PROTECTED AREA SELECTION FOR EFFICIENT AND EQUITABLE
BIODIVERSITY CONSERVATION IN BRITISH COLUMBIA
by
Karen T. Dietrich
B.Sc., Cornell University, 2014

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

UNIVERSITY OF NORTHERN BRITISH COLUMBIA
April 2022
© Karen Dietrich, 2022

Abstract
Conserving biodiversity is principally achieved through the designation of protected areas,
guided by the process of systematic conservation planning (SCP). SCP identifies networks of
protected areas that represent regional biodiversity. Ecologists, planners, and decision makers may
each use different spatial boundaries, scales, and extents for individual projects; these can be
misaligned with the jurisdictional scope and scale of management decisions, impacting the
effectiveness of resulting conservation actions. Using British Columbia as a study area, we explore
how analysis design effects SCP outputs. Using ILP solvers, we measured concordance, efficiency,
and equity between the varying extents (ecological and administrative) and scales (fine, coarse,
provincial). Higher efficiency but lower equity results when constraints are lifted. Inversely, higher
land requirements with higher equity result when constraints are imposed. We show how initial
design decisions influence the priority assigned to conservation areas and find insights for striking
a balance between landscape-scale and regional-scale analyses.

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TABLE OF C ONTENTS
Table of Tables .............................................................................................................................. vi
Table of Figures ............................................................................................................................ vii
Glossary of Terms and Acronyms ................................................................................................. ix
1. Introduction................................................................................................................................. 1
1.1 Background: Conservation Planning for Biodiversity .................................................. 1
Biodiversity Conservation in Canada ..................................................................... 1
Systematic Conservation Planning ......................................................................... 2
Spatial Scale and Units for Planning ...................................................................... 5
Spatial Constraints Commonly Used in the Study Area ......................................... 6
Conservation Data Limitations and Opportunities ................................................. 8
Climate Change and Climate-Adaptive Conservation Planning........................... 10
Climate-Adaptive Planning for British Columbia (CAP-BC) Tool...................... 12
1.2 Research Objectives.................................................................................................... 13
Scale, Extents, and Units for Planning.................................................................. 13
Efficient & Equitable Conservation ..................................................................... 17
Hypothesis............................................................................................................. 19
Research Design.................................................................................................... 19
2. Impact of Spatial Constraints on Systematic Conservation Planning Outcomes ..................... 22
2.1. Introduction................................................................................................................ 22

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2.2. Methods...................................................................................................................... 22
Overview............................................................................................................... 22
Study Area ............................................................................................................ 24
Selection and Development of Conservation Feature Data .................................. 24
Setting Conservation Targets ................................................................................ 34
Scenario Analysis Structure .................................................................................. 36
Measuring Equity and Efficiency ......................................................................... 38
2.3. Results........................................................................................................................ 40
Spatial Congruence ............................................................................................... 40
Effect of Grouping Unit Type............................................................................... 42
Efficiency .............................................................................................................. 43
Equity.................................................................................................................... 45
Effect of Long-Ranging Features.......................................................................... 46
2.4. Discussion .................................................................................................................. 47
Results and Implications ....................................................................................... 47
Further Research Opportunities ............................................................................ 51
Summary ............................................................................................................... 54
3. Conclusion: Contributions of this Project................................................................................. 55
3.1 Provincial Relevance .................................................................................................. 55
3.2 Climate-Adaptive Conservation Planning in British Columbia.................................. 56

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3.3 Other Effective Conservation Measures ..................................................................... 57
3.4 Conclusion .................................................................................................................. 58
4. References................................................................................................................................. 60
Appendix A: Spatial Data Used for Analysis ............................................................................... 70
Appendix B: Table of Grouping Units by Scenario...................................................................... 71
Appendix C: Additional Maps ...................................................................................................... 74

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TABLE OF TABLES
Table 1. 1 Definitions and examples of the spatial constraint terms used in this analysis. ........... 6
Table 1. 2 Questions, objectives, and methods for achieving them in this research, by chapter. 20
Table 2. 1 Spatial constraint for scenario analysis. The labels at the intersection of scale and
grouping unit are the extents used. ............................................................................................... 23
Table 2. 2 Targets assigned to conservation features based on provincial conservation status.... 35
Table 2. 3 Planning scenario outputs and total land area requirements....................................... 43
Table 2. 4 Sample results (n = 6) from equity analysis of each scenario by regional district (n =
61), showing the most dissimilar and most equitable solutions.................................................... 45

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TABLE OF FIGURES
Figure 2. 1 Ecological grouping units (left) including Ecoprovinces (outlined) and Ecoregions
(green tones), and administrative grouping units (right) including NR Districts (outlined) and NR
Regions (blue tones). .................................................................................................................... 27
Figure 2. 2 Geographical grouping units for all 5 scenarios......................................................... 38
Figure 2. 3 BC Regional Districts, the boundary used to calculate the equity of scenario results.
....................................................................................................................................................... 40
Figure 2. 4 Optimization results for all 5 scenarios, showing dissimilarity between results derived
from the different grouping units. ................................................................................................. 41
Figure 2. 5 (left) Plotting the number of cells selected against the number of scenarios those cells
were selected in shows an exponential decrease in overlap as the number of scenarios increases.
(right) Mapping the gradient of cells by number of scenarios in which they were selected shows
proportional selection concordance, as well as groupings of significant areas. ........................... 42
Figure 2. 6 Plotting the number of 1km2 cells required in each scenario solution against the number
of grouping units in their associated scenario shows that planning at smaller scales yields less
efficient solutions in terms of land area. ....................................................................................... 44
Figure 2. 7 Efficiency also scales with extent. Using larger grouping unit extents yielded more
efficient solution sets. ................................................................................................................... 44
Figure 2. 8 Land use requirements between the five scenarios based on the percentage of land
needed in each regional district to reach the solution. .................................................................. 46
Figure 2. 9 Instances where a small portion of a bird's range occurs in BC (black) did not seem to
bias the cumulative results (red tones).......................................................................................... 47

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Figure 3. 1 Existing protected areas (black) overlaid with the results of the 5 scenarios (green).
The summed scenario overlay (red tones) shows the cells that were selected in 3, 4, and 5 of the
scenarios........................................................................................................................................ 53

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GLOSSARY OF T ERMS AND ACRONYMS
These definitions reflect how terms are referred to and applied within the context of this research.
BC: British Columbia, a Canadian province.
BCPARF: British Columbia Protected Area Research Forum. A biannual conference of
conservation planners and researchers intended to facilitate research and its application to parks
and protected areas management.
Biodiversity: the ecological and evolutionary patterns and processes that form the basis of life on
Earth.
CAP-BC: Climate-Adaptiv
. A web-based, open-access
planning tool that will be flexible for diverse user groups, preloaded with data, and updatable for
future advances in BC climate change knowledge.
CBD: Convention on Biological Diversity. Originally convened in 1992 following the Rio Earth
Summit, the CBD is a global effort with 150 national signatories that is dedicated to sustainable
development. It has 3 main objectives: (1) The conservation of biological diversity, (2) the
sustainable use of the components of biological diversity, and (3) the fair and equitable sharing of
the benefits arising out of the utilization of genetic resources.
Efficiency: the measurement of how much or little land area is required in a given solution set.
Equity: the proportional availability (or conversely, the relative deprivation) of a resource
compared with its availability to others.
Grouping units: classifications of spatial constraints.
Protected area: a clearly defined geographical space, recognized, dedicated, and managed,
through legal or other effective means, to achieve the long term conservation of nature with
associated ecosystems services and cultural values.
Regional biodiversity: the relative uniqueness of local conditions; significant in its distinction
from global biodiversity.
SCP: Systematic Conservation Planning. A structured, six-step, systematic approach to
conservation planning.
Spatial scale: the geographic size or magnitude of a process (does not refer to resolution or grain
size).
Spatial extent: the geographic space enclosing the referenced data.
Spatial constraints: the various differences in scale, size, location, and type of data used to define
an SCP problem.
prioritizr: an R package which uses mixed integer linear programming techniques to provide a
flexible interface for building and solving conservation planning problems.

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1. Introduction
1.1 BACKGROUND : C ONSERVATION PLANNING FOR BIODIVERSITY
BIODIVERSITY CONSERVATION IN CANADA
describes the ecological and evolutionary patterns and processes that form
the basis of life on Earth (Noss and Cooperrider, 1994). Global loss of biodiversity has been
exacerbated by land use change (Boakes et al., 2010; Jones et al., 2018). Conflict between landuses is often decided against conservation goals, jeopardizing the success of protection initiatives
(Venter et al., 2018). Biodiversity conservation is a practice that aims to protect and manage these
values, which are essential to ecosystem function, the global food supply, aesthetics, recreation,
and spiritualism (Watson et al., 2005).
Conserving biodiversity is principally achieved through the creation and designation of protected
areas (Margules and Pressey, 2000).

and managed, through legal or other effective means, to achieve the long term conservation of
nature with associated ecosystems services and cultural values (Dearden et al., 2015)

These

protected areas are designed to be representative of regional biodiversity and to be resilient to
climatic or periodic changes in the ecosystem (Dearden et al., 2015).
Canada is a signatory on

2011-2020 Strategic Plan for

conservation objectives. Aichi Target 11 obligates the signatory countries to protect at least 17%
of their terrestrial land and 10% of their marine areas through

ecologically representative and well-connected systems of protected areas

(Convention

on Biological Diversity, 2010).
Ca
(Climate Change Canada, 2018). Achieving the Aichi Targets requires a rapid and systematic
expansion of the protected area network, especially as the 2030 and 2050 protection targets are
expected to increase (Convention on Biological Diversity, 2020). British Columbia (BC) is
outpacing other provinces, with 15.4% of its land currently in protected areas (Climate Change
Canada, 2018). Its high biodiversity, geographic connectivity, and relatively low land use change
rates make BC a prime candidate for new protected areas (Austin and Eriksson, 2009). These
characteristics also make the province vulnerable to widespread climate-related changes (Woods
et al., 2017). Incorporating climate variables is critical to ensuring new protected areas promote
the overall adaptive capacity of the total network (Groves et al., 2012; Pressey et al., 2007).
Importantly, the Aichi Target contains a critical mandate that established protected areas be
effective, equitable, connective networks selected for their ecological importance. This highlights
the importance that true improvements to the protected area network must progress the
incorporation of these components, ensuring that new protected areas are placed where they will
be effective in safeguarding biodiversity (Venter et al., 2018). To successfully improve protected
area networks, potential inconsistencies in the conservation planning process must be understood
and reconciled.
SYSTEMATIC CONSERVATION P LANNING
Conservation planning aims to systematically identify areas which safeguard biodiversity and
ecosystems against loss while still managing land appropriately for human use (Groves et al.,
2003). The process of Systematic Conservation Planning (SCP) (Margules and Pressey, 2000)

2

provides a structured approach to the creation of protected areas, and is a key tool for conserving
biodiversity. SCP requires (1) considered selection of features of biodiversity, (2) establishment
of specific goals and quantitative targets, (3) evaluation of the existing protected area network, (4)
complementary design of new reserves, (5) implementation procedures and direction, and (6)
management and review to maintain the ecological conditions. SCP is an iterative process,
necessitating continual review, additions, and managerial adjustments. Though some variation is
to be expected among process results (Wiersma and Sleep, 2016), it is important that SCP is
applied consistently to ensure optimal protected areas are created.
In the first step of this process, conservation planners select spatial data that represents relevant
conservation features, such as species habitat or forest composition (Margules and Pressey, 2000),
to analyze and assign protection priorities across the landscape of interest. These spatial data are
used to identify biodiversity conservation priorities and inform landscape-scale decisions. This
judicious selection of conservation features is integral to the SCP process.
The second step is to identify representative goals and quantifiable targets for conservation. Using
the features identified in step one, relevant numerical targets are assigned to these values as a
measure of how much

Using a science-based

approach, the most appropriate numerical target is determined after a thorough review of the best
available ecological and spatial information for each feature (Wiersma and Sleep, 2018). These
targets are used to instruct a prioritization algorithm and produce optimal spatial solutions that are
representative of the combined spatial requirements of the input features (Svancara et al., 2005).
The third step is to conduct a review of existing conserved areas to identify gaps and key areas for
new conservation. The objective of this gap analysis is to evaluate how well existing conservation
areas have achieved their goals (Burley, 1988, pp. 227 230) and where there is room for

3

improvement of the network. Protected areas that are not strategically selected can create or
exacerbate societal inequity, and are ineffective in protecting biodiversity (Venter et al., 2018).
Therefore, this step is important for ensuring that new protected areas are located in the right places
to conserve biodiversity effectively.
In the fourth step, the features, targets, and information about the current network of existing
protected areas are used to guide the selection of new conservation areas. This selection process is
generally conducted using optimization algorithms, which run multiple iterations and produce a
range of optimal solution sets. Integer linear programming (ILP) is a commonly used optimization
process which uses input data assemblages to iteratively select complementary sites to achieve a
solution to a SCP problem (Albuquerque and Beier, 2015). ILP achieves solutions at the lowest
cost, which optimizes both conservation and resource opportunity (Beyer et al., 2016). A standard
planning unit is employed throughout to delineate the size of each selection (Margules & Pressey,
2000).
Steps five and six (implementation and management, respectively) occur outside the purview of
conservation planners, and are implemented by policy makers and land managers. For these actors
to succeed, and for resources to be adequately directed towards meaningful conservation, diligent
work must be done in the preceding steps. SCP provides structure for evaluating and prioritizing
the conservation features, while decision-support technology is used to find optimal solutions for
reaching conservation targets. Getting the right data and making informed decisions during the
SCP setup is critical to the actualization of conservation goals.
The underlying assumption of SCP is that maximizing species representation within protected
areas will ensure their persistence. It follows that the maximum number of protected features
represented at the minimum land area should give the most efficient solution to the conservation

4

problem (Ball et al., 2009; Moilanen et al., 2009). However, the process and its outcomes may also
be influenced by the spatial constraints that are used to define the area of interest for any SCP
exercise.
SPATIAL S CALE AND UNITS FOR PLANNING
The spatial scale and extent of a conservation planning project are often determined before the
SCP process begins.

the geographic

size or magnitude of a process

data, and is often represented by and referred to as a boundary (National Geographic Society,
2020). The selection of these spatial constraints may be driven by the objective of the project, by
available funding, or by specific interests of the researchers, but nonetheless contribute to the
process.
Though biodiversity conservation analyses follow a formula, they are often conducted using
inconsistent spatial constraints and are frequently misaligned with the jurisdictional boundaries
used for management decisions (Warman et al., 2004b). While these may have excellent scientific
basis, their results can difficult to implement. For example, the Nature Conservancy Canada
Conservation Blueprints, the guiding document for national conservation in Canada, were
conducted using spatial regions purpose-designed for the study, and not used anywhere else
(Nature Conservancy Canada, 2010).
It is the nature of land conservation that different organizations and operators have differing
geographic priorities. Effective and consistent conservation planning should be conducted at
consistent scales and congruous extents, or provide justification for the constraints used, as
ecological relevance may not always translate well to jurisdictional applications (Huber et al.,

5

2010). It is possible that the constraints chosen cause evaluations to over- or under-emphasize the
ecological significance of areas relative to the distribution of the features in the larger landscape.
However, there is currently no way to measure the effect of geographic scale and extent of SCP
evaluations on the ability of models to accurately represent ecologically significant areas.
Table 1. 1
Definitions and examples of the spatial constraint terms used in this analysis.
Term

Use

Example

Spatial Scale

The relative size of a process or area

(Huber et al., 2010)

Spatial Extent

The geographic coverage of a process or area

(Saura and Martinez-Millan, 2001)

Grouping Unit

The classification of a given spatial scale and/or extent

(Selig et al., 2012)

SPATIAL CONSTRAINTS COMMONLY USED IN THE STUDY AREA
The Canadian province of British Columbia (BC) serves as the study areas for this research. BC is
home to more biodiversity than any other place in North America,
mammals, the majority of the global range for 99 species, high genetic diversity between species,
and a relatively short history of large-scale land use change (Austin et al., 2008). Terrestrial
protected areas total 15.4% of the
(Environment and Climate Change Canada, 2018; Environmental Reporting BC, 2016).
Biodiversity in BC is vulnerable to the effects of climate change, which are projected to intensify
in the coming decades (Bellard et al., 2012; Woods et al., 2017). Combined, these factors make
BC a candidate site for impactful SCP. However, to ensure meaningful conservation, it is important
to consider the role of spatial scale and extent in the SCP processes which occur in the province.
The current state of SCP in BC reveals disparity between the spatial scales and extents used in
planning and those used in administration.

6

There is disparity across the geographic extent groupings used by various provincial actors. BC
Ministry of Forests, Lands, Natural Resource Operations & Rural Development (FLNRORD) use
an administrative unit to define its operations. The administrative unit employed is not consistent;
Land Use Plan boundaries, for example, have been established through a collaborative process
involving Regional Districts, municipalities, government agencies, and First Nations, and often do
not reflect any distinct administrative boundary. By contrast, many conservation studies are
conducted using ecological groupings. Global ecoregions (Olson et al., 2001) are one such
grouping which gives a biogeographical framework to large-scale conservation strategies. The
global ecoregions are employed commonly, as evidenced by their 5,500+ citations. In BC, an
analog dataset of provincial ecoregions is often used.
Regarding spatial scale, studies conducted in BC are generally inconsistent (Warman et al., 2004b).
Some analyses are meant to represent the full province; however, sampling is carried out by region,
and do not deliberate on the effect of this artificial apportionment on the study results (Guerrero et
al., 2013). The major guiding documents for private land conservation in the southern ecoregions
of Canada, Nature Conservancy C

, are conducted in this manner.

Additionally, the scale of Old-growth Management Areas are chosen based on provincial
harvesting rates and limits, and are generally scattered and small in size compared to the
geographic distribution of BEC zones that would support contiguous old-growth forests
(Environmental Law Centre, 2013). Studies such as these which arbitrarily stratify data based on
artificially-constructed regions jeopardize the legitimacy of their own conservation efforts.
The impact of spatial constraints on SCP research design is an important one. However, this
relationship is not often explicitly explored. Many recent studies suggest that multi-scale studies
collectively yield more informative results than any singular spatial scale. In conservation biology,

7

it can be difficult to determine the most appropriate or ecologically relevant scale for management
of a species of interest, even though multiscale studies are employed to study selection patterns
(Johnson et al., 2004).
caribou, the argument is equally applicable to protected area selection, as it raises the issue of
processes which operate at multiple scales. SCP efforts which are limited to any single spatial scale
will neglect biodiversity patterns and ecological processes that are important at other scales (Huber
et al., 2010). These inconsistencies can result in largely disparate spatial optimization results.
Conservation is hindered by the spatial scale of global climate change and the difficulty in
reconciling its regionalized impact, and this mismatch may cause incomplete or inaccurate SCP
optimization results (Cadotte et al., 2017). Carroll et al. (2017) similarly identify that the results
for priority areas differ with ecological context and spatial scale. These results collectively suggest
a need for balanced integration of broad-scale ecosystems planning with regionalized
considerations for specific ecological processes. It is therefore evident that SCP should be
conducted complementarily, spanning several spatial scales and extents to emulate the variation in
ecological and physical processes which comprise biodiversity.
CONSERVATION DATA LIMITATIONS AND OPPORTUNITIES
Conservation planners have historically employed spatial data representing biodiversity to instruct
SCP optimizations (Margules and Pressey, 2000). However, it is nearly impossible to represent
the diversity of life within any single SCP problem (Lambeck, 1997; Margules et al., 2002).
Furthermore, data approximations are ubiquitous (Langford et al., 2011), contain commission and
omission errors, and are subject to sampling bias (Rondinini et al., 2006).
Because the full biotic composition of a landscape is too complex to be useful for systematic
conservation planning, surrogates are selected to represent the biodiversity of the ecosystem (Caro

8

1999). Coarse-filter surrogates represent ecological patterns and processes, such
as biogeoclimatic zone, land facets, and/or disturbance regimes. Fine-filter surrogates represent
individual habitat requirements of species that are endemic, threatened, and/or representative of
the ecosystem (Carroll et al., 2017). Choosing the right suite of features to represent biodiversity
is still plagued with uncertainty, and there is disagreement among practitioners as to the best
approach (Pressey, 2004; Rodrigues and Brooks, 2007)
The accuracy of SCP is limited by the quality of the obtainable data (Hermoso et al., 2013b).
Accurate datasets are necessary both at the planning stage of SCP, and as a baseline for long-term
monitoring and stewardship (Margules and Pressey, 2000). Accurate and/or high resolution
biodiversity data are infrequently and inconsistently available, creating a data availability gap
which hinders conservation efforts (Cadotte et al., 2017). Comprehensive surveys of entire taxa
are expensive and time-consuming endeavors (Ricketts et al., 1999). Even so, the most robust
surrogate datasets are subject to data approximation biases (Langford et al., 2011; Pressey, 2004).
Datasets derived from species point localities are subject to sampling bias as well as
misclassification (Pressey, 2004). The geographic range of a species can be used, but these ranges
often over-generalize, and only convey the presence or absence of a species, as opposed to its
abundance (Gaston and Rodrigues, 2003). Using the predicted distributions of a species carries
similar risk, as these distributions are inferred from environmental conditions and are not verified
on the ground (Rondinini et al., 2006).
Additionally, no single surrogate can represent the whole of ecological diversity (Lambeck, 1997).
Given the limitations of individual surrogate datasets, the use of several carefully-chosen
surrogates together can give a clearer picture (Pressey, 2004). Muti-taxa surrogate sets

9

Gamboa et al., 2019) and composite datasets (Hermoso et al., 2013a) have proven successful in
overcoming some of the limitations of biological surrogate data.
In recent years, computer modelling has advanced, global climate models have become more
accurate, and range estimates have become more precise (Conlisk et al., 2013; McKenney et al.,
2011). Recent data advances have fostered the development of improved biodiversity datasets for
entire taxa in some regions (McKenney et al., 2011; Stralberg et al., 2018; Wilsey et al., 2019).
There is evidence to support that the minimum set of data selected to represent well-documented
taxa perform well at representing other taxa

(Warman et al., 2004a).

The improved accuracy of these new datasets presents an opportunity to reevaluate the
effectiveness of surrogate data and perhaps improve upon the criticisms of the practice.
CLIMATE CHANGE AND CLIMATE -ADAPTIVE CONSERVATION PLANNING
Global climate change presents a major threat to the persistence of biodiversity. In the coming
decades, climate change is likely to overtake habitat destruction as the greatest threat to global
biodiversity (Leadley et al., 2010). It is now well-known that climate change will exacerbate and

changes in the distributions of natural habitat and of entire biota (Leadley et al., 2010). As global
mean temperatures reach 2-3

-industrial levels, 10-40% of all species on Earth face

high risk of extinction (Pachauri and Reisinger, 2008). Further indirect consequences will follow
as the global cascade of species interactions is disrupted (Gilman et al., 2010).
In British Columbia and all of northwestern North America, where changes are projected to occur
faster and be more pronounced than the global average, it is apparent that significant warming has
already taken place (Fields et al., 2007). Climate-driven changes are expected to exceed biological
tolerances for many species and ecosystems in BC (Hamann and Wang, 2006; Hebda, 1997).

10

Vulnerable ecosystems that are well represented in the province, such as boreal forests and alpine
mountains, are at high risk (Fields et al., 2007; Wilson and Hebda, 2008).
broad biogeoclimatic zones, or 5% of the land area, are considered under conservation concern,
while most the alpine regions of the province are expected to disappear entirely (Austin et al.,
2008). At the finer scale, vulnerable ecolo
biogeoclimatic zones (Austin et al., 2008).
gh susceptibility to climate change impacts, it is critical that SCP across the province
recognizes and adapts to these vulnerabilities. Traditional conservation strategies are predicated
upon the assumption that current species distributions are sufficient to represent the future. These
strategies are insufficient to tackle the challenge of conservation planning within climate change
(Belote et al., 2017; Groves et al., 2012; Hannah et al., 2002). Climate change effects will cross
protected area boundaries indiscriminately and alter the landscapes otherwise preserved there
(Belote et al., 2017). With the knowledge of anthropogenic climate change came the call for new
conservation tools to integrate climate-driven changes in the biota to the mission of SCP (Hannah
et al., 2002). To address the reality of a shifting biota, conservationists began to advance new
frameworks for considering climate change in the course of SCP, including conserving the
geophysical setting that supports biodiversity and prioritizing protecting unique land facets
(Groves et al., 2012; Jones et al., 2016; Schmitz et al., 2015), as well as incorporating climateadaptive approaches such as prioritizing connectivity corridors, protecting future climate space,
and protecting climate refugia
Johnson, 2012; Schmitz et al., 2015).
Climate impacts will exacerbate non-climate stressors related to human activity, such as land-use
changes, pollution and resource use (Austin et al., 2008). Human activity disrupts the natural forest

11

and watercourse processes and reduces ecosystem resilience through development and agriculture
(Austin et al., 2008). As climate change affects land use opportunities and community access to
resources, equity considerations are an important factor in climate-adaptive conservation planning.
Climate-adaptive conservation planning should mitigate these land-use conflicts (Halpern et al.,
2013).
CLIMATE -ADAPTIVE PLANNING FOR BRITISH COLUMBIA (CAP-BC) TOOL
The SCP process is time- and resource-intensive, and the data needs and technical skill
requirements for conservation practitioners are burdensome (Wiersma and Sleep, 2016). Using
multiple features and targets quickly makes the SCP problem complex (Wiersma and Sleep, 2018).
As climate change hastens the pace at which SCP must be executed and implemented (Pressey et
al., 2007), the complexity of the problem and challenge for conservation planners increases. These
limitations hinder scientifically-sound conservation decision making. New frameworks for
incorporating climate change data have been put forth (Belote et al., 2017; Groves et al., 2012;
Schmitz et al., 2015). However, there is still a need for more accessible tools with improved
functionality for solving climate-adaptive SCP problems (Moilanen et al., 2009; Runge et al.,
2016).
Large-scale mapping, data visualization, and decision-support advances allow conservation
planners to analyze these processes more quickly and effectively. Recent advances in computing
make these selection software packages readily available and flexible (Wiersma and Sleep, 2016).
These technological innovations have greatly increased capacity for tackling these multifaceted
problems (Ball et al., 2009).
To address these needs for BC practitioners, data from this study will be used to assist the
development of a fully-contained, open access planning tool for climate-adaptive SCP. The

12

Climate-Adaptive Planning for British Columbia (CAP-BC) web tool brings together recent
advancements in computing to address these problem (Pacific Institute for Climate Solutions,
2020). It will provide a common basis of pre-loaded data, removing the need to search for and
transform spatial data from numerous sources. It will be flexible and accessible to users without
specialized training and democratize the process by being online and open access. The tool will be
province-wide, facilitating use by ministries, NGOs, and other planners at all scales.
SCP work is complicated and time-consuming, and doing so necessitates better, more accessible
tools for landscape-scale planning. To best facilitate the practice, it is necessary to ensure the data
and process are democratized and readily available. It is critical to ensure that SCP is an adaptive
process, and conducted in a manner which can mitigate the effects of climate change in the
province.
To ensure this research would meet actual conservation data needs, I engaged conservation
planners from across the province and derived a list of priority biodiversity datasets. With this
input, I chose a suite of biodiversity data to be used in the following analyses. Additionally, I used
this list to compile all of datasets that these practitioners requested to be pre-loaded into the web
tool. All of these datasets will be supplemented with sourcing and citation data, as well as requested
information about the best uses of each spatial layer. The results of these analyses will be written
up and included as supplemental materials for web tool users.
1.2 RESEARCH OBJECTIVES
SCALE , EXTENTS , AND UNITS FOR PLANNING
Conserving biodiversity is principally achieved through the designation of protected areas, guided
by the process of systematic conservation planning (SCP) (Margules and Pressey, 2000). Decision-

13

support systems are used together with the SCP process to guide conservation planning. Despite
having the structure of the SCP process, ecologists, planners, and decision makers may each
employ different spatial constraints to individual projects. The choices made to delineate these
projects can be misaligned with the jurisdictional scope and scale of management decisions (Huber
et al., 2010).
Getting the right spatial scale is a challenge for conservation planning. Planning at too small a

conservation priorities (Groves et al., 2003). Fine-scale analyses can miss the importance of
regional-scale factors, and do not account for connectivity with neighboring or overlapping
grouping units (Huber et al., 2010). On the other hand, conservation plans that evaluate needs at a
coarse spatial scale fail to capture features of local importance (Hermoso and Kennard, 2012).
Variation is as important as any single feature, and may not be adequately accounted for in a
coarse-scale analysis (Groves et al., 2002). Coarse-scale planning also carries the risk of
disproportionately allocating conservation priority to one or few localities, placing the burden of
conservation unevenly across communities (Halpern et al., 2013). Conservation planning should
be conducted consistently, at a carefully considered spatial scale, and provide justification for the
spatial scale used, as ecological relevance alone may not coincide with jurisdictional application.
It is in the nature of land conservation that different organizations and operators have differing
geographic priorities, so choosing an appropriate project boundary, or extent, is also a challenge.
While there are plenty of good reasons to have these divergent priorities, using non-standard spatial
constraints can make SCP prioritization outcomes difficult to compare to one another. Three types
of spatial constraints for planning are generally used: ecological boundaries, administrative

14

boundaries, and purpose-fit, or custom, boundaries. These different extent categories are
sometim
Conservation studies are often conducted using ecological boundaries, while management actions
are regularly planned using administrative boundaries. An ecological boundary is a surface or
patch associated with an ecological system, which are composed of and included in other systems,
and which enclose discernible processes (Yarrow and Salthe, 2008). Administrative boundaries
capture the hierarchical structure of multilevel government entities (Kurian and Kojima, 2021).
This mismatch between planning and implementation can impact the resulting conservation
actions (Guerrero et al., 2013). Further, the ecosystem being studied does not adhere to the spatial
boundaries employed, nor does it behave subject to them (Huber et al., 2010). There is currently
no way to measure whether the spatial constraints used in SCP evaluations affect the ability of
models to accurately represent ecologically significant areas.
Conservation studies are most often conducted using several different types of ecological
groupings. The provincial Biogeoclimatic Ecosystem Classification (BEC) System (Krajina, 1959)
is often (though not always) used. The province is apportioned into these ecosystem zones
containing similar ecological processes and broadly homogenous macroclimates, which in turn
contain sub-zones describing the condition of the local ecosystem (Forests and Environment,
1995). BEC zones are valuable for summarizing the natural abiotic and biotic components of the
landscape, but they do not align with the boundaries of any administrative unit used by the
province. Other ecological studies use the provincially-defined ecoprovinces, ecoregions, and
ecosections; others use the national ecoregions established by Environment Canada; still others
use global terrestrial ecoregions established by World Wildlife Fund. Each of these groupings is

15

valuable for summarizing the landscape, but they do not align with the boundaries of any
administrative unit used by the province.
In BC, management organizations often use administrative boundaries to guide decision-making.
Natural Resource Areas, Regions, and Districts (GeoBC, 2018) are a set of hierarchical geographic
ural
Development (MFLNRORD). NR District boundaries are designated by the Lieutenant Governor
in council and published as regulations, which establish the Ministry's management areas (B.C.
Reg. 137/2014, 2014). MFLNRORD sometimes uses a conglomerate unit known as Land Use Plan
boundaries (B.C. Reg. 137/2014, 2014). These spatial grouping units were defined through a
collaborative process involving Regional Districts, municipalities, government agencies and First
Nations. Because these planning extents and grouping units do not reflect any distinct
administrative boundary, nor do they align with any ecological grouping, they can also be difficult
to understand when applied inconsistently.
Within conservation organizations, the early-stage decisions about what spatial constraints to use
for a study are inconsistently made. Though many use existing groupings, such as those described
above, sometimes purpose-fit boundaries are used. Studies done this way are difficult to compare
accurately with the results of other conservation plans. Conservation organizations often work
collaboratively; to do so, they need to know where their priorities align. For example, Nature

private land

ure Conservancy]

16

Blueprints are made harder to use alongside priorities identified by other analyses.
There have been few studies explicitly investigating the influence of spatial constraints on
conservation outcomes across large regions (Saura and Martinez-Millan, 2001), and relatively few
on the influence of spatial scale (Erasmus et al., 1999; Huber et al., 2010). The results of multiscale analyses conducted within the province highlight a lack of guidance on the effect of spatial
scale on SCP (Johnson et al., 2004; Wade et al., 2011), but draw no conclusions about its effect.
Our understanding of the magnitude of biodiversity loss depends on the scale at which it is
measured (McGill et al., 2015). Therefore, understanding the relationship between these spatial
variables and SCP is critical to the success of biodiversity conservation (Levin et al., 2016).
EFFICIENT & EQUITABLE CONSERVATION
The urgent need for conservation comes into conflict with competing interested on the land base.
Resource-dependent communities, such as those that rely on timber harvest, agriculture, fisheries,
etc, can be put at a disadvantage when land is slated into conservation. For this reason, as well as
those outlined in the previous section, there is a profound lack of clarity for conservation planners
on the optimal planning scale.
One way that conservation planners attempt to grapple with this issue is by pursuing maximum
efficiency through the SCP process. Site-selection algorithms used to perform SCP identify highly
efficient protected area solutions through complementarity (Kukkala and Moilanen, 2013). In most
-efficiency. Therefore, efficiency is high when the
conservation features of interest can be covered by a small number of sites, or more accurately,
the minimum necessary land area. Conservation efforts that are highly efficient tend to be more

17

socially popular, and therefore have greater chance of implementation (Game et al., 2011).
Efficient protected area networks are viewed as less costly, as they typically have lower overall
acquisition costs, have lower management costs (in terms of the number of land managers needed,
hours necessary to monitor land, access and road maintenance, etc), and reduce economic loss
from other land use interests (Possingham et al., 2006).
It is however possible to be overly focused on the algorithmic results and neglect the social context
on the land. Though biodiversity loss and the eradication of poverty are considered integrated
problems, historical conservation efforts have often come in direct conflict with economic
opportunity, exacerbating disenfranchisement (Adams et al., 2004; Miller et al., 2012). In many
cases, strict protectionism, where land is managed in isolation from human activity, has proven
futile and counterproductive (Adams et al., 2004; Roe, 2008; Wells and McShane, 2004). As
human and social factors greatly impact conservation success, it is important to quantify the equity

Aichi Target 11, protected area designation should recognize these effects, limit the procedural
harm, and accordingly distribute protected areas and enact any necessary mitigation. Conservation
ation of equity effects (Kark
Community support greatly increases success in achieving conservation objectives;
it is therefore critical to weigh the potential positive (ecosystem services, reduced human-wildlife
conflict, employment) and negative (reduced access to resources, management costs) social
impacts created by designating a protected area (Franks et al., 2018).
It is clear that planning at too broad a scale can sacrifice equity for the sake of efficiency. It is also
probable that planning at small scales carries a risk that a large amount of low conservation value

18

land will be selected to meet targets. This research investigates the intersection of equity:efficiency
tradeoffs and how they interact with scale and grouping unit type.
HYPOTHESIS
My research aims to explore: 1) How spatial constraints affect the outputs of algorithmic
optimization in systematic conservation planning, 2) What implications exist for human equity,
and for efficiency in biodiversity conservation, and 3) Whether there is there a balance of spatial
scale and extent that is best at achieving both equity and efficiency.
In this study, I analyze the spatial concordance, total area protected, and distribution of priority
areas between systematic conservation plans produced using administrative and ecological
boundaries as grouping units. I expect to find that there are tradeoffs between these measures
associated with the different spatial constraints. I hypothesize that there will be higher efficiency
but lower equity when spatial constraints are relaxed. Inversely, there will be higher land
requirements with higher equity when constraints are imposed. I further hypothesize that while
administrative units will contain clustered pockets of biodiversity, ecological units will be more
comprehensive in terms of capturing biodiversity.
RESEARCH DESIGN
The questions which guided the design of this MNRES research project research are: (1) Do the
boundaries employed in systematic conservation planning affect the areas selected for
conservation? (2) How could these difference affect the efficiency of conservation planning? (3)
How might these differences affect the equity in resource-dependent communities across the
province? And, (4) What insights can be gleaned from these analyses for CAP-BC users and other
conservation planners?

19

To answer the first question, I researched the existing literature around multi-scale analyses, and
the effects of spatial scale and extent on conservation planning. Having found little consensus on
the matter, I designed an analysis which varies the size and type of grouping unit for optimizations
in BC, and measures the efficiency and equity of the solutions to address the second and third
questions. The results of the second analysis will provide CAP-BC users with best-use information
for those datasets.
Thus, the main objectives of this MNRES research project research are to; (1) demonstrate the
potential incongruences of SCP planning conducted with inconsistent spatial boundaries, (2)
quantify the land use efficiency resulting from different spatial designs, (3) qualify the equity
effects of assigning heavier conservation priorities to fewer communities, and (4) make this
information readily available to users of these datasets through CAP-BC.
Table 1. 2
Questions, objectives, and methods for achieving them in this research, by chapter.
Description

Question

Objective

Method

Analysis where the
size and type of
grouping unit is
varied for SCP
optimizations in
BC.

Do the spatial
constraints
employed in SCP
affect the areas
selected for
conservation?

Demonstrate the
potential
incongruences of
SCP conducted with
varying spatial
constraints.

Measured the overlap of
the solutions to quantify
congruence.

How could these
affect the efficiency
of SCP?

Quantify the land use Measured and compared
efficiency resulting
the land area required
from different spatial by each of the solutions.
designs.

How might these
differences affect
the equity in
resource-dependent

Qualify the equity
effects of assigning
heavier conservation
priorities to fewer
communities.

Measured and compared
the relative land area
required by each of the
solutions per political /
community unit.

20

communities across
BC?
What insights can
be gleaned from
these analyses for
CAP-BC users and
other conservation
planners?

Make this
information readily
available to users of
these datasets
through CAP-BC.

Include the results in the
documentation of each
dataset when they are
incorporated into CAPBC.

21

2. Impact of Spatial Constraints on Systematic Conservation Planning Outcomes
2.1. INTRODUCTION
This analysis uses SCP to explore the relationship between spatial scale and extent on the
efficiency and equity of protected area selection in British Columbia. I employ ecological and
administrative boundaries, each at a fine and coarse spatial scale, in five scenarios. I measure
efficiency as the land-use requirement to meet targets, and equity as the relative distribution of
potential land-use restrictions on communities. By varying the spatial constraints, I quantify the
effect of these factors on the efficiency and equity of the solutions while meeting conservation
targets. I analyze the results relative to each other and against a baseline solution for the full
province.
2.2. METHODS
OVERVIEW
To identify the spatial data needs of conservation planners in BC, I engaged 42 stakeholders from
the conservation community at the British Columbia Protected Areas Research Forum in
December 2018. In a guided workshop, the stakeholders derived a list of biodiversity data which
they felt were essential for doing meaningful conservation work in the province. This list was then
used to guide the selection of datasets for the CAP-BC web tool. From this stakeholder-identified
list, I additionally identified and compiled those datasets for which province-wide data could
feasibly be rendered for this analysis.
The analyses for this study were done using the prioritizr R-package decision-support technology
(Hanson et al., 2020). prioritizr uses ILP (Beyer et al., 2016) and cloud-based solvers, which
outperform previously-used simulated annealing techniques (Schuster et al., 2020, 2019) .

22

My aim is to identify efficiency in terms of conserving features; therefore, I employed featurebased conservation targets. These targets instructed prioritizr on the percentage of area to select
for each conservation feature when running scenario optimizations. As this study is not intended
as an operational conservation plan for BC, the targets used here are illustrational and do not reflect
actionable targets for the individual species used as features.
This analysis was conducted using three nested scales, utilizing two contrasting grouping units
(Table 1). The targets are constant across these scenarios, and all solutions must meet or exceed
the targets for all features. In scenarios that use multiple grouping units, the features are treated as
sub-features and the feature targets must be met within every grouping unit. Repeating the same
target-based analyses, subject to these spatial constraint variables, allowed me to identify how
initial extent and scaling decisions influence the relative priority assigned to conservation areas.
Table 2. 1
Spatial constraint for scenario analysis. The labels at the intersection of scale and grouping unit
are the extents used.
Scale
Grouping Units
Administrative
Ecological
Provincial
BC Provincial Boundary
Coarse
Natural Resource Regions
BC Ecoprovinces
Fine
Natural Resource Districts BC Ecoregions

The resulting solutions were analyzed for three elements. First, measuring how the differing
grouping units affect the selection of land areas in the solutions provides a metric of spatial
congruence between solutions. Second, measuring the amount of land area required by the largerscale solutions informs how fine-scale planning affects comparative land use efficiency. Third,
measuring how unevenly the burden of conservation may be distributed across communities shows
how social equity is affected by land use decisions.

23

STUDY AREA
I used BC as a case study to explore the effects of the spatial constraints on the outputs of SCP
optimization, employing provincially-relevant biodiversity features and targets. BC is an
ecologically diverse province (relative to other North American jurisdictions), influenced by the
coastal landscape to the west and the mountainous terrain traversing most of its landscape. It
wide-ranging carnivores and ungulates, unique
populations of fish, and numerous mesocarnivores (Austin and Eriksson, 2009). In particular, BC
is globally significant for its richness in large carnivore and ungulate species (Austin and Eriksson,
2009). Each of these factors make important biological, cultural, and economic contributions to
the province. BC is considered a relatively undisturbed landscape; 15.4% of the land area of the
province has been gazetted in protected areas

- Environmental

2016).
Conservation planning in BC is the purview of several NGOs, provincial ministries, local agencies,
and funding bodies. Each of these actors has their own mission and conservation priorities. For
these organizations to work collaboratively, it is important to understand how they can achieve
complementary objectives.
SELECTION AND DEVELOPMENT OF C ONSERVATION FEATURE DATA
The majority of the datasets used were retrieved from BC Data Catalogue and from the BC
Ministry of Forests, Lands, Natural Resource Operations & Rural Development. Songbird
suitability distribution data was retrieved from Audubon (Wilsey et al., 2019) and tree distribution
models from Natural Resources Canada (McKenney et al., 2011). A full table of data and sources
is listed in Appendix A.

24

Geospatial data was prepared in ESRI ArcMap 10.5.1, and the optimizations were scripted in the
R programming language and executed using the prioritizr script. All data used in this analysis
were set to a 1000m BC Albers (ESPG: 3005) planning unit grid. The spatial datasets for each
feature were clipped to the grouping units to create one feature data sub-set per grouping unit (e.g.,
one grizzly bear (Ursus arctos horribilis) layer for each ecoregion). The targets for each feature
data sub-set were set the same across all grouping units, and the prioritizr scenarios were run
treating each feature data sub-set as a unique feature. Prioritizr was instructed to meet all subfeature targets within each grouping unit. This method simultaneously optimizes each of the
grouping units, with identical feature data and targets.
The key biodiversity datasets used for this analysis include the spatial range of several species.
Most of the selected species have experienced >20% contractions of their historic range: bighorn
sheep (Ovis canadensis), woodland caribou (Rangifer tarandis caribou), elk, or wapiti (Cervus
canadensis), fisher (Martes pennanti), mountain goat (Oreamnos americanus), and grizzly bear
(Ursus arctos horribilis). These charismatic mammalian fauna are flagship species for different
ecosystems in the province; their inclusion helps select for ecological diversity (Olson et al., 2001;
Laliberte and Ripple, 2004). I developed and used spatial layers for Bull trout- (Salvelinus
confluentus) and Sockeye salmon- (Oncorhynchus nerka) bearing waterways. The species
included are all significant to First Nations whose traditional territories lie within BC (Austin et
al., 2008). I also used spatial data for BEC zones, songbirds, and tree species distributions in this
analysis. Each of these layers is described in detail below.
G ROUPING UNIT B OUNDARIES
The ecological grouping units used in this study are the BC Ecoregions and Ecoprovinces (Figure
2.1, left). The coarse-scale ecological grouping units used for this analysis are the BC

25

Ecoprovinces. This is the parent unit of the fine-scale BC Ecoregions; one Ecoprovince contains
multiple Ecoregions (Figure 1; Appendix B, Map 1). Ecoregions are the fine-spatial scale grouping
unit. They contain strong interactions of biodiversity and ecological processes. These Ecoregions
are a significant unit because they group biodiversity regionally rather than globally, and therefore
represent unique local conditions more accurately (Olson et al., 2001). Biodiverse regions of
Canada, for example, are generally species-poor compared to global biodiversity hotspots, but are
species-rich compared to neighboring areas. These ecological boundaries are commonly used in
conservation studies (Dinerstein et al., 2017; Fleishman, 2017; Woolmer et al., 2008).
The administrative grouping units are the provincial Natural Resource (NR) Districts and Regions
(Figure 2.1, right). The coarse-scale administrative grouping units used for this analysis are BC
NR Regions. These are the parent unit of the fine-scale NR Districts; one NR Region contains
multiple NR Districts (Figure 2; Appendix B) (GeoBC, 2018). I use NR Districts as the fine spatialscale administrative grouping units for this analysis. These are administrative areas established by
the Ministry of FLNRORD. NR District boundaries are designated by the Lieutenant Governor in
council and published as regulations, which establish the Ministry's management areas. These
administrative boundaries are one of several different grouping units used to make planning
decisions in the province.
To further consider the impact of scale, this study applied the BC provincial boundary as another
administrative boundary. This absolute unit provides a baseline for comparison with the grouping
unit scenarios.

26

Figure 2. 1 Ecological grouping units (left) including Ecoprovinces (outlined) and Ecoregions
(green tones), and administrative grouping units (right) including NR Districts (outlined) and NR
Regions (blue tones).

27

C OARSE -S CALE B IODIVERSITY F EATURES
V EGETATION
The provincial Biogeoclimatic Ecosystem Classification (BEC) zones (Krajina, 1959) were used
as a vegetation feature. The province is apportioned into these assemblages which contain similar
ecological processes and a broadly homogenous macroclimate (Forests and Environment, 1995).

This layer incorporates
spatially-contiguous ecological assemblages into this analysis, which is advantageous for
achieving targets. Including such elements of the physical landscape, which change slowly over
time, improves protected

-term persistence against biodiversity loss (Margules and

Pressey, 2000). The BEC System utilizes four levels of classification: zones, subzones, variants,
and phases. Zones are the highest level, representing the broad macroclimate and named for the
dominant tree species. Subzones are the mid-division, further delineating the zones by climate and
precipitation. Variants are further divisions of subzones, describing localized conditions that may
be wetter, cooler, drier, or hotter than other areas of the subzone. Phases describe changes in
variants that occur based on topography, such as grasslands or rain shadows (Pojar et al., 1987).
Zones are the only level used in this analysis, as they are most appropriate for the large extent of
the province.
F INE -S CALE B IODIVERSITY F EATURES
B IGHORN S HEEP

28

The species range of Bighorn sheep in BC spans several ecosystems. I included their distribution
in this analysis to facilitate selection for multiple habitats: temperate coniferous forests; temperate
grasslands, savannas, and shrublands; and deserts and xeric shrublands. Bighorn populations have
contracted toward the center of their historic range (Laliberte and Ripple, 2004). Due to previous
species declines and habitat fragmentation, this species is recovering, yet potentially vulnerable.
The bighorn sheep layer was developed by extracting the species habitat data from BC Wild
Mountain Sheep Registry Distribution (FLNRORD, 2019) and verified against the current range
for the species (Shackelford et al., 2018).
B ULL TROUT -B EARING W ATERWAYS
Bull trout are associated with high-elevation streams; as these fish are highly sensitive to riparian
disturbance and pollution, their inclusion aids in prioritizing healthy watersheds. The distribution
of this fish also overlaps with the habitat of Arctic grayling (Thymallus arcticus) and Brook trout
(Salvelinus fontinalis) (Hagen and Decker, 2011). Cold-water fish such as these are identified as
key indicator species for areas resilient to climate change (Isaak et al., 2015).
I developed a layer for Bull trout-bearing waterways using BC lakes and rivers as selectable
features. To be included in this layer, these water layers were required to satisfy one of the
following criteria: overlap with Wildlife Habitat Area
with Fisheries Sensitive Watershed

Bull trout (legal or proposed); overlap

Bull trout (legal or proposed); be within 1km (selected by

location) of Known BC Fish Observations and BC Fish Distributions

Bull trout. The final layer

was produced using these points to create line segments that run from the ocean to the maximum
extent of fish distribution on each river or lake where the fish have been inventoried (Mason, 2019,
pers. comm).

29

C ARIBOU
Woodland caribou are highly associated with alpine and subalpine habitats, as well as old, midelevation forest stands containing arboreal lichen communities (Johnson et al., 2004). I included
woodland caribou because it is a highly threatened ungulate, and its inclusion helps prioritize
selection of forest habitats with ecologically significant attributes.
I developed the caribou layer using the publicly-available Caribou Herd Locations for BC dataset.
four populations (Boreal, Northern Mountain, Central
Mountain, and Southern Mountain populations) present in BC, which range in conservation
concern from vulnerable to critically imperiled, and have been identified as a high conservation
priority within the province. These herds were grouped into four population-based Designatable
Units identified by the Committee on the Status of Endangered Wildlife in Canada (COSEWIC)
and recognized under the federal Species-at-Risk Act (SARA).
E LK
The persistence of elk in BC contributes to the province

-significant richness in ungulates

(Ministry of Environment, Lands and Parks, 2000). Elk have lost a large portion of their range in
regions of high human influence (Laliberte and Ripple, 2004). Decline in this species exacerbates
loss of predatory species, especially wolves and grizzly bears (Laliberte and Ripple, 2004). Elk
occupy a variety of habitats, though they are generally constrained to areas with shallow snow
pack. Two of the four subspecies of North American elk (Rocky Mountain and Roosevelt elk)
occur in BC.

30

The elk range layer is a generalized distribution are

current

range in North America (Shackelford et al., 2018). This layer was selected due to the lack of
higher-resolution data of their distribution.
FISHER
Fisher is a widespread but low-density species, and the only mesocarnivore included in this
analysis. Fisher avoid open areas and rely on structural elements of mature and old forests to meet
several of their life history requirements: mainly reproduction and natal denning. Thus, fisher are
vulnerable to land use changes, and their inclusion in conservation planning provides a measure
of sensitivity to disturbance as well as surrogate for the elements of mature and old forest structure
(Laliberte and Ripple, 2004).
The fisher range layer is an area polygon representing generalized distribution developed from the
current North American range (Shackelford et al., 2018). This layer was selected due to a
lack of higher-resolution distribution data for this species.
M OUNTAIN G OAT
Mountain Goats have most of their global range in BC, and therefore have global importance
within their provincial context. Mountain Goat range was included as a fine-filter conservation
target because of their high-elevation range and provincial status as a vulnerable (S3) species.
Mapping the extent of this species helps guide selection in temperate coniferous forests, boreal
forests and taiga, and tundra biomes (Laliberte and Ripple, 2004).
I developed two Mountain Goat layers using the current North American range (Shackelford et al.,
2018) for this species. The first layer uses the full distribution of the species as represented by this
range. The second layer represents the vulnerable, high-

31

Most of their habitat is above the tree line, identified at the mid-latitude of the province (51°N) as
being at 2,400m / 7,900ft. Therefore, I used latitude-adjusted elevation classes corresponding with
elevation classes 9 (6000-7000ft) and 10 (7000-13000ft) (Michalak et al., 2018) as a mask to
extract a more accurate range for this species. Both layers are used to prioritize land selection for
mountain goat; the inclusion of the specific high-elevation range ensured that some of that
vulnerable range must be selected in order to meet the feature target meaningfully.
G RIZZLY B EAR
Grizzly bear was selected as a fine-filter conservation target mainly due to its broad range. Grizzly
bears have omnivorous diets which require several differing habitat types, and therefore can
function as an umbrella species (Noss et al., 1996).
overlaps with a large number of co-occurring species; it is expected that the conservation of an
umbrella species also confers protection to these other species (Lambeck, 1997). Mapping the
extent of this species captures large, intact landscapes in several diverse ecosystems. Grizzly bears
have seen some population decline due to habitat conversion (Laliberte and Ripple, 2004) and are
provincially considered potentially vulnerable.
The grizzly bear layer was developed using the publicly available BC Grizzly Bear Habitat
Classification and Rating dataset (Hamilton et al., 2018), a measure of the availability of vegetation
habitat. The habitat suitability rakings 1-3

v

) were extracted to a

classified density layer to represent suitable grizzly bear habitat for this analysis.
S OCKEYE S ALMON - B EARING W ATERWAYS
Sockeye salmon are a widely-distributed and provincially important species. Despite being a
marine species, Sockeye salmon are useful for the selection of healthy terrestrial ecosystems.

32

These migratory fish are associated with both upstream and downstream ecological processes, and
are recognized as providing vital ecosystem services in the marine and riparian nutrient cycles
(Darimont et al., 2010).
I developed the layer for sockeye salmon-bearing waterways using BC lakes and rivers as
selectable features (Appendix 1). To be included in this layer, these water layers were required to
satisfy one of the following criteria: be contained within a watershed identified in Provincial Fish
Ranges Sockeye Salmon; be within 1km (selected by location) of a Known BC Fish Observations
and BC Fish Distributions

Sockeye Salmon. The final layer was produced using these points to

create line segments that run from the maximum extent of fish distribution (on each river or lake
where the fish have been inventoried) to the ocean (Mason, 2019, pers. comm).
S ONGBIRD D ISTRIBUTION
The current distributions of 242 species of songbirds (Wilsey et al., 2019) with suitable breedingbird habitat in BC were included to account for the relative importance of species where the extent
of their suitable habitat exceeds that of the study area. By incorporating these ranges, I can analyze
where areas may be over-emphasized as important because a proportionally small piece of their
range crosses into the study area.
These layers were extracted from a dataset of 389 North American bird species, with both
breeding- and nonbreeding- range and suitability. Only those with breeding-bird suitability
occurring within British Columbia were included from that larger set.
T REE S PECIES D ISTRIBUTION
The distributions of 233 tree species (McKenney et al., 2011) with ranges in British Columbia
were also included to represent extended ranges. Projected climate change scenarios suggest these

33

species will experience poleward and up-slope shifts within the century; prioritizing these species
for conservation helps inform forward-looking forest management and protection actions.
These layers were extracted from a dataset of 325 North American tree species to include only
those with present-day ranges in British Columbia.
SETTING CONSERVATION TARGETS
This analysis employs target-based (as opposed to area-based) conservation, as this approach is
often taken by conservation planners in BC. As conservation targets are numerical goals that
quantify the minimum amount of a particular biodiversity feature sought to be conserved
(Possingham et al., 2006), target-based conservation is a reserve-selection strategy that aims to
protect enough habitat for a suite of target species, vegetation types, and landforms (Carwardine
et al., 2009). The most common objective of target-based SCP is to solve a minimum-set problem,
in which an area is selected that meets all of the conservation targets in the fewest number of sites
(Margules and Pressey, 2000). Target-based conservation problems are designed to maximize
efficiency while meeting all set targets (Albuquerque and Beier, 2015). The ILP prioritizr Rpackage (Hanson et al., 2020) is used to solve minimum-set problems within individual grouping
units, described below (Scenario Analysis Structure).
Generally, the targets for each feature are assigned according to the most appropriate guidance
from the contemporary literature. For example, BC has set a 90% target for most herds of southern
mountain caribou (Ministry of Environment, 2013). Protection of 60% of bull trout range has been
used for bull trout populations at smaller scales, as it was identified as a target for aquatic focal
species representation in the Muskwa-Kechika Management Area, where the species is abundant
(Heinemeyer, 2004). However, these targets were derived to achieve habitat retention, and not for
ongoing management of protected areas. It would therefore by inappropriate to directly adopt them

34

for this analysis. Therefore, an adjusted target-setting method was employed to achieve
representation. These datasets and conservation targets are described below in Table 2.
A target for each feature was assigned based on a scale derived from the BC Conservation Status
rankings. The minimum terrestrial area target values of 10, 17, and 30% mirror the progression of
global conservation targets. In the Convention s
initial inception, the signing parties committed to securing 10% of the global land in protected

Target 11) (Convention on Biological Diversity, 2011, 2014). For the next set of global targets,
presently in review, an increase to 30% by 2030 has been established (Convention on Biological
Diversity, 2021). 1
Table 2. 2
Targets assigned to conservation features based on provincial conservation status.
Coarse-filter Feature
Status
BEC Zone: Boreal Altai Fescue Alpine
S5
BEC Zone: Bunchgrass
S2
BEC Zone: Boreal White and Black Spruce
S5
BEC Zone: Coastal Douglas fir
S2
BEC Zone: Coastal Mountain-heather Alpine
S5
BEC Zone: Coastal Western Hemlock
S5
BEC Zone: Engelmann Spruce -- Subalpine fir
S5
BEC Zone: Interior Cedar -- Hemlock
S5
BEC Zone: Interior Douglas fir
S3
BEC Zone: Interior Mountain-heather Alpine
S5
BEC Zone: Mountain Hemlock
S5
BEC Zone: Montane Spruce
S5
BEC Zone: Ponderosa Pine
S2S3
BEC Zone: Sub-Boreal Pine -- Spruce
S5
BEC Zone: Sub-Boreal Spruce
S5
BEC Zone: Spruce -- Willow -- Birch
S5
Fine-filter Feature
Status
Caribou - Southern Mountain Population
S1
Caribou - Central Mountain Population
S1S2

Target
10
30
10
30
10
10
10
10
17
10
10
10
30
10
10
10
Target
30
30

1
Achieving the 10%, 17%, or 30% target for features does not constitute the Aichi targets being met in BC, as the
Aichi targets are area-based, and this study utilizes feature-based conservation targets.

35

Caribou - Boreal
Caribou - Northern Mountain Population
Bighorn Sheep
Bull Trout
Elk
Fisher
Grizzly Bear
Mountain Goat
Mountain Goat: Threatened Range
Sockeye
Birds
Trees

S2?
S3
S3?
S3S4
S5
S3
S3?
S3
S3
S4

30
17
17
17
10
17
17
17
17
17
10
10
target.

4) features were given the moderate 17% target.
arget of 10% to reflect the
minimum terrestrial area target proposed by the CBD. Features with overlapping status
assignments (i.e., Bull Trout at S3S4) or which were denoted as uncertain (?) were adjusted
accordingly.
SCENARIO ANALYSIS STRUCTURE
F INE -S CALE S CENARIO
At the fine spatial scale, the BC Ecoregion and Natural Resource District grouping units were used
as spatial constraints. There are forty-six (46) BC Ecoregions compared to twenty-three (23) NR
Districts used in this scenario (Figure 2.2). A complete list of these grouping units is in Appendix
B. This fine-scale scenario resembles the landscape-scale considered by managers and
conservation practitioners, such as NTBC, BCPF, and CPAWS.
For this and all scenarios, the conservation targets were held constant (described in Table 2.2,
above) as the grouping units varied. For each scenario, the feature datasets were geospatially
apportioned into the corresponding spatial constraint units (here: each of the BC Ecoregions and

36

each of the Natural Resource Districts), allowing prioritizr to achieve the targets within each of
the grouping units. By utilizing a spatial constraint, the targets for each feature had to be met within
the grouping unit, e.g., 17% of range must be protected within each unit.
C OARSE -S CALE S CENARIO
At the coarse spatial scale, the BC Ecoprovinces and Natural Resource Region grouping units were
used as spatial constraints. Ten (10) BC Ecoprovinces are compared to eight (8) NR Regions in
this scenario (Figure 2.2). See Appendix B for the complete list of these grouping units. This
coarse-scale scenario allowed me to compare the fine-scale results against the kind of broader
analyses used by national NGOs and conservation planning initiatives.
The conservation targets were again held constant while the grouping units vary. The feature
datasets were correspondingly apportioned into each of the BC Ecoprovinces and Natural
Resource Regions, again allowing prioritizr to achieve the targets within each of the grouping
units.
P ROVINCIAL S CENARIO
At the provincial scale, I lifted all spatial constraints, and the prioritizr optimization ran across the
full extent of all the features to achieve the targets (Figure 2.2). The solution set from this analysis
provides a baseline for analysis of the fine- and coarse-scale scenarios. Additionally, the provincial
boundary is truly a larger administrative boundary, and there will be room for future discussion
regarding the implications of this constraint.

37

Figure 2. 2 Geographical grouping units for all 5 scenarios.
MEASURING EQUITY AND EFFICIENCY
For each scenario, I conducted a prioritizr optimization where the input data were separated by
grouping unit and treated as though they were individual features. The prioritizr algorithm then
solved to meet the targets for each feature iteratively within each grouping unit. The total area
needed to achieve conservation targets in each scenario is meant to be representative of the
combined spatial needs of the individual features (Svancara et al., 2005), but the priorities
identified in each solution set should differ. I used efficiency and equity as response variables. I
measured the efficiency of each scenario as the land area required to capture the conservation
features. I measured the equity of each scenarios as the comparative percentage of land area that
38

the conservation solution would remove from other possible land uses, within their respective
communities.
The three nested scales for scenario analysis allowed me to determine whether the system gains
efficiency as the constraints are released. E
a given solution set comes to meeting the optimal solution (Wiersma and Sleep, 2018). In this case,
highly efficient solution sets will meet the conservation targets with the lowest possible land use
requirement. In this way, greater efficiency may mean less area is removed from the timber harvest
land base or from other land uses; it also generally assumes a lower acquisition and management
cost. I measured efficiency as the percentage of the land area required (relative to total area per
grouping unit) to meet the set conservation targets.
Measuring equity is a method of recognizing on whom the burden of conservation falls. Equity
refers to the proportional availability (or conversely, the relative deprivation) of a resource
compared with its availability to others (Halpern et al., 2013). Areas with higher concentrations of
conservation priority areas are subject to the tradeoffs associated with restricting other land uses.
Communities within high priority conservation areas are subject to these restrictions and any
associated losses (commercial, cultural, etc).
To analyze equity, I began by overlaying the provincial Regional Districts (n = 61) (Figure 2.3),
the administrative boundary most closely associated with local communities in BC, over the results
of each scenario. Within each Regional District, I calculated the land area requirement by summing
the binary scenario solutions with Zonal Statistics in ESRI ArcMap. I used the resulting values to
calculate the percentage of the land area required within each Regional District, took that
percentage in each solution to create a histogram, and binned by 5% increments.

39

Figure 2. 4 Optimization results for all 5 scenarios, showing dissimilarity between results derived
from the different grouping units.

41

Figure 2. 5 (left) Plotting the number of cells selected against the number of scenarios those cells
were selected in shows an exponential decrease in overlap as the number of scenarios increases.
(right) Mapping the gradient of cells by number of scenarios in which they were selected shows
proportional selection concordance, as well as groupings of significant areas.
EFFECT OF GROUPING U NIT TYPE
Ecological grouping units gave more evenly distributed solutions, which is consistent with the
hypothesis, but required more cells than their respective administrative counterparts. Solutions
generated using administrative grouping units had denser clustering and required fewer cells. This
difference in area requirement is significant, as the solutions vary by thousands of square
kilometers. The efficiency-equity tradeoff is also apparent here; planning conducted using
administrative grouping units will require a lower percentage of land area, but the burden of
conservation is unevenly distributed. Resource-dependent communities in areas that are located in
the clustered solution areas may face difficulties under traditional, restrictive conservation
management plans.

42

EFFICIENCY
The land area requirement of each scenario, determined by the number of cells required to meet
the targets, increased as the scenarios became finer in scale. This confirms the hypothesis that
efficiency decreases as the number of grouping units increases (Table 2.3; Figure 2.6). There is a
clear relationship (R2 = 0.9658, p = 0.0002) between land area requirement and planning scale.

Notably, there is a relatively large (13.3%) increase in land area requirement between the coarsest
scale (1 grouping unit) when compared with the increase (6.3%) between the next three scales (8,
10, 23 grouping units). Further analysis may be able to determine an optimal number of planning
units for efficient conservation in BC.
Table 2. 3
Planning scenario outputs and total land area requirements.
Scenario
BC
NRR
EP
NRD
ER

# Grouping Units
1
8
10
23
46

Solution Area (km2)
117,992
133,637
138,829
142,052
156,252

% Total Area
12.23%
13.86%
14.39%
14.73%
16.20%

43

Figure 2. 6 Plotting the number of 1km2 cells required in each scenario solution against the
number of grouping units in their associated scenario shows that planning at smaller scales yields
less efficient solutions in terms of land area.

Figure 2. 7 Efficiency also scales with extent. Using larger grouping unit extents yielded more
efficient solution sets.

44

EQUITY
The equity results demonstrate a pattern that implies equity increases as the spatial scale decreases.
The finer-scale scenarios placed greater spatial constraints on the analysis, generally resulting in
greater equity across Regional Districts (Figures 2.7, 2.8). There is some discernable pattern of
results becoming more equitable as scale decreases, which is consistent with the hypothesis. The
finer-scale Ecoregion and NR district scenarios show the most equitable results, with the majority
of solutions clustering around 17% land area. The provincial scale scenario stands out as having
some of most stratified land area requirements across Regional Districts, with some requiring as
high as 65% of the land area. This finding is also consistent with the hypothesis, as the most
efficient solution is the most disproportionate.
Table 2. 4
Sample results (n = 6) from equity analysis of each scenario by regional district (n = 61),
showing the most dissimilar and most equitable solutions.

High
Dissimilarity
High Equity

Analysis Unit
Regional District
Highland
South Saanich
Cedar

% Land Area Required by Scenario
BC
NRR EP
NRD ER
77
19
55
26
9
62
7
0
16
2
10
57
21
17
62

Index Value
Standard Dev
0.251
0.230
0.217

Peace River
Helmcken
Range 1 Coast

17
0
15

0.011
0.008
0.008

16
0
15

17
2
15

16
0
14

19
1
13

45

Figure 2. 8 Land use requirements between the five scenarios based on the percentage of land
needed in each regional district to reach the solution.
EFFECT OF LONG-RANGING F EATURES
Using the bird and tree feature data layers, I analyzed whether some areas may have been overemphasized in the solution due to a proportionally small piece of their range crossing into the study
area, as compared to their range across North America. These features did not seem to drive the
solution. The results did not display bias towards bird species with only a small amount of their
range in BC. This is particularly evident for bird species such as Rock Pigeon, Veery, Western
Bluebird, and Western Kingbird (Figure 2.9).

46

Rock pigeon

Western bluebird

Veery

Figure 2. 9 Instances where a small portion of a bird's range occurs in BC (black) did not seem to
bias the cumulative results (red tones).
2.4. DISCUSSION
RESULTS AND IMPLICATIONS
(1) D O THE BOUNDARIES EMPLOYED IN SYSTEMATIC CONSERVATION PLANNING AFFECT
THE AREAS SELECTED FOR CONSERVATION ?

The scale and extent of planning units have a demonstrable effect on the outcomes of conservation
planning optimizations. Congruence and efficiency both scale with the number of grouping units
employed. The units most similar in extent (ecoprovinces and natural resource regions) showed
the most similar results (3.8% difference) in terms of land area requirement.
Based on the features and targets used in this analysis , it appears the smaller administrative
grouping units (NRDs) are most effective at achieving both efficiency and equity. When the
number of grouping units increases (ex. ERs), efficiency is sacrificed without gaining much more
equity. At the larger scales (BC, EPs, NRRs) there is high inequity without a significant
improvement in efficiency. This suggests that there may be an optimal number of planning units
for conservation planning in the province. As the 23 grouping units from NRDs falls just below

47

the efficiency curve, the optimal number may be closer to 25-30 units, bringing the balance closer
to the more equitable ER scenario.
When the scenario results are overlaid, the clustering of highly-selected cells suggests there are
important areas despite the overall dissimilarity between results. This analysis is only meant to
illustrate the concepts behind SCP, and not act as a conservation plan for BC. Still, further analysis
into the relationships of these clusters might give conservation planners commonalities to use as a
framework for navigating the challenges outlined by this study.
(2) H OW COULD THESE DIFFERENCE AFFECT THE EFFICIENCY OF CONSERVATION
PLANNING ?

The most efficient grouping unit is that which allows ILP the freedom to select those cells that
achieve the highest level of complementarity. This points to the value of coarse-scale planning,
which provides an overview of conservation priorities at the provincial scale. It also serves as a
reminder that planning at even the provincial scale employs an artificial, administrative boundary,
and that there is value in looking both within and beyond it. While achieving high complementarity
is cost efficient, conservation priorities must be kept in context, as areas which are a high priority
in BC might not make the cut if the analysis were national, continental, or global.
Achieving efficiency at the provincial scale is nonetheless meritable. Highly efficient scenarios
outline actionable priorities for the strongest protection mechanisms (ex. Park Act designations).
They indicate where effort and resources can best be spent to add the most ecologically valuable
and relevant land to the provincial network. The results also indicate diminishing returns when
planning for efficiency at fine-scales, illustrating that prioritizing efficiency is more appropriate
for coarse-scale planning needs. Despite the evident tradeoffs in efficiency with the use of finer

48

scales, it is noteworthy that all of the solution sets came in under the Aichi Target 11 minimum
threshold of 17% land area. This implies that there is room within the scope of provincial
conservation planning to increase the focus on biodiversity conservation. Further, with an expected
new target of at least 30% land area (Convention on Biological Diversity, 2021), there is significant
latitude for future conservation in the province to increase and expand feature targets.
(3) H OW MIGHT THESE DIFFERENCES AFFECT THE EQUITY IN RESOURCE - DEPENDENT
COMMUNITIES ACROSS THE PROVINCE ?

These results also illustrate that it is critical to keep social context in mind when conducting SCP
analyses. As discussed, conservation efforts demonstrably fail when human equity is left out of
the discussion until after the fact (Halpern et al., 2013; Wilhere, 2008). The results here
demonstrate that similar conservation outcomes can be achieved even with moderate equity
considerations. The inclusion of equity as an explicit planning objective can help minimize the
tradeoffs and shortfalls in conservation. Planning conducted at too fine of a scale could results in
lower-value lands being slated into conservation, unnecessarily impeding needed development
opportunities. For small communities, this could be the difference between having local
employment or needing to relocate to find those opportunities. Interestingly, this is similar to the
problem that can arise from overly coarse-scale planning, where an unequal distribution of the
conservation burden can unduly fall on few communities. These effects highlight a need for
planners to be mindful of blindly prioritizing equity without taking on-the-ground context into
account.
It would be insensitive to assume that protected area priorities, as identified through Western
scientific methods, are the only way of knowing the value of the land. Indigenous knowledge

49

systems have historically been left out of conservation conversations. By incorporating these ways
of knowing into conservation planning, planners could gain a more robust understanding of the
landscape and the communities who rely on and often steward it. Here ecological boundaries often
make sense, as administratively-derived boundaries may unknowingly divide places that are
integrally connected.
It is also worth acknowledging that the equity results are influenced by the number of features
present within each Regional District and the size of the Regional Districts themselves. This part
of the analysis is reliant upon the assumption that all RDs are created equal. Since British Columbia
has such a large land area with few major cities, it is not possible that the RDs are perfectly
comparable. There are likely RDs that might be appropriately allocated a higher conservation
burden to offset their larger footprint, such as the Cariboo RD, home of Prince George, the largest
city in the northern part of the province. This would produce a solution that is truly more equitable,
rather than simply equal.
(4) W HAT INSIGHTS CAN BE GLEANED FROM THESE ANALYSES FOR CAP-BC USERS AND
OTHER CONSERVATION PLANNERS ?

For conservation planners, the questions of when to apply what planning scale has been unclear,
despite carrying potentially large consequences. The results provide guidance around the
appropriate applications for different scales. Coarse-scale analyses are especially appropriate for
climate change-related conservation planning, especially for connectivity corridor identification
and migration-related applications. The coarse-scale results prioritize complementarity between
cost efficiency and biodiversity target-meeting. Broad ecological patterns are easily recognized
here, providing a big-picture view of biodiversity conservation opportunities. Fine-scale analyses

50

are appropriate for seeing how the priorities shift when humans are factored in. They give a clearer
picture of necessary adjustments and compromises that a biodiversityaccount for. Rather than being at odds, these findings are complementary, and can help guide SCP
practitioners going forward. The speed of the CAP-BC tool allows planners to compare the results
of multiple scenarios, including at differing scales, as I have done here. CAP-BC users are able to
run several iterations of a SCP problem and see the results based on multiple considerations. These
results showcase the novel flexibility and utility of the CAP-BC tool, and can act as a guideline
for planners to balance landscape- and regional-scale considerations within a single SCP
application.
FURTHER RESEARCH OPPORTUNITIES
Alternative analysis structures, such as maximum coverage or utility maximization, could be more
successful in finding solutions that are balanced in terms of conservation effectiveness, efficiency,
and equity (Di Minin and Moilanen, 2012; Moilanen and Arponen, 2011). Further analysis might
take the structure used here to evaluate the effect of grouping units on these SCP approaches.
Target-based conservation, such as that utilized here, is a prescriptive process that has been
criticized. Target amounts may be set arbitrarily, features that are critical to ecosystem function
may be excluded, and the approach does not account for socioeconomic factors (Carwardine et al.,
2009). Over-emphasized features may also drive solution results, as may be the case in this
research with regards to caribou in the northeastern corner of the province. Attempts to counteract
these shortfalls can lead to over-complicating the SCP analysis, potentially leading to inefficient
solutions (Di Minin and Moilanen, 2012). Target-related uncertainty is exacerbated by climate
change, and incorporating climate-projected data is complicated (Hannah et al., 2007; Robillard et
al., 2015). In this research, I attempt to mitigate most of these issues by carefully selecting target

51

features for their life-history characteristics, role in ecosystem function, and relative threat level.
Additionally, the use of ILP solvers such as prioritizr helps mitigate the limits of minimum-set
r efficiency (a

To further contextualize this analysis, it would be beneficial to review the scenario results while
using road density as a cost overlay. This cost imposition would increase the efficiency of the
solutions as it would force the selections to be made from a smaller land base. The effect on
efficiency would likely be greatest at the broadest scales, as transportation corridors are not evenly
distributed across BC. Similarly, imposing this cost would further stratify the equity results;
resource-dependent communities that are located nearer to transportation would be more heavily
impacted by land-use restrictions. This consequence may be alleviated by using the Canadian
Human Footprint as a cost layer instead. This would prioritize conservation further from
communities as well as transportation infrastructure, reducing land-use conflict.
Conservation planning is designed to create solutions that are complementary to existing protected
areas, by evaluating the existing protection of features and the improving the system until all
features are adequately protected (Hannah et al., 2007; Margules and Pressey, 2000) However, this
approach assumes that existing protected areas are maintaining species representation (Venter et
al., 2018). As this analysis was not designed to yield and actionable conservation plan for BC, I
opted not to lock existing protected areas into or out of the solutions. The results of the 5 scenarios
did not overlap substantially with existing PAs (14-16% of solution area). However, locking in
existing PAs would substantially reduce the land area required in each solution, and might affect
the equity:efficiency tradeoffs. Locking PAs out would also change the results; assuming that

52

existing PAs are representing the target features well, eliminating PAs might yield solutions that
all require more land area to achieve the same targets.

Figure 3. 1 Existing protected areas (black) overlaid with the results of the 5 scenarios (green).
The summed scenario overlay (red tones) shows the cells that were selected in 3, 4, and 5 of the
scenarios.
Further analysis could explore the comparative effectiveness of the datasets used in this research
as different surrogate groups. Historically, there has been a limited set of data available to represent
biodiversity. Conservation planners are then restricted to using those datasets, which tend to
consist of environmental surrogates alongside a few charismatic species (Grantham et al., 2010).
Due to data advances in recent years, SCP has seen the development of robust, fine-scale datasets
for entire taxa which span the continent, such as the bird and tree distribution layers used here
53

(McKenney et al., 2011; Stralberg et al., 2018; Wilsey et al., 2019). These datasets present an
opportunity to reevaluate the effectiveness of surrogate data and perhaps improve upon the
criticisms of the practice (Grantham et al., 2010). It would be possible to demonstrate how effective
biological surrogates are at standing in for larger distribution datasets by comparing SCP solutions
generated using the tree and bird distribution data against the other individual, more generalized
data layers. Such an analysis would further inform how best to use these datasets in the context of
the province.
SUMMARY
The study results are intended to identify how initial extent and scaling decisions influence the
relative priority assigned to conservation areas, carve pathways to overcoming the limitations in
SCP, and provide rationale for keeping landscape-scale planning embedded within regional-scale
analyses. The results of this analysis should inform planners and practitioners about the strengths
and weaknesses of spatial optimizations to achieve biodiversity conservation targets. As some
significant difference is demonstrable between the scenarios, future conservation plans will need
to carefully consider the boundary and scale selected for their potential impact on the prioritization
outcomes. Additionally, places with polarized results across the scenarios might necessitate a
review of conservation plans where the scale and boundary design may have biased the results and
subsequent protection and management actions.

54

3. Conclusion: Contributions of this Project
This research project is intended to evaluate some of the underlying assumptions in SCP, and
anticipate how they might affect the results of conservation planning in practice. Rather than act
as a critique, the results of these analyses are intended to illustrate potential limitations of SCP and
highlight opportunities for proactive mitigation of such issues. Though these studies are focused
on British Columbia, many of the principles explored are applicable for conservation practitioners
anywhere.
These analyses demonstrate how varying spatial constraints produce potential incongruences in
SCP solutions. This is an early-stage decision for conservation planners, and understanding its
impacts is important for creating robust conservation plans going forward. In addition, these results
highlight where existing protected areas may prove inadequate, which is illustrative for ongoing
management and stewardship of these areas. This study adds to a small existing body of literature
that explicitly explores the effects of spatial constraints on SCP. Additionally, I explore the
relationship between the congruence of these solutions, their efficiency in terms of land use, and
the consequential inequity that can result from disproportional land-use restrictions. I examine
these tradeoffs and open a door towards finding an optimal balance.
3.1 PROVINCIAL RELEVANCE
This analysis is timely and relevant for conservation planners within the resource management and

exceptional place. Humans derive incalculable benefits from its vast natural resources, but the
ever-increasing rate of extraction threatens ecosystem health, and the system could collapse if it is
not managed. The impacts of climate change only amplify the urgency of the biodiversity crisis in
BC, as it compounds the existing impacts of land conversion, habitat degradation and

55

fragmentation, and invasive species while also introducing new environmental stressors. For all
that is known about the biodiversity of BC, there is much that is still unknown about its present
state, and even more uncertainty about exactly how climate change will affect the system. These
shortfalls limit our ability to understand and react to the situation.
This research is part of an effort to inform the ongoing conservation and management processes in
the province, and to fill the existing information gap around conservation planning efforts. Because
of the diversity and spatial enormity of BC, it would be impossible to create a singular plan to
effectively protect all of its resources. Still, it is critical that smaller assessments are done in an
integrative manner. The results of this research provide context for selecting appropriate
management boundaries where possible, and developing more useful ones when needed.
Moreover, these results illustrate the need for collaborative and intentional conservation planning
that keeps the big picture in mind; they show how siloed conservation efforts contribute to both
inefficiency and inequity overall.
3.2 CLIMATE -ADAPTIVE CONSERVATION PLANNING IN BRITISH COLUMBIA
As the datasets collected for this research are being made available to conservation planners in BC
through the CAP-BC tool, it is important to contextualize their utility to end-users. The project
partners specifically requested information about the apparent strengths and weaknesses of the
data and process behind the tool, and the results of this study should provide a comprehensive
look. These results provide context and guidance for selecting appropriate spatial parameters for
provincial SCP, and showcase the quality and flexibility of the included datasets.
The direct input of project partners at The Nature Trust of BC, Canadian Parks and Wilderness
Society, and the BC Parks Foundation has steered this research and helped ensure that it has further

56

practical applications. These NGOs are committed to the conservation of high biodiversity land,
and deal directly with the process of identifying and protecting land that is as resilient to the twin
crises of climate change and biodiversity loss as possible. Through this collaboration, project
partners have provided valuable insight for developing meaningful, necessary, and practical
information to support conservation planning practitioners in BC. This is a critical component of
the project, as cooperative partnerships are more likely to result in equitable and lasting
conservation solutions.
The CAP-BC project is an opportunity project supported and funded by the Pacific Institute for
Climate Solutions (PICS). PICS opportunity projects support innovative approaches for climate
change mitigation and adaptation. As conservation planners seek climate-adaptive SCP strategies
that are both comprehensive and manageable, there is a need for technology that can keep the pace.
By developing a climate-informed SCP tool that is accessible, fast, and flexible to users with less
intensive training, the CAP-BC project is helping to bring SCP up to speed, making it a more
viable tool in a rapidly changing world.
3.3 OTHER EFFECTIVE CONSERVATION MEASURES
Here we have also underscored the need for balanced solutions to both biodiversity protection and
accommodation of human resource needs. Multiple objectives can only be successfully achieved
when they are accounted for at the outset of conservation efforts. If we only begin to consider the
full context of a conservation plan at steps 5 and 6 of the SCP process, those efforts are destined
to fall short. Using the results of this research, we can better the practice of holistic conservation
planning and work towards refine, effective, and actionable conservation solutions.

57

Protected area designation is only one tool for effectively managing land, biodiversity, and
By 2020, at least 17% of terrestrial and inland water, and 10%
of coastal and marine areas, especially areas of particular imp
conserved through

and other effective area-based conservation measures

(Convention on Biological Diversity, 2010)
of other effective conservation measures (OECMs),
Protected Area, which is governed and managed in ways that achieve positive and sustained longterm outcomes for the in situ conservation of biodiversity, with associated ecosystem functions
and services and where applicable, cultural, spiritual, socioeconomic, and other locally relevant
values (Donald et al., 2019)
and Conserved Areas (IPCAs), community forests, elective private land conservation, and marine
protected areas, among others.
Many of these OECMs allow for more direct management of resources by those who use and
coexist with them, facilitating the achievement of multiple, more equitable objectives, including
the safeguarding of biodiversity. By better leveraging OECMs, we can advance a triple bottom
line of equity, efficiency, and effectiveness for conservation.
3.4 CONCLUSION
In conclusion, this research project contributes to the advancement and improvement of systematic
conservation planning, which should in turn create protected areas that robustly safeguard
biodiversity. It provides information and resources for a tangible conservation tool, which will be
used by conservation planners in BC to identify on-the-ground conservation opportunities for the

58

my intended career in large-landscape land conservation, and highlights the considerations that
will be critical for conservation planning in the near future.

59

4. References
Adams, William.M., Aveling, R., Brockington, D., Dickson, B., Elliott, J., Hutton, J., Roe, D.,
Vira, B., Wolmer, W., 2004. Biodiversity Conservation and the Eradication of Poverty.
Science 306, 1146 1149. https://doi.org/10.1126/science.1097920
Albuquerque, F., Beier, P., 2015. Rarity-Weighted Richness: A Simple and Reliable Alternative
to Integer Programming and Heuristic Algorithms for Minimum Set and Maximum
Coverage Problems in Conservation Planning. PLOS ONE 10, e0119905.
https://doi.org/10.1371/journal.pone.0119905
in British Columbia. Biodiversity BC.
Austin, M.A., Eriksson, A., 2009. The Biodiversity Atlas of British Columbia. Biodiversity BC.
Ball, I.R., Possingham, H.P., Watts, M., 2009. Marxan and relatives: software for spatial
conservation prioritisation. Spat. Conserv. Prioritisation Quant. Methods Comput. Tools
185 195.
Bellard, C., Bertelsmeier, C., Leadley, P., Thuiller, W., Courchamp, F., 2012. Impacts of climate
change on the future of biodiversity. Ecol. Lett. 15, 365 377.
Belote, R.T., Dietz, M.S., McKinley, P.S., Carlson, A.A., Carroll, C., Jenkins, C.N., Urban, D.L.,
Fullman, T.J., Leppi, J.C., Aplet, G.H., 2017. Mapping Conservation Strategies under a
Changing Climate. BioScience 67, 494 497. https://doi.org/10.1093/biosci/bix028
Beyer, H.L., Dujardin, Y., Watts, M.E., Possingham, H.P., 2016. Solving conservation planning
problems with integer linear programming. Ecol. Model. 328, 14 22.
https://doi.org/10.1016/j.ecolmodel.2016.02.005
Boakes, E.H., Mace, G.M., McGowan, P.J.K., Fuller, R.A., 2010. Extreme contagion in global
habitat
clearance.
Proc.
R.
Soc.
B
Biol.
Sci.
277,
1081 1085.
https://doi.org/10.1098/rspb.2009.1771
Burley, F.W., 1988. Monitoring biological diversity for setting priorities in conservation.
Biodivers. Natl. Acad. Press Wash. DC 227 230.
Cadotte, M.W., Barlow, J., Nuñez, M.A., Pettorelli, N., Stephens, P.A., 2017. Solving
environmental problems in the Anthropocene: the need to bring novel theoretical advances
into the applied ecology fold. J. Appl. Ecol. 54, 1 6. https://doi.org/10.1111/13652664.12855
Conserv. Biol. 13, 805 814. https://doi.org/10.1046/j.1523-1739.1999.98338.x

60

Carroll, C., Parks, S.A., Dobrowski, S.Z., Roberts, D.R., 2018. Climatic, topographic, and
anthropogenic factors determine connectivity between current and future climate analogs
in North America. Glob. Change Biol. 24, 5318 5331. https://doi.org/10.1111/gcb.14373
Carroll, C., Roberts, D.R., Michalak, J.L., Lawler, J.J., Nielsen, S.E., Stralberg, D., Hamann, A.,
Mcrae, B.H., Wang, T., 2017. Scale-dependent complementarity of climatic velocity and
environmental diversity for identifying priority areas for conservation under climate
change. Glob. Change Biol. 23, 4508 4520. https://doi.org/10.1111/gcb.13679
Carwardine, J., Klein, C.J., Wilson, K.A., Pressey, R.L., Possingham, H.P., 2009. Hitting the target
and missing the point: target-based conservation planning in context. Conserv. Lett. 2, 4
11. https://doi.org/10.1111/j.1755-263X.2008.00042.x
Conlisk, E., Syphard, A.D., Franklin, J., Flint, L., Flint, A., Regan, H., 2013. Uncertainty in
assessing the impacts of global change with coupled dynamic species distribution and
population models. Glob. Change Biol. 19, 858 869. https://doi.org/10.1111/gcb.12090
Convention on Biological Diversity, 2021. First draft of the post-2020 global biodiversity
framework.
Convention on Biological Diversity, 2020. Zero draft of the post-2020 global biodiversity
framework.
Convention on Biological Diversity, 2014. Global Biodiversity Outlook 4: a mid-term assessment
of progress towards the implementation of the strategic plan for biodiversity 2011-2020,
in: Convention on Biological Diversity (CBD), Montreal.
Convention on Biological Diversity, 2011. COP 10 Decision X/2. Strategic Plan for Biodiversity
Convention on Biological Diversity, 2010. The strategic plan for biodiversity 2011 2020 and the
Aichi biodiversity targets. Convention on Biological Diversity Montreal.
Dabiri, Z., Blaschke, T., 2019. Scale matters: a survey of the concepts of scale used in spatial
disciplines.
Eur.
J.
Remote
Sens.
52,
419 434.
https://doi.org/10.1080/22797254.2019.1626291
Darimont, C.T., Bryan, H.M., Carlson, S.M., Hocking, M.D., MacDuffee, M., Paquet, P.C., Price,
M.H.H., Reimchen, T.E., Reynolds, J.D., Wilmers, C.C., 2010. Salmon for terrestrial
protected
areas:
Salmon
for
Parks.
Conserv.
Lett.
3,
379 389.
https://doi.org/10.1111/j.1755-263X.2010.00145.x
Dearden, P., Rollins, R., Needham, M., 2015. Parks and Protected Areas in Canada, 4th ed. Oxford
University Press.
Di Minin, E., Moilanen, A., 2012. Empirical evidence for reduced protection levels across
biodiversity features from target-based conservation planning. Biol. Conserv. 153, 187
191. https://doi.org/10.1016/j.biocon.2012.04.015

61

Dinerstein, E., Olson, D., Joshi, A., Vynne, C., Burgess, N.D., Wikramanayake, E., Hahn, N.,
Palminteri, S., Hedao, P., Noss, R., Hansen, M., Locke, H., Ellis, E.C., Jones, B., Barber,
C.V., Hayes, R., Kormos, C., Martin, V., Crist, E., Sechrest, W., Price, L., Baillie, J.E.M.,
Weeden, D., Suckling, K., Davis, C., Sizer, N., Moore, R., Thau, D., Birch, T., Potapov,
P., Turubanova, S., Tyukavina, A., de Souza, N., Pintea, L., Brito, J.C., Llewellyn, O.A.,
Miller, A.G., Patzelt, A., Ghazanfar, S.A., Timberlake, J., Klöser, H., Shennan-Farpón, Y.,
Kindt, R., Lillesø, J.-P.B., van Breugel, P., Graudal, L., Voge, M., Al-Shammari, K.F.,
Saleem, M., 2017. An Ecoregion-Based Approach to Protecting Half the Terrestrial Realm.
BioScience 67, 534 545. https://doi.org/10.1093/biosci/bix014
Donald, P.F., Buchanan, G.M., Balmford, A., Bingham, H., Couturier, A.R., de la Rosa Jr., G.E.,
Gacheru, P., Herzog, S.K., Jathar, G., Kingston, N., Marnewick, D., Maurer, G., Reaney,
L., Shmygaleva, T., Sklyarenko, S., Stevens, C.M.D., Butchart, S.H.M., 2019. The
v. Lett. 12,
e12659. https://doi.org/10.1111/conl.12659
Can. Prot. Conserv. Areas Database. URL https://www.canada.ca/en/environmentclimate-change/services/environmental-indicators/conserved-areas.html
(accessed
6.24.20).
Environmental Law Centre, 2013. An Old Growth Protection Act for British Columbia [WWW
Document]. URL http://www.elc.uvic.ca/2013-oldgrowthprotectionact/ (accessed
6.24.20).
Environmental Reporting BC, 2016. rotected Lands & Waters in B.C. State of Environment
Reporting,
Ministry
of
Environment
[WWW
Document].
URL
http://www.env.gov.bc.ca/soe/indicators/land/protected-lands-and-waters.html (accessed
6.24.20).
Fields, C.B., Mortsch, L.D., Brklacich, M., Forbes, D.L., Kovacs, P., Patz, J.A., Running, S.W.,
Scott, M.J., 2007. Climate Change 2007: Impacts, Adaptation and Vulnerability.
Contribution of Working Group II to the Fourth Assessment Report of the
Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge,
UK, North America.
Fleishman, E., 2017. Methods for Assessment of Species Richness and Occupancy Across Space,
Time, Taxonomic Groups, and Ecoregions 107.
Forests, B.C.M. of, Environment, B.C., 1995. Biodiversity guidebook. University of British
Columbia Press.
Franks, P., Booker, F., Roe, D., 2018. Understanding and assessing equity in protected area
conservation. IEED Issue Pap. 40.

62

Game, E.T., Lipsett-Moore, G., Hamilton, R., Peterson, N., Kereseka, J., Atu, W., Watts, M.,
Possingham, H., 2011. Informed opportunism for conservation planning in the Solomon
Islands. Conserv. Lett. 4, 38 46. https://doi.org/10.1111/j.1755-263X.2010.00140.x
Gaston, K.J., Rodrigues, A.S.L., 2003. Reserve Selection in Regions with Poor Biological Data.
Conserv. Biol. 17, 188 195. https://doi.org/10.1046/j.1523-1739.2003.01268.x
GeoBC, 2018. Natural Resource Regions and Districts.
Gilman, S.E., Urban, M.C., Tewksbury, J., Gilchrist, G.W., Holt, R.D., 2010. A framework for
community interactions under climate change. Trends Ecol. Evol. 25, 325 331.
https://doi.org/10.1016/j.tree.2010.03.002
Grantham, H.S., Pressey, R.L., Wells, J.A., Beattie, A.J., 2010. Effectiveness of Biodiversity
Surrogates for Conservation Planning: Different Measures of Effectiveness Generate a
Kaleidoscope of Variation. PLoS ONE 5. https://doi.org/10.1371/journal.pone.0011430
Groves, C., Conservancy, C.T.N., Beck, M.W., Higgins, J.V., 2003. Drafting a Conservation
sland Press.
Groves, C.R., Game, E.T., Anderson, M.G., Cross, M., Enquist, C., Ferdaña, Z., Girvetz, E.,
Gondor, A., Hall, K.R., Higgins, J., Marshall, R., Popper, K., Schill, S., Shafer, S.L., 2012.
Incorporating climate change into systematic conservation planning. Biodivers. Conserv.
21, 1651 1671. https://doi.org/10.1007/s10531-012-0269-3
Guerrero, A.M., McALLISTER, R.R.J., Corcoran, J., Wilson, K.A., 2013. Scale Mismatches,
Conservation Planning, and the Value of Social-Network Analyses. Conserv. Biol. 27, 35
44. https://doi.org/10.1111/j.1523-1739.2012.01964.x
Hagen, J., Decker, S., 2011. The Status of Bull Trout in British Columbia: A Synthesis of Available
Distribution, Abundance, Trend, and Threat Information. Rep. Prep. MOE 114.
Halpern, B.S., Klein, C.J., Brown, C.J., Beger, M., Grantham, H.S., Mangubhai, S., Ruckelshaus,
M., Tulloch, V.J., Watts, M., White, C., Possingham, H.P., 2013. Achieving the triple
bottom line in the face of inherent trade-offs among social equity, economic return, and
conservation.
Proc.
Natl.
Acad.
Sci.
110,
6229 6234.
https://doi.org/10.1073/pnas.1217689110
Hamann, A., Wang, T., 2006. Potential Effects of Climate Change on Ecosystem and Tree Species
Distribution in British Columbia. Ecology 87, 2773 2786. https://doi.org/10.1890/00129658(2006)87[2773:PEOCCO]2.0.CO;2
Hamilton, A., Demarchi, Diane A, Demarchi, Dennis A, 2018. British Columbia Grizly Bear
Habitat Classification and Rating. Ministry of Environment and Climate Change Strategy,
Ecosystems Branch, Victoria, BC.

63

Hannah, L., Midgley, G., Andelman, S., Araújo, M., Hughes, G., Martinez-Meyer, E., Pearson, R.,
Williams, P., 2007. Protected area needs in a changing climate. Front. Ecol. Environ. 5,
131 138.
Hannah, L., Midgley, G.F., Millar, D., 2002. Climate change-integrated conservation strategies.
Glob. Ecol. Biogeogr. 11, 485 495. https://doi.org/10.1046/j.1466-822X.2002.00306.x
H.P., 2020. prioritizr: systematic conservation prioritization in R.
Hebda, R.J., 1997. Impact of climate change on biogeoclimatic zones of British Columbia and
Yukon. Responding Glob. Clim. Change Br. Columbia Yukon 1, 363.
Heinemeyer, K., 2004. Conservation area design for the Muskwa-Kechika Management Area
(MKMA). Vol. 1. Final report. British Columbia Ministry of Sustainable Resource
Management, Victoria. Br. Columbia Can.
Hermoso, V., Januchowski-Hartley, S.R., Pressey, R.L., 2013a. When the suit does not fit
biodiversity: Loose surrogates compromise the achievement of conservation goals. Biol.
Conserv. 159, 197 205. https://doi.org/10.1016/j.biocon.2012.11.026
Hermoso, V., Kennard, M.J., Linke, S., 2013b. Data Acquisition for Conservation Assessments:
Is
the
Effort
Worth
It?
PLOS
ONE
8,
e59662.
https://doi.org/10.1371/journal.pone.0059662
Huber, P.R., Greco, S.E., Thorne, J.H., 2010. Spatial scale effects on conservation network design:
trade-offs and omissions in regional versus local scale planning. Landsc. Ecol. 25, 683
695. https://doi.org/10.1007/s10980-010-9447-4
Isaak, D.J., Young, M.K., Nagel, D.E., Horan, D.L., Groce, M.C., 2015. The cold-water climate
shield: delineating refugia for preserving salmonid fishes through the 21st century. Glob.
Change Biol. 21, 2540 2553. https://doi.org/10.1111/gcb.12879
Johnson, C.J., Seip, D.R., Boyce, M.S., 2004. A quantitative approach to conservation planning:
using resource selection functions to map the distribution of mountain caribou at multiple
spatial scales. J. Appl. Ecol. 41, 238 251. https://doi.org/10.1111/j.00218901.2004.00899.x
Jones, K.R., Venter, O., Fuller, R.A., Allan, J.R., Maxwell, S.L., Negret, P.J., Watson, J.E.M.,
2018. One-third of global protected land is under intense human pressure. Science 360,
788 791. https://doi.org/10.1126/science.aap9565
Jones, K.R., Watson, J.E.M., Possingham, H.P., Klein, C.J., 2016. Incorporating climate change
into spatial conservation prioritisation: A review. Biol. Conserv. 194, 121 130.
https://doi.org/10.1016/j.biocon.2015.12.008
Keppel, G., Warde
Change Biol. 18, 2389 2391. https://doi.org/10.1111/j.1365-2486.2012.02729.x

64

Krajina, V.J., 1959. Bioclimatic zones in British Columbia.
Kukkala, A.S., Moilanen, A., 2013. Core concepts of spatial prioritisation in systematic
conservation planning. Biol. Rev. 88, 443 464. https://doi.org/10.1111/brv.12008
Laliberte, A.S., Ripple, W.J., 2004. Range Contractions of North American Carnivores and
Ungulates.
BioScience
54,
123 138.
https://doi.org/10.1641/00063568(2004)054[0123:RCONAC]2.0.CO;2
Lambeck, R.J., 1997. Focal Species: A Multi-Species Umbrella for Nature Conservation. Conserv.
Biol. 11, 849 856. https://doi.org/10.1046/j.1523-1739.1997.96319.x
Langford, W.T., Gordon, A., Bastin, L., Bekessy, S.A., White, M.D., Newell, G., 2011. Raising
the bar for systematic conservation planning. Trends Ecol. Evol. 26, 634 640.
https://doi.org/10.1016/j.tree.2011.08.001
Leadley, P., Pereira, H., Alkemade, R., Fernandez-Manjarres, J., Proenca, V., Scharlemann, J.,
2010. Biodiversity Scenarios: Projections of 21st Century Change in Biodiversity, and
3. Secr. Conv. Biol. Divers. Technical Series no. 50., 1 132.
Margules, C.R., Pressey, R.L., 2000. Systematic conservation planning. Nature 405, 243 253.
https://doi.org/10.1038/35012251
Margules, C.R., Pressey, R.L., Williams, P.H., 2002. Representing biodiversity: Data and
procedures for identifying priority areas for conservation. J. Biosci. 27, 309 326.
https://doi.org/10.1007/BF02704962
Mason, B., 2019. BC Fish Data.
McKenney, D.W., Pedlar, J.H., Rood, R.B., Price, D., 2011. Revisiting projected shifts in the
climate envelopes of North American trees using updated general circulation models. Glob.
Change Biol. 17, 2720 2730. https://doi.org/10.1111/j.1365-2486.2011.02413.x
Miller, B.W., Caplow, S.C., Leslie, P.W., 2012. Feedbacks between Conservation and SocialEcological Systems. Conserv. Biol. 26, 218 227. https://doi.org/10.1111/j.15231739.2012.01823.x
Ministry of Environment, Lands and Parks, 2000. Elk in British Columbia. Province of British
Columbia.
Moilanen, A., Arponen, A., 2011. Setting conservation targets under budgetary constraints. Biol.
Conserv. 144, 650 653. https://doi.org/10.1016/j.biocon.2010.09.006
Moilanen, A., Wilson, K., Possingham, H., 2009. Spatial conservation prioritization: quantitative
methods and computational tools. Oxford University Press.

65

vertebrates as surrogates for selecting conservation areas in a biodiversity hotspot in
Mexico. Conserv. Sci. Pract. 1, e12. https://doi.org/10.1111/csp2.12
National Geographic Society, 2020. Boundary | National Geographic Society [WWW Document].
URL https://www.nationalgeographic.org/encyclopedia/boundary/ (accessed 6.26.20).
Nature Conservancy Canada, 2010. Central Interior Ecoregional Assessment Map Volume.
Conservation Blueprints.
Noss, R.F., Cooperri
Island Press.
Noss, R.F., Quigley, H.B., Hornocker, M.G., Merrill, T., Paquet, P.C., 1996. Conservation biology
and carnivore conservation in the Rocky Mountains. Conserv. Biol. 10, 949 963.
Olson, D.M., Dinerstein, E., Wikramanayake, E.D., Burgess, N.D., Powell, G.V.N., Underwood,
Ricketts, T.H., Kura, Y., Lamoreux, J.F., Wettengel, W.W., Hedao, P., Kassem, K.R.,
2001. Terrestrial Ecoregions of the World: A New Map of Life on EarthA new global map
of terrestrial ecoregions provides an innovative tool for conserving biodiversity.
BioScience
51,
933 938.
https://doi.org/10.1641/00063568(2001)051[0933:TEOTWA]2.0.CO;2
https://doi.org/10.1080/13658816.2015.1053890

1933.

Pachauri, R.K., Reisinger, A., 2008. Climate change 2007. Synthesis report. Contribution of
Working Groups I, II and III to the fourth assessment report. Intergovernmental Panel on
Climate Change, Geneva, Switzerland.
Pacific Institute for Climate Solutions, 2020. Climate[WWW Document]. PICS Res. Proj. URL http://pics.uvic.ca/projects/climate-adaptiveplanning-bcs-protected-areas (accessed 6.26.20).
Pojar, J., Klinka, K., Meidinger, D.V., 1987. Biogeoclimatic ecosystem classification in British
Columbia. For. Ecol. Manag. 22, 119 154. https://doi.org/10.1016/0378-1127(87)901009
Possingham, H., Wilson, K., Andelman, S.A., Vynne, C.H., 2006. Protected areas: Goals,
limitations, and design, in: Groom, M.J., Meffe, G.K., Carroll, R.C. (Eds.), Principles of
Conservation Biology.[ 3rd Ed.]. Sinauer Associates, Sunderland, Mass, pp. 507 549.
Pressey, R.L., 2004. Conservation Planning and Biodiversity: Assembling the Best Data for the
Job. Conserv. Biol. 18, 1677 1681. https://doi.org/10.1111/j.1523-1739.2004.00434.x

66

Pressey, R.L., Cabeza, M., Watts, M.E., Cowling, R.M., Wilson, K.A., 2007. Conservation
planning in a changing world. Trends Ecol. Evol. 22, 583 592.
https://doi.org/10.1016/j.tree.2007.10.001
Protected Lands & Waters - Environmental Reporting BC [WWW Document], 2016. URL
http://www.env.gov.bc.ca/soe/indicators/land/protected-lands-and-waters.html (accessed
2.18.20).
America?Patterns of species richness and the utility of indicator taxa for conservation.
BioScience 49, 369 381. https://doi.org/10.2307/1313630
Robillard, C.M., Coristine, L.E., Soares, R.N., Kerr, J.T., 2015. Facilitating climate-changeinduced range shifts across continental land-use barriers. Conserv. Biol. 29, 1586 1595.
https://doi.org/10.1111/cobi.12556
Rodrigues, A.S.L., Brooks, T.M., 2007. Shortcuts for Biodiversity Conservation Planning: The
Effectiveness of Surrogates. Annu. Rev. Ecol. Evol. Syst. 38, 713 737.
https://doi.org/10.1146/annurev.ecolsys.38.091206.095737
Roe, D., 2008. The origins and evolution of the conservation-poverty debate: a review of key
literature,
events
and
policy
processes.
Oryx
42,
491 503.
https://doi.org/10.1017/S0030605308002032
Rondinini, C., Wilson, K.A., Boitani, L., Grantham, H., Possingham, H.P., 2006. Tradeoffs of
different types of species occurrence data for use in systematic conservation planning.
Ecol. Lett. 9, 1136 1145. https://doi.org/10.1111/j.1461-0248.2006.00970.x
Runge, C.A., Tulloch, A.I., Possingham, H.P., Tulloch, V.J., Fuller, R.A., 2016. Incorporating
dynamic distributions into spatial prioritization. Divers. Distrib. 22, 332 343.
Saura, S., Martinez-Millan, J., 2001. Sensitivity of landscape pattern metrics to map spatial extent.
Photogramm. Eng. Remote Sens. 67, 1027 1036.
Schmitz, O.J., Lawler, J.J., Beier, P., Groves, C., Knight, G., Boyce, D.A., Bulluck, J., Johnston,
K.M., Klein, M.L., Muller, K., Pierce, D.J., Singleton, W.R., Strittholt, J.R., Theobald,
D.M., Trombulak, S.C., Trainor, A., 2015. Conserving Biodiversity: Practical Guidance
about Climate Change Adaptation Approaches in Support of Land-use Planning. Nat.
Areas J. 35, 190 203. https://doi.org/10.3375/043.035.0120
Schuster, R., Germain, R.R., Bennett, J.R., Reo, N.J., Arcese, P., 2019. Vertebrate biodiversity on
indigenous-managed lands in Australia, Brazil, and Canada equals that in protected areas.
Environ. Sci. Policy 101, 1 6. https://doi.org/10.1016/j.envsci.2019.07.002
Schuster, R., Hanson, J.O., Strimas-Mackey, M., Bennett, J.R., 2020. Exact integer linear
programming solvers outperform simulated annealing for solving conservation planning
problems. PeerJ 8, e9258. https://doi.org/10.7717/peerj.9258

67

Selig, E.R., Casey, K.S., Bruno, J.F., 2012. Temperature-driven coral decline: the role of marine
protected areas. Glob. Change Biol. 18, 1561 1570. https://doi.org/10.1111/j.13652486.2012.02658.x
Shackelford, N., Standish, R.J., Ripple, W., Starzomski, B.M., 2018. Threats to biodiversity from
cumu
J. Soc. Conserv. Biol. 32, 672 684. https://doi.org/10.1111/cobi.13036
Stralberg, D., Carroll, C., Pedlar, J.H., Wilsey, C.B., McKenney, D.W., Nielsen, S.E., 2018.
Macrorefugia for North American trees and songbirds: Climatic limiting factors and multiscale
topographic
influences.
Glob.
Ecol.
Biogeogr.
27,
690 703.
https://doi.org/10.1111/geb.12731
Svancara, L.K., Brannon J., R., Scott, M., Groves, C.R., Noss, R.F., Pressey, R.L., 2005. Policydriven versus Evidence-based Conservation: A Review of Political Targets and Biological
Needs.
BioScience
55,
989 995.
https://doi.org/10.1641/00063568(2005)055[0989:PVECAR]2.0.CO;2
Venter, O., Magrach, A., Outram, N., Klein, C.J., Possingham, H.P., Marco, M.D., Watson, J.E.M.,
2018. Bias in protected-area location and its effects on long-term aspirations of biodiversity
conventions. Conserv. Biol. 32, 127 134. https://doi.org/10.1111/cobi.12970
Warman, L.D., Forsyth, D.M., Sinclair, A.R.E., Freemark, K., Moore, H.D., Barrett, T.W.,
Pressey, R.L., White, D., 2004a. Species distributions, surrogacy, and important
conservation regions in Canada. Ecol. Lett. 7, 374 379. https://doi.org/10.1111/j.14610248.2004.00590.x
Warman, L.D., Sinclair, A.R.E., Scudder, G.G.E., Klinkenberg, B., Pressey, R.L., 2004b.
Sensitivity of Systematic Reserve Selection to Decisions about Scale, Biological Data, and
Targets: Case Study from Southern British Columbia. Conserv. Biol. 18, 655 666.
https://doi.org/10.1111/j.1523-1739.2004.00538.x
Watson, R.T., Zakri, A.H., Arico, S., Bridgewater, P., Mooney, H.A., Cropper, A., 2005.
Millennium Ecosystem Assessment. United Nations Environment Program.
Wells, M.P., McShane, T.O., 2004. Integrating Protected Area Management with Local Needs and
Aspirations. AMBIO J. Hum. Environ. 33, 513 519. https://doi.org/10.1579/0044-744733.8.513
boreal: what is the right amount of area to protect? Biodivers. Conserv. 27, 733 748.
https://doi.org/10.1007/s10531-017-1461-2
Wiersma, Y.F., Sleep, D.J.H., 2016. A review of applications of the six-step method of systematic
conservation planning. For. Chron. 92, 322 335. https://doi.org/10.5558/tfc2016-059
Wilhere, G.F., 2008. The How-Much-Is-Enough Myth. Conserv. Biol. 22, 514 517.

68

Wilsey, C., Bateman, B., Taylor, L., Wu, X., LeBaron, G., Shepherd, R., Koseff, C., Friedman, S.,
Stone, R., 2019. Survival by Degrees: 389 Bird Species on the Brink. National Audubon
Society.
Wilson, S.J., Hebda, R.J., 2008. Mitigating and adapting to climate change through the
conservation of nature. Land Trust Alliance of British Columbia.
Woods, A.J., Coates, K.D., Watts, M., Foord, V., Holtzman, E.I., 2017. Warning signals of adverse
interactions between climate change and native stressors in British Columbia forests.
Forests 8, 280.
Woolmer, G., Trombulak, S.C., Ray, J.C., Doran, P.J., Anderson, M.G., Baldwin, R.F., Morgan,
A., Sanderson, E.W., 2008. Rescaling the Human Footprint: A tool for conservation
planning at an ecoregional scale. Landsc. Urban Plan. 87, 42 53.
https://doi.org/10.1016/j.landurbplan.2008.04.005
World Wildlife Fund, 2019. Ecoregions [WWW Document]. WWF. URL
https://www.worldwildlife.org/biomes (accessed 4.15.19).
1

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Appendix A: Spatial Data Used for Analysis
Dataset
BC Natural Resource
Districts
BC Natural Resource Regions
BC Ecoprovinces
BC Ecoregions
BC Regional Districts
Biogeoclimatic Ecosystem
Classification Zones
Caribou Herd Locations for
BC
Elk Range
Fish Ranges
(sockeye, bull trout)
Fisher Range
Freshwater Atlas
(rivers, lakes)
BC Grizzly Bear Habitat
Classification and Rating
(BEU)
Mountain Goat Range
Climate-based bird
distribution models (present)
Tree Species distribution
model projections (present)
Wild Mountain Sheep Range
(bighorn sheep)

Source
Ministry of Forests, Lands, Natural Resource
Operations and Rural Development - Forest
Tenures
Ministry of Forests, Lands, Natural Resource
Operations and Rural Development - Forest
Tenures
Ministry of Environment and Climate Change
Strategy - Knowledge Management
Ministry of Environment and Climate Change
Strategy - Knowledge Management
Ministry of Municipal Affairs and
Housing - Governance and Structure
Ministry of Forests, Lands, Natural Resource
Operations and Rural Development - Forest
Analysis and Inventory
Ministry of Environment and Climate Change
Strategy - Ecosystems
Shackelford et al. 2018
Ministry of Env and CC Strategy

Year
2019

Shackelford et al. 2018
Ministry of Forests, Lands, Natural Resource
Operations and Rural Development - GeoBC
Ministry of Environment and Climate Change
Strategy - Ecosystems

2018
2011

Shackelford et al. 2018
Audubon Survival by Degrees

2018
2019

McKenney et al. 2011b

2011

FLNRORD

2019

2019
2019
2019
2019
2018
2019
2018
2019

2019

70

Appendix B: Table of Grouping Units by Scenario
BC

NRR

EP

NRD

ER

British
Columbia

Cariboo Natural
Resource Region

Boreal Plains

100 Mile House
Natural Resource
District

Boreal Mountains
And Plateaus

Kootenay
Boundary Natural
Resource Region

Central Interior

Campbell River
Natural Resource
District

Boundary Ranges

Northeast Natural
Resource Region

Coast And
Mountains

Cariboo Chilcotin
Natural Resource
District

Central Alberta
Upland

Omineca Natural
Resource Region

Georgia
Depression

Cascades Natural
Resource District

Central Canadian
Rocky Mountains

Skeena Natural
Resource Region

Northern Boreal
Mountains

Chilliwack
Natural Resource
District

Chilcotin Ranges

South Coast
Natural Resource
Region

Southern Alaska
Mountains

Coast Mountains
Natural Resource
District

Chugach
Mountains And
Icefields

Thompson
Okanagan Natural
Resource Region

Southern Interior

Fort Nelson
Natural Resource
District

Coastal Gap

West Coast
Natural Resource
Region

Southern Interior
Mountains

Haida Gwaii
Natural Resource
District

Columbia
Highlands

Sub Boreal
Interior

Mackenzie
Natural Resource
District

Eastern
Continental
Ranges

Taiga Plains

Nadina Natural
Resource District

Eastern Hazelton
Mountains

North Island
Central Coast
Natural Resource
District

Eastern
Vancouver Island

Okanagan
Shuswap Natural
Resource District

Fraser Basin

Peace Natural
Resource District

Fraser Plateau

71

Prince George
Natural Resource
District

Georgia Puget
Basin

Quesnel Natural
Resource District

Gwaii Haanas

Rocky Mountain
Natural Resource
District

Hay Slave
Lowland

Sea to Sky Natural Hecate
Resource District Continental Shelf
Selkirk Natural
Resource District

Hyland Highland

Skeena Stikine
Natural Resource
District

Inner Pacific Shelf

South Island
Natural Resource
District

Interior Transition
Ranges

Stuart Nechako
Natural Resource
District

Liard Basin

Sunshine Coast
Natural Resource
District

Lower Mainland

Thompson Rivers
Natural Resource
District

Muskwa Plateau

Nass Ranges
Northern Alberta
Upland
Northern
Canadian Rocky
Mountains
Northern Cascade
Ranges
Northern
Columbia
Mountains

72

Northern
Continental
Divide
Okanogan
Highland
Omineca
Mountains
Outer Pacific
Shelf
Pacific Ranges
Peace River Basin
Pelly Mountains
Purcell
Transitional
Ranges
Selkirk Bitterroot
Foothills
Skeena Mountains
Southern Alberta
Upland
Southern Rocky
Mountain Trench
St Elias
Mountains
Thompson
Okanagan Plateau
Western
Continental
Ranges
Western
Vancouver Island
Yukon Southern
Lakes
Yukon Stikine
Highlands
1

8

10

23

46

73

Appendix C: Additional Maps

74

75

76

77

78

79

80

81

82

1

83