SYSTEMATIC CONSERVATION PLANNING IN TSAY KEH DENE TERRITORY:
INCORPORATING CLIMATE CHANGE AND INTERWEAVING TRADITIONAL
ECOLOGICAL KNOWLEDGE
by
Christopher Morgan
B.A., University of Wisconsin-Madison, 2016

THESIS SUBMITTED IN PARTIAL FULFILLMENT OF
THE REQUIREMENTS FOR THE DEGREE OF
MASTER
OF
NATURAL RESOURCES AND ENVIRONMENTAL STUDIES

UNIVERSITY OF NORTHERN BRITISH COLUMBIA
July 2021
© Christopher Morgan, 2021

ABSTRACT
Systematic Conservation Planning (SCP) is the practice of comprehensively assessing a
landscape for its conservation value via geospatial analysis. This research project applied
SCP principles and tools to Tsay Keh Dene Nation Territory in north-central British
Columbia, Canada. Working with the Tsay Keh Dene community, we articulated
conservation goals and determined important features on the landscape that helped attain
those goals. This effort also examined climate change and connectivity impacts on
conservation, comparing which lands are most worth conserving today versus 30 and 60
years from now. Finally, this work explored the interweaving of Traditional Ecological
Knowledge with the Western science-based SCP framework to ensure a more holistic and
inclusive outcome. Our findings both validated ongoing conservation efforts in the Territory
and identified additional high-value areas for future consideration. This research can also
serve as a guide for other accessible TEK-focused or community-led SCP efforts.

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TABLE OF CONTENTS

1.0 INTRODUCTION ..........................................................................................................1
1.1 The Biodiversity Crisis.................................................................................................1
1.2 Conservation Planning Need for the Tsay Keh Dene Nation.......................................3
1.3 Including Climate Change and Traditional Ecological Knowledge in Systematic
Conservation Planning .......................................................................................................7
1.4 Research Purpose .........................................................................................................8
2.0 LITERATURE REVIEW ............................................................................................10
2.1 Systematic Conservation Planning as the Gold Standard...........................................11
2.2 Selecting Key Biodiversity Features ..........................................................................15
2.3 Connectivity and Persistence of Conservation Areas .................................................21
2.4 Climate Change ..........................................................................................................25
2.5 Traditional Ecological Knowledge .............................................................................30
2.6 Conclusion ..................................................................................................................37
3.0 CASE STUDY...............................................................................................................38
3.1 The People of the Rocks .............................................................................................38
3.2 Geography and Natural History of Tsay Keh Dene Territory ....................................40
3.3 Resource Extraction History and the W.A.C. Bennett Dam ......................................43
3.4 Contemporary Resource Extraction and the Williston Reservoir ..............................45
3.5 The Tsay Keh Dene First Nation................................................................................46
3.6 Existing Conservation Areas ......................................................................................48
3.7 Historical and Projected Climates in Tsay Keh Dene Territory .................................49
4.0 METHODS ...................................................................................................................52
4.1 Stage 1: Set Conservation Goals/Select and Compile Conservation Feature Data ....55
4.2 Stage 2: Develop a Human Footprint Layer ...............................................................70
4.3 Stage 3: Quantify Landscape Permeability to Identify Connectivity Corridors.........71
4.4 Stage 4: Develop Conservation Targets .....................................................................77
4.5 Stage 5: Review Existing Conservation Areas Efficacy ............................................78
4.6 Stage 6: Identify a Portfolio of High-Value Conservation Lands ..............................79
4.7 Stage 7: Assess prioritizr Solutions Through Different Lenses.................................81
4.8 Documenting Adaptations to the SCP Process...........................................................83
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5.0 RESULTS......................................................................................................................84
5.1 Conservation Goals ....................................................................................................84
5.2 Conservation Feature Layers......................................................................................85
5.3 Human Footprint ......................................................................................................145
5.4 Landscape Resistance and Connectivity ..................................................................148
5.5 Locked-in Areas .......................................................................................................153
5.6 Gap Analysis ............................................................................................................159
5.7 prioritizr Scenario Outputs.......................................................................................162
5.8 Conservation Feature Complementarity ...................................................................183
5.9 Adaptations to the SCP Process ...............................................................................185
6.0 DISCUSSION .............................................................................................................186
6.1 Which portions of Tsay Keh Dene Territory have the highest ecological and cultural
value for select present-day conservation features? .......................................................187
6.2 Which portions of Tsay Keh Dene Territory retain conservation value when climate
change is considered? .....................................................................................................199
6.3 How can landscape connectivity be explicitly included in the Systematic
Conservation Planning process? .....................................................................................216
6.4 Which stages of the Systematic Conservation Planning process provide an
opportunity for the interweaving of Traditional Ecological Knowledge to produce a
more inclusive conservation plan? .................................................................................225
6.5 Other thoughts ..........................................................................................................236
7.0 RECOMMENDATIONS ...........................................................................................239
7.1 Validation .................................................................................................................240
7.2 Future Use Cases ......................................................................................................242
8.0 CONCLUSION ...........................................................................................................248
9.0 LITERATURE CITED ..............................................................................................251
10.0 APPENDIXES ..........................................................................................................277
Appendix A. Federal Species at Risk Act (SARA) Schedule 1 Species in Territory ....277
Appendix B. Conservation Feature Data Sources ..........................................................278
Appendix C. Human Footprint Feature Data Sources and Buffers ................................279

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LIST OF TABLES
Table 1. Modified stages of the Systematic Conservation Planning framework .................... 13
Table 2. Protected Areas by designation and size in Tsay Keh Dene Territory ..................... 49
Table 3. Climate Projections for the territory ........................................................................ 51
Table 4. List of Conservation Features used in analysis ........................................................ 59
Table 5. Disturbance and Biogeoclimatic Ecosystem Classifications from the Biodiversity
Guidebook of British Columbia that are found in Tsay Keh Dene Territory. ........................ 62
Table 6. Human footprint permanence framework and categorized layers. .......................... 71
Table 7. Resistance features to inform connectivity analyses................................................. 73
Table 8. BEC subzones and variants included in the ‘Rare BEC Zones’ layer...................... 88
Table 9. Representation of present-day conservation features by protected areas in the
Greater Tsay Keh Dene Territory Study Area. ..................................................................... 160
Table 10. Representation of current, future, and connectivity conservation features by
protected areas in the Greater Tsay Keh Dene Territory Study Area. ................................. 161
Table 11. Parameters for each scenario. .............................................................................. 162
Table 12. Percentage of each conservation feature by target and actual amount captured for
each scenario. ....................................................................................................................... 181
Table 13. Conservation feature complementarity matrix ..................................................... 184
Table 14. Percentage of each elevation classification captured by scenario....................... 188
Table 15. Percentage of each ecoregion captured by scenario............................................ 191
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Table 16. Percentage of each BEC zone captured by scenario by time period.................... 203

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LIST OF FIGURES
Figure 1. Contextual map showing the location of Tsay Keh Dene Territory, the study area,
and the Yellowstone to Yukon region within British Columbia and North America. ............... 3
Figure 2. Contextual map showing Tsay Keh Dene Territory within so-called British
Columbia................................................................................................................................... 5
Figure 3. An illustration of a moving window iteration in the Omniscape algorithm............ 24
Figure 4. The six faces of Traditional Ecological Knowledge and the key components of each
dimension. .. ............................................................................................................................ 33
Figure 5. Map depicting the traditional territory of the five historical Tsek’ene groups and
neighbouring First Nations..................................................................................................... 39
Figure 6. Topographical/hydrological map of Tsay Keh Dene Territory .............................. 41
Figure 7. The Systematic Conservation Planning process and points for community guidance
and the interweaving of Traditional Ecological Knowledge. ................................................. 53
Figure 8. Contextual map showing the selected study area in relation to Tsay Keh Dene
Territory.................................................................................................................................. 54
Figure 9. A stacked set of select conservation feature layers................................................. 58
Figure 10. A visual representation of how different block sizes are used to improve
computing time........................................................................................................................ 76
Figure 11. Spatial extent of the Land Facet Diversity layer used to prioritize lands for
conservation in the Greater Tsay Keh Dene Territory Study Area ........................................ 89

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Figure 12. Spatial extent of the Land Facet Rarity layer used to prioritize lands for
conservation in the Greater Tsay Keh Dene Territory Study Area ........................................ 90
Figure 13. Spatial extent of the Elevational Diversity layer used to prioritize lands for
conservation in the Greater Tsay Keh Dene Territory Study Area ........................................ 91
Figure 14. Spatial extent of the Ecotypic Diversity layer used to prioritize lands for
conservation in the Greater Tsay Keh Dene Territory Study Area. ....................................... 92
Figure 15. Spatial extent of the Heat Load Index Diversity layer used to prioritize lands for
conservation in the Greater Tsay Keh Dene Territory Study Area. ....................................... 93
Figure 16. Spatial extent of all the Forest Pattern and Process layers used to prioritize lands
for conservation in the Greater Tsay Keh Dene Territory Study Area................................... 94
Figure 17. Spatial extent of the Rare BEC Zones layer used to prioritize lands for
conservation in the Greater Tsay Keh Dene Territory Study Area. ....................................... 95
Figure 18. Spatial extent of the Wetlands layer used to prioritize lands for conservation in
the Greater Tsay Keh Dene Territory Study Area. ............................................................... 102
Figure 19. Spatial extent of the Lakes layer used to prioritize lands for conservation in the
Greater Tsay Keh Dene Territory Study Area. ..................................................................... 103
Figure 20. Spatial extent of the Karst Deposits layer used to prioritize lands for conservation
in the Greater Tsay Keh Dene Territory Study Area. ........................................................... 104
Figure 21. Spatial extent of the Grizzly Bear layer used to prioritize lands for conservation in
the Greater Tsay Keh Dene Territory Study Area ................................................................ 105

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Figure 22. Spatial extent of the Fisher layer used to prioritize lands for conservation in the
Greater Tsay Keh Dene Territory Study Area. ..................................................................... 106
Figure 23. Spatial extent of the Caribou layers used to prioritize lands for conservation in
the Greater Tsay Keh Dene Territory Study Area. ............................................................... 107
Figure 24. Spatial extent of the Moose layer used to prioritize lands for conservation in the
Greater Tsay Keh Dene Territory Study Area ...................................................................... 108
Figure 25. Spatial extent of the Stone Sheep layer used to prioritize lands for conservation in
the Greater Tsay Keh Dene Territory Study Area ................................................................ 109
Figure 26. Spatial extent of the Mountain Goat layer used to prioritize lands for conservation
in the Greater Tsay Keh Dene Territory Study Area ............................................................ 110
Figure 27. Spatial extent of the Wolverine layer used to prioritize lands for conservation in
the Greater Tsay Keh Dene Territory Study Area ................................................................ 111
Figure 28. Spatial extent of the Bank Swallow layer used to prioritize lands for conservation
in the Greater Tsay Keh Dene Territory Study Area. ........................................................... 112
Figure 29. Spatial extent of the Barn Swallow layer used to prioritize lands for conservation
in the Greater Tsay Keh Dene Territory Study Area ............................................................ 113
Figure 30. Spatial extent of the Western Toad layer used to prioritize lands for conservation
in the Greater Tsay Keh Dene Territory Study Area. ........................................................... 114
Figure 31. Spatial extent of the Horned Grebe layer used to prioritize lands for conservation
in the Greater Tsay Keh Dene Territory Study Area ............................................................ 115

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Figure 32. Spatial extent of the Little Brown Myotis (Bat) layer used to prioritize lands for
conservation in the Greater Tsay Keh Dene Territory Study Area. ..................................... 116
Figure 33. Spatial extent of the Northern Myotis (Long-eared Bat) layer used to prioritize
lands for conservation in the Greater Tsay Keh Dene Territory Study Area. ...................... 117
Figure 34. Spatial extent of the Olive-sided Flycatcher layer used to prioritize lands for
conservation in the Greater Tsay Keh Dene Territory Study Area. ..................................... 118
Figure 35. Spatial extent of the Rusty Blackbird layer used to prioritize lands for
conservation in the Greater Tsay Keh Dene Territory Study Area. ..................................... 119
Figure 36. Spatial extent of the Climate Corridors layer used to prioritize lands for
conservation in the Greater Tsay Keh Dene Territory Study Area ...................................... 123
Figure 37. Spatial extent of the Backward Velocity (2055) layer used to prioritize lands for
conservation in the Greater Tsay Keh Dene Territory Study Area. ..................................... 124
Figure 38. Spatial extent of the Backward Velocity (2085) layer used to prioritize lands for
conservation in the Greater Tsay Keh Dene Territory Study Area. ..................................... 125
Figure 39. Spatial extent of the Forward Velocity (2055) layer used to prioritize lands for
conservation in the Greater Tsay Keh Dene Territory Study Area. ..................................... 126
Figure 40. Spatial extent of the Forward Velocity (2085) layer used to prioritize lands for
conservation in the Greater Tsay Keh Dene Territory Study Area. ..................................... 127
Figure 41. Spatial extent of the Cool Headwater Refugia layer used to prioritize lands for
conservation in the Greater Tsay Keh Dene Territory Study Area. ..................................... 128

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Figure 42. Spatial extent of the Climatic Refugia layer used to prioritize lands for
conservation in the Greater Tsay Keh Dene Territory Study Area. ..................................... 129
Figure 43. Spatial extent of the Biotic Refugia layer used to prioritize lands for conservation
in the Greater Tsay Keh Dene Territory Study Area. ........................................................... 130
Figure 44. Spatial extent of the Bird Richness layer used to prioritize lands for conservation
in the Greater Tsay Keh Dene Territory Study Area ............................................................ 131
Figure 45. Spatial extent of the Carbon Storage layer used to prioritize lands for
conservation in the Greater Tsay Keh Dene Territory Study Area. ..................................... 132
Figure 46. Spatial extent of the Linkage Mapper layer used to prioritize lands for
conservation in the Greater Tsay Keh Dene Territory Study Area. ..................................... 135
Figure 47. Spatial extent of the Omniscape layer used to prioritize lands for conservation in
the Greater Tsay Keh Dene Territory Study Area. ............................................................... 136
Figure 48. Time series of each biogeoclimatic ecosystem classification zone found in the
Greater Tsay Keh Dene Territory Study Area. ..................................................................... 138
Figure 49. Proportions of each biogeoclimatic ecosystem classification zone found in the
Greater Tsay Keh Dene Territory Study Area through time................................................. 139
Figure 50. Spatial extent of the Elevational Representation layer used to understand
representation within selected conservation lands in the Greater Tsay Keh Dene Territory
Study Area............................................................................................................................. 140
Figure 51. Spatial extent of the Ecoregional Representation layer used to understand
representation within selected conservation lands in the Greater Tsay Keh Dene Territory
Study Area............................................................................................................................. 141
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Figure 52. Spatial extent of the BEC Zones 2020 layer used to understand representation
within selected conservation lands in the Greater Tsay Keh Dene Territory Study Area.... 142
Figure 53. Spatial extent of the BEC Zones 2050 layer used to understand representation
within selected conservation lands in the Greater Tsay Keh Dene Territory Study Area. ... 143
Figure 54. Spatial extent of the BEC Zones 2080 layer used to understand representation
within selected conservation lands in the Greater Tsay Keh Dene Territory Study Area .... 144
Figure 55. Spatial extent of the Human Footprint layers used to avoid development in the
conservation prioritization process for the Greater Tsay Keh Dene Territory Study Area. 147
Figure 56. Spatial extent of the Resistance layer used to quantify connectivity for the
conservation prioritization process for the Greater Tsay Keh Dene Territory Study Area. 150
Figure 57. Spatial extent of the Linkage Mapper connectivity layer used to inform the
conservation prioritization process for the Greater Tsay Keh Dene Territory Study Area. 151
Figure 58. Spatial extent of the Omniscape connectivity layer used to inform the conservation
prioritization process for the Greater Tsay Keh Dene Territory Study Area. ...................... 152
Figure 59. Spatial extent of the Protected Areas layer used to ‘lock-in’ areas as part of the
conservation prioritization process for the Greater Tsay Keh Dene Territory Study Area. 155
Figure 60. Spatial extent of the Protected Areas + Least-Cost Paths layer used to ‘lock-in’
areas as part of the conservation prioritization process for the Greater Tsay Keh Dene
Territory Study Area ............................................................................................................. 156
Figure 61. Spatial extent of the Omniscape “Bones” layer used to ‘lock-in’ areas as part of
the conservation prioritization process for the Greater Tsay Keh Dene Territory Study Area.
............................................................................................................................................... 157
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Figure 62. Spatial extent of the Omniscape “Meat” layer used to ‘lock-in’ areas as part of
the conservation prioritization process for the Greater Tsay Keh Dene Territory Study Area.
............................................................................................................................................... 158
Figure 63. Spatial extent of the conservation solution for Scenario A: Present Conservation
Features within the Greater Tsay Keh Dene Territory Study Area. ..................................... 164
Figure 64. Spatial extent of the conservation solution for Scenario B: Future (2050s)
Conservation Features within the Greater Tsay Keh Dene Territory Study Area ............... 166
Figure 65. Spatial extent of the conservation solution for Scenario C: Future (2080s)
Conservation Features within the Greater Tsay Keh Dene Territory Study Area. .............. 168
Figure 66. Spatial extent of the conservation solution for Scenario D: Present-day and
Future (2050s and 2080s) Conservation Features within the Greater Tsay Keh Dene
Territory Study Area. ............................................................................................................ 170
Figure 67. Spatial extent of solutions A-D in the Greater Tsay Keh Dene Territory Study
Area for comparison purposes.............................................................................................. 171
Figure 68. Spatial extent of the conservation solution for Scenario E: Protected Areas
Connectivity within the Greater Tsay Keh Dene Territory Study Area. ............................... 173
Figure 69. Spatial extent of the conservation solution for Scenario F: Landscape
Connectivity within the Greater Tsay Keh Dene Territory Study Area. ............................... 175
Figure 70. Spatial extent of conservation solutions for Scenarios A-F stacked atop one
another to reveal areas that were consistenly selected in the Greater Tsay Keh Dene
Territory Study Area ............................................................................................................. 177

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Figure 71. Spatial extent of all conservation features stacked atop one another to reveal
areas of high conservation feature diversity, or ‘hotspots’ in the Greater Tsay Keh Dene
Territory Study Area ............................................................................................................. 179
Figure 72. Spatial extent of solutions E and F in the Greater Tsay Keh Dene Territory Study
Area for comparison purposes; stacked solutions and conservation feature stack also shown
side by side for comparison purposes................................................................................... 180
Figure 73. Priority areas and protected areas overlaid with the spatial extent of the
conservation solution for Scenario A: Present-day Conservation within the Greater Tsay Keh
Dene Territory Study Area.................................................................................................... 192
Figure 74. A still from the “Wedzih - The Caribou” video illustrating the holistic importance
of caribou to the Tsay Keh Dene........................................................................................... 197
Figure 75. Priority areas and protected areas overlaid with the spatial extent of the
conservation solution for Scenarios B: Future (2050s) and C: Future (2080s) Conservation
within the Greater Tsay Keh Dene Territory Study Area. Present-day focal areas included for
comparison purposes ............................................................................................................ 205
Figure 76. Optimal climate connectivity corridor within the Greater Tsay Keh Dene
Territory Study Area. ............................................................................................................ 209
Figure 77. Priority areas and protected areas overlaid with the spatial extent of the
conservation solution for Scenario E: Protected Areas Connectivity within the Greater Tsay
Keh Dene Territory Study Area. ........................................................................................... 219

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Figure 78. Priority areas and protected areas overlaid with the spatial extent of the
conservation solution for Scenario F: Landscape Connectivity within the Greater Tsay Keh
Dene Territory Study Area.................................................................................................... 221
Figure 79. Spatial extent of a clustered conservation solution in the Greater Tsay Keh Dene
Territory Study Area ............................................................................................................. 243
Figure 80. Spatial extent of mature- and old-growth forests in the Greater Tsay Keh Dene
Territory Study Area. ............................................................................................................ 245
Figure 81. Spatial extent of the clustered conservation solution atop the planning units
selected in all six of the thesis scenarios for the Greater Tsay Keh Dene Territory Study Area
............................................................................................................................................... 247

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ABBREVIATIONS GLOSSARY
BEC: Biogeoclimatic Ecosystem Classification (zones)
CCE: Chu Cho Environmental
GIS: Geographic Information Systems
LRTO: Lands, Resources, and Treaty Operations office at Tsay Keh Dene Nation
SCP: Systematic Conservation Planning
TEK: Traditional Ecological Knowledge

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ACKNOWLEDGEMENTS
I would first and foremost like to express my gratitude to my supervisor Dr. Pamela
Wright, who convinced me to pack up my car and move 3,000 kilometers across an
international border to Prince George, BC. I will be forever grateful to her for taking me on
as a student, connecting me to this project, and sharing her vast knowledge with me along the
way. I would also like to thank the other members of my thesis committee: Dr. Sina
Abadzadesahraei and Dr. Richard Schuster, for their expert knowledge on Tsay Keh Dene
Territory and systematic conservation planning, respectively. Thank you to former Wright
Conservation Science Lab students Jerrica Mann, Ian Curtis, and Tim Burkhart for their
guidance in SCP work and critical GIS (and also for housing me for a week and providing
dog therapy in Tim’s case).
Many thanks to the team at Chu Cho Environmental (CCE) and Tsay Keh Dene’s
Lands, Resources, and Treaty Operations (LRTO) office for helping guide this work with
Tsay Keh Dene values and providing me workspace so I could build relationships with them.
I would specifically like to thank Evan MacKinnon at LRTO, and Kristen Marini, Caroline
Walter, Morgan Husereau, Sofia Favila, and Erica Bonderud at CCE. Erica in particular put
up with a lot of my questions. I am deeply grateful to Luke Gleeson for sharing his
knowledge and perspectives on issues in the Territory, as well as the history of his ancestors
with me – all of which immeasurably bettered this work. I would like to show my
appreciation to WCS Canada, Round River Conservation Studies, and John Hagen &
Associates for sharing their rigorous habitat data with me. I would also like to acknowledge
Mitacs, Chu Cho Environmental, and the Yellowstone to Yukon Conservation Initiative for
their continued support of this work.
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Additionally, I wish to thank my amazing support network that guided me through a
labour strike, global pandemic, and personal tragedy as I worked towards my degree: my
parents and brother for almost always picking up the phone when “BC WIRELESS” rang;
my partner Erika for always being my hype-woman from afar; and the amazing PG pals of
Rachelle, Megan, Ella, and Cale. And to Dan – I consider myself lucky to have known you. I
also want to thank the UNBC community for being so welcoming, UNBC Counselling
Services for always providing a safe space to talk, and the good folks at Degrees Coffee for
supplying me with Americano caffeination. Shout-out to Tommy Douglas for giving me a
taste of universal healthcare as well.
I reserve my final and deepest sense of gratitude to the people of the Tsay Keh Dene
Nation for allowing me to play a role in their conservation and land stewardship traditions.

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1.0 INTRODUCTION
1.1 The Biodiversity Crisis
Today, human activities dominate the earth and are having significant global,
regional, and local impacts on ecosystems and the critical services they provide to humanity.
Extinction rates are between 100- and 10,000-times evolutionary background rates, and
shrinking populations and ranges are contributing to a massive anthropogenic erosion of
biodiversity, which scientists have referred to as “biological annihilation” (Lamkin & Miller,
2016; Strona & Bradshaw, 2018). Habitat loss and degradation is a major driver of
biodiversity loss not restricted to tropical environments but equally problematic across
Canada and in northern British Columbia.
This is set against a backdrop of an increasingly changing climate where effects are
magnified in Canada’s boreal regions (Schindler & Lee, 2010). In our own backyard this
means that without radical action, mountain caribou will likely be extinct in our lifetimes
(Festa-Bianchet et al., 2011). Wolverine and other critical mid-trophic carnivores will be
further isolated into patchy, ultimately genetically impoverished pockets (McKelvey et al.,
2011). Moose populations will continue to decline (Rempel, 2011) and populations of boreal
birds that rely on structurally complex forests in the north will become destitute (Stralberg et
al., 2015).
Systematic Conservation Planning (SCP) represents a strategic area-based
conservation approach to counteract both development- and climate change-related
anthropogenic activity and safeguard biodiversity (Gillson et al., 2013). Conserving lands to
combat climate change and protect biodiversity also helps contribute to Aichi Target 11 of
1

the Convention on Biological Diversity (17% terrestrial and inland waters protected by 2020)
and Canada’s Pathway to Target 1, which supports Indigenous Protected and Conserved
Areas (IPCAs) as a vehicle to reach that 17% (Zurba et al., 2019). As 2020 came and went,
protecting 30% of the planet by 2030 is the new goal of conservationists, and this project can
help the Tsay Keh Dene Nation contribute to that goal within their mountainous corner of the
world (Campaign for Nature, 2020).
This SCP can also serve as an act of continued stewardship by Indigenous peoples,
not just counteracting anthropogenic activity in service to the world, but also counteracting
the last few centuries of settler colonialism. Eli Enns – a Nuu-chah-nulth member and cochair of Canada’s Indigenous Circle of Experts (ICE) – states that “Whenever you find intact
ecological biodiversity, you find intact, thriving, cultural holistic diversity” (Parks Canada,
2018, p. 73). The hope is that the products of this work strengthen the Nation’s own
ecological and cultural conservation initiatives, benefitting both the community and their
relationships with the land.

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1.2 Conservation Planning Need for the Tsay Keh Dene Nation
Tsay Keh Dene Nation Territory is located in the northcentral region of so-called
British Columbia, Canada (Figure 1). The Tsay Keh Dene community is located roughly 360
km north of the city of Prince George, with Mackenzie being the closest sizable town at
roughly 3,700 people. The Territory is a large, 32,000 km2 area encompassing critical
ecological and cultural values.

Figure 1. Contextual map showing the location of Tsay Keh Dene Territory, the study area, and the Yellowstone to
Yukon (Y2Y) region within British Columbia and North America.

Located within the Rocky Mountain Trench region and encompassing portions of the
Omineca and Rocky Mountains, the current resource development footprint is ecologically
3

significant in the region. My analysis revealed that 30% of the Territory has been impacted.
There are five small-scale dams located within the Territory (the Kemess North, South, and
East Diversion Dams, and the 3 Mile Creek and Woody Creek Dams) as well as the massive
W.A.C. Bennett Dam on the Peace River located just outside of the Territory (Figure 2).
Although the Bennett Dam site is located outside of the Territory, construction and
subsequent flooding to create the Williston Reservoir have had immeasurable impacts on the
Tsay Keh Dene people and the habitats within the Territory, both historically and today
(Tsay Keh Dene Nation, personal communication, September 30, 2019). The Williston
Reservoir's inundation resulted in the direct loss of approximately 1,500 km2 of highcomplexity lowland habitats, which are critical for many species (Fish and Wildlife
Compensation Program, 2014). Furthermore, access roads associated with the reservoir
allowed industry (e.g., forestry, mining) to expand throughout the area, resulting in
continued, indirect impacts on wildlife species and the habitats they utilize today (Fish and
Wildlife Compensation Program, 2014). Hunters and guide outfitters can also significantly
impact wildlife in the region (Abadzadesahraei, personal communication, May 7, 2020).

4

Figure 2. Contextual map showing Tsay Keh Dene Territory within so-called British Columbia.

Today, there are approximately 170 provincially red- or blue-listed species (including
several woodland caribou populations) found within the Territory, along with numerous rare
ecosystems and special features. In addition, 36 species listed under Schedule 1 of the
Species at Risk Act occur within the Territory (Appendix A. Federal Species at Risk Act
(SARA) Schedule 1 Species in Territory). Of particular interest to the Nation are the Chase,
Wolverine, and Finlay herds of woodland caribou. Other focal species include bull trout,
fisher, Stone sheep, mountain goat, moose, and grizzly bear (Tsay Keh Dene Nation,
personal communication, September 30, 2019). Regionally, connectivity is critical to

5

maintaining populations of caribou and other wide-ranging mammal and fish species,
particularly in light of the rapidly changing climate.
The intensity of current resource development in the Territory is such that the Nation
is often overwhelmed by the number of referrals for resource activities that they receive.
These referrals are, by their nature, limited in focus both to a specific geography and to a
single industry or development. As a result, referral comments provided by the Nation are
often site-specific and limited in scope. The Nation would like to take a much broader
perspective and holistic approach in planning and managing, including being able to
contextualize individual referrals within a larger scale conservation context (Tsay Keh Dene
Nation, personal communication, September 30, 2019).
In the twenty-first century, much of the global conservation planning response to
development pressures has been focused on the designation and management of parks and
protected areas (Maxwell et al., 2020). Only 9% of Tsay Keh Dene Territory is currently
under provincial protected status, though the Nation has recently declared an Indigenous
Protected and Conserved Area in the Ingenika River valley. The largest of these protected
areas (Omineca) is under 1,000 km2 – well below minimum thresholds for species
persistence of wide-ranging mammals (>3,000 km2) (Gurd & Nudds, 1999; Newmark, 1995;
Wright, 2016). While protected areas are an increasingly vital tool to combat loss of
biodiversity and the climate crisis, conservation planning must occur across all land
management systems and encompass a wide range of management tools and approaches.

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1.3 Including Climate Change and Traditional Ecological Knowledge in Systematic
Conservation Planning
Systematic Conservation Planning is widely considered the most effective method for
designing wide, regional conservation approaches, including the identification of protected
areas and other ecological networks (Pressey et al., 2007). The success and effectiveness of
SCP can be attributed to its efficiency in using limited resources to achieve conservation
goals, its flexibility and defensibility in the face of competing land uses, and its
accountability in allowing decisions to be critically reviewed (Margules & Pressey, 2000).
SCP uses detailed biogeographical information and selection algorithms to identify priority
conservation areas (Knight & Cowling, 2007; Watson et al., 2011). It strives to move the
prioritization of conservation lands beyond opportunism and toward scientific defensibility
and improved efficacy (Pressey et al., 1993). Furthermore, SCP supports the identification of
conservation networks that represent regional species and ecosystems diversity, are
comprised of enough habitat of specific types to maintain viable species populations, enable
continued community and population processes (including shifts in species’ ranges), and
allow natural patterns of disturbance (Baldwin et al., 2014).
Explicitly incorporating climate change as part of the Systematic Conservation
Planning process is now possible. As the field expands its scope and perspectives,
approaches become more effective at incorporating previously poorly understood or
connected variables (Mann, 2020). With the widespread availability of emission scenarios
and reliable climate change data, the SCP framework can incorporate climate information
and evolve into a climate change-conscious approach to conservation planning (Stralberg et
al., 2020).
7

This climate-conscious approach involves looking at areas of biotic refugia, or habitat
that will remain viable and desirable for species despite climate change (Michalak, Lawler, et
al., 2018). Another aspect of this approach is to consider climate velocity, which quantifies
how far species will have to travel from a given area to find similar habitat in the future
(forward velocity), as well as how far other species will have to travel to populate that same
area in its now altered state (backward velocity) (Carroll et al., 2015). One of the first
projects to undertake a climate-conscious conservation planning approach in Canada has
been done in British Columbia’s Peace Region (Mann, 2020). Methods developed and tested
in that project will be used to inform the approach in this research.
This project is unique from Mann’s (2020), however, in that it is being developed in
conjunction with a First Nation. Indigenous-led conservation initiatives are gaining
momentum in Canada and elsewhere in the world through the creation of Indigenous
Protected and Conserved Areas (IPCAs) (Zurba et al., 2019). Establishing conservation areas
is just one way to protect biodiversity, with other designations like Old Growth Management
Areas (OGMAs) and Ungulate Winter Range (UWR) also serving as options. These sorts of
initiatives help to further the self-determination of Indigenous peoples, creating conservation
areas that embody their own biodiversity and cultural values as opposed to the colonial
processes of the past that centered on a people-free ‘wilderness’. By initiating and guiding
this project, the Tsay Keh Dene Nation ensured that their values informed this project by
helping scope it from the outset and providing input at each step along the way.
1.4 Research Purpose
The purpose of my research project was to explore which areas in Tsay Keh Dene
Territory have high conservation value (both ecologically and culturally), landscape
8

connectivity, and resiliency to climate change. I used the Systematic Conservation Planning
framework and interwove Traditional Ecological Knowledge throughout by taking a critical
GIS perspective and community-led approach to identifying important conservation lands in
the Territory for the Nation’s consideration. By recognizing GIS as a colonial tool shaped by
the Western scientific spatial understanding of the world, I assisted the Nation in the form of
counter-mapping – using GIS technology to empower a community and share an accessible
and defensible expression of their conservation goals. By allowing Tsay Keh Dene values to
shape this process, local voices, views, and understandings were etched into this work
(Burkhart, 2018).
The result was an actionable systematic conservation plan and accompanying tool
that can assist the Nation with routine resource extraction referrals as well as long-range land
use planning decisions. While the Nation already possesses a great deal of GIS capacity, this
project complemented its strengths and provided a novel aspect to their geospatial
information and conservation planning efforts. The end product included a set of curated
maps and an updatable model for identifying the locations of key ecological and cultural
values within Tsay Keh Dene Territory. By providing the Nation with a robust ‘living tool’
that takes present-day biodiversity, climate change, and connectivity of lands into account,
diverse scenario planning can continue to take place once this research project has formally
ended. Planning and management of the Territory for cultural, ecological, or economic
purposes will be made easier and more defensible with this SCP framework in place. To
achieve these goals, I sought to answer the following questions:

9

•

Which portions of Tsay Keh Dene Territory have the highest ecological and
cultural value for select present-day conservation features?

•

Which portions of Tsay Keh Dene Territory retain conservation value when
climate change is considered?

•

How can landscape connectivity be explicitly included in the Systematic
Conservation Planning process?

•

Which stages of the Systematic Conservation Planning process provide an
opportunity for the interweaving of Traditional Ecological Knowledge to produce
a more inclusive conservation plan?

2.0 LITERATURE REVIEW
The Systematic Conservation Planning process consists of a set of common steps but
remains highly adaptable to account for other critical aspects of ecology and conservation.
This literature review explores how the SCP process became widely accepted and the
theories behind the field's foundational approaches. I will then delve into the importance of
movement corridors across the landscape and how to explicitly incorporate connectivity into
the SCP process. Next, I will explain how climate change threatens biodiversity and how to
conserve resilient habitats for future species assemblages by integrating climate metrics as
SCP conservation features. Finally, I will examine Traditional Ecological Knowledge (TEK)
as a way of knowing. I will explore the various ‘faces’ or aspects of TEK and how these

10

aspects have been used alongside Western science in the past, before concluding with how
TEK has been interwoven into the SCP process by various conservation efforts to date.
2.1 Systematic Conservation Planning as the Gold Standard
Systematic Conservation Planning is the product of decades of conservation theory
and practice, promoting representativeness and persistence of species and ecosystems in
conservation area design. From the inception of SCP as a concept in the 1980s until its
coalescence and increased prevalence in the early 2000s, the practice has become widely
accepted within the field of conservation biology (Pressey et al., 2007; Smith et al., 2019).
This is illustrated through its use by major organizations like the Nature Conservancy,
heavily influencing legislation, policy, and real-world conservation initiatives, and cementing
itself as a staple of relevant academic conferences (Pressey et al., 2007).
The development of this ecocentric, systematic approach to conservation in places
like New South Wales, Australia has resulted in an increase in the establishment of
conservation areas whose express purpose is to protect biodiversity – be it ecosystems,
biological assemblages, a specific species, or even a particular population of a species
(Margules & Pressey, 2000). Furthermore, integrating conservation areas within their larger
landscapes and focusing on connectivity in general (not just amongst conservation areas) is
critical (Schloss et al., 2011). Margules and Pressey (2000) also maintain that in order for
conservation areas to achieve their goal of protecting biodiversity, they must be
representative (a cross-section of the biodiversity of a region) and promote persistence
(allowing for species and processes to naturally remain on the landscape). While
representativeness remains a common goal of SCP, there has more recently been a shift

11

towards focusing on biodiversity hotspots and ecosystem services as either primary or
supplemental goals (Mitchell et al., 2021; Smith et al., 2019).
SCP requires that users make several decisions to inform model construction. The
SCP process is outlined in a series of stages (Margules & Pressey, 2000) that I have modified
to explicitly include connectivity and climate change elements based on Mann’s (2020) work
(Table 1).

12

1. Set Conservation Goals/Select and Compile Conservation Feature Data
a) Develop conservation goals for the study area.
b) Identify conservation features to serve as surrogates for present-day and future biodiversity.
c) Compile data with sufficient rigor and consistency for inclusion in the analysis.
2. Develop a Human Footprint Layer
d) Compile a human footprint model to quantify and spatially model the present-day state of
anthropogenic disturbance.
3. Identify Connectivity Corridors
e) Identify features that affect landscape permeability.
f) Create a landscape resistance spatial layer.
g) Perform a landscape connectivity analysis between conservation areas and ecologicallyintact areas in the greater territory (Linkage Mapper).
h) Perform a landscape connectivity analysis across the entire greater territory (Omniscape).
4. Set Conservation Targets
i) Translate goals into quantifiable targets for conservation in the greater territory that promote
present-day and future biodiversity.
5. Review Existing Conservation Areas
j) Determine the extent to which the existing conservation areas network and ecologicallyintact areas achieve the identified targets.
6. Identify a Portfolio of High-Value Conservation Lands
k) Use an SCP algorithm (prioritizr) to spatially delineate additional areas for conservation
while minimizing costs.
7. Assess prioritizr Solutions Through Different Lenses
l) Compare areas within the resulting portfolio of high-value conservation lands for their
diversity of conservation features captured.
m) Compare high-value conservation lands for present-day and multiple future climate
scenarios.
n) Validate solutions with Traditional Ecological Knowledge.
Table 1. Modified stages of the Systematic Conservation Planning framework

13

In Stage 1, the user must identify conservation goals (e.g., promoting biodiversity,
climate change resiliency, landscape connectivity, etc.). These goals set the basis for the rest
of the process, guiding the selection of conservation features, representational targets, and the
potential inclusion of other elements within the SCP framework. The user selects which
‘conservation features’ best represent their ideal ecological outcomes, realizing that this suite
must be numerous and comprehensive enough to achieve one’s conservation goals, yet not
include so many features as to become unwieldy. Data with sufficient rigor and consistency
must then be compiled for inclusion in the analysis.
In Stage 2, the user analyzes the location and severity of anthropogenic disturbance in
the study area by creating a present-day human footprint layer. This allows the user to
proceed to Stage 3 and identify features (natural and human footprint) that affect landscape
permeability in order to create a resistance layer. The user can then quantify the permeability
of the landscape (both overall and between identified core areas) and identify connectivity
corridors.
In Stage 4, targets must be set for each conservation feature to identify what
percentage of that feature’s area must be selected as part of a conservation solution. These
targets can be informed by the literature, best practices, or stakeholder priorities.
In Stage 5, the identified core areas are reviewed for how well they capture the
targets. This allows the user to understand how much of each conservation feature target will
have to be captured in the remaining portions of the study area.
In Stage 6, various scenarios are run to create a portfolio of conservation lands based
on the conservation features' targets. In Stage 7, these scenarios' outputs are assessed through
14

different lenses (e.g., conservation feature diversity, climate change resiliency) to better
understand their potential efficacy.
Further details on each stage will be outlined in the Methods section, but I will
explore key concepts behind the stages as part of this Literature Review. I will first examine
different approaches to capturing biodiversity (Stage 1), before moving on to connectivity
(Stage 3), and ending with climate change and Traditional Ecological Knowledge, which
inform each stage of the SCP process.
2.2 Selecting Key Biodiversity Features
Understanding how to distill biodiversity into selected features and providing wellvetted input data is critical, as these steps are the foundation of a conservation solution. The
goal is to conserve biodiversity by selecting conservation features at the ecosystem and
species levels using coarse- and fine-filter features, respectively, with the genetic diversity
level indirectly conserved through sufficiently large and connected conservation areas.
A representation goal’s intent is that a prospective conservation area is a microcosm
of the biodiversity in the area, mirroring the region's various ecosystems and species at large
(Wiersma, 2008). One option to achieve representation would be to procure spatial datasets
of every known species, land facet category, and vegetative history – a feat that Lambeck
(1997) argues is simply not possible in most instances. Conversely, one could strategically
select the most important conservation features– whether ecologically, socioculturally, or
economically.
Using one or just a few select conservation features is ill-advised, as the resulting
areas calculated for conservation will represent only those features and not capture larger
15

biodiversity goals (Margules & Pressey, 2000). Tingley et al. (2014) argue that best practice
is to employ several strategically selected conservation features at both coarse- and fine-filter
levels, capturing umbrella species as well as the ecological patterns, biophysical processes,
and abiotic components that shape the broader landscape.
Coarse-filter features such as ecoregional representation systems that focus on broad
vegetative classes or abiotic representation approaches such as land facets are intended to
capture the breadth of biodiversity across the landscape. Representation approaches focus on
ecosystem diversity and the attendant environments that support species and genetic
diversity.
An alternative or complementary approach is to explicitly conserve biodiversity
hotspots, or Key Biodiversity Areas (KBAs), focusing on species richness and rarity at local
scales (Ceauşu et al., 2015). This concept initially focused on the protection of endemic and
threatened species as opposed to protecting the species composition of a particular area
(Kullberg et al., 2019). The KBA approach has since broadened to include five criteria: A)
Threatened Biodiversity, B) Geographically Restricted Biodiversity, C) Ecological Integrity,
D) Biological Processes, and E) Irreplaceability Through Quantitative Analysis (IUCN,
2016). However, some suggest that KBAs, which can be rarely distributed across the
landscape and in limited occurrence, may be less useful for protected areas planning and
more suited towards the designation of Other Effective Area-Based Conservation Measures
(OECMs) (Dudley et al., 2017), Old Growth Management Areas (OGMAs), Special
Management Zones (BC Ministry of Forests, 2003; Government of British Columbia, 2020)
or Ecological Reserves.

16

2.2.1 Coarse-Filter Approaches
The reasons for protecting the landscape rather than its inhabitants are two-fold: 1)
developing and maintaining species data for the entirety of the plant and animal kingdoms is
time- and cost-prohibitive, and 2) present-day biological assemblages are ephemeral (Tingley
et al., 2014). The idea is that by conserving diverse ecosystems, present-day species
assemblages (including those that little to no information is known about) will be conserved
in the process, while conserving as diverse a collection of land facets as possible will
conserve the inherent adaptive or evolutionary potential of species under future conditions.
This way, both the present-day and future needs of most species will be inherent in the highvalue conservation lands that make up the final product of the SCP (Schneider et al., 2011).
A coarse-filter conservation approach focuses on representation of biotic diversity
within varied vegetative communities (e.g., biogeoclimatic zones) or abiotic diversity in
varied soil, slope, and elevation units (Curtis, 2018). Biogeoclimatic Ecosystem
Classifications, or BEC zones, are commonly used within British Columbia to ensure
representation of various areas of vegetative communities and the abiotic (e.g., slope,
elevation, and soils) and climatic environments that have shaped them (BC Environment,
1995). Other classification systems for vegetation-based ecological communities such as
terrestrial ecoregions (Olson et al., 2001) and ecozones (Parks Canada, 2003) also provide
approaches to understand and map biotic representation at global and national scales that are
less biased by a resource-extraction focus. Schloss et al. (2011) note that vegetation-based
ecological communities can also serve as a proxy for certain species when detailed species
distribution data is limited.

17

Disturbance regimes – the historical patterns of natural processes that alter the
landscape – act as a driver of ecological representation and diversity (Hobbs & Huenneke,
1992). Natural disturbances such as fires, storms, flooding, and insect outbreaks (among
others) act as regulators of species composition. Different species thrive with varying
frequency, intensity, and types of disturbance (Hobbs & Huenneke, 1992). British Columbia
has classified disturbances into five natural disturbance types (NDTs), and for the sake of
setting biodiversity objectives pairs them with BEC zones to aid in management decisions
(BC Environment, 1995).
Unique combinations of natural disturbance regimes, biogeoclimatic ecosystem
classifications (BEC) zones, and the age and burn history of a forest stand can translate to
high-quality, biodiverse habitat. By managing today’s forests to resemble historical forests
shaped by natural disturbances, there is a greater chance that native species and ecological
processes will persist (BC Environment, 1995). Seral stages (or forest ages) are also of
biodiversity interest, as specialist species are often associated with the early shrub or mature
stages of forest stands (BC Environment, 1995).
Abiotic, or land facet representation (a combination of aspect, elevation, slope, and
landform), is a more recent focus given its potential to allow for persistence in the face of a
changing climate (Tingley et al., 2014). Conservation plans that target high representation of
land facet rarity and diversity, along with ecosystem diversity, help promote ecological
processes, evolutionary interaction, and range shift – aiding in species’ resiliency against
climate change (Beier & Brost, 2010; Tingley et al., 2014). This approach requires the
extraction of topographic data from a digital elevation model (DEM), the products of which
are more stable and permanent in nature than the relatively ephemeral vegetation-based
18

ecological communities (Beier & Brost, 2010). The inclusion of land facets has been
described as preserving the ‘stage’ or ‘arena’ and not just the ‘actors’ (species) (Beier &
Brost, 2010).
Each of the above coarse-filter approaches and corresponding conservation features
do not necessarily represent mutually exclusive attributes for inclusion in an SCP analysis.
Rather, these options can be mixed and matched to provide different lenses for understanding
diversity in a region and to attain different conservation objectives.
2.2.2 Fine-Filter Approaches
Fine-filter conservation approaches provide a lens for prioritizing lands for
conservation that focuses on rarer landscape elements – like wetlands, mineral licks, or
wildlife species that are unevenly distributed across the landscape and may ‘fall through the
cracks’ of a coarse-filter approach (Curtis, 2018). Mammalian carnivores frequently serve as
focal species in fine-filter analyses as they generally operate at the ecosystem scale. This
helps to uncover habitat area thresholds lower in the food chain, as well as reveal levels of
connectivity via their distribution on the landscape (Carroll et al., 2001). These are referred
to as surrogate species because by accounting for these species1, others with similar habitat
needs are theoretically included.
In some instances, multiple focal species will be used to capture various facets of
biodiversity in an attempt to be more inclusive. This was the case in Carroll et al.’s (2001)

1

Frequently the selected surrogates are umbrella species, so named because the protection of the species in
question indirectly protects several other species under the same ‘umbrella’.

19

study in the Rocky Mountain region, where researchers incorporated carnivores with similar
ranges, but which occupied different trophic levels. The researchers chose fisher (Martes
pennanti), lynx (Lynx canadensis), wolverine (Gulo gulo), and grizzly bear (Ursus arctos),
for their varying levels of habitat overlap, as well as differing responses to the fragmentation
effects of human population and roads. These species complement one another, whereas a
single umbrella species might not capture the full diversity of an area.
Another approach to selecting focal species is to focus on species of conservation
concern. Grizzly bear, fisher, and wolverine could be included in this approach, as they are
categorized as blue- and red-listed (species of special concern and at risk of being lost,
respectively) according to British Columbia’s provincial Conservation Status Rankings
(Government of British Columbia, 2019). This approach can lead to a large number of
species as inputs in an attempt to reach conservation goals in each, as was the case in Horn’s
(2011) SCP work in the Central Interior of British Columbia, where 100 terrestrial animal
species were selected.
Some research has called into question the use of umbrella or ‘flagship’ species as
surrogates in SCP (Andelman & Fagan, 2000). In a situation where a database of species of
concern and their geographic ranges was available, Andelman and Fagan (2000) found that
strategically-selected species performed just as well as an equal number of randomly selected
species as surrogates for conservation. Furthermore, some argue for including as many
species as possible (provided suitable data exists) to aid in quantifying irreplaceability and
identifying geographically restricted biodiversity in the name of global persistence of
biodiversity (IUCN, 2016; R. Schuster, personal communication, May 1, 2020). While the
utility of umbrella species as conservation feature inputs may have some limitations, the use
20

of wide-ranging, keystone or flagship species (i.e., big carnivores and charismatic species) is
helpful for the attention they bring to SCP research (Andelman & Fagan, 2000), and practical
as data is likely more widely available (Watson et al., 2011).
Ultimately, combining a range of biotic and abiotic coarse-filter approaches with a
more nuanced species-based fine-filter approach is what makes for an effective SCP. Some
researchers suggest that conservation goals cannot be achieved without using both strategies
and that this will only grow truer in the face of uncertainty brought on by climate change
(Tingley et al., 2014).
2.2.3 Boundaries of Analysis
Political boundaries are often chosen as study areas given jurisdictional
considerations; however, ecological boundaries are better suited for ecological work.
Landscape units such as watersheds can be relevant for representation and analysis at a
number of scales (Groves, 2003). Watersheds provide natural units within which to assess
biodiversity, as well as acting as zoogeographic range boundaries (Groves, 2003). These
units are more organic than political boundaries, as they delineate natural processes as
opposed to human ones (Ffolliott et al., 2003). Additionally, other sub-boundaries like
climate regions and caribou herd ranges can be considered and assessed based on their
suitability and partner priorities.
2.3 Connectivity and Persistence of Conservation Areas
“‘Ecological connectivity’ is the unimpeded movement of species and the flow of
natural processes that sustain life on Earth” (Hilty et al., 2020, p. 2). Whether today or in a
climate-altered future, species must be able to effectively move across the landscape to
21

persist. Best practices in conservation planning regarding the concepts of fragmentation and
connectivity of conservation areas stem from landscape ecology. Landscape fragmentation
occurs when effective habitat is fractured – typically through human development and
resource extraction – into distinct and discontinuous patches. These patches have increased
edge and less ‘interior’ habitat, increasing vulnerability for disturbance-sensitive species and
biodiversity as one ‘island’ of core habitat is broken into multiple smaller ‘islands’ (Hansen
& Defries, 2007). Additionally, many species require large home ranges for daily, seasonal,
or genetic movement. Connectivity is important because it supports the movement of these
species from one patch of habitat to another, allowing them relatively safe passage within
their ranges during single animals’ life histories (Catchpole, 2016). This increases fitness of
wildlife populations while decreasing predation and human-wildlife conflict (Ghoddousi et
al., 2021). Additionally, connectivity can ensure long-term genetic diversity by functionally
connecting metapopulations (Stewart et al., 2019). This importance of contiguous habitats is
why conservation planners focus on conserving corridors of land to serve as a link between
existing conservation areas (Haber & Nelson, 2015).
By linking conservation areas, a landscape becomes more permeable (structural
connectivity), facilitating the movement of species and their genetic material (functional
connectivity) (Doerr et al., 2011). Connectivity between individual conservation areas of a
given region (in situ connectivity) has proven effective in promoting ecological persistence,
causing conservation scientists to explore the connection of much larger landscapes, even up
to the continental scale (ex situ connectivity) as in the case of the Yellowstone to Yukon
Conservation Initiative (Hodgson et al., 2011; Mann, 2020). These larger landscape
connections not only facilitate the movement of species within their life histories but may
22

prove to be vital movement corridors in the face of climate change as species seek new
habitat as climate refugees (Hilty et al., 2020).
Connectivity can be a meaningful addition to a systematic conservation plan, ensuring
species movement between ecologically intact areas and effectively making a network of
conservation areas greater than the sum of its parts. The SCP process provides a spectrum of
connectivity analysis options, ranging from a visual overlay to inform prioritization of
conservation lands after the solution has been generated (Mann, 2020), to a post hoc
statistical analysis of connectivity value to prioritize selected conservation lands (Fajardo et
al., 2014), all the way up to modelling the connectivity of a landscape and explicitly
including it as part of the SCP process (Heinemeyer et al., 2003).
Most connectivity modeling methods are based on graph theory, a third way of
thinking about the landscape beyond vector polygons and raster grids – a graph (or network)
of interconnected nodes (Urban & Keitt, 2001). The identification of least-cost paths between
core areas and the application of electronic circuit theory as a connectivity predictor are both
born out of graph theory (Hilty et al., 2020). To apply these theories and methods,
conservation scientists have developed a number of tools to model connectivity. Linkage
Mapper looks for least-cost corridors between core areas, identifying a path of least
resistance and assigning progressively lower values of connectivity as distance increases
from the least-cost path (McRae et al., 2013). Alternatively, Omniscape looks at the
connectivity of the landscape overall by using a ‘moving window analysis’ (Figure 3) to
measure connectivity between a single grid cell and every grid cell within a stated radius (the
‘window’) (McRae et al., 2016). This exercise is performed for each grid cell in the study

23

area (the ‘moving’ aspect) and summed for a cumulative output (Landau, 2020). Both tools
are open source and part of the Circuitscape library (McRae et al., 2013).

Figure 3. An illustration of a moving window iteration in the Omniscape algorithm (Landau, 2020).

Geographic context can play a large role in which tool is optimal in a given effort,
largely dependent on how fragmented or intact the landscape is and what level of protection
is already in place (Gallo et al., 2020). In a highly fragmented landscape with agreed-upon
core areas (protected or largely intact), Gallo et al. (2020) argue that Linkage Mapper, with
its ability to identify least-cost paths (as well as larger corridors), may be optimal for
identifying connectors between declared core areas. Conversely, a relatively intact landscape
with disparate protected areas may be better served by the omnidirectional nature of
Omniscape, not allowing the output to be so influenced by defined core areas (Gallo et al.,
24

2020). Efforts to glean the most useful information out of tools like Omniscape (McRae et
al., 2016) or combining tools like Linkage Mapper and Omniscape to maximize each of their
strengths (Gallo et al., 2020) are experimental and ongoing, with a common theme of local
context as an important factor.
2.4 Climate Change
Climate change from human activity is widely considered to be one of the greatest
threats to biodiversity worldwide, first being officially recognized by the United Nations
Convention on Biodiversity at its fifth meeting of the Conference of the Parties in Nairobi in
2000 (Lemieux et al., 2010). Despite climate change’s leading role in biodiversity loss, SCP
applications are just beginning to explicitly incorporate it within the framework (Reside et
al., 2018). Previous SCP analyses may have excluded climate change data given a lack of
reliable, conservation-focused climate data. In reality, conservation areas do not exist in a
vacuum, and climate change-induced fluctuations in temperature, precipitation, snow cover,
permafrost, and extreme weather events have just as much impact on areas that are protected
as those that are not (Mann, 2020).
Provincial parks make up the greatest portion of conservation areas in Tsay Keh Dene
Territory, yet a Canada-wide analysis suggests that they are some of the most susceptible
areas to biome shifts due to climate change (Lemieux & Scott, 2005). While present-day
ecoregional representation is important in a network of conservation areas, this approach
becomes problematic if the targeted biome begins to shift or becomes altogether elusive. If
an area is conserved with a certain species in mind, for example, and that species’ habitat
shifts outside of the area due to a change in climate, then that conservation area is no longer

25

serving its intended purpose and that species could be vulnerable to anthropogenic activity
(Mann, 2020).
Thus, climate-conscious conservation area design that protects both the present-day
and future needs of species has become the focus. Some researchers report success in
capturing range shifts for most species within their protected area system designs under
moderate climate change scenarios (Hannah et al., 2007). Rose and Burton (2009) furthered
this approach, identifying what they call “temporal corridors”, which are areas within the
geographic range of a conservation target (i.e., a species) that are predicted to remain
relatively consistent ecologically despite the generally warming climate. While traditional
corridors offer spatial connectivity, these areas offer a sort of connectivity through time.
Locations that are resistant to climatic changes are known as refugia – as they provide
refuge to species affected by climate change elsewhere in their range. One method of
planning with future habitats in mind is targeting these refugia since they are predicted to
remain as, or transition to, suitable habitat for some species in the future (Ashcroft, 2010).
While specific species can be targeted, identifying climatic refugia can also benefit
understudied and even undiscovered species since they represent habitats that are predicted to
remain relatively unchanged (Garcia et al., 2014). These climatic refugia can be identified
through an area’s ‘climate velocity’ and help delineate novel and disappearing climates of the
future.
In addition to protecting geophysically diverse land facets (combinations of geology,
soil, and topography), there are climate-specific metrics that serve specialized roles in
identifying areas for conservation. Climate change velocity is one, measuring the spatial and

26

temporal variations of climate on the landscape to determine the distance and rate of travel
that species must undertake to keep pace with climate change and find analogous habitat
(Loarie et al., 2009).
Forward velocity represents the rate at which climate is shifting across the landscape,
with low velocity meaning that future climate analogs can be found nearby, while high
velocity means that species must travel great distances to find analogous habitat (Carroll et
al., 2015). Alpine areas typically exhibit high climate velocities, as warming climates mean
that species must relocate to distant mountaintops to find a similar climate (Hamann et al.,
2015). Forward velocities are useful in assessing the conservation status and populations of
species under climate change and evaluating protected area networks (Carroll et al., 2015).
Backward velocity represents the distance from a location, given its projected future climate,
that various species from analogous habitats would have to travel from to colonize that
location in the future (Carroll et al., 2015). In contrast to the high forward velocity of
mountaintops, valley bottoms generally exhibit high backward velocities, as organisms here
must travel long distances to find analogous climates (Hamann et al., 2015). Backward
velocities can also be useful in evaluating protected area networks, as well as identifying
species that may require assisted migration to reach analogous climates (Carroll et al., 2018).
Sites with excessively high forward or backward velocities help identify climates that
are entirely novel or disappear altogether within the study area, while low climate velocities
help identify areas that are resistant to climate change, also known as refugia (Mann, 2020).
Locations with the highest forward velocity have extremely distant future analogous climates
in relation to their present-day climate, possibly to the point where the climate no longer
exists under certain climate change scenarios. In these cases of disappearing climates, an
27

area’s present-day inhabitants are projected to become homeless, raising fears of species
extinctions and the disruption of communities, especially in montane environments
(Williams et al., 2007). Identifying disappearing climates and their associated species
assemblages is vital to the proactive management of these species in order to combat genetic
bottlenecks and potential extinctions (Jackson & Overpeck, 2000). Locations with the highest
backward velocities have extremely distant present-day analogous climates in relation to
their projected climate, possibly to the point where the projected climate does not currently
exist elsewhere. In these cases of novel climates, unknowns abound as novel species
associations form and the potential for other unexpected ecological responses (also known as
“ecological surprises”) arise (Williams et al., 2007). Determining where these novel climates
are likely to arise can be valuable in anticipating and planning for novel communities, even if
predictions on species assemblages are fuzzy at best (Williams & Jackson, 2007).
With the shifting, disappearance, and emergence of various climates described above,
it is paramount that these areas of persistence be protected to safeguard the species found
within them (Loarie et al., 2009). Researchers like Roberts and Hamann (2012) have
examined the paleoecological record to better understand historical refugia in an effort to
predict the locations of future refugia sites. Both species that prefer warmer climates and
species that prefer cooler climates (glacial and interglacial refugia, respectively) should have
their refugia conserved for the sake of species persistence, but those that prefer cooler
conditions should be the immediate focus as global temperatures continue to rise (Ashcroft,
2010). Biotic refugia are species-specific subsets of climatic refugia that are bioticallyinformed (based on the behavior of taxa as opposed to climatic refugia that may not provide
habitat to many species) (Sedell et al., 1990). Based on our understanding of the
28

paleoecological record, refugia provide not only a safe haven during periods of unfavorable
climate but also serve as areas of recolonization if the climate becomes advantageous again
(Morelli et al., 2016). Willis and Whittaker (2000) even argue that refugia are “species
pumps”, as they develop new species and act as biodiversity hotspots due to their persistence
and isolation through time. Protecting these sites anticipated to experience climate change at
slower rates and smaller magnitudes should be of great importance in any conservation area
design.
While incorporating climate change into the SCP framework is highly desirable and
increasingly necessary, effectively doing so can prove challenging due to the complexity of
climate change models and the uncertainty that comes with predicting the future (Lawler &
Michalak, 2017). Any modeling relies on making assumptions, in this case mainly around
carbon emissions, and thus relying too heavily on a given model or set of models can prove
problematic (Groves et al., 2012). Conservation biologists specifically argue that modeled
simulations of climate change are not reliable enough to serve as the foundation of
conservation planning (Beier & Brost, 2010), particularly when considering that modeled
species distributions can carry their own level of uncertainty. These compounding effects of
uncertainty can prove too much for some scientists to include climate change metrics in an
SCP (R. Schuster, personal communication, May 1, 2020).
Furthermore, matching the scale of climate data to the scale of analysis is critical and
some of the available data has mismatched scales (Heller & Zavaleta, 2009). There have been
efforts to accurately downscale climate models for more local applications. For example,
Wang (2016) used bilinear interpolation and elevation adjustment to generate scale-free point
data for all of North America.
29

To facilitate incorporation of climate in SCP, Groves et al. (2012, p. 1652) developed
a framework of various approaches to account for climate change: “(1) conserving the
geophysical stage, (2) protecting climatic refugia, (3) enhancing regional connectivity, (4)
sustaining ecosystem process and function, and (5) capitalizing on conservation opportunities
emerging in response to climate change (e.g., Reducing Emissions from Deforestation and
Forest Degradation [REDD])”. Some of these strategies reinforce existing SCP principles,
but the explicit inclusion of climatic refugia is key. Gillson et al. (2013) advocate for a
similar three-pronged approach for area-based strategies to adapt to climate change:
identifying the geophysical stage, identifying and protecting refugia, and maximizing crossenvironment connectivity.
Previous SCP work has touched on climate change and implicitly included it via the
“persistence” goal inherent to the framework (Sarkar et al., 2006), but Mann’s (2020)
framework explicitly includes climate change data in addition to land facet diversity and
rarity data. Her work demonstrates that a climate change-conscious SCP framework can be
constructed, and my project will build on the groundwork that she laid. I am specifically
adopting her selection of various climate metrics, methods for molding them into a usable
format, and use of conservative targets to justify their inclusion in an SCP.
2.5 Traditional Ecological Knowledge
In addition to incorporating climate change, this research also sought to interweave
Traditional Ecological Knowledge with the SCP framework. Traditional Ecological
Knowledge is a way of knowing that is born out of local experiences of Indigenous peoples
who have harmoniously subsisted with the environment over many generations (Groves &
Game, 2016). Some scholars and Indigenous peoples prefer the term ‘Indigenous
30

Knowledge’ to emphasize locality and empower contemporary knowledge holders who may
not see themselves as ‘traditional’; however, other scholars and Indigenous peoples prefer
‘Traditional Ecological Knowledge’ given its ability to convey the ancient roots of much of
this knowledge and its transgenerational nature (Houde, 2007). I use the term Traditional
Ecological Knowledge as it is more commonly used by the Tsay Keh Dene Nation and Chu
Cho Environmental.
Knowledge of both species and the greater environment is often ingrained in the
management of natural resources by Indigenous peoples, as many cultures hold deep bonds
and social codes towards the land and its inhabitants that simply are not found in most settler
societies (Groves & Game, 2016). While Traditional Ecological Knowledge had previously
been considered inferior to Western science for purposes of resource management by the
scientific community, consensus has grown over the past thirty years that Western and
Indigenous Knowledge are complementary, with a combination of the two leading to better
outcomes in natural resource management (Karjala et al., 2004). The inclusion of TEK is
vital in any instance of natural resource co-management between Indigenous and settler
governments for the sake of genuine cooperation, but TEK is also valuable in any situation
where it is offered or available as a source of local ecological knowledge (Groves & Game,
2016).
In her article “What spiders can teach us about ecology”, Polfus (2018) advocates for
the legitimacy of TEK and its principles surrounding the interconnectedness of species,
people, and place. She argues that the scientific method and the English language both
prioritize the compartmentalization of information, “[E]mphasizing nouns, establishing
boundaries, creating boxes, and fitting things into those boxes.” If ecology claims to be the
31

study of relations among organisms and their physical surroundings, TEK and its inherent
holism should be considered whenever it is offered if complete knowledge is truly being
sought. In her book Braiding Sweetgrass: Indigenous Wisdom, Scientific Knowledge, and the
Teachings of Plants, Robin Wall Kimmerer (2013) describes the importance of combining
our ways of knowing, writing:
It was the bees that showed me how to move between different flowers—to drink the
nectar and gather pollen from both. It is this dance of cross-pollination that can
produce a new species of knowledge, a new way of being in the world. After all, there
aren’t two worlds, there is just this one good green earth. (p. 47)
In one prominent paper, Houde (2007) cautions that TEK cannot simply be reduced to
another collection of data that simply complements existing government datasets, but that it
must be considered in the value-based and cosmological contexts in which it was created.
TEK does not necessarily conform to Western rules and is often minimized down to
polygons for use in a Geographic Information System (GIS) since that is the language and
realm that resource extraction companies and colonial governments speak in and understand
(Houde, 2007). In doing so, there is an unavoidable loss of meaning, especially in cases
where the Indigenous knowledge producers do not have a voice in how that information will
be used. Houde goes on to outline six dimensions, or ‘faces’, of TEK which complement one
another and range in levels of non-Indigenous comprehension (Figure 4). The more easily
understood faces include factual observations, management systems, and past and current
uses. In contrast, the more abstract include ethics and values, culture and identity, and the
overarching concept of cosmology in which the other five faces are rooted (Houde, 2007;
Groves & Game, 2016).
32

Figure 4. The six faces of Traditional Ecological Knowledge and the key components of each dimension.
(Concept adapted from Houde, 2007; graphic adapted from Groves & Game, 2016).

33

Houde’s (2007) more material faces of TEK (factual observations, management
systems, and past and current uses) are understandably easier to implement, as was done by
researchers Bethel et al. (2014) in coastal Louisiana, USA. These scientists focused on
coastal restoration and leveraged TEK to help achieve their goals. Their methods for
incorporating TEK included land-based meetings with members of the local Indigenous
community, individual boating trips with each TEK expert to identify areas of importance
and management strategies to focus on, and finally, validation of the synthesized results.
Bethel et al. (2014) stressed that serious consideration should be placed on who from the
community is selected as a TEK expert to ensure adequate geographic coverage of the study
area, collect accurate information, and a consensus that the findings are valid. This means
choosing well-respected community members from various subregions to ensure buy-in from
the broader Indigenous community, other locals, and government agency personnel if
possible. Bethel et al.’s (2014) research project is self-proclaimed to be rooted in GIS, which
Groves and Game (2016) remind us leaves behind valuable context, but is an excellent first
step in building mutually beneficial relationships with Indigenous communities to bridge
TEK and Western science.
The more abstract of Houde’s (2007) faces of TEK (ethics and values, culture and
identity, and cosmology) are understandably more difficult to implement both in general and
in the SCP framework. In her paper “Indians Don’t Make Maps”, Lucchesi (2018) refutes the
colonial rhetoric stated in her article title, asserting that Indigenous peoples have historically
and still continue to produce maps, but that these cartographic products do not always adhere
to Western-style, resulting in Indigenous accomplishments being erased through racism and
imperialist attitudes. Lucchesi (2018) maintains that the continuation and development of
34

Indigenous mapping praxes is a meaningful way of furthering culture and community. She
uses the example of a Polynesian master navigator, Pius “Mau” Piailug, whose research
contributions helped prove the sophisticated geographic knowledge held by his ancestors in
non-instrumental wayfinding across the sea, revitalizing cultural identity and pride not only
in his native island of Satawal, but across the Pacific islands.
Weiss et al. (2013) note that cultural ethics and values permeate throughout the
natural resource management practices of Traditional Ecological Knowledge and Western
science, and that these influences must be acknowledged within each way of knowing. TEK
is fundamentally multidisciplinary, with nature, politics, and ethics all being interwoven, but
Western science is regulated by the values of Western cultures. Western science has taken
steps in the direction of viewing humans as part of the land-community thanks to ecologists
like Aldo Leopold (1949). However, TEK goes a step further in its embrace of social and
legal dimensions, emphasizing the web of relationships between humans, other species, and
the land, as well as with spirits and ancestors (Weiss et al., 2013). Because of the
cosmological threads in TEK, greater caution must also be placed on using this sacred
knowledge without corrupting its purpose, betraying the privacy of the knowledge holder or
of the sensitive information itself, as well as navigating legal constraints around said
information in regards to treaty negotiations and related endeavours (Berkes, 2018; Ramos,
2018).
Other initiatives have addressed methods of bridging local perspectives and
Traditional Ecological Knowledge into SCP approaches (Baker et al., 2011). This has
included setting objectives and targets, providing conservation and cultural feature mapped

35

data, and verifying map outputs of the SCP process. However, there is an opportunity to
further develop approaches to bridge SCP with Indigenous planning and perspectives.
SCP efforts involving Indigenous peoples have typically occurred when the plan is
being conducted in close proximity to Indigenous territories, and in some cases on behalf of
an Indigenous government itself. Working on behalf of the Taku River Tlingit First Nation of
the Yukon, British Columbia, and Alaska, Heinemeyer et al. (2003) applied TEK in the form
of distribution, ecology, and habitat use patterns to develop habitat suitability models for the
focal species of their SCP. Knowledge was collected from taped interviews and translated
into spatial data via community-drawn maps and verbal descriptions. This resulted in
enhanced habitat suitability index (HSI) models for grizzly bear, moose, Stone sheep,
mountain goat, caribou, and salmonids, in most cases utilizing extensive season-specific
knowledge to build seasonal HSI models (Heinemeyer et al., 2003).
TEK has also been used in SCP in Namibia, integrating the Herero, Himba, and
Damara people’s knowledge to design a conservation area that links existing areas
(Muntifering et al., 2008). This effort utilized local knowledge to build the land use and
water feature mapping component of the plan, creating datasets related to water sources and
usage, village characteristics, and grazing patterns in the region. These data were collected on
the ground with local leaders and hired Indigenous guides, entered into a GIS, and then
iteratively reviewed with local leaders for accuracy and final approval (Muntifering et al.,
2008). Assessing and documenting Indigenous peoples' values and priorities through
workshops that inform conservation tools and initiatives has been paramount in efforts
facilitated by groups such as Round River Conservation Studies (Muntifering et al., 2008)
and The Firelight Group (Kuntz & Vuntut Gwitchin First Nation, 2018).
36

In working on several Indigenous conservation efforts, Heinemeyer (2019) asserts
that TEK enhances the SCP process overall due to the deep, longstanding knowledge that
Indigenous peoples can provide for a specific area. This holds true for climate change – a
great concern for many Indigenous communities – by creating climate-informed species
distribution modeling that builds off TEK of species habitat requirements (Heinemeyer et al.,
2019). The possibility of flipping this structure – having TEK at the center of an SCP-like
analysis with Western science only playing a supplementary role – has been discussed by
Drs. Jean Polfus and Richard Schuster, but this approach has not been fully formulated to
date (R. Schuster, personal communication, May 1, 2020). Bridging Traditional Ecological
Knowledge with the Western science-based SCP approach ultimately empowers Indigenous
communities to develop a long-term vision for connected and ecologically resilient
landscapes in their homelands now and into the future (Heinemeyer et al., 2019).
2.6 Conclusion
The field of Systematic Conservation Planning was born out of lessons learned in the
historical establishment of conservation areas – that they should be designed as a linked
network and be ecologically representative of the larger landscape, taking into account both
ecosystems and the key species that inhabit them in order to effectively conserve
biodiversity. Additionally, these conservation areas must be designed in such a way as to
promote persistence on the landscape and provide corridors for movement that mirror
species’ natural migration. The tenets of representativeness (through informed conservation
feature selection) and persistence (through effective conservation area design and
connectivity) will continue to be the building blocks of Systematic Conservation Planning in
the face of climate change. A multifaceted, holistic approach – including a climate change
37

lens and the interweaving of Traditional Ecological Knowledge – is the best path forward to
effectively conserve the species and ecosystems of today, as well as be prepared for the shifts
that will occur in the future.

3.0 CASE STUDY
3.1 The People of the Rocks
The Tsek’ene2 (commonly spelled Sekani) are an ethnic group of Indigenous peoples
that includes the Kwadacha Nation, McLeod Lake Indian Band, Takla Nation, and Tsay Keh
Dene Nation (Sims, 2017). Their name means “people of the rocks” or “people of the
mountains”, and they share a common language, though there are dialects and regional
variations (Littlefield et al., 2007). Anthropologists generally agree that there were five
historical groups or bands that make up the Tsek’ene today, three of which went on to form
the Tsay Keh Dene (the Sasuchan, T’lotona, and Tseloni peoples) (Jenness, 1937; Sims,
2017) (Figure 5).
Oral tradition, as well as archaeological evidence, suggests that this stretch of the
Rocky Mountain Trench has had continuous human occupation since the last Ice Age,
roughly 11,700 years ago (Sims, 2017). This period, when Tsek’ene lived freely, is fondly
referred to as “The Singing Days”, before outsiders brought resource development to their
land (Christensen, 1987). Tsek’ene peoples traditionally viewed their relationship with the

2
“Tsek’ene”, “Tsek’ehne”, “Tse’khene”, or “Tse’kene” (depending on dialect) are considered to be more
accurate spellings for the ethnic group and language commonly anglicized as “Sekani” (Sims, 2017).

38

Figure 5. Map depicting the traditional territory of the five historical Tsek’ene groups and neighbouring First
Nations. This figure predates the W.A.C. Bennett Dam and flooding of the Rocky Mountain Trench, and thus
the channels of the Finlay, Parsnip, and Peace Rivers are shown (Jenness, 1937).

land and the concept of land ownership as similar to that of a marriage. The idea of
‘possession’ goes both ways, with the land conversely claiming the Tsek’ene as its own, with
the right to reject the people if they were irresponsible towards the land (Sims, 2017). As
such, Tsay Keh Dene and other Tsek’ene have sought to support the land since the beginning
of time – a feat made more difficult since European contact in 1793 (Sims, 2017).
The Tsek’ene traditionally had their own economy in the form of harvest and trade
activities prior to the advent of the Euro-Canadian fur trade. Each band held its own territory
and each family held a sub-territory (Sims, 2017). These territories were not rigid when it
came to other Tsek’ene, but Tsek’ene staunchly defend their territorial claims when other
First Nations are involved. Even members of groups that they intermarried with like the
39

Gitsxan were killed on sight if found hunting in Tsek’ene territory (Jenness, 1937).
According to the Gitsxan, “The Sekani came from a wolf, so they, like the Hagwilgate
Indians [descended from a grizzly], are always killing people” (Jenness, 1934, p. 241). In
practice, this meant that non-Tsek’ene cannot gain rights to the territory unless they are the
parent of a Tsek’ene child or are a spouse of a Tsek’ene member, and only for the duration of
the relationship (though exceptions have been made for those that have become ingrained in
the community) (L. Gleeson, personal communication, April 15, 2021; Sims, 2017). The
Tsek’ene were historically a nomadic people as well, moving with the seasons as game was
too scarce to stay in any one place for too long. The places they migrated between were
recognized by the Tsek’ene as village sites, even though a few were transformed into fur
trade posts that Euro-Canadians took credit for establishing after the fact (Sims, 2017).
3.2 Geography and Natural History of Tsay Keh Dene Territory
The Territory of Tsay Keh Dene Nation is a river-filled and mountainous landscape
north of Mackenzie, BC, and is a large, 32,000 km 2 area – similar in size to the Netherlands.
Located within the Rocky Mountain Trench region and encompassing portions of the
Omineca and Rocky Mountains, the Territory has an abundance of waterways, including the
Peace, Omineca, Ospika, Osilinka, Mesilinka, Manson, Nation, Ingenika, Akie, Swannell,
Parsnip, and Finlay Rivers (Figure 6). These rivers once naturally converged, but their
confluence today is the Williston Reservoir, a result of the W.A.C. Bennett Dam constructed
in 1968. The topography varies in the region from low elevation forested landscapes near the
reservoir (670 m) to rugged mountainous terrain at the northern edge of the Territory near
Mt. Ulysses (3,014 m) (Fish and Wildlife Compensation Program, 2014).

40

Figure 6. Topographical/hydrological map of Tsay Keh Dene Territory

The climate in the region is characterized by cold, snowy winters that result in deep
snowpack, and mild, rainy summers that make for a short growing season. The reservoir
moderates the temperature of its surroundings given its size, acting as a source of local
climate change since its flooding. The mean annual temperature is 0.5 °C, where January
sees an average of -18 °C and July sees an average of 13 °C. The common extreme
temperatures experienced are -47 °C in the winter up to 32 °C in the summer (Fish and
Wildlife Compensation Program, 2014). Annual snowfalls range from roughly 1 meter in the
valleys on up to 4 or more meters in the mountains. Rainfall also varies greatly across the
region, averaging anywhere from 250 mm to 1300 mm depending on location. The overall

41

average annual precipitation is 800 mm, split evenly between snow and rain (Fish and
Wildlife Compensation Program, 2014).
Natural disturbance regimes within the Territory have been affected by the reservoir,
with riparian and wetland habitats that fell in the confluence floodplains now lost following
the inundation of the reservoir (Fish and Wildlife Compensation Program, 2014). Wildfires
are a common occurrence in the region, with 112,000 hectares burned in 2014. Forest insects
are also a prevalent natural disturbance in the region, often occurring cyclically with fire.
Recently, the region has experienced significant mountain pine beetle outbreaks in 2005 and
2009, and western balsam bark beetle to a lesser extent. More recently, spruce beetle has
been detected with increasing frequency (Nicholls, 2014, 2019).
The Territory consists of several Biogeoclimatic Ecosystem Classification (BEC)
zones, as defined by British Columbia’s Biodiversity Guidebook (BC Environment, 1995).
These include Boreal White and Black Spruce, Engelmann Spruce-Subalpine Fir, Sub-Boreal
Spruce, Spruce-Willow-Birch, and Boreal Altai Fescue Alpine.
Some of the key wildlife species found in the Territory include woodland caribou,
grizzly bear, Stone sheep, moose, elk, fisher, mountain goat, and wolverine (Fish and
Wildlife Compensation Program, 2014; Tsay Keh Dene Nation, personal communication,
September 30, 2019). Riparian species like Arctic grayling, mountain whitefish, and rainbow
trout persist in the area despite the reservoir. In contrast, lacustrine species like lake whitefish
(half the fish population in the reservoir), bull trout, kokanee, lake trout, ling, and peamouth
thrive (Loo, 2007).

42

3.3 Resource Extraction History and the W.A.C. Bennett Dam
Tsay Keh Dene Territory saw only limited development and settlement by EuroCanadians from first contact up to 1956. Early on, Sir Alexander Mackenzie and later Simon
Fraser established fur trade posts in the Territory, which meant intermittent but increasing
exchanges as the fur trade evolved. Euro-Canadian trappers were the predominant nonTsek’ene population in the Territory until 1861, when prospectors unearthed gold in the
Parsnip River and ushered in the Peace River Gold Rush. After prospectors discovered gold
along tributaries of the Omineca River in 1868, the Omineca Gold Rush began and brought
even more settlers, including Roman Catholic missionaries. As migration flowed north, the
North-West Mounted Police established a presence in the area, but that ebbed with the bust
of gold mining interest (Sims, 2017).
Other settlement efforts in the Territory occurred throughout the first half of the
twentieth century as mining went boom or bust and speculators anticipated railway
construction through the Territory. The idea of clearing the land for agriculture in the region
was also proposed by settlers living to the south, but neither this nor any of the
aforementioned efforts ever truly materialized. This largely allowed the Tsek’ene to continue
with their traditional lifestyle (Sims, 2017). Wartime in the 1940s saw the forest industry
expand and start to reach northwards into the southern stretches of Tsek’ene Territory. This
provided work for many Tsek’ene and was nearby for McLeod Lake people. Tsay Keh Dene,
however, had much further to travel, ushering in a seasonal migration south in the spring and
summer before returning home in the fall and winter to trap (Sims, 2017). Summit Lake
north of Prince George was a popular destination for Tsay Keh Dene families seeking work

43

at the mill there; however, many families remained in their Territory (L. Gleeson, personal
communication, April 15, 2021).
Construction of the Hart Highway in 1952 represented the first substantial connection
between Tsek’ene Territory and the outside world, linking Tsek’ene Territory to Prince
George in the south and the Peace River Country to the east via the Pine Pass – a link to the
Alaska Highway. As transportation to, from, and within Tsek’ene Territory had been one of
the major impediments to increased Euro-Canadian presence, the highway's completion
brought more and more settlers to the region in the name of resource extraction (Sims, 2017).
This influx of settlers was further exacerbated by the construction of the Pacific Great
Eastern Railway from Prince George to Fort St. John, which runs alongside the Hart
Highway. Moose were a frequent casualty of the newly introduced cars and trains. The
highway and railway also contributed to the decline of local caribou herds by severing their
range and increasing access to industry, which led to deforestation (Sims, 2017).
With increased access, harnessing the power of the Peace River became feasible. The
Province and the recently nationalized BC Hydro crown corporation were ultimately the
parties leading the charge for construction of a dam, and did most of their consulting – or
rather, informing as Sims (2017) argues – of the Tsek’ene with Indian Affairs serving as a
go-between. Sims (2017) also contends that the Tsek’ene were not properly consulted, but
they may not have been overly concerned at the time even if they had been, as no
development or settlement efforts to date had materialized in the Territory (Stanley, 2010).
Construction of the W.A.C. Bennett Dam was completed in the fall of 1967 and initially
flooded in the spring of 1968. The Province had not fully prepared for this event, failing to
properly clear the reservoir basin and underestimating how quickly it would fill. The result
44

was a pool of debris that caught both people and wildlife in the area off guard, causing
Tsek’ene to retreat to camps at higher ground and animals like moose to lose large swaths of
habitat, or in some cases drown (Sims, 2017). The water body would come to be known as
Williston Lake3 (despite its unnatural formation), running 250 km north-south and 150 km
east-west and ultimately resulting in the loss of approximately 1,500 km2 of high-complexity
lowland habitat (Fish and Wildlife Compensation Program, 2014; Loo, 2007).
3.4 Contemporary Resource Extraction and the Williston Reservoir
The habitats lost to the Williston Reservoir include woodlands, wetlands, floodplains,
riverine, and lake habitats, which were replaced with homogenous, artificial reservoir
habitats. This destruction of large riverine habitat was likely why 24 populations of Arctic
grayling were lost from the drainage. Also lost were critical winter range and wildlife
corridors for woodland caribou, Stone sheep, and grizzly bear. Furthermore, a host of other
species were affected by the loss of riparian areas and forced migration to areas of higher
elevation, where deeper snowpack made foraging difficult (Fish and Wildlife Compensation
Program, 2014).
The effects of the Williston Reservoir were compounded with the opening of new
transportation routes for industries like forestry and mining (Ingram, 2012). The reservoir
serves as an easily navigable waterway, with booms and barges able to transport timber to the
Hart or Alaska Highway and beyond. The added reach of logging companies meant

3

While the Williston Reservoir is officially named a Lake on settler maps, I have chosen to refer to it as a
reservoir throughout given its artificiality and in accordance with the cultural and ecological practice of the
Tsay Keh Dene.

45

additional harvesting in areas adjacent to the reservoir, further fragmenting habitat for
species like caribou. Additionally, many of the impacts are linear in nature, with service
roads and bridges having a significant cumulative impact not only on habitat but also on
migration corridors. Road culverts hinder fish migration when poorly maintained. The
reservoir itself is also a linear feature, significantly affecting various species by severing their
home ranges and migration corridors (Fish and Wildlife Compensation Program, 2014).
Anthropogenic land use and the road network, as well as natural phenomena like land
cover and slope of topography, all contribute to decreased levels of permeability on the
landscape, making it more difficult for species to traverse landscapes in the Territory.
Additionally, the percentage of the Territory that has been modified – 30% to date – will
likely only increase over time.
3.5 The Tsay Keh Dene First Nation
In 1971, many Tsay Keh Dene abandoned the camps and government-selected
reserves they had been living on near Mackenzie to settle at Ingenika Point on the reservoir's
northwest tip, as well as other points along the Ingenika River (Grassy Bluff) and Pelly Creek
(Tucha Lake) in the northwest part of the Territory (L. Gleeson, personal communication,
April 15, 2021; Sims, 2017). Referred to by the Indian Act as the Ingenika Band at the time,
the Tsay Keh Dene defied both the provincial and federal governments in this action in an
attempt to live a more isolated and traditional lifestyle. They sought to have a new reserve
established at this location, and families that remained on the Parsnip Reserve near
Mackenzie eventually moved northward (L. Gleeson, personal communication, April 15,
2021). When requests to the government failed, the Ingenika Band appealed to public
opinion, ultimately coming to an agreement with BC Hydro, the Province of British
46

Columbia, and Canada in 1989 that granted them a community site 16 km to the north, at the
very northern tip of the reservoir. This agreement established not only the present-day Tsay
Keh community site, but also the Tsay Keh Dene First Nation (Sims, 2017). The Tsay Keh
Dene community site was officially designated a reserve in 2019 (L. Gleeson, personal
communication, April 15, 2021).
The Nation has approximately 500 members today, with about half of those living in
the Territory. Of those in the Territory, a majority live in the Tsay Keh community (British
Columbia Assembly of First Nations, 2020; L. Gleeson, personal communication, April 15,
2021; Littlefield et al., 2007). In addition to the 2,100 acres at the village site, the Nation has
1,000 acres at the Blackpine Reserve, five acres at the old Ingenika Point site for a cemetery,
and 320 acres at Police Meadows (Littlefield et al., 2007; Abadzadesahraei, personal
communication, May 18, 2020). There were also two former reserves near Mackenzie: Tutu
Creek (90 acres that were never inhabited) and Parsnip (80 acres that are no longer inhabited)
(L. Gleeson, personal communication, April 15, 2021). The Nation’s governance structure is
a custom electoral system with an elected Chief and Council independent of any treaty or
tribal association (British Columbia Assembly of First Nations, 2020). In addition to the
elected Chief, the Nation allots three of the elected councillor positions to members living in
the Tsay Keh community, with a fourth to an elected member that lives outside of the
community to represent Tsay Keh Dene members who also live elsewhere (Tsay Keh Dene
Nation, 2020b).
The Nation holds an administrative office in Prince George and has an Economic
Development Corporation to build capacity within the Nation, with “[S]everal businesses that
work hard to provide revenue and employment for the Nation and its members. The Nation's
47

businesses are overseen by a Governing Economic Development Board that reports to the
Elected Chief & Council” (British Columbia Assembly of First Nations, 2020). Chief &
Council work with the businesses to deliver on the vision of the Nation. These businesses
include Chu Cho Industries, Chu Cho Environmental, Chu Cho Forestry, Tsay Keh
Developments (which houses a guide outfitting business), and Ootsa Air (British Columbia
Assembly of First Nations, 2020). The natural resource businesses in particular provide
employment to many Tsay Keh Dene and provide a sustainable revenue stream to the Nation,
as well as the opportunity to perpetuate cultural practices on the landscape within their
Territory (Tsay Keh Dene Nation, 2020a).
3.6 Existing Conservation Areas
At the continental scale, Tsay Keh Dene Territory is nestled among large protected
area systems – the national parks of the central Rocky Mountains and the provincial parks in
and around the Muskwa-Kechika Management Area. Currently, 9.2% of the Territory is
under protected area status (Table 2). Five of these protected areas (all provincial parks) are
relatively large, each 200 km2 or greater in size. These larger parks are generally at least 50
km from one another though, making movement between them more difficult for species.
Designating additional conservation areas would further the Nation’s values of biodiversity,
and also help the federal government of Canada work toward its commitment of protecting at
least 25% of the nation’s land and inland waters by the end of 2025 (Liberal Party of Canada,
2019).

48

Protected Area Name
Omineca Park
Graham - Laurier Park
Sustut Park
Chase Park
Redfern-Keily Park
Muscovite Lakes Park
Ed Bird - Estella Lakes Park
Sikanni Chief River Ecological Reserve
Sustut Protected Area
Omineca Protected Area
Ospika Cones Ecological Reserve
Chunamon Creek Ecological Reserve
Blackwater Creek Ecological Reserve
Raspberry Harbour Ecological Reserve

Protection Designation
Provincial Park
Provincial Park
Provincial Park
Provincial Park
Provincial Park
Provincial Park
Provincial Park
Ecological Reserve
Protected Area
Protected Area
Ecological Reserve
Ecological Reserve
Ecological Reserve
Ecological Reserve

Area (km2)
914.1
779.1
498.3
361.9
215.4
57.1
55.9
21.8
18.0
17.1
12.8
3.4
2.9
1.2

Table 2. Protected Areas by designation and size in Tsay Keh Dene Territory

In 2020, the Nation proclaimed the establishment of the Ingenika Conservation and
Management Area (CMA), a 5,049 km2 area centered on the Ingenika River watershed that
connects Finlay-Russel and Chase Provincial Parks. An Indigenous-led management plan for
this IPCA is in development, with the goal of protecting the Ingenika’s biodiversity using
Tsay Keh Dene values, management practices, laws, and Traditional Ecological Knowledge
(Chu Cho Environmental, 2020b).
3.7 Historical and Projected Climates in Tsay Keh Dene Territory
The Territory warmed by 2-3 °C over the 20th century, although this was likely more
pronounced in the mountainous portions of the Territory. The 1900s also saw an increase of
30 mm of precipitation, with increases in all seasons except winter snowfall over the latter
half of the century (Pacific Climate Impacts Institute, 2013). More locally, the flooding of the
49

reservoir caused adjacent temperatures to drop by a few degrees, as well as causing a
significant increase in windiness in the reservoir valley and increased humidity in the form of
a fog layer that now occurs in the fall (Loo, 2007).
The climate of the Territory is projected to continue to warm through the 2020s,
2050s, and 2080s (Pacific Climate Impacts Institute, 2013) when compared against a 19611990 baseline (Table 3). The annual mean temperature is projected to consistently rise, as is
annual precipitation, with much of the increase coming in the winter rather than the summer.
Annual snowfall specifically will only modestly increase, while spring snowfall will decline
drastically. Finally, there will be a corresponding increase in frost-free and growing-degree
days and a decrease in heating-degree days as temperatures increase (Pacific Climate Impacts
Institute, 2013).
These changes will not be uniform in this topographically diverse geographic setting
given that temperature is affected by elevation and precipitation by topography. This means
that the mountaintops will be cooler and wetter, while the valley bottoms found along the
reservoir will experience higher temperatures (BC Agriculture & Food Climate Action
Initiative, 2013). Extreme temperature and precipitation weather events are also predicted to
increase in magnitude, frequency, and intensity in the region. Extreme cold temperatures are
predicted to occur less frequently, while abnormally warm temperatures are predicted to
occur more often. Longer droughts are also predicted in the summer along with spells of
extreme rainfall events. The overall increase in precipitation may result in rising water levels
in both the rivers and the reservoir (BC Agriculture & Food Climate Action Initiative, 2013).

50

2%
7%
5%

Summer
Winter
Winter

Table 3. Climate Projections for the territory

Annual

Annual

Heating Degree Days*
(degree days)

Frost-Free Days* (days)

Annual

+9 days

-352
degree days

+129
degree days

-30%

5%

Annual

Spring

+1.0 °C

Ensemble
Median

Annual

Season

Growing Degree Days*
(degree days)

Snowfall* (%)

Precipitation (%)

Mean Temperature (°C)

Climate Variable

51

+5 to +16 days

-616 to -180
degree days

+54 to +177
degree days

-48% to +2%

-1% to +11%

-1% to +12%

-4% to +9%

-2% to +10%

+16 days

-651
degree days

+225
degree days

-55%

7%

11%

3%

8%

+1.8 °C

+0.5 °C to +1.7

°C

Ensemble
Median

-69% to -16%

-7% to +17%

-4% to +22%

-7% to +12%

+1% to +16%

+1.4 °C to +2.8

+10 to +25 days

-989 to -485
degree days

+139 to +380
degree days

°C

Range (10th to
90th percentile)

2050s Projected Change from
1961-1990 Baseline

Range (10th to
90th percentile)

2020s Projected Change from
1961-1990 Baseline

+26 days

-996
degree days

+364
degree days

-70%

8%

16%

1%

10%

+2.8 °C

Ensemble
Median

-89% to -19%

-0% to +20%

+3% to +29%

-10% to +17%

+2% to +25%

+1.7 °C to +4.5

+13 to +40 days

-1625 to -617
degree days

+200 to +626
degree days

°C

Range (10th to
90th percentile)

2080s Projected Change from
1961-1990 Baseline

4.0 METHODS
This research project first sought to identify which portions of Tsay Keh Dene
Territory have the highest conservation value both now and in the future. Next, it examined
how to explicitly include connectivity within the systematic conservation planning
framework. Finally, it explored which stages of the SCP process provided an opportunity for
the interweaving of Traditional Ecological Knowledge to produce a more inclusive
conservation plan. To address these questions, I used a modified SCP framework originally
outlined by Margules and Pressey (2000) supported by community input and Traditional
Ecological Knowledge to assess Tsay Keh Dene Territory (Figure 7). I took a critical GIS
perspective and community-led approach, including input from the Nation whenever possible
to enhance this community-initiated research project and further the Nation’s goals in the
face of settler agendas (Wilson, 2009). The result is an actionable and defensible Systematic
Conservation Plan that can be used to assist the Nation with routine resource extraction
referrals and long-range land-use planning decisions.
Selecting a relevant study area was essentially Stage 0 of the SCP process given its
ecological relevance and downstream ramifications. I initially chose to use the Nation’s
Territory as my study area, as it is both the Tsay Keh Dene’s ancestral lands and the focus of
much of the environmental work performed by the Nation and their wholly-owned consulting
firm – Chu Cho Environmental. The appropriateness of this extent was assessed with the
Nation as part of establishing conservation goals for the SCP in Stage 1 of the process. The
study area was ultimately built with caribou herds and supplemented with important
watershed groups (Figure 8).

52

Figure 7. The Systematic Conservation Planning process and points for community guidance and the
interweaving of Traditional Ecological Knowledge.

53

Figure 8. Contextual map showing the selected study area in relation to Tsay Keh Dene Territory.

The SCP process formally begins with the development of conservation goals for a
chosen study area, so I first collaborated with the Nation to set goals for the greater territory.
The next step was selecting which features on the landscape help attain those goals. I curated
an initial set of conservation features based on previous work to present to the Nation for
refinement. I then gathered spatial data for the agreed-upon conservation features in a GIS,
utilizing provincial/federal government and Nation sources, as well as TEK-sourced data
whenever it was available. Next, I delineated areas of human footprint with location and
weighting input from the Nation in order to quantify connectivity between protected and
ecologically intact areas across the landscape. I then set targets for the selected conservation
54

features based on ecological best practices and prior SCP work, and reviewed the targets with
the Nation in an interactive format utilizing prioritizr, a conservation prioritization software
(Hanson et al., 2021). Next, I reviewed the existing network of conservation areas to quantify
how well they met those targets, and then identified a suite of ecologically valuable areas as a
final product. I identified a suite of high-value conservation areas that are ideal for the
present-day climate, but also for the projected climates of the 2050s and 2080s. This
provided comparisons between present and future scenarios to allow decision-makers to
select areas with the greatest resiliency for conservation.
4.1 Stage 1: Set Conservation Goals/Select and Compile Conservation Feature Data
I began by collaborating with the Tsay Keh Dene Nation to articulate conservation
goals for the Territory. These goals were established from existing Nation objectives and
conversations with employees and other representatives of the Nation and Chu Cho
Environmental. The objectives from The Greater Muskwa-Kechika report (Weaver, 2019)
served as an example. By establishing these goals, I identified which features on the
landscape could help attain each goal and form the basis for the selection of conservation
features.
The selection of conservation features (Table 4) was informed by the Systematic
Conservation Planning work completed by Curtis (2018) and Mann (2020) and incorporated
both fine- and coarse-filter features to focus on all levels of biodiversity. Including surrogate
species and rare habitat data as fine-filters ensure that specific species and biological
assemblages are conserved as part of the plan. Coarse-filter features capture ecosystem
diversity and complement the fine-filter approach. The coarse-filter approach can capture
representation of various habitats, with diverse topography and rare vegetative assemblages
55

rising to the top. The third level of biodiversity, genetic diversity, is indirectly accomplished
through the SCP steps by maintaining connectivity, conserving areas large enough to
maintain minimum viable populations of species, and the decision to represent species at fine
scales (e.g., each caribou herd in the Territory was its own feature).
Traditional Ecological Knowledge is often thought of in a material sense, such as
factual observations, management systems, and past and current uses of species and the land
(Houde, 2007). While I utilized TEK-sourced data provided to me by the Nation whenever
possible, the methods for this project were not designed to collect first-hand data through
interviews with elders or other means. Instead, I consulted with staff scientists and other
representatives of the Nation and Chu Cho Environmental, who gave voice to community
values. They communicated the Nation’s conservation goals and guided the selection of
conservation features. In many cases, this involved the Nation sharing species and ecosystem
data to include as part of the project. In some cases, those data were sourced from TEK
through Traditional Use Studies (TUS) or other documentation efforts by the Nation. Some
of these TEK-sourced spatial layers are of particularly sensitive information, such as
medicinal plant locations or the location of cultural and spiritual sites. Data of this nature was
used solely as an input to the conservation prioritization model and was never included in
any visual representations (e.g., maps) to protect privacy.
Once an initial list of conservation features was compiled, I iteratively reviewed it
with the Nation for feedback, data availability, and final approval to proceed. I then
delineated the conservation features into five categories (Figure 9; Table 4) and documented
how and why each was chosen within a spreadsheet for the sake of transparency. I noted the
source and specific dataset used for each conservation feature (Appendix B. Conservation
56

Feature Data Sources). Additionally, I documented any modifications (e.g., buffers) that were
made and the rationale for the changes. I then standardized each spatial layer into a consistent
set of shapes known as ‘planning units’, allowing prioritizr to assess each conservation
feature in a consistent fashion. While irregularly shaped planning units can be used (e.g.,
watersheds), I chose to systematically divide the landscape into a hexagonal grid,
constructing planning units of uniform size and alignment. While I maintained the 1 km2sized planning unit used by Curtis (2018) and Mann (2020), I decided to opt for vector-based
hexagons because hexagons possess a lower edge-to-area ratio than squares while still
maintaining contiguous links to their neighbours (Horn, 2011).

57

Figure 9. A stacked set of select conservation feature layers

58

Coarse-Filter Features
Abiotic

Diverse combinations of slope, aspect, elevation and landform

Land Facet Rarity

Rare combinations of slope, aspect, elevation and landform

Elevational Diversity

Cluster of varying elevations

Ecotypic Diversity

Diverse Ecological Land Units (made up of physical features, climate, and land cover type)

Heat Load Index Diversity

Diverse collection of solar radiation exposure (i.e. how hot or cool an area is)

(Disturbance)-(BEC Zone)-(Age/Burned)
Ex 1: NDT1-ESSF-Burned
Ex 2: NDT2-SBS-Mature/Old
Rare BEC Zones
Fine-Filter Features
Grizzly Bear

Combination of disturbance, Biogeoclimatic Ecosystem Classification Zone, and age/burn
history of tree stands to meet biodiversity goals from the Biodiversity Guidebook
(B.C. Environment, 1995)
Rare occurrances of BEC subzone variants in the territory
Description
Capable habitat for grizzly bear, enhanced with TEK
Critical habitat for bull trout focusing on spawning and juvenile rearing sites; enhanced with
TEK on bull trout and other fish species
Suitable habitat for fisher based on denning, resting, moving, and foraging needs
High quality habitat for caribou by herd, enhanced with TEK
Year-round habitat for moose, enhanced with TEK
Suitable habitat for Stone sheep, enhanced with TEK
Suitable habitat for mountain goat, enhanced with TEK
Suitable habitat for wolverine
Suitable habitat for bank swallow
Suitable habitat for barn swallow
Suitable habitat for western toad
Suitable habitat for horned grebe
Suitable habitat for little brown myotis
Suitable habitat for northern myotis
Suitable habitat for olive-sided flycatcher
Suitable habitat for rusty blackbird
Wetlands by size of complex
Lakes by size
Cave ecosystems based on likelihood of occurrence
Description

Biotic

Land Facet Diversity

Environmental

Description

Special
Features

Species

Bull Trout/Fish
Fisher
Caribou (by herd)
Moose
Stone Sheep
Mountain Goat
Wolverine
Bank Swallow
Barn Swallow
Western Toad
Horned Grebe
Little Brown Myotis
Northern Myotis
Olive-Sided Flycatcher
Rusty Blackbird
Wetlands
Lakes
Karst Deposits
Climate Change Features

Misc.

Refugia

Migration

Backward Velocity 2055
Backward Velocity 2085
Forward Velocity 2055
Forward Velocity 2085
Climate Corridors
Cool Headwater Refugia
Climatic Refugia
Biotic Refugia
Bird Richness
Carbon Storage (above and below ground)
Cultural Features
Sites of Cultural Importance
Cultural/Spiritual Areas
Subsistence Areas
Connectivity Features
Linkage Mapper
Omniscape

Distance from a projected 2055 climate location back to analogous existing climate locations
Distance from a projected 2085 climate location back to analogous existing climate locations
Distance from a single source to multiple projected 2055 climate analogs
Distance from a single source to multiple projected 2085 climate analogs
Connections between current and future locations of a climate type
Areas predicted to have a mean annual temperature change of <1°C by 2080
Areas where climate-threatened species can continue to exist or readily colonize
Climatic refugia that are further informed with biological thresholds
Predicted summer habitat for 604 climate vulnerable bird species under a 3°C warming
scenario
Above and below ground carbon storage
Description
TEK-sourced point data on habitation, subsistence, transportation, wildlife, and
cultural/spiritual locations
TEK-sourced polygon data on cultural areas like burial sites, medicinal plant locations,
battlegrounds, campsites, and teaching places
TEK-sourced polygon data on subsistence areas like berry picking sites, hunting grounds, and
fishing holes
Description
Highly connected areas between existing and proposed protected areas
Highly connected areas throughout the entire landscape

Table 4. List of Conservation Features used in analysis

59

4.1.1 Coarse-filter Conservation Features
Coarse-filter conservation features are used as a means of representing ecosystemlevel biodiversity, conserving the greatest diversity of physical landscapes as possible –
including biotic, abiotic, and climatic representation.
4.1.1.1 Abiotic
The abiotic coarse-filter conservation features of this SCP include land facet diversity
and land facet rarity. Using land facet data developed by Michalak et al. (2018), I developed
layers for land facet diversity and land facet rarity. Land facet diversity was selected because
it represents a varied collection of land facets within a relatively small area, allowing for
different niches in close proximity. Diversity was generated using the ‘variety’ option in
ArcMap’s Focal Statistics tool. The most diverse portions were selected until 10% of the
study area was achieved. Land facet rarity was selected because it represents unique
combinations of physical features that are relatively scarce. For rarity, the least common land
facets were selected until 10% of the study area was achieved.
4.1.1.2 Environmental
The environmental coarse-filter conservation features included elevational diversity,
ecotypic diversity, and heat load index diversity. Like land facets, varied occurrences of these
features in concentrated areas represent high conservation value for the diverse habitats they
can hold (Carroll et al., 2017). The data were already processed to be measures of diversity
(varied occurrences within a spatial neighbourhood) and were sourced from Carroll et al.
(2017). Elevational diversity mapped dissimilar elevations in close proximity. Ecotypic
diversity used ecological land units (ELUs), a classification developed by Sayre et al. (2014)
and derived from growing degree days, an aridity index, landform, lithology, and land cover
60

type. This metric is similar to land facets but goes further by including climate and land
cover data (Carroll et al., 2017). Heat load index (HLI) is a metric based on slope, aspect, and
latitude (Mann, 2020). HLI estimates the annual solar radiation exposure of an area,
predicting cool, warm, or hot microclimates (Carroll et al., 2017). The more desirable 50% of
values were selected for all three of these layers.
4.1.1.3 Biotic
Biotic coarse-filter conservation features represent biodiversity as expressed through
present-day vegetative communities, in this case derived from the concept of forest pattern
and process (Curtis, 2018). The ‘Mature/Old’ classification varies by biogeoclimatic zone
such that the stand is at a minimum anywhere between 80-120 years old. The ‘Burned’
designation, on the other hand, is consistent in that it means the stand has been exposed to
wildfire within the last 40 years. By using the BEC zone and disturbance type combinations I
ensured representation of each dominant tree species/climatic condition and disturbance
frequency. From there I selected for a biodiversity emphasis of mature/old or naturally young
(i.e., burned) forests. The classifications and methods described are derived from the
Biodiversity Guidebook of British Columbia (BC Environment, 1995) (Table 5).
BEC zones also have subzones based on precipitation, temperature, or continentality,
as well as further variants of those subzones reflecting their relative location or distribution
within the subzone (BEC Program, 2011). Chu Cho Environmental staff expressed an interest
in capturing rare BEC occurrences, so I adapted a rarity scoring spreadsheet from BC Parks
(2020) to create a layer of the rarest BEC zones in the Territory down to the variant level.

61

Natural Disturbance Type (NDT)
NDT1
NDT2
NDT3
NDT4
NDT5
BAFA
BWBS
ESSF
SBS
SWB

ecosystems with rare stand-initiating events
ecosystems with infrequent stand-initiating events
ecosystems with frequent stand-initiating events
ecosystems with frequent stand-maintaining events
alpine tundra and subalpine parkland

Biogeoclimatic Ecosystem Classification (BEC)
Boreal Altai Fescue Alpine
Boreal White and Black Spruce
Engelmann Spruce—Subalpine Fir
Sub-Boreal Spruce
Spruce—Willow—Birch

Table 5. Disturbance and Biogeoclimatic Ecosystem Classifications from the Biodiversity Guidebook of
British Columbia that are found in Tsay Keh Dene Territory.

4.1.1.4 Representation
Quantifying how various categories of the landscape are captured in a conservation
solution provides valuable information even in the absence of target setting. While certain
portions of existing BEC zones were targeted in other layers for their ecological value or
rarity, including the zones as a whole for representation purposes provided additional insight.
Elevational representation was important for understanding if the existing network of
protected areas or the identified potential conservation areas were biased towards low,
moderate, or high elevation areas. I used three elevation classes approximating the break
points used by Weaver (2019).
Ecoregions are a federal classification system used to assess ecological representation
(Mann, 2020). Greater Tsay Keh Dene Territory contains nine distinct ecoregions, though
62

just four (Omineca Mountains, Northern Canadian Rocky Mountains, Central Canadian
Rocky Mountains, and Boreal Mountains and Plateaus) make up most of the Territory. This
classification system is characterized by distinct assemblages of natural communities and
species (Olson et al., 2001). Consequently, assessing how well each ecoregion present in my
study area was captured by existing protected areas and identified potential conservation
areas was valuable information.
4.1.2 Fine-filter Conservation Features
Many of the focal species that applied to the Wild Harts Area of the Peace River
Break (Curtis, 2018; Mann, 2020) also apply to Tsay Keh Dene Territory given boundary
overlaps. These species include caribou, grizzly bear, fisher, and bull trout. Additionally, the
Nation asked for the inclusion of moose, wolverine, Stone sheep, and mountain goats (Tsay
Keh Dene Nation, personal communication, September 30, 2019). Data for these species
were screened for availability and tied to specific ecosystem conditions as surrogates.
Additional species were selected based on input from the Nation and Chu Cho
Environmental data from species at risk work they have performed previously. In addition to
Western science-sourced datasets, the Nation provided TEK-sourced data, some of which
was used to enhance species data. I also included fine-filter layers of ‘special features’, which
are ecosystem components that are highly biodiverse, sensitive, and/or rare on the landscape
(Heinemeyer et al., 2004). While Curtis (2018) and Mann (2020) used a consolidated special
features layer, I included wetlands, lakes, and karst deposits individually to ensure the
capture of each. While mineral licks also fall into the special feature category, the only data
available was TEK-sourced, and was included as a cultural feature (described later).

63

4.1.2.1 Species
There were a series of species with data constructed largely from Western science but
enhanced with TEK. These included grizzly bear, caribou, Stone sheep, mountain goat,
moose, and bull trout. The TEK-sourced species data was derived from traditional use studies
and is part of the Nation’s Cultural Knowledge Keeper database. While each species has
scientific justification for their selection outlined below, it is important to note that they each
hold inherent value to the Tsay Keh Dene and that was justification enough for their
inclusion in this study.
Grizzly bears were included because they represented large, intact landscapes, opencanopy forests and avalanche chutes, and provided umbrella representation for a multitude of
other species (Curtis, 2018). I used weighted habitat suitability data sourced from the BC
Data Catalogue.
Caribou also represented large, intact landscapes, but more specifically alpine and
subalpine parkland habitats. Additionally, they were characterized by old, mid-elevation tree
stands containing arboreal lichens (Mann, 2020). I constructed a spatial layer of high-value
caribou habitat in a stepwise fashion, beginning with data from Mann (2020) – largely based
on data from Gustine and Parker (2008). High-quality caribou habitat outside of Mann’s
study area was supplemented first with data from Weaver’s Muskwa-Kechika report (2019),
then provincial Ungulate Winter Range data, and finally Demarchi and Demarchi’s
provincewide habitat assessment (2003) to fill the remaining study area gaps.
Stone sheep and mountain goats represent precipitous terrain in remote wilderness
areas as well as areas of concentrated elevational diversity (Heinemeyer et al., 2004). These

64

layers were constructed in a similar stepwise fashion as caribou, each starting with data from
Heinemeyer (2004) that selected high-quality winter and growing season habitat. Data from
Weaver (2019) was used to supplement the sheep layer outside of Heinemeyer’s study area,
and Ungulate Winter Range was used to fill the study area extent for both species.
Moose represented a unique suite of ecosystems, including young deciduous (or
mixed-wood) forests, productive lakeshores and wetlands, and dense, mature/old forests
(Heinemeyer et al., 2004). This layer was constructed by beginning with the provincewide
Demarchi (2003) summer moose model and averaged with a localized model by Suzuki and
Parker (2016) in a concentrated area of overlap. The resulting layer was summed with the
Demarchi winter model to create a year-round moose layer.
Bull trout symbolize healthy watersheds and cold, high elevation streams with clean
gravel beds and undisturbed riparian vegetation (Weaver, 2019). This layer was constructed
from critical habitat conservation work performed by Hagen and Weber (2019) for the Fish
& Wildlife Compensation Program (FWCP) in the Williston Reservoir watershed. It focused
on critical spawning and juvenile rearing habitats.
For each of the aforementioned species, TEK-sourced spatial data in the form of
points and polygons were used to enhance the final conservation feature layer. This data was
both valuable and relatively sparse, so TEK-identified areas were automatically given the
highest score on each of the species’ habitat quality scales. In the case of the bull trout layer,
TEK data on other fish species were also included, resulting in a more generalized fish layer
but with a bull trout focus.

65

Fishers were included because they represent connected forests and dense shrub
cover, as well as coarse woody debris, large diameter trees, and complex forest structure (BC
Fisher Habitat Working Group, 2017). Robust fisher data exists for the province thanks to the
BC Fisher Habitat Working Group (2017). This data delineates four unique habitat zones
(Boreal, Sub-Boreal Moist, Sub-Boreal Dry, and Dry Forest) and four distinct habitat needs
(denning, resting, moving, and foraging). I laid each of these layers on top of one another,
with overlapping areas receiving higher scores.
Wolverines were identified by the Nation as another focal species, and data for them
along with eight other species from previous work by Chu Cho Environmental (Bonderud et
al., 2020) were included as conservation feature layers. These include bank swallows, barn
swallows, western toads, horned grebes, little brown myotis (bat), northern myotis (longeared bat), olive-sided flycatchers, and rusty blackbirds. Habitat quality for each was rated on
a scale of 1-4, with the quality levels included in the analysis being decided on a case-bycase basis depending on how much of the Territory was included.
4.1.2.2 Special Features
The rare or valuable ecosystem fine-filter special features I included were wetlands,
lakes, and karst deposits. Wetlands provide ecosystem services like water filtration and
provide important habitat for species like migratory waterfowl (Mann, 2020). I used data
from the Williston Wetland Explorer Tool (Filatow et al., 2020) supplemented with data
from the BC Data Catalogue to gain complete geographic coverage of my study area. To
implement a hierarchical scoring system, I adapted guidance from the Nation’s Expectations
for Industry in Tsay Keh Dene Territory document (2019), with larger wetlands complexes
receiving higher scores.
66

Lakes were also identified as an important landscape feature by the Nation, and there
is scientific consensus on the importance of safeguarding freshwater resources (Weaver,
2019). I adopted Weaver’s (2019) classification system for conservation value of lakes by
size, and used data from the BC Data Catalogue.
Karst deposits were included because they provide habitat for plant and animal
species that utilize cave ecosystems or calcareous deposits for some or all of their life
histories (Curtis, 2018). Karst features were given numeric scores based on their likelihood
of occurrence.
4.1.3 Climate Change Conservation Features
In order to identify which portions of the landscape have the highest ecological value
not just today, but also thirty and sixty years from now, incorporating climate change data is
paramount (Gillson et al., 2013). I included features relating to climate velocity, climate
connectivity, refugia, carbon storage, and bird richness.
4.1.3.1 Climate Migration
I identified climate velocity and climate corridor metrics to include as features in my
analysis. I looked at both forward and backward velocities of climate projections for two
time periods – the 2050s and 2080s – to understand progressive changes. In each case I took
the lower 50% of values from the median, as lower velocity is more desirable (Carroll et al.,
2017).
I also looked at climate corridors, or connections between present-day and future
locations of a climate type. These areas identify the best routes between climatic refugia, and

67

therefore represent connectivity in a future sense (Carroll et al., 2018). In that case I took the
upper 50% of values from the median, as greater current flow is more desirable.
4.1.3.2 Refugia
Refugia are locations that are predicted to remain as is or transition to suitable habitat
for some species in the future (Ashcroft, 2010). I looked at three different metrics with those
future habitats in mind. The first is climatic refugia, or areas where climate-threatened
species can continue to exist or readily colonize (Stralberg et al., 2020). Using data from
Stralberg et al. (2020), I took the upper 50% of values from the median. Biotic refugia are
climatic refugia that are further informed with biological thresholds, in this case with data on
mammal, bird, amphibian and tree species (Michalak et al., 2018). I used data from Michalak
et al. (2018), and stacked the results from their six different iterations (three models at two
time stamps) to generate a continuous layer with overlapping areas scoring higher. The last
feature was cool headwater refugia, where I targeted keystone sections of rivers given their
ecological value and refugia potential. I used data from ClimateBC (Wang et al., 2016) and
methodology from Weaver (2019) to identify areas predicted to have a mean annual
temperature change of <1C by 2080.
4.1.3.3 Bird Richness
Bird richness is a summary of predicted summer habitat for 604 climate vulnerable
bird species under a 3C warming scenario (Bateman et al., 2020). Using data from Bateman
et al. (2020) I took the upper 50% of values from the median.

68

4.1.3.4 Carbon Storage
Carbon storage is a method to counteract (or at least stave off) climate change by
keeping CO2 out of the atmosphere and locked in the earth (Chan et al., 2006). I used data on
above-ground biomass (Santoro et al., 2018) and below-ground carbon (Hengl et al., 2017).
Data was normalized and added together to create a carbon storage layer. I then kept the
upper 50% of values from the median.
4.1.3.4 Flying BEC Zones
With representation of present-day BEC zones included in the coarse-filter category, I
also included predicted BEC zones under climate change scenarios for the years 2050 and
2080. I used data from ClimateBC (Wang et al., 2016), which sees the Territory add six
novel zones by 2050 before losing one of those novel zones by 2080, all while maintaining
the five that exist at present.
4.1.4 Cultural Conservation Features
The Tsay Keh Dene Nation’s Office of Lands, Resources, and Treaty Operations
provided me with TEK-sourced data from their Cultural Knowledge Keeper database. This
data is derived from traditional use studies and is available as spatial data in the form of
points, lines, and polygons under strict confidentiality. I used the point data to form a layer
called ‘Sites of Cultural Importance,’ which contained data on habitation, subsistence,
transportation, wildlife, and cultural/spiritual locations. Planning units received scores based
on how many sites were located within them. I also used the polygon data, and divided it into
two categories – ‘Cultural/Spiritual Areas’ and ‘Subsistence Areas’. Examples include berry
picking sites, hunting grounds, fishing holes, burial sites, medicinal plant locations,
battlegrounds, campsites, and teaching places.
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4.1.5 Connectivity Conservation Features
The results of connectivity analyses from both the Linkage Mapper and Omniscape
approaches were incorporated as conservation features. In each case I kept the more desirable
50% of values from the median for possible selection in prioritizr. Details on the
development of the connectivity analysis are provided in methods section 4.3.
4.2 Stage 2: Develop a Human Footprint Layer
The anthropogenic footprint in the region has been significant, primarily due to
resource extraction activities. Just as prioritizr allows conservation features to be used as
desirable inputs to be included in the identification of high-value conservation areas, it also
allows for a ‘cost layer’ of areas that should be avoided. I categorized human development
into three levels of footprint permanence (permanent, semi-permanent, and ephemeral), with
the cost layer used in prioritizr growing progressively larger as additional footprint features
were added (Table 6). The model user can choose one of three compounding options as
human footprints to avoid: permanent, permanent plus semi-permanent, or permanent plus
semi-permanent plus ephemeral. The different categories allow the user to plan with different
levels of permanence in mind as some impacts like logging and agriculture can theoretically
be restored, while others (e.g., dams like the W.A.C. Bennett) are more permanent.
Furthermore, each footprint feature was assigned a buffer distance based on its impact on the
landscape and wildlife from relevant literature (Appendix C. Human Footprint Feature Data
Sources and Buffers). This categorization and buffering framework was vetted by the Nation
and edits were made accordingly. Valuable insight and local knowledge were provided at this
stage, namely in subcategorizing roads and cutblocks by their relative level of impact.

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Footprint Layer Permanence
Permanent
Dams
Drillsites and Wellsites

Semi-Permanent
Agriculture
Cutblocks
Active
Orphan
Dormant

Industrial Uses
Mines
Pipelines
Power and Telecom Lines
Quarries
Reservoirs
Residential Areas
Roads

Ephemeral
Alpine Skiing
Heliskiing
1940-1980 Snowmobiling
1981-2020 Trail Riding

Camps and Cabins
Roads
Outblock Trails
Inblock Resource Main
Inblock Resource Secondary
Inblock Trails

Outblock Major
Outblock Resource Main
Outblock Resource Secondary

Urban Areas
Wind Turbines
Table 6. Human footprint permanence framework and categorized layers.

4.3 Stage 3: Quantify Landscape Permeability to Identify Connectivity Corridors
Landscape permeability is the degree of difficulty that species experience in moving
across the landscape, with highly permeable routes between ecologically intact areas serving
as connectivity corridors. To quantify the landscape permeability of the Territory and
understand the resistance that species face in traversing ground, I performed connectivity
analyses using third-party extensions to ArcMap GIS software, as well as standalone scripts.
These include Gnarly Landscape Utilities (McRae, Shirk, et al., 2013), Linkage Mapper
(McRae, Shah, et al., 2013), and Omniscape (Landau, 2020; McRae et al., 2016). As these
exercises did not include empirical movement data and were not species specific, their
outputs are considered structural connectivity rather than functional connectivity. However,
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areas identified for connectivity that overlap with planning units selected for their
conservation value could be considered functionally connected (Keeley et al., 2021).
4.3.1 Resistance Layer
Building a resistance layer is the first step of performing connectivity analyses, and I
calculated one for the Territory using the Resistance and Habitat Calculator tool from Gnarly
Landscape Utilities. This entails populating a preformatted spreadsheet with each of the
resistance layers, their sub-classifications, and relative resistance scores that fall between 0
(no resistance) and 100 (extreme resistance). Resistance input layers included variables such
as land cover, slope, buffered roadways, private land, and the human footprint (minus roads
since they are accounted for separately in this analysis) (Table 7). After creating a
geodatabase of rasterized spatial files for each of the input resistance layers, the
geoprocessing plugin tool matches each of the spatial layers to their respective resistance
values to synthesize an overall resistance layer.

72

Data Layer

Land Cover

Slope (in degrees)

Buffered Roads

Human Footprint

Class Description

Agriculture
Alpine
Barren Surfaces
Fresh Water
Glaciers and Snow
Mining
Old Forest
Range Lands
Recently Burned
Recently Logged
Recreation Activities
Selectively Logged
Shrubs
Sub alpine Avalanche Chutes
Urban
Wetlands
Young Forest
0-5
5 - 10
10 - 15
15 - 20
20 - 25
25 - 30
30 - 35
35 - 40
40 - 45
45 - 50
50 - 55
55 - 60
60 - 65
65 - 70
70 - 75
75 - 80
80 - 85
Outblock Major Roads
Outblock Resource Main Roads
Outblock Resource Secondary Roads
Outblock Trails
Inblock Resource Main Roads
Inblock Resource Secondary Roads
Permanent Footprint (except roads)
Semi-Permanent Footprint (except roads)
Ephemeral Footprint

Resistance Value
30
0
20
40
60
70
0
25
10
20
10
10
0
10
100
5
0
1
1
1
1
1
1
1
1
1
10
20
30
40
50
60
100
100
70
50
30
10
40
20
70
30
10

Table 7. Resistance features to inform connectivity analyses. Resistance Value on scale of 0-100.

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4.3.2 Linkage Mapper
I then utilized the Linkage Pathways Tool from the Linkage Mapper Toolkit by
Circuitscape. This ArcMap extension looks at the ability of a generalized species to traverse
the landscape between a set of ‘core areas’, which in some instances are protected areas, but
could also be non-protected areas found to be relatively intact from the human footprint
analysis. I used legally designated protected areas plus the proposed boundary for the
Ingenika Conservation and Management Area as my core areas after consulting with the
Nation. After uploading the raster resistance layer from the previous step, this tool uses
random walk analysis and electric circuit theory to find the paths of least resistance (also
known as least-cost paths) between core areas by quantifying the level of permeability of
each raster cell (McRae et al., 2008).
Next, I adapted methods from Heinemeyer et al. (2004) to explicitly include
connectivity as part of the SCP process in an attempt to create an ‘ecological network’ of
core habitats and ecological corridors (Hilty et al., 2020). I used the least-cost paths (LCPs)
identified by Linkage Mapper to create a layer that could be ‘locked in’ as part of a
conservation solution so that prioritizr would automatically select it.
Least-cost paths are lines that represent the easiest route across the landscape for
species to move from one core area to another. While these paths can serve as important
movement corridors, they do not necessarily represent ecologically valuable land. As such, I
created a layer made up solely of the planning units traversed by LCPs and the core areas that
they connect. This ensures structural connectivity between core areas without
overemphasizing wider corridors that may not have ecological value. It does, however, still
allow planning units adjacent to the LCPs to be selected for their ecological value and
74

functionally build a larger corridor between core areas. This method differs from Heinemeyer
et al. (2004), who locked in larger corridors by including LCPs plus adjacent planning units
with equally low resistance values. I ultimately included the Linkage Mapper output as a
conservation feature (top 50% of values) despite the shortcomings outlined above, but did not
set any target at all just to see how well it was incidentally captured in various scenarios.
4.3.3 Omniscape
Like Linkage Mapper, Omniscape uses electric circuit theory to theorize connectivity
by analyzing how electric current flows across a landscape given its resistance. While
Linkage Mapper looks for connectivity between core areas, Omniscape looks at the
connectivity of the entire landscape.
Omniscape requires a set of parameters so the script knows how to execute the
analysis. To replicate the 50 km moving window used in McRae et al. (2016) I took my 100
m resolution resistance layer and set a radius of 500 (a 100 m raster cell multiplied by 500
cells to reach 50,000 m or 50 km). I used a block size of 49 to coarsen the analysis of the
resistance raster and speed up processing time (Figure 10). Larger block sizes have been
found to produce only negligible differences in the output (Landau, 2020). A source strength
raster defines how much electric current should be injected into each pixel, with intact areas
or high-quality habitat serving as potential high source values. This layer can be created
independently or be calculated on the fly as the inverse of the resistance raster. I opted to use
the inverse of the resistance raster, as this analysis was not species-specific (Keeley et al.,
2021).

75

Block Size of 3

Block Size of 1

Figure 10. A visual representation of how different block sizes are used to improve
computing time (Landau, 2020).

The default output of an Omniscape run is cumulative current flow, or the summed
flow of current for a given pixel after all of the moving window iterations have been added
together. Omniscape can also produce metrics known as flow potential and normalized
current flow. Flow potential is what flow would look like if there was no resistance, using the
same algorithm as cumulative current flow but with resistance set to 1 for the entire
landscape (Landau, 2020). Normalized current flow takes the cumulative current flow and
divides it by flow potential. This helps to identify channelized areas or ‘pinch points’ in more
developed areas while high flow areas in largely intact landscapes (as would be expected)
fade more into the background. Each output can be useful in interpreting flow, and local
context is key (McRae et al., 2016).
I used the cumulative current flow output for inclusion in my prioritizr analysis, as
this landscape remains highly connected relative to other locations, even given development
pressures. I produced two ‘locked-in’ options as a result, one using the top two quantiles of
flow, and another slightly thicker iteration using the top three quantiles of flow. My goal was
to choose break points that locked in enough planning units for connectivity to translate to
76

the conservation solution, but not be overbearing on how many planning units were locked in
to allow conservation prioritization to still occur.
Like with Linkage Mapper, I decided to also include my Omniscape output as a
conservation feature, but without targets. This allows the user to at least see how well the top
50% of Omniscape connectivity values are incidentally captured in scenarios focused on
other conservation features.
4.4 Stage 4: Develop Conservation Targets
The most common version of SCP problem solving is the ‘minimum set objective’.
This solution requires that overall conservation goals be translated into more specific,
quantifiable targets for functional use (Margules & Pressey, 2000). With conservation
features identified and compiled for our analysis, I set an initial target for each to inform the
ultimate conservation solution. prioritizr requires these inputs for the tool to know how much
of each conservation feature to capture when performing prioritizations (Hanson et al., 2021).
prioritizr’s purpose is to then maximize the selection of ecologically high-value areas while
minimizing the selection of areas with a high human footprint. I used prioritizr to return a
single best solution of potential conservation areas as opposed to the conventional Marxan
approach of comparing the selection frequency of different iterations (Beyer et al., 2016). For
example, caribou are highly endangered in the region and would warrant a high target
percentage such as 90%. A target of 90% tells prioritizr that in any prioritization process it
performs that at least 90% of caribou habitat area must be included in the solution.
The initial target percentages for each conservation feature were carefully considered
and drawn from a variety of sources. My draft set of targets was informed by the targets set

77

by Curtis (2018) and Mann (2020), as well as ecological best practices from other literature.
For example, the Forest Pattern and Process layer targets were informed by the ‘higher
biodiversity emphasis’ values of the Biodiversity Guidebook (BC Environment, 1995), while
the caribou targets were based on the management objectives in the Implementation Plan for
the Ongoing Management of South Peace Northern Caribou (British Columbia & Ministry of
Environment, 2013). The sources behind each conservation feature target and the rationale
for choosing the target was documented.
Traditional Ecological Knowledge and expertise provided by the Nation in the form
of species reports and other management documents was also used to inform draft targets,
along with other considerations unique to the study extent. I presented draft targets to the
Nation in the form of an interactive workshop so representatives could weigh in on the
targets and revise as necessary. While setting a specific and defensible target is important, the
tool also allows the user to test multiple targets by running different scenarios.
Experimenting with various targets for a specific conservation feature made sense when there
was uncertainty due to a lack of literature on that feature, or if new information comes to
light after the initial targets were set. The purpose was to imprint the Nation’s values on these
numbers to accurately portray the Nation’s objectives when it comes to relative importance
among conservation features, as well as taking relative abundance in the Territory into
account. This process allowed me to affirm targets in cases of agreement, document
disagreements and potential revisions, and provide additional justification for target choices.
4.5 Stage 5: Review Existing Conservation Areas Efficacy
A gap analysis quantifies how well an existing network of conservation areas captures
the conservation features outlined in Stage 1 by comparing those numbers to the targets set in
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Stage 4. This analysis was performed because designated conservation areas are unlikely to
lose their protected status and were thus included as an option to ‘lock in’ as part of the
conservation solution. By performing a gap analysis for the Territory I identified
shortcomings in the network of existing conservation areas. To do so, I opted to lock in
protected areas within prioritizr – revealing what percentage of conservation features
selected are present in those areas. This review also helped inform the targets of certain
conservation features in the context of this specific study area (Margules & Pressey, 2000).
For example, if fisher and NDT1-SBS-B forest stands (recently burned sub-boreal spruce
with rare stand-initiating events) were found to be poorly represented among the existing
conservation area network, their targets would be adjusted accordingly. There was no
community/TEK component to this stage, as this was a technical analytical process.
4.6 Stage 6: Identify a Portfolio of High-Value Conservation Lands
A conservation solution in this context is the most efficient array of planning units
that meets the target of each conservation feature, known as the minimum set objective. This
process was performed by the prioritizr R package within a planning tool developed for this
project that runs as a program on a local computer. The tool works by synthesizing the spatial
data of the selected conservation features and accounting for a cost (footprint) layer to
develop its solution of high-value conservation lands. Since the present-day network of
protected areas is pre-existing and unlikely to disappear, they were included as an option to
lock into the solution, as were three connectivity focused layers to ensure pathways for
movement. The planning tool also contains input options for a ‘Boundary Length Modifier’
and an ‘Edge factor”. These parameters encourage clustering of solutions and avoid
penalizing planning units at the outer edge of the study area, respectively. Each were left at
79

their default values for the formal thesis scenarios, as the focus was on high-value areas
regardless of clustering. The final step was entering the targets developed in Stage 4 for the
algorithm to select the most efficient conservation solution for each scenario.
I ran six prioritizr base scenarios to address my research questions and account for
climate change and connectivity within the growing human footprint. These included (a)
setting targets for present-day conservation features, (b) setting targets for future
conservation features focusing on the 2050s, (c) setting targets for future conservation
features focusing on the 2080s, (d) setting targets for both present-day and future
conservation features, (e) focusing on protected areas connectivity, and (f) focusing on
landscape connectivity. As the planning tool was ultimately transferred to the Nation,
additional scenarios can be run in the future as the Nation identifies other objectives or
questions, or if specific needs arise. Parameters and targets can then be set accordingly to
address those needs.
Present-day biodiversity is based on species and ecosystem presence today, while
future biodiversity is based on an area’s potential to serve as habitat under projected climatic
conditions. The prioritizr tool was run through various iterations with different conservation
features selected in order to compare the single best solution for the present with the single
best solutions for the 2050s and 2080s. I started by using the foundational conservation
features like species, forest pattern and process, and rare ecosystems data to produce a
solution that maximizes conservation at the present. I also ran iterations that looked solely at
the climate data outlined in section 4.1.3 (Climate Change Conservation Features), using
datasets derived from both 2050s and 2080s climate models to produce solutions that
maximized conservation for those respective timelines. A cumulative scenario was then run
80

where targets were set on conservation features for all three timeframes to see the extent of a
conservation solution that conserves biodiversity for the present, near future, and distant
future.
An additional two scenarios were run with a focus on connectivity. The first of these
scenarios locked in protected areas and their least-cost paths with targets on present-day
conservation features given that protected areas were generally designed with existing
conditions in mind. The connectivity aspect of this layer was also quite conservative in that
only planning units traversed by least-cost paths were locked in alongside the protected areas
themselves. The second connectivity scenario focused on overall landscape connectivity and
locked in select high values from the Omniscape output. This scenario set targets on future
conservation features, as Omniscape focuses on the naturalness of a landscape (considered a
coarse-filter conservation strategy) and represents structural connectivity (Keeley et al.,
2021).
As mentioned in Stage 4, conservation feature targets can be experimented with on
the fly, as prioritizr can quickly produce conservation solutions. Therefore, I held an
interactive workshop with the Nation where additional use cases were identified. While we
did not run any corresponding scenarios in real time, the Nation gained an understanding of
how to translate additional conservation goals into parameters for the tool.
4.7 Stage 7: Assess prioritizr Solutions Through Different Lenses
The areas selected by prioritizr containing high ecological value were then reviewed
through a set of various lenses to assess how the solution fared in regard to local biodiversity,
climate change, and agreement with local knowledge of biodiverse areas.

81

4.7.1 Assess Conservation Feature Complementarity
Complementarity is the degree to which conservation features are similar to one
another, measuring the percentage of each conservation feature’s extent that is contained
within a focal conservation feature. By setting a target of 100% for a lone feature, I
determined the percentage of each other feature that falls within this focal layer. This
exercise helped determine which features can potentially serve as surrogates for other
features, as well as provide general insight into the similarities of each feature.
4.7.2 Compare Diversity of Conservation Features within Solutions
While prioritizr solutions are simply presence/absence spatial data, the diversity of
selected conservation features can be calculated within ArcMap to help identify which of the
selected solution areas exhibit high diversity. This sort of insight can prove useful in probing
for which areas are ‘hotspots’ for conservation features and can potentially be considered for
even further prioritization.
4.7.3 Compare Ideal Conservation Lands for 2020s, 2050s, and 2080s Climates
With conservation land portfolios for the 2020s, 2050s, and 2080s in hand, the three
time periods can be compared for overlap to identify areas with the greatest levels of
biodiversity and climate resiliency. I also analyzed what percentage of conservation features
were incidentally captured as part of each scenario where they were not the focus and no
targets were set. For example, in the present-day (2020s) scenario, I noted what percentage of
climate conservation features for the 2050s and 2080s happen to be captured even though
they were not the focus of the scenario. I also compared the total amount of area required to
meet the conservation targets in each scenario – both as a raw total (in km 2 or ha) and as a
82

percentage of the total area of the Territory. These were also compared with the allencompassing scenario that uses targets for all three time periods. Finally, areas of overlap
between all six scenarios were quantified to identify those areas with the greatest
conservation value through time.
4.8 Documenting Adaptations to the SCP Process
In multiple instances, my research project adapted the methods of Systematic
Conservation Planning to include other elements. Documenting the process and approach I
took to include connectivity, incorporate climate change, and interweave Traditional
Ecological Knowledge into the SCP framework is critical to ensure rigor and transparency.
The methods I used to document each are described briefly below.
I outlined in sections 4.3.2 (Linkage Mapper) and 4.3.3 (Omniscape) how I chose to
explicitly include connectivity in this analysis, adapting methods from Heinemeyer et al.
(2004) to lock in connected areas, as well as including connected areas as a feature with no
targets. While coarse-filter and climate change conservation feature targets were largely
based on Mann’s (2020) methodology of incidental capture in a present-day conservation
feature scenario, any adjustments that were made were documented and justified.
There is even less precedent for using Traditional Ecological Knowledge in an SCP.
Considering that TEK can also be more qualitative in nature, I documented decisions
surrounding its use in both a factual and reflective manner. For example, in the conservation
feature selection process, when the Nation shared an idea for a specific feature and data
source, I documented the impetus for this sharing of information, what feature was shared,
the rationale behind it, who provided the idea, and where the data came from. After making

83

an effort to incorporate this knowledge, I documented the productiveness of this idea in a
Reflective Journal to record how well it could be integrated, what I learned or experienced
from this effort, and what could be done differently in the future. This serves to not only
document my attempts to interweave TEK with the SCP process, but also allowed me to
examine my own personal assumptions and values and how they may affect these attempts
(Ortlipp, 2008).

5.0 RESULTS
This section reviews the results of completing the seven stages of the systematic
conservation planning process outlined above. Many of these results are illustrated through
maps of the greater territory study area. These maps also contain inset maps to give the
reader a finer scale understanding of the results, not necessarily to highlight a specific area's
results.
5.1 Conservation Goals
The Nation voiced seven goals for this systematic conservation planning effort to
achieve:
1. Ensure the direction of systematic conservation planning reflects the vision and goals
of the Tsay Keh Dene Nation. Community engagement will provide guidance,
support a greater understanding of culturally-important areas and values, and inform
refinements to this work.
2. Represent the full range of natural ecosystem variation across the Territory.
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3. Identify and prioritize ecologically intact, high biodiversity habitats.
4. Identify and prioritize high-value habitat for priority plant, fish, and wildlife species.
5. Identify and prioritize rare ecosystems for conservation.
6. Identify and prioritize connectivity corridors of lands and waters across the landscape.
7. Incorporate an understanding of ecosystem shifts due to climate change into longterm resiliency planning, including identification of climate refugia.
5.2 Conservation Feature Layers
I developed a total of 41 conservation features for selection in this analysis. Some
features included subcategories (e.g., caribou herds and forest pattern and process
combinations), which resulted in a total of 64 layers for selection. An additional five
representation features and their subcategories accounted for 38 more layers. While some
layers were represented in planning units in a binary manner, those layers with continuous
values maintained their hierarchical nature to facilitate preferential selection within prioritizr.
Each conservation feature is described below, along with the proportion of the study area that
it occupies. The study area extends beyond Tsay Keh Dene Territory and encompasses
89,007 km2.
5.2.1 Coarse-filter Conservation Features
There were seven coarse-filter conservation features used in this analysis. The extent
of each layer is described below, with maps depicting each coarse-filter feature included at
the end of this group of descriptions.

85

5.2.1.1 Land Facet Diversity
The land facet diversity layer makes up 5% of the study area. This feature is speckled
across the Territory, with the most notable concentration occurring along the shores of the
Parsnip Arm of the Williston Reservoir near Mackenzie (Figure 11) representing the area of
highest topographic complexity.
5.2.1.2 Land Facet Rarity
The land facet rarity layer makes up 5% of the study area. This feature is scattered
across the Territory, generally found in river valleys and certain mountain ridges. The most
notable clustering, however, is found along the Peace and Parsnip Arms of the Williston
Reservoir (Figure 12).
5.2.1.3 Elevational Diversity
The elevational diversity layer makes up 50% of the study area. This feature is
predominantly found in the central and northern portions of the Territory, with the highest
values clustered around the Rocky Mountain Trench (Figure 13) where a full range of
elevational classes are located within close proximity.
5.2.1.4 Ecotypic Diversity
The ecotypic diversity layer makes up 50% of the study area. This feature is generally
found in less mountainous portions of the Territory and has its greatest concentration of high
values in the southwest along the Omineca River valley (Figure 14).
5.2.1.5 Heat Load Index Diversity
The heat load index diversity layer makes up 50% of the study area. This feature
centers on the northern part of the Territory, with an arm that reaches down the eastern side
86

of the Rocky Mountain Trench, as well as high value areas along the Finlay and Ingenika
Rivers (Figure 15).
5.2.1.6 Forest Pattern and Process
The twelve forest pattern and process layers (described in section 4.1.1.3 Biotic)
collectively make up 44% of the study area. These old or recently burned biodiverse forests
are distributed mainly along river valleys and in other low and moderate elevation portions of
the Territory (Figure 16). Given the criteria and data inputs, alpine portions of the Territory
are not represented in these layers.
5.2.1.7 Rare BEC Zones
The rare BEC zones layer makes up 25% of the study area. This feature is found at
the very center of the study area and along its eastern and western edges. The highest values
are found in high elevation areas just east of the Williston Reservoir, as well as along the
Sustut and Skeena Rivers to the west (Figure 17). The BEC subzones/variants included in
this layer are listed in Table 8.

87

BEC Subzone/variant Code

BWBS wk 1
BWBS wk 3
ESSF wvp
ESSF wv
SBS vk
SBS mc 2
ESSF wcp
SBS wk 3
ESSF mcp
ESSF mv 2
ESSF wk 2
BWBS mw
BWBS wk 2
ESSF wc 3
BWBS mk
ESSF mc
SBS wk 2
SBS mk 2
ESSF mvp
SBS mk 1
SWB mks
BWBS dk
ESSF mv 4

Descriptor

Murray Wet Cool
Kledo Wet Cool
Wet Very Cold Parkland
Wet Very Cold
Very Wet Cool
Babine Moist Cold
Wet Cold Parkland
Takla Wet Cool
Moist Cold Parkland
Bullmoose Moist Very Cold
Misinchinka Wet Cool
Moist Warm
Graham Wet Cool
Cariboo Wet Cold
Moist Cool
Moist Cold
Finlay-Peace Wet Cool
Williston Moist Cool
Moist Very Cold Parkland
Mossvale Moist Cool
Moist Cool Scrub
Dry Cool
Graham Moist Very Cold

% of Study Area

Table 8. BEC subzones and variants included in the ‘Rare BEC Zones’ layer.

88

0.004%
0.01%
0.1%
0.1%
0.2%
0.9%
0.9%
1.1%
1.3%
1.3%
1.6%
2.0%
2.3%
2.6%
3.1%
3.4%
3.5%
4.4%
4.8%
4.9%
6.8%
7.5%
8.9%

Rarity Score

3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
2
2
2
2
2
1
1

Figure 11. Spatial extent of the Land Facet Diversity layer used to prioritize lands for conservation in the
Greater Tsay Keh Dene Territory Study Area; inset represented by pink frame.

89

Figure 12. Spatial extent of the Land Facet Rarity layer used to prioritize lands for conservation in the
Greater Tsay Keh Dene Territory Study Area; inset represented by pink frame.

90

Figure 13. Spatial extent of the Elevational Diversity layer used to prioritize lands for conservation in the
Greater Tsay Keh Dene Territory Study Area; inset represented by pink frame.

91

Figure 14. Spatial extent of the Ecotypic Diversity layer used to prioritize lands for conservation in the
Greater Tsay Keh Dene Territory Study Area; inset represented by pink frame.

92

Figure 15. Spatial extent of the Heat Load Index Diversity layer used to prioritize lands for conservation
in the Greater Tsay Keh Dene Territory Study Area; inset represented by pink frame.

93

Figure 16. Spatial extent of all the Forest Pattern and Process layers used to prioritize lands for
conservation in the Greater Tsay Keh Dene Territory Study Area; inset represented by pink frame.

94

Figure 17. Spatial extent of the Rare BEC Zones layer used to prioritize lands for conservation in the
Greater Tsay Keh Dene Territory Study Area; inset represented by pink frame. Select level 3 (rarest)
subzones/variants are labeled.

95

5.2.2 Fine-filter Conservation Features
There were eighteen fine-filter conservation features used in this analysis. The extent
of each layer is described below, with maps depicting each fine-filter feature included at the
end of this group of descriptions. Features developed from Chu Cho Environmental Habitat
Suitability Indexes (wolverines, bank swallows, barn swallows, western toads, horned
grebes, little brown bats, northern long-eared bats, olive-sided flycatchers, and rusty
blackbirds) only have coverage for Tsay Keh Dene Territory and not for the entire study area.
Features that included traditional knowledge for SCP analysis have had their TEK data
removed from these maps for display purposes out of respect for the sacredness and
sensitivity of this knowledge.
5.2.2.1 Wetlands
The wetlands layer makes up 57% of the study area. This feature is spread widely
across the Territory with high values found adjacent to large river systems. There is also a
noteworthy cluster of large, high value wetlands in the northeastern corner of the study area
in the lower elevation Muskwa Plateau along the Alaska Highway (Hwy 97) (Figure 18).
5.2.2.2 Lakes
The lakes layer makes up 21% of the study area. This feature is well dispersed across
the Territory, but is denser on the western half and contains more high value large lakes as
well. The most noteworthy large lakes are found in the northwest (Kitchener, Tatlatui, and
Thutade Lakes) and southwest (Tsayta, Tchentlo and Nation Lakes) (Figure 19).

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5.2.2.3 Karst Deposits
The karst deposits layer makes up 27% of the study area. This feature is found in
bands across the landscape within the Rocky Mountains, with the highest values and greatest
abundance found in the Muskwa Ranges in the east (Figure 20).
5.2.2.4 Grizzly Bear
The grizzly bear layer makes up 29% of the study area. This feature is best
represented in the southern half of the Territory in moderate to high elevation areas, with
arms of high-value habitat reaching into the northern stretches of the study area along the
Finlay and Skeena Rivers (Figure 21).
5.2.2.5 Bull Trout/Fish
The bull trout/fish layer makes up just 3% of the study area. This feature is heavily
concentrated in the north-central part of the study area, though there is representation to the
west and south along the Omineca River and Clearwater Creek, respectively. As this layer
was solely sourced from confidential Fish & Wildlife Compensation Program (FWCP) data
and TEK data, no map is provided.
5.2.2.6 Fisher
The fisher layer makes up 38% of the study area. This feature is found in low
elevation areas, almost exclusively along river valleys, reaching like tentacles into
mountainous areas off of the Rocky Mountain Trench. The one notable exception is the
concentration of high-value habitat in the Muskwa Plateau in the northeastern corner of the
study area (Figure 22).

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5.2.2.7 Caribou
The caribou layers (divided by herd) collectively make up 35% of the study area. This
feature is found almost exclusively in moderate and high elevation areas, but with a few river
valleys also represented. High-value habitat can be found in much of the study area, with
notable gaps in the northeast, northwest, south, and one central location along the Ospika
River (Figure 23).
5.2.2.8 Moose
The moose layer makes up 39% of the study area. This feature is found
predominantly in low elevation areas along the Rocky Mountain Trench, with notable
collections in the eastern and southwestern portions of the study area (Figure 24).
Unsurprisingly, this feature overlaps significantly with the wetlands layer.
5.2.2.9 Stone Sheep
The Stone sheep layer makes up 6% of the study area. This feature is found in high
elevation areas in the northern half of the study area in the Rocky and Omineca Mountains,
with a few exceptions of patches in the southern Muskwa Ranges, northern Misinchinka
Ranges, and southern Omineca Mountains (Figure 25).
5.2.2.10 Mountain Goat
The mountain goat layer makes up 10% of the study area. Similar to Stone sheep, this
feature is found in the northern half of the study area in the Rocky and Omineca Mountains
with a few stretches to the south. The difference, however, is that mountain goats have a
greater presence in the west in the mountains on either side of the Skeena River (Figure 26).

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5.2.2.11 Wolverine
The wolverine layer makes up 19% of the study area. This feature is predominantly
found in moderate elevation areas and is well spread throughout the Territory, likely due to
their general preference of conifer forests that occur in many BEC zones (Figure 27).
5.2.2.12 Bank Swallow
The bank swallow layer makes up 6% of the study area. This feature is highly
concentrated along the Williston Reservoir and its southwestern valley of the Rocky
Mountain Trench, with offshoots along the many rivers that feed the reservoir (Figure 28).
This is likely due to their nesting requirements along riverbanks (specifically cutbanks) as
their name suggests.
5.2.2.13 Barn Swallow
The barn swallow layer makes up 8% of the study area. Similar to the bank swallow,
this feature is highly concentrated along the Williston Reservoir and its valleys. However, the
barn swallow is also found at moderate elevations throughout the Territory, with a unique
high-value clustering along Thutade Lake (Figure 29).
5.2.2.14 Western Toad
The western toad layer makes up 12% of the study area. This feature is fairly well
spread across the Territory, namely at low and moderate elevations with a few pockets of
higher quality habitat (Figure 30). Many of the locations are along lakes and wetlands as
western toads require shallow, warm water for breeding.

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5.2.2.15 Horned Grebe
The horned grebe layer makes up 10% of the study area. This feature is clustered
around the Williston Reservoir and the rivers that feed it, with the highest habitat values
found in low elevation valleys along the southwestern shore of the reservoir (Figure 31).
5.2.2.16 Little Brown Myotis
The little brown myotis bat layer makes up 25% of the study area. This feature is
widespread throughout the Territory, save for high elevation areas. The greatest
preponderance of high habitat values are found in the Muskwa Ranges, overlapping with the
karst deposits that represent cave habitat (Figure 32).
5.2.2.17 Northern Myotis
The northern myotis bat layer makes up 11% of the study area. This feature is less
widespread than the little brown myotis and is found predominantly in river valleys. Clusters
of high-value habitat are found along the shores of the Williston Reservoir and the Ospika
River, among others (Figure 33).
5.2.2.18 Olive-Sided Flycatcher
The olive-sided flycatcher layer makes up 9% of the study area. This feature has
partial coverage across the Territory, namely at low and moderate elevations along the
Mesilinka River, Finlay River, and the southwestern shores of the Williston Reservoir
(Figure 34).
5.2.2.19 Rusty Blackbird
The rusty blackbird layer makes up 13% of the study area. This feature has moderate
coverage across the Territory along riparian corridors and low elevation forests. Similar to
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the olive-sided flycatcher and horned grebe, its high-value habitat groupings are found in the
low elevation wetlands just southwest of the reservoir (Figure 35).

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Figure 18. Spatial extent of the Wetlands layer used to prioritize lands for conservation in the Greater
Tsay Keh Dene Territory Study Area; inset represented by pink frame.

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Figure 19. Spatial extent of the Lakes layer used to prioritize lands for conservation in the Greater Tsay
Keh Dene Territory Study Area; inset represented by pink frame.

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Figure 20. Spatial extent of the Karst Deposits layer used to prioritize lands for conservation in the
Greater Tsay Keh Dene Territory Study Area; inset represented by pink frame.

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Figure 21. Spatial extent of the Grizzly Bear layer used to prioritize lands for conservation in the Greater
Tsay Keh Dene Territory Study Area; inset represented by pink frame.

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Figure 22. Spatial extent of the Fisher layer used to prioritize lands for conservation in the Greater Tsay
Keh Dene Territory Study Area; inset represented by pink frame.

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Figure 23. Spatial extent of the Caribou layers used to prioritize lands for conservation in the Greater
Tsay Keh Dene Territory Study Area; inset represented by pink frame. ‘Faux’ herds are areas where
high-quality caribou habitat is found but no existing herd boundaries overlap.

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Figure 24. Spatial extent of the Moose layer used to prioritize lands for conservation in the Greater Tsay
Keh Dene Territory Study Area; inset represented by pink frame.

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Figure 25. Spatial extent of the Stone Sheep layer used to prioritize lands for conservation in the Greater
Tsay Keh Dene Territory Study Area; inset represented by pink frame.

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Figure 26. Spatial extent of the Mountain Goat layer used to prioritize lands for conservation in the
Greater Tsay Keh Dene Territory Study Area; inset represented by pink frame.

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Figure 27. Spatial extent of the Wolverine layer used to prioritize lands for conservation in the Greater
Tsay Keh Dene Territory Study Area; inset represented by pink frame.

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Figure 28. Spatial extent of the Bank Swallow layer used to prioritize lands for conservation in the
Greater Tsay Keh Dene Territory Study Area; inset represented by pink frame.

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Figure 29. Spatial extent of the Barn Swallow layer used to prioritize lands for conservation in the
Greater Tsay Keh Dene Territory Study Area; inset represented by pink frame.

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Figure 30. Spatial extent of the Western Toad layer used to prioritize lands for conservation in the
Greater Tsay Keh Dene Territory Study Area; inset represented by pink frame.

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Figure 31. Spatial extent of the Horned Grebe layer used to prioritize lands for conservation in the
Greater Tsay Keh Dene Territory Study Area; inset represented by pink frame.

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Figure 32. Spatial extent of the Little Brown Myotis (Bat) layer used to prioritize lands for conservation
in the Greater Tsay Keh Dene Territory Study Area; inset represented by pink frame.

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Figure 33. Spatial extent of the Northern Myotis (Long-eared Bat) layer used to prioritize lands for
conservation in the Greater Tsay Keh Dene Territory Study Area; inset represented by pink frame.

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Figure 34. Spatial extent of the Olive-sided Flycatcher layer used to prioritize lands for conservation in
the Greater Tsay Keh Dene Territory Study Area; inset represented by pink frame.

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Figure 35. Spatial extent of the Rusty Blackbird layer used to prioritize lands for conservation in the
Greater Tsay Keh Dene Territory Study Area; inset represented by pink frame.

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5.2.3 Climate Change Conservation Features
There were ten climate change conservation features used in this analysis. The extent
of each layer is described below, with maps depicting each climate change feature included
at the end of this group of descriptions.
5.2.3.1 Climate Corridors
The climate corridors layer makes up 54% of the study area. This feature occurs in
mountainous portions in both the eastern and western portions of the study area. The greatest
concentration of high values is found in the east like a spine through the Rocky Mountains
(Figure 36).
5.2.3.2 Backward Velocity 2055
The backward velocity 2055 layer makes up 50% of the study area. This feature
occurs as a band across the middle of the study area from east to west, straddling the
Williston Reservoir. The most desirable areas are found at moderate to high elevations in the
southern Muskwa Ranges (Figure 37).
5.2.3.3 Backward Velocity 2085
The backward velocity 2085 layer makes up 50% of the study area. This features
occurs almost entirely in the western half of the study area, save for some high elevation
occurrences in the east. The greatest concentration of desirable values is found in the
northwest corner of the study area in the Omineca Mountains surrounding the Skeena River
(Figure 38).

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5.2.3.4 Forward Velocity 2055
The forward velocity 2055 layer makes up 50% of the study area. This feature is
clustered in the southern third of the study area with an arm up the Rocky Mountain Trench
connecting to lower value areas along the northern edge of the study area. The most desirable
areas are clustered in the Misinchinka Ranges and the Wolverine Range to the west of
Parsnip Arm (Figure 39).
5.2.3.5 Forward Velocity 2085
The forward velocity 2085 layer makes up 50% of the study area. This feature is quite
similar to its 2055 counterpart in both extent and location of desirable values; however, in
this instance, the most desirable values are even more concentrated in the Misinchinka
Ranges of the Rocky Mountains (Figure 40).
5.2.3.6 Cool Headwater Refugia
The cool headwater refugia layer makes up 6% of the study area. This feature is
found predominantly at high elevation in the northeastern portion of the study area in the
Muskwa Ranges. There is also a smattering of values to the west at high elevation in the
Omineca Mountains (Figure 41).
5.2.3.7 Climatic Refugia
The climatic refugia layer makes up 57% of the study area. This feature is spread
across the northern half of the study area in the Omineca Mountains and the Muskwa Ranges.
The greatest concentration of high values are in the Ominecas northeast of Thutade Lake and
in the eastern Muskwa Ranges near the Rocky Mountain Foothills (Figure 42).

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5.2.3.8 Biotic Refugia
The biotic refugia layer makes up 16% of the study area. This feature is similar in
extent to the cool headwater refugia layer, but somewhat more widespread. Small pockets of
high values can be found at high elevations in the Muskwa Ranges, Misinchinka Ranges, and
Omineca Mountains (Figure 43).
5.2.3.9 Bird Richness
The bird richness layer makes up 52% of the study area. This feature is fairly evenly
spread across the Territory save for gaps in the Misinchinka Ranges and the Rocky Mountain
Foothills. High value locations are exclusively along riparian corridors, specifically the
Nation, Omineca, Osilinka, and Mesilinka Rivers (Figure 44).
5.2.3.10 Carbon Storage (above and below ground)
The carbon storage layer makes up 49% of the study area. This feature is well spread
out across the Territory save for a gap in the Rocky Mountain Foothills. There is a
concentration of high values in the Muskwa Ranges between the Ospika and Peace Arms of
the Williston Reservoir. The greatest concentration, however, is found along the western
edge of the study area in the valleys of the Skeena, Bear, and Sustut Rivers (Figure 45).

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Figure 36. Spatial extent of the Climate Corridors layer used to prioritize lands for conservation in the
Greater Tsay Keh Dene Territory Study Area; inset represented by pink frame.

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Figure 37. Spatial extent of the Backward Velocity (2055) layer used to prioritize lands for conservation
in the Greater Tsay Keh Dene Territory Study Area; inset represented by pink frame.

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Figure 38. Spatial extent of the Backward Velocity (2085) layer used to prioritize lands for conservation
in the Greater Tsay Keh Dene Territory Study Area; inset represented by pink frame.

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Figure 39. Spatial extent of the Forward Velocity (2055) layer used to prioritize lands for conservation
in the Greater Tsay Keh Dene Territory Study Area; inset represented by pink frame.

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Figure 40. Spatial extent of the Forward Velocity (2085) layer used to prioritize lands for conservation
in the Greater Tsay Keh Dene Territory Study Area; inset represented by pink frame.

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Figure 41. Spatial extent of the Cool Headwater Refugia layer used to prioritize lands for conservation
in the Greater Tsay Keh Dene Territory Study Area; inset represented by pink frame.

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Figure 42. Spatial extent of the Climatic Refugia layer used to prioritize lands for conservation in the
Greater Tsay Keh Dene Territory Study Area; inset represented by pink frame.

129

Figure 43. Spatial extent of the Biotic Refugia layer used to prioritize lands for conservation in the
Greater Tsay Keh Dene Territory Study Area; inset represented by pink frame.

130

Figure 44. Spatial extent of the Bird Richness layer used to prioritize lands for conservation in the
Greater Tsay Keh Dene Territory Study Area; inset represented by pink frame.

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Figure 45. Spatial extent of the Carbon Storage layer used to prioritize lands for conservation in the
Greater Tsay Keh Dene Territory Study Area; inset represented by pink frame.

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5.2.4 Cultural Conservation Features
There were three cultural conservation features used in this analysis. No maps nor
descriptions of their extent beyond areal percentages are provided given the sacred and
sensitive nature of the knowledge that informed the data for these layers. The percent that
each layer makes up of the study area is as follows: sites of cultural importance 0.5%,
cultural/spiritual areas 9%, subsistence areas 1%.
5.2.5 Connectivity Conservation Features
There were two connectivity conservation features used in this analysis. These two
layers were products of the aforementioned connectivity analyses. The extent of each layer is
described below, with maps depicting each connectivity feature included at the end of the
descriptions.
5.2.5.1 Linkage Mapper
The Linkage Mapper layer makes up 50% of the study area. This feature is found
between protected areas given its nature of linking “core” areas. Noteworthy groupings of
high-value corridors are found in the east – a triangle between Ed Bird-Estella Lakes,
Redfern-Keily, and Graham-Laurier Provincial Parks, and as a mass in the southern Muskwa
Ranges between the Ospika and Peace Arms of the Williston Reservoir (Figure 46).
5.2.5.2 Omniscape
The highest quality (top 49%) omnidirectional connectivity values are predominantly
found at low and moderate elevations away from the Williston Reservoir, with the highest
flows of current found at pinch points and along riparian corridors. Sizable areas of high

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current flow are found along the Omineca River, the northern span of the Rocky Mountain
Trench, and the Graham River (Figure 47).

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Figure 46. Spatial extent of the Linkage Mapper layer used to prioritize lands for conservation in the
Greater Tsay Keh Dene Territory Study Area; inset represented by pink frame.

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Figure 47. Spatial extent of the Omniscape layer used to prioritize lands for conservation in the Greater
Tsay Keh Dene Territory Study Area; inset represented by pink frame.

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5.2.6 Representational Zones
There were five representation conservation features used in this analysis. The intent
was to not necessarily set targets on these features for selection by prioritizr, but rather to
assess how well each category was captured in a given scenario’s solution to understand how
well different aspects of the landscape were represented. Each layer covers the entirety of the
study area by nature, but notable patterns will be described below with maps depicting each
representation feature included at the end of this group of descriptions.
5.2.6.1 Elevational Representation
The elevational representation layer divided the study area into three classifications –
high (17%), moderate (42%), and low (42%). The low elevation class is found along the
Williston Reservoir, river valleys throughout the Territory, and the Muskwa Plateau in the
northeast. The division of moderate elevations is apparent between the Rocky Mountains in
the east and the Omineca Mountains in the west, with the high elevation class perched within
these groupings (Figure 50).
5.2.6.2 Ecoregional Representation
The ecoregional representation layer is dominated by four ecoregions that converge
near the Tsay Keh Dene community at the northern tip of the Williston Reservoir (clockwise
from the northwest: Boreal Mountains and Plateaus, Northern Canadian Rocky Mountains,
Central Canadian Rocky Mountains, and Omineca Mountains. There are an additional five
ecoregions that have only a marginal presence in the greater territory and are found along the
fringe of the study area (Figure 51).

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5.2.6.3 BEC Zones (2020, 2050, 2080)
BEC zones are included for three time periods to illustrate the shifts that are predicted
to occur under a changing climate. While only five BEC zones exist in the Territory at
present, BEC zones are predicted to diversify to eleven by 2050 before subsiding to ten by
2080 (Figure 48). While SBS, ESSF, and BWBS are predicted to remain fairly consistent in
area, they are expected to migrate northward over time. Meanwhile, BAFA and SWB decline
significantly, largely in favour of ICH (Figure 49, Figure 52, Figure 53, and Figure 54).

2020s

2050s

2080s

BAFA Boreal Altai Fescue Alpine
BWBS Boreal White and Black Spruce
ESSF Engelmann Spruce—Subalpine Fir
SBS Sub-Boreal Spruce
SWB Spruce—Willow—Birch
CWH Coastal Western Hemlock
ICH Interior Cedar—Hemlock
IDF Interior Douglas-Fir
IMA Interior Mountain-heather Alpine
MH Mountain Hemlock
MS Montane Spruce

Figure 48. Time series of each biogeoclimatic ecosystem classification zone found in the Greater Tsay Keh Dene
Territory Study Area; 2020s are the existing conditions while 2050s and 2080s are predicted conditions.

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BEC Zone Proportions Through Time
100%
90%
80%
70%
60%
50%
40%

30%
20%
10%
0%
2020

2050

BAFA

BWBS

ESSF

SBS

SWB

CWH

2080

ICH

IDF

IMA

MH

MS

Figure 49. Proportions of each biogeoclimatic ecosystem classification zone found in the Greater Tsay Keh Dene
Territory Study Area through time; 2020s are the existing conditions while 2050s and 2080s are predicted
conditions. Five BEC zones (CWH, IDF, IMA, MH, MS) are so scarce as to barely register on the graphic.

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Figure 50. Spatial extent of the Elevational Representation layer used to understand representation within
selected conservation lands in the Greater Tsay Keh Dene Territory Study Area; inset represented by pink
frame.

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Figure 51. Spatial extent of the Ecoregional Representation layer used to understand representation
within selected conservation lands in the Greater Tsay Keh Dene Territory Study Area; inset represented
by pink frame.

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Figure 52. Spatial extent of the BEC Zones 2020 layer used to understand representation within selected
conservation lands in the Greater Tsay Keh Dene Territory Study Area; inset represented by pink frame.

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Figure 53. Spatial extent of the BEC Zones 2050 layer used to understand representation within selected
conservation lands in the Greater Tsay Keh Dene Territory Study Area; inset represented by pink frame.

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Figure 54. Spatial extent of the BEC Zones 2080 layer used to understand representation within selected
conservation lands in the Greater Tsay Keh Dene Territory Study Area; inset represented by pink frame.

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5.3 Human Footprint
I developed three human footprint layers to operate as cost layers within prioritizr.
These layers are represented in a binary manner and allow the user to choose how
conservative to be in avoiding human disturbances as part of a conservation solution. The
classifications are permanent-sensitive, semi-permanent-sensitive, and ephemeral-sensitive,
with each category building on the one before it.
5.2.1 Permanent-Sensitive Footprint
The permanent-sensitive human footprint makes up 24% of the study area. This layer
forms much of the entire human footprint, as a majority of the features in this analysis are
considered permanent. The highways of the Territory – both literal roads and easily
traversable river valleys – provide access to this remote area and thus are where the greatest
footprint is found. Populated areas to the south near Mackenzie and in the east along the
Alaska Highway (Hwy 97) make up the greatest concentrations of footprint near the
periphery of the study area. The Williston Reservoir and the valleys of the rivers that feed it
provide access to the interior of the Territory, and thus the greatest concentrations of
footprint at the core of the study area are found along their banks and shores (Figure 55).
5.2.2 Semi-Permanent-Sensitive Footprint
The semi-permanent-sensitive human footprint is comprised of both permanent and
semi-permanent features and makes up 29% of the study area. That additional 5% is
predominantly made up of cutblocks and is almost exclusively found adjacent to the
permanent footprint layer. Notable clusters of the semi-permanent footprint can be found just
west of Mackenzie and along the Ingenika and Finlay Rivers (Figure 55).
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5.2.3 Ephemeral-Sensitive Footprint
The ephemeral-sensitive human footprint is comprised of permanent, semipermanent, and ephemeral features and makes up 30% of the study area. Ephemeral features
in this analysis are all recreation-based and generally found near roadways. Notable clusters
of ephemeral footprint can be found east of Mackenzie, west of the Skeena River, and near
Redfern-Keily Provincial Park (Figure 55).

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Figure 55. Spatial extent of the Human Footprint layers used to avoid development in the conservation
prioritization process for the Greater Tsay Keh Dene Territory Study Area; inset represented by pink
frame.

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5.4 Landscape Resistance and Connectivity
The connectivity analysis informed multiple aspects of this project, with output data
serving as both conservation features and locked-in areas. The data for the foundational
resistance layer – as well as the outputs for both Linkage Mapper and Omniscape – cover the
entirety of the study area. In contrast, derivative data products focus on high-value subsets of
that data and cover only portions of the study area.
5.3.1 Resistance Layer
The resistance layer looks strikingly similar to the human footprint layers because the
footprint is one of the main components of resistance. However, the resistance layer also
takes land cover and slope into account, and these more subtle variables can be seen in light
grey in the steepest portions of the Rocky and Omineca Mountains (Figure 56). Their nuance
is most visible in the red diffuse connected areas in the Omniscape Cumulative Current map
(Figure 58).
5.3.2 Linkage Mapper
The Linkage Mapper layer is based on connections between ‘core areas’ (protected
areas in this case), and thus high-value portions of the landscape appear as fairly direct
corridors between existing protected areas and the proclaimed Ingenika Conservation and
Management Area. These corridors are generally found along river valleys and relatively
undisturbed mountain ranges, most notably along the Omineca River and in the Germansen
Range in the southwest of the study area, as well as the mountain ranges surrounding the
Peace Arm and a triangle between Ed Bird-Estella Lakes, Redfern-Keily, and GrahamLaurier Provincial Parks in the east (Figure 57).
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5.3.3 Omniscape
The Omniscape layer assesses connectivity for the entire landscape regardless of
protected status, though there were portions of the landscape identified as high value by both
Omniscape and Linkage Mapper. The most notable areas identified by both tools include the
Omineca River and Germansen Range corridors mentioned above, as well as the area
between Finlay-Russel and Kwadacha Wilderness Provincial Parks north of the community
of Kwadacha. High-value areas identified solely by Omniscape include areas within GrahamLaurier Provincial Park and Ingenika Conservation and Management Area (Figure 58).

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Figure 56. Spatial extent of the Resistance layer used to quantify connectivity for the conservation
prioritization process for the Greater Tsay Keh Dene Territory Study Area; inset represented by pink
frame.

150

Figure 57. Spatial extent of the Linkage Mapper connectivity layer used to inform the conservation
prioritization process for the Greater Tsay Keh Dene Territory Study Area; inset represented by pink
frame.

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Figure 58. Spatial extent of the Omniscape connectivity layer used to inform the conservation
prioritization process for the Greater Tsay Keh Dene Territory Study Area; inset represented by pink
frame.

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5.5 Locked-in Areas
‘Locked-in areas’ are portions of the landscape that the user wants to guarantee for
inclusion in a conservation solution. By choosing to lock in a certain layer we are ensuring
that it is selected, in this case for practical or connectivity purposes. I included four layers as
lock-in options, though the user can also choose to not lock anything in. Protected areas are
included for practicality, as they are already legally protected and unlikely to be delisted.
Thus, the existing network of protected areas can logically be part of a conservation solution.
The exception is the Ingenika Conservation and Management Area, which was included but
is not yet recognized by the provincial or federal governments. The remaining three locked-in
options focus on connectivity, seeking to include a skeleton of connected lands that can be
built out further with ecologically valuable portions of the landscape. The Omniscape layers
are colloquially named for accessibility and to describe their relative breadth.
5.4.1 Protected Areas
Protected areas make up 11% of the study area, or 17% when including the proposed
Ingenika Conservation and Management Area. The protected areas network is fairly well
distributed across the greater territory, with the Ingenika serving as a valuable addition given
its significant size (504,857 ha) and strategic location for connectivity (Figure 59). However,
a noticeable gap exists in the Muskwa Ranges near Ospika Cones Ecological Reserve,
notable because this is the heart of Tsay Keh Dene Territory and leaves the Finlay Caribou
Herd virtually unprotected.

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5.4.2 Protected Areas + Least-Cost Paths
This layer is made up of the protected areas from the previous layer plus the least-cost
paths derived from Linkage Mapper, and makes up 18% of the study area. With smaller and
more numerous protected areas in the south, more parallel and intersecting least-cost paths
exist. For example, the paths from Omineca and Nation Lakes Provincial Parks are
coterminous for much of their routes to Heather-Dina Lakes Provincial Park. Additionally,
there are numerous paths converging near and traversing the Peace Arm, while paths east
from Muscovite Lakes Provincial Park take a 6 km crossing of the Williston Reservoir rather
than circumnavigating its southern shore (Figure 60).
5.4.3 Omniscape ‘Bones’
The ‘bones’ layer is made up of values from the top two quantiles of the Omniscape
analysis, and makes up 19% of the study area. This thin layer has clusters in the northern
reaches of the greater territory, along the Omineca River, and the relatively flat area east of
Nation Lakes Provincial Park (Figure 61).
5.4.4 Omniscape ‘Meat’
The ‘meat’ layer is made up of values from the top three quantiles of the Omniscape
analysis, and makes up 29% of the study area. This thicker layer is a slightly more built-out
version of the ‘bones’ layer, and contributes additional areas of connectivity in the Muskwa
Ranges east of the Tsay Keh Dene community, in addition to augmenting clusters from the
‘bones’ layer (Figure 62).

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Figure 59. Spatial extent of the Protected Areas layer used to ‘lock-in’ areas as part of the conservation
prioritization process for the Greater Tsay Keh Dene Territory Study Area; inset represented by pink
frame.

155

Figure 60. Spatial extent of the Protected Areas + Least-Cost Paths layer used to ‘lock-in’ areas as part
of the conservation prioritization process for the Greater Tsay Keh Dene Territory Study Area; inset
represented by pink frame.

156

Figure 61. Spatial extent of the Omniscape “Bones” layer used to ‘lock-in’ areas as part of the
conservation prioritization process for the Greater Tsay Keh Dene Territory Study Area; inset represented
by pink frame.

157

Figure 62. Spatial extent of the Omniscape “Meat” layer used to ‘lock-in’ areas as part of the
conservation prioritization process for the Greater Tsay Keh Dene Territory Study Area; inset represented
by pink frame.

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5.6 Gap Analysis
The representation of each conservation feature within the existing network of
protected areas varied greatly, ranging from no protection up to 61% protected (Table 9,
Table 10). It is worth noting, however, that most features fall somewhere in the middle, and
that extreme high and low levels of protection were generally found in features with sparse
extents. On average, conservation features were 17% protected.
Conservation features that were relatively well represented include cool headwater
refugia (37%), Tsay Keh Dene subsistence areas (38%), and the Chase Caribou Herd (39%).
The best protected conservation feature, however, was the NDT2-SWB-Burned layer at 61%
(recently burned areas of the Spruce–Willow–Birch biogeoclimatic zone with infrequent
stand-initiating events). Conversely, poorly protected conservation features include recently
burned and mature/old portions of the Engelmann Spruce–Subalpine Fir biogeoclimatic zone
with rare stand-initiating events (NDT1-ESSF-Burned: 0.0%; NDT1-ESSF-Old: 0.8%), as
well as the Finlay Caribou Herd at just 0.5%. More generally, present-day biodiversity
conservation features (16%) were only slightly less represented than future biodiversity
conservation features (18%).

159

Conservation Feature (present-day)
Sites of Cultural Importance
Cultural/Spiritual Areas
Subsistence Areas
Wetlands
Lakes
Karst Deposits
Grizzly Bear
Bull Trout/Fish
Fisher
Chase Caribou Herd
Finlay Caribou Herd
Frog Caribou Herd
Gataga Caribou Herd
Graham Caribou Herd
Kennedy Siding Caribou Herd
Klinse-za Caribou Herd
Pink Mountain Caribou Herd
Thutade Caribou Herd
Wolverine Caribou Herd
Faux North Caribou Herd
Faux Central Caribou Herd
Faux South Caribou Herd
Moose
Stone Sheep
Mountain Goat
Wolverine
Bank Swallow
Barn Swallow
Western Toad
Horned Grebe
Little Brown Myotis
Northern Myotis
Olive-Sided Flycatcher
Rusty Blackbird

% of Study Area
0.5
9
1.3
57
21
27
29
3
38
6
5
1.1
1.0
4
0.1
2
2
4
4
4
0.7
0.1
39
6
10
19
6
8
12
10
25
11
9
13

% Protected
11
21
38
14
21
21
14
20
10
39
0.5
5
4
15
0.0
14
21
33
8
31
0.0
0.0
12
28
24
25
11
20
21
14
24
24
15
16

Target %
100
80
80
50
70
60
60
80
60
90
90
90
90
90
90
90
90
90
90
0
0
0
70
70
70
60
40
60
60
40
60
60
40
60

Table 9. Representation of present-day conservation features by protected areas in the Greater Tsay Keh
Dene Territory Study Area.

160

Conservation Feature (present-day)
NDT1-ESSF-Burned
NDT1-ESSF-Old
NDT2-ESSF-Burned
NDT2-ESSF-Old
NDT2-SBS-Burned
NDT2-SBS-Old
NDT2-SWB-Burned
NDT2-SWB-Old
NDT3-BWBS-Burned
NDT3-BWBS-Old
NDT3-SBS-Burned
NDT3-SBS-Old
Rare BEC Zones

% of Study Area
0.1
2
2
10
0.4
0.8
1.0
4
2
16
0.5
11
25

% Protected
0.0
0.8
24
13
4
2
61
27
35
18
2
4
5

Target
100
70
100
56
100
88
100
52
100
52
100
62
60

Conservation Feature (future)
Land Facet Diversity
Land Facet Rarity
Elevational Diversity
Ecotypic Diversity
Heat Load Index Diversity
Climate Corridors
Backward Velocity 2055
Backward Velocity 2085
Forward Velocity 2055
Forward Velocity 2085
Bird Richness
Carbon Storage (above and below ground)
Cool Headwater Refugia
Climatic Refugia
Biotic Refugia

% of Study Area
5
5
50
50
50
54
50
50
50
50
52
49
6
57
16

% Protected
12
12
23
15
24
19
17
21
5
8
15
14
37
21
30

Target
80
80
67
59
68
61
64
60
50
52
58
61
80
63
70

Conservation Feature (connectivity)
Omniscape
Linkage Mapper

% of Study Area
49
50

% Protected
23
19

Target
0
0

Table 10. Representation of current, future, and connectivity conservation features by protected areas in
the Greater Tsay Keh Dene Territory Study Area.

161

5.7 prioritizr Scenario Outputs
This section reports the solutions of the six prioritizr scenarios developed to prioritize
lands for conservation in the greater territory study area (Table 11). Each of the six scenarios
represent the most efficient collection of planning units that still meet the targets set for each
conservation feature. The target percentages and actual percent captured for each scenario are
reported in Table 12.

Targets
Set for:
Footprint:

Locked-in
Areas:

Scenario A

Scenario B

Scenario C

Scenario D

Scenario E

Scenario F

Present

Future
(2050s)

Future
(2080s)

Present +
Future (both)

Present

Futures
(both)

Permanent +
SemiPermanent
Permanent

None

None

Permanent

Permanent + Permanent +
SemiSemiPermanent
Permanent Permanent

None

Protected
Areas +
Least-Cost
Paths

None

Table 11. Parameters for each scenario.

162

Omniscape
“Bones”

5.6.1 Scenario A – Present Conservation Features
Scenario A’s solution encompasses 59% of the greater territory study area. Given its
focus on existing high-quality habitat for a broad collection of species, the solution contains
clusters along both river valleys and moderate to high elevation mountain ranges. River
valleys of note include the Omineca, Ospika, Mesilinka, and Ingenika. Important montane
areas include the northern Misinchinka Ranges and Wolverine Range in the southern extent
of the study area, as well as the Omineca Ranges west of Kwadacha in the north (Figure 63).

163

Figure 63. Spatial extent of the conservation solution for Scenario A: Present Conservation Features
within the Greater Tsay Keh Dene Territory Study Area; inset represented by pink frame.

164

5.6.2 Scenario B – Future (2050s) Conservation Features
Scenario B’s solution encompasses 52% of the greater territory study area. Given its
emphasis on future climates, moderate to high elevation areas are well represented as they
serve as refugia in a warming landscape. Clustering is quite evident in this solution,
particularly in the Rocky Mountains along the eastern shores of the Williston Reservoir.
Additional clusters can be found along the Skeena River, in the Muskwa Ranges west of
Redfern Lake, and along the Finlay River (Figure 64).

165

Figure 64. Spatial extent of the conservation solution for Scenario B: Future (2050s) Conservation
Features within the Greater Tsay Keh Dene Territory Study Area; inset represented by pink frame.

166

5.6.3 Scenario C – Future (2080s) Conservation Features
Scenario C’s solution encompasses 52% of the greater territory study area. As only a
few of the climate change features are time period specific, this solution is incredibly similar
to that of Scenario B. A notable shift northward is evident, however, with low elevation
patches in the southwest and east migrating northward and bolstering higher elevation
patches (Figure 65).

167

Figure 65. Spatial extent of the conservation solution for Scenario C: Future (2080s) Conservation
Features within the Greater Tsay Keh Dene Territory Study Area; inset represented by pink frame.

168

5.6.4 Scenario D – Present-day and Future (2050s and 2080s) Conservation Features
Scenario D’s solution encompasses 61% of the greater territory study area. This is the
largest solution yet, which is expected given that targets were set on both present and future
features. It is noteworthy, however, that this solution is only 2% larger than the present
scenario. As it combines targets from the previous three scenarios, the clustered high value
areas are understandably similar, with the most notable blocks found on either side of the
northerly half of the Williston Reservoir (Figure 66). Figure 67 allows for comparisons
among the first four solutions.

169

Figure 66. Spatial extent of the conservation solution for Scenario D: Present-day and Future (2050s
and 2080s) Conservation Features within the Greater Tsay Keh Dene Territory Study Area; inset
represented by pink frame.

170

Figure 67. Spatial extent of solutions A-D in the Greater Tsay Keh Dene Territory Study Area for
comparison purposes; inset represented by pink frame.

171

5.6.5 Scenario E – Protected Areas Connectivity
Scenario E’s solution encompasses 62% of the greater territory study area. Ignoring
existing protected areas – as they were locked in for this solution – the most notable clusters
are found along the Ospika and Omineca Rivers, the Mesilinka River south of Chase
Provincial Park, and the northern edge of the Misinchinka Ranges (Figure 68).

172

Figure 68. Spatial extent of the conservation solution for Scenario E: Protected Areas Connectivity
within the Greater Tsay Keh Dene Territory Study Area; inset represented by pink frame.

173

5.6.6 Scenario F – Landscape Connectivity
Scenario F’s solution encompasses 54% of the greater territory study area. Despite its
focus on overall landscape connectivity and not just connectivity between protected areas,
scenario F shares a remarkable amount of overlap with scenario E. Scenario F is generally a
leaner version of Scenario E, with the exception of more robust collections in the southern
Muskwa Ranges, south of Omineca Provincial Park, and along the Skeena and Omineca
Rivers (Figure 69).

174

Figure 69. Spatial extent of the conservation solution for Scenario F: Landscape Connectivity within
the Greater Tsay Keh Dene Territory Study Area; inset represented by pink frame.

175

5.6.7 Scenario Stack
I stacked each of the aforementioned six scenarios to reveal which areas were
consistently selected despite each scenario having different focuses on the present, future,
and connectivity. This exercise acknowledged areas of cultural significance, as we placed the
same high targets on them in each scenario, but also revealed other portions of the landscape
that were consistently selected. These include the northern edge of the Misinchinka Ranges,
the southern Muskwa Ranges, the Omineca, Sustut, and Skeena River valleys, the
Toodoggone and Fox River watershed groups, and portions of the Besa and Prophet River
valleys (Figure 70).

176

Figure 70. Spatial extent of conservation solutions for Scenarios A-F stacked atop one another to
reveal areas that were consistenly selected in the Greater Tsay Keh Dene Territory Study Area; inset
represented by pink frame and referenced areas outlined and labeled in green.

177

5.6.8 Conservation Feature Diversity
I also stacked each of the conservation features for which targets were set (52 in all)
to identify ‘hotspot’ areas of overlap, or conservation feature diversity. These were
predominantly in river valleys near confluences. Notable hotspots included: the Sustut River
near Red Creek; Pelly Creek near the Ingenika River and Tucha Creek; the Mesilinka River
near Carina Lake; the headwaters of the Davis River; the Ospika River from McCusker
Creek to the Williston Reservoir; the Wicked River near Ignatieff and Cowart Creeks; and
the Omineca River near Henschel Creek. One additional, non-riparian hotspot was located
north of the Nation Arm of the Williston Reservoir near Maybeline Lake. Each of the
aforementioned locations contained one or more planning units with at least 25 overlapping
conservation features. The maximum number of overlapping conservation features was 28 –
found in just two planning units across the study area. One was at the Davis River
headwaters hotspot, the other on Stevenson Creek near the Ospika River hotspot. These two
planning units were selected in all six of the scenarios I ran (Figure 71). Figure 72 allows for
comparisons between the connectivity-centric solutions, as well as between the stacked
outputs.

178

Figure 71. Spatial extent of all conservation features stacked atop one another to reveal areas of high
conservation feature diversity, or ‘hotspots’ in the Greater Tsay Keh Dene Territory Study Area; inset
represented by pink frame.

179

Figure 72. Spatial extent of solutions E and F in the Greater Tsay Keh Dene Territory Study Area for
comparison purposes; stacked solutions and conservation feature stack also shown side by side for
comparison purposes; inset represented by pink frame.

180

100
80
80
50
70
60
60
80
60
90
90
90
90
90
90
90
90
90
90
0
0
0
70
70
70
60
40
60
60
40
60
60
40
60

100
80
80
58
70
68
64
80
60
90
90
90
90
90
92
90
90
90
90
27
85
20
70
70
70
72
45
60
71
52
71
71
59
62

Scenario A
%
%
Target Captured
100
80
80
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

100
80
80
48
54
67
58
64
37
54
66
62
45
69
47
79
61
57
49
48
70
93
47
66
65
61
27
43
60
33
60
51
42
44

Scenario B
%
%
Target Captured
100
80
80
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

100
80
80
48
54
68
58
66
36
54
68
63
49
57
86
83
66
61
49
57
69
65
47
67
66
61
28
43
60
33
60
52
42
45

Scenario C
%
%
Target Captured
100
80
80
50
70
60
60
80
60
90
90
90
90
90
90
90
90
90
90
0
0
0
70
70
70
60
40
60
60
40
60
60
40
60

181

100
80
80
60
70
74
64
80
60
90
90
90
90
90
91
90
90
90
90
36
87
22
70
76
70
73
45
60
71
51
72
71
59
62

Scenario D
%
%
Target Captured

Table 12. Percentage of each conservation feature by target and actual amount captured for each scenario.

Sites of Cultural Importance
Cultural/Spiritual Areas
Subsistence Areas
Wetlands
Lakes
Karst Deposits
Grizzly Bear
Bull Trout/Fish
Fisher
Chase Caribou Herd
Finlay Caribou Herd
Frog Caribou Herd
Gataga Caribou Herd
Graham Caribou Herd
Kennedy Siding Caribou Herd
Klinse-za Caribou Herd
Pink Mountain Caribou Herd
Thutade Caribou Herd
Wolverine Caribou Herd
Faux North Caribou Herd
Faux Central Caribou Herd
Faux South Caribou Herd
Moose
Stone Sheep
Mountain Goat
Wolverine
Bank Swallow
Barn Swallow
Western Toad
Horned Grebe
Little Brown Myotis
Northern Myotis
Olive-Sided Flycatcher
Rusty Blackbird

Conservation Feature
100
80
80
50
70
60
60
80
60
90
90
90
90
90
90
90
90
90
90
0
0
0
70
70
70
60
40
60
60
40
60
60
40
60

100
80
80
60
70
75
65
80
60
90
90
90
90
90
91
90
90
90
90
44
82
13
70
83
70
75
45
60
73
51
75
72
58
62

Scenario E
%
%
Target Captured
100
80
80
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

100
80
80
50
54
68
60
67
38
58
65
68
49
64
47
83
60
56
52
56
69
80
48
62
64
64
28
44
61
34
62
53
43
46

Scenario F
%
%
Target Captured

(Table 12 continued)

Total Area

NDT1-ESSF-Burned
NDT1-ESSF-Old
NDT2-ESSF-Burned
NDT2-ESSF-Old
NDT2-SBS-Burned
NDT2-SBS-Old
NDT2-SWB-Burned
NDT2-SWB-Old
NDT3-BWBS-Burned
NDT3-BWBS-Old
NDT3-SBS-Burned
NDT3-SBS-Old
Rare BEC Zones
Land Facet Diversity
Land Facet Rarity
Elevational Diversity
Ecotypic Diversity
Heat Load Index Diversity
Climate Corridors
Backward Velocity 2055
Backward Velocity 2085
Forward Velocity 2055
Forward Velocity 2085
Bird Richness
Carbon Storage
Cool Headwater Refugia
Climatic Refugia
Biotic Refugia
Omniscape
Linkage Mapper

Conservation Feature
0
0
0
0
0
0
0
0
0
0
0
0
0
80
80
67
59
68
61
64
0
50
0
58
61
80
63
70
0
0

74
77
42
65
30
45
72
63
44
39
22
37
50
80
80
67
59
68
61
64
60
50
49
58
61
80
63
70
65
67

Scenario B
52.1%

100
70
100
61
100
88
100
65
100
63
100
62
60
58
48
67
59
68
61
64
60
49
52
58
61
57
63
62
71
70

Scenario A
58.8%

100
70
100
56
100
88
100
52
100
52
100
62
60
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

Scenario B
%
%
Target Captured

Scenario A
%
%
Target Captured
77
79
44
61
32
44
75
66
48
42
22
32
49
80
80
67
59
68
61
60
60
44
52
58
61
80
63
70
65
67

182

Scenario C
51.9%

0
0
0
0
0
0
0
0
0
0
0
0
0
80
80
67
59
68
61
0
60
0
52
58
61
80
63
70
0
0

Scenario C
%
%
Target Captured
100
72
100
61
100
88
100
65
100
62
100
62
60
80
80
69
59
70
64
64
61
51
53
59
62
80
65
71
73
72

Scenario D
60.6%

100
70
100
56
100
88
100
52
100
52
100
62
60
80
80
67
59
68
61
64
60
50
52
58
61
80
63
70
0
0

Scenario D
%
%
Target Captured
100
72
100
60
100
88
100
69
100
64
100
62
60
60
54
72
61
74
66
66
63
49
53
61
64
75
68
72
76
75

Scenario E
62.1%

100
70
100
56
100
88
100
52
100
52
100
62
60
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

Scenario E
%
%
Target Captured

80
78
46
65
34
46
72
66
48
43
26
36
50
80
80
67
59
68
61
64
62
50
52
58
61
80
63
70
76
73

Scenario F
53.7%

0
0
0
0
0
0
0
0
0
0
0
0
0
80
80
67
59
68
61
64
60
50
52
58
61
80
63
70
0
0

Scenario F
%
%
Target Captured

5.8 Conservation Feature Complementarity
I ran auxiliary scenarios for each conservation feature where a 100% target was set
for that feature alone. In doing so, I determined the degree to which each feature contributed
to a focal feature (i.e., how well each feature complemented one another). For example,
referencing the fisher row in Table 13, I evaluated what percentage of other conservation
features (columns) fall within the extent of the fisher layer. As the fisher layer takes up much
of the lower elevation portions of the study area, it contains 62.9% of the moose layer and
100% of the bank swallow layer. Conversely, the fisher layer contains only 21.7% and 29.4%
of the Chase caribou herd and grizzly bear layers, respectively – species that gravitate
towards higher elevations. Features that were 50% or more represented within another
feature were considered to have high complementarity and are identified in bold. Where a
column and row of the same feature align, values of “100” are highlighted diagonally across
the complementary matrix. This also revealed instances where one feature was completely
contained within another feature.

183

Cultural/Spiritual Areas

3.3
100
4.5
68.7
22.4
27.3
25.4
14.6
51.6
13.4
6.6
0
0
0.8
0
0.2
0
3.7
5.3
49.2
3.5
6.0
44.2
25.0
20.6
30.4
40.3
64.8
36.6
28.4
48.0
0.0
0.9
2.0
6.7
0.5
0.8
0.2
7.3
2.2
23.6
0.6
11.0
5.3
4.5
3.6
71.7
53.5
50.0
39.9
56.0
48.9
33.9
52.2
62.3
48.3
2.4
47.9
12.7
39.8
41.8

Sites of Cultural Importance

100
41.9
14.0
80.4
27.5
25.4
24.2
21.5
69.2
7.4
5.3
0
0
1.1
0
0
0
3.6
0.9
63.6
2.7
4.1
38.9
45.8
20.9
32.3
60.6
73.9
55.6
36.4
66.9
0
0.8
0.8
3.1
0.9
1.6
0.6
3.8
3.9
24.3
1.7
23.4
11.9
2.7
4.9
82.4
46.8
64.1
26.2
37.1
26.8
55.3
69.5
57.6
50.6
2.0
54.5
9.9
31.4
36.7

Subsistence Areas

7.5
25.0
100
61.9
20.1
38.5
39.9
15.7
54.6
24.1
3.7
0
0
0
0
0
0
10.9
0
42.4
5.3
3.1
44.5
33.2
26.6
26.4
44.5
64.9
35.5
26.3
49.8
0
0.3
3.9
2.3
0.3
0.8
6.3
6.4
11.6
21.4
0.5
19.1
0.6
4.6
11.6
82.5
41.5
67.2
13.3
46.4
54.1
31.1
48.2
49.8
43.5
2.4
53.2
9.9
38.5
37.0

Wetlands

0.9
11.3
1.4
100
33.7
23.1
29.3
4.4
51.9
7.1
3.6
1.0
1.1
1.6
0.1
1.8
0.3
4.5
5.4
53.5
1.2
2.7
20.9
10.1
9.3
17.6
16.4
28.7
16.5
16.8
20.3
0.1
1.7
1.8
9.5
0.4
0.9
0.8
5.4
3.1
21.8
0.8
17.4
20.8
5.9
7.5
43.3
54.6
39.6
44.1
44.8
44.4
59.6
58.2
66.4
48.5
1.7
46.4
9.7
42.1
43.1

Lakes

0.9
9.6
1.5
83.7
100
18.0
24.4
3.1
42.9
7.7
1.2
1.3
0.9
1.1
0.0
1.3
0.9
5.2
4.6
43.9
2.3
7.0
16.4
6.4
8.2
16.1
11.8
21.7
11.9
14.6
14.9
0.0
0.7
0.9
8.2
0.1
0.3
0.8
6.3
2.1
16.3
0.5
18.2
18.4
6.4
6.8
39.6
57.3
41.0
50.5
55.0
57.9
54.8
51.6
73.2
42.0
4.2
50.0
17.7
43.3
44.9

Karst Deposits

0.4
9.8
1.4
52.1
13.5
100
26.6
5.1
30.2
2.9
16.1
1.8
2.2
8.6
0.0
3.5
4.1
2.5
0.8
34.3
13.1
14.6
23.5
4.2
7.3
17.7
8.4
29.4
13.4
6.9
12.0
0.5
3.9
2.0
5.7
0.3
1.5
1.9
5.1
2.3
13.5
0.3
5.7
18.3
5.7
7.6
73.2
38.6
78.1
67.0
36.5
40.6
38.9
45.2
41.0
47.5
13.7
73.1
21.9
60.7
72.0

Grizzly Bear

0.5
9.4
1.9
60.8
19.5
25.2
100
2.6
29.4
9.9
4.7
0.6
0.3
4.6
0.3
4.7
0.5
2.7
6.0
37.6
3.0
6.8
30.8
5.2
10.8
17.8
8.0
36.1
10.8
11.5
12.3
0.2
3.8
3.5
20.9
0.6
1.2
0.4
2.9
1.8
9.3
0.6
10.9
33.0
5.4
4.5
54.0
53.6
51.0
50.2
61.2
53.5
51.9
51.6
51.7
62.0
1.5
52.1
11.3
52.9
50.8

Bull Trout/Fish

4.6
43.6
7.8
79.2
21.5
44.8
26.1
100
72.0
17.4
12.8
0
0
2.3
0
0
0.2
1.6
0.6
65.6
3.6
3.5
50.1
25.5
11.7
32.2
50.0
77.9
54.8
26.7
62.2
0
1.3
3.0
6.0
0.0
3.2
1.5
5.0
8.3
44.8
0.4
10.8
7.0
3.0
1.0
87.5
33.6
74.2
22.0
40.4
37.3
31.6
66.2
59.1
58.6
2.3
62.6
4.6
51.0
55.6

Fisher

1.1
11.4
2.0
72.3
22.4
22.5
24.0
5.7
100
3.4
2.3
0.3
1.0
1.7
0
0.3
0.0
0.0
3.4
71.4
0.2
0.3
16.2
14.8
7.6
10.7
25.5
25.7
22.3
15.5
29.9
0.2
1.0
1.7
4.0
0.8
2.4
0.7
0.4
5.1
43.6
1.3
31.3
40.9
6.1
9.9
44.4
55.3
33.4
28.6
27.5
18.7
71.4
75.6
58.8
50.9
0.0
43.0
0.2
25.0
24.8

Chase Caribou Herd

0.6
19.2
4.9
65.5
27.1
14.3
43.2
7.9
21.7
100
0
0
0
0
0
0
0
0
0
37.2
5.2
5.3
55.1
3.9
20.1
27.9
13.7
63.9
21.4
20.0
24.4
0
0
6.4
18.3
0.0
0.4
0.6
8.8
3.5
15.1
1.6
4.7
0.1
2.5
2.0
73.8
39.3
68.0
34.1
92.0
90.5
5.3
16.7
63.1
47.1
4.1
48.2
22.9
62.6
42.7

Finlay Caribou Herd

0.5
14.3
1.0
46.3
5.4
74.6
25.6
7.6
17.4
0
100
0
0
0
0
0
0
0
0
17.4
22.5
22.7
27.5
0.8
3.7
17.7
3.3
31.5
9.4
2.6
7.7
0
0.1
0.4
6.9
0.1
0.1
1.5
6.4
2.1
14.2
0.1
0.6
0.8
5.1
1.5
96.9
14.4
95.1
69.6
29.2
54.9
13.7
26.0
32.9
51.1
22.8
74.4
39.7
67.9
81.5

Frog Caribou Herd

0
0
0
57.1
25.1
50.3
12.0
0
9.1
0
0
100
0
0
0
0
0
0
0
13.3
3.1
23.4
0
0
0
0
0
0
0
0
0
0
0
0
0.4
0
0
1.5
3.4
1.1
8.4
0
0
0
2.2
6.9
90.6
39.4
100
77.0
0
69.9
64.8
61.1
32.5
36.8
11.1
72.8
36.0
67.4
63.6

Gataga Caribou Herd

0
0
0
62.4
16.5
45.4
8.7
0
40.4
0
0
0
100
0
0
0
0
0
0
38.6
11.7
12.9
0
0
0
0
0
0
0
0
0
0
0
0
0.8
0
0
0.8
4.4
2.4
38.9
0
0
0
4.3
1.4
41.5
53.5
61.1
85.7
0
0.6
84.3
45.9
40.6
51.7
12.5
54.3
23.7
62.4
97.4

Graham Caribou Herd

0.2
2.1
0
25.1
6.1
54.3
31.6
1.6
16.7
0
0
0
0
100
0
0
0
0
0
28.5
8.6
3.2
21.3
0.1
6.7
17.6
1.2
25.4
10.9
4.2
2.6
0.5
1.9
3.6
18.8
0.0
0.2
0
0.1
0.6
10.5
0
1.5
23.8
5.3
3.7
30.0
49.3
63.6
87.9
96.4
35.3
35.5
33.4
45.6
54.8
2.8
92.0
13.1
75.4
62.6

Kennedy Siding Caribou Herd

0
0
0
90.9
7.8
10.4
89.6
0
0
0
0
0
0
0
100
0
0
0
0
36.4
0
0
0
0
0
0
0
0
0
0
0
0
40.3
0
0
0
0
0
0
0
0
0
0
100
0
3.9
27.3
100
6.5
61.0
0
0
100
100
0
83.1
0
16.9
62.3
0
0

Klinse-za Caribou Herd

0
0.9
0
63.0
16.0
42.6
64.5
0
8.2
0
0
0
0
0
0
100
0
0
0
21.0
2.3
27.5
0.5
1.8
0.4
1.3
1.8
1.8
1.8
1.4
1.8
1.5
18.4
4.8
6.8
0.5
0.6
0
0
0
0
0
1.5
96.7
5.7
7.7
48.5
72.2
36.2
85.6
15.4
0.4
100
98.7
15.2
61.4
0
59.4
13.2
62.3
82.7

Wolverine Caribou Herd

Thutade Caribou Herd

Pink Mountain Caribou Herd

0
0.5
0.2
0
8.0 14.9
0
3.7
0
6.0 64.9 80.1
8.3 29.2 27.4
57.9 24.0 5.2
7.3 18.9 36.2
0.3
1.1
0.5
0.5
0.6 38.6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
100
0
0
0
100
0
0
0
100
14.8 6.0 58.7
48.5 11.3
0
32.7 17.5 5.9
1.2 34.3 21.3
0
0
3.9
0.7 14.6 5.8
1.3 24.4 10.3
0
0.1 11.8
1.4 38.1 27.0
0.3
5.6 14.0
0.2
9.8
9.6
0
1.1 15.2
0
0
0
0
0
0
0
0
1.9
0
0.3 23.0
0
0
0
0
0
0.6
2.8
2.2
0
8.4 19.3
0
0.1
0.2
0.4
0.3
0.5 19.8
0
0
0.2
0
0
11.9
0.5
0.2
0.8
3.4
1.9
4.9
1.2
2.9
2.4
50.8 50.8 41.3
42.8 47.0 91.4
78.3 58.4 12.4
92.8 80.0 26.0
17.0 56.4 83.7
72.1 100.0 71.8
5.1
6.7 49.3
9.0 13.6 38.5
23.7 38.8 80.4
15.7 35.9 68.7
24.5 6.6
0
96.9 86.1 16.0
43.9 42.3 5.3
64.6 70.7 55.2
74.0 87.1 42.5

Moose

1.0
11.2
1.4
68.6
21.5
26.2
26.6
4.8
62.9
4.4
3.2
0.5
1.4
3.5
0.1
1.0
1.2
0.9
4.4
100
1.0
1.4
22.2
9.9
7.1
13.9
17.8
31.3
18.9
13.6
21.8
0.2
1.8
2.4
9.5
0.4
1.4
1.2
2.6
4.4
28.6
0.9
16.8
26.0
5.9
7.2
47.8
55.4
43.2
38.6
40.5
27.4
58.0
59.6
59.0
53.9
0.2
51.9
3.1
39.6
39.4

Stone Sheep

0.3
6.1
1.1
15.7
8.9
57.4
13.2
1.8
2.4
5.5
18.7
0.6
2.0
6.1
0
0.7
18.3
7.6
0
4.4
100
75.1
4.2
0
1.7
4.6
0.0
4.2
0.3
0.3
0.0
0.0
0.1
1.1
2.6
0.0
0
2.1
4.7
0.2
1.6
0.0
0.1
4.9
1.9
3.3
72.2
30.8
82.5
86.5
35.8
79.3
17.0
19.8
21.8
19.7
35.9
84.3
59.1
62.8
78.6

Mountain Goat

0.2
6.1
0.4
20.4
16.0
36.2
17.5
1.0
1.3
3.3
11.1
2.6
1.3
1.3
0
5.0
7.2
6.9
2.1
4.5
44.0
100
4.2
0.1
3.8
6.6
0.2
4.8
0.3
0.7
0.2
0.1
0.7
1.0
8.1
0.0
0.0
0.8
3.0
0.0
0.3
0
0.6
21.5
2.9
3.9
66.2
29.1
77.3
84.3
48.6
83.2
27.3
32.0
30.1
22.5
30.9
78.3
57.0
60.6
71.2

Barn Swallow

Bank Swallow

Wolverine

1.1
3.9
1.3
22.0 36.3 23.5
3.0
7.8
4.5
64.5 78.1 62.7
18.2 20.5 19.3
31.8 21.0 23.4
47.1 22.8 34.7
7.5 10.2 4.0
31.3 100 48.1
18.2 3.8 14.7
7.1
0.5
2.1
0
0
0
0
0
0
4.7
0.1
3.3
0
0
0
0.1
0.5
0.1
0.1
0
0.2
7.2
0
6.9
4.1
2.3
2.5
47.4 67.4 37.4
1.3
0
1.2
2.2
0.1
4.4
100 21.1 37.5
7.2 100 33.5
17.0 49.8 100
39.8 29.1 38.8
19.5 100.0 41.5
96.2 51.4 47.9
37.1 51.4 15.3
26.2 45.9 27.6
32.0 100 48.4
0.3
0
0.2
2.8
0.1
0.5
5.1
0.2
7.7
19.7 0.0
8.7
0.2
0.4
0.2
1.4
0.7
0.5
0.9
0.1
1.2
9.1
0
8.2
2.8
3.8
3.2
17.0 22.4 6.9
0.7
1.6
1.4
7.5 33.9 14.5
13.4 1.1
8.9
4.5
5.0
4.8
3.9
8.6
4.7
67.6 66.6 59.9
46.1 31.8 37.7
62.1 37.1 48.9
46.9 8.4 37.1
72.4 3.2 55.6
57.6 2.1 48.6
23.8 88.0 44.2
36.7 99.3 50.6
59.7 72.5 67.9
66.2 29.5 40.0
0.7
0
1.3
56.1 23.9 44.0
8.6
0
9.9
62.2 10.1 34.3
62.0 13.3 43.2

Western Toad

1.5
23.2
3.0
72.4
28.5
36.5
41.7
7.8
33.3
14.6
7.2
0
0
6.2
0
0.2
0.2
8.1
3.1
48.8
2.3
5.5
63.1
15.5
27.4
100
27.0
80.2
34.1
41.8
35.9
0.3
2.2
2.8
14.6
0.2
0.5
0.8
10.5
2.2
15.0
0.5
10.7
14.3
6.4
5.6
59.9
44.2
58.8
51.7
69.3
55.8
28.9
39.4
66.3
56.3
1.6
57.1
10.1
52.1
59.5

Horned Grebe

3.5
35.6
6.8
81.0
21.7
23.2
22.3
12.4
99.5
6.6
1.2
0
0
0.3
0
0.4
0
0.0
3.5
71.5
0.0
0.2
30.2
75.9
41.0
30.5
100
60.4
59.9
46.0
99.8
0.1
0.3
1.4
1.1
0.4
1.5
0.2
0.2
5.0
30.9
1.6
30.2
3.4
4.6
7.7
68.5
38.6
40.2
11.8
11.8
10.1
75.0
94.0
70.5
36.5
0
29.5
0.2
15.5
18.7

Little Brown Myotis

1.7
24.4
3.4
67.9
18.8
32.1
42.8
9.1
40.0
16.4
6.4
0
0
4.6
0
0.1
0.1
6.2
4.0
52.7
1.0
2.0
75.6
14.2
16.8
39.6
28.2
100
44.0
28.3
39.9
0.2
2.4
4.6
16.3
0.3
1.3
0.8
7.4
3.1
18.6
0.8
10.4
12.9
4.6
4.7
68.4
44.5
59.9
42.1
65.4
50.4
29.5
43.6
60.3
64.6
0.6
54.5
7.5
56.3
57.8

Northern Myotis

2.9
30.4
4.5
83.2
23.0
34.3
30.0
14.6
76.5
12.7
4.5
0
0
4.3
0
0.3
0.1
2.1
4.9
75.5
0.2
0.3
67.0
32.7
12.4
38.6
64.5
99.4
100
43.9
76.7
0.2
1.5
4.1
9.8
0.4
2.0
0.6
5.0
5.9
37.1
1.5
21.6
7.7
4.1
6.7
68.3
47.2
52.9
28.3
39.7
28.7
44.9
70.2
64.0
58.2
0.1
48.3
1.3
40.5
40.8

Olive-Sided Flycatcher

2.3
28.8
4.1
95.8
36.3
23.5
40.3
8.8
64.9
14.8
1.5
0
0
2.1
0
0.3
0.1
4.6
4.1
72.0
0.2
0.9
58.6
33.9
27.1
58.3
55.9
80.5
54.2
100
69.5
0.0
1.2
3.0
11.1
0.2
1.0
0.3
6.3
3.3
26.5
0.8
24.3
5.7
5.3
8.0
54.9
53.7
41.2
30.5
47.1
39.6
44.7
63.0
71.2
47.8
0.3
37.3
5.0
27.7
32.1

Rusty Blackbird

2.8
32.0
4.9
85.2
23.2
27.4
29.7
14.0
90.1
12.5
3.1
0
0
1.0
0
0.3
0
0.4
4.5
75.4
0.0
0.2
49.8
42.5
26.2
34.6
74.7
78.7
65.5
47.6
100
0.2
1.3
3.7
7.0
0.5
2.5
0.4
2.0
6.0
36.4
1.5
25.5
8.9
3.9
7.2
70.3
47.1
46.6
19.4
31.3
25.5
54.2
80.5
66.8
50.4
0.0
39.7
0.9
29.0
31.6

NDT1-ESSF-Burned

0
0.8
0
50.0
4.0
81.5
44.4
0
51.6
0
0
0
0
13.7
0
20.2
0
0
0
54.8
0.8
5.7
34.7
0
10.5
28.2
9.7
33.9
13.7
1.6
19.4
100
18.6
1.6
0.8
41.9
8.9
0
0
0
0
0.8
0.8
100
4.0
38.7
83.1
56.5
70.2
87.9
46.0
0
94.4
94.4
47.6
64.5
0
73.4
4.8
45.2
79.0

0.4
6.5
0.2
71.2
10.0
56.3
65.9
2.4
21.6
0
0.4
0
0
5.2
2.2
21.3
0
0
0
42.8
0.2
4.3
34.0
0.2
2.5
16.5
3.2
35.4
9.8
6.2
10.3
1.6
100
0.1
2.0
0.6
17.1
0
0
0
0
0
0.1
100
3.6
8.9
53.6
58.2
55.8
85.7
37.7
7.6
83.0
83.6
28.9
82.2
0.1
71.1
7.9
59.6
76.4

NDT1-ESSF-Old

184

Table 13. Conservation feature complementarity matrix. Values above 50% are bolded, values of 100% are highlighted.

Sites of Cultural Importance
Cultural/Spiritual Areas
Subsistence Areas
Wetlands
Lakes
Karst Deposits
Grizzly Bear
Bull Trout/Fish
Fisher
Chase Caribou Herd
Finlay Caribou Herd
Frog Caribou Herd
Gataga Caribou Herd
Graham Caribou Herd
Kennedy Siding Caribou Herd
Klinse-za Caribou Herd
Pink Mountain Caribou Herd
Thutade Caribou Herd
Wolverine Caribou Herd
Moose
Stone Sheep
Mountain Goat
Wolverine
Bank Swallow
Barn Swallow
Western Toad
Horned Grebe
Little Brown Myotis
Northern Myotis
Olive-Sided Flycatcher
Rusty Blackbird
NDT1-ESSF-Burned
NDT1-ESSF-Old
NDT2-ESSF-Burned
NDT2-ESSF-Old
NDT2-SBS-Burned
NDT2-SBS-Old
NDT2-SWB-Burned
NDT2-SWB-Old
NDT3-BWBS-Burned
NDT3-BWBS-Old
NDT3-SBS-Burned
NDT3-SBS-Old
Rare BEC Zones
Land Facet Diversity
Land Facet Rarity
Elevational Diversity
Ecotypic Diversity
Heat Load Index Diversity
Climate Corridors
Backward Velocity 2055
Backward Velocity 2085
Forward Velocity 2055
Forward Velocity 2085
Bird Richness
Total Biomass
Cool Headwater Refugia
Climatic Refugia
Biotic Refugia
Omniscape
Linkage Mapper

Conservation
Feature

NDT2-ESSF-Burned

0.3
9.2
2.5
55.9
9.0
25.1
49.0
4.2
41.8
19.4
1.0
0
0
7.4
0
4.2
0
0
3.4
51.6
3.1
4.8
46.4
0.6
32.0
16.2
10.4
54.3
20.8
12.4
21.3
0.1
0.1
100
21.4
8.3
1.8
0.3
0.1
25.8
23.2
5.6
5.9
21.5
2.7
1.2
58.1
52.7
52.7
29.7
67.1
54.9
27.3
40.3
52.6
58.9
0.1
48.8
4.4
43.8
44.2

NDT2-ESSF-Old

0.2
6.7
0.3
54.8
17.8
15.0
60.4
1.7
15.5
11.2
3.3
0.0
0.1
7.7
0
1.2
0
0.1
8.2
36.7
1.5
8.0
36.5
0.0
7.3
17.2
1.7
38.1
10.1
9.2
8.4
0.0
0.3
4.3
100
0.4
1.7
0
1.5
0.6
4.3
0.2
8.4
30.1
4.3
0.8
46.4
57.1
52.7
49.7
86.2
72.5
47.7
45.5
62.6
74.1
0.3
47.6
6.9
64.5
59.0

NDT2-SBS-Burned

1.1
7.4
1.1
65.1
6.0
16.5
43.1
0.3
100
0.3
1.1
0
0
0.3
0
2.2
0
0
0
37.9
0.3
0.3
9.6
5.2
4.7
5.8
11.8
15.1
11.0
4.1
16.8
14.3
2.2
42.9
9.1
100
16.2
0
0
26.4
23.6
4.7
4.7
77.5
3.0
12.6
31.9
37.1
23.4
25.3
6.6
0
97.3
99.5
15.1
62.6
0
20.1
0
11.0
16.5

NDT2-SBS-Old

1.2
9.0
1.2
75.8
8.6
41.2
42.0
11.2
100
3.1
0.4
0
0
0.8
0
1.4
0
0
2.7
67.3
0
0.5
32.4
5.5
5.5
7.5
23.6
36.5
24.5
10.2
36.6
1.5
33.0
4.6
21.5
7.9
100
0
0
1.6
1.9
0
4.6
67.3
1.5
13.3
56.3
61.6
49.3
47.6
15.8
0.7
88.6
94.4
18.8
75.9
0
57.1
0
26.1
38.4

NDT2-SWB-Burned

0.2
2.3
8.2
45.9
14.4
61.9
12.0
4.0
37.1
3.8
7.1
1.6
0.8
0
0
0
6.2
8.5
0
34.0
12.1
8.3
15.9
0.4
10.0
9.2
2.9
19.0
6.4
2.4
5.0
0
0
0.6
0
0
0
100
13.5
34.4
23.3
0
0
5.5
5.1
2.6
79.0
66.9
79.0
60.2
28.0
47.1
38.4
43.0
40.8
37.8
2.4
86.8
11.3
42.9
56.2

NDT2-SWB-Old

0.4
15.9
2.0
62.5
28.1
30.8
18.5
3.4
3.4
12.7
7.3
0.9
1.0
0.1
0
0
4.3
17.6
0
18.3
6.3
7.1
39.7
0
15.7
29.1
0.8
41.5
12.2
12.3
5.7
0
0
0.1
3.6
0
0
3.2
100
0.4
3.1
0
0.1
1.2
3.5
5.4
41.5
54.5
59.8
88.8
66.8
76.8
25.8
30.1
51.4
48.8
4.0
80.9
20.8
70.3
77.5

NDT3-BWBS-Burned

0.7
9.2
7.1
71.5
18.1
30.8
22.6
10.2
100
10.1
4.7
0.6
1.1
1.1
0
0
0.1
0.4
0.6
73.5
0.6
0.1
24.3
11.3
12.7
11.9
27.1
34.8
28.9
12.9
33.6
0
0
24.6
2.8
4.9
0.6
16.0
0.8
100
78.7
0.4
0.3
35.4
2.8
7.5
62.2
54.4
57.4
25.2
20.0
26.9
51.9
77.9
55.7
37.9
0
62.7
1.0
28.6
30.9

NDT3-BWBS-Old

0.9
13.5
1.8
67.9
21.3
23.6
16.9
8.0
100
6.1
4.5
0.6
2.5
2.8
0
0
0.0
0.1
4.6
74.1
0.6
0.2
20.6
9.6
3.8
11.3
23.3
28.7
24.8
14.4
28.7
0
0
3.1
2.8
0.6
0.1
1.5
0.9
10.9
100
0.0
0.1
40.8
4.1
5.1
52.4
53.7
43.7
33.8
28.5
23.3
44.8
66.7
59.4
45.6
0.0
59.7
0.5
36.5
34.5

NDT3-SBS-Burned

2.2
7.4
1.5
83.1
19.9
21.8
34.6
2.7
100
22.6
0.7
0
0
0
0
0
0
0
1.5
78.4
0.5
0
27.7
21.3
26.5
12.8
36.8
41.7
35.5
15.4
41.9
0.3
0
25.5
5.2
4.2
0
0
0
2.0
1.5
100
90.0
22.1
4.9
4.7
49.0
50.3
41.9
11.3
44.1
28.7
76.7
63.5
73.3
46.8
0
23.5
0
22.3
25.0

NDT3-SBS-Old

1.1
8.3
2.4
84.3
30.4
16.3
31.5
2.9
100
2.8
0.3
0
0
0.6
0
0.3
0
0
4.2
66.8
0.0
0.5
13.7
19.9
11.3
12.0
27.6
23.9
21.5
19.8
30.6
0.0
0.0
1.2
8.3
0.2
0.4
0
0.1
0.1
0.2
3.9
100
24.9
9.6
10.7
33.5
63.4
25.4
14.7
41.2
32.3
93.5
68.5
73.2
57.4
0
18.8
0.1
18.6
15.8

Rare BEC Zones

0.3
2.7
0.0
46.6
17.6
20.0
37.2
1.0
57.8
0.0
0.2
0
0
3.7
0.2
6.6
0.0
0.0
0.2
41.3
1.4
9.4
9.4
0.4
3.2
6.6
2.3
11.7
3.5
1.8
4.7
0.6
6.0
1.9
10.9
1.5
2.7
0.1
0.1
2.7
19.6
0.7
16.7
100
4.4
10.3
43.7
45.9
42.9
61.3
52.5
36.9
79.8
79.1
43.3
56.9
0.7
59.0
7.0
38.4
38.2

Land Facet Diversity

0.3
8.7
1.3
65.6
27.7
37.2
33.4
1.8
42.8
3.2
6.2
0.5
1.0
5.2
0
2.6
1.7
1.9
3.8
49.6
2.6
7.4
19.6
6.2
8.8
17.2
8.2
25.6
8.4
8.7
8.8
0.2
1.6
1.1
10.5
0.3
0.3
1.0
3.6
1.1
12.6
0.5
20.0
24.2
100
9.4
46.8
52.8
46.0
46.6
47.7
38.5
58.8
51.1
59.0
49.5
2.4
49.3
8.6
44.4
45.2

Land Facet Rarity

0.3
6.0
3.5
69.5
24.8
47.8
26.3
0.5
63.1
1.8
1.5
1.5
0.2
3.5
0.1
3.2
0.6
1.8
1.0
57.3
4.7
9.4
14.2
10.8
7.3
12.4
14.8
21.8
13.6
12.4
17.6
1.2
3.1
0.5
1.5
1.1
2.4
0.4
3.9
3.1
12.5
0.3
20.0
44.6
7.6
100
49.2
33.8
36.6
48.4
30.1
25.4
76.8
79.0
48.9
44.4
10.4
49.6
16.4
23.0
38.5

Elevational Diversity

1.0
13.8
2.5
53.8
17.0
37.9
30.5
5.2
36.4
9.5
9.5
2.1
0.8
2.5
0.0
1.9
2.3
4.1
2.9
36.7
8.8
13.4
26.6
8.4
10.1
14.6
14.6
34.7
15.2
9.8
19.2
0.3
1.8
2.4
9.4
0.3
1.0
1.8
3.5
2.8
16.8
0.5
7.3
20.2
4.1
4.9
100
39.2
78.4
51.0
45.3
53.8
41.8
51.3
47.7
50.2
9.0
64.6
19.3
55.5
59.6

Ecotypic Diversity

0.5
10.2
1.1
63.2
23.2
23.1
30.7
1.9
41.6
5.1
1.4
0.9
1.1
4.2
0.2
2.7
1.9
3.8
6.9
43.6
3.7
6.0
17.9
4.1
6.5
10.8
8.9
22.1
10.1
9.3
12.0
0.2
1.9
2.2
11.9
0.3
1.0
1.4
4.8
2.4
17.2
0.5
13.5
21.9
5.2
4.0
40.0
100
34.9
48.2
52.9
48.9
54.1
51.4
53.1
54.5
0.7
46.4
9.4
50.0
46.8

Heat Load Index Diversity

0.7
9.3
1.8
48.6
17.5
42.3
29.3
4.4
26.5
8.6
10.0
2.4
1.2
5.6
0.0
1.4
3.6
4.8
0.9
31.1
10.4
16.2
24.3
4.4
8.3
14.6
8.4
29.7
11.4
6.9
12.0
0.2
1.9
2.2
10.8
0.2
0.9
1.8
5.3
2.7
13.9
0.4
5.5
20.3
4.0
3.8
79.9
34.1
100
64.0
50.0
58.8
38.0
45.3
46.3
47.9
10.9
75.0
23.6
61.4
66.2

Climate Corridors

0.3
7.0
0.3
47.1
20.0
34.1
26.3
1.2
19.2
3.9
6.9
1.6
1.6
7.2
0.1
3.1
4.1
5.8
1.7
25.9
10.3
16.8
16.5
0.9
5.8
11.7
2.5
18.8
5.5
4.8
4.4
0.3
2.7
1.1
9.3
0.2
0.7
1.1
7.2
1.0
9.5
0.1
2.7
29.9
3.7
4.9
49.0
43.3
60.0
100
53.6
57.8
43.7
45.2
42.5
44.6
11.2
76.5
27.3
59.8
63.4

Backward Velocity 2055

0.4
9.4
0.7
45.7
22.0
20.4
40.3
1.5
15.0
10.4
1.4
0
0
13.3
0
0.9
0.4
3.6
4.5
27.4
4.0
11.7
28.2
0.2
10.1
18.7
1.8
31.9
6.5
7.7
5.4
0.2
1.3
2.5
22.4
0.0
0.1
0.4
5.7
0.4
5.8
0.2
6.6
33.8
4.6
3.8
44.7
50.7
57.4
67.8
100
75.4
35.7
34.3
58.9
54.6
2.4
64.6
20.2
65.7
59.6

Backward Velocity 2085

0.3
9.2
1.0
47.0
27.3
14.8
32.4
1.6
10.1
11.8
3.9
0.3
0
2.0
0
0
1.9
8.7
3.5
16.5
7.6
19.2
20.7
0.1
8.7
14.3
1.6
23.0
3.9
6.1
4.3
0
0.3
1.0
17.8
0
0
0.7
8.1
0.4
3.1
0.1
6.5
27.8
3.1
3.2
46.7
46.3
56.5
71.3
86.1
100
35.2
37.2
53.1
45.0
8.1
65.2
32.7
64.6
60.0

Forward Velocity 2055

0.3
4.2
0.7
68.7
20.1
23.6
37.5
1.0
59.5
0.2
0.2
0.3
0.2
3.2
0.2
6.5
0.0
0.1
4.1
46.0
0.7
4.1
10.2
8.6
7.2
6.8
11.6
14.3
8.2
8.2
13.6
0.5
4.7
1.7
11.7
1.6
2.9
0.1
0.4
1.5
6.6
0.8
25.3
45.7
6.3
12.1
35.6
61.9
26.9
39.3
33.7
20.5
100
86.8
48.1
59.6
0.4
33.1
2.1
30.7
33.8

Forward Velocity 2085

0.5
6.4
1.0
66.2
18.2
26.3
38.6
3.3
58.1
0.8
1.1
0.6
0.5
1.1
0.6
11.8
0.1
0.2
0.9
45.6
0.8
6.1
8.2
10.4
6.3
5.3
13.6
12.9
9.3
7.2
15.2
0.4
7.5
2.7
8.8
1.7
3.9
0.4
0.8
2.7
14.6
0.4
12.5
57.7
4.3
8.8
42.8
62.2
32.4
51.0
17.1
15.2
93.7
100
33.3
57.8
0.7
43.4
4.9
34.4
38.6

Bird Richness

0.6
11.4
1.3
68.9
29.1
21.0
28.5
3.4
45.9
7.8
3.1
0.7
0.8
3.7
0
0.5
1.0
2.9
5.8
47.8
2.4
5.7
22.1
8.3
10.7
15.7
13.5
28.5
13.4
12.2
17.6
0.1
0.9
2.1
12.4
0.1
0.3
0.8
4.3
2.4
18.6
0.7
15.5
21.0
5.6
5.7
45.7
51.2
44.4
44.3
57.3
51.1
52.3
49.2
100
49.1
2.5
49.4
10.9
46.1
45.2

Total Biomass

0.6
9.4
1.1
59.3
16.8
25.5
38.1
3.4
38.1
6.1
5.2
0.8
1.0
4.7
0.2
2.3
0.7
2.9
5.3
41.7
2.4
4.8
26.0
3.6
6.8
14.0
8.2
32.0
12.2
8.1
12.6
0.2
2.8
2.4
16.4
0.5
1.3
0.8
4.3
1.6
13.9
0.4
12.4
30.1
4.5
4.7
50.7
54.9
48.8
49.9
55.5
49.2
54.5
52.4
51.9
100
1.5
53.8
7.8
54.1
52.4

Cool Headwater Refugia

0.1
4.7
0.6
22.5
17.3
57.4
8.2
1.2
0.1
4.8
20.6
2.3
2.3
2.1
0
0
10.0
4.8
0
0.8
39.0
57.2
2.3
0
2.0
3.6
0
2.8
0.3
0.5
0.0
0
0.0
0.0
0.6
0
0
0.5
3.2
0
0.1
0
0
2.3
1.5
8.1
81.7
5.9
93.1
94.9
30.8
82.0
11.9
20.3
24.6
12.9
100
82.8
82.2
56.3
74.9

Climatic Refugia

0.5
7.7
1.2
45.8
18.2
35.3
25.4
3.1
28.1
5.1
6.3
1.5
0.9
7.0
0.0
1.9
4.4
6.2
0.9
34.4
9.4
14.3
18.3
2.5
6.3
11.9
5.6
22.5
8.8
5.3
8.5
0.2
1.9
1.7
8.3
0.1
0.8
1.7
6.3
2.4
16.6
0.2
3.3
27.7
3.8
4.6
56.0
39.7
65.0
71.8
51.1
53.7
38.3
44.0
44.4
45.2
8.4
100
22.0
57.8
58.5

Biotic Refugia

0.3
7.6
0.7
36.4
23.6
36.9
14.7
0.9
0.4
8.7
12.8
2.4
1.4
3.1
0.3
1.9
6.2
10.3
0.9
4.5
24.7
40.6
7.8
0
4.3
6.0
0.2
8.7
0.6
2.1
0.5
0.0
0.8
0.4
3.4
0
0
0.5
4.5
0.1
0.3
0
0.0
8.8
2.1
5.9
62.7
25.7
74.5
88.5
51.9
86.4
13.9
20.3
33.9
19.3
36.0
79.0
100
63.9
73.9

Omniscape

0.4
8.2
1.0
52.2
19.3
32.2
30.0
3.0
20.9
8.1
6.5
1.6
1.3
6.7
0
2.3
2.7
5.5
4.2
29.9
7.0
11.6
24.3
1.4
5.9
12.7
4.1
28.0
9.0
4.9
7.4
0.1
2.0
1.8
13.5
0.1
0.4
0.9
6.1
1.4
12.2
0.2
4.0
19.6
3.9
2.4
55.0
50.2
60.4
64.2
62.0
63.1
38.2
39.1
48.8
53.1
5.8
65.7
20.6
100
74.3

0.5
8.5
0.6
53.8
18.3
38.1
29.6
3.6
20.0
5.6
8.9
1.6
2.3
5.8
0
3.4
3.4
6.0
3.3
28.7
9.1
14.0
26.0
1.6
7.4
15.6
4.2
30.4
8.5
5.5
8.0
0.3
3.0
1.7
12.9
0.2
0.7
0.7
6.6
1.1
10.9
0.2
3.0
20.5
4.1
3.9
59.9
47.3
65.5
66.9
56.6
59.7
41.2
40.7
47.5
53.1
7.9
65.0
22.5
77.9
100

Linkage Mapper

5.9 Adaptations to the SCP Process
5.8.1 Climate Change
Given the compounding uncertainties of including both modeled species habitat data
and modeled climate change data in this SCP, I followed Mann’s (2020) conservative
approach in target setting on climate change features. This entailed running a scenario where
targets were only set for present-day conservation features and the percentages of future
conservation features that were incidentally captured were noted. After assessing these
figures, they were either adopted as targets for future-focused scenarios or adjusted with
documented rationale.
5.8.2 Connectivity
To explicitly include connectivity within prioritizr – not just as auxiliary data in the
SCP process – I included outputs from both Linkage Mapper (protected areas connectivity)
and Omniscape (overall landscape connectivity) as both conservation features and as lockedin options. Targets were not set on the connectivity conservation features in any of the
scenarios I ran, but including them allows the user to see what percentage of highly
connected lands are incidentally captured in a given scenario. Including prime connectivity
lands as locked-in options allows the user to run connectivity-focused scenarios and ensure a
certain degree of connectedness in their conservation solution.
5.8.3 Traditional Ecological Knowledge
Much of the TEK applied to this project was in guiding the process and influencing
each stage with the Nation’s values rather than explicitly changing or adding stages to the
SCP framework. This was the case in selecting a study area boundary, setting conservation
185

goals, selecting conservation features, outlining criteria for continuous data within
conservation features, honing the human footprint, and translating community values into
conservation targets. Including TEK-sourced cultural data as the basis for some conservation
features and the enhancing of others represents the most material addition to the standard
SCP process, as this is a relatively new category that goes beyond the more conventional
ecological-, recreational-, or industrial-based conservation features for selection. The various
discussions and workshops held around guiding the SCP process with the Tsay Keh Dene’s
values are detailed in the Discussion chapter.

6.0 DISCUSSION
The Rocky Mountain Trench has been home to the Tsay Keh Dene since time
immemorial – a landscape sacred to both their ancestors and Tsay Keh Dene that live there to
this day. The effects of both human development and climate change are increasingly felt in
the territory. The purpose of this project was to identify areas of this landscape that contain
high conservation value (both in an ecological and cultural sense) and to help the Tsay Keh
Dene Nation provide further evidence for the protection of these areas in the face of change.
The prioritization tool developed over the course of the project was also transferred to the
Nation to facilitate ongoing planning and management efforts.
The methods of Systematic Conservation Planning were adapted to address this task,
with the goals of: (a) identifying which portions of the landscape have the highest
conservation value today; (b) identifying which portions of the landscape are predicted to
have the highest conservation value 30 and 60 years from now considering climate change;
186

(c) explicitly accounting for landscape connectivity across the study area; and (d)
interweaving the Traditional Ecological Knowledge of the Tsay Keh Dene throughout the
project to ensure an inclusive and more holistic outcome. This final goal intended to develop
a product that is not only accurate and useful, but one that is accepted as valid by the Nation,
scientists, and government.
This chapter is centered on my four specific research questions and goals, and weaves
together reflections on the methods, results, and other literature. It also includes reflective
notes I took throughout the process on the efficacy of interweaving TEK. Finally, I discuss
other noteworthy concepts that arose but that did not fit neatly within the four specific goals.
6.1 Which portions of Tsay Keh Dene Territory have the highest ecological and cultural
value for select present-day conservation features?
In the face of ever-increasing development in the Territory, a scientifically defensible,
systematic approach to identifying high-value conservation areas could prove invaluable both
in the short- and long-term. By including community knowledge, the tool provides an
inclusive and comprehensive view of the greater territory to help place specific natural
resource referrals into the larger landscape. This enables decision-makers within the Nation
to better understand how the impacts of a single project may affect the broader landscape
puzzle cumulatively. Furthermore, it sets a course for a range of conservation interventions,
including identifying areas for restoration, areas to informally avoid development in, and the
potential addition of conservation areas – among other avenues. The findings can also serve
as the basis for future, more targeted conservation plans.

187

6.1.1 Solution Characteristics
Scenario A focused on present-day conservation value and biodiversity, with targets
set and achieved for each of the species and ecosystem conservation features. This solution
occupied 59% of the landscape and met targets for 44 features. By comparison, Mann’s
(2020) and Curtis’ (2018) counterpart solutions required 68% and 46% of the landscape,
respectively, in the adjacent Peace River Break. All three percentages fall in the aggressive
yet imperative range near 50% – the target of the Nature Needs Half Movement (Dinerstein
et al., 2019; Locke, 2013).
Given the number of features that tend to favour lower elevations (ecosystems like
old and burned forests, lakes, and wetlands; species like bull trout, fisher, moose, birds, toad,
and bats), one might expect valleys to be disproportionately represented in this conservation
solution. Instead, low elevation is the least represented classification at 55%, with 62% of
both moderate and high elevation captured (Table 14). The inclusion of several species that
prefer moderate to high elevations, and the aggressive targets on some (caribou, Stone sheep,
and mountain goat) were likely the cause of increased representation of higher elevations.

Elevation Classification
High Elevation (> 1678m)
Moderate Elevation (1220 - 1677m)
Low Elevation (< 1220m)

PAs
28
18
10

Scen
A
62
62
55

% Captured
Scen Scen Scen
B
C
D
60
60
68
62
62
63
39
38
55

Scen
E
70
66
56

Scen
F
63
64
40

Table 14. Percentage of each elevation classification captured by scenario. These were purely
representational features and no targets were set for them.

Scenario A was configured to avoid both permanent and semi-permanent human
footprint features. As cutblocks are considered semi-permanent and can theoretically be
restored, they appear to be one part of the landscape that was often selected despite human
188

development. For example, the mouth of the Ingenika River has seen significant forestry
activity, but the area retains high-value wetlands and moose habitat. While many SCPs
rightfully focus on ecologically intact habitats, that is only one piece of the puzzle, and a
restorative conservation lens must also be placed on these compromised landscapes
(Schuster, 2014). Additional time and effort will be required to restore these areas relative to
existing intact areas; however, the only alternative is to lower the targets which ultimately
degrades the conservation goals.
Larger resource roads throughout the territory and study area were considered
permanent features, and yet these areas were selected as having high conservation value in
many instances – likely because roads tend to be found in valleys and run roughly parallel to
the rivers themselves. As roads are highly disruptive and fragmenting landscape features
(Polfus et al., 2011), further examination is needed to see if the impacts from these roads are
significant enough to offset the value of the conservation features selected in these areas.
This examination could also assess whether road impacts can be mitigated. As targets are
uncompromising, human-impacted areas will be selected if they contain a significant number
of conservation features or if those features are not found elsewhere. These areas that serve
as transportation corridors for people have long served as habitat and transportation corridors
for many species, and their value for conservation purposes cannot be undervalued moving
forward. Thus, restoring these linear features to their natural states should be a priority once
they are no longer needed, as Tsay Keh Dene Nation and Chu Cho Environmental are
actively doing near Johanson Lake as part of their Chase Caribou Herd Road Restoration
Program (Chu Cho Environmental, 2021).

189

The four predominant ecoregions in the study area were all well represented: Boreal
Mountains and Plateaus – 61%, Northern Canadian Rocky Mountains – 65%, Central
Canadian Rocky Mountains – 67%, and Omineca Mountains – 55% (Table 15). The large
number and diversity of conservation features in this scenario likely resulted in this
widespread geographic coverage of the study area. Furthermore, much of the human footprint
in the Territory is found along the southern and eastern peripheries of the study area, which
also happen to be the edges of other ecoregions in several instances. The 59% of the total
area that was ultimately selected trended towards the more remote portions of the study area
and was well distributed, likely due to the heterogeneity of the landscape. While this solution
is quite scattered, my approach was intended to be idealistic in finding planning units with
the highest conservation value – not just moderately valuable, neatly grouped areas that may
be easier to designate for conservation purposes. Thoughtful delineation of areas for
conservation interventions can come later from the Nation. Furthermore, this broad
distribution of valuable conservation lands should be considered an asset, as it gives the
Nation choices for conservation action based on their priorities rather than being resigned to
a few obvious areas that may require restoration.

190

Ecoregion
Boreal Mountain and Plateaus
Central Alberta Upland
Central Canadian Rocky Mountains
Fraser Basin
Muskwa Plateau
Northern Canadian Rocky Mountains
Omineca Mountains
Peace River Basin
Skeena Mountains

PAs
38
0
9
0
1
19
10
0
0

Scen
A
61
29
67
43
39
65
55
13
75

% Captured
Scen Scen Scen
B
C
D
56
61
65
9
9
29
61
58
68
30
20
44
4
5
39
61
64
70
50
48
55
80
82
95
56
53
93

Scen
E
69
28
68
41
39
74
55
13
83

Scen
F
60
9
61
29
7
62
52
80
43

Table 15. Percentage of each ecoregion captured by scenario. These were purely representational
features and no targets were set for them. Purple ecoregions are significant in size and collectively
make up 90% of the study area.

6.1.2 Focal Areas
There were five focal areas representing clusters of high-value conservation lands
identified by the tool outside of existing and proposed protected areas (Figure 73). The first
was the Omineca River valley (Area 1) connecting Sustut and Omineca Provincial Parks,
likely selected because it contains bull trout habitat and low to moderate elevation caribou
habitat – both features with very high targets. As the most linear of the five identified areas,
protecting this area would not only safeguard these species’ habitat, but also provide a crucial
connectivity corridor for wildlife between sizable protected areas in the region. This area is
also at the interface of the Chase and Wolverine caribou herds, and could contribute to gene
flow among these threatened populations (Roffler et al., 2012).

191

Figure 73. Focal areas (outlined in orange and numbered) and protected areas overlaid with the spatial
extent of the conservation solution for Scenario A: Present-day Conservation within the Greater Tsay
Keh Dene Territory Study Area; inset represented by pink frame.

192

The next focal area was the Ospika River valley and adjacent mountain ranges (Area
2) that contain karst deposits and high-quality habitat for a great deal of focal species (fish,
fisher, moose, three herds of caribou, Stone sheep, mountain goat, wolverine, bats, and
birds). Protecting this area would not only conserve these species’ habitat, but also protect a
number of cultural/spiritual areas of the Tsay Keh Dene, as the Ospika, or 'Əsbagah, River
valley is of great importance. Additionally, this somewhat linear focal area could serve as a
connectivity corridor for wolverine between Redfern-Keily and Graham-Laurier Provincial
Parks.
The Mesilinka River valley south of Chase Provincial Park (Area 3) was selected for
its abundance of burned Engelmann Spruce–Subalpine Fir forest stands with infrequent
stand-initiating events – a feature with a 100% target. While these young, biodiverse forests
stands were the reason for this area’s selection, the area is also home to fisher, bull trout,
moose, and caribou habitat. Protecting this area would add roughly 1,000 km 2 of habitat for
fire-obligate species and contain features like burned snags that are not found in young
managed forests (Curtis, 2018). This addition would also complement the adjacent forests
found in Chase Provincial Park and the proposed Ingenika Conservation and Management
Area while also building out the conservation complex.
The northern Misinchinka ranges (Area 4) house both karst deposits and a number of
territorially-rare BEC subzone variants, but the leading cause of this selection was likely the
fact that it holds high-quality moderate elevation habitat for the Klinse-za caribou herd. This
finding confirmed the location of a portion of the Caribou Conservation Partnership
Agreement between British Columbia, Environment and Climate Change Canada, West
Moberly First Nation, and Saulteau First Nation (Environment and Climate Change Canada
193

et al., 2020). While the areas identified in both efforts largely overlap, my findings suggest
that the governments lower their elevation threshold to include a broader portion of the
mountain range. While this change adds only slightly more land to an industry moratorium, it
significantly reduces the edge-to-area ratio of the patch, therefore increasing vital interior
habitat for Klinse-za caribou – one of the focal herds of the partnership agreement.
The final focal area is in the Omineca Mountains west of Kwadacha (Area 5), nestled
between the Ingenika Conservation and Management Area, Finlay-Russel Provincial Park,
and Tatlatui Provincial Park. This region has several high-value wetlands complexes, as well
as high-quality habitat for the Thutade caribou herd, moose, and Stone sheep that likely led
to its selection. There is currently only one significant resource road in the area for mining
and forestry. Protecting this entire area – or at least the highly connected corridors between
the aforementioned protected areas – would minimize further road-building and reduce
fragmentation. It would also create a robust conservation network with a complex that
includes Mount Edziza, Stikine River, Spatsizi Plateau Wilderness, and Chase Provincial
Parks, Gladys Lake Ecological Reserve, and Pitman River and Chukachida Protected Areas.
This network would span over 400 km from Chase Provincial Park in the southeast to Mount
Edziza Provincial Park in the northwest.
6.1.3 Protected Areas Placement
Completing the SCP allowed me to look at the location of the existing protected areas
relative to the conservation feature information mapped in the SCP – information that wasn’t
available when these protected areas were designated. According to this SCP solution, the
existing protected areas network has mixed results regarding the efficacy of their placement.
Provincial parks like Omineca, Nation Lakes, Klinse-za, and Graham-Laurier had much of
194

their extent included as part of the present-day solution, while Northern Rocky
Mountains/Kwadacha Wilderness and Tatlatui had very little. Additionally, the proposed
boundary of the Ingenika Conservation and Management Area is substantiated by the
conservation solution.
Accordant parks consistently contained biodiverse forests from the forest pattern and
process layers and a collection of wildlife habitats. Klinse-za Provincial Park and its
expansion is one of the best placed parks according to the solution, largely due to the caribou
herd of the same name. Old and burned Engelmann Spruce–Subalpine Fir stands are among
the biodiverse forests included in the Addition. Mountain goat, and to a lesser extent grizzly
bear habitat, likely also played a role in confirming its location. This park expansion, born
out of the same Caribou Conservation Partnership Agreement mentioned earlier, is entirely
validated by this solution and a great example of contemporary informed protected area
design.
The parks that do not align with the solution appear to be lacking in biodiverse
forests. This does not mean that protected areas not in agreement with the solution do not
hold value (e.g., Tatlatui contains several large lakes, which are of the utmost importance to
the Nation), but that the features located within them are either not diverse enough or not rare
enough to merit their selection given the parameters of the SCP scenario. While Tatlatui may
be an example of the global practice of protecting high elevation areas of “rock and ice”
(Joppa & Pfaff, 2009), it still contains some present-day conservation features. It is important
to recognize that these parks were selected as part of multi-stakeholder land use planning
initiatives and likely include a range of values that my SCP did not account for.

195

The placement of the proposed Ingenika Conservation and Management Area was
also broadly confirmed by the present-day SCP solution – an important finding given the
deep cultural bonds the Tsay Keh Dene have to this area. Containing several major river
valleys, this area contains high-value wetland complexes and habitat for moose, fisher,
western toad, bats, and birds. The moderate to high elevation areas found in this region
contain high-value caribou habitat for the Thutade and Chase herds. The Nation has been
working specifically to recover the population of the Chase herd, and this solution includes
hundreds of square kilometers of habitat within their range. This SCP’s affirmation of the
Ingenika CMA’s placement is vital, as the Tsay Keh Dene have a profound connection with
the caribou, or wədzih (Figure 74). Historically, the sight of healthy, migrating caribou has
brought the Tsay Keh Dene comfort and indicated the changing of seasons (Chu Cho
Environmental, 2020a). More recently, their noted absence has weighed heavily on the hearts
and minds of the people. Tsay Keh Dene feel a responsibility for the well-being of the
caribou, and have voluntarily stopped hunting them out of respect and to reduce strains on
the population (Chu Cho Environmental, 2020a). The concurrence of the conservation
solution with the Nation’s proposed conservation and management area boundary further
validates their efforts.

196

Figure 74. A still from the “Wedzih - The Caribou” video illustrating the holistic importance of caribou to the Tsay
Keh Dene (Chu Cho Environmental, 2020).

6.1.4 Avoiding Triage and Defeatism
One extreme approach to conservation has been the adoption of the triage concept of
medical care employed in battle or times of disaster, where the wounded are classified based
on the severity of their injury. The result is that some individuals are deemed beyond saving
and resources are then focused on treating those with a good likelihood of survival. In a
conservation context, this means that certain species are deemed too far gone to viably be
saved due to dangerously low population numbers or lack of habitat (Tingley et al., 2014).
However, urgency is one of the catalysts for action and scientific innovation, and by simply
disregarding the most endangered species we are essentially lowering our sense of urgency
towards finding conservation solutions (Bottrill et al., 2008).

197

The target setting function of SCP allows users to avoid the triage method, as highly
vulnerable species or ecosystems can have aggressive targets to select most or all of the
specified land that remains. This avoids outcomes of only moderately diverse, moderately
threatened species assemblages – a major criticism of the triage approach, which is seen as
well-intentioned but promoting defeatism (Bottrill et al., 2008). While I set default wildlife
targets of 60%, the flexibility to set loftier targets on threatened species – particularly bull
trout and caribou – ensures that almost all of their identified habitat will be included as part
of a conservation solution. These bold targets may give the tool less room to prioritize
complementarity (as only the target minimum was captured for bull trout and all but one
caribou herd), but it guarantees the inclusion of habitat for these vital populations.
6.1.5 Data Availability
The availability and completeness of data for identified conservation features
presented one of the biggest hurdles in the process, as accurate input data from reputable
sources is vital to a valid model. There was a spectrum of how much data gathering and
processing needed to take place for each layer. In some instances, data could be directly
downloaded from provincial sources and used with only minimal processing (e.g., grizzly
bear, karst deposits). In other instances, data had to be pieced together from several sources
and standardized to suit our needs (e.g., caribou, Stone sheep, mountain goat). In some
instances, trustworthy data existed but was not at the resolution required to match up with the
selected 1 km2 planning units. In these cases, data was resampled to match the grid and was
represented at a scale that it was not necessarily intended to be. The broad scale nature of this
project served to mitigate these resampling issues, however. Additionally, completing
geographic coverage of the study area was not feasible in all cases. The species at risk
198

Habitat Suitability Indexes developed by Chu Cho Environmental were only for the Territory
proper and supplementary data was not available to round out the study area. With high
enough targets on these species at risk, the solution will be biased towards the Territory
proper, but including these available data gives the user greater capabilities. While features
should be meaningful and not just included because data is available (Wiersma & Sleep,
2016), the user always has the option of not setting targets on these features or setting
conservative targets as I did.
There was one noteworthy potential conservation feature that was not included at this
time. Key Biodiversity Areas, or KBAs, are sites that significantly contribute to the global
persistence of biodiversity, and are identified using a standard IUCN process worldwide
(IUCN, 2016). While KBAs would have been a worthwhile addition to present-day
conservation scenarios and this study overall, the work being done to identify and document
these areas in Canada was not far enough long at the time of writing to be included.
Additionally, of the initial KBAs identified by this working group, none fall within the study
area of this project, with the closest being the Liard River Hot Springs near British
Columbia’s northern border. If KBA mapping is completed within the study area it will be
interesting to see the extent to which it overlaps with areas already selected in the SCP
model.
6.2 Which portions of Tsay Keh Dene Territory retain conservation value when climate
change is considered?
Areas identified as biodiverse or otherwise containing high conservation value today
may look very different 30 and 60 years from now. Using conservation-focused metrics
based on the best climate change predictions available allows us to identify which areas are
199

predicted to remain or become ecologically valuable in the future. When these areas overlap
with areas identified for their present-day conservation value, they are considered
ecologically valuable and climate-resilient and should be further prioritized.
6.2.1 Solution Characteristics
A bias towards higher elevations was expected in climate change-focused future
scenarios given their relative coolness, and that proved to be true. Solutions B (2050s) and C
(2080s) each captured more than 60% of both moderate and high elevation classes in the
territory while only capturing 39% and 38% of the low elevation class, respectively. This was
likely due to coarse-filter and climate change features favouring montane environments for
their lower backward velocity and refugia potential. Solution D (all time periods), however,
captured 55% of low elevation, 63% of moderate elevation, and 68% of high elevation. As
this scenario had to capture both current biodiversity often found in lower elevations and
predicted future biodiversity that trends towards moderate and high elevations, this solution
was calibrated to select planning units that can serve both roles.
Solutions for the 2050s, 2080s, and multi-time periods were each able to capture over
50% of the predominant four ecoregions in the study area, save for the 2080s solution only
capturing 48% of the Boreal Mountains and Plateaus ecoregion. As the name suggests, the
decreased topographic variation of plateaus in this ecoregion likely led to its lower
representation in the 2080s climate change scenario. The overall strong representation across
the landscape in these climate change-focused scenarios was likely due to how mountainous
this region is as a whole (Loarie et al., 2009). Of the four main ecoregions, the highest
percentage captured in this set of scenarios was the Northern Canadian Rocky Mountains in
the multi-time periods solution with 70%, presumably due its role as both critical present-day
200

habitat for Finlay and Pink Mountain caribou, Stone sheep, and mountain goat, as well as its
high refugia potential into the future.
The 2050s and 2080s solutions were configured to avoid permanent human footprint
features, with the understanding that semi-permanent features could theoretically be
ecologically restored over time. Permanent features like roads and mines were largely
avoided, however areas adjacent to the Williston Reservoir that fell within its buffered
impact area were selected in many cases – likely because this altered landscape still contains
diverse and rare land facets. This was particularly true along the Peace and Parsnip Arms. If
further access to these areas remains limited, then they have the potential to serve as
important habitat for several bird species despite their alterations. The Parsnip Arm has seen
more development with roads and forestry, leaving the Peace Arm with greater conservation
potential.
Human development (especially that which is permanent in nature) is not often found
in the moderate to high elevation areas that these climate-conscious scenarios favour. The
footprint likely played less of a role in which areas were selected as part of this solution. Like
the present-day solution, the multi-time period solution was configured to avoid both
permanent and semi-permanent footprints. The multi-time period solution expectedly
selected footprint areas in a similar fashion to the present-day solution (cutblocks) and 2050s
and 2080s solutions (reservoir adjacent areas). The conservation implications of each hold
true – cutblocks must be viewed through a restoration lens for how they could contribute to
the larger landscape, and topographically-diverse reservoir-adjacent areas can still hold
conservation value if further development is limited.

201

The predicted changes in climate for this landscape can be readily understood by
shifts in BEC zones (Table 16). Sub-Boreal Spruce, Engelmann Spruce—Subalpine Fir, and
Boreal White and Black Spruce are predicted to remain in abundance and likely migrate
slowly enough to allow their inhabitants to track with them. In contrast, Boreal Altai Fescue
Alpine is expected to contract further and further up mountain peaks until only ‘islands’
remain. This could spell serious trouble for high elevation species like Stone sheep and
mountain goat, not only limiting movement within single animals’ life histories, but cutting
off gene flow among populations and potentially leading to extinction (Parks et al., 2015).
Perhaps even more dramatic is the almost complete collapse of the Spruce—Willow—Birch
zone. The most northerly subalpine zone in British Columbia, this zone is predicted to
drastically shrink and migrate northwestward within the study area. This means current
moose and caribou populations (namely the Pink Mountain, Thutade, and Frog herds)
accustomed to summering in this zone for its open, shrubby valley bottom habitats will either
have to adapt or move northward as soon as 2050 (British Columbia Forest Service, 2007).

202

16

12

3

29

0*

0*

0*

0*

0*

0*

BWBS

ESSF

SBS

SWB

CWH

ICH

IDF

IMA

MH

MS

0*

0*

0*

0*

0*

0*

60

48

62

57

62

Scen
A

0*

0*

0*

0*

0*

0*

57

31

62

34

62

0*

0*

0*

0*

0*

0*

61

28

60

36

62

0*

0*

0*

0*

0*

0*

63

50

62

57

68

Present (% Captured)
Scen Scen Scen
B
C
D

0*

0*

0*

0*

0*

0*

70

49

63

57

70

Scen
E

0*

0*

0*

0*

0*

0*

58

31

64

37

63

Scen
F

0

0

67

0

6

0

23

13

22

11

40

PAs

100

60

0

100

54

50

43

56

66

55

48

Scen
A

50

19

33

0

49

50

54

43

63

35

78

50

55

33

0

46

30

61

43

62

36

78

100

62

33

100

55

60

52

57

67

56

74

2050s (% Captured)
Scen Scen Scen
B
C
D

100

60

67

100

56

50

52

57

70

56

72

Scen
E

50

19

67

0

50

40

59

45

64

37

79

Scen
F

3

0

0*

7

10

0

19

17

26

9

45

PAs

63

59

0*

34

59

70

33

60

64

54

43

Scen
A

41

35

0*

12

53

80

45

47

61

35

85

47

60

0*

13

51

77

55

47

61

36

85

63

61

0*

34

60

70

44

59

66

55

77

2080s (% Captured)
Scen Scen Scen
B
C
D

60

59

0*

35

60

69

39

61

70

55

74

Scen
E

203

Table 16. Percentage of each BEC zone captured by scenario by time period. These were purely representational features and no targets were set for them.

28

PAs

BAFA

BEC
Zone

50

34

0*

13

54

80

52

49

63

37

84

Scen
F

6.2.2 Focal Climate Areas
The 2050s and 2080s solutions are strikingly similar, likely because they share a
majority of the same conservation features and only a few are time specific. While these
solutions share many of the same focal areas with the present-day solution, they contain a
few unique groupings of values (Figure 75). The first is in the southern Muskwa Ranges
between the Ospika and Peace Arms (Area 1), likely selected for its rare land facets,
elevational diversity, heat load index diversity, low backward velocity, and carbon storage.
Protecting this area would provide a link to Graham-Laurier Provincial Park and safeguard
climate resilient habitat for wolverine, grizzly bear, and the Graham caribou herd.
Furthermore, this area contains regionally significant carbon storage, a crucial ecosystem
service. Keeping carbon sequestered is a widely-accepted protected areas strategy to mitigate
climate change globally (Mitchell et al., 2021).

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Figure 75. Climate focal areas (outlined in green and numbered) and protected areas overlaid with the
spatial extent of the conservation solution for Scenarios B: Future (2050s) and C: Future (2080s)
Conservation within the Greater Tsay Keh Dene Territory Study Area; Present-day focal areas outlined
in orange for comparison purposes; inset represented by pink frame.

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The other novel focal area is in the Omineca Mountains on either side of the Skeena
River (Area 2). Similar to the previous region, this area was likely chosen due to its rare land
facets, elevational diversity, low backward velocity, and carbon storage; however, low
forward velocity also likely played a role in this area’s selection. The climate change
implications of protecting this area are twofold: the valleys of the Skeena River and its
tributaries are carbon storage hotspots, while the surrounding higher elevation mountain
ranges are climatic refugia. The 2050s and 2080s solutions each occupied 52% of the
landscape.
The multi-time period scenario had targets set for both present-day and climate
change conservation features, and thus its solution is a sort of hybrid of the solutions for each
individual time period. Somewhat expectedly, this solution does not reveal any novel large
groupings of selected planning units; rather, it looks largely similar to the present-day
solution but with slight shifts northward and upslope in the name of climate resiliency. As
targets are uncompromisingly met in this tool, the multi-time period solution required
slightly more land base to reach every target, occupying 61% of the landscape as opposed to
59% in the present-day solution. While more land was needed to meet the targets, this
modest increase is remarkable in that only 2% more of the study area was needed to meet the
needs of two very different scenarios. The tool had been successfully calibrated to efficiently
account for both present-day and climate change conservation features.
6.2.3 Protected Areas Placement
Many protected areas fared differently in the prioritization solutions when climate
change was the focus of the scenario. Of the protected areas that were poorly represented in
the present-day scenario, the Northern Rocky Mountains/Kwadacha Wilderness Provincial
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Parks were very well represented in this iteration, while Tatlatui was poorly represented once
again. Tatlatui does contain moderately valuable planning units for a number of coarse-filter
and climate change metrics, but largely did not rise to the level of selection save for a valley
east of its major lakes. All of the protected areas that fared well in the first iteration
(Omineca, Nation Lakes, Klinse-za, and Graham-Laurier Provincial Parks) were only
moderately climate resilient according to this scenario. Additionally, the Ingenika
Conservation and Management Area’s core was selected, while its northern and southern
edges were not.
Sustut and Redfern-Keily Provincial Parks were almost entirely selected in the 2050s
and 2080s solutions. For Sustut, this suggests that it will continue to serve its role as pristine
wilderness habitat for species like grizzly bear, caribou, and Stone sheep. Its basaltic cliffs in
particular serve as high-value habitat for mountain goats, while its waterways should sustain
salmon and steelhead trout (BC Ministry of Environment, 2021b). Redfern-Keily contains
considerable amounts of refugia from all three types used in this study. The rare land facets
of its western peaks in particular are poised to maintain their high-value habitat for Pink
Mountain caribou, Stone sheep, and mountain goat. The park’s numerous forests, as well as
valley and alpine meadows should also continue to support wide-ranging grizzly bears,
which are provincially blue-listed (of special concern) (BC Ministry of Environment, 2021a).
The Kwadacha Wilderness/Northern Rocky Mountains Provincial Park complex
holds far greater value under climate change conditions according to the tool, largely for their
refugia potential. Both parks exhibit elevational diversity, while Kwadacha Wilderness
boasts rare land facets and Northern Rockies contains ecotypic diversity. These parks
currently support wildlife including moose, fisher, and wolverine, and the highly diverse
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elevations found there could prove to be invaluable microrefugia for these species (Stralberg
et al., 2020). Additionally, the most important climate corridor in the entire study area runs
directly north from Graham-Laurier Provincial Park, winding past Redfern-Keily before
ending at Northern Rocky Mountains Provincial Park (Figure 76). Thus, climate refugee
species to the south could very well colonize the Northern Rocky Mountains/Kwadacha
Wilderness complex in the future. As this area already holds reasonable landscape
connectivity under current conditions, it should be considered for protection as a climateresilient connectivity corridor.

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Figure 76. Optimal climate connectivity corridor within the Greater Tsay Keh Dene Territory Study
Area; inset represented by pink frame.

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The placement of the proposed Ingenika Conservation and Management Area was
less substantiated by the 2050s and 2080s solutions; however, its Cultural Epicenter subunit
was almost entirely selected given its climate resiliency, as was the western half of the
Management Area subunit. This is beneficial, as the Cultural Epicenter of the Ingenika is the
portion that is planned to receive legal protections, while the rest will be heavily managed to
preserve values but not formally protected. This finding provides climate-conscious Western
scientific justification to parallel the cultural and spiritual value of this area as the Nation
campaigns for an official conservation designation.
6.2.4 Considering the Yale Framework
The Yale Mapping Framework was introduced in 2015 and continues to gain
attention as an important tool for integrating climate adaptation into landscape conservation
planning (Carrasco et al., 2021; Schmitz et al., 2015; Wineland et al., 2021). The framework
provides a set of six objectives that users can choose from, and a suite of appropriate tools
based on the level of ecological analysis (species, ecosystems, or landscape) to help attain
each objective (Conservation Biology Institute, 2021). The adaptation objectives include: (1)
Protect current patterns of biodiversity, (2) Protect large, intact, natural landscapes and
ecological processes, (3) Protect the geophysical setting, (4) Identify and appropriately
manage areas that will provide future climate space for species expected to be displaced by
climate change, (5) Identify and protect climate refugia, and (6) Maintain and restore
ecological connectivity (Conservation Biology Institute, 2021). This project did not formally
adopt the Yale Framework, and while its principles were largely followed given the multifaceted nature of this project, some steps could have been conducted differently.

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While all six objectives of the framework are touched on, a few could have been more
fully developed. The first three objectives focus on strengthening current conservation
efforts. Current patterns of biodiversity were protected through the delineation of highquality habitat for sixteen species and several key ecosystems. This project did separate out
caribou herds to recognize population level issues but could have delved further on other
species to assess population sizes/viability and genetic patterns at a finer scale as the
framework suggests. Large, intact natural landscape and ecological processes were protected
through the inclusion of umbrella species, representative and biodiverse forest types, and the
scale of the project in general. The project did not explicitly consider ecosystem services,
however, as Mitchell et al. (2021) did as the focus of their national-scale conservation
planning effort. Doing so in the future would be beneficial given the Nation Government’s
responsibility towards its people and ongoing issues like dust storms near the community.
Finally, I was explicit in addressing the protection of the geophysical setting in which
ecological processes play out and would not approach this objective any differently. My use
of landscape features like land facets, elevational diversity, ecotypes, and heat load index
thoroughly addressed this objective.
The last three objectives focus on anticipating and responding to future conditions.
Future climate-altered habitats were identified and selected for by including features like
forward and backward climate velocity for two future time periods. While BEC zones were
included as non-target representational features for the 2020s, 2050s, and 2080s, rare BEC
subzone variants were only included as a conservation feature in their current state. Including
the predicted locations of rare BEC subzone variants in a climate-altered future could help
address the framework’s goal of protecting climate-vulnerable rare ecosystems. The
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identification of climate refugia was also sufficiently addressed. I incorporated three separate
refugia layers based on different criteria, including one that explicitly addressed the
framework’s specification for spring-fed streams – the cool headwater refugia feature
(Weaver, 2019). Finally, ecological connectivity was maintained by explicitly including
protected area and landscape connectivity data in the SCP process. Both instances address
structural connectivity and are well-suited for the ecosystem and landscape scale; however,
other connectivity measures (e.g., those that include empirical movement data) would need to
be included to adequately address functional connectivity for the life histories and migration
of specific species.
While using the Yale Mapping Framework from the outset of this project may have
changed the lens through which I made some decisions along the way, the essence of the
project would have remained largely unchanged. Adequately addressing all six objectives at
all three levels of ecological analysis would have added a considerable amount of work and
likely would have pushed the boundaries of the scope of a master’s level project – especially
given the TEK aspect of my work. The ecosystem services and functional connectivity
aspects in particular would have required significant additional effort and likely extended the
project’s timeline for completion.
6.2.5 Use of Climate Change Metrics
While climate change metrics are based on models that carry uncertainty, Mann
(2020) outlined a measured approach to utilize this valuable data without being over-reliant
on it. Some conservation scientists argue that the compounding uncertainty of climate change
metrics within an SCP (a simulation in itself) can prove too risky (Schloss et al., 2011). If

212

used correctly, however, these metrics are certainly worthwhile for the insights they can
provide.
Mann’s (2020) method was one of incidental capture of a variety of climate change
metrics. She took a multi-faceted approach by including strategies for protecting the
geophysical stage, low climate velocity areas, and climate refugia – objectives 3-5 of the
Yale Framework. This entailed setting targets solely on present-day conservation features
and documenting the percentage of each climate change feature that was captured by
happenstance. Using these incidental capture percentages as conservative targets on the
climate change features, she was able to calibrate her model for future biodiversity scenarios
and ultimately fine-tune a scenario that captured both present-day and predicted future
biodiversity. In her case this final scenario resulted in a solution that did not select any more
land than the initial present-day biodiversity scenario, but one that was more informed in
which planning units to select to account for climate change. In my case it resulted in a 2%
increase in area selected. This shows that climate change considerations do not automatically
result in either/or decisions of present and future biodiversity when identifying what land to
conserve. Mann’s (2020) work and this project confirm that little to no additional land is
required to better select for climate resilient lands – at least in these more mountainous
landscapes of northern BC – just a modest reshuffling of which planning units are selected
once the tool has been calibrated.
Species-centered approaches to climate change have also been used, as opposed to the
climate/environment-centered approach of this project (Reside et al., 2018). While species
habitat modeling carries its own uncertainty, this method can look at species movements
from recent decades and reasonably predict species’ range shifts into the future. In contrast,
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Mann’s (2020) approach can be seen as both more holistic and further removed from reality
as it seeks to be inclusive of all species by being more theoretical. Mann’s method is superior
if the goal is overall biodiversity, but if robust data exists for range shifts of a surrogate
species or if a conservation plan is being developed with a specific species in mind, then her
method (and this project) could be seen as having limitations.
Lawler and Michalak (2017) argue that any climate-conscious SCP approach is
worthwhile by virtue of being a thoughtful attempt to consider future conditions. They
maintain that concerns of uncertainty surrounding model-based climate change metrics are
misguided given that methods for protecting the geophysical setting and quantifying
connectivity also contain large amounts of uncertainty but are generally taken at face value.
For example, soil data that informs ecotypic diversity and land cover data that informs
resistance for connectivity calculations can each hold a great deal of error, yet these methods
are more often accepted (Lawler & Michalak, 2017). No model will be perfect, but that is no
reason to dismiss the use of climate change data and cease the work of developing novel
approaches.
One creative method has been to apply abiotic goals to reconstructed ice age
conditions – an exercise that allowed researchers to test how well geophysical feature targets
performed over time given that we can observe present conditions as the outcome of the ice
age’s future (Williams et al., 2013). While the results showed that biotically-informed
strategies were more effective at larger scales (e.g., climate velocity), abiotic methods can
still be effective at identifying microrefugia. By using features that represent the geophysical
stage, climate velocity, and refugia, I took a diverse climate change approach that mitigates
uncertainty by not being overly reliant on any one strategy. Furthermore, I explicitly
214

addressed connectivity – another climate strategy rooted in the idea that connecting intact
ecosystems boosts their inherent value and provides safe passage for climate refugee species
(Groves et al., 2012). By building on an already multi-faceted approach with connectivity, I
further diversified my climate change portfolio to develop a strategy that mitigates any one
facet’s shortcomings through variety (Groves et al., 2012; Mann, 2020).
To make my climate change strategy even more robust according to Groves et al.
(2012), I would need to focus further on ecosystem process and function, as well as
capitalizing on opportunities emerging in response to climate change. The former would
entail expanding the forest pattern and process layers I used to include more disturbance
regimes. This proves difficult given how poorly understood some processes are and the
limited data surrounding them, as well as problematic given how much it could shift the
focus away from biodiversity itself (Groves et al., 2012). The latter proves challenging
because it requires building alliances with activities that may not be conservation-focused,
coupled with the fact that these initiatives may not easily translate into spatial data for use in
an SCP (Groves et al., 2012).
The multi-faceted approach that I used translates well to this relatively intact
landscape. Considering the geophysical stage, climate velocity, refugia, and connectivity
addresses three of the five approaches examined by Groves et al. (2012), while addressing all
six of the Yale Framework objectives to at least some degree (Schmitz et al., 2015). There is
still room for improvement given local context, however. My strategy could be enhanced by
steps such as integrating beetle disturbance regimes to address the ecosystem process and
function approach, as well as an increased focus on ecosystem services like carbon storage to
capitalize on climate change-driven opportunities. All approaches carry uncertainty, and so
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the pursuit of ever more robust strategies for effectively incorporating climate change within
SCPs should continue.
6.3 How can landscape connectivity be explicitly included in the Systematic
Conservation Planning process?
Landscape connectivity has been recognized as a crucial ecological component of
systematic conservation plans, but most efforts to date have used it as a post-hoc or
supplementary analysis rather than a core component of the model (Fajardo et al., 2014;
Mann, 2020). There are many existing tools to quantify permeability and connectivity of the
landscape, but efforts to explicitly include their outputs within terrestrial SCPs have been
limited (Heinemeyer et al., 2004; Hermoso et al., 2012). Keeley et al. (2021) have
synthesized the relevant literature and provided a helpful decision tree for users to decide
which tool best suits their needs based on the context of their work. I incorporated
connectivity explicitly in my model by using current flow metrics from Linkage Mapper and
Omniscape to capture structural connectivity. The relative intactness of the Greater Tsay Keh
Dene Territory Study Area justified my use of current flow metrics according to Keeley et al.
(2021), the nuances of which are described below.
6.3.1 Choice and Use of Connectivity Tools
The Omniscape connectivity tool requires users to input both a resistance layer (how
difficult it is to traverse the landscape) and a source layer (intact areas). I used the inverse of
the resistance layer as my source layer (also known as naturalness), which Keeley et al.
(2021) equate to structural connectivity and consider to be a coarse-filter conservation
strategy. This was my goal, as I set out to quantify generalized landscape connectivity – not

216

structural connectivity of a specific species or functional connectivity for a specific species
based on movement data.
SCP prioritization is designed to select the most efficient array of planning units that
meet each of the user’s targets. Individual planning units are assessed in a vacuum, and thus
it can be a challenge to ensure that a conservation solution is sufficiently contiguous so as to
promote connectivity across the landscape. Including connectivity data as a conservation
feature does not address this issue – though I did include both Linkage Mapper and
Omniscape outputs as conservation features to be able to assess incidental capture. My
strategy to ensure that conservation solutions could be connected was to include options for
‘locking in’ connected lands.
As Keeley et al. (2021) argue that assessing structural connectivity and using
naturalness is a coarse-filter conservation strategy, it follows that scenarios relying on these
locked-in connected areas should focus on future-oriented climate change features. I used
this strategy in scenario F when I locked in the leaner Omniscape ‘Bones’ layer, setting
targets solely on coarse-filter and climate change features (including both 2050 and 2080
time periods). However, in scenario E existing protected areas (PAs) and the least-cost paths
between them served as the locked-in connectivity layer. This scenario has more of a presentday focus given that these protected areas were designated with their current attributes in
mind. Thus, I set targets on present-day features for this scenario.
6.3.2 Solution Characteristics
While the PA connectivity solution was influenced by the locking in of protected
areas, its solution was similar to the present-day solution – its counterpart in present-day
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conservation feature target setting. The least-cost paths seemed to have little effect on the
overall solution, likely due to how lean the corridors were. At just one to two planning units
wide, these corridors were only somewhat latched onto by high-value conservation planning
units to create broader, functionally connected corridors as intended. The LCPs – particularly
those in the south – were often not selected in the present-day solution, suggesting that they
do not hold ecological value on their own.
There were two focal areas selected in the PA connectivity solution but not the
present-day solution (Figure 77). The first is in the Rocky Mountains between Ed BirdEstella Lakes Provincial Park and Sikanni Chief River Ecological Reserve (Area 1). This
region was likely selected given its high patch connectivity potential due to the number of
protected areas in the region (including Graham-Laurier and Redfern-Keily Provincial Parks)
coupled with high biodiversity in the Ospika River valley and surrounding mountain ranges.
The other focal area is in the southwest corner of the study area along Takla Lake (Area 2).
With protected areas and LCPs locked in, this solution had to look elsewhere to capture
targets, resulting in a 3% larger solution than scenario A. This area was likely selected due to
its proximity to the large lake, quality fisher habitat, rare BEC subzone variants, and
abundance of old growth Sub-Boreal Spruce forests with frequent stand-initiating events.

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Figure 77. Focal areas (outlined in light blue and numbered) and protected areas overlaid with the
spatial extent of the conservation solution for Scenario E: Protected Areas Connectivity within the
Greater Tsay Keh Dene Territory Study Area; inset represented by pink frame.

219

The landscape connectivity scenario can be compared to the 2050s and 2080s climate
scenarios given their almost identical targets. The landscape connectivity solution was
influenced by the locking in of Omniscape connectivity corridors, resulting in the selection of
novel focal areas and a 2% increase in area over the 2050s and 2080s solutions. Many of
these novel areas were likely identified by Omniscape as highly connected but did not
necessarily have value in a climate-altered landscape (Figure 78). These instances were
found along the northern Rocky Mountain Trench between Finlay-Russel and Kwadacha
Wilderness Provincial Parks (Area 1), the Sikanni and Osilinka Ranges east of Sustut
Provincial Park (Area 2), the Tenakihi Range south of Chase Provincial Park (Area 3), and
the Rocky Mountain Foothills north and east of Graham-Laurier Provincial Park (Area 4).
The most sizable focal area was in the south between Omineca Provincial Park and Tchentlo
Lake, though it was somewhat dispersed (Area 5). This area appears to be partially
influenced by landscape connectivity, but ecotypic diversity, carbon storage, and low climate
velocities also seem to have played a role in its selection.

220

Figure 78. Focal areas (outlined in purple and numbered) and protected areas overlaid with the spatial
extent of the conservation solution for Scenario F: Landscape Connectivity within the Greater Tsay Keh
Dene Territory Study Area; inset represented by pink frame.

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6.3.3 Traditional Ecological Knowledge of Connectivity
The connectivity stage could have potentially benefited from further community input
and local knowledge on movement corridors as part of the factual observations face of TEK.
While the Nation’s knowledge and values did inform the human footprint layer and therefore
the resistance/permeability input in my connectivity analyses, existing TEK-sourced
connectivity data was not available. As previously mentioned, the Nation does have polyline
data within their Cultural Knowledge Keeper database; however, this data is limited in extent
and largely focuses on trails used by people and not necessarily wildlife. I did not pursue
procuring connectivity data from other Nation sources in the interest of time and due to the
difficulty of first-hand data collection from the community given the pandemic. Charting
movement or migration corridors known to elders and other community members who know
the land would be a worthwhile addition to this project by the Nation after the fact, or for
other community-guided SCP projects.
Had known migration corridor data existed it would have been interesting to develop
a methodology for explicitly including it – whether it be standalone or interwoven with the
outputs of the least-cost path or moving window analysis methods. This stage demonstrated
that even in an Indigenous-led project, deferring to the Western science default might occur
as a backup when no documented TEK exists, provided the community understands and
approves of this method. Further exploration of how to appropriately collect TEK-sourced
connectivity data and how best to interweave this knowledge is needed.

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6.3.4 Future Use of Connectivity within SCPs
Solely including connectivity data as a regular layer for prioritzr to select from in an
SCP analysis is not ideal because the tool assesses each conservation feature in a vacuum at
the planning unit level, ignoring whether that specific conservation feature is found in
neighbouring planning units or not. Only after the data have been synthesized into the
conservation solution can weights be adjusted to encourage grouping or clumping of selected
areas. That is to say, a connectivity dataset does not prove most useful as a regular input
layer in the prioritizr analysis. As disparate portions of different corridors would likely be
selected, this method defeats the purpose of including a layer whose inherent value comes
from its contiguity (Mann, 2020). Including connectivity data as an option to ‘lock-in’ as part
of the solution is one simple and accessible path forward, as it gives the user an opportunity
to ensure a connected landscape within their solution. A post-hoc or other connectivity
analysis can always be used later instead of, or in addition to, a locked-in option. Deciding
how to utilize the outputs of Linkage Mapper and Omniscape to develop locked-in layer
options then becomes the question. prioritizr can also encourage connected and contiguous
solutions through penalties and constraints, even supporting connectivity data as inputs to
parameterize these functions.
Research surrounding best practices for considering connectivity in conservation
planning is ongoing (Keeley et al., 2021). I found helpful guidance after completing my
scenario simulations that would likely change my approach to including protected areas
connectivity in the future. In the absence of species-specific movement data, I would adopt a
2 km minimum buffer as a useful rule of thumb for adequate connectivity corridor width
(Beier, 2019). In my protected areas connectivity locked-in layer, I only included planning
223

units traversed by least-cost paths. As my 1 km 2 planning units are just over 1 km across, the
resulting corridors fall below this threshold in most cases. Thus, in future efforts I would
buffer least-cost paths to achieve the minimum 2 km width.
Omniscape is preferable to Linkage Mapper in a relatively intact landscape such as
this given its comprehensive focus. The only exception is if the objective is connectivity
between specific areas, in which case Linkage Mapper may be better suited. There is a
delicate balance of choosing how much of the Omniscape output to include as a locked-in
option to ensure connectivity, yet not be heavy-handed and still allow the tool to perform
conservation prioritization. My method of dividing the Omniscape output into quantiles and
including multiple locked-in options is a helpful way to navigate this predicament, as it
allows the user to experiment and find the optimal level of locked-in connectedness for their
conservation purposes.
The Nation has already expressed confidence in the connectivity outputs, to the extent
that they have amended the proposed internal boundaries of the Ingenika Conservation and
Management Area based on the Omniscape analysis. Specifically, the connectivity corridor
portion of the CMA between the Cultural Epicenter (the portion to receive legal protected
status) and Chase Provincial Park was altered to maximize areas with concentrated current
flow. My connectivity outputs could also help inform other portions of the CMA boundary as
well as any new protected area efforts as project results are reviewed.

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6.4 Which stages of the Systematic Conservation Planning process provide an
opportunity for the interweaving of Traditional Ecological Knowledge to produce a
more inclusive conservation plan?
The most novel and challenging portion of this project was determining how to
effectively and inclusively interweave Traditional Ecological Knowledge and community
input with the SCP process. As Western and Indigenous worldviews can sometimes appear to
be at odds, blending these two ways of knowing in a manner that is respectful to TEK's
sacred and cosmological origins while adhering to SCP methodology proved to be a delicate
balance at times. While I was in regular contact with the Nation and working in their office
throughout the process, I had to remain careful not to corrupt this knowledge by using it in a
way that it was not intended.
I used GIS not just as a tool, but also as a critical approach to understanding
community and conservation needs. GIS inherently privileges the Western scientific spatial
understanding of the world in which it was created and can marginalize other ways of
knowing (Burkhart, 2018). Recognizing GIS as a colonial tool and working to interweave
local community knowledge from Indigenous peoples is an attempt to counter or disrupt that
status quo. While the SCP methodology is also rooted in Western science, it is worth noting
that this project was initiated by the Tsay Keh Dene First Nation and seeks to further their
conservation and natural resource management goals – not solely for, or in co-management
with a colonial government. This Indigenous community-led distinction is what makes this a
TEK project, as the Nation’s ethics and values were imprinted on the project as they guided
my use of the SCP framework throughout (Figure 7). This is consistent with Houde’s (2007)

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depiction of TEK as being holistic and multi-faceted – not just factual observations as TEK
can sometimes be portrayed.
6.4.1 Contrasting with Other TEK-guided SCP Efforts
Community-based research is more involved than a strictly academic exercise by
virtue of listening to more voices; however, the additional time and effort required results in
a more meaningful and fulfilling project with a greater chance of uptake and real-world
impact (Strand et al., 2003). Building relationships, holding workshops, and fostering
productive discussions certainly extended the amount of time required to complete this
project, but were vital steps in producing a plan that reflected the visions and goals of the
Tsay Keh Dene Nation.
Previous SCP work performed in close proximity to Tsay Keh Dene Territory has
provided meaningful insights into the Wild Harts and Peace River Break areas; however
these efforts were not community-led (Curtis, 2018; Mann, 2020). These projects were more
of an academic exercise, though they did have support and interest from the Yellowstone to
Yukon Conservation Initiative (Y2Y). Backing from larger organizations like Y2Y can be
crucial for broader awareness and financial support but can lack the invaluable local insights
and buy-in that community engagement often provides. Furthermore, when the local
community in question is a First Nation there is even more nuance to consider.
Communications and decision-making must be viewed through a culturally appropriate lens
by considering centuries of colonialism and making good faith attempts to understand a
Nation’s worldview (Reid et al., 2021). Presenting a project and its facets must be done in a
manner that respects all belief systems.

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Heinemeyer et al.’s (2003) work with the Taku River Tlingit First Nation is a great
example of taking a TEK-driven SCP even further than this project was able to. Their effort
was a multi-year, multi-organization feat that included several champions, and the resulting
breadth and depth of their work is apparent. Working with many community members over a
longer time period, they were able to gather TEK through taped interviews and mapping
workshops to chart species distributions. This exhaustive effort included semi-structured
interviews with 60 potential questions, and resulted in over 1200 pages of transcribed
responses (Heinemeyer et al., 2003). Their data collection was extensive enough to inform
season-specific habitat suitability models for several focal species. In contrast, I utilized
existing TEK wildlife data that was not necessarily intended to inform a habitat suitability
model. Its limited extent also meant using it as an enhancement to existing Western science
models rather than standalone TEK wildlife layers. With more time and greater freedom to
visit with community members in a post-pandemic world, this work could be improved with
a more intensive TEK data collection effort.
6.4.2 Community-centric Planning
A truly community-centric approach would look more like the Taku River Tlingit
effort of knowledge collection as opposed to this project that focused more on using already
available information. An enhanced knowledge gathering effort would have to be
thoughtfully organized and include community mapping workshops to document the
distribution of features on the landscape. Individual semi-structured interviews with elders
would also be beneficial if circumstances allowed. With more robust TEK-sourced feature
data layers in hand, additional decisions would have to be made on how to implement
hierarchy within datasets, as well as how and if to attempt to blend this information with
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Western science models to create hybrid layers. This cross-pollination of perspectives or
marrying of both worlds holds real potential for better conservation outcomes if navigated
thoughtfully (Polfus, 2018). Furthermore, ‘biocultural approaches’ such as this are better set
up for success given that local communities have contributed to the work and have a vested
interest in conservation goals coming to fruition (DeRoy et al., 2021). Accomplishing any or
all of the above tasks would likely require increased staff time and resources from the Nation
to assist an outside researcher like me.
Decisions still had to be made with the cultural data that was available, but these were
more technical than philosophical. I was provided with cultural data from the Nation in the
form of points, lines, and polygons from their Cultural Knowledge Keeper database. I
ultimately did not use the linear data since it was solely made up of trails, but there is an
argument to be made for the cultural importance of these routes and they could be included in
future efforts by the Nation. I scored planning units commensurate with the number of ‘Sites
of Cultural Importance’ found within them (i.e., a planning unit with four sites received a
score of four). Finally, given the uncertainty around the spatial extent of some of the
polygons behind the ‘Cultural/Spiritual Areas’ and ‘Subsistence Areas’ layers, planning units
were scored higher based on the number of overlapping polygons present following Darvill
and Lindo’s (2015) methodology for quantifying community-generated spatial data.
The decisions on which portions of the Cultural Knowledge Keeper database to use
and how to use them within the tool were largely made by me individually. While these
decisions were ultimately signed off on by Nation representatives, determinations on how
many layers to create, how to score them, and what targets to set on them may well have
looked different if discussions with staff from the Nation and Chu Cho had taken place from
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the outset. The target setting workshop I held was one instance of collaborative discussion
that served to confirm my actions with only slight adjustments. Nation staff are generally
aware of community consensus but decided to go a step further and send out a survey to
community members to help quantify the relative importance of cultural/spiritual areas as
well as specific species and ecosystems. There were twelve respondents in the end, and their
answers reinforced the preferences voiced by Nation staff. This led me to adjust targets on
moose, Stone sheep, and mountain goat, elevating them above the default value set for other
species. It also prompted me to raise the lakes value, as this was the unanimously most
important conservation feature according to Tsay Keh community members.
The survey also provided additional useful information. There was an open-ended
question on what additional features should be included in future efforts, which could prove
useful if the Nation wants to expand the tool. Other features mentioned included: weasels,
eagles, elk, additional cultural elements, low elevation pine lichen habitat, beaver, blue
grouse, ptarmigan, and natural springs. The survey also provided a short answer box that
allowed participants to provide context for why they answered the way they did. These
comments centered around the belief that all life is important and interconnected in the
territory. To quote one response, “All living things hold importance to each other. Our great
ecosystem that creates a natural balance of all life. Each value has equal importance; if we
take away one the other will be affected in some way. We must protect this life balance for
our future generations.”
This brief survey provided just a taste of what a more community-centric approach
could look like. Rather than serving to confirm notions of importance, future efforts could
concentrate on community engagement from the outset, learning which features matter most
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and how to measure them. This would require research ethics approval if being performed by
the student and would ideally be done via in-person workshops in the community rather than
the online survey performed by the Nation due to the COVID-19 pandemic. Delineated
features could then be plotted on large maps at subsequent workshops for further discussion
and validation. We discussed the feasibility of these tasks early on in this project; however,
given its already large scope and the remoteness of the community we realized how difficult
it might be. The pandemic all but made the final decision for us.
Beyond learning more about the community’s values as they relate to my project, the
COVID-19 pandemic precluded me from forming relationships with many Nation members,
as visiting the Tsay Keh Dene community was not possible given safety measures. This not
only kept me from gaining an understanding of local context from site visits but also robbed
me of the ability to make connections with the people this work sought to benefit. It would
have been deeply beneficial for me to learn of Tsay Keh history through the voices of elders
and not just existing literature, as hearing stories of customs, traditions, and the devastation
caused by the dam from the source would have better grounded my work in the worldview of
the Tsay Keh Dene.
6.4.3 Partner Backgrounds
I was also somewhat limited in my ability to coordinate with Nation staff members
and Chu Cho Environmental employees in a face-to-face manner in Prince George –
especially at the beginning of the pandemic. Remote communication methods such as email,
video chats, and phone calls were utilized throughout the project. As pandemic safety
protocols were developed, I was eventually able to work out of Chu Cho’s office once a
week. The ability to be in the same space as my collaborators proved invaluable in building
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connections and relationships and improved the project overall. It allowed me to better
understand the Nation’s capacity and specific interests surrounding my work, allowing me to
better tailor products to suit their needs.
Of the six Nation and Chu Cho staff members that made up the informal team
supporting this project, five were non-Indigenous. The one Tsay Keh Dene member provided
invaluable guidance regarding Nation priorities and community values and was a great
resource in building out the case study chapter with history of the people and the land. NonIndigenous staff also possess a meaningful understanding of the community’s values and
were able to convey those perspectives as well as provide their own valuable feedback given
their backgrounds in Western science. For example, the biologists were able to offer context
on how to use the Habitat Suitability Indexes they had developed for species at risk within
the Territory. While these Western and community-informed perspectives were valuable,
they are not a stand-in for being immersed in the community and truly engaging Tsay Keh
Dene members to help guide the project.
Having community partners on the ground is a tremendous asset, as they can serve as
champions of this work and take advantage of its findings. This distinction sets this effort
apart from SCPs without a local community aspect. Furthermore, the staff at Chu Cho
Environmental and Tsay Keh Dene’s Lands, Resources, and Treaty Operations office are
highly capable. Together they are extremely involved in natural resource decisions in the
Territory – from resource extraction referrals to the creation of Indigenous Protected and
Conserved Areas. With their leadership, the final products of this work can have real-world
impacts in instances like identifying which areas to avoid timber harvest in, or where to focus
efforts for a second IPCA. The staff’s broad, existing capacity in fields like forestry, biology,
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conservation science, and Geographic Information Systems meant I only had to focus on
teaching the specifics of the tool I created. Once they understood its innerworkings, their
local knowledge and extensive experience in environmental science allowed them to fully
leverage the tool and truly make it their own.
6.4.4 Reflections on SCP Stages
The main theme that emerged from this community-led approach to the SCP process
was the importance of providing sufficient context and conveying the implications of each
decision that had to be made. Selecting a study area boundary was an early example. While
this step is relatively simple in theory, it carries huge ramifications. It would be very timeconsuming to change course after data gathering and manipulation has taken place, so if it is
possible that the study extent might be revisited then it is better to err on the side of too large
of a study boundary. It is significantly easier to cut down extent rather than go back and redownload and process data for a larger extent.
A good example of providing context was in the goal articulation stage. I found that
providing the Nation with the goals from a similar, albeit non-Indigenous-led effort was
helpful in beginning the discussion (Weaver, 2019). After receiving a first draft of goals from
the Nation I had to provide further context and propose some changes to bring their goals in
line with what the SCP approach was capable of accomplishing. For example, wetlands
restoration was important to the Nation, but SCP can only identify and prioritize areas
containing wetlands. The Nation’s original goal is valid and can stand as written for their
own conservation actions stemming from this work, but for this project the modification was
necessary.

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As the project progressed towards more complex and sometimes nebulous tasks, it
became apparent that discussions were more productive when ample background was
provided. I also began to provide a first draft of ideas for the Nation to respond to based on
my reading of relevant literature and understanding of the Nation’s priorities for conservation
feature selection, target setting, and human footprint features and buffers. This was a balance
of not going too far down a single path before consulting with partners while still providing
enough material to react to and not overburden or overwhelm already busy staff members. I
had to be especially conscious of not overpriming community partners given the Western
biases of GIS and SCP. While I wanted to effectively convey the parameters of the project, I
still wanted authentic ideas from the Nation and not answers that were overly pigeonholed by
the conventional SCP framework.
Another benefit of providing substantial context is that it allowed the Nation to assess
the appropriate personnel to involve at each stage. For example, Chu Cho brought their GIS
specialist into the conversation at the human footprint stage given their expertise on data
availability and awareness of ongoing and imminent work being carried out on the land. This
led to more accurate and precise road, drill site, mine, and cutblock features for inclusion in
the human footprint layer. While not explicitly a material face of TEK, this contemporary
local knowledge of the Territory and community values on regional issues provided valuable
insight on a number of occasions. Chu Cho has since expressed enough confidence in the
resulting human footprint layer to use it as a detailed disturbance layer to inform moose and
caribou habitat modeling work in the territory.
The target setting stage proved especially challenging – both for the cultural
sensitivity involved in quantifying the relative importance of features, and the general
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uncertainty that underlies the selection of these targets. Targets were informed by ecological
best practices or Nation priorities and were both transparent and well-documented. In cases
where neither of these approaches could help inform a conservation target or there were
concerns around data quality, conservative estimates were chosen to ensure consistency and
adequate feature capture. Additionally, prioritizr’s nimble nature allows for flexibility by
running multiple scenarios with differing targets set for an uncertain conservation feature,
thereby mitigating the risks associated with a given feature.
The staff’s experience in navigating both Western and Indigenous knowledge was
indispensable in the target setting stage, as quantifying the relative importance of features is
challenging enough on its own. Undertaking a task that can appear contradictory to the
underlying beliefs of the community further complicated the matter, but it is a necessary step
in the SCP process. Hearing staff perceptions of community values towards wildlife and
ecosystems helped translate priorities into numeric targets, and having the Nation initiate a
survey of community members that ultimately backed up those claims with data was
affirming. By asking community members to essentially prioritize certain cultural elements,
species, and ecosystems ahead of others, I knew there was the potential of offending them
with a question that could be interpreted as antithetical to their worldview. The Nation
deemed it appropriate though, and it was a valuable addition to the project in the end.
Another takeaway was to earnestly consider any idea that was offered by our partners
and think creatively about how it could be integrated into the project, within reason. For
conservation features that had not been used in previous efforts (e.g., moose, Stone sheep,
and mountain goats), this meant compiling trustworthy habitat data and assembling it in a
piecemeal fashion to cover the study area in a defensible manner. Another example was
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leveraging knowledge from existing sources like an ‘expectations for industry’ document
developed by the Nation with knowledge sourced from many elders (Chu Cho Environmental
& Tsay Keh Dene Nation Lands, Resources and Treaty Operations Department, 2019). This
helped me develop an ordinal structure within certain features. Wetlands, for example, were
scored based on their size: 3 points if greater than 10 ha, 2 points if less than 10 ha but
greater than 1 ha, and 1 point if less than 1 ha. When values are voiced, it is the researcher’s
responsibility to examine how they can be effectively integrated into the project.
Engaging Nation representatives throughout the process undoubtedly required more
time and effort than if I had made executive decisions based on my understanding of their
wants and needs. Their insights on community values undeniably resulted in a better product,
as has been discussed previously. What their involvement also added, however, was a sense
of investment in the project that led to meaningful understanding and eventual uptake of the
tool. When the time came for a final technical workshop to put the completed product on
display, staff were better equipped to appreciate the tool and how they might use it in the
future. This demonstration was paramount in providing tangibility to what often seemed like
a nebulous concept when described in scoping meetings. It also served as the culmination of
previous workshops, displaying how the information and guidance they provided along the
way was leveraged within the now fully assembled tool.
The community was able to conceive of potential applications given their
understanding of the inner workings of the tool and some potential applications that I
suggested. With a tangible tool in front of them, their attention was captured and several
types of analysis were formulated. Requests for additional functionality like subunit analysis
(i.e., planning at the watershed level within the study area) and area budget-based analysis
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(as opposed to feature target-based) were communicated and confirmed as feasible additions.
These inquiries signaled a clear interest and engagement in the project that indicated a sense
of enthusiasm for its use moving forward to assist in activities like systematic resource
extraction referral responses and the identification of a core area for a second IPCA in the
Territory.
6.5 Other thoughts
A few concepts were born out of this project that did not fit neatly into one of the
above research questions but are still worthy of discussion. These revolved around
contributions to national and global conservation efforts, the decision to set persistent targets
on cultural features in every scenario, the decision not to use a certain human footprint layer,
and the logistics of transferring the planning tool to community partners.
6.5.1 Contributions to larger conservation efforts
While not an explicit goal of this project, any conservation efforts stemming from this
work will indirectly contribute to conservation on the national and global scale – particularly
if new protected or conserved areas are established as a result. For example, the Government
of Canada has committed to protecting 25% of Canada’s lands and oceans by the year 2025,
while global efforts are focused on protecting 30% of the Earth’s lands and oceans by 2030
(Campaign for Nature, 2020; Liberal Party of Canada, 2019).
As it stands, 9% of the Territory and 11% of the study area are currently protected.
When including the entirety of the Ingenika Conservation and Management Area, the
Territory reaches 25% protection while the study area reaches 17% protection. As it stands
the Ingenika CMA is only recognized by the Nation itself though, and provincially236

recognized protection is only being sought for the core of the CMA with the rest being
managed through other conservation means. While the Ingenika effort is first and foremost
by and for the Tsay Keh Dene, its establishment contributes to Canada’s area-based goal of
25% by 2025. The establishment of any additional Indigenous Protected and Conserved
Areas by the Nation would likely be informed by the products of this collaborative research
project and advance towards the global goal of 30% protection by 2030.
These contributions are meaningful, but area-based goals are political and aspirational
in nature and may be ecologically flawed, which is why I focused on conservation featurebased targets throughout this project (Wiersma & Sleep, 2016). The Nation still sees value in
area-based target setting though, as it could be a means to an end for appealing to the federal
government while advancing their own political agenda of IPCA-creation. During our final
workshop one staff member voiced their intention of doing so, referring to the products of
this effort as, “[A] really dynamic negotiating tool.” With a defensible methodology for
identifying the 30% of the Territory with the greatest conservation value, the Nation could
present powerful evidence for their conservation efforts to be recognized.
6.5.2 Persistent targets on cultural features
I used consistently high targets on each of the three cultural conservation features
given the sacredness of the places they represented. This was my initial proposal based on the
nature and extent of these features, and that idea was affirmed by the Nation in our target
setting workshop. When I stacked each of the six solutions to reveal which areas were
consistently selected, the result naturally displayed these important cultural, spiritual, and
subsistence areas. This could be interpreted as a bias towards cultural features; however,
other areas were selected in all six scenarios purely on their ecological merit. Second, there is
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nothing inherently wrong with weighting cultural areas so strongly that they heavily
influence the outcome. The concept of human culture and nonhuman nature as separate
worlds is a European construct that perpetuates imperialist attitudes on the division of culture
and nature (Pacini-Ketchabaw & Nxumalo, 2015). This conflicts with the Nation’s
worldview of life as a web of related beings, and so this artificial Settler division was
discarded given that this is a project of and for the Nation. The Tsay Keh Dene are the
foundation of this project and this work seeks to further their holistic conservation goals.
6.5.3 Ephemeral-Sensitive Footprint Layer
I ultimately did not use the ephemeral-sensitive footprint layer in any of the scenarios
for the thesis, but it remains an option within the tool for the Nation to use moving forward.
This will prove useful should they decide to run scenarios focused on entirely undisturbed
areas, which is relevant for extremely sensitive species like caribou. As ephemeral features
were activities such as heliskiing and horseback riding, it felt short-sighted to dismiss areas
that could otherwise hold great conservation value simply because recreation occasionally
took place there. While management, policy, and land tenure decisions can be complicated,
these areas could be reverted to undisturbed conditions overnight if the political will was
there instead of semi-permanent cutblocks that can take decades or even centuries to fully
regenerate.
6.5.4 Tool Handoff
While not a stage of the SCP process, working with a community means ensuring that
they understand the process you are undertaking and can continue to use the tool you are
developing on their behalf once the project is formally over. From the outset of this project,
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the goal was to have a living tool that the Nation could update and expand as new data
became available. This could be replacing or augmenting the spatial data for an existing
conservation feature (e.g., new ungulate habitat modeling work was completed) or the
addition of an altogether new conservation feature like one of the species named in the
community survey. For this to happen, the tool needed to be made accessible to Nation staff.
I packaged the vast amount of spatial data generated over the course of the project
into a user-friendly format and uploaded it, as well as the tool itself, onto an external hard
drive to give to Chu Cho staff. I then assisted in loading the tool and underlying data onto the
Nation’s network so anyone with an organizational computer can run the tool. I also
developed a guide outlining how to both update and add data, as well as ensure that the
various components of the tool are working properly so that the entire package can continue
to operate. These technical steps were paramount in ensuring the tool's efficacy moving
forward and making good on our promised deliverable of an adaptive tool that can respond to
the Nation’s dynamic needs.

7.0 RECOMMENDATIONS
This research analyzed conservation value across greater Tsay Keh Dene Territory
and served to both confirm ongoing conservation efforts and identify where future initiatives
could be focused. The scenario stack solution, in particular, should be referenced in
conservation efforts, as it combines a number of relevant scenarios to delineate which
portions of the Territory meet goals on present-day biodiversity, climate change, and
connectivity. The Nation should perform validation of these findings to ensure that they are
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well-founded, but then there are countless possibilities for how they can apply the tool to
future use cases to meet their needs.
7.1 Validation
This entire process is a simulation, and so any conservation solution will need to be
verified and validated before decisions are made on the ground. One final way to include
local, Indigenous knowledge is to have members of the Nation validate the conservation
solutions to ensure that the areas they deem to be ecologically or culturally significant are
properly captured in the analysis. The solutions will also require increased scrutiny and
potential site visits when local decisions are being made. Initial discussions have taken place
to have a Chu Cho staff member who lives in the community perform TEK validation by
plotting results on maps and sitting down with elders to discuss findings and mark up the
maps. This exercise could serve to endorse or challenge research findings but could also
further develop a TEK-SCP methodology by formalizing this interaction and making it part
of the interweaving process.
A complementary Western validation approach could also be undertaken to
systematically verify the solutions. Hirsh-Pearson (2020) developed a method of inspecting
the level of human footprint visible in satellite imagery using random sampling and
contrasted those visual results with what available spatial data stated was present. When used
systematically, this method generates confidence metrics for the findings as a whole. Using
satellite imagery to verify specific areas of interest is also possible though. This could be
used as a cost-effective approach to vetting specific areas, with genuine ground-truthing
efforts taking place when an area starts to receive serious consideration for conservation
action.
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A supplementary approach to validation could also be performed with auxiliary data.
These could be spatial datasets that depict an aspect of conservation value, but for one reason
or another do not merit inclusion in the tool itself. For example, if reputable elk habitat
suitability data is developed in an adjacent area but does not cover the entire extent of the
study area, it could be used as an overlay. This allows the user to validate a conservation
solution for its elk potential without having to incorporate that data within the tool explicitly.
Another potential validation dataset could be a conservation plan performed by a
neighbouring First Nation. This could identify areas of mutual interest, foster cooperation,
and potentially lead to conservation designation and co-management efforts like the West
Moberly and Saulteau First Nations in their Caribou Conservation Partnership Agreement
(Environment and Climate Change Canada et al., 2020). For example, the northern periphery
of the study area overlaps with the Kaska Dena’s Dene K'éh Kusān IPCA, allowing for a
potential partnership for conservation efforts in the region (Dena Kayeh Institute, 2019).
Finally, the Tsay Keh Dene Nation is in the process of performing archaeological
work within the Territory, though this effort is not yet finalized. Spatial data from this effort
could also be overlaid with solutions to validate whether culturally important areas beyond
those explicitly included in the tool were captured in a solution. Depending on the nature of
the archaeological findings and available data, this layer could eventually move beyond
being a supplementary feature and explicitly be included in the tool for target setting
purposes.

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7.2 Future Use Cases
There are a multitude of ways that the Nation can use the tool to help solve
community-specific needs moving forward. At the final workshop, I outlined some potential
uses to initiate further thought and discussion amongst Nation staff, the end-users of the tool.
These talks centered around examining tool parameters, focusing on specific conservation
features, using project outputs to better inform ongoing conservation efforts, and exploring
novel conservation efforts.
One scenario that gained traction in the final workshop and merits additional
exploration was to increase clustering within the tool. None of the scenarios run for the thesis
encouraged clustering via the ‘boundary length modifier’ parameter, opting instead for the
most efficient array of high conservation value planning units. The scattered result can be
difficult to protect in practice, though. Clustered solutions provide more coherent areas that
allow for more straightforward delineation and eventual protection, though they generally
require a greater percentage of the landscape to meet conservation targets (Figure 79). This
areal issue can be countered with a conservative blanket target for each feature (e.g., 40%);
however, these lower targets also represent a compromise. Ultimately, clustered outputs can
be more relevant and pragmatic for conservation action and should be further explored.

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Figure 79. Spatial extent of a clustered conservation solution in the Greater Tsay Keh Dene Territory
Study Area; inset represented by pink frame. A conservative 40% blanket target was used.

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The Nation should also consider honing in on specific conservation features and
setting targets accordingly. This could mean setting aggressive targets on a single feature or
set of features, or focusing on one feature and setting conservative targets on the rest. This
strategy could provide valuable insight when the Nation has conservation concerns about a
specific feature or a few related features. One example is a focus on mature and old-growth
forests throughout the Territory (Figure 80). Setting 100% targets on the mature/old forest
pattern and process layers would clearly delineate these invaluable ecosystems for
conservation action.

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Figure 80. Spatial extent of mature- and old-growth forests in the Greater Tsay Keh Dene Territory Study
Area; inset represented by pink frame. 100% targets were set on relevant layers, with no targets set for
other layers.

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The clustered and scenario stack solutions provide the optimal catalog for identifying
coherent high-value areas. They should be considered in tandem when scouting locations for
an additional IPCA or other effective area-based conservation measures (OECMs) in the
Territory (Figure 81). The scenario stack provides a modestly grouped solution that
represents consistently selected areas across varying conservation goals. The clustered
solution – based on grouping with conservative blanket targets – provides a structure of
consolidated areas to choose from. Together they co-produce a pragmatic framework to
facilitate delineation around consistently selected areas.

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Figure 81. Spatial extent of the clustered conservation solution atop the planning units selected in all six
of the thesis scenarios for the Greater Tsay Keh Dene Territory Study Area; inset represented by pink
frame.

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Finally, the connectivity analysis – particularly the outputs of Omnicape – should be
used to inform conservation decisions. Omniscape connectivity has already proven helpful in
refining the internal subunit boundaries of the Ingenika Conservation and Management Area.
It could also prove useful in designing additional conservation areas – either as overall
boundary delineation or strategically placing a smaller area as a potential stepping stone for
movement across the landscape.
These are just a few possibilities out of the countless use cases that Nation staff can
develop and ultimately apply the tool to help solve. By mixing and matching conservation
features, adjusting targets, locking in different areas, and avoiding varying levels of
development, the tool provides users at Chu Cho Environmental and Tsay Keh Dene
Nation’s Lands, Resources, and Treaty Operations office with a dynamic and agile tool to
meet their conservation planning needs now and into the future.

8.0 CONCLUSION
Both people and wildlife are being forced to adapt to environmental changes at
contemporarily unprecedented rates. Human development presents continued disturbance to
species and ecosystems – even in this remote corner of a country with low population
density. Climate change is exacerbating these issues, even altering areas that have remained
relatively unaffected by encroaching industry and settlement. This systematic conservation
planning project was an effort to counter ongoing biodiversity loss using an area-based
strategy that focuses on important landscape features like key species and ecosystems. I
sought an outcome of inclusion and reciprocity by attempting to interweave the Traditional
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Ecological Knowledge of the Tsay Keh Dene throughout the process. The main goal of this
work was to benefit the Tsay Keh Dene and the land they call home. At the same time, this
effort benefitted immensely from the invaluable knowledge of these Indigenous land
stewards and those working on their behalf.
The Tsay Keh Dene have occupied this mountain- and river-filled landscape in socalled northern British Columbia since time immemorial. They have thoughtfully managed
these landscapes for millennia and continue to even in the aftermath of a devastating
reservoir and continually encroaching development. A consistent message throughout this
effort, from the scoping stages to the final workshop, was that the Nation was burdened with
the number of resource extraction referrals they routinely receive and could benefit greatly
from a systematic review process. This project's final product planning tool will allow the
Nation to place these forestry and mining requests in a broader context so more holistic
decisions can be made. Furthermore, the tool facilitates location scouting for high-value
conservation areas, allowing for proactive conservation efforts and furthering ambitions for
IPCAs.
The adaptability of the SCP process proved vital as I adopted methods for considering
climate change, explicitly included landscape connectivity data, and sought to interweave
TEK throughout the project. By including data on the geophysical stage, areas with low
climate velocity, and refugia, I accounted for predicted changes on the landscape over the
next 30 to 60 years. A conservative target-setting approach helped mitigate the uncertainty
underlying some of these metrics. Landscape connectivity was explicitly included as an
option to lock into conservation solutions, both ensuring movement corridors for wildlife
today and acting as a climate adaptation strategy into the future. The last but most important
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modification to the SCP framework was using a community-led approach and taking a
critical GIS perspective. Tsay Keh Dene TEK and community input were the foundation of
this project and undoubtedly led to an enhanced and more inclusive outcome.
By building a Western science-based GIS tool, Tsay Keh Dene knowledge, values,
and priorities are articulated in a manner that is readily understood by the provincial and
federal governments. My findings can serve to not only validate ongoing conservation efforts
in the Territory but help guide future efforts by identifying additional high-value areas. These
efforts will encourage not only ecological health in the Territory, but also further cultural
restoration through the preservation of spiritual areas. The defensible nature of the SCP
framework will help justify Nation-led conservation initiatives if and when formal
negotiations take place between governments. Living in harmony with the land will always
be a tenet of the Tsay Keh Dene way of life. By interweaving TEK with sound conservation
science on biodiversity, climate change, and connectivity, we have developed an Indigenousled SCP framework. This work can serve as a guide for conservation planning that promotes
multiple ways of knowing – a strategy that is our best path forward given the urgency of the
climate and biodiversity crises.

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Ashcroft, M. B. (2010). Identifying refugia from climate change. Journal of Biogeography,
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Baker, N., Beger, M., McClennen, C., Ishoda, A., & Edwards, F. (2011). Reimaanlok: A
national framework for conservation area planning in the Marshall Islands. Journal of
Marine Biology, 2011, 1–11. https://doi.org/10.1155/2011/273034
Baldwin, R., Scherzinger, R., Lipscomb, D., Mockrin, M., & Stein, S. (2014). Planning for
land use and conservation: Assessing GIS-based conservation software for land use
planning (RMRS-RN-70; p. RMRS-RN-70). U.S. Department of Agriculture, Forest
Service, Rocky Mountain Research Station. https://doi.org/10.2737/RMRS-RN-70
Bateman, B. L., Wilsey, C., Taylor, L., Wu, J., LeBaron, G. S., & Langham, G. (2020). North
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10.0 APPENDIXES
Appendix A. Federal Species at Risk Act (SARA) Schedule 1 Species in Territory
Darker highlighted species included in analysis
English Name

Hotwater Physa
White Sturgeon
Northern Abalone

Yellow-breasted Chat
Whitebark Pine
Black Swift
Northern Myotis
Little Brown Myotis
Porsild's bryum
Crumpled Tarpaper
Barn Owl
Lewis's Woodpecker
Bobolink
Marbled Murrelet
Canada Warbler
Olive-sided Flycatcher
Barn Swallow
Western Screech-Owl
Common Nighthawk
Western Grebe
Northwest Waterfan
Yellow Rail
Green Sturgeon
Peregrine Falcon
Wolverine
Cryptic Paw
Grizzly Bear
Long-billed Curlew
Short-eared Owl
Collared Pika
Band-tailed Pigeon
Rusty Blackbird
Red-necked Phalarope
Western Toad
Coastal Tailed Frog
Evening Grosbeak

Scientific Name

Category

Physella wrighti
Acipenser transmontanus
Haliotis kamtschatkana

Invertebrate Animal
Vertebrate Animal
Invertebrate Animal

Icteria virens
Pinus Albicaulis
Cypseloides niger
Myotis septentrionalis
Myotis lucifugus

Vertebrate Animal
Vascular Plant
Vertebrate Animal
Vertebrate Animal
Vertebrate Animal

Haplodontium macrocarpum
Collema coniophilum
Tyto alba
Melanerpes lewis
Dolichonyx oryzivorus
Brachyramphus marmoratus
Cardellina canadensis
Contopus cooperi
Hirundo rustica
Megascops kennicottii
Chordeiles minor
Aechmophorus occidentalis
Peltigera gowardii
Coturnicops noveboracensis
Acipenser medirostris
Falco peregrinus
Gulo gulo
Nephroma occultum

Nonvascular Plant
Fungus
Vertebrate Animal
Vertebrate Animal
Vertebrate Animal
Vertebrate Animal
Vertebrate Animal
Vertebrate Animal
Vertebrate Animal
Vertebrate Animal
Vertebrate Animal
Vertebrate Animal
Fungus
Vertebrate Animal
Vertebrate Animal
Vertebrate Animal
Vertebrate Animal
Fungus

Ursus arctos
Numenius americanus
Asio flammeus
Ochotona collaris
Patagioenas fasciata

Vertebrate Animal
Vertebrate Animal
Vertebrate Animal
Vertebrate Animal
Vertebrate Animal

Euphagus carolinus
Phalaropus lobatus
Anaxyrus boreas
Ascaphus truei
Coccothraustes vespertinus

Vertebrate Animal
Vertebrate Animal
Vertebrate Animal
Vertebrate Animal
Vertebrate Animal

277

SARA Status

Endangered
Endangered
Endangered
Endangered
Endangered
Endangered
Endangered
Endangered
Threatened
Threatened
Threatened
Threatened
Threatened
Threatened
Threatened
Threatened

Threatened
Threatened
Threatened
Special Concern
Special Concern
Special Concern
Special Concern
Special Concern
Special Concern
Special Concern
Special Concern
Special Concern
Special Concern
Special Concern
Special Concern
Special Concern
Special Concern
Special Concern
Special Concern
Special Concern

Cool Headwater Refugia
Climatic Refugia
Biotic Refugia

Rare BEC Zones - at variant level
Elevational Representation
Ecoregional Representation
Land Facet Diversity
Land Facet Rarity
Elevational Diversity
Ecotypic Diversity
Heat Load Index Diversity
BEC Zones 2020
Climate
BEC Zones 2050
BEC Zones 2080
Climate Corridors (Current-Flow Centrality)
Backward Velocity 2055
Backward Velocity 2085
Forward Velocity 2055
Forward Velocity 2085
Bird Richness (3.0 degrees summer)
Carbon Storage (above and below ground)

Wolverine
Bank Swallow
Barn Swallow
Western Toad
Horned Grebe
Little Brown Myotis
Northern Myotis
Olive-Sided Flycatcher
Rusty Blackbird
Coarse-Filter
Forest Pattern & Process

Mountain Goat

Moose
Stone Sheep

Lakes
Karst Deposits
Grizzly Bear
Bull Trout/Fish
Fisher
Caribou (by herd)

Cultural
Sites of Cultural Importance
Cultural/Spiritual Areas
Subsistence Areas
Fine-Filter
Wetlands

Layers by Category

Source(s)

http://www.climatewna.com/ClimateBC_Map.aspx
http://www.climatewna.com/ClimateBC_Map.aspx
https://adaptwest.databasin.org/pages/climate-connectivity-north-america
https://adaptwest.databasin.org/pages/adaptwest-velocitywna
https://adaptwest.databasin.org/pages/adaptwest-velocitywna
https://adaptwest.databasin.org/pages/adaptwest-velocitywna
https://adaptwest.databasin.org/pages/adaptwest-velocitywna
https://adaptwest.databasin.org/pages/audubon-survival-by-degrees
https://globbiomass.org/wp-content/uploads/GB_Maps/Globbiomass_global_dataset.html
https://data.isric.org/geonetwork/srv/eng/catalog.search#/metadata/c02ddf8b-cbfb-4533-a9c3-7bf0790fd041
http://www.climatewna.com/ClimateBC_Map.aspx
https://adaptwest.databasin.org/pages/climate-informed-priorities
https://adaptwest.databasin.org/pages/distribution-and-protection-climatic-refugia

Climate BC
Climate BC
AdaptWest
AdaptWest
AdaptWest
AdaptWest
AdaptWest
AdaptWest
GlobBiomass
ISRIC
Climate BC
AdaptWest
AdaptWest

278

https://catalogue.data.gov.bc.ca/dataset/bec-map
https://catalogue.data.gov.bc.ca/dataset/vri-2019-forest-vegetation-composite-polygons
https://catalogue.data.gov.bc.ca/dataset/vri-2019-forest-vegetation-composite-layers-all-layershttps://catalogue.data.gov.bc.ca/dataset/bec-map
https://catalogue.data.gov.bc.ca/dataset/digital-elevation-model-for-british-columbia-cded-1-250-000
https://catalogue.data.gov.bc.ca/dataset/ecoregions-ecoregion-ecosystem-classification-of-british-columbia
https://adaptwest.databasin.org/pages/adaptwest-landfacets
https://adaptwest.databasin.org/pages/adaptwest-landfacets
https://adaptwest.databasin.org/pages/environmental-diversity-north-america
https://adaptwest.databasin.org/pages/environmental-diversity-north-america
https://adaptwest.databasin.org/pages/environmental-diversity-north-america
https://catalogue.data.gov.bc.ca/dataset/bec-map

https://catalogue.data.gov.bc.ca/dataset/ungulate-winter-range-approved
https://catalogue.data.gov.bc.ca/dataset/ungulate-winter-range-approved

http://a100.gov.bc.ca/pub/acat/public/viewReport.do?reportId=1434

https://www.bcfisherhabitat.ca/wp-content/uploads/2017/05/Users-guide-to-spatial-data-May-2017.pdf

https://catalogue.data.gov.bc.ca/dataset/freshwater-atlas-wetlands
https://governmentofbc.maps.arcgis.com/apps/MapSeries/index.html?appid=5a59fc13b9064cf7b19398f29ceaac9e
https://catalogue.data.gov.bc.ca/dataset/freshwater-atlas-lakes
https://catalogue.data.gov.bc.ca/dataset/reconnaissance-karst-potential-mapping
https://catalogue.data.gov.bc.ca/dataset/bc-grizzly-bear-habitat-classification-and-rating

Link (if public)

BC Data Catalogue
BC Data Catalogue
BC Data Catalogue
BC Data Catalogue
BC Data Catalogue
BC Data Catalogue
AdaptWest
AdaptWest
AdaptWest
AdaptWest
AdaptWest
BC Data Catalogue

BC Data Catalogue
Williston Wetland Explorer Tool
BC Data Catalogue
BC Data Catalogue
BC Data Catalogue
John Hagen & Associates; Tsay Keh Dene Cultural Knowledge Keeper Database
BC Fisher Habitat Working Group
Mann (2020); WCS Canada; BC Data Catalogue; Demarchi & Demarchi (2003);
Tsay Keh Dene Cultural Knowledge Keeper Database
Suzuki & Parker (2016); Demarchi & Demarchi (2003)
Round River Conservation Studies; WCS Canada;
BC Data Catalogue; Tsay Keh Dene Cultural Knowledge Keeper Database
Round River Conservation Studies; BC Data Catalogue;
Tsay Keh Dene Cultural Knowledge Keeper Database
Chu Cho Environmental
Chu Cho Environmental
Chu Cho Environmental
Chu Cho Environmental
Chu Cho Environmental
Chu Cho Environmental
Chu Cho Environmental
Chu Cho Environmental
Chu Cho Environmental

Tsay Keh Dene Cultural Knowledge Keeper Database
Tsay Keh Dene Cultural Knowledge Keeper Database
Tsay Keh Dene Cultural Knowledge Keeper Database

Appendix B. Conservation Feature Data Sources

1940-1980
1981-2020

Trail Riding

Outblock Trails
Inblock Resource Main
Inblock Resource Secondary
Inblock Trails
Ephemeral
Alpine Skiing
Heliskiing
Snowmobiling

Roads

Wind Turbines
Semi-Permanent
Agriculture
Camps and Cabins
Cutblocks

https://catalogue.data.gov.bc.ca/dataset/baseline-thematic-mapping-present-land-use-version-1-spatial-layer#edc-pow
https://catalogue.data.gov.bc.ca/dataset/tantalis-crown-tenures
https://catalogue.data.gov.bc.ca/dataset/results-openings-svw

2 km
2 km
2 km
250 m

https://catalogue.data.gov.bc.ca/dataset/tantalis-crown-tenures
https://catalogue.data.gov.bc.ca/dataset/tantalis-crown-tenures
https://snoriderswest.com/northernbc
https://catalogue.data.gov.bc.ca/dataset/tantalis-crown-tenures

BC Data Catalogue
BC Data Catalogue
Snoriders West destination maps
(georeferenced)
BC Data Catalogue

1 km
1.5 km
120 - 240 m
120 m
240 m
.25 - 1 km
250 m
1 km
500 m
250 m

2 km

250 m
2 km
1.5 km
250 m
.5 - 2 km
2 km
1 km
500 m
1.5 - 8 km

250 m

ftp://ftp.geobc.gov.bc.ca/sections/outgoing/bmgs/DRA_Public

279

Buffer
1 km large; 0.5 km small
.25 - 1 km
1 km
500 m
250 m
1 km
2 km
250 m

BC Data Catalogue

NR Can

BC Data Catalogue
BC Data Catalogue
BC Data Catalogue

https://catalogue.data.gov.bc.ca/dataset/tantalis-crown-tenures
https://catalogue.data.gov.bc.ca/dataset/reservoir-permits-over-crown-land#edc-pow
https://catalogue.data.gov.bc.ca/dataset/tantalis-crown-tenures
ftp://ftp.geobc.gov.bc.ca/sections/outgoing/bmgs/DRA_Public

https://catalogue.data.gov.bc.ca/dataset/baseline-thematic-mapping-present-land-use-version-1-spatial-layer
https://catalogue.data.gov.bc.ca/dataset/vri-2019-forest-vegetation-composite-polygons
https://open.canada.ca/data/en/dataset/79fdad93-9025-49ad-ba16-c26d718cc070

Mann & Wright (2018)
BC Data Catalogue
BC Data Catalogue
BC Data Catalogue
BC Data Catalogue

https://catalogue.data.gov.bc.ca/dataset/bc-transmission-lines
http://ftp.maps.canada.ca/pub/nrcan_rncan/publications/ess_sst/299/299660/as_0900A_66.zip
https://catalogue.data.gov.bc.ca/dataset/tantalis-crown-tenures
https://catalogue.data.gov.bc.ca/dataset/oil-and-gas-commission-pipeline-segment-permits
https://catalogue.data.gov.bc.ca/dataset/bc-transmission-lines
https://catalogue.data.gov.bc.ca/dataset/tantalis-crown-tenures

https://catalogue.data.gov.bc.ca/dataset/b-c-dams
https://catalogue.data.gov.bc.ca/dataset/tantalis-crown-tenures
https://www.bcogc.ca/data-reports/data-centre/?category=60040

Link (if public)

BC Data Catalogue

BC Data Catalogue

Private Land
Quarries
Reservoirs
Residential Areas
Roads
Outblock Major
Outblock Resource Main
Outblock Resource Secondary
Urban Areas

BC Data Catalogue
BC Data Catalogue
BC Data Catalogue

BC Data Catalogue
BC Data Catalogue
BC Oil & Gas Commission for status

Source(s)

Power and Telecom Lines

Industrial Uses
Mines
Pipelines

Active
Orphan
Dormant

Permanent
Dams
Drillsites and Wellsites

Footprint by Category

Appendix C. Human Footprint Feature Data Sources and Buffers

Seip et al. (2007)
Mann & Wright (2018)

Recommended by Ecosystems Branch of FLNRO
Recommended by Ecosystems Branch of FLNRO

Laurance et al. (2007)
Nation input on younger cutblocks
Province of BC (2015); Polfus et al. (2011)

Mann & Wright (2018)
Polfus et al. (2011)

Mann & Wright (2018)

Mann & Wright (2018)

Mann & Wright (2018)
Based on Polfus et al. (2011) approach
Based on Polfus et al. (2011) approach
Mann & Wright (2018)
Province of BC (2015); Polfus et al. (2011)

Mann & Wright (2018)

Mann and Wright (2018)
Polfus et al. (2011)
Mann & Wright (2018)

Based on Polfus et al. (2011) approach
Polfus et al. (2011)

Buffer Source