USING BIOCLIMATIC ENVELOPE MODELLING TO INCORPORATE SPATIAL AND TEMPORAL DYNAMICS OF CLIMATE CHANGE INTO CONSERVATION PLANNING by Nancy-Anne Rose B.Sc., University of Guelph, 1998 THESIS IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN NATURAL RESOURCES AND ENVIRONMENTAL STUDIES (BIOLOGY) THE UNIVERSITY OF NORTHERN BRITISH COLUMBIA December 2009 © Nancy-Anne Rose, 2009 Reproduced with permission of the copyright owner. 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Conformement a la loi canadienne sur la protection de la vie privee, quelques formulaires secondaires ont ete enleves de cette these. While these forms may be included in the document page count, their removal does not represent any loss of content from the thesis. Bien que ces formulaires aient inclus dans la pagination, il n'y aura aucun contenu manquant. 1+1 Canada Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Abstract Current and predicted trends in climate are diverging from historic norms, thereby compromising the equilibrial basis of our resource management frameworks. This study investigates the impacts of climate change on biodiversity in the context of conservation planning for British Columbia's Central Interior. I used bioclimatic envelope modelling and a climate interpolation and general circulation model downscaling tool to assess 73 rare plant species, 103 biogeoclimatic variants, and 30 terrestrial ecosystem units. I mapped areas projected to support climate suitable for the persistence of those conservation targets through to the 2080s. Results illustrate the potential for disruptive change; only 12% (24) of the 206 targets are projected to experience persistent climate at their current locations. Although strong overlap among locations projected to persist for different targets was not found, and those areas meeting multiple objectives (including value independent of climate change) are clear priorities for protection. This methodology can function as a valuable tool for conservation planners and resource managers. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table of Contents Abstract i Table of Contents ii List of Tables iii List of Figures iv Acronyms v Acknowledgements vi Chapter 1 - Introduction: Conserving biodiversity in a changing climate 1 Abstract 1 Introduction 2 Conclusions 24 Chapter 2 - Proof of concept: Using bioclimatic envelopes to identify persistent climate corridors in support of conservation planning 26 Abstract 26 Introduction 27 Methods 32 Results 37 Discussion 44 Conclusions 50 Chapter 3 - Bioclimatic envelopes of selected conservation targets in B.C.'s Central Interior and the identification of candidate areas for conservation 51 Abstract 51 Introduction 53 Results 67 Discussion 83 Conclusions 92 Chapter 4 - Synthesis: Dynamic conservation planning and climate change. 94 Abstract 94 Introduction 95 Methods 96 Results 99 Discussion 103 Conclusions 109 References Ill Appendix A - Conservation target and climate data for the conservation target groups...124 ii Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. List of Tables Table 1.1. A summary of predicted geographic responses to climate change of biomes, plant communities 6 Table 1.2. The environmental characteristics used to classify terrestrial ecosystems 18 Table 1.3. A summary of the diagnostic classifiers used to describe ecological systems in NatureServe's International Vegetation Classification system 20 Table 1.4. A summary of the sources of uncertainty pertinent to the use of bioclimatic envelopes to project ecological responses to climate change 25 Table 2.1. A summary of the mean values and standard deviationsfor elevation (m) and latitude (°N) of the bioclimatic envelope for each B.C. biogeoclimatic zone for all four timeslices and its associated persistent climate corridor 39 Table 2.2. Summary of the area (km2) within the bioclimatic envelope for each the B.C. biogeoclimatic zones and their projected changes over time 40 Table 2.3. Selected Interior Cedar-Hemlock biogeoclimatic variants found in British Columbia, and their expected persistence 41 Table 3.1. Data sources accessed for rare plant occurrence data 59 Table 3.2. Description of annual climate variables produced by ClimateBC and ClimatePP 59 Table 3.3. a) The standardized loadings from the top 4 principal components (PC) and b) a partial summary of the Pearson's Correlation Matrix of provincial climate data used to select climate variables for the development of bioclimatic envelopes 62 Table 3.4. A summary of the four storyline and scenario families 66 Table 3.5. Maximum, minimum, median, mean and standard deviation (SD) for the mean annual temperature (MAT,°C) from 16 GCM and scenario combinations, 66 Table 3.6. Biogeoclimatic variants, currently found in the study area, that are predicted to have suitable climate space and the area and degree of change associated with persistent climate corridors based on CGCM3 A2 projections and ClimateBC downscaling 71 Table 3.7. A summary of the suitable climate space, persistent climate corridors and percent of the current area represented by projected PCCs for eight terrestrial ecosystem units 72 Table 3.8. A summary of the suitable climate space, persistent climate corridors (PCC) and percent PCC representing the current distribution of 30 rare plant species 75 Table 4.1. An areal summary (km2) of the scores assigned to the area-based PCCs with parks locked in to the Marxan suitability run 104 Table 4.2. An areal summary (km2) of the scores assigned to the area-based PCCs without parks locked in to the Marxan suitability run 105 Table 4.3. The Marxan output scores for the B.C. Conservation Data Centre plant species PCCs 106 Table Al. Target plant species names and their conservation 131 Table A 2. A summary of the conservation status codes assigned by the B.C. Conservation Data Centre 134 Table A 3. Synonyms for some of the B.C. Conservation Data Centre "At Risk" plant species investigated in this study 135 Table A 4. A summary of the results using CGCM3 for the B.C. biogeoclimatic variants 138 Table A 5. A summary of the results using CGCM3 for the Nature Conservancy of Canada's 142 Table A 6. A summary of the results using CGCM3 for rare plant species 144 Table A7a. A complete summary of the fatal trends to B.C. Conservation Data Centre listed plant populations for the baseline and 2020s timeslices 147 Table A7b. A complete summary of the fatal trends to B.C. Conservation Data Centre listed plant populations for the 2050s and 2080s timeslices 150 Table A8. A summary of a species' projected suitable climate space and the proportional change from the baseline to the 2080s timeslice 153 iii Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. List of Figures Figure 1.1. A map of the Central Interior study area 21 Figure 2.1. An example of the conceptual or functional space describing the bioclimatic envelope of the dainty moonwort fern, Botrychium crenulatum Wagner 29 Figure 2.2. Graphical gap analysis showing the locations of persistent climate corridors projected for the biogeoclimatic zones of south-central British Columbia and for the province 38 Figure 2.3. Locations of persistent climate corridors projected for the Nass Moist Cold Interior CedarHemlock (ICHmcl) biogeoclimatic variant 42 Figure 2.4. The current distribution, locations expected to exhibit persistent suitable climate, and the resulting persistent climate corridors projected for the North Pacific Interior Lodgepole Pine Douglas-fir Woodland and Forest ecosystem unit in the study area 43 Figure 2.5. Locations of suitable climate space and persistent climate corridor projected for Nephroma occultum in the Central Interior study area 44 Figure 3.1. An illustration of the intersect-overlay process used to identify candidate areas for conservation of Nephroma occultum 64 Figure 3.2. Maps of the current distribution, suitable climate space and resulting persistent climate corridor 69 Figure 3.3. Maps of persistent climate corridor of Boreal Altai Fescue Undifferentiated 70 Figure 3.4. An illustration of the current distribution, suitable climate space and persistent climate corridor projected for the Northern Rocky Mountain Lower Montane Riparian Woodland and Shrubland terrestrial ecological unit 73 Figure 3.5. A map illustrating the current distributions and persistent climate corridors 74 Figure 3.6. An illustration of the current distribution, suitable climate space 76 Figure 3.7. An illustration of the current distribution, suitable climate space 77 Figure 3.8. The frequency (across all four timeslices) that a variable prevented a species' location from meeting the conditions defined by its bioclimatic envelope 78 Figure 3.9. A comparison of the frequency of different degrees of change in the area covered by suitable climate space of rare species grouped by four broad habitat types 79 Figure 3.10. A comparison of the number of targets with suitable climate space and persistent climate corridors as projected by the CSIRO A2, CGCM3 A2 and PCM B1 scenarios 80 Figure 3.11. A comparison of the percent change in suitable climate space for six species 81 Figure 4.1. Marxan output for the Central Interior study area showing the range of conservation value scores generated from a suitability index without parks "locked in" 99 Figure 4.2. A map illustrating the locations in the Central Interior study area with more than one persistent climate corridor 102 Figure 4.3. A comparative illustration showing the Marxan suitability index output with and without parks "locked in" 103 iv Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Acronyms Acronym Definition ANHIC Alberta Natural Heritage Information Centre B.C. British Columbia BEC Biogeoclimatic ecosystem classification BEM bioclimatic envelope modelling BGC biogeoclimatic CDC Conservation Data Centre CGCM3 Canadian General Circulation Model Generation 3 COSEWIC Committee on the Status of Endangered Wildlife in Canada CSIRO Australian Commonwealth Scientific and Industrial Research Organization DEM digital elevation model ERAP ecoregional assessment process GBIF Global Biodiversity Information Facility GCM general circulation model GIS geographic information system (computer mapping program) ILMB Integrated Land Management Bureau IPCC Intergovernmental Panel on Climate Change NCC Nature Conservancy of Canada PCC persistent climate corridor PCM US Department of Energy's Parallel Climate Model PRISM Parameter Regression of Independent Slopes Model SCS suitable climate space SRES Special Report on Emissions Scenarios TEU terrestrial ccological unit TNC The Nature Conservancy (U.S.A.) UBC University of British Columbia UNBC University of Northern British Columbia UVIC University of Victoria V Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Acknowledgements I would like to thank Phil Burton for his guidance and mentorship throughout my research, as well as Chris Johnson and Brian Menounos for their expertise. I would like to thank Ping Bai, Nancy Alexander, Darin Brooks and Roger Wheate of the University of Northern British Columbia for lending their GIS expertise. I thank Brian Aukema (Canadian Forest Service and University of Northern British Columbia) for helping me refine my thinking on the distinction between suitable climate and persistent climate corridors. I am grateful to the Nature Conservancy of Canada for providing valuable spatial coverages and financial support. The British Columbia Forest Investment Account's Forest Science Program (FIA-FSP) graduate student pilot program and a Canadian National Science and Engineering Research Council (NSERC) Industrial Partnership Scholarship have also supported this research. Finally, I would like to thank the following government agencies for providing data on the occurrence and status of rare plant species: B.C. Conservation Data Centre, Alberta Natural Heritage Information Centre, Washington Natural Heritage Program, and the Idaho and Montana Conservation Data Centres. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Chapter 1 - Introduction: Conserving biodiversity in a changing climate Abstract The ecological repercussions of this century's anthropogenic climate change are expected to devastate the environment. Climate change is driving species to extinction and altering life-sustaining ecosystem processes. These changes are happening at a rate that exceeds the physiological capabilities of most ecological units and consequently, the ability to effectively manage these resources is hampered. In response to these changes, ecologists and resource managers are starting to incorporate the spatial and temporal dynamics of ecosystems into their planning frameworks. A number of tools are available to assist with this transition and there is evidence that a dynamic, non-equilibrium approach to ecosystem management is emerging. Using the Nature Conservancy of Canada's Central Interior ecoregional assessment as a case study, this research explores the identification of persistent climate corridors as a means of addressing the spatiotemporal dynamics of climate change on the landscape and its subsequent impact on conservation planning. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Introduction Climate has shaped the structure and function of ecosystems and is partly responsible for the existing character and distribution of plant and animal species. However, the current rate of climate change is unprecedented (Leemans and Eickout 2004; IPCC 2007; McKenney et al. 2007b) and will likely exceed the ability of many species to respond (Schwartz et al. 2006). When considered on a geological scale, the impacts of contemporary climate change are immediate, as demonstrated by the mountain pine beetle (Dendroctonus ponderosae) epidemic in central B.C. (Carroll et al. 2003) and by global changes in the geographic ranges of many butterfly species (McCarty 2001). According to the Intergovernmental Panel on Climate Change (IPCC 2007), the primary force driving this century's climate change is anthropogenic, and is expected to cause extreme weather events, global changes in temperature and precipitation, and increases in sea levels. In the same report, IPCC (2007) identified ecosystems, water resources, food security, settlements and society, and human health as the primary systems most vulnerable and highly impacted by anthropogenic climate change. From a biodiversity perspective, the impacts of climate change on ecosystems can include an increase in the magnitude of local extinctions of plant and animal species (Schwartz et al. 2006), as well as an increase in the incidence of species invasions (BCMFR 2006a; Gayton 2008). These impacts will lead to major changes in ecosystem structure and function, ecological interactions and species distributions. Overall, these changes have predominantly negative consequences for biodiversity and the provision of ecological services (McCarty 2001). Anthropogenic drivers of other aspects of global change, such as resource exploitation, and land conversion and 2 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. degradation are also expected to intensify the consequences of climate change (Hansen et al. 2001; Hannah et al. 2002b). Individually these impacts will have harmful effects on the environment, but interaction of climate change with other global changes will have a far greater synergistic impact on biodiversity (Dale et al. 2001; Hijmans and Graham 2006). Invasive species, for example, are a serious problem for many indigenous species and biotic communities, and their projected increase is expected to exacerbate current extirpation rates as they outcompete and replace native species (Hansen et al. 2001; Malcolm et al. 2002). Coupled with climate driven changes to natural disturbance regimes, the increase in invasive species is expected to create a positive feedback, which will continue to drive the introduction and establishment of invasive species and intensify natural disturbances such as wildfire (BCMFR 2006a). More intense and frequent fires will change successional trajectories through changes to community structure and composition, such as the difference between native and exotic grass fire cycles which are responsible for woodland conversion to grasslands in the Sonoran woodland deserts and the shrub and steppe habitat in the Great Basin of North America (D'Antonio and Vitousek 1992). The purpose of this thesis is to explore how climate change will impact biodiversity through changes in species distribution. It will also explore the ability of managers and ecologists to mitigate these changes and develop new adaptive conservation strategies. Specifically the objectives of this thesis are to develop bioclimatic envelopes for three groups of conservation targets (i.e., rare plant species, terrestrial ecological units and B.C. biogeoclimatic variants), and to introduce the concept of persistent climate corridors and their application to the site selection and prioritization of a network of protected areas. The utility 3 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. of this concept is demonstrated by identifying potential persistent climate corridors for each target which I will argue represent superior priority areas for conservation. Species and Ecosystem Migration In response to the anticipated adversities associated with climate change, species must adapt to or evolve with the new climate, migrate to new more suitable areas, or go extinct. Individual species are expected to respond idiosyncratically, which will lead to a redistribution of individual species and widespread re-organization of ecological communities (Shafer et al. 2001; Hamann and Wang 2006; Hijmans and Graham 2006). A species' response to climate change is a function of its physiological and life history characteristics, such as reproductive biology and phenology (Berry et al. 2003), phenotypic plasticity and genetic adaptation (Hamann and Wang 2006; McKenney et al. 2007b), resilience to disturbance (Fitzpatrick et al. 2008), dispersal ability, biotic interactions and abiotic factors (Hansen et al. 2001; Pearson and Dawson 2003; McKenney et al. 2007b). Human activities will also impact how species will respond to climate change. Land uses such as urban development may create barriers to dispersal and species may become trapped and unable to a move to more suitable areas (Hansen et al. 2001; Williams and Jackson 2007). At the community level, change is a function of direct and interacting global changes including the impact of invasive species, biochemical changes in the atmosphere, differential species dispersal, and changes to natural disturbance regimes, land use and interspecific interactions (Hansen et al. 2001). Asynchronous responses of individual taxa within a community will have significant ecological consequences for current community dynamics 4 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. and the persistence of ecological communities, and are likely to result in novel species associations (Shafer 2001; Williams and Jackson 2007). Inevitably, these idiosyncratic responses within a community will lead to the creation of new ecological communities without current analogues (Suffling and Stocks 2002; Lemieux and Scott 2005; Williams and Jackson 2007). According to Schweiger et al. (2008), one of the potential consequences of differential species responses within a community is spatial mismatching of trophically interacting species. For example, it has been noted that Boloria titania (Purple Bog Fritillary), a monophagous butterfly, and its host plant Polygonum bistoria (bistort) have a pronounced mismatch of the future geographic ranges projected for their climatic niches (Schweiger et al. 2008). A small area of spatial overlap occurs among the projected areas characterized by their suitable bioclimatic envelopes, which leads to the conclusion that interspecies interactions and species-specific dispersal characteristics will contribute to some dynamic responses to climate change. Collectively these responses are expected to express themselves on the landscape as 1) poleward migrations (Pearson and Dawson 2003; Parmesan and Yohe 2003); 2) contractions of lower latitudinal biomes and plant communities (Malcolm et al. 2002; Pearson and Dawson 2003; Schwartz et al. 2006); 3) the encroachment or replacement of higher latitude biomes, such as open taiga, with closed forests (Bachelet et al. 2005); and 4) elevational migrations and losses (McCarthy 2001; Walther et al. 2002). The effects of climate change are expected to be strongest in northern sub-boreal, boreal and subarctic ecosystems (Scott et al. 2002; Hamann and Wang 2006). However, this is not a simple conclusion, and there are a number of issues associated with these predictions; for example, species at the southern limit of their range, and the suite of "at risk" species which are sensitive to environmental perturbations face the likelihood of local extinction (Honnay et al. 2002; Parmesan and Yohe 5 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 2003; Schwartz et al. 2006). Table 1.1 describes how a variety of different plant species and ecosystems are expected to respond to climate change. Table 1.1. A summary of predicted geographic responses to climate change of biomes, plant communities Ecosystem or plant species Geographic location Geographic Response Hot Desert Ecosystems Global Remain stable relative to other ecosystems Alpine Biome Coterminous United States South Africa Disappear from western mountains and replaced by forests Projected loss of area (51-65%) which equates to the loss of a 1/3 of fynbos associated species Expand into southwest deserts Fynbos Biome Grassland Biome Tundra Biome Temperate Forests Arctic-Alpine/montane heath Beech Woodland Ecosystems Lowland raised bog Lowland Proteaceae species Acer saccharum (sugar maple) Pseudotsuga menziesii (Douglas-fir) Banksia spp. (Proteaceae) Potamogeton filiformis (slender leaved pondweed) Ranunculus scleratus (cursed crowfoot) Lecanora populicola (rim lichen) Pinus albicaulis (whitebark pine) Quercus gambelii (Gambel oak) Pinus virginiana (Virginia pine) Fagus grandifolia (American beech) Scleranthus perennis (perennial knawel) Ilex aquifolium (European holly) Artemisia tridentata (big sagebrush) Coterminous United States Canada Reference Leemans and Eickhout 2004 Hansen et al. 2001 Mideley et al. 2002 Hansen et al. 2001 Scott et al. 2002 South Africa 6 climate change scenarios predict a loss of suitable habitat Increased representation Highly sensitive; projected loss of suitable habitat. Sensitive; projected loss of suitable habitat in southern Britain. Vulnerable; susceptible to summer drying, many species would lose suitable habitat Projected to experience rapid loss of range North America General poleward increase. British Columbia, Canada Western Australia Europe Overall increase in frequency across B.C. with the largest decrease in the Ponderosa Pine zone Varied in degree; a general range contraction is projected Losing suitable climate space Fitzpatrick et al. 2008 Berry et al. 2003 Europe Gaining valuable climate space Berry et al. 2003 Northern Britain Overall increase in the likelihood of occurrence in eastern and northeastern Scotland. Modest changes; suitable climate is expected to decline Currently not present but suitable climate is projected to exist Projected decrease in suitable habitat and a fairly small northward migration Projected 90% reduction in suitable area Ellis et al. 2007 Canada Britain Britain Europe Yellowstone National Park Yellowstone National Park Eastern United States Eastern United States Europe Dramatic area reductions and redistribution Europe Northward and northeastward range expansion North America Projected to migrate northward accompanied by a significant contraction of its current range Scott et al. 2002 Berry et al. 2002 Berry et al. 2002 Berry et al. 2003 Hannah et al. 2005 McKenney et al. 2007b Hamann and Wang 2006 Bartlein et al. 1997 Bartlein et al. 1997 Iverson et al. 1999 Iverson and Prasad 2002 Bakennes et al. 2002 Walther et al. 2005 Shafer et al. 2001 6 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Range shifts are also determined by other environmental factors, including edaphic and hydrological conditions and topography (Hamann and Wang 2006), and constraints such as geographic barriers and lack of sufficient dispersal opportunities (Costa et al. 2008). These overall results will further impact future distributional patterns and consequently biodiversity and its associated ecological processes and services (Hansen et al. 2001; Berry et al. 2003). Management perspectives: Protected area planning in a changing climate Managing natural resources for economic or ecological values is a challenging task given the dynamic character of heterogeneous environments. Many government agencies and environmental non-profit organizations are developing innovative management strategies with the objectives to prepare for, mitigate and adapt to the potential impacts of climate change. The B.C. Ministry of Forests and Range (BCMFR), for example, is developing a proactive strategy to address the short- and long-term consequences of climate change on forest and range resources. The recommended actions outlined in this strategy consist of improving the Ministry's ecological knowledge through increasing analysis and research, reviewing current operational policies and practices, as well as building awareness and capacity within and outside the Ministry. Some of the challenges of this adaptive approach include the uncertainty in the magnitude and timing of climate change impacts, the difficulty of balancing multiple values (and hence management objectives), and a variety of institutional and policy barriers. Factors which influence adaptive management in a changing climate include scale of the area of interest, target species, landscape processes, natural disturbance and societal values (Hannah et al. 2002a; Spittlehouse 2005). Many global change ecologists agree that climate change poses one of the greatest threats to native biodiversity (McCarty 2001; Bakkenes et al. 2002; Berry et al. 2002; Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Hannah et al. 2005; Ellis et al. 2007; Fitzpatrick et al. 2008; Gayton 2008). The large-scale, cascading consequences of climate change are bringing current conservation practices into question. The current conservation management paradigm emphasizes equilibrium between biotic communities and their abiotic environment (including soils, terrain and climate), and assumes that this abiotic environment is essentially stable. Consequently, exercises such as ecosystem mapping place static boundaries on an inherently dynamic system, but as plant species migrate in response to climate change this paradigm's flaw becomes apparent (Hijmans and Graham 2006; Leroux et al. 2007). Parks and protected area networks, for example, are unlikely to maintain their conservation objectives as climate driven changes re­ assemble and re-organize ecosystems (Scott et al. 2002; Araujo et al. 2004; Leemans and Eickhout 2004). This classical paradigm of ecological stasis is based on the assumption that ecosystems have discrete, recognizable boundaries and that recovery from disturbance follows a linear progression to a stable or climax state. In contrast, the modern nonequilibrium paradigm states that ecosystems are open and heterogeneous, spatially and temporally variable, and their interactions on the landscape influence the mechanics of other ecosystems (Hannah and Salm 2005; Wallington et al. 2005). Emphasizing the temporal and spatial dynamics of ecological systems is fundamental to the successful integration of a nonequilibrium approach to conservation (Suffling and Stocks 2002; Lemieux and Scott 2005). By closing the gap between ecological theory and practical application, current policy and practice may begin to reflect emerging scientific perspectives and lead to more effective resource management (Wallington et al. 2005; Shultis and Way 2006; Scott and Lemieux 2007). One example of such an application is re-assessing representation and persistence criteria in order to develop a dynamic network of protected areas. By tracking the temporal 8 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. and spatial dynamics of parks and reserves, managers can more effectively ensure that suitable habitat is continuously available (Lcroux et al. 2007; Rayficld ct al. 2008). Other strategies that are recommended to address the contradiction between ecosystem dynamics and conserving ecological values include floating reserves (Cumming et al. 1996; Rayfield et al. 2008) and the provision for dispersal corridors (Williams et al. 2005). A paradigm shift in our conservation management practices would require a more multidisciplinary approach, which involves incorporating biogeography, conservation biology and practical resource management. The fundamental goal of process-integrated conservation strategies is to account for changes in species distribution, and consequently persistence and vulnerability to global changes (Margules and Pressey 2000; Hannah et al. 2002a; Araujo et al. 2004; Botkin et al. 2007). A number of general recommendations and considerations for conserving biodiversity in a changing climate have been proposed: • • • • • • • • • • • Focus on ecosystem pattern with consideration of ecological process (Hannah et al. 2002b; Scott and Lemieux 2007); Direct conservation efforts towards preserving areas where species are projected to persist (Shafer et al. 2001; Miller et al. 2007; Fitzpatrick et al. 2008); Manage a percentage of the current habitat area as a reserve until populations are established elsewhere (Hansen et al. 2001); Consider trans-boundary or potential range shifts (Hamann and Wang 2006; Lee and Jetz 2008); Conserve and maintain habitats in an appropriate condition in order to facilitate the migration of species (Halpin 1997; Berry et al. 2003); Prioritize the creation of northward and upslope migration corridors (Hansen et al. 2001; Gayton 2008); Identify and protect core areas within the ranges of targeted species (Miller et al. 2007; Fitzpatrick et al. 2008); Place greater emphasis on longer term ecological monitoring in order to determine the success of stated conservation goals (Welch 2005; Gayton 2008); Establish seed banks and nurseries for species at risk (Hansen et al. 2001); Help avert species extinction and keep up with climate change using mitigative measures, such as assisted migration (BCMFR 2006a; Schwartz et al. 2006; Van der Veken et al. 2008); Manage the surrounding matrix, including stressors, in order to alleviate their exacerbating effect on climate stress (McCarty 2001; Hannah and Salm 2005); 9 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. • • Coordinate conservation actions across political boundaries and agency jurisdictions (Hannah et al. 2002b; Hannah and Salm 2005; Lee and Jetz 2008); and Increase redundancy (representation) of conservation targets and the buffers around them (Halpin 1997; Miller et al. 2007). These general recommendations can be grouped into different categories of management actions including the selection of redundant reserves and reserves that provide habitat diversity and management for buffer zone flexibility, landscape connectivity and habitat maintenance. In order to maximize the efficacy of these actions, managers must identify the goals and objectives of their projects and prioritize them according to ecological principles (Halpin 1997; Botkin et al. 2007; Miller et al. 2007). The use and development of bioclimatic envelope models The responses of species and ecological communities to climate change are difficult, if not impossible, to predict with certainty, but there are a variety of tools available to assist global change ecologists with predicting the probable response of target species and communities. Bioclimatic envelope modelling (BEM) is one technique used to predict species dynamics and community formation (McKenney et al. 2007a; Williams and Jackson 2007). Bioclimatic envelope modelling is used to describe the present and potential future distribution of a species based on defining a set of suitable climate conditions (Thuiller 2003, 2004). Other species distribution modelling strategies use environmental and ecological data other than (or in addition to) climate, such as vegetation type (Segurado and Araujo 2004), geology (Zaniewski et al. 2002) and ecological processes such as competition and succession (Austin 2002). Different modelling strategies utilize a variety of data types including presence-only, presence/absence and abundance estimates, and are analyzed using general linear models, general additive models, classification and regression trees or artificial neural 10 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. networks (Heikkinen et al. 2006) to predict where plant species should be able to establish and persist. Bioclimatic envelope modelling has its conceptual underpinnings in Hutchinson's ecological niche theory (Hutchinson 1957), which describes a species' fundamental niche as a conceptual space occupied by a species, the multidimensional axes of which are described by environmental factors. This space, also termed a hypervolume, defines the range of a species' physiological tolerances and its position in the ecosystem. The realized niche, a subset of the fundamental niche, is the functional space a species actually occupies, as constrained by biotic factors such as predation and competition (Pearson and Dawson 2003; Beaumont et al. 2005). In principle, bioclimatic envelopes are larger than fundamental niches because they only consider climatic limitations, which at a global level are typically the dominant influence controlling plant species' establishment, growth and survival. It is for this reason that a bioclimatic envelope is often referred to as a species' "climatic niche" (Pearson and Dawson 2003; Hannah et al. 2005; McKenney et al. 2007a). Overall, BEM provides a practical tool that allows for a relatively quick first assessment to address ecological objectives such as the following (Berry et al. 2002; Kadmon et al. 2003; McKenney et al. 2007b): • • • • • • Estimating the spread of invasive species; Evaluating potential planting areas; Identifying climate-based disease expression in plant communities; Mapping wildlife habitats; Identifying potential areas for endangered species re-introductions; and Investigating potential responses of species to climate change. BEM is often criticized for its exclusion of important ecological dynamics including biotic interactions (e.g., competition and predation), dispersal ability, evolutionary adaptation and the influence of additional abiotic factors (e.g., local topography, soil conditions) and human 11 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. pressure on the landscape. Proponents of bioclimatic envelope modelling recognize these shortcomings, and argue that this strategy is typically undertaken as a first step in climate impact projections rather than for precise habitat suitability assessment, employed at a coarse scale where climate is the dominant factor controlling species distributions (Pearson and Dawson 2003; Heikkinen et al. 2006; Ellis et al. 2007). Bioclimatic envelope modelling is particularly useful for ecologists in the fields of ecological restoration, conservation planning and plantation forestry where managers are interested in matching species to suitable environments (Hamann and Wang 2006). Other advantages of bioclimatic envelopes include a valuable cost-benefit ratio from the perspectives of data availability and budgetary constraints. For example, BEM can typically be conducted on the basis of collection records associated with voucher specimens deposited in museums and herbaria, providing a feasible alternative to field surveys and their high and often prohibitive costs. They also have the potential to provide the only method of estimating the current potential and future distributions of poorly understood or under-researched species. Finally, a large number of datasets such as online herbarium records provide collection locations but no abundance information; such presence-only data are not suitable for many statistical approaches, but are ideally suited for bioclimatic envelope modelling (Kadmon et al. 2003; Beaumount et al. 2005). Bioclimatic envelopes are developed by associating current species occurrences with a set of climate variables or through an understanding of a species' physiological relationship with climate. When identifying a plant species' climatic space those variables which most limit successful survival, growth and reproduction are ideally used. Typical climate variables (which must be available or calculated from standard meteorological records) include mean annual temperature (MAT), mean temperature of the warmest month (MWMT), mean 12 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. temperature of the coldest month (MCMT), precipitation in the warmest season, and precipitation in the coldest season (Berry et al. 2002; Thuiller 2003, 2004; McKenney et al. 2007a,b). Ideally, occurrences from across the entire range of a conservation target are needed to fully describe its bioclimatic envelope. However, this information is not always achievable, especially when dealing with rare or uncommon species. There does not appear to be a consensus regarding a minimum number of records required for developing bioclimatic envelopes. However, Bakkenes et al. (2002) and Fitzpatrick et al. (2008) used a minimum of 20 occurrence records per target to describe bioclimatic envelopes for Europe's higher plants and Kadmon et al. (2003) used a minimum of 50 records to analyze the general performance of climatic envelope models. Persistent climate corridors: Collapsing the fourth dimension in conservation biology The Nature Conservancy of Canada (NCC) is a large non-profit organization dedicated to the conservation of native biodiversity (http://www.natureconservancy.ca). To achieve its goals, NCC participates in the acquisition of ecologically valuable parcels of land with specific conservation intentions, (e.g., stewardship programs, conservation covenants or easements). It also assists (or sometimes leads) governments in the process of planning for the designation of protected area networks, through development of a rigorous, multistakeholder conservation plan called an ecoregional assessment process (ERAP). NCC and its American counterpart, The Nature Conservancy (TNC), have completed ecoregional assessments for over 45 American and 14 Canadian or trans-boundary terrestrial ecoregions. 13 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. In 2007, the B.C. chapter of NCC launched the Central Interior Ecoregional Assessment (Nature Conservancy of Canada 2007b). The NCC's ecoregional assessments are an example of conservation planning designed to identify priority areas for the protection of biological diversity. A system of ecoregional planning is used to create a conservation blueprint, which attempts to incorporate natural processes, including species migration, predator/prey relationships, and species' response to a variety of disturbances in order to identify and document a portfolio of sites desired for protection (e.g., a reserve network). If conserved, this portfolio should secure the long-term survival of viable native species and community types currently found in the region. NCC takes a "multi-filter approach" to conservation planning, attempting to provide fine-filter protection for individual rare elements, and a full range of representative habitats for coarse-filter ecosystem conservation (Scott et al. 1993). The ecoregional assessment is carried out by NCC's conservation science and planning team, which consists of conservation planners, geographic information system (GIS) technicians, and ecologists who specialize in aquatic and terrestrial vertebrates, invertebrates, and plants, as well as the ecological processes which drive these ecosystems. These ecoregional assessments are carried out in collaboration with a wide range of government agency and environmental organization partnerships, and provide the rationale for making science-based, strategic investments in the conservation of biodiversity. The products of this process provide the information from which stakeholders can determine optimal conservation outcomes for proposed resource development projects (NCC 2007a,b). The steps to an ecoregional assessment are: 1. Identify conservation targets; 2. Assemble information on the locations or "occurrences" of targets; 3. Determine how to represent and rank target occurrences; 14 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 4. 5. 6. 7. 8. Set goals for each target; Rate suitability of each part of the ecoregion for conservation; Assemble draft conservation portfolios using reserve network design algorithms; Refine the portfolios through expert review; and Prioritize the potential conservation sites. Marxan, an optimal reserve selection algorithm, is currently a fundamental tool used in the NCC's ecoregional assessment process. According to the developers (Ball and Possingham 2000), Marxan, "... receives spatially-explicit data generated through GIS and applies spatial optimization algorithms to achieve a reasonably efficient solution to the problem of selecting a system of spatially cohesive reserves that meet a suite of multiple conservation targets (both coarse and fine filter) simultaneously." Marxan is a greedy, heuristic, simulated annealing algorithm that prioritizes site selection based on the least cost (weighted sum of area and boundary length) for the most benefit. This particular algorithm is used because it identifies a large number of near-optimal solutions (termed "portfolios") to a set of stated objectives, which are based on user-defined parameters, (e.g., size, connectivity, representativeness and complementarity). Portfolios are refined using expert knowledge, and include recommended conservation-based prescriptions, as well as maps of the various Marxan outcomes. These final products are then used by planners and researchers to explore multiple scenarios when designing conservation networks (Ball and Possingham 2000). The research described in this thesis is specific to the issue of biodiversity persistence and conservation network design, and may provide a framework applicable to the NCC's ERAP and to other protected area agencies, such as Parks Canada and B.C. Parks. The general purpose of this research is to explore the capacity of existing inventories and climate projection tools to identify priority areas for conservation having good prospects for relatively persistent climate over time. To address the impacts of climate change on the 15 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. management of biodiversity, the concept of a "persistent climate corridor" is developed. A persistent climate corridor is the intersection of a target's current distribution with locations projected to remain within its bioclimatic envelope as projected into the 2080s. This intersection identifies areas where a particular climate zone (as uniquely defined for each conservation target) is expected to persist, based on the best available information of target distribution and localized expectations of climate change (Hannah et al. 2005). Areas so identified represent candidate areas for particular management practices, such as conservation prioritization and assisted migration of target species. The identification of areas estimated to meet the requirements of particular bioclimatic envelope coincidence across different timeframes is becoming a popular tool in the field of conservation biology and climate change. For example, Berry et al. (2003) used the overlap between current and projected future species distributions based on bioclimatic envelopes to describe the degree of vulnerability a species might face in a changing climate. Overlap analysis may also prove to be a useful tool for exploring the role of competitive interactions or other influences on species distributions (Costa et al. 2008). Vos et al. (2008) combined bioclimatic envelope overlap with dispersal models to identify areas of spatial cohesion for successful colonization of new climate space. Conservation Targets The conservation targets used for my research were B.C. biogeoclimatic variants found in the Central Interior (103), NCC defined terrestrial ecological units (30) and "at risk" plant species (73). Rare plant communities or associations were not included in this analysis because the B.C. Conservation Data Centre (CDC) did not have any occurrence records for the Central Interior study area. These conservation targets represent a combined 16 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. fine and coarse filter approach, which constitutes a basic component of the NCC's ecoregional assessment. An important part of this research is the exploration of how a species' biology affects its response to climate change. The identification of persistent climate corridors for Neck's Teas and B.C. BGC variants will offer insight into how spatial and temporal scales might influence the distribution and availability of suitable climate for particular coarse-scaled conservation targets. The resulting distribution of suitable climate space and any resulting persistent climate corridors should help support the decision-making components of the ERAP. The research will also identify important gaps in our knowledge, as well as aspects of our planning and management practices, which still need to be identified and addressed. B.C. Biogeoclimatic Ecosystem Classification (BEC): Variants The B.C. ecosystem classification system is a framework that groups ecosystems at regional, local and successional levels. At the regional level, vegetation, soil type and topography are used to infer the regional climate and identify areas (biogeoclimatic units) with relatively uniform climate. Locally, ecosystems are classed into site units according to relatively uniform areas of soil, vegetation and topography (Pojar et al. 1987). In order to arrange these levels of integration (regional, local, successional) into a practical tool, the BGC framework combines vegetation, climate (zonal) and site characteristics and sometimes serai stage into a hierarchical classification system. Table 1.2 provides a summary of how each characteristic is used to classify ecosystems into progressively smaller, more site specific units. In terms of the BGC classification system, this research focused on BGC subzones and any affiliated variants within the study area because they are the smallest units where 17 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. climate is still the dominant control over ecosystem distribution. The B.C. BGC classification framework also provided one of the foundations for the classification of the terrestrial ecological units for the study area. Table 1.2. The environmental characteristics used to classify terrestrial ecosystems (BCMFR 2009). Classification Vegetation Description and Method • e.g., Montane Spruce North Thompson Dry Mild Describes the vegetation of a mature ecosystem Units are determined by grouping plot data and comparing results in a series of vegetation tables • The result is hierarchical: Class —> Order —> Alliance —> Association (basic unit, differentiated by diagnostic species) • Regional (macro) climate that influences an ecosystem over an extended period of time, as well as prevailing soil processes • Geographic extent inferred by climax or late serai plant communities, less influenced by local topography or soil properties • Basic unit of climatic classification • May include significant climatic variation marked by changes in vegetation (which are divided into variants, e.g., wetter, snowier, colder) • Derived from relative precipitation and temperature or continentality • Represents a geographic name given to a relative location or distribution within a subzone • Often have distinctive biogeographic elements within the subzone Site • • Climate (Zonal) e.g., Montane Spruce Subzones e.g., Montane Spruce Dry Very Cold Variants • Serai or Successional • • • The basic unit is an association followed by series and type Based on edaphic features (soil moisture and nutrients) Poorly described due to limited sampling Incorporates complex interactions associated with disturbance history and ecosystem recovery May span several variants and structural stages NatureServe's Terrestrial Ecological Unit (TEU) Classification The classification of the terrestrial ecological units was based on NatureServe's International Vegetation Classification (Comer et al. 2003), and as such reflects a standard methodology employed for ecosystem classification across North America. NatureServe defines an ecological system as "a group of plant communities that tend to occur within landscapes with similar ecological processes, substrates and/or environmental settings" (Comer et al. 2003). This classification system is based on a multiple criteria framework, which incorporates biotic composition (species abundance), environmental settings (moisture 18 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. regime) and dynamic ecological processes (fire, flooding). Comer et al. (2003) describe the ecological concepts governing their classification framework as: 1) An ecosystem unit is explicitly scaled to represent spatial scales of tens to thousands of hectares and temporal scales of 50 to 100 years; 2) The variability in the system is explicitly described in terms of a consistent list of abiotic and biotic criteria, and by linking ecological systems to plant community types, which describe community variation; 3) Long-term sustainability and local stability are considered by mapping and evaluating the occurrence of ecological systems at local and regional levels; and 4) Population processes are not considered as explicit system dynamics, but through knowledge of the component plant communities. This framework is based on recurring groups of biological communities that are found in similar habitats and are influenced by similar ecological processes, such as natural disturbance. Ecological factors termed diagnostic classifiers are integrated into this framework to further define and evaluate each classification unit, and to explain the spatial co-occurrence of plant associations. These diagnostic classifiers are described in Table 1.3 (Cromer et al. 2003). In terms of the Central Interior study area, the classification of the terrestrial ecological units (TEU) was based on the B.C. BGC variant and site series classifications, as well as the B.C. CDC provincial classification of forested and non-forested units (Gwen Kittelpers. comm.). Ecological characteristics (e.g., species composition and abundance, serai stage) refined these ecosystems into manageable units were obtained from the Prince Rupert, Prince George and Cariboo Forest Region guidebooks (Banner et al. 1993; Delong et al. 1993; Steen et al. 1997; Delong 2003) and BCMFR's Vegetation Resource Inventory (VRI) (Gwen Kittel, pers comm), which is essentially a forest cover map. 19 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table 1.3. A summary of the diagnostic classifiers used to describe ecological systems in NatureServe's International Vegetation Classification system (Cromer et al. 2003). Diagnostic Classifier Description Ecological Divisions Bioclimatic Variables Environment Continental bioclimate, phytogeography, biogeography Regional bioclimate Landscape position, hydrogeomorphology, soil characteristics, specialized substrate Hydrological and fire regimes Upland-wetland mosaics Physiognomy, spatial pattern and patch type, composition and abundance of plant associations Soil chemical and physical properties, natural disturbance Ecological Dynamics Landscape Juxtaposition Vegetation Other To create the TEU map of the study area, NCC commissioned a reclassification of an existing map of the BGC variants into a different set of ecological systems. Vegetation data were augmented with VRI forest inventory data and leading species polygons using ArcMap® overlay analysis. No new line work was created in this mapping exercise. The final name of each TEU is a combination of regional distribution, environmental setting, and vegetation structure and composition, e.g., the North Pacific Sub-boreal (Ecodivision regional distribution) Mesic (environmental setting) Hybrid Spruce Forest (vegetation structure and composition). B.C. Conservation Data Centre (CDC) plant species The target plant species for this research were selected based on the occurrence records found in the CDC data warehouse. Their conservation status and level of protection varies across the study area and are summarized in Appendix A, Tables Al and A2. These species are designated "of conservation concern" for a variety of reasons such as habitat loss and low population numbers. Unlike BGC variants and TEUs these rare plants are designated by point occurrences (which may be incomplete) and often have ranges outside of B.C. As 20 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. noted in Table Al, many of these species are more abundant elsewhere, and central B.C. populations are often marginal or incidental to the total range of a species. Study Area The Nature Conservancy of Canada's Central Interior ecoregion corresponds with the Central Interior and Sub-boreal ecoprovinces of Environment Canada's Ecological Classification system (Ecoregions Working Group 1989). The study area is approximately 246,000 km2 (24.6 million hectares) and its geographic location ranges from 50.868° to 57.408 °N latitude and 131.166° to 119.987 °W longitude. The Central Interior includes several physiographic systems including the Chilcotin, Cariboo, Nechako and McGregor plateaus, the Chilcotin Ranges west to the centre of the Pacific Ranges, the southern portion of the Northern Rocky Mountain Trench, the Bulkley, Tahtsa and Hart Ranges, and the southern Muskwa Ranges and their associated foothills. The southern Skeena and Omineca Mountains are also included in the study area (Demarchi 1995; Figure 1.1). ce George Quesnel Kamloops Kelowna ; 0 208 Kilometers 400 Vancouver Victoria Figure 1.1. A map of the Central Interior study area (NCC 2007b). 21 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Climate The study area has a continental climate characterized by cold winters and warm summers. The influence of the topography and climate is typified by the Sub-boreal Spruce (SBS) and Interior Douglas-Fir (IDF) biogeoclimatic zones, which dominate much of the study area. The other biogeoclimatic zones, more fully described in Meidinger and Pojar (1991) are: • Bunchgrass (BG), • Alpine Tundra (AT), • Engelmann Spruce-Subalpine Fir (ESSF), • Montane Spruce (MS), • Spruce-Willow-Birch (SWB), • Sub-Boreal Pine-Spruce (SBPS), • Boreal White and Black Spruce (BWBS) and • Interior Cedar-Hemlock (ICH) Wildlife The Central Interior ecoregion supports a diversity of wildlife, including over 50% of the bird species that live and breed in B.C. This area also supports many ungulate species including A Ices alces (moose) and Odocoileus hemionus (mule deer), as well as some of North America's fiercest predators, e.g., Ursus arctos (grizzly bear) and Felis lynx (lynx). Soils and Land Use According to the Soil Landscapes of British Columbia (B.C. Ministry of Environment, 1985), the soils in this area are dominated by Grey Luvisols with pockets of Humo-Ferric Podzols, Eutric and Dystric Brunisols, and Dark Brown and Dark Grey Chernozems, and provide opportunities for rangeland, agriculture, and forestry activities. Other resource-based industries include oil and gas exploration and development, and mining for base metals. According to the Protected Areas Strategy for B.C. (Ministry of 22 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Environment, Land and Parks 1993), significant conservation and recreational features of Central Interior ecorcgion include: • Douglas-fir {Pseudotsuga menziesii) dry forest and scrub grassland habitats; • Largely undisturbed subalpine spruce-fir forests and alpine tundra; • Populations of the federally endangered woodland caribou {Rangifer tarandus caribou), present as both mountain and northern ecotypes; • Deep fjord-like lakes with natural hydrology; • Large unregulated rivers, with important salmonid spawning habitat for Fraser, Skeena, and Nass River runs; • Lake-headed (warmer, more productive) rivers supporting sockeye salmon spawning habitat; • Historic trails utilized by First Nations and fur traders; and • Recreation corridors on both land and water. Uncertainty The sources of uncertainty are plentiful in ecology and the climate sciences, and often have a cumulative effect on the research as it progresses from the collection and generation of data to the analysis. The challenge of obtaining occurrence records which span a species' full range is one of many limitations that contribute to the uncertainty associated with BEM. Given their ubiquity, addressing all sources of uncertainty is impossible; however, an attempt should be made to identify and account for those uncertainties which will directly influence final results and ultimately final policy decisions. Some of the uncertainties associated with studying the effects of climate change on species distributions are summarized in Table 1.4. Despite the uncertainty associated with combining climate change projections and bioclimatic envelopes to project potential future ranges for various species or ecosystems, the results can provide valuable biogeographic information, so long as model behaviour is well understood (Pearson et al. 2006). The performance of BEM is partially influenced by ecological and geographic characteristics of the distribution pattern of conservation targets 23 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. including area and extent of occupancy, marginality, niche breadth and prevalence (rarity). The area and extent of occupancy relates to the geography of where a species is found on the landscape, which may be partially defined in terms of latitudinal or elevational limits that reflect strong climate limitations (Heikkinen et al. 2006). Understanding the role of these characteristics and how they impact model performance will help to reduce uncertainties, and improve the ability to differentiate between statistical artefacts and inherent biogeographic or ecological differences in the potential distribution of species (Heikkinen et al. 2006). Conclusions The concepts of bioclimatic envelopes, suitable climate space, and persistent climate corridors provide a simple and powerful tool kit for conservation planning under a changing climate, pertinent to the development and application of variety of management strategies. For example, the Nature Conservancy of Canada will use the final outcomes of this research as a pre-processing layer in their conservation plan for the Central Interior ecoregion in British Columbia. Government agencies such as the B.C. Ministry of Forests and Range can use the concept of persistent climate corridors in the development of monitoring programs and strategies for facilitating the expected migration of valuable tree provenances and species. As research continues to reveal the impacts of climate change on ecological systems, the need to develop and adapt new management strategies becomes increasingly urgent. Persistent climate corridors have the potential to assist managers as they cope with the challenges presented by climate-driven changes to the world's ecosystems. 24 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table 1.4. A summary of the sources of uncertainty pertinent to the use of bioclimatic envelopes to project ecological responses to climate change. Source of Uncertainty Species response to climate and to climate change Source data Validity of predictions based on general circulation models (GCM) Time lags Magnitude of error Description There are often key ecological features of species that remain unknown or have limited information: ecological plasticity, capacity for genetic adaptation, dispersal barriers and migration ability. Acquiring this knowledge is hampered by complex interactions and processes, e.g., inter- and intraspecies interactions such as predation and competition. Co-evolved species associations will not adapt synchronously, and these new associations will have unknown impacts on ecosystem function and structure. Describing the climate across the known range of a species is constrained by the distribution of weather stations, how complete their records are, and by the limitations of tools designed to interpolate climatic conditions between those weather stations. Uncertainties arise from: insufficient or incomplete distribution data, poor or low quality data, and lack of or under-developed methodologies for quantifying certain types of information. Failure to validate data can lead to erroneous assumptions about data accuracy and invalid output. Factors leading to uncertainties include lack of funding, disagreement among multiple sources, vague concepts and imprecise terms, lack of expertise, interpersonal dynamics and how data are solicited. GCMs are subject to substantial uncertainty due to assumptions from difficult to measure parameters, ecosystem and atmospheric processes and interactions, and socio-economic conditions, e.g., the effects of land use/conversion on the atmosphere. Different GCMs (e.g., CGCM, CS1RO, Hadley) and different scenarios for future carbon emissions result in different projections of future climate, with no limited indications as to which is most realistic for a given area. Current GCMs have a limited ability to resolve the spatial distribution of climate and vegetation in regions of complex topography. Interpolation from a coarse scale model (e.g., GCM) to the landscape scale of a study area or an even finer scale of an occurrence record introduces cumulative errors. A biogeographic lag exists between climate change and biome response, (i.e., changes in distribution or composition.) Time lags are very difficult to measure. The difficulty in quantifying or assessing the degree to which uncertainties impact results as well as the direct impact of climate change References Hansen et al. 2001, Honnay 2002, Malcolm et al. 2002, Parmesan and Yohe 2003, Pearson and Dawson 2003 Hannah et al. 2002b Malcolm et al. 2003, Parmesan and Yohe 2003, Pearson and Dawson 2003, Botkin et al. 2007 Hannah et al. 2002b, Pearson and Dawson 2003, Botkin et al. 2007, Williams and Jackson 2007 Hijmans et al. 2005, Wang et al. 2006 Johnson and Gillingham 2004, and Winte 2005, Moilanen et al. 2006, Botkin et al. 2007, Guisan et al. 2007 Pearson and Dawson 2003, Moilanen et al. 2006 Hansen et al. 2001, Johnson and Gillingham 2004 Malcolm et al. 2003, Kueppers et al. 2005, Pyke et al. 2005, Pyke and Fischer 2005 Araujo and New 2006, IPCC 2007, Bartlein et al. 1997, Hamann and Wang 2006, Daly et al. 2000,2002 Pyke et al. 2005, Pearson et al. 2006 Malcolm et al. 2002, Parmesan and Yohe 2003, Leemans and Eickhout 2004, Hannah et al. 2005 Kadmon et al. 2003, Araujo et al. 2005, Heikkinen et al. 2006 25 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Chapter 2 - Proof of concept: Using bioclimatic envelopes to identify persistent climate corridors in support of conservation planning * Abstract Current and expected shifts in climate are threatening global biodiversity and are forcing managers to re-evaluate how they manage natural resources. Using the third generation of the Canadian general circulation model and ClimateBC, bioclimatic envelopes were developed for eleven Interior Cedar Hemlock biogeoclimatic variants, a North Pacific Interior Lodgepole Pine-Douglas-fir Woodland and Forest ecosystem type, and uncommon (B.C. blue-listed) lichen, Nephroma occultum. The geographic distribution of the resulting envelopes was projected for four timeslices, and then overlaid using ArcMap GIS software. The resultant intersection of areas is presumed to indicate locations of suitable climate over the study's timeframe. Next, the current distribution of the species or ecological unit was overlaid with its suitable climate space; the intersection of these points is considered the target's "persistent climate corridor." Current locations with persistent climate are thus expected to provide climatic continuity over time, sufficient to sustain the conservation target. The identification of such locations facilitates prioritization of sites for the designation of protected areas, and provides guidance on where other management policies can persist. The notion of persistent climate corridors is conceptually simple, yet this can be a powerful tool with many potential applications to assist natural resources managers in a rapidly changing environment. * A slightly modified version of this chapter has been published as "Using bioclimatic envelopes to identify temporal corridors in support of conservation planning in a changing climate" Forest Ecology and Management 258 (Suppl.l):S64-S74. 26 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Introduction Climate is one of the dominant influences on plant species distribution over large areas, such as an ecoprovince or forest region. Understanding its mechanics and subsequent manifestation on the landscape is critical to the successful management and conservation of forest resources (Spittlehouse 2005). Integrating a greater understanding of climate into our management practices is becoming increasingly important as the impacts of climate change on the sustainability of natural resources become more apparent. Some potential climatedriven impacts include the extirpation or even extinction of rare and specialized species (Hansen et al. 2001; Schwartz et al. 2006), an increase in invasive species (Dale et al. 2001; Hannah et al. 2002b), and more frequent and intense forest fires (Flannigan and van Wagner 1990; He et al. 2002) and insect outbreaks (Volney and Fleming 2000; Bale et al. 2002). As a consequence of idiosyncratic adaptations to climate, species displacement and community re­ organization will complicate current ecosystem knowledge and subsequent management practices (Suffling and Scott 2002). As a result of the multitude of individual and interacting species' responses to climate change, large-scale changes in plant species distribution are expected (Thuiller et al. 2005). As our understanding of how ecosystems respond to climate change improves, it is becoming increasingly important to review current paradigms of ecosystem inventory and management, which tend to apply static boundaries to dynamic systems (Margules and Pressey 2000; Walther et al. 2002; Spittlehouse 2005). The dynamic nature of ecosystems, communities and populations is gradually being recognized and accommodated, as indicated by the development of climate prediction tools (Beaumont et al. 2005; Hannah et al. 2005), and the advent of innovative planning tools such as floating reserves (Cumming et al. 1996; Rayfield 27 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. et al. 2008) and provision for dispersal corridors (Williams et al. 2005). The importance of re-evaluating the current "static ecosystem" paradigm is illustrated with current networks of protected areas. For example, as species respond individualistically to climate change and new ecological communities emerge, parks may no longer be able to support the values for which they were originally designed (Suffling and Scott 2002; Scott and Lemieux 2005). The purpose of this chapter is to explore the capacity of existing inventories and climate projection tools to identify candidate areas (for conservation or other management objectives) that have good prospects for relatively persistent climate over time. The ecological foundation for this research is supported by niche theory, as well as concepts well established in conservation biology, namely the value of habitat connectivity and the use of gap analysis in conservation planning. Central to this process is the well-developed concept of the bioclimatic envelope, and the novel concept of the persistent climate corridor. Bioclimatic envelope modelling Bioclimatic envelope modelling is used to describe the present and future distribution of ecological elements, whether individual species or entire life zones, based on suitable climate conditions. The model's development and subsequent application is supported by niche theory (Vandermeer 1972; Austin 2002; Leibold 1995), which describes the climatic niche as a functional or conceptual space defined on multiple axes of climatic variables (Figure. 2.1). The climatic niche is one aspect of an organism's or ecosystem's fundamental niche, excluding several admittedly important environmental constraints based on soils, topography, and biotic interactions such as competition or predation. Furthermore, the climatic niche is assumed to remain static and does not take dispersal ability or evolutionary adaptation into consideration when extrapolating from current distributions to future potential 28 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. distributions (Pearson and Dawson 2003; MeKenney et al. 2007b). Despite their inability to account for these key ecological processes, bioclimatic envelopes are appropriately employed at regional scales where climate has a dominant influence on species distribution (Pearson and Dawson 2003). Geographically calibrated bioclimatic envelopes are an inherently conservative tool for habitat modelling in that there is no danger of identifying 'false positives' for climatically suitable habitat; they allow firm identification of some known acceptable climates, even if the definition of all acceptable climates (which could be occupied by the target but are not) is incomplete. Furthermore, this modelling strategy is ideally suited to presence-only data, a characteristic of most conservation targets (Kadmon et al. 2003; Beaumont et al. 2005). Figure 2.1. An example of the conceptual or functional space describing the bioclimatic envelope of the dainty moonwort fern, Botrychium crenulatum Wagner. Axes shown here represent the B.C.-wide range for mean annual precipitation (MAP, mm), mean annual temperature (MAT, °C), and number of frost free days (NFFD), with only a subset of each being suitable for this species. The bioclimatic envelope for a specific conservation target can be further narrowed by consideration of additional or alternative climate attributes. 29 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. There is a vast array of methods available for generating bioclimatic envelopes. Most of these methods use one of the following statistical approaches: general linear models (GLM), general additive models (GAM), artificial neural networks (ANN), ecological niche factor analysis (ENFA), or classification and regression tree (CART) analysis. More recently, innovation in the field of statistical modelling has generated an exponential distribution model using maximum entropy (MAXENT), and a multivariate adaptive regression splines modelling approach (MARS) that combines linear regression, the mathematical construction of splines, and binary recursive partitioning to produce a model with linear and non-linear relationships (Guisan and Zimmermann 2000; Heikkinen et al. 2006). Examples of bioclimatic envelope models include BIOCLIM (Busby 1991), HABITAT (Walker and Cocks 1991) and DOMAIN (Carpenter et al. 1993). For a detailed summary of these modelling approaches, including the well described ENVELOP approach, see Guisan and Zimmermann (2000). Shortcomings of the bioclimatic envelope approach include the misrepresentation of suitable climate (commission and omission errors; Guisan and Zimmermann 2000; Heikkinen et al. 2006), the exclusion of possible interactions and partial substitutions, a propensity for autocorrelation and multi-collinearity, and problems with model validation (Kadmon et al. 2003; Araujo et al. 2005; Beaumont et al. 2005). It has also been recommended that bioclimatic envelopes be coupled with processbased models for a more refined projection of climate change impacts on biodiversity. For example, Pearson et al. (2002) coupled bioclimatic envelope models with a climatichydrological process model to predict the potential distribution of Protea species under climate change scenarios. Pyke and Fishcer (2005) also incorporated hydrological variables into their bioclimatic representation of fairy shrimp (Anostraca species) vernal habitat in the Central Valley ecoregion of California. Other studies exploring the impacts of climate change 30 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. on plant species and community distribution coupled GCM output with dynamic vegetation models (Burton and Cumming 1995; Malcolm et al. 2002; Scott et al. 2002; Lenihan et al. 2003; Lemieux and Scott 2005). Coupling dynamic models with bioclimatic envelopes is beyond the scope of the preliminary analysis and proof of concept reported here. These projections will be used in conjunction with The Nature Conservancy of Canada's large scale multi-filter ecoregional assessment (as outlined in Chapter 1), which uses expert knowledge and stakeholder input to address some of the shortcomings associated with any one modelling approach. Persistent climate corridor modelling To address the impacts of climate change on the management of biodiversity, the concept of a "persistent climate corridor" is developed. A persistent climate corridor extends the theoretical basis for landscape (spatial) corridors to provide continuity in time as a fourth dimension. In general, the purpose of landscape corridors is to provide continuity in geographic space. Maintaining genetic and habitat diversity support species persistence over time (Shafer 1990; Primack 2006). Consequently, the inclusion of climatic continuity over time in conservation planning enhances the decision making process and improves the prospects for resource sustainability. A persistent climate corridor is identified through the intersection of an ecological feature's current distribution with locations expected to remain within that feature's bioclimatic envelope as projected for the foreseeable future. This intersection identifies areas where a particular climate is expected to persist, based on the best available information of the feature's distribution and downscaled prediction of climate change. Areas so identified represent candidate areas for particular management practices, such as conservation 31 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. prioritization or the assisted migration of target species. Persistent climate corridors only indicate that certain locations are less at risk from climate change than other locations, and do not address other threats such as habitat destruction or displacement by invasive species. The idea for my thesis developed from the post-doctoral work of Drs. A. Hamann and T. Wang (2004, 2006), who used bioclimatic envelopes to project future distributional changes to biogeoclimatic zones. These zones represent landscape units based on climatic and physiographic features, and provided a valuable stepping-stone towards the conceptualization of persistent climate corridors. From here I moved to finer-scaled targets for which I hoped to refine my methods and develop a decision support tool which could more fully describe a target's potential future distribution. This chapter is offered as a proof of concept in applying these tools to three different types of conservation targets. It can serve as a template by which researchers and managers can begin to practically address the challenge of a changing climate. Methods The primary tools used to identify persistent climate corridors were the 3rd Generation of the Canadian general circulation model (CGCM3; Environment Canada 2008) and ClimateBC and ClimatePP climate downscaling and interpolation software (Hamann 2008). The identification of persistent climate corridors comprises the following four steps: 1) the development of bioclimatic envelopes for management targets; 2) the identification of locations projected to have future climates within each target's bioclimatic envelope for four timeslices ("current," defined as 1961 to 1999; the 2020s; 2050s and 2080s); 3) the overlay and intersection of these four timeslices using ArcMap® 9.2 GIS software (the identification of locations with a suitable climate space); and 4) a final overlay of suitable climate with a 32 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. target's current distribution. In order to illustrate the development and application of the persistent climate corridor concept, the following three management targets found in the Central Interior and Sub-Boreal ecoprovinces of B.C., Canada, were used in this analysis: • • • Biogeographical variants of the Interior Cedar-Hemlock (ICH) biogeoclimatic zone (Ketcheson et al. 1991); The North Pacific Interior Lodgepole Pine-Douglas-fir Woodland and Forest vegetation type, as defined by the Nature Conservancy of Canada; and An uncommon (B.C. blue-listed) lichen, Nephroma occultum Wetm. These management targets are a subset of those used in the Nature Conservancy of Canada's Central Interior Ecoregional Assessment process for protected area planning. This project area, constituting the Central Interior and Sub-Boreal ecoprovinces, served as the study area defining the spatial extent of the conservation targets explored here. Some conservation targets considered in that planning process are rare plant species or plant communities with individual locations of known occurrence, while other targets represent broad vegetation or ecosystem types, mapped over relatively large areas. The full set of persistent climate corridors identified in this study (of which only some are presented here) will be used in a site prioritization and selection process as part of the Nature Conservancy of Canada's ecoregional assessment, which is expected to provide fine-filter protection for those rare elements, and a full range of representative habitats for coarse-filter conservation as well (Noss 1987; NCC 2007). Defining bioclimatic envelopes for different conservation targets The selection of modelling and projection tools is dependent on research goals (e.g., to project species distribution, abundance, habitat suitability, probability of occurrence, or vulnerability), data type (absence and/or presence, relative abundance), data quality or reliability, and sample size. The ENVELOP-type modelling approach (Guisan and 33 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Zimmerman 2000) employed for this research was chosen because it is well suited for presence-only data, which were largely obtained from online herbaria and conservation and natural heritage data warehouses and from pre-existing map polygons. ClimateBC was used for climate interpolation and projection because it is easily accessible and calibrated for the study area (Hamann and Wang 2004, 2006; Spittlehouse 2006), it generates data amenable to this modelling approach, and it includes a wide selection of general circulation model (GCM) outputs from which to chose for future climate scenarios (Hamann 2008). Occurrence data (longitude, latitude, elevation) were collected for each conservation target. Since there were two types of distribution data (area-based and point-based), two separate methods were devised to capture the data necessary for the development of bioclimatic envelopes. Mapped coverages of the Interior Cedar-Hemlock (ICH) variants and The Nature Conservancy of Canada's North Pacific Interior Lodgepole Pine-Douglas-fir Woodland and Forest were each overlaid with a 1-km grid covering their entire range in B.C. A simple overlay of these coverages using ArcMap® produced a layer of points, which provided latitude, longitude and elevation values representing each 1-km2 of the target's current mapped range. In contrast, point locations for all documented locations of populations of the rare Nephroma occultum lichen were collected from a variety of conservation data centres, online sources and university herbaria. This extensive search for all possible species occurrence data (including locations beyond our study area) ensured that the resulting bioclimatic envelope was described as fully as possible. The bioclimatic envelope for Nephroma occultum was generated using 86 unique locations from across the geographic range covered by ClimateBC and ClimatePP including as far south as Idaho and as far east as Ontario. The four known locations of this species in the B.C. Central Interior 34 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. study area were then evaluated in terms of their potential to support persistent climate corridors. ClimateBC and ClimatePP (Mbogga et al. 2009) were used to describe current (1961 to 1990) and projected future climates (based on the A2 scenario of the CGCM3 model) for each point. Climate data interpolated or estimated for each target point consisted of 19 variables, which were narrowed down to four orthogonal indicators to reduce collinearity: • ® • • Mean annual temperature (MAT, in °C); Continentality, or temperature differences (TD, the difference in mean temperature of the warmest month and mean temperature of the coldest month, in °C); Annual heat moisture index (AHM, calculated as (MAT+10)/(mean annual precipitation in mm/1000)); and Precipitation as snow (PAS, in units of mm water equivalent). The variables selected to define bioclimatic envelopes were the most strongly correlated with the first four principal components of a simple principal components analysis, and explained >95% of the variance in current province-wide climate. A Pearson's covariance matrix of the province-wide climate data verified that MAT, TD, AHM and PAS were the least correlated, and therefore represent a set of largely orthogonal variables that can describe most of the variation in B.C.'s climate. In order to capture the core range of targets, devoid of anomalous and possibly erroneous data, the 5th and 95th percentiles of these variables were calculated for each target's current climate using PROC MEANS (SAS Institute 2004). Collectively, these values describe a target's current bioclimatic envelope. 35 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Identifying a target's persistent climate corridor Locations expected to be within the current bioclimatic envelope of each management target were projected for the current, 2020s, 2050s and 2080s timeslices using a 1-km grid for the province as a whole, or for just the Central Interior and Sub-Boreal ecoprovinces. A series of conditional statements in SAS was used to query each 1-km grid point to ascertain whether it was within the envelope (5th to 95th percentiles for each of the four selected climate variables) for a given conservation target in each timeslice. Maps portraying locations projected to be suitable, as defined by the target's current envelope are described as envelope areas; the envelope areas for each timeslice were then overlaid using the "OverlayIntersect" tool in ArcMap. Where the intersection of these timeslices identifies locations of suitable climate for all four timeslices, I infer that those locations are expected to remain adequately constant for the specified conservation target over the 75-year planning period; collectively, those locations are referred to as the "suitable climate space". Locations where the current distribution of a target and the locations of suitable climate space coincide designate a persistent climate corridor, and therefore represent priority candidate areas for management or conservation. Given the uncertainties inherent to original location information, recorded to the nearest minute, any point within 500 m of a target location projected to have a persistent climate was considered to be within its persistent climate corridor. The approach is illustrated by providing mapped output and area-based tabular summaries for some coarse (e.g., biogeoclimatic zones) and fine (e.g., individual rare plant species) conservation targets. 36 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Results Biogeoclimatic Zones and Interior Cedar-Hemlock (ICH) variants The overlay-intersection method was initially applied to the biogeoclimatic zones in B.C. In addition to portraying important contractions in the geographic distribution of these broad ecological zones, the analysis of climatic envelopes, suitable climate space and persistent climate corridors permits a simple summarization of expected range shifts, thereby providing an illustration of the potential magnitude of climate change impacts for a given area. Despite the inherent uncertainty, the findings presented in Tables 2.1 and 2.2 (based on the zonal projections published by Hamann and Wang 2006) show a general shift poleward of biogeoclimatic zones, as well as an average shift from low to higher elevations. The overlay and intersection procedures possible through the use of GIS utilities greatly aids in the visualization and analysis of projected conditions over multiple timeslices. Such overlay work is central to any sort of gap analysis in support of regional conservation planning. Figure 2.2, for example, shows B.C.'s current parks and protected area network overlaid with the persistent climate corridors for the biogeoclimatic zones of the province (derived from projections published as Figure 2.2 of Hamann and Wang (2006), and summarized here in Tables 2.1 and 2.2). The fact that there is little temporal climatic connectivity for many parks and protected areas illustrates a flaw in treating conservation areas as fixed and static. It is probable that the distribution of persistent climate corridors across the landscape is also restricted by the complex topography of B.C.'s landscape, providing very little opportunity for climatic stability and spatiotemporal connectivity. 37 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. N | | Protected Areas | ICH Corridor | BC Border IDF Corridor HH| AT Corridor MH Corridor m|BG Corridor MS Corridor BWBS Corridor PP Corridor - None CDF Corridor S8PS Corridor - None CWH Corridor SBS corridor ESSF Coiridor SWB Corridor - '* A V: • i...... M 120 km I Figure 2.2. Graphical gap analysis showing the locations of persistent climate corridors projected for the biogeoclimatic zones of south-central British Columbia and for the province as a whole relative to the distribution of existing parks and protected areas. Abbreviations for the biogeoclimatic zones are defined in Table 2.1 and 2.2. 38 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 73 CD "O -5 o Q. C o CD Q. "O CD Table 2.1. A summary of the mean values and standard deviations for elevation (m) and latitude (°N) of the bioclimatic envelope area for each B.C. biogeoclimatic zone for all four timeslices and its associated persistent climate corridor (PCC) C/) (j) o' 3 O o o "O cq' Base (1960-1990) Biogeoclimatic Zones AT, Alpine Tundra BG, Bunchgrass Mean Latitude CDF, Coastal Dougls-fir 3. 3CD CWH, Coastal Western Hemlock o Q. C a SSF, Engelmann Spruce Sub-alpine Fir ICH, Interior Cedar Hemlock o "O IDF, Interior Douglas-fir MH, Moutain Hemlock MS, Montane Spruce PP, Ponderosa Pine C/) C/) SBPS, Sub-boeal Pine Spruce 1844 1.68 51.97 526 69.41 60.01 601 26.98 49.99 37 25.76 58.82 360 26.29 60.00 1727 1.24 57.17 1059 4.64 52.43 1210 62.70 58.84 1127 1.19 52.23 1621 0.22 0.00 NA* 0.00 0.00 NA* 0.00 59.94 1052 0.91 59.72 488 0.03 1844 55.11 1825 (-480) (-3.3) (-518) 50.62 58.16 (1.39) 49.04 51.64 53.32 (2.61) 51.99 50.83 52.79 50.88 49.96 52.37 54.37 58.54 (0.88) 593 50.90 702 50.90 773 50.83 838 (199) (1.06) (237) (1.13) (248) (1.25) (266) 706 58.29 768 58.62 786 58.86 926 (208) (1.40) (277) (1.11) (318) (1.01) (395.30) 37 49.08 74 49.67 73 50.22 74 (56) (0.52) (113) (1.19) (106) (1.67) (101.84) 348 51.77 550 51.89 636 51.99 724 (314) (2.12) (484) (2.15) (522) (2.18) (555) 1552 55.18 1573 55.83 1625 56.39 1700 (347) (3.11) (360) (3.04) (355) (2.88) (347) 942 52.97 1096 53.15 1177 53.24 1288 (341) (2.29) (329) (2.49) (351) (2.78) (404) 1004 51.90 1062 53.55 979 54.33 1077 -242 (-2.18) (-295) (2.33) (284) (2.58) (264) 1085 53.30 1224 53.63 1360 53.98 1520 (265) 2.327 406.4567 (2.30) (368) (2.45) (366) 1424 53.14 1358 54.53 1358 55.82 1457 (165) (3.03) (378) (3.16) (357) (3.22) (361) 636 50.17 786 52.66 823 55.77 787 (188) (0.67) (193) (2.70) (231) (2.67) (228) 1143 52.76 1256 52.77 1394 52.80 1575 (139) (1.57) (163) (1.80) (180) (1.00) (96) 889 54.65 940 56.06 1089 55.43 1284 (167) (2.03) (224) (3.11) (233) (3.33) (219) 1273 58.89 1448 58.80 1661 58.62 1716 (219) (1.21) (265) (0.77) (354) (0.73) (519) * These values are not applicable because a persistent climate corridfor doesn't exist for these zones 39 60.00 55.30 (1.26) SWB, Spruce Willow Birch %of Current Area (-3.35) (0.56) SBS, Sub-boreal Spruce Mean Elevation 1819 (0.58) CD Mean Latitude (-427) (1.20) "O Mean Elevation 55.69 (2.44) Q. Mean Latitude (-3.18) (-0.91) CD Mean Elevation 1844 (2.29) o Mean Latitude (-327) (2.05) CD "O -5 Mean Elevation Persistent Climate Corridors 2080 55.47 (0.37) -5 -5 Mean Latitude 2050 (-2.16) (0.77) BWBS, Boreal White and Black Spruce Mean Elevation 020 Table 2.2. Summary of the area (km2) within the bioclimatic envelope for each of the B.C. biogeoclimatic zones and their projected changes over time, their associated persistent climate corridors and the current representation of those persistent climate corridor. Biogeoclimatic Zone Basline 2020 2050 2080 Persistent climate Corridor (PCC) % PCC of Current Area km2 PCC protected % PCC Protected AT, Alpine Tundra 187,644 73,385 44,879 33,065 31,613 2.0 7,679 24 BG, Bunchgrass 3,299 13,215 26,427 44,452 2,290 6.0 258 11 BWBS, Boreal White and Black Spruce 163,056 163,182 139,873 88,246 43,993 27.0 1,860 4 CDF, Coastal Douglas-Fir 14,140 6,072 10,355 16,015 3,642 26.0 164 5 CWH, Coastal Western Hemlock 398,503 155,633 169,175 179,100 104,758 26.0 1,178 1 ESSF, Engelmann Spruce - Subalpine Fir 148,087 192,225 187,228 132,339 1,834 1.0 588 32 ICH, Interior Cedar-Hemlock 53,502 127,350 152,346 184,827 2,483 5.0 891 36 IDF, Interior Douglas-Fir 44,410 61,722 139,625 111,565 2,765 63.0 324 12 MH, Mountain Hemlock 36,558 26,117 16,486 7,232 435 1.0 61 14 MS, Montane Spruce 28,098 27,302 23,736 17,254 62 0.0 8 13 PP, Ponderosa Pine 3,567 9,257 21,734 14,0657 0 0.0 0 0 SBPS, Sub-Boreal Pine Spruce 24,050 14,369 5,048 489 0 0.0 0 0 SBS, Sub-Boreal Spruce 103,012 81,336 28,687 14,139 934 1.0 44 5 SWB, Spruce-Willow-Birch 74,944 18,964 4,529 750 21 0.0 14 67 Of the eleven ICH variants in the study area, only two are expected to have persistent climate corridors. Table 2.3 summarizes the extent of the each variant's current distribution, its associated suitable climate and persistent climate corridor, as well as the percentage of the current distribution represented by the PCC. Figure 2.3 maps the locations in which climate suitable for ICHmcl is expected to remain suitable, and thus the locations where this BGC variant can be expected to exhibit a PCC. Despite a relatively large current distribution, there is little overlap with the locations expected to show persistent climate; consequently, the ICHmcl persistent climate corridor is expected to represent only approximately 4% of its current distribution. In contrast, the ICHvc experienced the only increase in its suitable climate space of the eleven ICH variants. More interestingly, this increase is expected to result in a potential range covered by suitable climate that is approximately 9.25 times its current distribution in the study area (Table 2.3). Overlaid on its current distribution, this 40 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. expected expansion of persistent climate contributed to identification of a persistent climate corridor (182 km2), constituting approximately 13% of this variant's current distribution. Table 2.3. Selected Interior Cedar-Hemlock biogeoclimatic variants found in British Columbia, and their expected persistence. Variant Description Current Area (km2) Persistent Climate Range (km2) Persistent Climate Corridor (PCC) (km2) Current area represented by PCC (%) ICHdk Interior Cedar-Hemlock, dry cool 351 0 0 0 ICHmw3 ICHmk2 ICHmk3 ICHmcl ICHmc2 ICHwk2 ICHwk3 ICHwk4 ICHvk2 ICHvc Interior Cedar-Hemlock, Thompson moist warm Interior Cedar-Hemlock, Thompson moist cool Interior Cedar-Hemlock, Horsefly moist cool Interior Cedar-Hemlock, Nass moist cold Interior Cedar-Hemlock, Hazelton moist cold Interior Cedar-Hemlock, Quesnel wet cool Interior Cedar-Hemlock, Goat wet cool Interior Cedar-Hemlock, Cariboo wet cool Interior Cedar-Hemlock, Slim very wet cool Interior Cedar-Hemlock, very wet cold 3,541 891 1,072 5,343 3,276 2,038 943 1,425 2,834 1,449 0 0 0 3,677 0 0 0 0 0 13,403 0 0 0 203 0 0 0 0 0 182 0 0 0 4 0 0 0 0 0 13 North Pacific Interior Lodgepole Pine - Douglas-fir Woodland and Forest The extent of suitable climate for the North Pacific Interior Lodgepole Pine -Douglasfir Woodland and Forest under current climate conditions is estimated to occupy some 57,000 km2 of the study area, though only 11,828 km2 of this area is currently occupied by this ecosystem unit (Figure 2.4). A large area (22,661 km2) covered by such suitable climate is expected to persist, but the current distribution of this relatively warm and dry vegetation type means that the persistent climate corridor is projected to occupy only 1,131 km2, which would represent only 10% of its current area. 41 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Figure 2.3. Locations of persistent climate corridors projected for the Nass Moist Cold Interior CedarHemlock (ICHmcl) biogeoclimatic variant. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Figure 2.4. The current distribution, locations expected to exhibit persistent suitable climate, and the resulting persistent climate corridors projected for the North Pacific Interior Lodgepole Pine -Douglasfir Woodland and Forest ecosystem unit in the study area. Nephroma occultum For many individual species as well, current climatic envelopes suggest that persistent climate can be expected over large areas, but often where populations are not currently found. This is particularly evident for rare species such as Nephroma occultum as shown in Figure 2.5. In this example, there are four occurrences of Nephroma occultum in the study area, with 43 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. only one population located in an area projected to exhibit persistent climate. Legend @ Persisten! Climate Corridor O Current Distribution Suitable Climate Range OO Figure 2.5. Locations of suitable climate space and persistent climate corridor projected for Nephroma occultum in the Central Interior study area. Discussion Overview Many caveats apply to the identification of locations expected to have suitable climate space and those having the possibility of providing continuity over time as persistent climate corridors. For all area-based targets, whether biogeoclimatic zones, terrestrial ecosystem units, or plant communities, there is some degree of arbitrary delineation in their definition, as constrained by their current expression under associated climatic and geographic 44 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. parameters. In other words, how they are distinguished from another similar unit can be very arbitrary, e.g., the Interior Cedar Hemlock (ICH) versus the Coastal Western Hemlock (CWH), or the ICHmcl versus the ICHmc2 variant. It is also important to recognize that a biogeographic lag exists between climate change and vegetation response such as observable changes in distribution or composition (Parmesean and Yohe 2003; Fitzpatrick et al. 2008). This lag is highlighted by my distinction between locations characterized by persistent climate and those identified as suitable persistent climate corridors: the contradiction of climate change is that there are expected to be larger areas suitable for most of the conservation targets in my study area, however they do not coincide with locations in which these sedentary targets are currently found (Figures 2.2 to 2.5, Tables 2.2, 2.3). Overall, my results agree with other studies (e.g., Pearson and Dawson 2003; McKenney et al. 2007b) which show that conditions suitable for the persistence of many existing plant species and ecological communities are expected to contract. In my study area, this is shown by the ICH variants (Table 2.3 and Figure 2.3), and the Lodgepole PineDouglas-fir Woodland and Forest ecological unit (Figure 2.4). Many rare plant communities (not specifically explored in this thesis) represent unique combinations of climate, soils, floristics and disturbance history, which may not be sustainable under changing climate conditions (Hansen et al. 2001; Gayton 2008; Van der Veken et al. 2008). The goal of this research was to assist the Nature Conservancy of Canada in refining their ecoregional assessment process to include the impacts of climate change (see http://science.natureconservancy.ca/centralinterior). The concept of persistent climate corridors is designed as an addendum to their fine-filter approaches to conserve individual species which are considered rare or of conservation concern, and coarse-filter approaches to conserve representative ecological communities. The Nature Conservancy of Canada's 45 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. ecoregional assessment is an intensive data gathering undertaking, which considers multiple inputs (e.g., the range of target animal species, aquatic features, the extent or frequency of natural and anthropogenic disturbance), as well as scientific expertise and priorities of various stakeholders. The climate change component of this ecoregional assessment also incorporates expert knowledge to address the vast uncertainties associated with climate change scenarios and species distribution projections. Recommendations for conservation priorities based on suitable climate and persistent climate corridors will serve as one of many inputs to an iterative, heuristic site selection process using the Marxan reserve selection software (Ball and Possingham 2000). Trends in my results identifying locations with higher elevations and latitudes (than what is current) as becoming more suitable for lower-elevation and more southerly ecosystems (Table 2.1) likewise concur with the majority of the literature exploring the potential outcomes of climate change (Parmesan and Yohe 2003; Spittlehouse 2005; Hamann and Wang 2006). On the other hand, some of my results may be counter-intuitive to what might be expected. For example, despite an increase in climatically suitable area over time, the Ponderosa Pine (PP) zone and the Sub-Boreal Pine-Spruce (SBPS) zone are not expected to have persistent climate corridors (Figure 2.1). The lack of a persistent climate corridor for the PP zone is particularly ironic given that this zone is characterized by a hot, dry climate (Hope et al. 1991), and thus might be expected to persist and expand under global warming as projected by some models. Unfortunately (from an ecosystem conservation perspective), most of the area expected to be suitable for the characteristic ponderosa pine ecosystem is not currently occupied by those forests or woodlands, while current areas will become so hot and dry that they may only support grassland or sagebrush (BCMFR 2006b). 46 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. The lack of identifiable persistent climate corridors for the PP and SBPS zones, plus most of the ICH variants explored in this paper (Table 2.3) presents conservation managers with a number of challenges. For example, if current locations occupied by these identifiable ecosystems are not suitable for sustaining them, should programs of facilitated migration and ecosystem engineering be employed at locations expected to support persistent climate for these conservation targets? The expected loss of nine out of the 11 ICH variants in my study area is particularly disturbing, considering that these inland rainforests are globally unique, with old-growth phases supporting many rare and disjunct lichen species including Nephroma occultum (Goward and Spribille 2005). A number of important questions regarding a conservation target's ecology and its subsequent management arise with the identification of persistent climate corridors. For example, despite an expansion of suitable climate space, Nephroma occultum has only one occurrence within the area expected to sustain a persistent climate. Consequently, available information suggests that there can only be one location expected to serve as a persistent climate corridor for this species in my study area. Although protection logically becomes a priority for that location, this result also demonstrates the need to incorporate additional expert knowledge into the conservation planning framework. Using this example, it is reasonable to infer that Nephroma occultum is not limited by climate. Rather, its limited distribution depends on old-growth forest habitat, which is threatened by logging, wildfire and defoliation by the hemlock looper (Lambdina fiscellaria lugubrosa (Hulst)). Other ecological factors which make Nephroma occultum vulnerable are its poor dispersal and competitive abilities (Brodo et al. 2001; COSEWIC 2006). Adapting management practices to maintain or increase its presence in the study area may involve altering timber harvesting practices to encourage the conservation of old- growth forests. Given its rarity and the 47 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. clustering of current occurrences near the location identified as a persistent climate corridor (Figure 2.5), it may still be prudent to target all known population locations for conservation management. Expected contractions in the range of conservation targets highlight the utility of identifying persistent climate corridors as potential adaptive strategies for forest management and conservation (Hannah et al. 2005). For example, a target's suitable climate space can provide target areas for the translocation or facilitated migration of plant species or populations which are the target of conservation or management. Facilitated migration represents a degree of active intervention to avoid the extinction of desired species or populations by transporting sensitive or economically important species or populations to more climatically suitable locations (Van der Veken et al. 2008). According to the B.C. Ministry of Forests and Range (BCMFR 2006a), facilitated migration is potentially the most effective and least expensive forest management option to address the effects of climate change on commercially important timber species. A common challenge is that establishment of species or seedlots in locations where they are expected to experience a more favourable future climate depends first on surviving the period of current climate; the mapping of persistent climate corridors gets around this problem. Whether or not a focused program of facilitated migration has applications in the conservation of rare plant communities or ecosystems remains to be seen. McKenney et al. (2007b) used a similar method to develop new plant hardiness zones for wild and cultivated plant species in Canada. Hannah et al. (2005) used projected bioclimatic envelopes to map areas of overlap in an attempt to protect the remaining distribution of key Protea (Proteaceae) species in South Africa. Bioclimatic envelope modelling has also been used to project the distribution of commercial tree species in British 48 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Columbia (Hamann and Wang 2006) and for all of North America (McKenney et al. 2007a). Climatic threats to the persistence of existing habitat, plus the potential for range expansion or range shifts of some biotic elements, were identified in all of these studies. Sources of uncertainty All climatic and biogeographic projections are subject to substantial uncertainty, which is largely a function of assumptions that are difficult to validate, parameters difficult to estimate, mechanisms difficult to confirm, and socio-economic conditions difficult to project (Pyke et al. 2005). Therefore, an uncertainty analysis is a critical component of any climate change study. At the very least, possible sources of uncertainty need to be recognized and accounted for. Although a detailed uncertainty analysis is beyond the scope of this chapter, I have summarized some possible sources of uncertainty relevant to this study in Table 1.4. The uncertainty inherent in the identification of persistent climate corridors is evident at each step in the overlay-intersection process. To begin with, the extent to which the occurrence data represent the full range of some species is questionable given the low number of "calibration points." Due to the challenges of collecting rare occurrence data, such as the difficulty of accurate species identification (especially in reference to varieties and sub-species), plus notoriously incomplete searches in mountainous and roadless terrain, data quality is often questionable (Hannah et al. 2005; McKenney et al. 2007a,b). The ability of general circulation models (GCMs) to accurately predict the relevant changes in future climate, particularly those related to precipitation, are also a significant source of uncertainty. One of the main reasons for this flaw is the large discrepancy of scale between a given study area and the large area covered by a GCM cell (Kueppers et al. 2005). The ability of the CGCM3 model to make realistic projections is also confounded by B.C.'s diverse and 49 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. mountainous topography, which heavily influences the climate from one area to another. Climate and vegetation both change rapidly over short distances in the mountainous terrain of jurisdictions such as B.C. Consequently, efforts to match and project changes in vegetation with climatic attributes modelled at the scale of GCM cells depend on the calibration and sensitivity of downscaling tools. British Columbia's terrain also constrains the number and placement of weather stations, which heavily influences the outcomes produced by these climate interpolation tools (Pyke et al. 2004; Hannah et al. 2005). Some of these limitations are addressed by the fact that elevational effects and the degree of spatial correlation are incorporated into the spatial interpolation algorithms of ClimateBC and ClimatePP (Daly et al. 2000, 2002; Hamann and Wang 2006). Conclusions The concepts of bioclimatic envelopes, suitable climate space, and persistent climate corridors provide a simple and powerful tool kit for conservation planning under a changing climate, pertinent to the development and application of a variety of management strategies. For example, the Nature Conservancy of Canada will use the final outcomes of this research as a pre-processing layer in their conservation plan for the Central Interior of British Columbia. Government agencies, such as the B.C. Ministry of Forests and Range, can use the concept of persistent climate corridors in the development of strategies for facilitating the climate-adapted migration of valuable tree provenances. As research continues to reveal the impacts of climate change on ecological systems, the need to develop and adapt new management strategies becomes increasingly urgent. Persistent climate corridors have the potential to assist managers as they cope with the challenges presented by climate-driven changes to forested ecosystems. 50 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Chapter 3 - Bioclimatic envelopes of selected conservation targets in B.C.'s Central Interior and the identification of candidate areas for conservation in a changing climate Abstract One of the threats climate change poses to global biodiversity is a widespread reorganization and redistribution of ecological communities. To address this issue, bioclimatic envelopes were developed to identify persistent climate corridors for 206 conservation targets (30 terrestrial ecological units (TEUs), 103 B.C. biogeoclimatic (BGC) variants, and 73 rare plant species) in B.C.'s Central Interior. Bioclimatic envelopes were developed using ClimateBC, a computer program that interpolates current climate data and downscales general circulation model climate projections. For this research, I chose the 3rd generation of the Canadian general circulation model and a "business as usual" scenario (CGCM3 A2) to generate climate data of the current and potential future distributions of a target. The 5th and 95th percentiles of each target's climate data were used to define the core bioclimatic envelope. Ares were identified which met bioclimatic envelope requirements for 4 timeslices of climate including a baseline (1961-1990s), the 2020s, the 2050s and the 2080s. The identification of areas of coincidence among these envelopes areas revealed a target's suitable climate space (SCS) in which climate suitable for that particular target is expected to persist for the foreseeable future. Subsequently, the intersection of a target's SCS with its current distribution characterized a target's persistent climate corridor (PCC). My analysis produced PCCs for 6 TEUs (20%), 7 plant species (10%) and 10 BGC variants (10%). For those TEUs and BGC variants with PCCs, an average of only 320 km2 and 19 km2, respectively, is projected to remain stable through the 2080s, highlighting the severity of climate change impacts to coarse filter biodiversity conservation. Persistent climate 51 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. corridors for plant species were scattered around the centre of the study area. It is predicted that rare plant populations will be most strongly limited by reduced snowfall and increased continentality. Persistent climate corridors for the BGC variants were concentrated in the northwest, and TEUs in the southeast and eastern edge of the study area. These areas of persisting suitable climate represent priority areas for conservation as they are projected to provide a degree of climatic refuge. Although this type of analysis is quite sensitive to the choice of models and scenarios for climate change, it represents a reasonable means of incorporating anticipated spatiotemporal ecosystem dynamics into conservation planning. 52 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Introduction Climate is the dominant abiotic control over large-scale environmental elements such as ecosystems (Pearson and Dawson 2003); and over geological time, climate affects biodiversity through its influence on the dispersal and migration patterns of plant and animal species (Leemans and Eickout 2004; McKenney et al. 2007b). According to the Intergovernmental Panel on Climate Change (IPCC 2007), the existing climate crisis is unequivocal, and global increases in temperatures are due to increases in anthropogenic greenhouse gases. From an ecological perspective, the current rate and magnitude of climate change poses a threat to native biodiversity, a threat considered by some analysts to ultimately be more serious than other anthropogenic activities, such as land use change and resource extraction (Bakkenes et al. 2002; Berry et al. 2002; Ellis et al. 2007; Gayton 2008) For a variety of socio-economic and scientific reasons, the forces driving climate change are arguably irreversible within our lifetime (Schneider 2004; IPCC 2007). The conservation of biodiversity is one of many resource management objectives demonstrating the need for innovative climate-driven management strategies (Halpin 1997; Hannah et al. 2002b; Spittlehouse 2005). Incorporating foreseeable climate shifts into management practices reflects a paradigm shift to a dynamic, non-equilibrium approach to resource management. The importance of this shift is illustrated with species migrating outside of reserve networks and the complete re-organization and redistribution of ecological communities (Scott et al. 2002; Suffling and Scott 2002; Lemieux and Scott 2005; Hamann and Wang 2006). At the root of these expected ecosystem changes are species extinctions, extirpations and declines, species invasions, the introduction or proliferation of pests and disease, as well 53 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. as changes in the frequency and magnitude of natural disturbances such as wildfire and flooding (Dale et al. 2001; Gayton 2008). Individual species are expected to respond idiosyncratically to climate change and current ecological communities are likely to evolve into new communities (Hamann and Wang 2006; Hijmans and Graham 2006; Williams and Jackson 2007). These changes will have cascading effects on community function and consequently important ecosystem services, such as water purification and waste decomposition (BCMFR 2006a). It is difficult to predict how ecological communities will re-organize themselves because our tools for the analysis and projection of climate predictions and understanding of ecological processes are imperfect. However, there are a variety of technological and conceptual approaches available to approximate the probable outcomes. For this research, I used bioclimatic envelope modelling as a foundation for projecting some potential ecological changes to the Central Interior of British Columbia (B.C.), Canada. The Nature Conservancy of Canada's ecoregional assessment of B.C.'s Central Interior The Nature Conservancy of Canada's ecoregional assessment of the Central Interior and Sub-boreal ecoprovinces of B.C. provides a case study to explore the integration of spatiotemporal dynamics into the site selection and prioritization processes of conservation planning (http://science.natureconservancv.ca/centralinterior/central.php. Or Chapter 1). Consequently, the research described here focuses on this geographic area (spanning 50.9 to 57.4 °N latitude and ranging from 131.2 to 120.0 °W longitude) (Figure 1.1), with the goal of aiding the design of a conservation network in the face of impending climate change. The specific objectives of this chapter are to: 1) define bioclimatic envelopes for three 54 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. conservation target groups (73 plant species identified as rare by the B.C. Conservation Data Centre (CDC), 30 terrestrial ecological units (TEUs) as defined by the Nature Conservancy of Canada, and 103 subzone variants as mapped under Version 7 of the B.C. Biogeoclimatic Ecosystem Classification; and 2) identify the current locations of each target's suitable climate space and where each target's suitable climate space will persist over a 75-year planning period. Plant species were selected based on their vulnerability to anthropogenic (e.g., habitat fragmentation, urbanization, invasion of exotic species) and to a lesser extent, natural threats (e.g., herbivory, competition, disturbance). For a description of CDC plant species and their conservation status see Appendix A Tables Al and A2. The B.C. biogeoclimatic (BGC) variants were selected as conservation targets because they represent climatically homogenous units that correspond to differences in vegetation, soil and ecosystem productivity, and they provide the basis for classification frameworks such as forest management practices and the TEU schema (outlined in Chapter 1) (Pojar et al. 1987). For a complete list of these targets please see Appendix A, Tables A5 and A6 respectively. In order to meet these objectives, bioclimatic envelopes were developed for each conservation target based on its current documented distribution using climate interpolation and downscaling tools. Geographic information system (GIS) software was used to perform climatic (niche) overlay and gap analysis to identify a target's suitable climate space and the locations where climatic conditions are projected to remain within the limits defined by its bioclimatic envelope. Subsequent analyses of the bioclimatic envelopes were performed to determine the climate variables which most strongly limit the distribution of the conservation targets. 55 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Bioclimatic envelope modelling Bioclimatic envelope modelling provided the foundation for this study. This modelling strategy is used to predict species dynamics and community formation (McKenney et al. 2007a; Williams and Jackson 2007), and to describe the current and possible future distribution of a conservation target (e.g., a rare plant species) based on a set of suitable climate conditions defined by target-specific physiological tolerances (Thuiller 2003, 2004). More information about bioclimatic envelope modelling is found in Chapter 1 and the feasibility of this approach for each type of conservation target has been demonstrated in Chapter 2. Development of bioclimatic envelopes - Data collection and amalgamation The first step in the development of bioclimatic envelopes is to collect occurrence data for each conservation target. The approach to the data gathering process depended on whether a target's occurrence data were point-based (i.e., individual plant species occurrences) or area-based (i.e., consisting of pre-existing spatial coverages of TEUs and BGC variants). In order to fully describe a species' bioclimatic envelope, a variety of online databases, conservation data centres and university herbaria were accessed. Ideally, occurrences from across the entire range of a conservation target are needed to fully describe a target's bioclimatic envelope (Bakkenes et al. 2002; Kadmon et al. 2003; Fitzpatrick et al. 2008). For this research, the development of a target's bioclimatic envelope was based on the climate across Canada, including the territories and northern portions of Washington, Idaho and Montana using ClimateBC and ClimatePP. In general, the occurrence records for the plant species were predominately found east and south of the study area. Their presence in 56 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. the study area appears to represent marginal populations relative to their distributions outside of ClimateBC and ClimatePP (see http://www.plants.usda.gov). Target species occurrence data collection The data sources and herbaria which were accessed to collect as many records of species occurrence as possible are summarize in Table 3.1. These records are typically based on physical voucher specimens deposited in herbaria and identified (or their identity confirmed) by expert plant taxonomists. In some CDC and Natural Heritage Program records, conservation specialists recorded populations of rare species without a corresponding voucher specimen deposited at a herbarium. Although additional information on soils, topography, elevation, plant community, etc., is usually associated with those occurrence records, the analysis reported here depended only on the precise identification of latitude and longitude. All synonyms for each scientific name were searched for; however, any other subspecies or variety other than the listed taxon were excluded from this search. See Appendix A, Table A3, for a list of the species synonyms used in the data collection. This decision was based on the lack of clear universally recognized taxonomic standards and that rarity (and hence my analysis) was specific to the subspecies or variety in some cases. Allium geyeri var. teneri, for example, is considered imperilled or of special concern and is found sporadically across B.C. On the other hand, A. geyeri var. teneri is not recognized to occur in Alberta; however, Allium geyeri occurs but is unlisted in Alberta and most of the western states in its range (Issac et al. 2004; Haig et al. 2006). The ability to successfully protect rare taxa is a challenging goal in itself, but it is made more difficult by a lack of clearly defined taxonomic standards (Issac et al. 2004; Haig et al. 2006; Garnett and Christidis 2007). 57 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Terrestrial Ecological Unit and Biogeoclimatic variant spatial coverages - Area-based data collection In order to collect occurrence data for area-based target groups (BGC variants and TEUs), spatial GIS coverages of their current distribution were obtained from the B.C. Integrated Land Management Bureau (ILMB) and the B.C. Chapter of the Nature Conservancy of Canada, respectively. A 1-km grid of B.C., where each point represented a latitude and longitude coordinate and elevation, was overlaid with each of those coverages using ArcMap® 9.2. To ensure that the bioclimatic envelopes of these conservation targets were described, the climate prevailing across the range of those area-based targets was determined from province-wide distributions rather than for the study area only. Climate data generated for the BGC variants were derived from their province-wide distribution and applied to the variants which occurred in the study area. Climate data for the TEUs were derived from coverages provided by the Nature Conservancy of Canada, including the TEU coverage for the adjacent Okanagan Ecoregional Assessment (NCC 2007a). Data Amalgamation Occurrence data were amalgamated in an Excel file and organized according to target group and data source. ClimateBC and ClimatePP (Mbogga et al. 2009) are two climate interpolation and downscaling software programs which can be used to generate 19 climate variables for both historical conditions and a number of future climate change scenarios (Table 3.2). 58 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table 3.1. Data sources accessed for rare plant occurrence data. Name of Dataset Type of data Element occurrence records Alberta Natural Heritage Centre and status report Element occurrence records B.C. Conservation Data Centre and status report Element occurrence records Idaho Conservation Data Centre and status report Element occurrence records Montana Natural Heritage and status report Centre Washington Natural Heritage Element occurrence records Centre and status report database Eflora Reference John Rintoul, (780) 427-6639 Email: john.rintoul@gov.ab.ca http://www.env.gov.bc.ca/cdc/ http://fishandgame.idaho.gov/cdc/ http://mtnhp.org. http://www.dnr.wa.gOv/ResearchScience/T opics/NaturalHeritage/Pages/ampnh.aspx. /index.shtml. Global Biodiversity Information Occurrence data from a wide variety of sources* www.gbif.org. Facility University of Victoria Herbaria Occurrence data Email: herb@uvic.ca or 250-21-7097 University Northern B.C. Occurrence data Email: reav@unbc.ca Herbaria Occurrence data, areaYukon Biodiversity Database http://www.aina.ucalgary.ca/yb/ specific articles •Sources include UBC and Canadian Museum of Natural History Table 3.2. Description of annual climate variables produced by ClimateBC and ClimatePP. For a more detailed review of these variables, see Spittlehouse (2006). Climate Variable Description MAT MWMT MCMT TD MAP MSP AH:M SH:M DD<0 DD>5 DD<18 DD>18 NFFD FFP bFFP eFPP PAS DD5 100 EXT Cold Mean annual temperature (°C) Mean temperature of the warmest month (°C) Mean temperature of the coldest month (°C) Continentality - difference between MWMT and MCMT (°C) Mean annual precipitation (mm) Mean May to September precipitation (mm) Annual heat: moisture index (MAT + 10)(MAP/1000) Summer (May to September) heat: moisture index (MWMT)(MSP/1000) Degree days below 0 °C (chilling degree days) Degree days above 5 °C (growing degree days) Degree days below 18 °C (heating degree days) Degree days above 18 °C (cooling degree days) Number of frost free days Frost free period (days) Beginning of frost free period (days) End of frost period Precipitation as snow (mm) Day of the year on which DD>5 reaches 100, date of budburst Extreme minimum temperature over 30 years (°C) 59 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Variable selection Nineteen variables provide the user of ClimateBC and ClimatePP with the opportunity to explore a variety of climate-based hypotheses. However, many of the climatic attributes listed in Table 3.2 are strongly correlated with each other. The presence of collinearity suggests that there is some degree of overlap or redundancy among variables which might lead to a loss of statistical power and make it difficult to interpret the results. In order to reduce collinearity and maximize the predictive power and reliability of my model, I selected four largely orthogonal variables from the original dataset. Variable selection for the development of target bioclimatic envelopes was based on principal components analysis (PCA) using the SAS PROC PRINCOMP procedure (SAS Institute 2004) followed by a Pearson's correlation analysis (SAS PROC CORR; SAS Institute 2004) based on provincewide climate data. From the PCA, I selected the variables most strongly correlated with the first four principal components, which resulted in MAT, AHM, TD and PAS being the strongest contributors to the eigenvalues (Table 3.3a). I confirmed my variable selection with a Pearson's correlation matrix to make sure that none of the stated variables were highly correlated (Table 3.3b). A brief review of the literature further confirmed that these selected variables are considered to be critical for plant survival and reproductive success (Araujo et al. 2005; McKenney et al. 2007a; Fitzpatrick et al. 2008). The baseline climate data for ClimateBC and ClimatePP were derived from commercially available coverages that were generated using PRISM (Parameter Regression of Independent Slopes Model (Oregon State University Corvallis, Oregon, USA) (Daly et al. 2000, 2002). According to Hamann and Wang (2006), the available PRISM datasets at 2-km and 4-km resolution were insufficient for B.C.'s complex terrain and ultimately led to the 60 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. overestimation of the changes to future climates. The biased distribution of weather stations which generally excludes difficult to reach, higher altitude mountainous areas also confounds and distorts inferred bioclimatic envelopes. Using high-resolution digital elevation models, Wang et al. (2006) developed simple elevation adjustment formulas that facilitated the intelligent downscaling of the PRISM model (Daly et al. 2000, 2002). Climate change components are incorporated into ClimateBC and ClimatePP with the provision of outputs from a number of general circulation models for the user to choose from. The third generation of Canadian General Circulation Model (CGCM3) was selected for this study because it was easily accessible and internationally recognized (IPCC 2007; McKenney et al. 2007b). The A2 "business as usual" scenario (Environment Canada 2008) was chosen to take a conservative approach and provide the worst possible circumstances (i.e., to follow the precautionary principle). ClimateBC was chosen because it was developed specifically for B.C.'s complex terrain; other climate interpolation tools such as ANUSPLIN (The Fenner School of Environment and Society, The Australian National University) lack the ability to incorporate the influences of complex terrain on climate (Daly et al. 2000, 2002). Determining a conservation target's bioclimatic envelope Once the target group's occurrence data were amalgamated, the latitude, longitude and elevation of each occurrence were run through ClimateBC and ClimatePP with the CGCM3 A2 scenario to generate climate variables at each location. The resulting dataset was refined to include the selected variables (i.e., MAT, TD, AHM, PAS), and the target's bioclimatic envelopes were limited to a more certain range defined by the 5th and 95th percentiles of each climate variable (as recommended by Kadmon et al. 2003; McKenney et al. 2007a). Using the 5th and 95th percentiles to capture the core of a target's bioclimatic 61 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. envelope excluded any anomalous data such as species persistence in regionally peculiar microsites which might skew the results (Walker and Cocks 1991; Carpenter et al. 1993; Beaumont et al. 2005). Table 3.3. a) The standardized loadings from the top 4 principal components (PC) and b) a partial summary of the Pearson's Correlation Matrix of provincial climate data used to select climate variables for the development of bioclimatic envelopes a) Principal Component Analysis Loading 1 2 3 4 Eigenvalue 0.5135 0.3168 0.0692 0.0465 Factor Loadings (Pearson's Correlation, r) AHM TD PAS MSP MWMT MAT 0.3437 0.0092 0.0300 -0.0672 0.0746 0.3945 -0.2106 0.2807 -0.1063 0.3234 0.4246 -0.0591 -0.3417 0.2810 -0.1459 0.5556 0.2361 0.2764 0.3018 -0.0842 0.0840 -0.3621 0.4071 0.0279 MAP 0.1597 -0.3571 0.2780 0.1265 0.9694 b) Pearson's Correlation Matrix Variable MAT AHM TD PAS MWMT MSP MAP MAT AHM TD PAS MWMT MSP MAP 1 0.1136 1 -0.5769 0.4669 1 -0.1975 -0.6072 -0.2929 1 0.7022 0.5126 0.1616 -0.5088 1 0.1526 -0.6727 -0.4747 0.6832 -0.2186 1 0.3810 -0.6742 -0.6563 0.6689 -0.1024 0.9070 1 NB: Prior to this selection process bFFP, eFFP, DD5100 were eliminated because they were not always available for each location and they denote Julian days of the year, the particular identity of which is not usually relevant to the persistence of a conservation target. To determine the locations meeting the requirements of a target's current bioclimatic envelope in the study area, a SAS® 9.1.2 (SAS Institute 2004) program consisting of conditional statements (e.g., Equation 1) was written to determine whether or not each datum in the 1-km grid of the study area fell within the target's core envelope. Locations that satisfied all four variable conditions were considered to meet the requirements of a target's 62 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. bioclimatic envelope. The resulting dataset of mapped envelope areas provided the locations in the study area where the climatic conditions are suitable for a given target. Equation 1. IF MAT > MATSth and MAT< MAT95th, THEN MAT_calc = 1; ELSE MATCALC = 0; where MAT CALC denotes the suitability (if 1) or unsuitability (if 0) of that location for its climate falling within the 5th to 95th percentiles of MAT (mean annual temperature) derived for locations currently occupied by that target This procedure was repeated for four timeslices (1961-1990s, 2020s, 2050s and 2080s) using a 1-km grid across the B.C. Central Interior study area. A timeslice simply represents the projected climate for a predefined time in the future. The purpose of projecting a target's bioclimatic envelope over four timeslices is to assess the continuity of a target's suitable climate space over time. Chapter 2 provides a more detailed account of my rationale for multiple timeslices and the need for the continuity of suitable climate space. Determining a conservation target's suitable climate space and persistent climate corridor In order to identify a target's suitable climate space, the locations meeting the requirements of a target's bioclimatic envelope for the baseline (1961 to 1990s), 2020s, 2050s and 2080s timeslices were overlaid. Collectively, the points of intersection in which target envelope conditions were predicted to be satisfied in all four timeslices are termed 'suitable climate space' (SCS), and represent the locations where tolerable climatic conditions are expected to persist over the study's timeframe. Next, a target's current distribution was overlaid with its suitable climate space, and the coinciding locations were considered a target's "persistent climate corridor" (PCC) and represent priority areas for conservation. Figure 3.1 illustrates the steps and results for this procedure with Nephroma occultum (Cryptic Paw) as the target. 63 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Intersection 1961-2080s - Suitable Climate Space Baseline 2050 s 2020s A Occurrence 9 Persistent Climate Corridor 2080s Figure 3.1. An illustration of the intersect-overlay process used to identify candidate areas for conservation of Nephroma occultum. Like its spatial counterpart, which provides in situ connectivity, a persistent climate corridor denotes a place where an existing population or ecosystem can expect to experience temporal connectivity in the form of climatic continuity or persistence over time. Determining climate constraints of conservation targets The purpose of this analysis was to determine why the occurrences of some species were completely or partially excluded from their bioclimatic envelopes. I used SAS® 9.1.2 (SAS Institute 2004) to determine which of the four selected variables (i.e., MAT, TD, AHM, PAS) at each species' location fell within the 5th and 95th percentiles or core of its distribution. If the climate data at a particular location was outside its core, that location was classed as either "too high" or "too low". Climate constraints were generated for all of the 64 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. occurrences which did not fall within the 5th and 95th percentiles, and therefore did not result in persistent climate corridors. I determined the climate constraints for all four timeslices (i.e., baseline, 2020s, 2050s, 2080s) and calculated the average number of times a variable was deemed too high or too low. Testing a range of general circulation model (GCM) and scenario assumptions In order to test the range of GCM and scenario assumptions, I used ArcMap's Hawth Tools to generate a random spatial sampling of 1000 points (geographic locations with the study area) for the 2080s timeslice. Next, I projected the climate of this random sample for the 2080s using 16 different GCM and scenario pairings and calculated the maximum, minimum, median and mean values for the mean annual temperature of each model (Table 3.5). These 16 models represent a subset of GCMs that are currently available with ClimateBC. In order to simplify this procedure the newest generation of a particular GCM was used (e.g., the HadCM3 was chosen over HadCM2). Each scenario family (i.e., Al, Bl, A2, and B2) represents two divergent tendencies or storylines (A and B) with one set varying between strong economic and strong environmental values, and the other between increasing globalization and regionalization (IPCC 2000) (Table 3.4). The Australian Commonwealth Scientific and Industrial Research Organization (CSIRO) A2 scenario generated the highest MAT prediction and the US Department of Energy's Parallel Climate Model (PCM) Bl scenario represented the lowest MAT prediction compared to CGCM3 A2.. Therefore, the CSIRO A2 (high) and PCM Bl (low) output were chosen to illustrate the potential uncertainty in climate change projections. This test of the range of assumptions was carried out for those conservation targets which had suitable 65 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. climate space projected by the CGCM3 A2 combination. This subset included 30 target plant species, 16 B.C. BGC variants and eight terrestrial ecological units. Table 3.4. A summary of the four storyline and scenario families representing two divergent tendencies, one set varying between strong economic and strong environmental values and the other between increasing globalization and regionalization (IPCC 2007) Storyline and scenario family Al (including A1F1) A2 (including A2x) Bl B2 Description A future world of very rapid economic growth, global population that peaks in mid-century and declines thereafter, and rapid introduction of new and more efficient technologies. A1F1 represents a fossil fuel intensive scenario. A very heterogeneous world with continuously increasing global population and regionally oriented economic growth that is more fragmented and slower than in other storylines. A2x is a custom scenario based on the average output of the A2 scenarios A convergent world with the same global population as in the Al storyline but with rapid changes in economic structures toward a service and information economy, with reductions in material intensity, and the introduction of clean and resource-efficient technologies. A world in which the emphasis is on local solutions to economic, social, and environmental sustainability, with continuously increasing population (lower than A2) and intermediate economic development. Table 3.5. Maximum, minimum, median, mean and standard deviation (SD) for the mean annual temperature (MAT,°C) from 16 GCM and scenario combinations, as projected for 1000 random points in the study area. General Circulation Model CSIR02 A2 HADCM3 A1F1 CSIR02 A1F1 CSIR02 B2 HADCM3 A2 HADCM3 A2x CSIR02 Bl PCM A1F1 CGCM3 A2 PCM A2 HADCM3 B2 HADCM3 Bl PCM B2 ECHAM4 B2 ECHAM4 A2 PCM Bl Minimum (°C) Maximum (°C) Median (°C) Mean (°C) SD* (°C) -6.10 3.80 0.70 0.48 1.61 -5.50 -6.60 -6.90 -6.60 -6.50 -7.40 -7.00 3.40 3.20 3.10 2.30 2.30 2.00 2.00 0.70 0.20 -0.10 -0.30 -0.30 -0.90 -0.80 0.53 -0.04 -0.28 -0.51 -0.44 -1.11 -0.95 1.48 1.59 1.63 1.48 1.45 1.52 1.44 -7.20 1.80 -1.00 -1.13 1.47 -7.70 -7.70 -7.90 -8.80 -8.70 -8.80 1.30 1.20 1.00 0.20 0.20 0.00 -1.40 -1.50 -1.70 -2.55 -2.60 -2.70 -1.59 -1.65 -1.89 -2.70 -2.72 -2.82 1.44 1.48 1.47 1.46 1.44 1.43 -9.10 -0.10 -2.90 -3.01 1.46 implications of bold - combinations are explored in the Results 66 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Results The final results describing the projected bioclimatic envelopes of each target group are summarized in Appendix A, Table A4-A6. For purposes of brevity and simplicity, the data pertinent to those targets with projected suitable climate space are provided in the following text. Many conservation targets are expected to experience large areas of suitable climate space. All conservation target groups are projected to have some examples of persistent climate corridors but usually over a minority of their current range. Conservation Target Groups B.C. Biogeoclimatic (BGC) Variants Only sixteen (16%) of the 103 variants had suitable climate space and only 10 (9%) had any PCCs (Table 3.6, Appendix Table A4). The Coastal Mountain-heather Alpine Undifferentiated and Parkland (CMAunp) and the Engelmann Spruce Subalpine-fir Very Wet Very Cold (ESSFwv) variants provide excellent examples of how climate change might impact the bioclimatic envelope features (i.e., suitable climate space, PCC) of particular targets (Figure 3.2). The CMAunp variant experienced an increase in suitable climate space relative to its current distribution, while the ESSFwv variant experienced an overall greater proportional increase from the baseline to 2080s timeslice. The resulting bioclimatic envelope areas of some other targets are illustrated in Figure 3.3. Overall, the CGCM3 A2 projections generated very low levels of PCC representation for the variants with suitable climate space. Persistent climate corridors were scattered in the northwestern and southeastern corners and eastern edge of the study area. The total area for all variant PCCs was projected to be 1936 km2. 67 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Nature Conservancy of Canada Terrestrial Ecosystem Units (TELO The results for the Nature Conservancy of Canada TEUs are also highly variable (Table 3.7). Please refer to Appendix A Table 5A for projected dynamics of all 30 TEUs. The contraction of the TEUs with suitable climate space and persistent climate corridors represent a reduction of approximately 90% of their cumulative current area. The greatest increases of a TEU's current distribution to its projected suitable climate space TEU5 (1372%) followed by TEU3 (91%) and TEU8 (76%). Despite a near doubling of its current distribution, TEU8 has a relatively small PCC which represents a mere 4% of its current distribution (Figure 3.4). At first glance, the suitable climate space illustrated in Figure 3.4 appears smaller in area, however; the current distribution of TEU8 is linear and represents a riparian ecosystem, while the SCS is nonlinear and represents climate irrespective of topographic features. The current distributions and PCCs of TEUs 1, 2, 5 and 6 are illustrated in Figure 3.5 and demonstrate the concentration of PCCs in the northwestern and southeastern corners, and eastern edge of the study area. The total area of the PCCs for the TEUs is 2561 km2. B.C. Conservation Data Centre (CDC) Listed Plant Species Climate change is expected to influence the distribution of bioclimatic envelopes of most of the rare plant species I evaluated. Overall, 130 out of 162 (80%) plant species occurrence records did not yield PCCs (Table 3.8, Appendix A6). Fourteen of the 162 rare plant occurrences are expected to experience climate conditions suitable for their persistence through the 2080s. Many species are projected to have large envelope areas and suitable climate space (Table 3.8). The low percentage of species with PCCs is largely a function of the low number of species' occurrence records in the study area, and the fact that many of the Central Interior B.C. occurrences are already on the margin of their range. 68 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. a) Engelmann SpruceSubalpine Fir Wet Very Cold (ESSFwv) HQ \y///\ Suitable Climate Space | | Persistent Climate Corridor Current Distribution 10 20 40 km Figure 3.2. Maps of the current distribution, suitable climate space (SCS) and resulting persistent climate corridor (PCC) of a) Engelmann Spruce-Subalpine Fir Wet Very Cold and b) Coastal Mountain-heather Alpine Undifferentiated and Parkland. The circled areas represent other locations of suitable climate space in the study area. 69 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. | Boreal Altai Fescue AJpine Undifferentiated I Boreal Altai Fescue AJpine Undifferentiated and Parkland | Coastal Western Hemlock Central Dry Submaritime | Engelmann Spruce-Sub-alpine Fir Moist Warm Mountain Hemlock Undifferentiated Mountain Hemlock Leeward Moist Maritime MHmm2 14 km 100 km 20 km 10 I 20 km i Figure 3.3. Maps of persistent climate corridor (PCC) of Boreal Altai Fescue Undifferentiated and Parkland and Mountain Hemlock Undifferentiated (1) Boreal Altai Fescue Undifferentiated (2,3) Coastal Western Hemlock Central Dry Maritime and Engelmann Spruce Subalpine fir Moist Warm (4), Mountain Hemlock Leeward Moist Maritime (5). The circled area in 5 represents a very small portion of the MHmm2 persistent climate corridor which is south of those locations shown in the fifth inset. 70 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table 3.6. Biogeoclimatic variants, currently found in the study area, that are predicted to have suitable climate space (SCS), and the area and degree of change associated with persistent climate corridors (PCCs) based on CGCM3 A2 projections and ClimateBC downscaling. Current Area (km2) % Change in Envelope Area, Baseline to 2080 SCS (km2) PCC (km2) Boreal Altai Fescue Alpine Undifferentiated (BAFAun) 31,255 -97.07 184 34 0.11 Boreal Altai Fescue Alpine Undifferentiated and Parkland (BAFAunp) 46,386 -99.72 10 9 0.02 Coastal Mountain-heather Alpine Undifferentiated and Parkland (CMAunp) 49,788 -67.17 1,396 182 0.37 Coastal Western Hemlock Central Dry Submaritime (CWHds2) 816 3182.34 352 64 7.84 Coastal Western Hemlock Wet Maritime (CWHwm) 5,359 386.09 2,702 0 0.00 Engelmann Spruce-Subalpine Fir Moist Warm (ESSFmw) 2,664 210.49 357 16 0.60 Engelmann Spruce-Subalpine Fir Wet Very Cold (ESSFwv) 1,933 -93.05 3,337 1,233 63.79 Interior Cedar Hemlock Nass Moist Cold (ICHmcl) 5,343 -13.81 3,677 203 3.80 Interior Cedar Hemlock Very Wet Cold (ICHvc) 1,449 -38.15 13,403 182 12.56 Description of Biogeoclimatic Variant % Current Area Represented by PCC Interior Douglas-fir Dry Cold (IDFdc) 745 0.01 123 0 0.00 Interior Douglas-fir Wet Warm (IDFww) 1,198 2578.77 96 0 0.00 Interior Mountain-heather Alpine Undifferentiated (IMAun) 12,991 -97.94 9 0 0.00 Interior Mountain-heather Alpine Undifferentiated and Parkland (IMAunp) 1,195 1.23 413 0 0.00 Mountain Hemlock Leeward Moist Maritime (MHmm2) 12,394 322.65 106 9 0.07 Mountain Hemlock Moist Maritime Parkland (MHmmp) 2,243 287.35 31 0 0.00 Mountain Hemlock Undifferentiated (MHun) 4,579 -64.37 3,172 4 0.09 180,338 6,398 29,368 1,936 1.07 Totals *A detailed account of the results for the Interior Cedar Hemlock (ICH) including figures and tables is found in Chapter 2. 71 Table 3.7. A summary of the suitable climate space (SCS), persistent climate corridors (PCCs) and percent of the current area represented by projected PCCs for eight terrestrial ecosystem units. 1 2 3 4 5 6 7 8 Nature Conservancy of Canada - Terrestrial Ecological Unit (TEU) Current Area (km2) % Change in Envelope Area, Baseline to 2080 SCS (km2) PCC (km2) % Current Area Represented by PCC Boreal Alpine Fescue Dwarf Shrubland and Grassland North Pacific Dry and Mesic Alpine Dwarf-Shrubland, Fell-field and Meadow North Pacific Interior Lodgepole Pine - Douglas-Fir Woodland and Forest* North Pacific Interior Wetland Composite North Pacific Montane Riparian Woodland and Shrubland North Pacific Sub-Boreal Mesic Subalpine Fir - Hybrid Spruce Forest North Pacific Sub-Boreal Mesic Subalpine Fir - Hybrid Spruce Parkland Northern Rocky Mountain Lower Montane Riparian Woodland and Shrubland 17,748 3,604 11,866 7,558 1,294 47,680 9,259 2,433 -95.61 -93.23 -33.63 -98.86 -70.61 -94.06 -93.73 -89.26 715 347 22,661 200 19,053 1,205 3,005 4,278 549 46 1,131 0 133 611 0 91 3.09 1.28 9.53 0.00 10.28 1.28 0.00 3.74 TOTAL 91,170 -668.99 51,264 2,561 2.81 *For a more detailed account of the projected outcomes for North Pacific Interior Lodgepole Pine - Douglas-Fir Woodland and Forest (TEU3) see Chapter 2 72 Figure 3.4. An illustration of the current distribution, suitable climate space and persistent climate corridor projected for the Northern Rocky Mountain Lower Montane Riparian Woodland and Shrubland terrestrial ecological unit. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Current Distributions North Pacific Dry and Mesic Alpine Dwarf-Shrubland, Fell-field and Meadow North Pacific Sub-Boreal Mesic Subalpine Fir - Hybrid Spruce Forest North Pacific Montane Riparian Woodland and Shrubland Boreal Alpine Fescue Dwarf Shrubland and Grassland f\ s~V~ Persistent Climate Corridors m North Pacific Dry and MesicAlpine Dwarf-Shrubland, Fell-field and Meadow m|| North Pacific Sub-Boreal Mesic Subalpine Fir - Hybrid Spruce Forest North Pacific Montane Riparian Woodland and Shrubland Boreal Alpine Fescue Dwarf Shrubland and Grassland Figure 3.5. A map illustrating the current distributions and persistent climate corridors of Boreal Alpine Fescue Dwarf Shrubland and Grassland, North Pacific Dry and Mesic Alpine Dwarf-Shrubland, Fellfield and Meadow, North Pacific Montane Riparian Woodland and Shrubland and North Pacific SubBoreal Mesic Subalpine Fir-Hybrid Spruce Forest. 74 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table 3.8. A summary of the suitable climate space, persistent climate corridors (PCC) and percent PCC representing the current distribution of 30 rare plant species. B.C. Conservation Data Centre Plant Species Allium geyeri var. tenerum Botrychium simplex Carex heleonastes Carex lenticularis var. dolia Carex scoparia Carex sychnocephala Carex tenera Chenopodium atrovirens Draba ruaxes Draba ventosa Dryopteris cristata Epilobium halleanum Epilobium leptocarpum Juncus albescens Juncus arcticus ssp. Alaskanus Juncus stygius Koenigia islandica Malaxis paludosa Minuartia austromontana Montia chamissoi Muhlenbergia glomerata Nephroma occultum * Nymphaea tetragona Potentilla nivea var. pentaphylla Salix boothii Salix serissima Saxifraga nelsoniana ssp. Carlottae Sparganium fluctuans Stellaria umbellata TOTAL # in Study Area Calibration Points Proportion Change Baseline to 2080 SCS (km2) PCC (km2) % PCC Representii Current Area 1 3 4 3 1 1 7 2 2 1 1 2 2 3 2 2 2 2 2 2 4 4 5 1 9 1 1 1 1 13 34 28 50 12 34 24 25 13 22 91 10 25 27 18 24 25 34 7 4 22 86 20 4 157 21 15 11 16 -100.00 -1.84 -93.76 -6.86 0.19 -82.62 -18.50 -1.52 -44.06 -97.30 -16.47 0.35 -11.71 -61.95 -15.75 -17.27 308.70 -14.73 -13.42 71.75 -65.29 -12.31 0.35 57.94 -97.09 3.68 -36.31 4.04 -20.86 11,965 5,993 53 178,348 810 6,175 49,081 55,356 1,751 49,941 17,356 25 97,321 19,529 7,549 80,991 34,669 92,612 1,651 17 26,043 11,585 158,015 654 2,973 6,196 2,166 590 241 0 0 0 1 0 0 2 0 0 0 0 0 0 0 0 1 1 2 0 0 0 1 5 1 0 0 0 0 0 0.00 0.00 0.00 2.00 0.00 0.00 8.33 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 4.17 4.00 5.88 0.00 0.00 0.00 1.16 25.00 25.00 0.00 0.00 0.00 0.00 0.00 72 872 2.81 919,656 14 19.44 * For more details concerning the projections for Nephroma occultum please see Chapter 2 75 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Current Distribution Outside of SCS Inside of SCS-PCC a) Nymphaea tetragona b) Muhlenbergia glomerata Figure 3.6. An illustration of the current distribution, suitable climate space (coloured polygons) and persistent climate corridors projected for a) Nymphaea tetragona (White-Pygmy water lily) and b) Koenigia islandica (Iceland purslane). Climatic constraints to conservation targets Out of 73 rare plant species, only Malaxis paludosa, Potentilla nivea var. pentaphylla and Nymphaea tetragona were free of climatic constraints and all of their occurrences resulted in PCCs (Appendix A7a, b). Precipitation as snow (PAS) was most often the factor limiting the distribution for many plant species throughout the planning period, followed by continentality (TD) and mean annual temperature (MAT) (Figure 3.8). Many of the plant species were constrained by the same climate variables within each timeslice and in general the climatic constraints remained relatively consistent. At the same occurrence locations, species are predicted to be constrained in a single timeslice which excluded it from an otherwise suitable climate space and consequently prevented the occurrence from serving as a PCC. For example, Allium geyeri var. tenerum were constrained in the baseline timeslice by a high PAS and a low TD value, and Epilobium leptocarpum was constrained by a high TD 76 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. value. Juncus stygius and Nephroma occultum were constrained in the 2080s timeslice by high values of AHM and MAT, respectively. Current Distribution • Ouside of SCS • Inside SCS - Persistent Climate Corridor b) Carex tenera d) Carex lenticularis jor rlnFin fc? A Figure 3.7. An illustration of the current distribution, suitable climate space (coloured polygons) and persistent climate corridors projected for a) Malaxispaludosa (bog adder's-mouth orchid), b) Carex tenera (quill sedge), c) Juncus stygius (moor rush), and d) Carex lenticularis var dolia (Enander's sedge). 77 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 300 • 257 250 207 220 200 >. o 161 " 150 • 128 CT s LI. 100 - 50 • 26 MAT too MAT too TD too low high low 14 25 TD too AHM too AHM too PAS too PAS too high low high low high Limiting Variable Figure 3.8. The frequency (across all four timeslices) that a variable prevented a species' location from meeting the conditions defined by its bioclimatic envelope. Species response according to habitat To explore theories that predict habitat-based climate driven changes to plant species distribution (e.g., expansion or contraction of suitable climate), I categorized the CDC plant species into four broad habitat types (i.e., alpine/subalpine, conifer forests, grasslands and wetlands) and evaluated the proportion change (%) in the areas of the bioclimatic envelope from the baseline to the 2080s timeslices (Appendix A Table A8). The bioclimatic envelopes for 14 of the 18 alpine/subalpine species are expected to contract (Figure 3.9). The remaining species which are expected to experience an expansion of suitable climate space (a positive proportional change) included Allium geyeri var. tenerum (+432%), Delphinium bicolor ssp. bicolor (+2560%) and Draba lonchocarpa var. vestita (+30%), while that for Polemonium boreale remained the same (0%). Fifty percent of conifer forest plant species were expected to expand with Chamaesyce serpyllifolia ssp. serpyllifolia experiencing the greatest proportional change (860%). Of the grassland species, 19 out of 26 experienced a loss of suitable climate. With the exception of Silence drummondii var. drummondii (+206%) 78 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. and Koenigia islandica (+309%), a significant increase in suitable climate was not necessarily reflected in the proportional change of suitable climate space from the baseline to the 2080s. Similarly, the majority (14 out of 19) of the wetland species are also projected to contract. The remaining wetland species, including Megalodonta beckii var. beckii (+183% in envelope area) and Montia chamissoi (+72% in envelope area) are projected to experience increases in suitable climate area, while Nymphaea tetragona was one of the few species occurrences expected to have persistent climate corridors. 11 10 (0CD O c £ 9 • alpine, subalpine 8 • conifer forests 3 Oo o o a W > O c a> 3 a>XJ 7 • grasslands 6 • wetlands 5 4 3 2 1 -81 to100 -31 to80 -11 to- -1 to -10 0 to 10 30 11 to 100 n 101 to 500 501 + Change in envelope space from baseline to 2080s timeslice, %area' Figure 3.9. A comparison of the frequency of different degrees of change in the area covered by suitable climate space (SCS) of rare species grouped by four broad habitat types. Testing a range of GCM and scenario assumptions There were strong discrepancies among the three selected GCMs in terms of their impl i c a t i o n s t o s u i t a b l e c l i m a t e s p a c e a n d p e r s i s t e n t c l i m a t e c o r r i d o r s ( i . e . , P C M B l , CGCM3, CSIRO A2; Figures 3.10 and 3.11). The differences between projected persistent 79 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. climate corridors are based on a subset of conservation targets, which had suitable climate space projected by the CGCM3 A2 (Figure 3.10). A sampling of target species were selected to illustrate the percent change in current area represented by persistent climate corridors in Figure 3.11. A full tabulation of the projections from each GCM and scenario combinations are presented for rare plant occurrences in Table 3.10 and for the area-based targets in Table 3.9. Of the 30 species with a suitable climate space under CGCM3 A2, five (Carex lenticularis var. dolia, C. tenera, Juncus stygius, Muhlenbergia glomerata and Nymphea tetragona) had at least one population projected to persist in each model x scenario combination (Table 3.10). In contrast, only three area-based conservation targets are projected to have PCCs under all three combiuations, with the CSIRO A2 projecting the least amount of area meeting envelope requirements through time. a o. 100 b 90 P -O 4J 80 £ 2> •| s 70 -(— W oQ 0S> a 30 — a) 01 20 c| 3L. CA 10 u a. 0 • Species • TEU Variant 1 - SCS T PCC CSIRO A2 H SCS PCC CGCM3 A2 SCS PCC PCM B1 Bioclimatic envelope feature Figure 3.10. A comparison of the number of targets (for each group) with suitable climate space (SCS) and persistent climate corridors (PCCs) as projected by the CSIRO A2, CGCM3 A2 and PCM B1 scenarios. Given the low number of occurrence records for rare species in the study area, the potential error associated with the actual persistent climate corridor is difficult to assess. Some 80 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. consistent findings can be acted on, however. For example, the results projected by each GCM for Carex tenera and Nephroma occultum were noticeably different. On the other hand, the results produced by each GCM for Juncus stygius were the same, making planning for the conservation of this species more robust, even though one model scenario combination projects an expansion of SCS and the other two project a contraction (Figure 3.11). O GO O CNJ 0) JZ 200 150 - 0) c "a> (0 _Q PCM B1 CGCM3A2 CSIRO A2 100 - CD _c i 50 o CO O O) l^u Q. -50 - -100 1 , i 1 n u 1 1 1 KOENISL JUNCSTY MALAPAL NEPHOCC NYMPTET POTENIV Species Figure 3.11. A comparison of the percent change in suitable climate space (SCS) for six species as projected by three different scenarios (CSIRO A2, CGCM3 A2, PCM Bl) scenarios for Koenigia islandica (KOENISL), Juncus stygius (JUNCSTY), Malaxis paludosa (MALAPAL), Nephroma occultum (NEPHOCC), Nymphaea tetragona (NYMPTET) and Potentilla nivea var. pentaphylla (POTENIV). 81 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table 3.9. A comparison of the percentage current range of biogeoclimatic variants and terrestrial ecological units projected to fall in persistent climate corridors under the assumptions of three different climate model and scenario combinations. Conservation Target Group B.C. Biogeoclimatic Variant Boreal Altai Fescue Alpine Undifferentiated Boreal Altai Fescue Alpine Undifferentiated and Parkland Coastal Mountain-heather Alpine Undifferentiated and Parkland Coastal Western Hemlock Central Dry Submaritime Coastal Western Hemlock Wet Maritime Engelmann Spruce-Subalpine Fir Moist Warm Engelmann Spruce-Subalpine Fir Wet Very Cold Interior Cedar Hemlock Nass Moist Cold Interior Cedar Hemlock Very Wet Cold Interior Douglas-fir Dry Cold Interior Douglas-fir Wet Warm Interior Mountain-heather Alpine Undifferentiated Interior Mountain-heather Alpine Undifferentiated and Parkland Mountain Hemlock Leeward Moist Maritime Mountain Hemlock Moist Maritime Parkland Mountain Hemlock Undifferentiated % current area represented by persistent climate corridors PCM B1 CGCM A2 CSIRO A2 7.03 5.73 0.59 19.85 0.21 0.64 0.11 29.63 35.69 0.03 1.00 0.00 1.34 0.40 0.18 0.44 0.11 0.02 0.37 7.84 0.00 0.60 6.90 3.80 18.94 0.00 0.00 0.00 0.00 0.07 0.00 0.09 0.00 0.47 0.00 4.66 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 39.90 39.10 29.25 69.35 25.02 0.00 0.01 0.00 0.00 1.28 3.10 1.28 0.00 11.68 41.18 9.56 0.00 0.00 0.00 8.36 0.00 0.00 0.00 0.00 NCC Terrestrial Ecological Units Boreal Alpine Fescue Dwarf Shrubland and Grassland North Pacific Dry and Mesic Alpine Dwarf-Shrubland, Fell-field and Meadow North Pacific Interior Lodgepole Pine - Douglas-Fir Woodland and Forest North Pacific Interior Wetland Composite North Pacific Montane Riparian Woodland and Shrubland North Pacific Sub-Boreal Mesic Subalpine Fir - Hybrid Spruce Forest North Pacific Sub-Boreal Mesic Subalpine Fir - Hybrid Spruce Parkland Northern Rocky Mountain Lower Montane Riparian Woodland and Shrubland 82 Table 3.10. A comparison of the number of populations (occurrences) of rare plants projected to fall in persistent climate corridors under the assumptions of three different climate model and scenario combinations. B.C. Conservation Data Centre Listed Plants Allium geyeri var. tenerum Botrychium simplex Carex lenticularis var. dolia Carex scoparia Carex sychnocephala Carex tenera Chenopodium atrovirens Delphinium bicolor ssp. bicolor Draba cinerea Draba ruaxes Draba ventosa Dryopteris cristata Epilobium halleanum Epilobium leptocarpum Juncus albescens Juncus arcticus ssp. alaskanus Juncus stygius Koenigia islandica Malaxis paludosa Minuartia austromontana Montia chamissoi Muhlenbergia glomerata Nephroma occultum Nymphaea tetragona Potentilla nivea var. pentaphylla Salix boothii Salix serissima Saxifraga nelsoniana ssp. carlottae Sparganium fluctuans Stellaria umbellata ^ 'n ^tUt^ ^rea 1 3 3 1 1 7 2 1 3 2 1 1 2 2 3 2 2 2 2 2 2 4 4 5 1 9 1 1 1 1 General Circulation Model PCM B1 0 1 2 1 0 7 2 0 0 0 1 0 1 0 1 0 1 ] 2 0 1 2 4 5 0 3 0 0 0 0 CGCM A2 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 2 0 0 1 2 5 0 0 0 0 0 0 CSIRO A2 0 0 1 0 0 2 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 2 0 1 0 0 0 0 0 0 Discussion B.C. biogeoclimatic (BGQ variant bioclimatic envelopes There appears to be a general migration or preservation of suitable climate in the northwestern corner of the study area where the persistent climate corridor of the Engelmann Spruce-Subalpine fir wet very cold (ESSFwv) and the Interior Cedar Hemlock very wet cold 83 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. (ICHvc) variants are found. These variants are adjacent to each other and are characterized by a colder, wetter climate relative to their southern counterparts and are found across a wide elevational range (Banner et al. 1993). These variants have the greatest range of mean annual temperature compared to other variants in their respective subzones. The ranges for the remaining variables (continentality, annual heat moisture index, precipitation as snow) are less consistently high but lie within the top quartile of the ranges for all other variants. A broader ecological niche may provide bioclimatic flexibility and the ability to persist as the climate changes. Mean annual temperature explained the most variance according to a Pearson's correlation matrix and principal components analysis of province-wide climate data. The Coast Mountain-heather Alpine Undifferentiated Parkland (CMAunp) and the Boreal Altai Fescue Alpine Undifferentiated (BAFAun) constitute minor components of the northwestern corner of the study area. The predominance of PCCs for these four variants (Appendix A Table A4) is contrary to other studies which project contractions of subalpine, alpine or boreal ecosystems (Pearson and Dawson 2003; McKenney et al. 2007a). However, this result might be explained by a projected increase in precipitation, a distinguishing characteristic of these variants in northern B.C. (CGCM3; Environment Canada 2008) as well as an anomaly in relation to the remainder of the province. A recent re-classification of the Alpine Tundra BGC zone has led to the recognition of three new BGC zones: the CMA, BAFA and IMA (Interior Mountain-alpine). Ice, snow and rock are also characteristic of the alpine tundra zone and remain classified as such despite the fact that these substrates cannot support much of the alpine flora and fauna. However, over the long-term (decades to millennia) the climates and soils of these areas may become more suitable for a greater complement of other species and ecological communities (Figures 4.3, Appendix A Table A4). 84 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Terrestrial ecological units (TEU) bioclimatic envelopes Persistent climate corridors for the TEUs are found in the southeastern corner and along the eastern edge of the Rocky Mountain Trench within the study area. Physiognomy and elements of ecosystem classification are two possible explanations for this result. For example, at the southeastern corner of the study area, the Cariboo-Quesnel Flighlands region is characterized by rolling hills and plateaus and a relatively homogenous climate which is reflected in the vegetation. The greater area of persistent climate corridors for TEUs compared to BGC variants is potentially explained by natural disturbances, such as wildfire (and consequently fire weather) which influence the ecological characteristics of a given ecosystem. In the Central Interior, for example, wildfire maintains the composition and age structure of Pinus contorta (lodgepole pine) ecosystems. As such, these ecosystems rarely reach the climax stage which can be tightly tied to climate and their composition remains determined more by the disturbance regime, which results in a comparatively uniform forest composition across several BGC variants. Plant species bioclimatic envelopes The low percentage of species with PCCs is largely a function of the low number of species' occurrence records in the study area, and the fact that many of the Central B.C. occurrences are already on the margin of their range. Species that are constrained by climate might be considered marginal because the climate associated with their occurrences is outside of what I have defined as the "core" of their bioclimatic envelope (i.e., the 5th and 95th percentiles). Using the 5th and 95th percentiles to define the core of the bioclimatic envelope is a somewhat conservative measure since I have excluded some populations for consideration as conservation priorities a priori. This definition is potentially erroneous 85 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. because it rejects 10% of species occurrences as unsuitable at the outset, and does not consider a population's genetic diversity or phenotypic plasticity. I chose to define the core as I did in an attempt to address any potential uncertainties associated with rare species occurrence records, such as unlikely record locations or transcription errors in online herbarium records. Ultimately, the choice of how to define a species' core bioclimatic envelope requires a cost-benefit analysis of the trade-offs dependent on project-specific goals and objectives. The inconsistencies between the distribution of a species' suitable climate and its current distribution in the study area also suggest that factors other than climate are limiting the distribution and establishment of most of these species (Figures 3.6 and 3.7). That is to say, climate is not currently the primary cause of rarity. Rarity of any given species is a function of a number of plausible anthropogenic factors including the loss of valuable habitat to urban and agricultural development (Ledig 1993). Secondary consequences of these activities with deleterious effects on the survival of a species include air and soil pollution (Mosquin 2000; Goward 1994) and the introduction of exotic species for horticultural and commercial purposes (Harper et al. 1993). Natural causes influencing rarity include natural disturbance, insects and pathogens (Harding 1994), a naturally discontinuous or sporadic habitat range (Schofield 1994) or range restriction by northern latitudes (Harper et al. 1993; Harding and McCullum 1997). In some cases a species' physiological and ecological characteristics pose severe challenges to long-term persistence, such as Nephroma occultum's poor competitive and dispersal abilities (Brodo et al. 2001; COSEWIC 2001). However, climate driven changes to the distribution of certain habitat types may threaten associated plant species. For example, climate change is likely to alter the thermal and hydrological regimes of wetlands, thereby drastically affecting proper ecological 86 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. function and productivity of wetland species such as Nymphaea leibergii and N. tetragona (Burkett and Kusler 2000: Johnson et al. 2005; Pittock et al. 2008). According to the literature, the projected impact of climate change on grassland habitat, and consequently grassland plants such as Juncus, Carex and Poa species, varies from one study to another. My results show that the CGCM3 A2 projected a general decrease of suitable climate space for the Juncus and Carex species and an increase for Poa fendleriana ssp. fendleriana is projected. However, despite slight decreases in suitable climate space for Juncus arcticus ssp. alaskanus, J. stygius, Carex lenticularis var. dolia, C. backii and C. bicolor, the area meeting the requirements of their bioclimatic envelopes is projected to remain relatively constant (Table 3.10, Appendix A, Table A6). Multi-filter approach to conservation planning The premise of a multi-filter approach is to first select sites that are supporting single species or communities of conservation concern (fine filter). Larger scale ecological units (coarse filter) are then used to select sites with multiple values such as an ecosystem service, a unique natural feature, or a representative ecosystem or a variety of species or talon (Nature Conservancy 1982; Noss 1987; Groves et al. 2002; Molina et al. 2006). To complement their multi-filter approach and address some sources of uncertainty, the Nature Conservancy of Canada's ecoregional assessment process requires a number of data inputs on anthropogenic and natural attributes, including wildlife species of conservation concern, aquatic features, ecosystem services (e.g., carbon storage, flood mitigation and recreation), land use classifications (e.g., agriculture, urban development), and natural disturbances (e.g., extent of the mountain pine beetle epidemic). 87 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Augmenting these inputs is an intensive expert-based site selection exercise supported by Marxan. See Chapter 1 for a brief description of Marxan and how it is used. For the Central Interior ecoregional assessment, the CDC-listed plant species represent fine filters, while the BGC variants and the TEUs represent coarse filters. Each of these target groups represents a Marxan input that will be included in a decision support tool for resource managers in the study area. The climate change component of the Central Interior ecoregional assessment will be supported by the findings of a climate change working group, which consists of experts from each input group. Through a series of workshops, these experts identified which targets are the most vulnerable to climate change and their probable response. Together with my empirical model, this expert-based approach will provide valuable ecological information from two different methodologies. A dynamic approach to resource management The B.C. biogeoclimatic ecosystem classification provides a level of climatic detail that is reflected in differences in plant, soil and ecosystem productivity (MacKinnon et al. 1992). The Nature Conservancy of Canada's terrestrial ecological units, for example, are in part based on the B.C. biogeoclimatic variants. The projected loss of suitable climate for a number of BGC variants (Table 3.5, Figures 3.5 and 3.6) and TEUs (Table 3.6, Figures 3.7 and 3.8) provide a warning of the drastic level of changes that might be expected in ecosystem structure and composition, and the subsequent impact of climate change on future resource management practices (BCMFR 2009). In all likelihood, new ecological communities will emerge; the composition, function and the role of those new species assemblages are difficult to predict, and may displace the familiar communities on which many of our management practices are based. 88 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. The efforts to integrate dynamic processes into a management framework are potentially undermined by how ecologists and resource managers classify ecological units. Despite their basis in reflecting relatively uniform ecology (species composition, soil type, and climate), ecosystem classes and mapped ecological zones or regions ultimately reflect a subjective process. Furthermore, these human constructs may have limited flexibility and adaptability because they describe the current expression of some ecological attributes (e.g., climax vegetation) under local climatic and geographic parameters. The delineation of boundaries for ecological units is also subject to interpretation, debate and uncertainty. The sources of uncertainty (and ultimately of error) afflicting projections of bioclimatic envelopes for terrestrial ecological units, for example, include a lack of ground truthing, and the inclusion of information from a range of different sources which differ in their underlying assumptions. In comparison, BGC variants have been more consistently sampled, evaluated, updated and refined to reflect consistent principles and hence represent a reliable source of information (BCMFR 2009). It is important to consider the biogeographic lag which exists between climate change and vegetation response, and recognize its contribution to landscape heterogeneity (Shafer 1990). These lags are also expected to cause sub-optimal ecological functioning and reduced resilience to natural disturbance (Parmesean and Yohe 2003; Leemans and Eickout 2004; Fitzpatrick et al. 2008). To date, biogeographic lags have not been incorporated into species distribution modelling and their impact on individual species and communities remains unclear (Pearson and Dawson 2003; Thuiller et al. 2005). The potential influence of time lags in this study is evidenced by the widespread distinction between the locations defining a target's suitable climate space and those identified as persistent climate corridors. 89 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Adaptive management: The application of suitable climate space and persistent climate corridors to resource management Effective adaptive management requires adjustments to our ecological and socio­ economic responses to the environment. It should incorporate risk analysis and require resource managers and conservation planners to educate stakeholders, establish future management objectives that consider cost-effective actions and develop monitoring programs that aid in the regular assessment of newly implemented strategies (Spittlehouse 2005; BCMFR 2006a; Millar et al. 2007). Some of the challenges of adaptive management include coordinating conservation initiatives with multiple protected areas and other resource-based activities, incorporating uncertainties, such as time lags and the emergence of new communities into our decision-making frameworks and making improvements to ecological modelling, (e.g., the coupling of GCMs with dynamic process-based simulations) (Hannah et al. 2002a; Spittlehouse 2005; Botkin et al. 2007; Rayfield et al. 2008). Addressing these challenges requires an emphasis on ecological process rather than structure and composition, and an understanding that no single approach will suit all situations (Millar et al. 2007). The intended purpose of persistent climate corridors is to provide refuge in the form of climatic connectivity or persistence. During the warm stages of Quaternary and Tertiary geological eras, climate refugia fostered speciation; and across topographically diverse areas, climate refugia allowed habitats to persist through shifts in elevation and diverge during periods of climate change. On a small scale such as a planning unit, climate refugia can be important for maintaining the unique floristics of species assemblages which differ from those communities adapted to the dominant climate (Noss 2001; Taberlet and Cheddadi 2002). 90 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. The identification of suitable climate space has managerial implications for conservation planning. Facilitated migration, for example, is a proactive management strategy designed to mitigate possible species extinctions by translocating species along expected climate gradients to more suitable climates (Millar et al. 2007; Van der Veken et al. 2008). It is potentially the most effective and economically feasible adaptive management strategy currently favoured by foresters and ecologists (Hannah et al. 2002a; BCMFR 2006a; McKenney et al. 2007b). In general, the identification of a target's suitable climate space allows silviculturalists and foresters to optimize their management of commercially valuable timber species by maximizing their deployment to the best possible growing conditions (i.e., core bioclimatic envelopes). Other research exploring climate change impacts on natural processes using a similar overlay-intersect approach include the redefinition and projection under climate change of North America's plant hardiness zones (McKenney et al. 2007b), predicting the future distribution of North American trees (McKenney et al. 2007a) as well as key British Columbian tree species (Hamarrn and Wang 2006), the mapping of candidate areas to protect key Proteaceae species in South Africa (Hannah et al. 2005), exploring the spatial mismatching of trophically interacting species (Schweiger et al. 2008), and identifying hotspots of response to climate change (Post et al. 2009). Model-based uncertainty The variability among different general circulation models can significantly compromise the usefulness of the results for guiding policy development and decision making processes. Ideally, an ensemble forecast would address some of the uncertainty-based issues because it is more narrowly defined by several different models across a set of 91 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. conditions, and model classes and parameters (Araujo and New 2006). In order to address the uncertainties associated with predicting future climates and avoid overly optimistic estimates, model projections would ideally be validated. A variety of validation techniques exist including resubstitution and grouped cross validation, but the best option for bioclimatic envelope modelling is to use independent test data from another region or timeframe. Unfortunately, given the data limitations of projecting future species distributions, validation of most bioclimatic envelope modelling research is rarely performed. Some of the limitations which might hamper a model's predictive ability include the assumption that species response to climate change is immediate, and the potential of climate change to occur more rapidly and at a greater magnitude than experienced in the past (Araujo et al. 2005; Heikkinen et al. 2006). Conclusions Bioclimatic envelope modelling provided the foundation for the identification of persistent climate corridors. These corridors represent locations where a conservation target's bioclimatic envelope is expected to be met over a 75-year period, and are designed to assist with the site selection and prioritization process of conservation planning. According to the CGCM3 A2 general circulation model and scenario, 24 (12%) of the 206 conservation targets were projected to have persistent climate corridors. Although a rational and moderate projection, this result is subject to a number of uncertainties including the accuracy and validity of the CGCM3 model and A2 scenario. The concept of persistent climate corridors provides a simple and powerful tool kit for conservation planning under a changing climate. It is also pertinent to the development and application of a variety of management strategies, including the Nature Conservancy of 92 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Canada Central Interior ecoregional assessment, and federal (Canadian Forest Service) and provincial (BCMFR) strategies for facilitating the climate-adapted migration of valuable tree provenances and seed sources. As the impacts of climate change continue to threaten global biodiversity, the need to develop new proactive management strategies becomes increasingly urgent. Persistent climate corridors give planners and ecologists some priorities for conservation and mitigation as they cope with the challenges presented by climate-driven changes to the protection of valued ecosystems and the ecological services they provide. 93 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Chapter 4 - Synthesis: Dynamic conservation planning and climate change Abstract Climate change represents significant and unforeseen changes to the natural environment and subsequently exacerbates the challenge of managing natural resources. Bioclimatic envelope modelling was used to predict the future distribution of suitable persistent climate for three conservation target groups, namely biogeoclimatic (BGC) variants, terrestrial ecological units (TEU) and selected rare plant species. Results from chapter 3 projected persistent climate corridors for 9% of the 103 BGC variants, 20% of the 30 TEUs and 11% of the selected plant species. Of these individual targets, only 4 TEUs and 5 BGC variants coincided to create overlapping areas of persisting suitable climate which equated to 327 km2 (or 0.13 % of the study area). I consider areas of overlap (coincidence) to be of high conservation value because they theoretically supported the persistence of more than one target. Results were evaluated according to the final scores of one of the outputs generated by Marxan, a reserve selection program set to prioritize areas with low human disturbance. The average scores (conservation value, on a scale of 0 to 100) for the areas of coincidence were 80 (without parks locked in) and 82 (with parks locked in). The identification of persistent climate corridors that also coincide with other high conservation values provides a means of designating areas that can be expected to have greater persistence in a changing climate. 94 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Introduction The impact of the coming century of climate change will alter the environment at an indeterminate rate and magnitude. A changing climate, in concert with other global pressures, seriously threatens native biodiversity, the protection of which represents a formidable challenge to resource managers (Halpin 1997; Scott and Lemieux 2005). Some of the anticipated threats associated with global warming are shifts in species distributions leading to the displacement and loss of biodiversity (Suffling and Scott 2002; Williams and Jackson 2007). The consequences of these changes are expected to have cascading effects throughout a number of ecosystems and will directly affect Canada's current network of parks and protected areas (Scott et al. 2002; Suffling and Scott 2002; Lemieux and Scott 2005). To mitigate the loss of biodiversity and to effectively protect critical species and ecological communities, ecologists have started to incorporate a more process-based approach to protected area planning. Using the Nature Conservancy of Canada's Central Interior Ecoregional Assessment as a case study, the research reported in this thesis explored the temporal dynamics of a changing climate and their implications to planning processes (Chapter 2). Bioclimatic envelope modelling (Pearson and Dawson 2003; Hamann and Wang 2006) and the concept of a suitable climate space (Berry et al. 2003; Pearson et al. 2006) provided the foundation to develop the concept of persistent climate corridors and their role as candidate areas for conservation (Chapter 2, 3). In this study, "suitable climate space" represents the spatial distribution of a conservation target as defined by its bioclimatic envelope (Pearson and Dawson 2003), and specifically where it is expected to persist over time (Berry et al. 2003; Pearson et al. 2006). "Persistent climate corridors" (PCCs) are locations where a target's current location is 95 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. coincident with its suitable climate space. The application reported here sought to 1) identify areas of high conservation value, which in this study were defined by areas with multiple persistent climate corridors; and 2) compare the location of PCCs with areas of high conservation value as denoted by the Nature Conservancy of Canada using Marxan, an iterative reserve selection program (Ball and Possingham 2000). Methods As illustrated in Chapter 2 and executed for multiple conservation targets in Chapter 3, the identification of persistent climate corridors is a four-step process. These steps consist of: 1) the development of bioclimatic envelopes for each conservation target for the Central Interior study area (i.e., 103 B.C. biogeoclimatic variants, 30 terrestrial ecological units, 73 plant species); 2) the identification of locations in the study area meeting a target's bioclimatic envelope requirements for the baseline (current), 2020s, 2050s and 2080s timeslices; 3) the intersection of these four timeslices (suitable climate space), and 4) an overlay of a target's current distribution with its suitable climate space. The tools used in this process were ArcMap® 9.2 geographic information systems (GIS) software, ClimateBC (Mbogga et al. 2009) and output from the third generation of the Canadian general circulation model (CGCM3; Environment Canada 2008). Developing bioclimatic envelopes involved collecting occurrence records or mapped distributions of a target's range and generating climate variables at each location using ClimateBC (Spittlehouse 2006). ClimateBC generated 19 climate variables but due to high collinearity among them, four key discriminators of climate were used: mean annual temperature (MAT), annual heat moisture index (AH:M), continentality (TD) and precipitation as snow (PAS); see Chapter 3. The target's bioclimatic envelopes were limited 96 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. to a core range defined by the 5th to 95th percentiles of each climate variable (Kadmon et al. 2003; Beaumont et al. 2005; McKenney et al. 2007b). Within each timeslice, the locations meeting the requirements of a target's core bioclimatic envelope were overlaid using ArcMap; the climate at these intersecting locations was termed suitable climate space and was presumed to persist over the defined timeframe, thereby providing temporal connectivity in climatic conditions (Berry et al. 2003; Pearson and Dawson 2003; see Chapters 2 and 3). Next, a target's current distribution was overlaid with its suitable climate space, and locations where these two coverages coincided were identified as persistent climate corridors. Because these locations already support populations and ecologies of a particular target, and they are expected to undergo less perceptible climate change than elsewhere, they arguably represent priority areas for conservation. Applying persistent climate corridors to conservation planning A reserve network that protects many conservation targets in a given area is the optimal solution for which planners and agencies such as the Nature Conservancy of Canada (NCC) strive. To explore this idea in concert with the concept of persistent climate corridors (PCCs), the projected PCCs for different conservation target groups were overlaid singly and in combination to form a single PCC layer. Locations exhibiting high conservation value were then themed to identify areas where one, two or three PCCs were projected. To illustrate how Marxan and persistent climate corridors can be used together, the aggregate PCC layer was compared with two of NCC's final outputs for the Central Interior study area. Conservation portfolios were created using Marxan, a reiterative reserve selection program (Ball and Possingham 2000; see Chapter 1) which generates a suite of potential protected area networks based on a stable climate and widely accepted conservation values. 97 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Marxan conservation value scores are a function of a number of built-in metrics including a cost threshold penalty, a specics penalty factor and a boundary length modifier designed to maximize the benefit of a reserve network at the least cost (Game and Grantham 2008; http://www.uq.edu.au/marxan/index.html?p=l.1.1). Final Marxan scores for the Central Interior were assigned to individual 500-ha hexagons covering the study area and represent a set of conservation targets as defined by expert knowledge. Depending on the conditions or requirements set by NCC's goals and objectives, each hexagon receives a score which represents the number of times that it was selected to be included in one of the Marxan iterations. Each Marxan output was created from 100 runs, each with 1 million iterations. For this exploratory analysis, NCC provided the Marxan outputs for the suitability index of terrestrial-based conservation targets with and without parks and protected areas "locked in" to the solution. The suitability index was a "cost function" measured in this case by the absence of human impact on the landscape as indicated by low road density and average distance to roads within each hexagon. Hexagons with a high cost receive a low score and are considered less attractive for conservation. For this particular Marxan output, areas with high human influence were thus less likely to be chosen; no other priorities such as rare habitats or endangered ecosystems were included in these Marxan runs. In this procedure each hexagon is scored between 0 and 100, with 100 being the highest value areas (Figure 4.1). When exploring conservation solutions built around existing parks and protected areas, these locations are assigned scores of 100, and so are "locked in" to the solution to more closely approximate a reasonable land use planning process for the region (Sarah Loos, NCC GIS Analyst, pers. comm.). To identify high conservation value areas that also are expected to support PCCs, or conversely, PCC locations that have high conservation 98 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. value, the aggregate layer showing multiple PCCs was overlaid with the Marxan scores with and without fixed parks and protected areas. Marxan Output score 0-15 tZZ]15"35 36-57 " " ' 58-84 EH 85-100 Figure 4.1. Marxan output for the Central Interior study area showing the range of conservation value scores generated from a suitability index without parks "locked in". Results Conservation Target Groups B.C. Biogeoclimatic (BGC) Variants According to the CGCM3 projections and my overlay analysis (Chapter 3), only 16 of the 103 BGC variants found in the study area had suitable climate space and eight had 99 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. persistent climate corridors (Appendix A, Table A4). Overall, the resulting projections indicated the potential for a substantial loss in representative climatic regions and vegetation assemblages represented by the BGC variants (Table 3.6). Terrestrial Ecological Units (TELO The results for NCC's TEUs (Table 3.7) were similar to the BGC variants in that there is the potential for a significant loss of valued ecosystem types (Appendix A, Table A5). With CGCM3 projections, only eight of the 30 TEUs currently found in the Central Interior were expected to have suitable climate space and six had PCCs, though still representing only 1-10% of their corresponding current areas. There is projected to be a loss of suitable climate space from the baseline timeslice to the 2080s for some TEUs, even while the suitable climate space for other units was projected to occupy more land than now. Plant Species listed by the B. C. Conservation Data Centre (CDC) Most of the rare plant species listed as being found in the Central Interior study area were expected to be threatened by the levels of climate change anticipated over the rest of this century. The CGCM3 projections and associated overlay analysis indicated that 29 of the 73 target plant species evaluated would have suitable climate space, and only nine would be able to persist in one or more of their currently documented locations (Appendix A, Table A6). Because lots of suitable climate space is available for most species under current as well as future climates, these projections imply that the distributions of these species are not limited by climate. However, the low sample sizes available for envelope calibration and point-based climate projections, as well as the exclusion of occurrences outside the 5th and 95th percentiles, undoubtedly affect the probability of a PCC for any particular species. In 100 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. other words, these are probably very conservative estimates of the potential for the persistence of rare plant populations; several other ones are likely to be suitable too. Applying persistent climate corridors to conservation planning The creation of a single aggregate PCC layer identified areas where BGC variants and terrestrial ecological units coincided. Other target group combinations did not result in areas of coincidence (Figure 4.2). Across the study area, the extent of target persistent climate corridors and the locations of coincidence are relatively sparse. The study area is approximately 246,000 km2, whereas the areas of the BGC variant PCCs, TEU PCCs, and their coincidence (TEU + BGC variant) are 18,108 km2 (7 %), 2,372 km2 (0.96 %) and 327 km2 (0.13 %), respectively. In terms of the CDC plant species, there were only 27 out of a possible 162 (17%) persistent climate corridor locations (based on occurrences of 73 species) for these rare plant species, none of which coincided with the PCCs of other conservation targets. Though potentially alarming from an overall biodiversity conservation perspective, these results nevertheless give strong direction to planners in terms of some priority areas for conservation. Comparing Marxan suitability indices with persistent climate corridors The "locking in" of parks into Marxan solutions resulted in a 44% increase in the number of hexagons with scores of 100, which on the landscape means 73,639 km2 of land with potential for conservation compared to 41,209 km2 for the solution without parks. In general, the TEUs persistent climate corridors had greater representation across the five score classes than the BGC variants' PCCs and BGC/TEU combinations for both the suitability index with (Table 4.1) and without (Table 4.2) parks locked into the solution. Conversely, the 101 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. score of a plant species is based on the score of the hexagon where it is located and did not appear to vary too greatly between Marxan outputs (Table 4.3). 100 200 km Variant and TEU Variant TEU Plant Species 40 km Figure 4.2. A map illustrating the locations in the Central Interior study area with more than one persistent climate corridor. B.C. biogeoclimatic variants and terrestrial ecological units (TEU) were the only target groups with areas of coincidence (red). 102 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. without parks locked in with parks locked in score score with parks locked without parks Score In (km2) locked In (km2) 0-20 21-40 41-60 84028 40084 28004 86663 49064 37854 61-80 81-100 16235 88523 24550 58724 Total 256854 km2 Figure 4.3. A comparative illustration showing the Marxan suitability index output with and without parks "locked in". The area (km2) of each score class is summarized according to the suitability index with and without parks in the table below the below the maps. (NCC 2009, unpublished). Discussion The map in Figure 4.2 illustrates those conservation targets with multiple persistent climate corridors and provides guidance for the selection of areas expected to exhibit relative ecological suitability under a changing climate. From the perspective of a conservation organization and agencies, areas which could potentially conserve more than one target are ideal candidates for protection. The patterns of temporal connectivity or climatic persistence as represented by a target's PCC facilitate the mobilization of a concerted effort for preservation and allocation of resources in these general areas. 103 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table 4.1. An areal summary (km2) of the scores assigned to the area-based PCCs with parks locked in to the Marxan suitability run. Marxan Output score classes (0-100) Conservation Target Group Biogeoclimatic (BGC) variants 0-15 16-36 37-60 61-86 87-100 Total Area (km2) Boreal Altai Fescue Alpine Undifferentiated (BAFAun) Boreal Altai Fescue Alpine Undifferentiated and Parkland (BAFAunp) Coastal Mountain-heather Alpine Undifferentiated and Parkland (CMAunp) Coastal Western Hemlock Central Dry Submaritime (CWHds2) Engelmann Spruce-Subalpine Fir Moist Warm (ESSFmw) Engelmann Spruce-Subalpine Fir Wet Very Cold (ESSFwv) Interior Cedar Hemlock Nass Moist Cold (ICHmcl) Interior Cedar Hemlock Very Wet Cold (ICHvc) Mountain Hemlock Moist Maritime (MHmm2) Mountain Hemlock Undifferentiated (MHun) 0 0 0 0 0 60 15 75 0 0 0 0 0 0 0 535 150 80 0 0 10 20 135 0 20 1,160 460 60 0 10 30 10 95 0 10 530 185 100 0 60 145 0 145 260 140 2,150 110 425 50 5 185 30 375 260 170 4,435 920 740 50 75 5 0 1,665 2,170 100 8,805 500 0 960 5,560 495 5,465 510 0 420 13,670 790 1,790 200 20 120 4,315 430 255 1,020 185 1,175 12,085 1,675 4,885 2,235 205 4,340 37,800 3,490 21,200 0 0 0 5 60 65 0 0 0 0 20 20 0 0 0 0 0 0 0 0 130 220 0 350 0 0 145 450 80 85 5 0 95 195 10 5 15 10 105 950 0 1,615 20 10 475 1,815 90 2,055 12,895 16 14,445 18 19,815 24 6,675 8 27,230 34 81,060 Terrestrial Ecosystem Units (TEU) Boreal Alpine Fescue Dwarf Shrubland and Grassland North Pacific Dry and Mesic Alpine Dwarf-Shrubland, Fell-field and Meadow North Pacific Interior Lodgepole Pine - Douglas-Fir Woodland and Forest North Pacific Montane Riparian Woodland and Shrubland North Pacific Sub-Boreal Mesic Subalpine Fir - Hybrid Spruce Forest Northern Rocky Mountain Lower Montane Riparian Woodland and Shrubland BGC and TEU combination BAFAun - Boreal Alpine Fescue Dwarf Shrubland and Grassland BAFAun - North Pacific Dry and Mesic Alpine Dwarf-Shrubland, Fell-field and Meadow CMA unp - North Pacific Dry and Mesic Alpine Dwarf-Shrubland, Fell-field and Meadow ESSFwv - Boreal Alpine Fescue Dwarf Shrubland and Grassland ESSFwv - North Pacific Montane Riparian Woodland and Shrubland ESSFwv - North Pacific Sub-Boreal Mesic Subalpine Fir - Hybrid Spruce Forest ICH mc 1 - North Pacific Montane Riparian Woodland and Shrubland ICH vc - North Pacific Montane Riparian Woodland and Shrubland Total area (km2) for score class Proportion (%) of each score class 104 Table 4.2. An areal summary (km2) of the scores assigned to the area-based PCCs without parks locked in to the Marxan suitability run. Conservation Target Group BGC variants 0-15 16-35 36-57 58-84 85-100 Total Area (km2) Boreal Altai Fescue Alpine Undifferentiated (BAFAun) Boreal Altai Fescue Alpine Undifferentiated and Parkland (BAFAunp) Coastal Mountain-heather Alpine Undifferentiated and Parkland (CMAunp) Coastal Western Hemlock Central Dry Submaritime (CWHds2) Engelmann Spruce-Subalpine Fir Moist Warm (ESSFmw) Engelmann Spruce-Subalpine Fir Wet Very Cold (ESSFwv) Interior Cedar Hemlock Nass Moist Cold (ICHmcl) Interior Cedar Hemlock Very Wet Cold (ICHvc) Mountain Hemlock Moist Maritime (MHmm2) Mountain Hemlock Undifferentiated (MHun) 0 0 0 0 10 0 20 85 0 0 20 0 30 10 10 355 95 65 0 0 65 20 145 10 15 1,500 465 70 15 5 40 10 160 35 115 905 240 100 20 45 20 10 35 85 20 400 70 105 5 35 145 40 370 140 170 3,160 890 425 40 85 0 0 2,190 3,020 35 9,245 530 70 855 3,755 390 5,345 490 35 215 12,805 1,090 1,380 340 35 205 7,985 850 900 895 50 875 10,235 1,125 4,330 2,255 190 4,340 37,800 3,490 21,200 0 0 50 25 0 75 0 0 0 0 20 20 0 0 0 0 0 0 5 0 5 95 0 5 15 0 215 650 80 100 0 0 150 335 10 85 0 10 105 760 0 1,530 20 10 475 1,840 90 1,720 14,605 19 11,640 15 19,435 25 12,590 16 20,720 78,845 Marxan Output score classes (0-100) TEUs Boreal Alpine Fescue Dwarf Shrubland and Grassland North Pacific Dry and Mesic Alpine Dwarf-Shrubland, Fell-field and Meadow North Pacific Interior Lodgepole Pine - Douglas-Fir Woodland and Forest North Pacific Montane Riparian Woodland and Shrubland North Pacific Sub-Boreal Mesic Subalpine Fir - Hybrid Spruce Forest Northern Rocky Mountain Lower Montane Riparian Woodland and Shrubland BGC and TEU combination BAFAun - Boreal Alpine Fescue Dwarf Shrubland and Grassland BAFAun - North Pacific Dry and Mesic Alpine Dwarf-Shrubland, Fell-field &Meadow CMAunp - North Pacific Dry and Mesic Alpine Dwarf-Shrubland, Fell-field & Meadow ESSFwv - Boreal Alpine Fescue Dwarf Shrubland and Grassland ESSFwv - North Pacific Montane Riparian Woodland and Shrubland ESSFwv - North Pacific Sub-Boreal Mesic Subalpine Fir - Hybrid Spruce Forest ICHmcl - North Pacific Montane Riparian Woodland and Shrubland ICHvc - North Pacific Montane Riparian Woodland and Shrubland Total area (km2) for score class Proportion (%) of each score class 105 26 Table 4.3. The Marxan output scores for the B.C. Conservation Data Centre plant species PCCs. Without parks "locked in" 4 16 28 Marxan Output Score (0-100) 42 64 78 80 Mean Score 86 Carex lenticularis var. dolia Carex tenera 100 1 100 2 100 4 Malaxis paludosa 1 1 Muhlenbergia glomerata Nephroma occultum 82 1 Nymphaea tetragona 1 1 64 1 1 Potentilla nivea var. pentaphylla With parks "locked in" 3 16 38 Marxan Output Score (0-100) 42 50 60 74 1 53 1 100 Mean Score 80 Carex lenticularis var. dolia 100 1 100 1 100 2 100 1 3 Juncus stygius Malaxis paludosa 1 1 Muhlenbergia glomerata Nephroma occultum Nymphaea tetragona 1 1 Juncus stygius Carex tenera 100 65 1 1 1 1 1 Potentilla nivea var. pentaphylla 74 1 51 1 100 Given the general paucity of conservation resources and an abundance of issues surrounding multiple stakeholders associated with conservation planning, large areas consisting of more than one PCC are ideal "coarse filter" conservation areas, suitable for the protection of a number of conservation targets. In terms of the overlap with the rare plant species it is especially unfortunate that none of the rare plant persistent climate corridors, indicating priority investments for successful "fine filter" conservation of rare species, overlap with PCCs for either of the area-based conservation targets. They may yet, however, coincide with the locations of other NCC conservation priorities as planning for this ecoregion progresses. Areas of the Central Interior with multiple persistent climate corridors are concentrated in the northwestern and southeastern corners, and the eastern edge of the study area. Englemann Spruce-Subalpine fir Wet Very Cold (ESSFwv) and the Interior Cedar Hemlock Very Wet Cold (ICHvc) variants are the primary BGC variants found in the 106 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. northwestern corner, while the Coast Mountain-heather Alpine Undifferentiated Parkland (CMAunp) and the Boreal Altai Fescue Alpine Undifferentiated Parkland (BAFAunp) constitute minor components of the northwestern corner of the study area. The potential expansion by these variants is contrary to the projections of other similar habitat types in that boreal, subalpine and alpine ecosystems are expected to contract (Pearson and Dawson 2003; McKenney et al. 2007a). For this study, their expansion might be explained by a projected increase in precipitation, a distinguishing characteristic of these variants in northwestern B.C. (Woods et al. 2005; Environment Canada 2008) as well as an anomaly for the remainder of the province. The persistent climate corridors for the terrestrial ecological units were concentrated in the southeastern corner and the eastern edge of the study area. These areas are relatively less diverse and are characterized by rolling hills and plateaus. The climate of these areas is also relatively homogeneous, and targets with broader climate niches might be more flexible and therefore more likely to persist as the climate changes. The analysis of suitable climate and persistent climate corridors of these conservation targets is based at a scale where climate is generally the dominant factor limiting species distributions (Pearson and Dawson 2003; Heikkinen et al. 2006). Realistically, species distributions are a function of genetics, adaptive capacity, biotic interactions and other abiotic factors such as the natural disturbance regime. Human activities including modern climate change alter these mechanisms and further exacerbate our ability to accurately predict the probable outcomes (McCarty 2001; Gayton 2008). For example, plant species are expected to respond individually to climate change, leading to a widespread redistribution and re­ organization of plant communities (Shafer et al. 2001; BCMFR 2006a). 107 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. The relatively large area of suitable climate and overall low percentage (17%) of PCCs for most rare plant species occurrences suggests that, as a group, they are not primarily limited by climate at the regional scale of the Central Interior. Although the inclusion of rare plant PCCs will be a useful contribution to an overall conservation strategy, protection and the recovery of any individual plant species requires identification of the threats limiting the distribution of that species in order to recommend pertinent management strategies. Persistent climate corridors which have high Marxan scores (e.g., >80), will be especially important targets for protection in conservation planning, contributing to continuity and high conservation values under current conditions as well has having high probability of persistence in the face of climate change. The analysis of PCCs in conjunction with two of the Nature Conservancy of Canada (NCC) Marxan outputs was an exploration into the applicability of persistent climate corridors to conservation planning and is by no means complete. The suitability index based on roadlessness with and without parks is one of many outputs which NCC has created and continues to create for the Central Interior study area. Various Marxan outputs and PCCs will be incorporated into a decision support tool designed by NCC for the Central Interior study area. The purpose of this tool is to allow users to create their own conservation portfolio or determine the advantages and disadvantages of any particular reserve network. A thorough understanding of species and ecosystems is central to successfully predicting their future distribution in a changing climate and subsequently prescribing appropriate conservation strategies. However, the inability to confidently predict ecological responses to climate change reflects a substantial uncertainty originating from a variety of sources (Chapter 1, Table 1.4). These uncertainties are cumulative, difficult to quantify and inevitably lead to error. The probable origins of error in this research include low sample 108 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. sizes for listed plant species, incomplete occurrence data of a target's range (or distribution) leading to a misrepresentation of a target's suitable climate space and consequently its persistent climate corridor. A number of GCM limitations which introduce error into the analysis include differences in scale, output variability from one model and scenario combination to another, and a generally poor ability to accurately project some climatic features such as cloudiness and the seasonality of precipitation (Chapter 1, Table 1.4) (Hannah et al. 2002; Millar et al. 2007a,b). Finally, it is recognized that many features of the landscape (topography; soils) and species biology (dispersal and competitive abilities) contribute additional factors that further constrain or facilitate the persistence of species and communities in a changing climate. Nonetheless, the identification of locations predicted to meet bioclimatic envelope requirements for the foreseeable future is an important first step for conservation planning in a word now facing some drastic changes. Conclusions The addition of a climate change perspective into a conservation planning framework attempts to recognize and account for the spatiotemporal dynamics of an ecosystem and its subsequent manifestation on the landscape. Within the Nature Conservancy of Canada's planning framework, projections of ecological resistance to the climate change component of these dynamics is one of many potential inputs to the planning process, along with considerations such as the distribution and habitat preferences of target wildlife species, natural disturbance regimes, aquatic features and ecosystem services. The results of the research reported here, juxtaposed with the research derived from NCC's Climate Change Working Group, provide some insight into how climate change will impact the Central Interior of B.C. and how some of the impacts can be minimized with careful spatial planning. 109 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. This collective effort also demonstrates the adaptive potential of a dynamic-based approach to resource management and conservation. 110 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. References Araujo, M.B., M. Cabeza, W. Thuiller, L. Hannah, and P.H. Williams. 2004. Would climate change drive species out of reserves? An assessment of existing reserve-selection methods. Global Change Biology 10:1618-1626. Araujo, M.B., and M. New. 2006. Ensemble forecasting of species distributions. Trends in Ecology and Evolution. 22:42-47. Araujo, M.B., R.J. 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Appendix A - Conservation target and climate data for the conservation target groups. Table A 1. Target plant species names and their conservation status (*See Table A2 for a description of codes describing conservation status). Scientific Name Allium geyeri var. tenerum Anemone canadensis Apocynum x floribundum Arabis holboellii var. pinetorum Arabis sparsiflora Atriplex argentea ssp. argentea Botrychium simplex Bouteloua gracilis Camissonia breviflora Carex backii Carex bicolor Carex heleonastes Carex lenticularis var. dolia Carex scoparia Carex simulata Carex sychnocephala Carex tenera Carex tonsa var. tonsa Carex xerantica Chamaerhodos erecta ssp. nuttallii Chamaesyce serpyllifolia ssp. serpyllifolia Chenopodium atrovirens Delphinium bicolorssp. bicolor Draba alpina Draba cinerea Draba fladnizensis 124 Common Name Geyer's onion Canada anemone western dogbane Holboell's rockcress sickle-pod rockcress silvery orache least moonwort blue grama short-flowered eveningprimrose Back's sedge two-coloured sedge Hudson Bay sedge Enander's sedge pointed broom sedge short-beaked fen sedge many-headed sedge tender sedge bald sedge dry-land sedge American chamaerhodos thyme-leaved spurge dark lamb's-quarters Montana larkspur alpine draba gray-leaved draba Austrian draba # of occurrences Calibration Study Points* Area Status Global Provincial BC List 13 19 4 8 7W 4 34 19 1 1 3 3 3 1 3 2 G4G5T3T5 G5 GNA G5T5? G5 G5T5 G5 G5 S2S3 S2S3 S2S3 S2S3 SI SI S2S3 SI Blue Blue Blue Blue Red Red Blue Red 4 32 S,W 18 E,N 28 50 W 12 E 8 34 24 S,E 4 18 19 W 2 1 2 4 3 1 8 1 8 1 4 5 2 2 1 2 3 1 G5 G4 G5 G4 G5T3Q G5 G5 G4 G5 G5T4T5 G5 G5T4T5 G5T5 G5 G4G5T4T5 G4G5 G5 G4 SI S2S3 S2S3 S2S3 S2S3 S2S3 S2S3 S3 S2S3 S2S3 S2 S2S3 S2S3 SI S2S3 S2S3 S2S3 S2S3 Red Blue Blue Blue Blue Blue Blue Blue Blue Blue Red Blue Blue Red Blue Blue Blue Blue 8 25 S 10 10 E,N,W 20 N 16 S,N COSEWIC Scientific Name Draba glabella var. glabella Draba lactea Draba lonchocarpa var. vestita Draba reptans Draba ruaxes Draba ventosa Dryopteris cristata Entosthodon rubiginosus Epilobium halleanum Epilobium leptocarpum Eutrema edwardsii Festuca minuti/lora Glyceria pulchella Hesperostipa spartea Impatiens aurella Juncus albescens Juncus arcticus ssp. alaskanus Juncus stygius Koenigia islandica Lloydia serotina var. flava Malaxis paludosa Megalodonta beckii var. beckii Melica bulbosa var. bulbosa Melica spectabilis Minuartia austromontana Montia chamissoi Muhlenbergia glomerata Nephroma occultum Nymphaea leibergii Nymphaea tetragona Platanthera dilatata var. albiflora Poa fendleriana ssp. fendleriana Polemonium boreale 125 Common Name smooth draba milky draba lance-fruited draba Carolina draba coast mountain draba Wind River draba crested wood fern rusty cord-moss Hall's willowherb small-fruited willowherb Edwards wallflower little fescue slender mannagrass porcupinegrass orange touch-me-not whitish rush arctic rush bog rush Iceland koenigia alp lily bog adder's-mouth orchid water marigold oniongrass purple oniongrass Rocky Mountain sandwort Chamisso's montia marsh muhly Cryptic Paw small white waterlily pygmy waterlily fragrant white rein orchid mutton grass northern Jacob's-ladder # of occurrences Calibration Study Points* Area 3 ION 9 5 13 22 91 5 10 25 10 E,N,W 25 7W 15 27 18 W 24 25 N 14 34 E, W 7 11 S 7 4 S,W 22 E 86 12 20 E 5 9 27 N,W 6 4 2 1 1 2 1 1 1 2 2 1 3 2 2 3 2 2 2 1 2 2 1 2 2 4 4 1 5 2 2 1 1 1 1 Status Global Provincial BC List G4G5T4 G4 G5T3 G5 G4 G3 G5 G1G3 G5 G5 G4 G5 G5 G5 G4? G5 G5T4T5 G5 G4 G5T3 G4 G4G5T4T5 G5TNRQ G5 G4 G5 G5 G4 G5 G5 G5T3T5 G5T5 G5 S2S3 S2S3 S2S3 SI S2S3 S2S3 S2S3 SI S2S3 S2S3 S2S3 S2S3 S2S3 S2 S2S3 S2S3 S2S3 S2S3 S2S3 S3 S2S3 S3 S2 S2S3 S2S3 S2S3 S3 S2S3 S2S3 S2S3 S2S3 SI S2S3 Blue Blue Blue Blue Red Blue Blue Red Blue Blue Blue Blue Blue Red Blue Blue Blue Blue Blue Blue Blue Blue Red Blue Blue Blue Blue Blue Blue Blue Blue Red Blue COSEWIC Scientific Name Polygonum ramosissimum var. ramosissimum Polypodium sibiricum Potentilla nivea var. pentaphylla Pyrola elliptica Sagina nivalis Salix boothii Salix serissima Saxifraga nelsoniana ssp. carlottae Senecio plattensis Silene drummondii var. drummondii Sparganium fluctuans Stellaria umbellata Woodsia alpina Common Name bushy knotweed Siberian polypody five-leaved cinquefoil white wintergreen snow pearlwort Booth's willow autumn willow dotted saxifrage plains butterweed Drummond's campion water bur-reed umbellate starwort alpine cliff fern # of occurrences Calibration Study Points* Area 4 13 E 5N 157 E,N 21 W 15 14 5 11 16 3 11 18 E 1 1 9 1 1 6 4 1 1 2 4 1 1 Status Global Provincial BC List G5T5 G5? G5T4 G5 G5 G5 G4 G5T3? G5 G5T5 G5 G5 G4 SI SH S2S3 S2S3 S2S3 S2S3 S2S3 S3 S2S3 S3 S2S3 S2S3 S2S3 Red Blue Blue Blue Blue Blue Blue Blue Blue Blue Blue Blue Blue COSEWIC * Some additional documented occurrences were available but not within the geographic range of ClimateBC and ClimatePP. These were excluded from use in envelope calibrations with additional distribution in the directions noted. E = east of 88 °W N = north of 60.42 °N (if east of 113.02 °W) or north of 70°N (if west of 113.02 °W) S = in the U.S.A. south of46.98 °N W + in Alaska, west of 142.02 °W 126 Table A 2. A summary of the conservation status codes assigned by the B.C. Conservation Data Centre. Plant species are ranked according to B.C., provincial, global and COSEWIC (The Committee on the Status of Endangered Wildlife in Canada) definitions. STATUS CODE STATUS DESCRIPTION (and source for more information) British Columbia (www.gov.bc.ca/atrisk/red-blue.htm) RED Any indigenous species or community that have or are candidates for Extirpated, Endangered, or Threatened status in B.C. Any indigenous species or community considered to be of Special Concern (formerly Vulnerable) B.C.. Taxa of Special Concern have characteristics that make them particularly sensitive or vulnerable to human activities or natural events. Any species that are apparently secure and not at risk of extinction. Yellow listed species may have Red- or Blue-listed subspecies. BLUE YELLOW Provincial Code/Global Code (www.natureserve.org) S1(N1)/G1 CRITICALLY IMPERILLED At very high risk of extinction due to extreme rarity, very steep declines, or other factors. IMPERILLED At high risk of extinction due to very restricted range, very few populations, steep declines, or other factors. VULNERABLE At moderate risk of extinction due to a restricted range, relatively few populations, recent and widespread declines, or other factors. APPARENTLY SECURE Uncommon but not rare; some cause for long-term concern due to declines or other factors. SECURE Common; widespread and abundant. S2(N2)/G2 S3(N3)/G3 S4(N4)/G4 S5(N5)/G5 COSEWIC Code 127 (www.cosewic.ge.ca) E ENDANGERED. A species facing imminent extirpation or extinction. SC SPECIAL CONCERN. A species of special concern because of characteristics that make it is particularly sensitive to human activities or natural events. Table A 3. Synonym for some of the B.C. Conservation Data Centre "At Risk" plant species investigated in this study. These synonyms were also used in the data collection process. Species Name Synonyms* Acorus americanus Acorus calamus var. americanus Agrostis pallens Agrostis diegoensis, Agrostis lepida, Agrostis pallens var. vaseyi Allium geyeri var. tenerum Allium geyeri subs, tenerum, Allium geyeri var. tenerum Anemone canadensis Anemonidium canadense Arabis lignifera Boechera lignifera Astragalus bourgovii Tragacantha bourgovii, Homalobus bourgovii Astragalus umbellatus Astragalus littoralis, Phaca littoralis, Astragalus alpinus var. littoralis, Astragalus frigidus var. dawsonensis, Astragalus frigidus var. littoralis, Phaca frigida var. demissa, Phaca frigida var. littoralis Atriplex argentea ssp. argentea Atriplex argentea subs, argentea argentea, Atriplex argentea subs, typica Botrychium crenulatum Botrychium dusenii Botrychium simplex Botrychium tenebrosum, Botrychium simplex var. compositum, Botrychium simplex var. laxifolium, Botrychium simplex var. tenebrosum, Botrychium simplex spp. typicum Bouteloua gracilis Chondrosum oligostachyum, Chondrosum gracile, Bouteloua oligostachya, Bouteloua gracilis var. stricta Camissonia breviflora Oenothera breviflora, Taraxia breviflora Carex backii Carex durifolia, Carex durifolia var. subrostrata, Carex backii var. subrostrata Carex lenticularis var. dolia Carex eurystachya, Carex hindsii, Carex enanderi, Carex plectocarpa Carex rostrata Carex rostrata var. ambigens Carex tenera Carex tenera var. echinodes Carex tonsa var. tonsa Carex umbellata var. tonsa, Carex rugosperma var. tonsa Chamaerhodos erecta ssp. nuttallii Chamaerhodos erecta var. nuttallii Chenopodium atrovirens Chenopodium wolfii, Chenopodium aridum, Chenopodium fremontii var. atrovirens Draba alpina Drabapilosa, Draba micropetala, Draba eschscholtzii, Draba alpina var. nana Draba corymbosa Draba macrocarpa, Draba bellii, Draba barbata Draba densifolia Draba sphaerula, Draba nelsonii, Draba caeruleomontana Draba lactea Draba allenii, Draba fladnizensis var. heterotricha 128 Species Name Synonyms* Draba reptans Draba micrantha, Draba caroliniana, Draba reptans var. stellifera, Draba reptans var. typica, Draba reptans var. micrantha, Draba reptans spp. stellifera Draba ruaxes Draba exalata, Draba ventosa var. ruaxes Eleocharis kamtschatica Scirpus kamtschaticus Epilobium halleanum Epilobium pringleanum, Epilobium pringleanum var. tenue, Epilobium brevistylum var. subfalcatum, Epilobium brevistylum var. tenue, Epilobium glandulosum var. tenue Festuca minutiflora Festuca ovina var. minutiflora, Festuca brachyphylla var. endotera Galium labradoricum Galium tinctorium var. labradoricum Galium multiflorum Galium bloomeri, Galium matthewsii var. scabridum, Galium multiflorum var. hirsutum, Galium multiflorum forma hirsutum, Galium multiflorum spp.. hirsutum, Galium bloomeri var. hirsutum Hesperostipa spartea Stipa spartea Juncus albescens Juncus conicinnus Koenigia islandica Macounastrum islandicum Malaxis brachypoda Malaxis monophyllos var. brachypoda, Malaxis monophyllos spp.. brachypoda Malaxis paludosa Hammarbya paludosa Melica smithii Avena smithii, Bromelica smithii Melica spectabilis Bromelica spectabilis, Melica bulbosa var. spectabilis Mimulus breweri Minuartia austromontana Minuartia austromontana Arenaria rossii var. columbiana, Arenaria rossii spp.. columbiana Montia chamissoi Crunocallis chamissoi, Claytonia chamissoi Muhlenbergia glomerata Muhlenbergia racemosa var. cinnoides, Muhlenbergia glomerata var. cinnoides Nymphaea leibergii Nymphaea tetragona var. leibergii, Nymphaea tetragona spp.. leibergii Nymphaea tetragona Castalia leibergii, Castalia tetragona Oxytropis maydelliana Oxytropis campestris var. melanocephala, Oxytropis campestris var. glabrata Poa fendleriana ssp. fendleriana Stipa spartea Polemonium occidentale ssp. occidentale Polemonium occidentale spp.. amygdalium, Polemonium occidentale spp..typicum Potentilla ovina var. ovina Potentilla ovina var. pinnatisecta Pyrola elliptica Pyrola compacta 129 Species Name Synonyms* Ranunculus pedatifldus ssp. afftnis Ranunculus pedatifldus spp.. affinis, Ranunculus pedatifldus var. leiocarpus Sagina nivalis Sagina intermedia, Spergella intermedia Salix boothii Salix myrtillifolia, Salix curtiflora, Salix pseudomyrsinites, Salix pseudocordata, Salix novae-angliae, Salix pseudocordata var. aequalis, Salix pseudomyrsinites var. aequalis, Salix myrtillifolia var. curtiflora Salix serissima Salix arguta var. pallescens, Salix lucida var. serissima, Salix arguta var. alpigena Scolochloa festucacea Arundo festucacea, Fluminia festucacea Senecio plattensis Packera plattensis Silene drummondii var. drummondii Stellaria obtusa Melandrium drummondii, Lychnis drummondii, Gastroluchnis drummondii, Wahlbergella drummondii, Lychnis pudica Alsine washingtoniana, Alsine viridula, Alsine obtusa, Stellaria washingtoniana, Stellaria viridula Stellaria umbellata Stellaria weberi, Alsine baicalensis, Stellaria gonomischa Thermopsis rhombifolia Thermopsis arenosa, Thermopsis annulocarpa, Thermia rhombifolia, Scolobus rhombifolius, Drepilia rhombifolia, Cytisus rhombifolius, Thermopsis rhombifolia var. rhombifolia, Thermopsis rhombifolia var. arenosa, Thermopsis rhombifolia var. annulocarpa Trichophorum pumilum Scirpus rollandii, Baeothryon pumilum, Trichophorum rollandii, Scirpus pumilus, Trichophorum pumilum var. rollandii, Scirpus pumilus var. rollandii, Trichophorum pumilum spp. rollandii, Scirpus pumilus spp.. rollandii Woodsia alpina Woodsia glabella var. bellii, Woodsia alpina var. bellii *According to the Global Biodiversity Information Facility (GBIF) http://data.gbif.org/welcome.htm 130 73 CD "O -5 o Q. C o CD Q. "O CD C/) (J) 3 O Table A4. A summary of the results using CGCM3 for the B.C. biogeoclimatic variants currently found in the study area. This table provides the resulting areas of suitable climate for each timeslice (number of points, which roughly equate to area in km2), the proportional change from the baseline area to the 2080s area, the area of suitable climate space and persistent climate corridors as well as the percent of the current area represented by the persistent climate corridor. o Persistent Climate Corridor (PCC) (km2) %of Current Area Represented by PCC Current Area (km2) Base (km2) 2020s (km2) 2050s (km2) 2080s (km2) Boreal Altai Fescue Alpine Undifferentiated (BAFAun) 31,255 24,208 16,234 4,242 710 -97.07 184 34 0.11 Boreal Altai Fescue Alpine Undifferentiated and Parkland (BAFAunp) 46,386 27,025 6,745 993 75 -99.72 10 9 0.02 Bunchgrass Thompson Very Dry Hot (BGxh2) 679 0 19 58 51 0 0 0 0.00 Bunchgrass Fraser Very Dry Hot (BGxh3) 377 306 588 172 0 -100 0 0 0.00 o o Description of Biogeoclimatic Variant "O cq' Suitable Climate Space (km2) Change From Base to 2080s (%) Suitable Climate Space Bunchgrass Alkali Very Dry Warm (BGxw2) 550 143 2,433 3 0 -100 0 0 0.00 Boreal White and Black Spruce Stikine Dry Cool (BWBSdkl) 28,541 23,174 556 0 0 -100 0 0 0.00 Boreal White and Black Spruce Peace Moist Warm (BWBSmwl) 30,769 2,814 18 0 0 -100 0 0 0.00 -5 -5 Boreal White and Black Spruce Murray Wet Cool (BWBSwkl) 3,399 16,885 5,488 13 5 -99.97 0 0 0.00 "O -5 Boreal White and Black Spruce Graham Wet Cool (BWBSwk2) 3,530 1,052 0 0 0 -100 0 0 0.00 o Q. Coastal Mountain-heather Alpine Undifferentiated and Parkland (CMAunp) 49,788 6,671 5,438 3,329 2,190 -67.17 1,396 182 0.37 a Coastal Western Hemlock Southern Dry Submaritime (CWHdsl) 2,618 42 137 3,323 12,038 28561.9 0 0 0.00 o Coastal Western Hemlock Central Dry Submaritime (CWHds2) 816 1,365 17,247 39,326 44,804 3182.34 352 64 7.84 "O Coastal Western Hemlock Central Moist Submaritime (CWHms2) 1,744 2 4 80 424 21100 0 0 0.00 3. 3CD CD C o CD Q. "O CD C/) Base (km2) 2020s (km2) 2050s (km2) 2080s (km2) Change From Base to 2080s (%) Suitable Climate Space (km2) Persistent Climate Corridor (PCC) (km2) %of Current Area Represented by PCC Interior Douglas-fir Chilcotin Dry Cool (IDFdk4) 3,729 23,982 23,161 1,186 5 -99.98 0 0 0.00 ^ Interior Douglas-fir Dry Warm (IDFdw) 1,115 6,650 41,278 33,314 13,692 105.89 0 0 0.00 CD Interior Douglas-fir Thompson Moist Warm (IDFmw2) 1,943 4,963 18,683 7,252 7,066 42.37 0 0 0.00 O Interior Douglas-fir Wet Warm (IDFww) 1,198 2,647 34,727 85,493 70,907 2578.77 96 0 0.00 *< Interior Douglas-fir Thompson Very Dry Hot (IDFxh2) 3,482 1,414 13,799 24,862 18,928 1238.61 0 0 0.00 Interior Douglas-fir Very Dry Mild (IDFxm) 2,590 6,650 11,195 221 16 -99.76 0 0 0.00 q Interior Douglas-fir Very Dry Warm (IDFxw) 445 2,797 5,870 4,026 677 -75.8 0 0 0.00 ^ Interior Mountain-heather Alpine Undifferentiated (IMAun) 12,991 8,137 7,436 1,962 168 -97.94 9 0 0.00 P Interior Mountain-heather Alpine Undifferentiated and Parkland (IMAunp) 1,195 6,771 14,376 10,813 6,854 1.23 413 0 0.00 -p| Mountain Hemlock Leeward Moist Maritime (MHmm2) 12,394 1,960 6,297 8,579 8,284 322.65 106 9 0.07 g. Mountain Hemlock Moist Maritime Parkland (MHmmp) 2,243 1,162 3,456 4,576 4,501 287.35 31 0 0.00 CD Mountain Hemlock Undifferentiated (MHun) 4,579 30,147 31,017 17,934 10,740 -64.37 3,172 4 0.09 Jg Montane Spruce Tatlayoko Dry Cold (MSdc2) 482 4,093 17,849 12,857 3,890 -4.96 0 0 0.00 "g Montane Spruce North Thompson Dry Mild (MSdm3) 1,018 6,908 34,168 7,017 1,412 -79.56 0 0 0.00 Montane Spruce Dry Very Cold (MSdv) 283 7,366 11,293 4,127 1,731 -76.5 0 0 0.00 Montane Spruce Undifferentiated (MSun) 93 2,705 15,429 12,970 5,724 111.61 0 0 0.00 Montane Spruce South Thompson Very Dry Cool (MSxk2) 2,277 1,451 16,828 8,333 888 -38.8 0 0 0.00 Montane Spruce Pavillion Very Dry Cool (MSxk3) 1,046 1,469 5,849 5,106 361 -75.43 0 0 0.00 Montane Spruce Very Dry Very Cold (MSxv) 8,789 14,219 9,787 307 4 -99.97 0 0 0.00 Ponderosa Pine Thompson Very Dry Hot (PPxh2) 1,250 195 969 716 131 -32.82 0 0 0.00 Sub-boreal Pine-Spruce Dry Cold (SBPSdc) 4,054 9,796 2,621 31 0 -100 0 0 0.00 ^ Sub-boreal Pine-Spruce Moist Cold (SBPSmc) 3,165 13,567 2,295 52 0 -100 0 0 0.00 O Sub-boreal Pine-Spruce Moist Cool (SBPSmk) 4,082 23,254 23,469 981 147 -99.37 0 0 0.00 ^ Sub-boreal Pine-Spruce Very Dry Cold (SBPSxc) 11,353 22,139 12,653 116 1 -100 0 0 0.00 ^ Sub-boreal Spruce Dry Cool (SBSdk) 10,612 23,713 6,333 85 5 -99.98 0 0 0.00 ij. Sub-boreal Spruce Horsefly Dry Warm (SBSdwl) 3,993 25,202 39,179 6,115 1,363 -94.59 0 0 0.00 52. Sub-boreal Spruce Blackwater Dry Warm (SBSdw2) 5,286 35,588 33,609 3,165 471 -98.68 0 0 0.00 p Sub-boreal Spruce Stewart Dry Warm (SBSdw3) 9,718 45,702 25,572 737 184 -99.6 0 0 0.00 Sub-boreal Spruce Moffat Moist Cold (SBSmc1) 516 14,641 34,445 3,503 784 -94.65 0 0 0.00 Sub-boreal Spruce Babine Moist Cold (SBSmc2) 22,112 51,420 16,598 526 94 -99.82 0 0 0.00 Sub-boreal Spruce Kluskus Moist Cold (SBSmc3) 2,613 7,292 1,212 9 0 -100 0 0 0.00 Sub-boreal Spruce Moist Hot (SBSmh) 1,083 5,321 8,832 2,591 2,561 -51.87 0 0 0.00 3" T3 a o "O —i o O" ^ CD 133 ZJ CD "O s O Q. C o CD Q. "O C/) (J) o 3 O 2050s (km2) 2080s (km2) Change From Base to 2080s (%) Suitable Climate Space CD Description of Biogeoclimatic Variant Current Area (km2) Base (km2) 2020s (km2) Suitable Climate Space (km2) Persistent Climate Corridor (PCC) (km2) %of Current Area Represented by PCC Sub-boreal Spruce Mossvale Moist Cool (SBSmkl) 13,975 41,785 6,355 3 0 -100 0 0 0.00 I-H Sub-boreal Spruce Williston Moist Cool (SBSmk2) 3,909 11,782 123 0 0 -100 0 0 0.00 CD Sub-boreal Spruce Moist Mild (SBSmm) 707 7,498 11,496 853 205 -97.27 0 0 0.00 Sub-boreal Spruce Moist Warm (SBSmw) 2,194 17,839 16,259 66,906 263 -98.53 0 0 0.00 Sub-boreal Spruce Very Wet Cool (SBSvk) 5,035 23,162 9,605 138 28 -99.88 0 0 0.00 Sub-boreal Spruce Willow Wet Cool (SBSwkl) 7,858 23,437 14,330 662 241 -98.97 0 0 0.00 Sub-boreal Spruce Finlay-Peace Wet Cool (SBSwk2) 5,090 32,940 1,213 0 0 -100 0 0 0.00 Sub-boreal Spruce Takla Wet Cool (SBSwk3) 4,448 33,423 5,385 8 3 -99.99 0 0 0.00 Spruce-Willow-Birch Moist Cool (SWBmk) 59,663 3,653 3 0 0 -100 0 0 0.00 Spruce-Willow-Birch Moist Cool Scrub (SWBmks) 10,467 3,098 5 0 0 -100 0 0 0.00 7X Spruce-Willow-Birch Moist Undifferentiated (SWBun) 8,748 23,598 1,291 5 0 -100 0 0 0.00 CD TOTAL 626,967 1,613,763 1,566,818 905,958 467,464 -71.03 29,368 1936 3.09 O O "O -5 -5 CD "O -5 O Q. C a o "O o CD Q. "O CD C/) C/) 134 X) CD "O s O Q. C o CD Q_ "O CD C/) (J) o 3 O Table A5. A summary of the results using CGCM3 for the Nature Conservancy of Canada's (NCC) terrestrial ecological units currently found in the study area. This table provides the resulting areas for each timeslice (number of points, which roughly equate to area in km2), the proportional change from the baseline area to the 2080s area, the area of suitable climate space and persistent climate corridors as well as the percent of the current occurrences represented by the persistent climate corridor. Suitable Climate Space Current Area (km2) Base (km2) 2020s (km2) 2050s (km2) 2080s (km2) Change From Base to 2080s (%) Boreal Alpine Fescue Dwarf Shrabland and Grassland 17,748 33,993 20,162 6,038 1,493 -95.61 715 549 3.09 North Pacific Dry and Mesic Alpine Dwarf-Shrubland, Fell-field and Meadow 3,604 14,997 10,950 4,524 1,015 -93.23 347 46 1.28 o o "O 2 CQ' Description of NCC's Terrestrial Ecosystem Units Suitable Climate Space (km2) Persistent Climate Corridor PCC (km2) %of Current Area Represented by PCC North Pacific Interior Dry Douglas-Fir Forest 2,265 7,535 13,920 609 0 -100 0 0 0 North Pacific Interior Dry-Mesic Conifer Forest (PI, Fd, Sxw, Cw, Bl) 117,031 105,998 80,763 12,404 4,349 -95.9 0 0 0 North Pacific Interior Wet Toeslope/Riparian Hybrid Spruce - Western Red Cedar Forest 4,886 112,397 49,106 1,715 238 -99.79 0 0 0 3. 3- North Pacific Interior Wet Toeslope/Riparian Mixed Conifer Forest 3,997 60,195 34,442 1,754 622 -98.97 0 0 0 CD North Pacific Interior Wetland (Swamp, Bog, Fen and Marsh) Composite 7,558 144,621 87,393 5,750 1,647 -98.86 200 0 0 CD North Pacific Maritime Mesic-Wet Douglas-fir-Westem Hemlock Forest 274 2,848 18,012 24,163 22,401 551.47 0 0 0 North Pacific Mesic Western Hemlock-Silver Fir Forest 147 7,583 22,424 24,184 18,554 144.68 0 0 0 North Pacific Montane Riparian Woodland and Shrubland 1,294 110,602 109,618 59,818 32,508 -70.61 19,053 133 10.28 North Pacific Mountain Hemlock Forest 887 15,433 24,014 15,257 9,749 -36.83 0 0 0 North Pacific Mountain Hemlock Parkland 224 10,964 14,404 8,090 10,964 0 0 0 0 North Pacific Sub-Boreal Dry Lodgepole Pine Forest 28,106 61,480 56,202 4,609 647 -98.95 0 0 0 North Pacific Interior Lodgepole Pine - Douglas-Fir Woodland and Forest 11,866 57,219 77,191 50,204 37,977 -33.63 22,661 1,131 9.53 North Pacific Sub-Boreal Mesic Hybrid Spruce Forest 57,165 81,796 25,041 519 109 -99.87 0 0 0 North Pacific Sub-Boreal Mesic Hybrid Spruce-Douglas Fir Forest 18,844 50,244 43,284 4,277 913 -98.18 0 0 0 North Pacific Sub-Boreal Mesic Subalpine Fir - Hybrid Spruce Forest 47,680 79,335 49,476 11,831 4,716 -94.06 1,205 611 1.28 North Pacific Sub-Boreal Mesic Subalpine Fir - Hybrid Spruce Parkland 9,259 67,605 22,834 4,897 4,242 -93.73 3,005 0 0 North Pacific Sub-Boreal Riparian Woodland and Shrubland 531 45,388 33,036 11,931 2,949 -93.5 0 0 0 Northern Rocky Mountain Dry-Mesic Montane Mixed Conifer Forest (Fd and Py) 293 1,976 2,805 0 0 -100 0 0 0 Northern Rocky Mountain Lower Montane Riparian Woodland and Shrubland 2,433 74,119 47,267 15,282 7,963 -89.26 4,278 91 3.74 Northern Rocky Mountain Lower Montane, Foothill and Valley Grassland 773 2,021 3,575 10 0 -100 0 0 0 Northern Rocky Mountain Ponderosa Pine Woodland and Savanna 9 0 0 0 0 0 0 0 0 13,323 18,926 56,013 45,303 26,157 38.21 0 0 0 Rocky Mountain Subalpine-Montane Riparian Shrubland 74 48,715 14,248 1,542 178 -99.63 0 0 0 North Pacific Sub-Boreal Wet Toeslope/Riparian Hybrid Spruce Forest 3,795 35,359 13,766 209 75 -99.79 0 0 0 Boreal Open Scrub/Willow Peatland 795 18,030 350 2 0 -100 0 0 0 -5 -5 "O -5 o Q. C a o "O o CD Q. "O CD C/) CO o' Description of NCC's Terrestrial Ecosystem Units 13 Boreal White Spruce Forest and Woodland North Pacific Hypermaritime Sitka Spruce Forest Current Area (km2) Base (km2) 2020s (km2) 6,231 38,228 21 2,434 216,249 361,113 (%) Suitable Climate Space (km2) Persistent Climate Corridor PCC (km2) %of Current Area Represented by PCC 0 Change From Base to 2080s 2050s (km2) 2080s (km2) 5,469 7 0 -100 0 0 1,107 26 0 -100 0 0 0 1,310,041 936,872 314,955 -12.78 51,464 2,561 1.18 CD O O •o v< TOTAL cq' 3" i—H o £ CD c p. IT CD CD •o O Q. C a o •o —5 o CD Q. O c •o CD C/i CO o' 3 Table A6. A summary of the results using CGCM3 for rare plant species listed by the B.C. Conservation Data Centre (CDC) as occurring in the study area. This table provides the resulting areas for each timeslice (number of points, which roughly equate to area in km2), the proportional 136 change from the baseline area to the 2080s area, the area of suitable climate space and persistent climate corridors, as well as the percent of the current area represented by the persistent climate corridor. Suitable Climate Space Change From Base to 2080s (%) Suitable Climate Space (km2) Persistent Climate Corridor (km2) % of Current Points in PCC -100 11,965 0 0 3,518 -95.62 0 0 0 3,019 3,857 -86.48 0 0 0 23,249 8,766 10,056 -36.8 0 0 0 217,934 231,965 235,327 230,380 5.71 0 0 0 1 385 1,316 45 2 -99.48 0 0 0 Botrychium simplex 3 246,951 250,860 248,935 242,408 -1.84 5,993 0 0 Bouteloua gracilis 2 144,422 142,524 51,330 44,989 -68.85 0 0 0 Camissonia breviflora 2 70,946 113,810 70,736 0 -100 0 0 0 Carex backii 1 149,567 28,869 298 26 -99.98 0 0 0 Carex bicolor 2 218,521 227,063 230,988 228,769 4.69 0 0 0 Carex heleonastes 4 206,697 158,161 40,992 12,894 -93.76 53 0 0 Carex lenticularis var. dolia 3 237,767 233,824 224,410 221,445 -6.86 178,348 1 33.33 Carex scoparia 1 227,333 231,205 228,068 227,768 0.19 810 0 0 Carex simulata 8 12,477 5,039 2 0 -100 0 0 0 Carex sychnocephala 1 117,279 85,582 18,458 20,378 -82.62 6,175 0 0 Carex tenera 7 209,132 214,157 181,302 170,450 -18.5 49,081 2 28.57 Carex tonsa var. tonsa 1 42,820 6,937 11 8 -99.98 0 0 0 Carex xerantica 4 22,910 36,328 17,698 19,963 -12.86 0 0 0 Points in Study Area Base Area (km2) 2020s (km2) 2050s (km2) 2080s (km2) Allium geyeri var. tenerum 1 35,236 119,633 189,880 187,311 Anemone canadensis 1 80320 35,754 4,689 Apocynum x floribundum 3 28,534 19,853 Arabis holboellii var. pinetorum 3 15,912 Arabis sparsiflora 3 Atriplex argentea ssp. argentea Species Name Chamaerhodos erecta ssp. nuttallii 5 33,985 15,094 1,700 2,231 -93.44 0 0 0 Chamaesyce serpyllifolia ssp. serpyllifolia 2 9,003 26,470 53,890 86,287 858.42 0 0 0 Chenopodium atrovirens 2 195,832 217,560 204,187 192,848 -1.52 55,356 0 0 Delphinium bicolor ssp. bicolor 1 5,201 207,518 173,591 138,341 2559.89 0 0 0 Draba alpina 2 50,065 22,737 4,515 547 -98.91 0 0 0 Draba cinerea 2 147,852 40,110 2,233 381 -99.74 2 0 0 Draba fladnizensis 3 50,318 24,656 4,493 999 -98.01 0 0 0 Draba glabella var. glabella 1 40,037 24,211 10,035 2,867 -92.84 0 0 0 Draba lactea 2 25,922 72 2,811 576 -97.78 0 0 0 Draba lonchocarpa var. vestita 1 15,800 26,355 24,456 20,481 29.63 0 0 0 137 Suitable Climate Space Species Name Points in Study Area Base Area (km2) 2020s (km2) 2050s (km2) 2080s (km2) Change From Base to 2080s (%) Suitable Climate Space (km2) Persistent Climate Corridor (km2) %of Current Points in PCC Draba reptans 1 8,075 14,177 1,885 37 -99.54 0 0 0 Draba ruaxes 2 174,894 147,033 120,544 97,831 -44.06 1,751 0 0 Draba ventosa 1 54,564 25,844 7,812 1,472 -97.3 49,941 0 0 Dryopteris cristata 1 113,581 157,411 103,736 94,879 -16.47 17,356 0 0 Entosthodon rubiginosus 1 1,534 4,042 53,559 212 -86.18 0 o • 0 Epilobium halleanum 2 233,060 242,307 242,771 233,869 0.35 25 0 0 Epilobium leptocarpum 2 188,968 186,451 169,639 166,843 -11.71 97,321 0 0 Eutrema edwardsii 1 18,241 1,991 136 1 -99.99 0 0 0 Festuca minutiflora 2 248,562 244,985 206,844 130,590 -47.46 0 0 0 Glyceria pulchella 1 44,620 1,689 0 0 -100 0 0 0 Hesperostipa spartea 2 132,094 164,058 154,660 133,368 0.96 0 0 0 Juncus albescens 3 247,071 231,794 169,248 94,015 -61.95 19,529 0 0 Juncus arcticus ssp. alaskanus 2 171,556 161,157 146,340 144,531 -15.75 7,549 0 0 Juncus stygius 2 175,517 165,874 149,924 145,198 -17.27 80,991 1 50 Koenigia islandica 2 18,010 150,868 92,987 73,607 308.7 34,669 1 50 Lloydia serotina var.flava 1 70,424 62,283 53,488 50,112 -28.84 0 0 0 Malaxis paludosa 2 180,154 170,455 155,217 153,612 -14.73 92,612 2 100 Megalodonta beckii var. beckii 2 57,387 116,388 150,466 162,566 183.28 0 0 0 Melica spectabilis 1 164,481 212,477 222,392 214,329 30.31 0 0 0 Minuartia austromontana 2 110,415 105,669 95,432 95,602 -13.42 1,651 0 0 Montia chamissoi 2 66,917 127,081 137,620 114,928 71.75 17 0 0 Muhlenbergia glomerata 4 207,655 170,641 75,737 72,075 -65.29 26,043 2 25 Nephroma occultum 4 112,782 146,634 125,213 98,893 -12.31 11,585 1 25 Nymphaea leibergii 1 115,773 10,443 39,179 37,660 -67.47 0 0 0 Nymphaea tetragona 5 235,285 236,629 235,194 236,105 0.35 158,015 5 100 Platanthera dilatata var. albiflora 2 41,625 59,744 69,983 77,243 -48.75 0 0 0 Poa fendleriana ssp. fendleriana 2 150,717 184,326 198,580 194,740 26.5 0 0 0 Polemonium boreale 1 153,944 77,549 19,566 7,143 -56.62 0 0 0 Polygonum ramosissimum var. ramosissimum 16,466 27,361 10,095 457 -83.42 0 0 0 Polypodium sibiricum 2,757 5 0 0 -100 0 0 0 Potentilla nivea var. pentaphylla 111,151 181,767 195,584 175,555 57.94 654 1 100 Pyrola elliptica 127,258 176,408 204,706 218,649 71.82 0 0 0 138 Suitable Climate Space Species Name Points in Study Area Base Area (km2) 2020s (km2) 2050s (km2) 2080s (km2) Change From Base to 2080s (%) Suitable Climate Space (km2) Persistent Climate Corridor (km2) % of Current Points in PCC 0 0 0 Sagina nivalis 1 102,329 58,350 15,474 6,168 -93.97 Salix boothii 9 177,641 110,818 16,674 5,176 -97.09 2,973 0 0 Salix serissima 1 185,995 197,075 194,831 192,846 3.68 6,196 0 0 Saxifraga nelsoniana ssp. carlottae 1 220,193 198,056 171,507 140,244 -36.31 2,166 0 0 Senecio plattensis 6 72,120 65,045 8,136 1,245 -98.27 0 0 0 Silene drummondii var. drummondii 2 36,959 40,546 5,720 112,902 205.48 0 0 0 Sparganium fluctuans 2 108,508 111,763 104,903 112,896 4.04 590 0 0 Stellaria umbellata 1 147,701 136,108 120,828 116,896 -20.86 241 0 0 Torreyochloa pallida 2 3,781 4,585 186 3 -99.92 0 0 0 Trichophorum pumilum 4 95,306 47,510 2,649 315 -99.67 0 0 0 Woodsia alpina 1 2,601 0 0 -100 0 0 0 0 Total 162 7,767,830 7,706,309 6,486,310 5,984,593 -88.16 919,658 16 9.88 Table A7a. A complete summary of the fatal trends to B.C. Conservation Data Centre listed plant populations for the baseline and 2020s timeslices. Cells indicate number of populations constrained by different bioclimatic envelope attributes. 139 X) CD "O s O Q. C o CD Q. BASELINE "O 2020 s CD Occurrences C/) (J) o 3 O « U u < •o s aj o o "O CQ' 3 n •2 H •5 u .5 z* MAT too low TD too high too low AHM too high too low PAS too high Allium geyeri var. tenerum 1 1 Anemone canadensis 1 1 Apocynum x floribundum 3 3 Arabis holboellii var. pinetorum 3 3 Arabis sparsiflora 3 3 Atriplex argentea ssp. argentea 1 0 1 1 1 1 -5 Camissonia breviflora 2 2 CD Carex backii 1 1 1 o Carex bicolor 2 2 1 C a Carex heleonastes 4 0 2 o Carex lenticularis var. dolia 3 2 2 "O Carex simulata 1 1 o Carex scoparia 8 8 Carex sychnocephala 1 0 Carex tenera 7 6 "O CD C/) C/) too high too low too high too low 1 1 2 1 1 1 too high 1 4 Chamaerhodos erecta ssp. nuttallii 5 5 Chamaesyce serpyllifolia ssp. serpyllifolia 2 2 Chenopodium atrovirens 2 2 Delphinium bicolor ssp. bicolor 1 1 1 Draba alpina 2 2 2 2 0 Draba glabella var. glabella 1 1 Draba lactea 2 2 Draba lonchocarpa var. vestita 1 1 140 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 2 1 2 1 1 2 1 2 2 6 1 1 4 1 2 8 1 1 1 1 1 1 1 4 5 4 1 1 1 4 1 2 5 2 1 2 2 1 1 1 1 1 3 1 1 1 2 2 2 1 1 1 2 1 2 2 1 1 1 1 1 2 1 1 3 1 1 1 4 2 1 3 7 Carex xerantica Draba cinerea 1 2 Carex tonsa var. tonsa Draba fladnizensis 1 1 1 3 Q. 3 2 1 2 CD too low 3 3 Q. too high 1 2 "O -5 too low PAS 1 1 Bouteloua gracilis CD too high AHM TD Species Botrychium simplex 3. 3- too low MAT 2 1 2 2 3 1 1 1 2 2 2 1 X) CD "O s O Q. C o CD Q. BASELINE "O 2020 s CD Occurrences MAT TD AHM PAS MAT AHM TD PAS C/) (J) o « V im 3 O < -o 3 VI o o "O CQ' 3. 3- •W 3 (A •2 H £ w ^ a. Draba reptans l 1 Draba ruaxes 2 2 Draba ventosa 1 1 Dryopteris cristata 1 1 1 Entosthodon rubiginosus 1 1 1 Epilobium halleanum 2 2 Epilobium leptocarpum 2 0 Eutrema edwardsii 1 1 Festuca minutiflora 3 3 CD Glyceria pulchella 2 2 o Hesperostipa spartea 2 2 C a Juncus albescens 3 3 o Juncus arcticus ssp.aAlaskanus 2 2 "O Juncus stygius 2 1 Koenigia islandica 2 1 Lloydia serotina var. flava 1 1 Megalodonta beckii var. beckii 2 1 "O -5 Q. o CD Q. too high too low too high too low too high too low too high too low too high too low too high too low too high too low too high Species -5 CD too low 1 1 1 1 1 2 2 1 1 1 1 1 2 1 1 1 1 1 1 1 2 1 1 2 1 1 1 1 1 1 1 1 1 1 2 2 1 1 2 1 1 1 1 1 1 1 1 1 1 2 Melica spectabilis 1 1 1 1 Minuartia austromontana 2 2 2 2 Montia chamissoi 2 2 Muhlenbergia glomerata 4 2 CD Nephroma occultum 4 3 C/) C/) Nymphaea leibergii 1 1 Platanthera dilatata var. albiflora 2 2 Poa fendleriana ssp. fendleriana 2 2 Polemonium boreale 1 1 Polygonum ramosissimum var. ramosissimum 1 1 Polypodium sibiricum 1 1 Pyrola elliptica 1 1 "O 141 1 1 1 1 1 1 2 1 1 1 1 2 2 1 1 1 1 1 1 2 1 1 1 1 2 1 1 1 1 1 ZJ CD "O s O Q. C o CD Q. BASELINE "O 2020 s CD MAT Occurrences AHM TD PAS MAT TD AHM PAS O o o "O CD 3. 3" CD CD "O O Q. C a o "O -5 o Without PCCs o 3 Study Area C/) (J) 1 1 1 8 1 too low too high too low too high too low too high too low too high 1 1 too low too high too low too high too low too high too low too high Species Sagina nivalis 1 1 Salix boothii 8 Salix serissima 1 1 Saxifraga nelsoniana ssp. carlottae 1 1 1 1 1 Senecio plattensis 6 6 1 1 1 1 4 Silene drummondii var. drummondii 4 4 2 2 2 1 Sparganium fluctuans 1 1 1 Stellaria umbellata 1 3 1 Torrevochloa pallida 2 2 1 Trichophorum pumilum 4 4 1 Woodsia alpina 1 1 1 Mean 1 1 3 1 1 1.20 1.00 1 1.12 1.00 1.10 1 1 1 1 1 1 4 2 1 1 1 1 5 1 1 4 1 2 1 1 2 2 1 1 2 1 1 2 1 1 4 1 1 1 1 1.39 1.64 1.20 1.17 2.11 1.00 1.00 1.29 2.22 1.31 N.B. Nymphea tetragona does not appear in this list because each of its occurrences met the conditions of its bioclimatic envelope. CD Q. "O CD C/) (f) Table A7b. A complete summary of the fatal trends to B.C. Conservation Data Centre listed plant populations for the 2050s and 2080s timeslices Cells indicate number of populations constrained by different bioclimatic envelope attributes. 142 ZJ CD "O s O Q. C o CD Q_ 2050s "O 2080s CD Occurrences Study Area Without PCCs C/) (J) o 3 O Allium geyeri var. tenerum 1 1 Anemone canadensis 1 1 Apocynum x floribundum 3 3 Arabis holboellii var. pinetorum 3 3 Arabis sparsiflora 3 3 Atriplex argentea ssp. argentea 1 0 I-H CD O O "O 7X CD -5 -5 CD "O -5 O Q. C a o "O o CD Q. "O CD C/) C/) MAT too low AHM TD too high too low 1 2 too high PAS too low too high too low 1 1 2 2 3 1 1 MAT too high too low TD too high too low 1 1 1 1 1 2 2 2 AHM too high PAS too low too high too low 1 1 2 3 3 1 1 too high Species 1 2 1 Boliychium simplex 3 3 Bouteloua gracilis 2 2 Camissonia breviflora 2 2 1 1 Carex backii 1 1 1 1 3 1 1 2 2 2 1 2 Carex heleonastes 4 0 2 4 Carex lenticularis var. dolia 3 2 Carex simulata 1 1 Carex scoparia 8 8 Carex svchnocephala 1 0 1 Carex tenera 7 6 2 Carex tonsa var. tonsa 1 1 1 1 Carex xerantica 4 4 2 3 1 Chamaerhodos erecta ssp. nuttallii Chamaesyce serpyllifolia ssp. serpyllifolia 5 5 5 5 4 2 2 Chenopodium atrovirens 2 2 Delphinium bicolor ssp. bicolor 1 1 Draba alpina 2 2 Draba cinerea 2 2 1 1 Draba fladnizensis 3 0 2 2 Draba glabella var. glabella 1 1 1 1 Draba lactea 2 2 2 2 Draba lonchocarpa var. vestita 1 1 1 1 1 1 Carex bicolor 143 2 1 1 2 3 1 2 2 2 1 1 1 2 2 3 4 2 8 2 3 2 1 8 8 8 4 2 1 1 3 2 3 2 3 5 5 5 4 5 1 2 1 2 2 1 8 1 2 1 2 1 1 2 2 1 8 1 1 2 1 2 1 1 1 2 2 1 2 1 1 1 2 2 1 3 2 3 1 1 1 1 1 2 2 2 2 2 2 1 1 1 1 3 73 CD "O -5 o Q. C o CD Q. 2050s "O 2080s CD o o "O cq' Study Area Without PCCs Occurrences C/) (J) o 3 O MAT too low TD too high too low 1 1 Draba reptans 1 1 Draba ruaxes 2 2 Draba ventosa 1 1 1 1 1 1 1 1 1 Epilobium halleanum 2 2 Epilobium leptocarpum 2 0 Eutrema edwardsii 1 1 Festuca minutiflora 3 3 CD Glyceria pulchella 2 o Hesperostipa spartea 2 C a "O -5 Q. too low 1 1 1 AHM too high too low PAS too high too low too high 1 1 1 1 2 2 1 1 1 1 2 2 2 "O 2 1 1 1 2 1 1 1 1 1 2 1 1 2 Juncus stygius 2 2 2 2 1 1 2 2 1 1 2 2 1 1 Koenigia islandica 2 1 Lloydia serotina var. flava 1 1 Megalodonta beckii var. beckii 2 1 Melica spectabilis 1 1 1 Minuartia austromontana 2 2 1 Montia chamissoi 2 2 Muhlenbergia glomerata 4 2 Nephroma occultum 4 3 Nymphaea leibergii 1 1 Platanthera dilatata var. albiflora 2 2 Poa fendleriana ssp. fendleriana 2 2 1 Polemonium boreale Polygonum ramosissimum var. ramosissimum 1 1 1 1 1 1 1 1 Polvpodium sibiricum 1 1 1 1 Pyrola elliptica 1 1 144 2 1 3 1 1 2 2 C/) C/) too high 1 3 CD too low 1 2 "O too high 1 Juncus arcticus ssp. Alaskanus Q. too low 2 J uncus albescens CD too high TD 1 o o too low MAT 2 Dryopteris cristata CD too high PAS Species Entosthodon rubiginosus 3. 3- AHM 1 1 1 1 1 1 1 1 1 2 4 1 2 2 2 1 1 1 4 1 2 2 3 3 1 1 3 1 1 1 2 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 2080s 2050s Occurrences CS 0> u < •e 3 t*> 3 « •a R § w TD MAT too low too high too low PAS AHM too high too low too high too low MAT too high too low TD too high too low AHM too high too low PAS too high too low too high Species Sagina nivalis 1 1 1 Salix boothii 8 8 7 7 1 1 Salix serissima 1 1 1 1 1 Saxifraga nelsoniana ssp. carlottae 1 1 Senecio plattensis 6 6 2 Silene drummondii var. drummondii 4 4 4 Sparganium (luctuans 1 1 1 1 1 1 1 8 7 1 3 1 1 1 1 1 1 5 2 5 6 5 3 2 4 4 3 1 2 1 1 2 6 3 4 1 Stellaria umbellata 1 3 1 Torrevochloa pallida 2 2 2 2 2 2 2 2 2 2 Trichophorum pumilum 4 4 4 4 1 4 4 4 1 4 Woodsia alpina 1 1 Average 1 1 1 1 1.94 2.05 1.00 1 1 1 1.41 2.13 1.30 1 1 1 2.21 2.05 1.00 1 1 1.53 2.26 N.B. Nymphea tetragona does not appear in this list because each of its occurrences met the conditions of its bioclimatic envelope. Table A8. A summary of a species' projected suitable climate space (SCS) and the proportional change from the baseline to the 2080s timeslice (Proportional Change) according to 4 broad habitat types (alpine/subalpine, conifer forests, grasslands and wetlands). 145 1.38 Species Allium geyeri var. tenerum Delphinium bicolor ssp. bicolor Draba alpina Draba cinerea Draba fladnizensis Draba glabella var. glabella Draba lactea Draba lonchocarpa var. vestita Draba reptans Draba ruaxes Draba ventosa Lloydia serotina var. flava Minuartia austromontana Polemonium boreale Polypodium sibiricum Sagina nivalis Saxifraga nelsoniana ssp. carlottae Woodsia alpina Baseline 2080s 35,236 5,201 50,065 147,852 50,318 40,037 25,922 15,800 8,075 174,894 54,564 70,424 110,415 153,944 2,757 102,329 220,193 50,999 187,311 138,341 547 381 999 2,867 576 20,481 37 97,831 1,472 50,112 95,602 7,143 0 6,168 140,244 0 Average Apocynum x floribundum Arabis sparsiflora Chamaesyce serpyllifolia ssp. serpyllifolia Chenopodium atrovirens Epilobium halleanum Malaxis paludosa Nephroma occultum Pyrola elliptica 146 431.59 2559.89 -98.91 -99.74 -98.01 -92.84 -97.78 29.63 -99.54 -44.06 -97.30 -28.84 -13.42 0.00 -100.00 -93.97 -36.31 -100.00 Suitable Climate Space Habitat 0 1,651 0 0 0 2,166 0 alpine, subalpine alpine, subalpine alpine, subalpine alpine, subalpine alpine, subalpine alpine, subalpine alpine, subalpine alpine, subalpine alpine, subalpine alpine, subalpine alpine, subalpine alpine, subalpine alpine, subalpine alpine, subalpine alpine, subalpine alpine, subalpine alpine, subalpine alpine, subalpine 0 0 0 55,356 25 92,612 11,585 0 conifer forests conifer forests conifer forests conifer forests conifer forests conifer forests conifer forests conifer forests 0 grasslands 11,965 0 0 2 0 0 0 0 0 1,751 49,941 106.69 28,534 217,934 9,003 195,832 233,060 180,154 112,782 127,258 3,857 230,380 86,287 192,848 233,869 153,612 98,893 218,649 Average Arabis holboellii var. pinetorum Proportion Change Baesline to 2080 -86.48 5.71 858.42 -1.52 0.35 -14.73 -12.31 71.82 102.66 15,912 10,056 -36.80 Species Bouteloua gracilis Camissonia breviflora Carex backii Carex bicolor Carex heleonastes Carex lenticularis var. dolia Carex scoparia Carex simulata Carex sychnocephala Carex tenera Carex tonsa var. tonsa Carex xerantica Chamaerhodos erecta ssp. nuttallii Festuca minutiflora Glyceria pulchella Hesperostipa spartea Juncus albescens Juncus arcticus ssp. alaskanus Juncus stygius Koenigia islandica Melica spectabilis Poa fendleriana ssp. fendleriana Senecio plattensis Silene drummondii var. drummondii Torreyochloa pallida Baseline 2080s Proportion Change Baesline to 2080 144,422 70,946 149,567 218,521 206,697 237,767 227,333 12,477 117,279 209,132 42,820 22,910 33,985 248,562 44,620 132,094 247,071 171,556 175,517 18,010 164,481 150,717 72,120 36,959 3,781 44,989 0 26 228,769 12,894 221,445 227,768 0 20,378 170,450 8 19,963 2,231 130,590 0 133,368 94,015 144,531 145,198 73,607 214,329 194,740 1,245 112,902 3 -68.85 -100.00 -99.98 4.69 -93.76 -6.86 0.19 -100.00 -82.62 -18.50 -99.98 -12.86 -93.44 -47.46 -100.00 0.96 -61.95 -15.75 -17.27 308.70 30.31 26.50 -98.27 205.48 -99.92 Average Anemone canadensis Atriplex argentea ssp. argentea Botrychium simplex Dryopteris cristata Entosthodon rubiginosus 147 Suitable Climate Space Habitat 0 0 0 0 53 178,348 810 0 6,175 49,081 0 0 0 0 0 0 19,529 7,549 80,991 34,669 0 0 0 0 0 grasslands grasslands grasslands grasslands grasslands grasslands grasslands grasslands grasslands grasslands grasslands grasslands grasslands grasslands grasslands grasslands grasslands grasslands grasslands grasslands grasslands grasslands grasslands grasslands grasslands 0 0 5,993 17,356 0 wetlands wetlands wetlands wetlands wetlands -26.06 80,320 385 246,951 113,581 1,534 3,518 2 242,408 94,879 212 -95.62 -99.48 -1.84 -16.47 -86.18 Species Epilobium leptocarpum Eutrema edwardsii Megalodonta beckii var. beckii Montia chamissoi Muhlenbergia glomerata Nymphaea leibergii Nymphaea tetragona Platanthera dilatata var. albiflora Polygonum ramosissimum var. ramosissimum Salix boothii Salix serissima Sparganium fluctuans Stellaria umbellata Trichophorum pumilum Average 148 Baseline 2080s Proportion Change Baesline to 2080 188,968 18,241 57,387 66,917 207,655 115,773 235,285 41,625 16,466 177,641 185,995 108,508 147,701 95,306 166,843 1 162,566 114,928 72,075 37,660 236,105 77,243 457 5,176 192,846 112,896 116,896 315 -11.71 -99.99 183.28 71.75 -65.29 -67.47 0.35 -48.75 -83.42 -97.09 3.68 4.04 -20.86 -99.67 -33.20 Suitable Climate Space Habitat 97,321 0 0 17 26,043 0 158,015 0 0 2,973 6,196 590 241 0 wetlands wetlands wetlands wetlands wetlands wetlands wetlands wetlands wetlands wetlands wetlands wetlands wetlands wetlands