Climate Change Vulnerability Assessment of Species of Concern in West Virginia Project Report Elizabeth Byers and Sam Norris West Virginia Division of Natural Resources P.O. Box 67, Elkins WV 26241 February 14, 2011 Table of Contents Abstract ........................................................................................................................................... 1 Acknowledgements ......................................................................................................................... 2 Introduction ..................................................................................................................................... 2 Methods........................................................................................................................................... 3 Results ............................................................................................................................................. 7 Amphibians ............................................................................................................................. 8 Birds ........................................................................................................................................ 8 Fish .......................................................................................................................................... 9 Mammals............................................................................................................................... 10 Reptiles ................................................................................................................................. 11 Mollusks................................................................................................................................ 11 Crayfish ................................................................................................................................. 12 Cave Invertebrates ................................................................................................................ 13 Odonata and Lepidoptera ...................................................................................................... 14 Other Insects and Spiders...................................................................................................... 15 Plants ..................................................................................................................................... 16 Discussion ..................................................................................................................................... 17 Conservation Status Rank and Climate Change Vulnerability ............................................. 17 Primary Risk and Resilience Factors .................................................................................... 18 The Geography of Vulnerability ........................................................................................... 19 Conclusions and Management Recommendations ....................................................................... 22 References ..................................................................................................................................... 25 Appendix A: Key to Codes………………………………………………………………………29 Appendix B: Vulnerability Index Scores………………………………………………………...30 Appendix C: Intrinsic and Modeled Risk Factor Scores………...………………………………49 Appendix D. Exposure and Geography Risk Factors……………………………………………61 Appendix E. Sample Vulnerability Assessment Form…………………………………………..68 Cover photos (clockwise from upper left): Cheat Mountain Salamander (Plethodon nettingi) photo by Craig Stihler, Northern Saw-whet Owl (Aegolius acadicus) photo by Rob Tallman, Red Spruce (Picea rubens) photo by Robert H. Mohlenbrock. USDA NRCS, Virginia Big-eared Bat (Corynorhinus townsendii virginianus) photo by Jeff Hajenga, Crimson-ringed Whiteface (Leucorrhinia glacialis) photo © Stephen Cresswell www.stephencresswell.com, Pink Mucket (Lampsilis abrupta) photo by Janet Clayton. Abstract This project assessed and ranked the relative climate change vulnerability of 185 animal and plant species in West Virginia. Most species were selected based on their status as Species of Greatest Conservation Need within the West Virginia Wildlife Conservation Action Plan. Among the species identified in the state plan, priority was given to globally vulnerable or imperiled species identified by NatureServe (G1-G3), and selected species that are critically imperiled at the state level (S1). A small number of more common species were assessed. More than half of the taxa assessed were scored as vulnerable to climate change. Amphibians were the taxonomic group at highest risk, followed closely by fish, mollusks, and rare plants. Highly mobile taxonomic groups including birds and mammals appear to be somewhat less vulnerable, as are common and widespread habitat foundation plants. Obligate cave invertebrates are predicted to have strong resistance to climate change impacts. Species with high global Conservation Status Ranks (at risk throughout their range) are statistically only slightly more vulnerable to climate change than globally abundant species. State-level Conservation Status Ranks and climate change vulnerability are more closely correlated, but scores for individual species still vary widely. In other words, rare species are not always vulnerable to climate change, and common species are not necessarily resilient. Six of the twenty-three risk factors assessed were strongly correlated with vulnerability to climate change across all taxonomic groups in the state. They are (a) natural barriers to movement and dispersal, (b) anthropogenic barriers to movement and dispersal, (c) physiological thermal niche, (d) physiological hydrological niche, (e) genetic variation, and (f) modeled response. In terms of the relative vulnerability of different geographies in the state, downscaled climate models indicate that species in the northern part of the state may experience slightly greater warming than those at the southern margin. Species dependent on moist habitats or ephemeral streams and wetlands in the eastern and western portions of the state are likely to experience greater drought stress than those in the higher-elevation Allegheny Mountains, but all habitats are likely to face increased drought stress, especially during the summer and early fall. Species on the southern, or “trailing” edge of their global range are more likely to disappear from the state. High elevation species restricted to the cool, moist summits and plateaus of Allegheny Mountain region of the state are at increased risk because they have no possibility of migrating upward, and potential migration northward is blocked by significant low-elevation natural barriers to the north. Based on the results of the assessment and review of current literature, management recommendations were developed for consideration in the next revision of the West Virginia Wildlife Conservation Action Plan. Key recommendations are to increase habitat connectivity; manage for ecosystem function and habitat integrity; protect natural heritage resources; protect water quality and streamflow; aim for representation, resiliency, and redundancy; consider innovative and unconventional strategies; reduce existing non-climate change ecosystem stressors; monitor, model, and adaptively manage; forge new partnerships; and mitigate. 1 Acknowledgements This project would not have been possible without the assistance of several experts who shared their knowledge of particular species or species groups with the authors. Zachary Loughman of West Liberty University provided range maps, natural history information, and worked jointly with the authors to assess West Virginia’s crayfish species. Donna Mitchell and David Thorne, of West Virginia Division of Natural Resources (WVDNR) provided distribution and natural history information for bird and fish species, respectively, and worked jointly with the authors to complete the assessments for these groups. Cathy Johnson of the Monongahela National Forest contributed independent assessments of five vertebrate species. Information on species distribution and natural history was provided by WVDNR biologists Sue Olcott (dragonflies and damselflies), Michael Welch (mammals, amphibians, reptiles), Craig Stihler and Jack Wallace (mammals, amphibians), Janet Clayton (mussels), Dan Cincotta (fish), and Mike Shingleton (brook trout). Helpful exchanges of information regarding species vulnerability occurred with the Pennsylvania Natural Heritage Program, the Virginia Natural Heritage Program, and NatureServe. Review comments were kindly provided by Walt Kordek, Jim Vanderhorst, Craig Stihler, Barb Sargent, Michael Welch, and Zac Loughman. Overall project supervision and support were provided by Walt Kordek, Assistant Chief at WVDNR. Introduction Ongoing climate change will have major impacts on wildlife and wildlife habitats in West Virginia, including range shifts, population declines or expansions, and extinctions. While some of the most visible impacts of climate change such as sea level rise, ocean acidification, and melting glaciers are not of immediate concern to West Virginia wildlife managers, climate change nevertheless is bringing severe stresses to wildlife in the form of increasing temperatures, potential net drying of habitats, an increase in the frequency and intensity of extreme events, and changes in atmospheric composition (IPCC 2007, TWS 2008, BPC 2009, Young et al. in press). Some of the most sensitive taxonomic groups, such as amphibians, are already being negatively impacted by climate change (Pauley 2006). West Virginia Division of Natural Resources (WVDNR) is currently revising its state Wildlife Conservation Action Plan. The Association of Fish and Wildlife Agencies (AFWA) has provided voluntary guidance for states to incorporate climate change into their wildlife action plans. Vulnerability assessment is a critical part of this guidance. Identifying which species and habitats are vulnerable and understanding the factors contributing to their vulnerability are key to developing effective adaptation strategies. The relative vulnerability of species or habitats can be used to set goals, determine management priorities, and to direct resources where they will be most effective (AFWA 2009, Glick et al. 2011). Climate change is only one of the many stresses that species and habitats are currently experiencing. In many cases, the management strategies that would ameliorate negative impacts of climate change are the same as those needed to address conventional threats to biodiversity. However, the particular species and habitats most at risk may shift as the climate changes, and some new or re-emphasized strategies may become more important as habitats change. There are likely to be “geographies of risk” that emerge as a result of climate change as well, especially in our mountainous state with its steep precipitation and temperature gradients. Adaptation to climate change will involve strategic conservation of terrestrial and freshwater habitats and the ecological functions that sustain them, within larger connected landscapes. 2 Methods This project assessed and ranked the relative climate change vulnerability of 185 animal and plant species in West Virginia. The project was conducted in six consecutive steps: 1. Select species for assessment: Animal species were selected based on their status as Species of Greatest Conservation Need within the West Virginia Wildlife Conservation Action Plan. Among the 517 animal species identified in the state plan, priority was given to globally vulnerable or imperiled species identified by NatureServe (G1-G3), species that are critically imperiled at the state level (S1), and rare species with a center of distribution in West Virginia. Rare plant species were selected based on NatureServe ranks (G1-G3, S1). A small number of more common species were assessed. Some of the common species, e.g., brook trout (Salvelinus fontinalis) and red spruce (Picea rubens), were selected based on their perceived vulnerability to the impacts of climate change. Other common species, such as red oak (Quercus rubra) and white oak (Quercus alba), were selected because they are important habitat foundation species upon which many other species depend. 2. Assemble natural history and distribution information: WVDNR and other state natural heritage programs within the NatureServe network have developed extensive information about the distribution, natural history, and conservation status of rare species and habitats. Following review of the existing information, data gaps were identified and a literature search and/or expert consultation were conducted as needed for particular species. References used in the assessments are listed at the end of this report. 3. Assess the relative vulnerability of species: Vulnerability assessment involves describing the severity and scope of the exposure that species experience, and combining this with species’ sensitivity and capacity to adapt to climate change. NatureServe’s newly developed Climate Change Vulnerability Index (Young et al. 2010) provides a rapid, scientifically defensible assessment of species’ vulnerability to climate change. The index was developed to serve the needs of wildlife managers for a practical, multi-faceted, rapid assessment tool. It is designed to complement, and not duplicate, information contained in the NatureServe conservation status ranks (Master et al. 2000), and may be used to update conservation status ranks to include the additional stressor of climate change. Using regionally-specific climate models, the index examines how the changed climate will impact a species using factors known to be associated with vulnerability to climate change, including species-specific factors as well as external stressors imposed by human actions. Downscaled climate data representing an ensemble of 16 global circulation models was downloaded from Climate Wizard (Girvetz et al. 2009) and displayed in a GIS format. Climate data was available on a 4-km grid for historic data, and a 12-km grid for predicted future data. The climate data, together with distributional and natural history information for each species to be assessed, was entered into the index calculator (an Excel workbook tool) to obtain scores for each species. Outputs were reviewed by WVDNR biologists most familiar with the species under evaluation. 3 Figure 1. Components of Vulnerability Assessment The factors considered in evaluating species response may be divided into general categories including direct exposure, indirect exposure, sensitivity, documented response, and modeled response. Complex interactions such as shifts in competitive, predator-prey, or hostparasite interactions are likely to be important as well, but they are not included in this rapid assessment because of the difficulty and unpredictability inherent in simultaneous evaluation of climate change on interacting species. Detailed information including the scientific references used to develop each factor and the limitations of the methodology are given in Young et al (2010) and Young et al (in press). Brief definitions of the factors are given below. • Direct exposure o Temperature change: predicted change in annual temperature by 2050, calculated over the range of the species in West Virginia. o Moisture change: predicted net change in moisture based on the Hamon AET:PET Moisture Metric, calculated over the range of the species in West Virginia. • Indirect Exposure o Exposure to sea level rise: not a factor in West Virginia o Distribution relative to natural and anthropogenic barriers: The geographical features of the landscape where a species occurs may naturally restrict it from dispersing to inhabit new areas. Similarly, dispersal may be hindered by intervening anthropogenically altered landscapes such as urban or agricultural areas for terrestrial species and dams or culverts for aquatic species. o Predicted impact of land use changes resulting from human responses to climate change: strategies designed to mitigate greenhouse gases, such as creating large wind farms, plowing new cropland for biofuel production, or planting trees as carbon sinks, have the potential to affect large tracts of land and the species that use these areas in both positive and negative ways. 4 • Sensitivity o Dispersal and movements: species with poor dispersal abilities may not be able to track shifting favorable climate envelopes. o Predicted sensitivity to temperature and moisture changes: species requiring specific moisture and temperature regimes may be less likely to find similar areas as climates change and previously-associated temperature and precipitation patterns uncouple. Predicted sensitivity to changes in temperature. • Historic thermal niche: exposure to past variations in temperature. • Current physiologic thermal niche. Predicted sensitivity to changes in precipitation, hydrology, or moisture regime. • Historical hydrological niche: exposure to past variations in precipitation. • Current physiologic hydrologic niche. Dependence on a specific disturbance regime likely to be impacted by climate change: Species dependent on habitats such as longleaf pine forests, floodplain forests, and riparian corridors that are maintained by regular disturbances (e.g., fires or flooding) are vulnerable to changes in the frequency and intensity of these disturbances caused by climate change. Dependence on ice, ice-edge, or snow-cover habitats: the extent of oceanic ice sheets and mountain snow fields are decreasing as temperatures increase, imperiling species dependent on these habitats. This factor is of minor significance in West Virginia. o Restriction to uncommon geological features or derivatives: species requiring specific substrates, soils, or physical features such as caves, cliffs, or sand dunes may become vulnerable to climate change if their favored climate conditions shift to areas without these physical elements. o Reliance on interspecific interactions: because species will react idiosyncratically to climate change, those with tight relationships with other species may be threatened. Dependence on other species to generate habitat. Dietary versatility (animals only). Pollinator versatility (plants only). Dependence on other species for propagule dispersal. Forms part of an interspecific interaction not covered above. o Genetic factors: a species'ability to evolve adaptations to environmental conditions brought about by climate change is largely dependent on its existing genetic variation. Measured genetic variation. Occurrence of bottlenecks in recent evolutionary history. o Phenological response to changing seasonal temperature and precipitation dynamics. Recent research suggests that some phylogenetic groups are declining due to lack of response to changing annual temperature dynamics (e.g., earlier onset of spring, longer growing season), including some bird species that have not advanced their migration times, and some temperate zone plants that are not moving their flowering times. 5 • Documented or Modeled Response to Climate Change (optional, if available) o Documented response to recent climate change. Although conclusively linking species declines to climate change is difficult, convincing evidence relating declines to recent climate patterns has begun to accumulate in a variety of species groups. This criterion incorporates the results of these studies when available. o Modeled future change in range or population size. The change in area of the predicted future range relative to the current range is a useful indicator of vulnerability to climate change. o Overlap of modeled future range with current range. A spatially disjunct predicted future range indicates that the species will need to disperse in order to occupy the newly favored area, and geographical barriers or slow dispersal rates could prevent the species from getting there. o Occurrence of protected areas in modeled future distribution. For many species, future ranges may fall entirely outside of protected areas and therefore compromise their long-term viability. • Factors not considered.—The climate change vulnerability score does not include factors that are already considered in existing conservation status assessments. These factors include population size, range size, and demographic factors. The goal is for the NatureServe Climate Change Vulnerability Index to complement NatureServe Conservation Status Ranks and not to partially duplicate factors. Ideally, Index values and Conservation Status Ranks should be used in concert. • Confidence. A measure of confidence in species information is provided with the final score. This confidence relates specifically to the level of uncertainty indicated by the assessor based on the range of values given for each factor. Checking a range of values for particular factors tends to decrease confidence in species information. 4. Compile and analyze results: Climate Change Vulnerability Index results were compiled and analyzed in order to (a) highlight those species most (and least) vulnerable to climate change, (b) identify and rank causative factors, (c) identify geographic areas or habitat types at high risk. Statistical analysis included (a) scatterplots showing the linear regression between factors and final index scores, (b) calculating indicator values of factors for final index scores using the method of Dufrene and Legendre (1997), and (c) evaluating factor linkages through hierarchical agglomerative cluster analysis (McCune and Grace 2002). 5. Share CCVI results with partners: U.S. Fish and Wildlife Service, the Monongahela National Forest, The Nature Conservancy, NatureServe, and other partners have expressed interest in climate change vulnerability assessment results. Presentations on the project were made in 2010 to a variety of stakeholders, including a multi-agency meeting sponsored by Monongahela National Forest, a workshop with the USFWS Ecological Services staff in Elkins, two webinars for NatureServe partners, and a presentation to the West Virginia Academy of Sciences. The project report will be distributed by email to interested partners and constituents. 6 Results The vulnerability index scores for 185 species in West Virginia reflect the combined effects of exposure and sensitivity in estimating the relative impacts of climate change on a species. Exposure to climate stress is based on the predicted temperature rise and potential net drying of habitats within the species’ range during the next 50 years. Sensitivity is derived from 15 intrinsic species-specific factors based on the particular characteristics and life history of the species. An additional six factors consider the impacts of geography and human response to climate change. Four final factors take into account documented or modeled responses to climate change by the species. The scores should be considered in concert with NatureServe Conservation Status Ranks, which they are designed to complement, and not duplicate. Species assessment details including the global and state conservation rank, relation of the species range in West Virginia to its global range, subscores for exposure to climate change, subscores for each risk factor, and confidence in the species data for each assessment are included in the Appendices. The results by taxonomic group are given below. Care should be exercised in interpreting the results by taxonomic group, since only a small sampling of the total species in the state were assessed. Nevertheless, the results by taxonomic group are consistent with those obtained by other states (Young et al. 2009, PNHP 2010) and may represent real differences in vulnerability of various groups. In our sample, amphibians were the taxonomic group at highest risk, followed closely by fish, mollusks, and rare plants. Highly mobile taxonomic groups including birds and mammals appear to be somewhat less vulnerable, as are common and widespread habitat foundation plants. Obligate cave invertebrates, known as troglobites, are predicted to have strong resistance to climate change impacts. Many troglobites, in fact, were able to survive the rigors of the last ice age in their buffered underground habitats. Vulnerability by Taxonomic Group Number of Species Assessed 25 20 Extremely Vulnerable 15 Highly Vulnerable Moderately Vulnerable 10 Presumed Stable 5 Increase Likely 0 Taxonomic Group Figure 2. Vulnerability by Taxonomic Group 7 Amphibians As a taxonomic group, amphibians are characterized by very high vulnerability to negative impacts of climate change. Nine at-risk amphibian species were assessed, eight of which appear vulnerable to climate-change related declines. Key risk factors for most amphibians include anthropogenic barriers to dispersal or movements, poor ability to disperse or move large distances, and narrow historic and physiological hydrological habitat niche. Some species, such as the extremely vulnerable Cheat Mountain Salamander, Cow Knob Salamander, and Shenandoah Mountain Salamander are also Figure 3. Cheat Mountain Salamander (Plethodon constrained by natural barriers to dispersal nettingi), photo by Craig Stihler (mountaintop habitats), and an apparently narrow physiological thermal niche within which they are able to compete successfully against other species. Certain species with more generalized habitat requirements, such as the red-backed salamander (Plethodon cinereus) may benefit from climate change and expand their range to outcompete these specialist species. The West Virginia Spring Salamander, while it has many risk factors unrelated to climate change, is scored as stable since its streamside cave habitat is largely buffered from external changes in climate. $ % ! ! & "# "# !&! '( $ # ) Birds Birds appear to be somewhat less vulnerable to climate change than other taxonomic groups, due largely to their excellent ability to disperse and move large distances, and to a lesser degree to their lack of specificity in terms of geological substrate. Those bird species that are vulnerable to climate change are likely to shift their range out of West Virginia. A variety of factors may lead to vulnerability including Figure 4. Peregrine Falcon (Falco peregrinus), photo © Gary Hartley 8 physiological thermal or hydrological niche, dependence on a particular disturbance regime that is likely to change with changing climates, dietary limitations, and dependence on vulnerable plants or plant communities. The USDA provides modeled responses of 150 bird species to climate change (Matthews et al. 2004). Phenological mismatches between nestling hatches and food supply have the potential to cause widespread decline in some bird species (Moller 2008), but this phenomenon is not yet well-studied in the United States. * / 1 ) / ) * " ' / ' + , , .& 0 1 # 2 + , , + , .' 2 ' , ' , ,2 . $ 0 # ' " ' # 5 ## 2 1 # $0$ 0 0 1 0 #' % ! ! ! ! 0 # ! ! ! ! ! & $ # ! ! ! ! -3 / -3 / -3 / / !- "# ) ) ) ) ) ) 4, 4, 4, 4, 4, 4, 4, 4, 2 2 2 2 2 2 2 2 Fish Evidence is growing that higher water temperatures resulting from climate change are negatively impacting cold- and cool-water fish populations across the country (Field et al. 2007). Fish are strongly impacted by natural barriers to dispersal, particularly those species that already inhabit the upper reaches of a watershed and thus have no possibility of migrating to colder waters. Cold-water Figure 5. Candy Darter (Etheostoma osburni), WVDNR photo fish that inhabit small, high elevation streams that may be subject to both drying (direct habitat loss) and warming are especially sensitive. Other important risk factors for individual fish species include anthropogenic barriers to movement (e.g., dams, perched culverts, chemical barriers from acid mine drainage), physical habitat specificity for certain springdependent species, and dependence on a disturbance regime (flood patterns) that may be disrupted by climate change. 9 ,2 7 7 , $ , $ 8 , & $ $ 6 $ % ! 8 5 ! ! ! ) ) #% 8 , $ & / * ! ! " " " " " 2& # 7 , ! $ # # # # # ' % Mammals Nine at-risk mammal species and one common species were assessed for climate change vulnerability. In general, mammals tend to have good dispersal abilities, which confers some resilience to climate change, since they are sometimes able to move or disperse along with a shifting climate envelope. They vary greatly in terms of other risk factors. The Virginia Big-eared Bat and WV Northern Flying Squirrel have the highest vulnerability to climate change impacts of the mammal species assessed. Key risk factors for the WV Northern Flying Squirrel are its distribution relative to natural topographic barriers (restriction to high elevations), its dependence on a vulnerable species (red spruce) for habitat, and its low genetic variability. The Virginia Big-eared bat is ranked as vulnerable due to its narrow historic and physiological thermal habitat niche, its physical habitat specificity (cave hibernacula), and its low genetic variability. Four additional at-risk species are also vulnerable to climate change: Southern Rock Vole, Indiana Bat, Allegheny Woodrat, and Southern Water Shrew. Eastern Small-footed Bat and Hoary Bat are presumed stable under climate change stress, although their populations are still at risk due to non-climate stresses. Fisher appears resilient to climate change but is likely to shift its range and move out of West Virginia. The common North American Deermouse is likely to be a climate change winner, with populations increasing. Figure 6. WV Northern Flying Squirrel (Glaucomys sabrinus fuscus), photo by Craig Stihler ( # ' (/ - #0 + # 9 ! $ & !& "# "# 10 4 * # " + / ( ' 7 ,2 ( ' 0% ( * , ' ( - & !& ( ! ! ! 8 ! ) ) ) 4, 2 Reptiles Two at-risk reptile species were assessed and both are vulnerable to climate change. The Mountain Earthsnake has moderate risk from a number of factors, including natural barriers, anthropogenic barriers, poor dispersal, somewhat narrow physiological thermal and hydrologic niche, physical habitat specificity, and partial dependence on other species for suitable habitat (ant tunnels). The Spotted Turtle also suffers from anthropogenic barriers to movement, a Figure 7. Spotted Turtle (Clemmys guttata), WVDNR photo archive diet. 2 " $ & narrow physiological hydrological niche, and physical habitat specificity, but it gains resilience from its good dispersal ability and generalized !& & ! "# Mollusks Figure 8. Pink Mucket (Lampsilis abrupta), photo by Janet Clayton Mollusks exhibit generally heightened vulnerability to the negative impacts of climate change, with the exception of cave obligates that are largely buffered from climate alterations. Lack of ability to disperse or move large distances is a key constraint. Many mussels are vulnerable in part because they rely on other species (fish) for dispersal. A variety of factors lead to increased vulnerability for mollusks, depending on the species, including strong natural and anthropogenic barriers to movement, narrow physiological thermal habitat niche, physical habitat specificity, and genetic bottlenecks. 11 + 0$ & 0 - 2+ ( # 2 # $ , 5 # " " " " + $2 : + ) 2 ! $ & / 7 # # # # # ,2 $ 2 7 %% $ , %% $ , /7 & ) & # ) ) 1 # Crayfish All known crayfish species in the state, including both common and rare taxa, were assessed in cooperation with Zac Loughman at West Liberty University. Half of West Virginia’s crayfish species are vulnerable to climate change. The most important risk factors for this group are strong natural and anthropogenic barriers to movements and a sometimes narrow physiological hydrological niche. A few species have added vulnerability due to a narrow physiological thermal niche, dependence on a particular natural disturbance regime, physical habitat specificity, dependence on other species to create habitat, and genetic bottlenecks. The generalized diet of crayfish confers some resilience to this group. Figure 9. New River Crayfish (Cambarus chasmodactylus), photo by Zac Loughman 8 ## -# ' 7 % $ ! % % ! "# "# 12 27 % * # % ,%; * , $ % <$ # % <$ # % % % % % % / 7 % <$ # % $ <$ # % - #7 % 5 # $ % * * # % * . % * *$$ , - 2 % 7 ,2 % & 7 % ( % * ,%; ! ! ! ! ! ! 6 /7 6 0 0& 0$ 0 6 ! 6 /7 6 ! ) ) ! ! ! ! ! ! ! ! ! ! ! ) ) ) ) ) ) ) ) ) ) ) 0" 0 # ! ! ! /* Cave Invertebrates Obligate cave invertebrates, or troglobites, while at great risk due to factors unrelated to climate such as isolation and cave pollution, are not likely to suffer significant additional stress due to climate change impacts. Troglobites experienced little or no response to the Pleistocene glacial era (Culver et al. 2003, Lamoreux 2004), and we may hope that the current era of warming will have similarly minor impacts on these unique organisms. Figure 10. Cave Beetle (Pseudanophthalmus sp.), photo by Craig Stihler * * * * * * * * - $ $ $ $ 8$ 1 # , ) $ + $ % $; ) ) ) ) ) ) ) ) 13 8 8 0 $ * $ $ . .) + 2( + 2( + ) ) ) ) ) , $ , 2 $ # ( 5 1 # 1 $ ) ) , ) ) , $ , $ * $ $ * $ $ ) , $ , ( $ # $ * $ $ ) , $ $ $ $ ) $ , - & $ 7 8 $ . 7 * 1 # * $ 6 4 $ ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) Odonata and Lepidoptera Twelve species of at-risk dragonflies, damselflies, butterflies, and moths were assessed for climate change vulnerability, with a wide range of resulting scores. Some of these species are mobile and already on the southern edge of their range, and are predicted to shift their populations entirely out of West Virginia due to climate change stress. Species associated with ephemeral wetlands and headwater streams tend to have the highest risk, especially where these are tied to cold-temperature habitats. Dietary specialization confers additional risk for half of the Figure 11. Crimson-ringed Whiteface (Leucorrhinia glacialis), photo by www.stephencresswell.com 0 - ,2 8 $ # ' %, species assessed. ! "# ,2 8 14 & $ 2 $$ $ : # - # $$ 8 ,2 + # ) 20 # $ == 2 $$ / 2 &# $2 ! ! ! ! $ ! ) ) ) ) Other Insects and Spiders Ten rare stonefly species, two tiger beetles, and two spider species were assessed. The life history of some of these species is poorly understood, but from the data available, these at-risk species are likely to have a wide range of vulnerabilities to climate change. Several of the stonefly species are constrained by anthropogenic barriers to movement (dams, culverts) during their larval stage. The stoneflies assessed are poor dispersers, generally moving less than 100 meters per dispersal event. The most vulnerable species have a narrow physiological hydrological niche, depending on small headwater streams with specific substrate types for habitat. Several of the stonefly species have probably experienced genetic bottlenecks in their recent evolutionary history. The two tiger beetle species have increased risk due to their physical habitat specificity and presumed genetic bottlenecks, but they gain resilience from their tolerance of varying disturbance regimes, somewhat broad temperature tolerance, and relatively good dispersal ability. The two spider species, while dissimilar in their risk profiles, do not have strong known risks and are presumed stable under climate change. Figure 12. Tiger Beetle (Cicindela sexguttata), WVDNR photo archive $ % $ % $ # / . % &# % ) $ ( % % ! ) , * , % + % * 8 2 % " . *$$ , * $ * $ / *$$ , &# - "# "# "# $ % % ) ) ) 15 Plants Eighteen at-risk plant species, 12 common habitat foundation tree species, and two invasive plant species were assessed. A large proportion of the at-risk plants are also vulnerable to climate change. They are vulnerable for a wide variety of reasons, including poor dispersal ability, dispersal constrained by natural and anthropogenic barriers, dependence on wetland habitats, restriction to calcareous substrates, genetic bottlenecks, and dependence of particular natural disturbance regimes that may be altered by climate change. Four of the at-risk plants, which have few known risk factors and Figure 13. Monongahela Barbara's Buttons (Marshallia grandiflora), photo by Elizabeth Byers prefer less vulnerable habitats (warm and dry slopes, larger streams, or generalized habitats), are scored as presumed stable. Seven of the 12 habitat foundation tree species assessed are presumed stable, and one species (black gum) is likely to increase, which is good news for the animal and plant species that depend on forest types where these trees are co-dominant. However, four of the foundation tree species are apparently vulnerable to climate change. Red spruce is considered highly vulnerable and may disappear entirely from West Virginia. Black cherry, sugar maple, and pin oak are moderately vulnerable. Their abundance and/or range in West Virginia will likely decrease by the middle of the century. Species that depend on red spruce forest, northern hardwood forest, or pin oak swamp will face severe stresses as regeneration in these forest types shifts to species with greater tolerance for warmer, drier conditions. The two invasive plant species assessed are, not surprisingly, expected to remained stable or increase under climate change stress. / $ # .+ 1 , " " " " " " " $ ' - ) # 7 ,2 + # $ 7 $ , * .& ( # $ . 0 # %) ) $ 7 # - %% & # - ,2 ! . 0- ( # # # # # # $ # # $ $ ! ! 16 ) # & & 5 1 2 0 $ . . ! ! ! ! 0 ) 1 1 2 0 ) , ) 7 $ 7 1 2 0 , ( # ) ' 1 2 0 ) - ,2# ' , ! ! ! ! ! ! ! /* ! /* ! ! ! ! ! ! ! /* ! /* ) ) ) ) ) ) ) ) ) ) ) ) 4, 4, 2 2 Discussion Conservation Status Rank and Climate Change Vulnerability An important result to come out of this assessment is the knowledge that we cannot predict the climate change vulnerability of a species based on its current Conservation Status Rank. In other words, rare species are not always vulnerable to climate change, and common species are not necessarily resilient. Each species behaves and responds according to its unique life history characteristics, habitat requirements, and distribution. The implications of this are important to conservation and management strategies, since our current understanding of the costs and benefits of certain strategies does not yet take into account the new landscape of risk. We need to re-examine and re-align our strategies to best conserve species and habitats with the resources available to us. Species with high global rank (at risk throughout their range) are statistically only slightly more vulnerable to climate change than globally abundant species. Obligate cave invertebrates (troglobites) are all presumed stable under climate change regardless of their Conservation Status Rank and have been excluded from the comparison. At the state level, Conservation Status Rank and climate change vulnerability are more closely correlated, but scores for individual species still vary widely. Critically imperiled species are more likely to be extremely vulnerable to the negative impacts of climate change than less threatened species. However, some at-risk species may actually benefit from changing climate. Most of the common species assessed are presumed stable under climate change, but some may experience declines. 17 Vulnerability by Global Rank (excluding cave obligates) Number of Species Assessed 30 25 Extremely Vulnerable 20 Highly Vulnerable 15 Moderately Vulnerable Presumed Stable 10 Increase Likely 5 0 G1 G2 G3 G4 G5 GNA Global Conservation Status Rank Figure 14. Vulnerability and Global Conservation Status Rank Vulnerability by State Rank (excluding cave obligates) Number of Species Assessed 30 25 Extremely Vulnerable 20 Highly Vulnerable 15 Moderately Vulnerable Presumed Stable 10 Increase Likely 5 0 S1 S2 S3 S4 S5 SNA State Conservation Status Rank Figure 15. Vulnerability and State Conservation Status Rank Primary Risk and Resilience Factors Each risk factor was evaluated against vulnerability index scores for West Virginia using scatterplots and linear regression. The R2 values of the top six factors are shown below, along with their rank (out of 23 total) in terms of highest overall risk scores, and the difference between scores of vulnerable vs. non-vulnerable species. The six factors shown rank high on all counts, and are probably representative of the most consistent risk factors across all taxonomic groups in 18 the state. Indicator value analysis was applied to factors that were scored for most species. Factors that are significantly (p<0.05) associated with high vulnerability statewide are, in order of importance, natural barriers, physiological thermal niche, anthropogenic barriers, WV range relative to global range, physiological hydrological niche, historical hydrological niche, and movements/dispersal ability. Finally, hierarchical agglomerative cluster analysis was used to evaluate the factors that cluster most closely with the overall index score. This clade of important risk factors includes natural barriers, anthropogenic barriers, physiological thermal niche, and physiological hydrologic niche. Statewide, upon evaluation of all statistical methods, the top two risk factors appear to be natural and anthropogenic barriers to dispersal. Important natural barriers for the species assessed include low elevation barriers for mountaintop species in the red spruce zone and watershed barriers for aquatic species. Anthropogenic barriers of importance in the assessment include dams and improperly sized culverts for aquatic species, and roads or powerlines for some amphibians. The next two pervasive factors across species in West Virginia are no surprise: physiological thermal and hydrological niche. As temperature and moisture regimes change, those species with specific requirements for cooler and moister microhabitats will certainly suffer. Genetic variation and modeled response factors were only available for a small number of species. When these factors were available, they were very strongly correlated with the final vulnerability score. ! / * ) ) $ # #, #, , , , #, $ , >; ? >; @ >; >; >;A >;! ? ! A A ! ! B B " " " " # # # # B B The Geography of Vulnerability Predicted climate warming in West Virginia over the next 40-50 years ranges from 4.55 F in the southern part of the state to 5.0-5.6oF in the northern part of the state. These estimates are based on downscaled climate data using an ensemble average of 16 global circulation models and the medium emissions (A1B) scenario (Gervitz et al. 2009). Species in the northern part of the state may experience slightly greater warming than those at the southern margin. o 19 Figure 16. Predicted Change in Annual Temperature by the 2050's Of equal importance, but fraught with greater uncertainty, are the climate predictions related to net drying or wetting of habitats. Most precipitation models show increasing precipitation for West Virginia in the next half century, but the coincident warming means that habitats are unlikely to maintain their current moisture status. Gervitz et al. (2009) and others predict net drying of habitats throughout the contiguous United States, including West Virginia. However, it should be noted that these models are significantly less consistent than the temperature models. The drying is predicted to be less severe in the portions of the state that are currently wettest, i.e., at higher elevations in the Allegheny Mountains. Drying is predicted to be more severe in the already-dry eastern panhandle and in the western hills. Species dependent on moist habitats or ephemeral streams and wetlands in the eastern and western portions of the state are likely to experience greater drought stress than those in the higher-elevation Allegheny Mountains, but all habitats are likely to face increased drought stress, especially during the summer and early fall. Extreme events such as drought, severe storms, and flooding are likely to increase statewide. 20 Figure 17. Predicted Change in Annual Moisture by the 2050's Species, even rare ones, whose ranges extend beyond West Virginia in all directions, are less likely to experience range contractions within West Virginia due to the stress of climate change. The next most resilient category is species at the northernmost edge of their range in West Virginia. As the climate warms, these species may move northward from the southeastern states into West Virginia. For those species on the southern, or “ trailing” edge of their range, the opposite is true. These species, if they are mobile and if suitable migration corridors exist, may move northward out of the state. Species on the east or west edge of their range, or whose entire range is restricted to West Virginia, also have relatively high vulnerability. High elevation species restricted to the cool, moist summits and plateaus of Allegheny Mountain region of the state are at increased risk because they have no possibility of migrating upward, and potential migration northward is blocked by significant low-elevation natural barriers to the north. 21 West Virginia Range Relative to Global Range Average Vulnerability Index (0=Increase LIkely, 4=Extremely Vulnerable) 3 2.5 2 1.5 1 0.5 0 Center of range Northern edge of range Entire range Southern edge of range East/west edge of range WV Portion of Global Range Figure 18. Species Range in West Virginia Relative to Global Range Conclusions and Management Recommendations Climate change is progressively impoverishing the biodiversity of our state. Species and habitats in West Virginia face significant stresses due to climate change, resulting in an on-going increase in extinctions, out-migrations, population declines, and range reductions. A few species are likely to benefit from changing climates, and some will be able to adapt to the warmer and potentially drier conditions and remain stable in the state. The majority, however, are unlikely to be able to successfully adapt to the unprecedented, rapidly changing conditions. In order to persist, species must not only adapt to climate change, but must also find a way to survive the serious “ conventional” threats from extractive industries, energy development, pollution, rural sprawl, invasive species, pathogens, and other stressors. A vulnerability assessment is only a first step toward conservation of species and ecosystems. It provides a science-based approach for differentiating between species and habitats likely to decline and those likely to thrive. Managers must then make tough choices to allocate scarce resources between the most vulnerable and the most viable conservation targets, balancing the greatest need with the highest probability of success. Economic, legal, and social factors will of necessity be part of this triage (Glick et al. 2011). Management strategies to combat climate change are based on a combination of (a) reducing the sensitivity of species and ecosystems, (b) reducing exposure to climate change impacts, and (c) increasing adaptive capacity to deal with those impacts. Consideration of these principles should ideally be embedded into all of WVNDR’ s planning and decision-making processes. Most of the management strategies that effectively address conventional threats are also likely to assist in combating the negative effects of climate change. However, the risk profile of individual species and of some taxonomic groups is strongly impacted by climate change, and may require additional conservation attention. Ten management recommendations to address climate change impacts on wildlife and habitats are presented below. 22 1. Increase Habitat Connectivity. Climate change is degrading current habitats and will likely create novel habitats as species with good dispersal mechanisms redistribute themselves to track a shifting climate envelope or shifting food resources. The key to successful movements and migration is the presence of contiguous suitable habitat that species are able to colonize or at least traverse. Protecting and restoring large blocks of unfragmented habitat and using linkages and corridors to enhance connectivity between habitats will facilitate this movement. New public land acquisitions in addition to management of habitats within existing Wildlife Management Areas should reflect these priorities. In West Virginia, forested ridgetops currently provide important corridors of unfragmented habitat, but these remnant natural areas are facing rapid fragmentation from energy development. Barriers to movement need to be identified and mitigated where possible. Barriers to aquatic species are in some cases amenable to management action, for example, perched or undersized culverts can be replaced with correctly-designed culverts that allow the passage of aquatic species. Powerlines and roads that constrain the movement of certain amphibians can be narrowed or vegetated to improve habitat connectivity. 2. Manage for Ecosystem Function and Habitat Integrity. Healthy and biologically diverse ecosystems will be better able to withstand or recover from the impacts of climate change. Key ecological processes such as pollination, seed dispersal, nutrient cycling, natural disturbance cycles, and predator-prey relations are the glue that holds ecosystems together, and should be aggressively maintained and restored wherever possible. These processes function best in landscapes composed of large habitat blocks connected by well-placed corridors, with minimal human disturbance. Proactive management and restoration that actively facilitates the ability of species, habitats, and ecosystems to accommodate climate change are necessary. For example, in designing critical habitat buffers, more buffer area may be needed in the direction of cooler, moister habitats (e.g., upstream, upslope, on cooler northerly aspects, or under denser forest cover). 3. Protect Natural Heritage Resources. Climate change is impacting and changing the species composition of natural habitats, but the refugia where these natural habitats occur nevertheless represent our best option for long-term biodiversity conservation. Existing natural communities are defined by unique arrays of environmental characteristics and the suite of species that interact within them. Rare species are often indicators of specialized or unique habitats. Even if some species are lost from special habitats, and some migrate in or out, the unique set of environmental characteristics will remain to provide the basis for a rich palette of opportunities for species in the future. Rare species and special habitats, such as those identified and tracked by the West Virginia Natural Heritage Program, should be a priority for conservation action. 4. Protect Water Quality and Streamflow. Climate change will alter the distribution, abundance, and quality of water by affecting precipitation, air and water 23 temperatures, and snowmelt. Riparian restoration and conservation projects can help to improve water quality by reducing stream temperatures, e.g., by expanding riparian vegetation, protecting cold-water refugia, or increasing cold-water spill from existing reservoirs. Watershed restoration and reforestation initiatives provide stable base flow, reduce flood runoff, and reduce sediment to streams. 5. Aim for Representation, Resiliency, and Redundancy. Among the most powerful strategies for the long-term conservation of biodiversity is establishment of networks of intact habitats or conservation land that represent the full range of a region’ s species and ecosystems, and include multiple, robust examples of each type. These principles are at the core of many conservation planning efforts, and are increasingly important as the stresses of climate change erode existing habitats. 6. Consider Innovative and Unconventional Strategies. With the unprecedented scale and speed of environmental change, it may become necessary to risk new and untried management strategies. Radical management options such as assisted migration, i.e., physically moving species to suitable habitat, need to at least be on the table for discussion. 7. Reduce Existing Ecosystem Stressors. Successful adaptation strategies for fish and wildlife will require understanding and reducing the combined effects of both climate-related and non-climate stressors. The cumulative effects of habitat loss and alteration, pollution, invasive species, and pathogens in addition to climate change may prove to be a deadly combination for many species. 8. Monitor, Model, and Adaptively Manage. As new conditions affect wildlife and habitats, managers need to monitor these changes and incorporate them into new action strategies. Ecological change is likely to be nonlinear and difficult to predict. As biological thresholds are reached, changes such as trophic cascades may occur with alarming rapidity, affecting many species within a habitat. Model predictions, even when handicapped by uncertainties, will help managers to understand the range of scenarios they need to plan for. 9. Forge New Partnerships. Success in combating the loss of significant numbers of species and habitats in West Virginia is only achievable through an unprecedented level of collaboration and cooperation between WVDNR and other agencies, organizations, scientists, and the public. Rather than working within traditional hierarchies and comfort zones, wildlife managers will need to reach out to build science-driven, landscape-scale strategies that maximize the use of scarce resources. An important element of this will be support for legislative and policy changes that support wildlife and habitats, and address climate change stresses. 10. Mitigate: WVDNR can work to reduce its own carbon emissions to set an example to partners, to the public and to employees. WVDNR can reduce the energy use and carbon footprint of its buildings, facilities, vehicle fleet, workforce, and operations to the maximum extent possible. 24 References This reference list includes sources cited in the body of this report, as well as references consulted for the individual species assessments, as listed in the Appendices. Acciavatti, R. E. 2001. Status of the cobblestone tiger beetle (Cicindela marginipennis Dejean 1831) (Coleoptera: Cicindelidae) in West Virginia. Interim research report to WVDNR. Allen, T. J. 1997. The butterfies of West Virginia and their caterpillars. University of Pittsburgh Press. AmphibiaWeb: Information on amphibian biology and conservation. [web application]. 2010. Berkeley, California: AmphibiaWeb. Available: http://amphibiaweb.org/ Arbogast, B. S., R. A. Browne, P. D. Weigl, and G. J. Kenagy. 2005. Conservation genetics of endangered flying squirrels (Glaucomys) from the Appalachian mountains of eastern North America. Animal Conservation 8: 123-133. Association of Fish & Wildlife Agencies (AFWA). 2009. Voluntary guidance for states to incorporate climate change into state wildlife action plans and other management plans. AFWA, Washington, DC. Bentley, S. L. 2000. Native orchids of the southern Appalachian Mountains. Univ. N.C. Press. Beattie, A. J. 1985. The evolutionary ecology of ant-plant mutualisms. Cambridge Studies in Ecology. Cambridge University Press. Bipartisan Policy Center (BPC). 2009. Beyond Seasons’ End: A Path Forward for Fish and Wildlife in the Era of Climate Change. Edited by The Wildlife Management Institute and the Theodore Roosevelt Conservation Partnership in cooperation with Ducks Unlimited, Trout Unlimited, BASS/ESPN Outdoors, Izaak Walton League of America, Association of Fish and Wildlife Agencies, Coastal Conservation Association, American Sportfishing Association, Pheasants Forever, and Boone and Crockett Club. 126 pp. Brown, P. M. 2008. A new fringed Platanthera (Orchidaceae) from the central Appalachian Mountains of eastern North America. N. Am. Native Orchid Journal 14:4. Buckelew, A. R., Jr. and G. A. Hall. 1994. The West Virginia Breeding Bird Atlas. University of Pittsburgh Press, Pittsburgh. Burns, R. M., and B. H. Honkala, tech. coords. 1990. Silvics of North America: 1. Conifers; 2. Hardwoods. Agriculture Handbook 654. U.S. Department of Agriculture, Forest Service, Washington, DC. vol.2, 877 p. Burton, M. and R. Burton, ed. 1969. The international wildlife encyclopedia. Marshall Cavendish. Culver, D. C., M. C. Christman, and W. R. Elliot. 2003. The North American obligate cave fauna: regional patterns. Biodiversity and Conservation 12: 441–468. Dourson, D. 2010. Statewide Land Snail Survey for West Virginia 2008. Report submitted to WVDNR. Elkins, WV. Dufrene, M. and P. Legendre. 1997. Species assemblages and indicator species: the need for a flexible asymmetrical approach. Ecological Monographs 67: 345-366. Dunkle, S. W. 2000. Dragonflies through binoculars. Oxford University Press. Field, C. B., L. D. Mortsch, M. Brklacich, D. L. Forbes, P. Kovacs, J. A. Patz, S. W. Running and M. J. Scott. 2007. North America. Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Flebbe, P. A., Roghair, L. D., and J. L. Bruggink. 2006. Spatial modeling to project Southern Appalachian trout distribution in warmer climate. Transaction of the American Fisheries Society. 135: 1371–1382. 25 Flora of North America Editorial Committee, eds. (FNA). 1993+. Flora of North America North of Mexico. 12+ vols. New York and Oxford. Freudenstein, J. V. 1999. A new species of Corallorhiza (Orchidaceae) from West Virginia, USA. Novon 9:511-513. Frost, S. W. 1959. Insect life and insect natural history. Dover Publications. Frumhoff, P.C., J.J. McCarthy, J.M. Melillo, S.C. Moser, and D.J. Wuebbles. 2007. Confronting Climate Change in the U.S. Northeast: Science, Impacts, and Solutions. Synthesis report of the Northeast Climate Impacts Assessment (NECIA). Cambridge, MA: Union of Concerned Scientists (UCS). 146 pp. Gallego, M. T., M. J. Cano, and J. Guerra. 2006. Syntrichia ammonsiana (Pottiaceae) new to South America. The Bryologist 109:236-238. Girvetz, E. H., C. Zganjar, G. T. Raber, E. P. Maurer, P. Kareiva, and J. J. Lawler. 2009 Applied climate change analysis: the climate wizard tool. PLoS ONE 4: e8320. doi:10.1371/journal.pone.0008320. Glick, P., B.A. Stein, and N.A. Edelson, editors. 2011. Scanning the Conservation Horizon: A Guide to Climate Change Vulnerability Assessment. National Wildlife Federation, Washington, D.C. 168 pp. Green, N. B. and T. K. Pauley. 1987. Amphibians and reptiles in West Virginia. Univ. Pittsburgh Press. 241 pp. Heiss, J. S. and M. L. Draney. 2004. Revision of the nearctic spider genus Calymmaria (Araneae, Hahniidae). Journal of Arachnology 32: 457-525. Hellquist, C. B. and A. R. Pike. 2003. Potomogeton strictifolius A. Bennett straight-leaved pondweed: Conservation and research plan for New England. New England Wildflower Society. Holsinger, J. R., R. A. Baroody, and D. C. Culver. 1976. The invertebrate cave fauna of West Virginia. WV Speleological Survey Bulletin 7. Hotopp, K. and T. A. Pearce. 2008. Land Snail Distributions in West Virginia. Unpublished report prepared for WVDNR. Elkins, WV. 126 pp. Hubricht, L. 1985. The distributions of the native land mollusks of the eastern United States. Fieldiana: Zoology, New Series, 24: 1-191. Intergovernmental Panel on Climate Change (IPCC). 2007. Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, M. L. Parry, O. F. Canziani, J. P. Palutikof, P. J. van der Linden and C. E. Hanson, eds., Cambridge University Press, Cambridge, UK, 976 pp. Kane, T. C. and T. Ryan. 1983. Population ecology of carabid cave beetles. Oecologia 60: 46-55. Knisley, C. B. and T. D. Schultz. 1997. The Biology of Tiger Beetles. VA Museum of Natural History Special Publication No. 5. Kondratieff, B. C. and R. W. Baumann (coordinators). 2000. Stoneflies of the United States. Jamestown, ND: Northern Prairie Wildlife Research Center Online. http://www.npwrc.usgs.gov/resource/distr/insects/sfly/index.htm (Version 12DEC2003). Lamoreux, J. 2004. Stygobites are more wide-ranging than troglobites. Journal of Cave and Karst Studies 66 (1): 18-19. Levi, H. W. and L. R. Levi. 1968. Spiders and their kin. Western Publishing Company. 160 pp. Loughman, Z. J. 2010. Personal communication on 14 September 2010 and 26 October 2010 regarding crayfish species in West Virginia. Master, L. L., B. A. Stein, L. S. Kutner, and G. A. Hammerson. 2000. Vanishing assets: Conservation status of U.S. species. Pages 93-118 in B. A. Stein, L. S. Kutner, and J. S. Adams (editors), Precious Heritage: The Status of Biodiversity in the United States. New York, Oxford University Press. Matthews, S. N., R. J. O’ Connor, L. R. Iverson, A. M. Prasad. 2004. Atlas of Climate Change Effects in 150 Bird Species of the Eastern United States. United States Department of Agriculture, Forest Service, Northeastern Research Station. General Technical Report NE-318. 26 McCune, B. and J. B. Grace. 2002. Analysis of ecological communities. MjM Software Design, Gleneden Beach, OR. 300 pp. Merritt, R. W. and K. W. Cummins. 1978. An introduction to the aquatic insects of North America. Kendall/Hunt Publishing Company. 1158 pp. Moller, A. P., D. Rubolini, and E. Lehikoinen. 2008. Populations of migratory bird species that did not show a phenological response to climate change are declining. PNAS 105 (42):16195-16200. Neville, B. 2007. Diplurans Species Account. The Subsurface Life in Mineral Environments (SLIME) Team. Available: http://caveslime.org/ Norris, S. J. and R. E. Sullivan. 2002. A community conservation assessment for mid-Appalachian shale barrens. Unpublished report prepared for the U. S. Department of Agriculture, Monongahela National Forest, Elkins, WV. 106 pp. Ogle, D. W. 1991. Spiraea virginiana Britton. Castanea 56(4):287-296. Olcott, S. P. 2010. Personal communication on 22 October 2010 regarding Odonata in West Virginia. Parmalee, P. W. and A. E. Bogan. 1998. The freshwater mussels of Tennessee. University of Tennessee Press. 328 pp. Parry, M. L., O. F. Canziani, J. P. Palutikof, P. J. van der Linden, and C. E.Hanson, (eds.) IPCC, Geneva, Switzerland. Pages 617-652. Intergovernmental Panel on Climate Change. 2007. Climate Change 2007: Synthesis Report. Contribution of Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, Pachauri, R. and A. Kand Reisinger (eds.)]. IPCC, Geneva, Switzerland. 104 pp. Pauley, T. K. 2006. Upland Wetlands: Amphibians and Reptiles. Unpublished report provided to WVDNR Natural Heritage Program, 14 April 2006. 22 pp. Pearce, T. A. 2008. The rare land snail Glyphyalinia raderi in Pennsylvania, USA. Tentacle, 16: 7-8. Pearson, D. L. 1988. Biology of tiger beetles. Annual Review of Entomology 33:123-47. Pennsylvania Natural Heritage Program (PNHP). 2010. Climate Change Vulnerability Index results. Available: http://www.naturalheritage.state.pa.us/ccvi.htm Piaggio, A. J., K. W. Navo, and C. W. Stihler. 2008. Intraspecific comparison of population structure, genetic diversity, and dispersal among three subspecies of Townsend’ s big-eared bats, Corynorhinus townsendii townsendii, C. t. pallescens, and the endangered C. t. virginianus. Conserv. Genet. DOI 10.1007/s10592-008-9542-0. Piel, W. H. 1994. A new Chrosiothes spider from West Virginia (Araneae, Theridiidae). The Journal of Arachnology 22:181-184. Prasad, A., L. S. Iverson, S. Matthews, M. Peters. 2009. Atlases of tree and bird species habitats for current and future climates. Ecological Restoration. 27: 260-263. Rogers, R. 2010. Personal communication on 3 February 2010 regarding Fisher (Martes pennanti) in West Virginia. WVDNR. Silvis Lab. 2010. Wildland-Urban interface maps. University of Wisconsin-Madison and the USDA Forest Service North Central Research Station. Available: http://silvis.forest.wisc.edu/old/Library/WUILibrary.php Stauffer, J. R., Jr., J. M. Boltz, and L. R. White. 1995. The fishes of West Virginia. West Virginia Department of Natural Resources. Academy of Natural Sciences of Philadelphia, Philadelphia, PA. 389 pp. Stone, J. 2006. AFLP fingerprints of the rare orchid Isotria medeoloides suggest little genetic variation within or among populations. Department of Biology, Colby College, Waterville, ME. Unpublished manuscript. 4 pp. Available: http://www.colby.edu/biology/BI320/evolab/isotria_report06.doc Tarter, D. C. and C. H. Nelson. 2006. A revised checklist of the stoneflies (Plecoptera) of West Virginia (USA). Proc. Entomol. Soc. Wash. 108(2) 429-442. The Wildlife Society (TWS). 2008. Special Issue on Adapting to Climate Change. Wildlife Professional 2(3) 9-68. 27 Trujillo, R. G. and S. K. Amelon. 2009. Development of microsatellite markers in Myotis sodalis and cross-species amplification in M. gricescens, M. liebii, M. lucifugus, and M. septentrionalis. Conservation Genetics 10:1965-1968. Trumbo, B., M. Hudy, E. P. Smith, D.-Y. Kim, B. A. Wiggins, K. H. Nislow, and C. A. Dolloff. 2010. Sensitivity and Vulnerability of Brook Trout Populations to Climate Change, pp. 62-68 in Carline, R.F., and C. LoSapio, editors. 2010. Conserving Wild Trout. Proceedings of the Wild Trout X symposium, Bozeman, Montana. 370 pages. U.S. Fish and Wildlife Service (USFWS). 1990. Appalachian northern flying squirrels (Glaucomys sabrinus fuscus and G. s. coloratus) recovery plan. Unpublished report. 62 pp. Available: http://www.fws.gov/ecos/ajax/docs/recovery_plan/900924c.pdf U.S. Fish and Wildlife Service (USFWS). 1991. Cheat Mountain salamander (Plethodon nettingi) recovery plan. Unpublished report. 36 pp. Available: http://www.fws.gov/ecos/ajax/docs/recovery_plan/910725.pdf U.S. Fish and Wildlife Service (USFWS). 1992. Virginia spiraea (Spiraea virginiana Britton) recovery plan. Unpublished report. 47 pp. Available: http://ecos.fws.gov/docs/recovery_plan/921113a.pdf U.S. Fish and Wildlife Service (USFWS). 2010. Rising to the Urgent Challenge: Strategic Plan for Responding to Accelerating Climate Change. 32 pp. Available: http://www.fws.gov/home/climatechange/pdf/CCStrategicPlan.pdf Ware, D. 2008. The natural history and distribution of the mountain earthsnake (Virginia valeriae pulchra) in West Virginia. MS thesis, Marshall Univ. 54 pp. Watters, G. T., M. A. Hoggarth, and D. H. Stansbery. 2009. The freshwater mussels of Ohio. Ohio State University Press, Columbus. 421 pp. West Virginia Division of Natural Resources (WVDNR). 2010a. Biotics database records of rare species and natural communities. West Virginia Natural Heritage Program. WVDNR. Elkins, WV. West Virginia Division of Natural Resources (WVDNR). 2010b. Unpublished data maintained by the West Virginia Natural Heritage Program, WVDNR, Elkins, WV. West Virginia Partners in Flight (WVPIF). 2006. Bird point counts and associated habitat database. West Virginia Division of Natural Resources, Wildlife Resources Section, Wildlife Diversity Unit. Elkins, WV. Williams, R. N. 1999. Genetic polymorphisms in fishers (Martes pennanti). Am. Midl. Nat. 141: 406410. Willis, C. G., B. Ruhfel, R. B. Primack, A. J. Miller-Rushing, and C. C. Davis. 2008. Phylogenetic patterns of species loss in Thoreau’ s woods are driven by climate change. PNAS 105 (44): 1702917033. Young, B. E., E. A. Byers, K. Gravuer, K. R. Hall, G. A. Hammerson, A. Redder, K. Szabo, J. E. Newmark. 2009. Using the NatureServe Climate Change Vulnerability Index: A Nevada Case Study. NatureServe, Arlington, Virginia, U.S.A. 10 pp. Young, B. E., E. A. Byers, K. Gravuer, K. Hall, G. A. Hammerson, A. Redder. 2010. NatureServe Climate Change Vulnerability Index Version 2.01. NatureServe, Arlington, VA. Guidelines (54 pp.) and spreadsheet. Available: http://www.natureserve.org/prodServices/climatechange/ccvi.jsp Young, B. E., K. R. Hall, E. Byers, K. Gravuer, G. Hammerson, A. Redder, and K. Szabo. In press. Rapid assessment of plant and animal vulnerability to climate change. In Conserving Wildlife Populations in a Changing Climate, edited by J. Brodie, E. Post, and D. Doak. University of Chicago Press, Chicago, IL. 28 ! " # % & # % ' $ ! " & ( ) + ! " ( * ' + ( ( $ ! " " ( ! " $$ ( . 0 0 ' 1 1 ' 2 ' ( ,( - " / 1 1 ' 2 ' ( 3 ( 04 5 4 3 $ " 0 5 )0 $ " 07 5 7 )0 5 08 5 8 0! 5 ! )0 . )0 *6 *6 $ *63 $ ) $ $ *6 " )0 ( ( $ 5( $ *62 9 ! $ 5 9 " $ : *5( 5 5 ( 5 $ 5 $ " )' 0 * ' % $ 0! 4; & # ( ) *9< 1 '. 91 ? @ = ' ( / " " >5 " " >90 =< " 9 >5 " " >" ). % * ) 0! % * 0! ! ! & ' + % : B * 0! ; # % ( 5& $ % " ' : )' ( 5& ( " ; B # 4 < 3 4 + 85' 52 1 3 5; 52 1 3 5; 3 # 3 # " 3 5< 85' ; ; ( A 4 A 4 " " + 5; $ " " : )' : B * 0! + 7' # ) 1 * 08 4 % # ) : * 5; ( # " < '# & 5' 3 4 CD ) $ " $B ( ( 5% *" ( " ) * < '# & " 08 9& % 5 ); / * )0 )+ % * B 07 4 0' ' + # * 0 E 7 % & + ; "' 4 D9< = "# " 0 ( $ " < '# & 9& ; 4 D9< " 9' ( " ' ( 5< 3 $ & " ' ? ( 5< '# & 5' )0 5% 5% 5 3 *5; 4 D! ) *" " ( )% % 0 $ * # ) $ * 07 )1 4 % % $ * 07 4 % % ' ' ' ' # )' ' ' & ( >5% ( >5% ( *" ( >5% 5< '# & 1 3 5< '# & 1 3 $ 5A 4 CD52 0 5A 4 CD52 0 ( 5< '# & 1 3 5A 4 CD52 0 " 5 $ ) * 08 + 4 $ " < '# & ' ( ( 5@ ? & 9 " "5F ( >" >" >" $ 4 D 5 $ 4 7 "; 5 " ) 0! * 4 # < '# & # 3 )# , + 5; * 08 + 4; )0 0708 ) 7 % % * 08 & # $B # 3 ; " ' ( 5< '# & ' < 9 $ ( 9 3 ( )1 / . 74 3 ( & ) 3 * * ; 0 08 4 & # 04 4 & # 0 # + ' 5' ( ? "4 ( 5# ( )0 " !/ $ 9 $ $ *" ) < '# & ' $$ $ ( "3 ( )% " 5< ; ; 852 0 2 % ) *52 1 4 3 $ < * ( ( # 3 * 5' 5< '# & 5# 5& " 4 D7" 5' 3 ( *" 7) 3 * )' % * F * ) ) 04 & # 0708 & # 08 % # 3 $" $"* $ < '# & * 04 ) 4 08 4 7 & % % % % + )3 3 $ * 0! ! $ ( " ) ; 3 $ * 0! ! & " # ). 3 5' < '# & 9' % 4 D9 4 C" < '# & 9' B ) " "* ' ( 5# ) *" 3 ( " & < "# ( : " + $ A $ $ 0 " A 4 ". A 4 D7" < . $ < H 3 5< B .( 3 $ * 0! ! & # ( " ( 9B ( 91 9 1 8 " B @G "A ( 3 $ " $ " & " 0 =$ 0 " ( $ " ( " )' .( 3 $ * 0! 7 & # :# 3 )2 7 )2 $ * ; ; 0! :% 3 $ * 0! 7 7 & % 0 " ) $ 5 ". $ $ ". $ *" # " # # " 1 , & $ 5 & $ % ) .( " *" @ " $ < : )2 ; 3 :7! $ * 0! 7 & 5$ $ 5 " ( " I( $ # ) *5 "F ( " )2 :A 3 ; ) 3 $ * 0! 7 % " ' ) 5I % . $ % .( $ *" " " A ( $ * " 3" 3" " A ( 0 4 % 5 % : .( ( ) *" 1 " " & ) 3 $ * ); 0! 3 4 % 3 " % ( F ' : $ * 3 " .( "& ( 5 $ " % "% 0!)07J* 8) J* % ) ; + ( ( D $* "/ : $ : ); 3 ' ( " 0: ( ( K" + $ 8) 7J* % # " % "& ( $ " & 3" ( ) ; D : $ 77 : "& $ * 0! " $* "/ " " 0: ( : K" + " )0 3 ( 3 $ * 0 4 & # ( " 3 "@ ) ( * $0 ( $ $ $ ( $ ) ( ' ); . ( 3 ' .( ( ( 5 *" " 5 ) $ * $ .( @ 5 0! 8 & # - *" # 7: #5 5 ( 5 " A , 5 " )A 3 $ * 3 $ * .( 0! ! & # 0'.)04J* '.) 4J* % # 0! 8 & # )0 )+ % * 3 $ * ; ); 0 07 4 # # $ " ' .( B " A $ ' .( ' 5 K" + 1 4 "A $0 $ % 5 " 7 " % $ & 3 " 0: : K" + " ' ( "& 5 5 5 $ "1 $ ( " ' ( "< 3 )& 3 * 1 / $A . ( " < " + 5 3" B$ $ $$ 5 ( $$ " < ) # 3* " & $ : : * 0! 7; + # 5 ' 3 $ " 5; ( 3 " # ; 9 ) $ *99 " $ " 5 $ 78 " :$ A )< .( 4 852 1 $$ ) A , * 0! 4; % # 3 ; ' $ ( + $B 5; 52 1 3 ; ( 5< # 3 4 85 ; " " ). * & 040 ) 4 & ' # * & 04 4 & A ; * )3 A ; * ) A 07 7 & 0 4 % % 4 % 891 9# ). 0708 * # + )& 0! 4 = 3 " ' ( 5@ & 4 DD" ' ( )% 3 *5< '# & & 4 DD5 ( < '# & 5' % % H4 # C5 5& 5< H4 5@ 4" ( 9 C5 ( $ " ' ( < "3 < '# & # $ $ 9 : * = + 4 4 D7" : 1 5# ( * 0! 4 $ " ) ( 9 $$ 5 "4 !/ $ " 9' ( 9 4 C" $ ( " )B ( : / ' * 08 4; & $ # ( 5; # 4 85< & / >" ( " ); 3 , < '# & 5' 040 4 & + =; 9 *9 )A * 7! ( * 08 4 & # 4 " < '# & 9' $: H 9 ( 5; 9 )4 L =/ 91 9 " < '# & ( ; * ) ; : 5' ( 9 $ # 08A " + = ( 90 ( ( )& ) * )3 04J * )@ # 0! * 4 ); * ) 0 ' ( < 9 ' ( < " ' ( < 9 < '# & # " & < '# & " 9' 3 + 4 89< ; ; " 3 + 5; ( # 4 # # 4 * 0 )/ * 04E 4 08 8; & # % # )3 < * ); + # * $ 0! ; % ( + ) * 0! 7 # % ) / * 0! 7; # 9 : 9 $ % ( 3 " $$ "4 " "4 $$ 9 D*" !/ $ !/ $ ; $ : " " A " 4 C>" ' 9& " $ 3" ' ( J 4 D9< ; " 9; 52 1 3 ( $B ; $B # 4 3 $ " 3 ( 9& ; # 3 5< ; ; 852 0 2 % ) *52 1 " < '# & " 9' = 3 + ; ; ' ( 9& < '# & " < '# & " 9& ; " ( 4 D9< " $B 5; " 5< # 4 85 " )' . $$ 0 A * ) 7> $$ * 07 4 % % # % 9' ( 9& 9' ( ; " ; " 4 D9< 4 D9< )+ 1 * 1 * $ )3 )0 08 4 # # 07 4 # # % 07 * )A 08 * ); 1 ' < "F ' < " ' 4 % # * 08 4 % # ( 9 .( 1 ( ( & $$ "4 !/ $ $$ "4 !/ $ " 9 < '# & 5< 5< '# & 9' < '# & 9' 3 $ ( 9 4 C" ( 9 4 C" $ ( " )A % ! / % 08 * # )& * ' < " 3 - ( + 9 $$ $B "4 5' : !/ ( $ 9 : $ 08 4;5 ' & 5 # $ * " < $ ) ) : * 5 ( " ! 3 )1 ' $ * ( I 0! "< "% 7C )B 0 * )0 04 * )< / - * 0!A 4 5 " # 4 " ! 3 ( ! 3 ( " ' < .( & # & # # % % " / 5 < " ' ( " ' ( 5< '# & < '# & & " 5' ( " 5'/ . ( " 0 ' )% * )% " 3 " % 0 * )< * # )# # ) , $ * * ) # B # * )< < # 4 & # 04 4 & # % 0 4 04 4 # % & # : * 0! "4) 3 ( $ + !; 5# ( 0! 07 ( & D5& D )A # * $ < '# & 2 < '# & $ $ *" 9' " 5 ( ( < 91 " " ' ( 5< '# & 1 5A ' >5% 3 4 CD52 ' ( 5< '# & 1 5A ' >5% 3 4 CD52 / 5 $ )' ( *" < '# & )# & 3 *" 3 + $B 5< ; 5; # 4 852 0 2 ( 3 52 1 3 ; 5' ( " < '# & )B ( - % 0 >" 0 @ >" ; % 3 3 *9 : 04 4 & # ' $ 5 )3 ( $ : $ < % 3 ( & 7D * 04 4 & # ) & * )B * 0 4 # # 04 4 & # " < '# & ) 0 ( & 3 *5 ( 4 >!91 = " < '# & ) *5' ( < & . ( & 9 ). ( & *90 ( ) >*" ' ( 5# 4 D7" 5 $: % )B % )& & & 3 ( & * 3 ( 04 4 & # 04 4 & # 0 4 % ' ( 5# 4 D7" ' ( 5# 4 D7" * )& & )% % + * # * 08A7E + 4; " # < '# & " 9' 3 + 5; ; ) 9& ( $B # $ 4 ; 4 " 5< ; 852 1 3 ( D9< ; 3 *5' ( " )# & ' ; * / 0! 7 & # 3 + & / & )0 )0 $ * )3 & < $ * # 07 * 0! ! < % & ' < < # : 0! 4 # $ + ( ( 5; 3 & $ " 2 0 / 3 < A = ( 3 =M2 0 $ $ ( $$ ( 5 ( $ * ' '# & 9& " 9' ( ( 9 $$ "3 " '# & 9' ( B ) " "* $ ( " ( 3 5; 3 $ $ $ " $ 5 ) $ "M ; " 4 "4 D9< !/ $ 91 9 " # " & ) 7 ,< * 08 ; & # 3 4 # + 89< ; 4 D7" $B ; 9; 5' # ( 5 4) 1 & * ); & 3 04 4 & < '# & ) ( < '# & # * 080! 4 % # 5' *" 9' ( 5' ( ( 9 C 4 C" $ ( " ). & $ 0! * ' )% ' ; )/ , :; * * % 0 ' < "% ' # 7 # ( ( $ ( & ( 7 & % < $ 4 4 5 D ( ( $ 0 ' ' $ ) ) . * 4 # % 08A7 % % : " 5 ( : " 5< '# & 1 >" 0 ) * )' ( *" 5< '# & 5A $ ' ( " 5' ( $ 4 & = # )3" ) ; * ( "5A I 5' *" ( $ 4 % 8 ( ( 90 )3" 9 9& = % ( ( " < '# & 0 9 9& 90 ' $ ( >52 0 < '# & 07 " $ ' ' ; * $ 5 $ $ * $ " . $ ) !/ $ $ 4 > " @ ' ' "4 5< 3 5< '# & " 9. . )< 1 '.* 4 "& $ $ 5 ( 9< "A " 0! $$ " ' $ 9 " ( "5A I *" ) ( < ' % * ( 9< 1 '. / ( $$ ' 9 " ; ( )' I ( 7 4 *" ; ( 5 $ ( ( ( I 9 ( 5 $ < )3F*" 1 " 0 N7 ) 0708 7 % *" / $ # ) ( 4> 9 4 5 ( ( 7 4 *5 ( $ $ " )' ( .( * ); ( * 0! ! # + # ) ) 3 ' $ * 0! 84 % 08 ' < @ ' 8 & # $ 5 $ 5 $ )' ( $ ) " "54: 8 * ) "7:4 * : )% 0 7*" A : ) *9% I ( ) * ( $$ " H : 5 ( " / :3 ) 5 *53 >" 0 =3 ) *5/ >5# # 4 " ( 9 $$ "4 !/ $ ". ' .( $ / " ( 52 1 3 3 A 5& " 5; # 4 " ( "3 $ " + $ : ( $ " # 7: 5 " A #5 ( 5 5 , " ( 4 * )3 ) 3 ) ' $ * 0! 8 & # " ( 5 A "# 7: 5 "# ( 5 " 7: 5 "# ( 5 " 7: " #5 , ) 3 ) , ' $ * 0! 8 & # " ( 5 A " #5 , ) ) 3 $ * ' 0! ' & # " ( 5 A , ) / ) * * ). * * ) * ; * % * )3 07 4 % # 04 4 # % 07 4 % % 07 4 , )' * 1 * ) & * 8 % % )@ * * % ' 0! ! 04 4 & # 0708 4 # # + ( >5% ( 5 5 " 5< '# & 1 5A 3 4 CD52 0 5 ) $ * )' ( *" D) $ & # & 3 *" # 4 D!5# 4 8D" < '# & 9# " I < '# & 9' ( $( ( = $ *" & = 0 = 5 5 )' ( *" : ( * 0 * % ' ' . ); $ * " #5 % ( < '# & 3 *5# ' ( < " & >" D5& " 4 7 : D9' ( " ( 91 , = $ " & )' ) ( ( ( O $ *" J " )% H % " 4 C>" 9 $$ A "4 !/ $ ) * $& ' * ? 1 % 0 ( 5< '# & 5& 5 $ & "$ 8 ) '*" ; )0 = # ) '*" ( 5' ( *9 5 3 = = " ). * * ' 0! 7 # # ( & $ 5< 5< '# & 3 $ " ( " )& * & * ) * & 0! ! & % 0! ! & % ' ( 5& * 52 1 3 5; 52 1 3 5; " ' ( 5& " 3 # 3 # A 4 A 4 " " " * / ) B ; ( , * 04 4 # # " $ * )3 * % * * )3 @ 07 ) 87 )F * )3 * ' + # + 4 # * 040 * % * & & 0 07E * )3 ! 0 * * % * 0! 4 # 04 4 % 0' ' & + # # 5 H 9 5 $& )< ) * D) *9 " ( 5& < '# & " 5 < '# & 33 $ < '# & < " < '# & " 9' = < '# & " 9' ' ( & " 5' D*" 52 1 3 5; < 3 # A 4 " " ( 5 $ '# & " 5' ( 9& ( 5 ; " 4 D9< ; " 4 D9< " 9& ( 5< 3 $ < '# & * )A & * & 0 9' # $ )< .( ( "< "% I 5 5E 4 # )# < .( F 53 " 5 "% 53 " ' % ( " < $ 5 $ 5 % " 3" ( $< )& * < * " 0! ; & # 3 ; ' ) $ + ; ( " $B 5; 52 1 3 $< 5 ( 5 " "5 ( ); * 3 * ' 0! ! % # $ ( 5& " 5 " "5 5< # 3 4 88 . 3 ( ; 3 ( ; / * 3 ( ; 3 ( ; * * * 4) 3 ( * 04 4 & # 52 1 3 5; 3 # A 4 ( 04 4 & # 04 4 & # 04 4 & # 04 4 & # < . < . < . < . < . '# & 4 D7 ) '# & 4 D7 ) '# & 4 D7 ) '# & 4 D7 ) '# & 4 D7 ) 5' ( 5' ( 5' ( 5' ( 5' ( 85 ; *" " * )A * )+ * )1 * ) * ; - 7*" ' $ * 0! 9& 9& & * 3 ( ( ( *" ( *" ( *" ( *" ( *" 5@ 5@ 5@ 5@ 5@ " * ; * ; * 3 ( * ) 3 ( * 7) 3 ( * )0 3 * )0 3 ( % * ) * ; * 3 ( % * )% * 3 ( % 4 )0 * * 1 ( % * ) 4 & # 04 4 & # 04 4 & # 04 4 & # 04 4 & # 04J 4 & # 0!A! 4 % % # % # % 07 $ * )# * 04 < '# & 5' . 4 D7 ) ( < '# & 5' . 4 D7 ) ( < '# & 5' +% C) ( < '# & 5' 4 >D ) ( *" < '# & 5' 4 >D ) ( *" < '# & 5' 4 >D ) ( *" ' ( " ' ( ' >5% < '# & * ( *" ( *" ( )A * % : 0 )0 HH * + )< + 8! * )& B + + B * )3 ). B B * * 4 & + " & 040 E 4 & # 0! ! & # 0! ! % # 0! ! & # 0! ! & # & < '# & ' + ( ( 5+ ( + ( ( 5+ ( + ( ( ' " ( 5& ' " ( 5& ' " ( 5& 5A 4 CD52 0 >" 9 H 9 $ 5 $9 ( 91 ( 9 ( " 9' 5& 4 ! 5+ ( 5< '# & 1 3 9' ( $$ 2 /< " 9' ( $ < '# & 5/ ( = , * * 4 5@ *" ( 0 5@ " 52 1 5; 52 1 5; 52 1 5; 52 1 5; 3 3 3 3 3 # 3 # 3 # 3 # 4 A 4 A 4 A 4 A 4 C" " " " " ); , A * ' < "; ( 9 $ 5 $$ "4 !/ 5 ( 5 ) 5 * " ( ( $" $ I " 0! ! # # M $ > 5 : "/ $ L P8"!/ : $ ( $ "# $ 5 $ ( "& $ ( $ : : ) , $ A , )' 0! * ; 4 # # * 07 ) * , ) < 8> * 0! 8;5 7' & 0!A7 4 % # % 4 $ / 5' 9' " ( 50 ( 9 ( ? 9< " 1 = ( , < " " % 4 >" < '# & & 4 DC" < '# & 2 ( I "M A 3 + 4 89< ; # 4 D7" ' ( % " @ $ " $B ; ' $ * 9< 1 '. / $ ) < " & 9; 5' # ( 9 " 5 )3 ( , , & , * ) * ); , 3 * )3 , 3 ( * 04 4 & # 0 4 % # 0 '. % % , 040 4 & # )3 ( , , 3 ( * 04 4 & # )& , 3 ( * 04 4 & # )& , 3 ( * 04 4 & # < '# & )% H % A 3 *5# " 4 C>" ' ( 5< 3 & 5< '# & " # & D5# 4 D!" < '# & ; 4 > ) 5' ( $ 5; ? ( *" 5' < '# & ; 4 > ) $ ( 5; ? ( *" 5' < '# & ; 4 > ) $ ( 5; ? ( *" 5' < '# & ; 4 > ) $ ( 5; ? ( $ *" )& , $ * , ) & % 0 ,A 04 * 4 % # + )% ' ' 3 ' 7 % % 5< '# & 1 5A 3 4 CD" 2 5; 50 ( 5< '# & " ' ? % 3 )0 5% *5@ * 07 ( >5% $& ( 5< '# & 50 5# 5% 5% 4 D7 ) " 5' 5# 5& $ ( 5 5A *9 9 " )1 / * ; $$ 3 ( * 0! 4 % % < '# & ' ). 07 7 % ( & % $ " 8C 9' + 5< 3 5< '# & ( 91 ); 52 /< 9 C*" $ )@ % ' ? 3 ( * ( ); 07 7 # + # 3 50 5% 5% 5< '# & 5' 50 5# 5% 5& ( 5 *" " ) - A * ) * A - @ )/ : 08 % # 07 % % ' # : * 04 < '# & 1 % 4 * 04 4 % % 5< 3 5< '# & & D5# 5< $ " 4 D!" 5' & 4 5# % ( ' )& 3 ); A ( & ? & 5# 53 50 ( D) 3 *" & 5F ( *9 5 D 5& ( 5+ ; ; " ); - B * 0! + 4;5 4' 3 # )0 . ' $ ' $ * 07 )0 / < 4 # + 5B + * 08 + ; ) ( *5< '# & 1 5A >5% 3 4 CD" + "' " 3 " ( ) *9; " " C *90 =& H Q R $ )3F*9 " " !) *9% ) 3 3 ; *" # % / )% * ' < 0 $ 0!A7A8 # + ( 5< " ( & ' ) : $B " ( $ 50 5& # & / & 5< " ( 8D " 4 DC5< 1 '. 5 1 " $ D" 2 $ ". " 1 * ). % * % ) * : )' : )' : B * B * : ) 1 * ) ' ' ' :' ' ' 0: ' ' ' ' ' ' ' ' ' ' ' ' ' ' ': 1 ' ' ' ' 1 ' 0 ' ' : :' ' :' 1 ' ' ' 1 ' ' ' ' ' ' ' ' ' :' ' :' 0 :' ' :' ' ' 0: * : ) : )1 : $ * * * 1 ' ' : 1 : ' : ' & % ! ) # ! )& * & * & ) ' ' * # ' :' 0 * & & )& ' ' $ * 8 ' :' )% ) ' ' $ * ) ' ' * )+ :' 1 )0 B ' ' * % :' 1 ); / ' ' & $ # & & & # ! % & & & & )' 0 % ( & ' & & ' $ & & ' ! ' ' ' ' ' ' 2 ' ' ' ' 2 2 ' 2 2 2 ' 2 2 2 : 2 2 ' 2 ' 2 ' ' ' ' 2 2 2 2 2 2 2 ' ' ' ' ' 2 2 2 2 2 2 2 ' ' ' ' ' ' 2 2 2 2 2 2 2 ' ' ' ' ' ' 2 ' 2 2 2 2 2 ' ' ' ' ' ' ' 2 ' 2 2 2 2 2 ' 1 ' 1 ' ' ' ' ' 2 2 1 2 2 2 2 1 ' ' ' ' ' ' ' 2 ' 2 2 2 2 2 ' ' ' ' ' ' ' 2 2 2 2 2 2 2 ' ' ' ' ' ' ' 2 2 2 2 2 2 ' ' ' ' ' ' ' 2 2 2 2 2 2 ' ' ' ' ' ' ' ' 2 2 2 2 2 2 ' ' ' ' ' ' ' ' 2 2 2 2 2 2 ' ' 1 ' ' ' ' ' 2 2 2 2 2 2 : : : :' 0: 0: ' : : : : ' 2 ' ' * ) * )% 3 ( 1 : ' * F * 3 ) $" $"* $ * )3 $ * ; 3 $ * ). )' 2 ' 2 2 2 2 2 ' ' ' ' 2 ' 2 ' 2 2 2 2 2 ' ' ' ' ' 1 ' ' ' 2 2 2 2 2 2 2 0 ' :' ' ' ' ' ' : ' ' 2 2 2 2 2 2 2 ' 1 1 : ': 1 ' ' ' ' 2 2 2 2 2 2 ' ' 0 ' ' ' ' ' ' ' 2 2 2 2 2 2 2 ' ' ' 1 ' ' ' ' ' ' ' ' 2 ' 2 2 2 2 2 ' ' ' 0 ' ' ' ' ' ' ' ' 2 ' 2 2 2 2 2 ' ' ' ' ' ' ' ' ' ' 2 2 2 2 2 2 2 ' ' ' ' ' ' 1 ' ' ' ' ' ' 2 2 2 2 ' ' ' ' ' ' 1 ' ' ' ' ' ' 2 2 2 2 ' ' :' ' ' ' ' 1 ' ' ' ' ' ' 2 2 2 2 ' ' :' ' ' ' ' 1 ' ' ' ' ' ' 2 2 2 2 ' ' ' ' ' ' ' 1 ' ' ' ' ' ' 2 2 2 2 ' ' ' 3 ' ' ' ' 1 ' ' ' ' ' ' 2 2 2 2 ! ' $ * 0: : ' ; : :' : :# )2 $ * ) ' ' * ) 3 % ' ' * % .( ! ' ' 0: $ * & ' ' ; 7) 3 )' 3 ! ' * ) # ' ' 3 ( ) 3 )& ' )1 / & ) * & ) ' ' 0 * 3 * & # * & & )& * )0 . ' & $ # & & & , % ( ' & & ' & & & $ # ! % & & & & )# : : ' : ' ' ' : ' )2 ; )2 ; 3 :% 3 $ * : 3 $ * :A 3 $ * ) .( $ * ) 3 $ * ); ); 3 ( 3 3 3 : $ * : $ * )0 $ * ); . ( 3 $ * 3 $ * 3 $ * % * 3 $ * )A .( )0 )+ ; ); )< : ) A & !4 : * * ). * , : & % ! ! ) # ' ' ' ' ' 2 2 2 2 ' ' ' ' 1 ' ' ' ' ' ' 2 2 2 2 ' ' ' ' 1 ' ' ' ' ' ' 2 2 2 2 :' ' ' ' ' 1 ' ' ' ' ' ' 2 2 2 2 :' ' ' ' ' 1 ' ' ' ' ' ' 2 2 2 2 ' ' ' ' 1 ' ' ' 2 2 2 2 2 2 2 ' ' ' ' 1 ' ' ' 2 2 2 2 2 2 2 ' ' ' 1 ' ' ' 2 2 2 2 2 2 2 2 2 2 2 2 2 ' 2 2 2 2 ' ' ' ' ' :' ' ' ' ' ' ' ' ' ' ' ' ' 0 ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' 1 ' ' ' 1 ' : :' 0: 0: 0: : ' : ' : ' : ': 1 ' ' ' 1 ' ' ' 2 ' ' ' 1 ' ' ' ' ' ' ' 1 ' ' ' 2 2 2 2 2 2 2 ' ' ' 1 ' ' ' 2 2 2 2 2 2 2 ' ' ' 1 ' ' ' 2 2 2 2 2 2 ' ' 1 ' : ' ' ' ' 2 ' 2 2 ' 2 ' ' ' ' ' ' ' ' 2 2 2 2 0 2 2 :' ' ' ' ' : ' ' 2 2 2 2 2 2 2 ' ' ' )& ' ' ' * & 1 ' 0 * & ' ' : ) ' ' ' # ' ' : * & & )& ' ' :' ' & $ # & & & ; % ( ' & & ' & & & $ # ! % & & & & )2 ' ' 1 ' A )3 ; A * ; * ' ': 1 0 ' ' ': 1 ' ' ': 1 ' ': 1 0 ' 1 ' ' 0 0 1 ' 0 ': 1 ' ) A * ). 1 * )& : * )B ( : / 1 ': 1 ': 1: 1 : ' : ' ' ); , * & % ! ! ) # )& * & ) 2 ' ' ' ' ' ' ' ' ' 2 2 2 2 2 2 2 2 2 2 2 2 ' 2 2 2 2 2 2 ' ' : ' ' 1 ' ' ' 2 ' 2 2 2 2 2 ' ' ' ' ' ' ' ' 2 2 2 2 2 2 2 ' ' ' ' ' ' ' 2 ' 2 2 2 2 2 ' ' ' : : ' ' ' ' 2 2 2 2 2 2 2 ' ' ' ' 2 ' 2 2 2 2 2 ' ' ' ' ' ' 2 ' 2 2 2 2 2 2 2 2 2 2 ' ' ' ' ' :' 0 ' ' 0: : ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' 2 2 2 2 2 2 2 ' ' ' ' ' ' ' ' 2 2 2 2 2 2 2 ' ' ' ' ' ' ' ' 2 2 2 2 2 2 2 0: : : ' * )@ ' * 0: : 4 ); ' ' ' * )3 ' 1 1 ) ' ' 1: ; * ' ' )A ; : ' ' * ) :' * & # ' * & & )& ' * 3 ! ' ' ' ' & $ # & & & ) * % ( ' & & ' & & & $ # ! % & & & & ) ': 1: 1 ': 1 * ' < ); ) * / ' ' ' ' 1 ' ' ' ' ' ' ' ' ' : : ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' 1 ' ' ' ' ' 1 ' ' ' ' ' ' ' ' . $$ $ 1 :' ' ' :' ' ' ' ' ' ' ' * $$ * * * )0 )A ); )A % 1 * )& : ' * * 0 )1 ' ' ' & % ! ! ) # )& * & * & ' ' 2 2 2 2 2 2 2 ' 2 ' 2 2 2 2 2 ' ' 2 ' 2 2 ' 2 ' ' 2 2 2 2 0 2 2 ' 2 ' 2 2 2 2 2 ' 2 2 2 2 2 2 2 ' 2 ' 2 2 2 2 2 ' 2 ' 2 2 2 2 2 ' ' 2 2 2 2 2 2 2 ' ' 2 2 2 2 2 2 2 ' ' 2 2 2 2 2 2 2 : : ' : : ' ' :' ' 0 ' ' ' ' ' ' ' 2 ' 2 2 2 2 2 ' ' :' 0 0 ' ' ' ' ' ' ' 2 ' 2 2 2 2 2 1 ' ' ' ' ' ' ' ' ' ' 2 2 2 2 2 2 2 1 ' ' ' ' ' ' ' ' ' : ' 2 2 2 2 2 2 2 ' ' 0 : ': ' 1 ' ' ' 2 2 2 2 2 2 * $ * ) ' ' :' # 1 ' : * & & ' ' * !7 ' ' )3 ! / ! 3 ' ' 1 ) 1 ' ' * )' )+ ' ' * ) ' ' :' * ': 1 ' * ' ' )/ )3 ' ' * % & )& & & & # $ & & ' ( & & ' & $ # ! % & & & & 0: ) ' : : ' : ' : ' & ' ! ! ) # )& * & * & ) * & & )& # $ # & & & % & % ( ' & & ' & & & $ # ! % & & & & 1 )B ! 3 ( * ! 3 ( * " ' / " 0 * " 3 * " )0 )< - * )% 0 0 ' ' :' ' ' ' ' ' ' ' 2 2 2 2 2 2 2 0 0 ' ' :' ' ' ' ' ' ' ' 2 2 2 2 2 2 2 ' ' ' ' ' ' ' ' ' ' ' 2 2 2 2 2 0 ' ' ' ' ' : ' ' ' ' ' 2 : ' 2 2 2 2 2 1 ' ' ' ' ' ' ' ' ' ' ' 2 ' 2 2 2 2 2 ' ' : :' ' ' ' ' ' ' ' 2 2 2 2 2 2 2 ' ' :' ' ' ' ' ' ' ' 2 2 2 2 2 2 ' ' 0 ' ' ' ' ' ' ' 2 2 2 2 2 2 ' ' 0 : ': 1 ' ' ' : ' ' ' ' 2 2 2 2 2 2 ' ' ' ' ' ' ' ' ' ' 2 2 2 2 ' ' 2 ' : ': 1 0 : ': 1 ' ' ' ' ' ' ' 2 2 2 2 2 2 2 ' ' 0 ' ' ' ' ' ' ' ' 2 2 2 2 2 2 ' ' 0 ' ' ' 2 2 2 2 2 0 ' ' :' ' ' 2 2 2 2 2 )% )< * : # )# # ) , $ * : * ) # B # 0 * )< < : * 1 0: $ $ : $ < % 3 ( & !8 & "4) 3 ( )3 ( * ) * )B * * 0: ' : 1 ' ' ' : ' ': 1 ' ' ' ' : ' : ' ' : : ' ' 2 2 ' & & / & )& % * )# ; * )3 & ) : ,< ); 3 ). $ * ; * 2 2 2 2 2 2 0 ' ' :' ' ' ' ' ' : ' ' 2 2 2 2 2 2 2 ' ' ' ' ' ' ' ' ' ' 2 ' 2 2 2 2 2 1 ' ' ' ' ' 1 ' ' ' ' ' 2 2 2 2 2 2 2 1 ' ' ' ' ' ' ': 1 : ' ' ' ' 2 2 2 2 2 2 2 ' 0 ' :' ' ' ' ' ' ' ' 2 ' 2 2 2 2 2 ' ' ' ' ' ' ' ' ' ' ' 2 ' 2 2 2 2 2 0 ' ' ' ' ' ' ' ' 2 ' 2 2 2 2 2 ' ' : ' ' ' ' ' 2 2 2 2 2 2 2 ' 1 ' ' ' 2 2 2 2 2 2 2 ' ' ' 2 ' 2 2 2 2 2 ' ' ' 2 2 2 2 2 2 2 ' ' 2 2 2 2 2 2 2 ' 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 :' : ' ' : 1 : ' ' ' ' ' ' ' : ' ' : ' ' ' ' ' ' ' ' 2 ' ' ' ' ' ' ' 2 ' ' ' ' ' ' ' 2 1 ' 0 ' ' ' :' 0 0 ' ' ' ' : ' : . : : ' ' $ * ) ) 2 * * % ' ,: )/ ) ! : ' 0 * & ' 1 4) 1 & & & ! ' ' * # ' 1 * )& ' * $ * & ) ' :' )0 * & # * & & )& :' )0 < !! ' * + & ' ' ' ' ' * $ * ' )% ; 0 * 3 ( & & )% ' & $ # & & & 3 ( & % ( ' & & ' & & & $ # ! % & & & & % )B % )& & ' ' ' ' ' : ' : ' ' ' ' ' : 2 ' & ' ! ! ) # )& * & * & ) * & & )& # $ # & & & % & % ( ' & & ' & & & $ # ! % & & & & * ) ' $ : ; * ) ' ( < ( 1 ; * ) * )' .( * ); ( ) ) 3 ) 3 ) ) / * * ) 3 $ * ) $ * ) , $ * ) 3 ); $ * ). * * )' 1 * * ) & * ' ' ' ' ' 2 2 2 2 2 2 2 ' ' ' ' ' ' ' ' ' 2 2 2 2 2 2 2 ' ' ' ' ' ' ' ' ' 2 2 2 2 2 ' ' ' ' ' ' ' ' ' 2 2 2 2 2 2 2 ' : ' ' ' ' ' ' 2 ' 2 2 ' 2 ' ' ' 1 ' ' ' 2 2 2 2 2 2 2 ' ' ' ' 1 ' ' ' 2 2 2 2 2 2 2 ' ' ' 1 ' ' ' 2 2 2 2 2 2 2 ' ' ' 1 ' ' ' 2 2 2 2 2 2 2 ' ' ' ' ' 2 2 2 2 2 2 ' ' ' 2 2 2 2 2 2 ' ' ' 2 2 2 2 2 2 2 ' ' ' 2 ' 2 2 2 2 2 ' ' 2 : ' 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' 0 ' 0 ' ' ' : ' 0 :' 0: ' 0 ' : :' ' ' ' ' ' ' ' 0 0: !> ' ' : ' , ' ' ) )3 ' ' * : ( * 0: ' ' $ * ) : ' : * * ; * % * : ' : ' 1 1 ': 1 : ': 1 ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' : 1: 1 ' ' ' ': 1 ': 1 ' ' ' ' ' 1 ' ' ' 2 ' : ' ' ' ' 2 : 1 ' $& * )& * & : ': 1 : ': 1 & * ( , ) * ) * * % * )3 ) * * % * & * )A )3 )F * .( * ' : @ !C ': 1 ' ' ' ' ' 1 1 ' 1 1 ' ' ' : :' : : & % ! ! ) # )& * & ) ' * & # ' ' ' 2 2 2 2 2 2 2 ' ' ' ' 2 ' 2 2 2 2 2 ' ' ' ' ' 2 2 2 2 0 2 2 ' ' ' ' ' 2 ' 2 2 ' ' 2 ' ' ' ' ' ' 2 ' 2 2 ' ' 1 ' ' ' ' : ' 2 2 2 2 ': 1 ' ' ' ' ' ' ' 2 ' 2 2 1 ' 2 ' ' ' :' ' ' :' ' ' ' ': 1 ': 1 : 2 2 ' ' ' ' 2 2 2 2 2 2 2 ' ' ' ' ' 2 2 2 2 2 2 2 ' ' ' ' ' 2 2 2 2 2 2 2 ' 2 ' 2 2 2 2 2 ' 2 ' 2 2 2 2 2 : ' ' ' ' ' ' ' ' ' :' ' ' ' ' ' ' ' ' ' ' ' : :' :' ' ' ' ' ' ' ' ' 2 2 2 2 2 2 2 ' ' ' ' ' ' ' ' ' ' ' 2 2 2 2 2 2 2 ' ' ' : ' ' ' 1 ' ' 1: 1 1 ' 0: 0 $ * 1 ' ' ' : * & & )& ' 2 :' 1: 3 ' ' )3 )< ' 0 * * ' ' * & : ' ' 1 * * ' ' ' * * % ' ' * * )3 ' ' * ) B * ' & $ # & & & ) ). * * * * / ': 1 0: * % ( ' & & ' & & & $ # ! % & & & & )@ * % ' : ': 1 ' : 1 ' ' ' 2 2 2 2 2 2 2 ' ' ' ' 2 2 2 2 2 2 2 ' 3 ( ; 1 0: )% * * 1 ( !D & % ! )& * & # 2 ' ' ' 2 2 2 2 2 2 2 ! ) # * & & )& 2 2 2 2 2 2 ' ' 0 ' ' ' ' ' ' ' ' 2 2 2 2 2 2 2 ' ' ' ' ' ' ' ' ' ' 2 2 2 2 2 2 2 ' ' ' ' ' ' ' ' ' ' 2 2 2 2 2 2 2 ' ' ' ' ' ' ' ' ' ' 2 2 2 2 2 2 2 0 ' ' ' ' ' ' ' ' ' ' 2 2 2 2 2 2 2 0 ' ' 0 ' ' ' ' ' ' ' ' 2 2 2 2 2 2 2 0 ' ' 0 ' ' ' ' ' ' ' ' 2 2 2 2 2 2 2 0 ' ' ' ' ' ' ' ' ' ' 2 2 2 2 2 2 2 0 ' ' ' ' ' ' ' ' ' ' 2 2 2 2 2 2 2 ' :' ' ' ' ' ' ' ' 2 ' 2 2 2 2 2 ' ' ' ' ' ' ' ' ' 2 2 2 2 2 2 ' ' : : ' ' ' ' 2 2 2 2 2 2 : : * 0 2 $ * * )# 2 2 % ) * 2 ' 0: 3 ( % 4 )0 * ' ' ' ) 3 ( ) * ; * 3 ( % * ' 2 ' 0: )0 ' ' ' 4) 3 ( * ' ' ' * 3 ( % ' ' ' * )0 * ' ' 0: 3 ( ' ' * 7) 3 ( ' 0 * * ' ' 0: 3 ( ; ' ' * 3 ( ; ' ' 0: / ' 0: ' * 3 ( ; ' * & . * ) * 3 ); ' & $ # & & & < % ( ' & & ' & & & $ # ! % & & & & )& * * )A * )+ * )1 * ) * ; * ; * ; * 3 * 0: 0: 0: 0: 0: ' ' ' ' ' ' 2 ' ' B * )& B * )3 B ). B * ); ' ' ' ' ' )< * 1 * ' ) $ A , )' * ; ' * ) , < , ) 1 < ' * ': 1 ' ' ' ' 0: ' 1 ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ': 1 : ' ' ' ' ' ' ' ' 0: )3 ( , ' ' ' ' ' ); , : 3 * )3 * )3 ( , * )& * , & % ! ' 2 2 2 2 2 2 2 ' ' 2 ' 2 2 2 2 2 ' ' ' ' ' ' ' ' 2 2 2 2 ' ' ' ' 2 2 2 2 2 2 2 2 1 ' ' ' ' ' 2 2 2 2 ' ' ' ' ' ' ! ) # ' ' ' ' ' ' )& ' ' ' ' ' ' ' 2 ' 2 2 ' ' ' ' 2 ' 2 2 2 2 2 : ' ' 2 2 2 2 2 2 2 :' 0 ' ' ' ' ' 0 : ' ' ' ' ' ' ' ' ' : ' ' ' ' 2 2 2 2 2 2 2 ' ' ' ' ' ' ' : ' 2 2 2 2 2 2 2 ' ' ' : ' ' ' ' 2 2 2 2 2 2 ' ' 2 2 2 2 2 2 ' ' ' 2 2 2 2 2 2 2 ' ' ' 2 2 2 2 2 2 2 ' ' ' 2 2 2 2 2 2 2 ' ' ' 2 2 2 2 2 2 2 ' ' ' ' 0 ' ' ' * ) ' * & # ' * & & )& 1 ' ' * ! 0: ' 0: : * , & , , 3 ( , 3 ( , 3 ( ' * & )0 HH * + + + + , A , 0: ) , * ' & $ # & & & : % ( ' & & ' & & & $ # ! % & & & & )A * % * : ' 0 ' ' 0 ' ' 0 ' ' 0: : ': 1 ' ' ' ' ' ' :' ' ' 0 ' ' ' ' ' ' ' ' ' ' ' ' 0: 0: ' ' ': 1 ': 1 ': 1 ': 1 ' ' ' : )& 0: , ) ,A * )% & * )1 / * ). ; $$ 3 ( * )@ % 3 ( * ) A - 0 * : @ )/ : : * ); 0 ' ' : ': 1 ': 1 ': 1 0: ' 0: ); B 1 * % ' 2 ' ' ' ' ' ' ' 2 : ' ' ' ' 2 ' 2 2 ' ' ' ' ' ' ' 0 ' ' ' ' ' ' ' : :' : ': 1 ': 1 ' ' ' ' ' ' ' ' ' :' ' ' 0 :' ' ' ' :' ' ' ' ' : :' :' ' ' ' :' ' ' : ' ' : )0 )0 / < * * : 1 ' ' ' ' :' ' : ! ) # ! ' ': 1 ' & )& * & ' ' : * & ': 1 ': 1 ' ) ' ' : # ' ' ' * & & )& ' ' ' ' $ * > ' 0: 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 ' 2 2 2 2 2 2 2 2 2 2 2 : : ' ' 2 2 ' ' ' ' ' ' 2 2 2 2 2 2 2 ' ' : ' : ' ' 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 ' ' ': 1 ' ' 2 2 ' 1 ' ' ' 2 ' ' ' ' ' ' 2 ' 1 ' : ' ' ' ' 2 ' ' : ' ' ' ' ' ' 2 ': 1 ' 1 ' ' ' ' ' 2 ' ' 2 ' ' 2 ' : ' : ' ' ' ' ' 2 2 2 2 2 2 2 ' * . / )% ' ' 0: : A ' ' A ) * ' ' & $ # & & & 0 * % ( ' & & ' & & & $ # ! % & & & & )& , 3 ( , $ * : ' : : ' ' 0 * % ); $ 3 3 * )' )' 1 * ) )0 )+ )% ) )1 * : : B * B * / 3 $ * % $ * * B 3 $ $ $ $ $ $ $ * $ * * * )0 ) )% , * * 3 3 ' * 3 ( * )1 / $ $ $ 3 ( . 3 * * % F $ * 3 * 3 $"* ' ' 1 % ' % ' & 8 8! D 4 ! 78 ! 8 D D ! 7! ! 7D >C $ > C $ $ 4 $ 4 4! 4! ! > ! ' ' ' ' ' ' ' ' ' ' ' ' ' 0 0: ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' :' S ' ' ' ' ' ' ' ' ' ' ' ' :' ' ' ' ' ' ' ' ' ' ' ' ' ' S ' ' ' ' ' ' ' ' ' ' :' ' ' ' ' ' ' ' ! ! ! ! 7 4 ! 4 4 4 ! 4 >> ! > 4 4 * 3 $ $ * ! ! ' C! ! ! 4 > 4 4 C D 4 4 4 * ) ; 7) 3 )' ) ) $" ) )3 D ! ! 4 > !! ! $ $ * )# >4 $ $ $ ) ) & $ $ * ! % ' )' ). % ) : : ) ' ' ! , 0." " - & ./" ' - # + ! ! ! 8 7 4! ! 4 77 4 8 7 4 4 ! 4 !! 4 4 4 ! ' S 8 D! 4 ! ! ' ' ' ' ' ' 0: ' ' ' ' ' ' ' ' ' ' ' :' :' ) 3 $ * 3 $ * ; :# $ * .( 3 )2 :% )2 : :A ) ) 3 )' )2 3 $ * $ * ; ' ; ; 3 $ * 3 $ * ). ) > ! $ C D 3 ' ! $ $ 4 ! 4 7 3 ' 3 > $ $ ! $ $ A * 1 * * / ,3 ; ; * * * * 3 3 3 $ $ $ $ $ $ $ > 4 4 C ' ' ! 1 % ' % ' ! ! 4 ! 4 ! 8 4 ! 4 ' ' ' ' ' :' ' :' ' ' :' ' :' :' ' :' :' ' ' ' ' ' ' ' ' ' ' ' ' 0 ' ' ' ' ' ' ' ' 4 C 7 ' ' ' ' ' ' ' ' ' > 8 ' ! ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' :' ' ' ' ' ' ' 4 4 ! ! 4 S 4 4 ! 4 > 4 4 4 7 S 8 >! D 4 ' ' ' 4 4 4 7 4 ' ' ' ' 4 8 ' ' ' 4 4 * !! 4 4 A A : )B ( : ); & 4 $ * ) )3 4 D 8 4 4 $ ! * $ * : : * ,A * & ) )& ' 3 $ * % 3 ); )< ). ! ! 3 3 ) ! ! 4 4 ; $ * 3 $ * : ); 3 $ * : ); 3 $ * )0 3 ( 3 $ * ); . ( 3 $ * )A .( 3 $ * )0 )+ $ $ $ ; )2 .( ' - % ' ). 0." ./" ' - , 4 ! 4 :' 0 0 ' ' 0: :' : ' ' ' ' ' ' ' ' ' 1 * 3 ) > 7! ! ' ) * )3 )@ * * * < ) )/ )3 ); ) * ) ) )3 ! ! ! " " " " # # >7 )0 )A ); )A )& )1 )B )0 )% )% $ $ $ $ * * 4 ); ! $ ; : ; * ' ' $ * 3 $ * / $ $ $ $ $ $ * )' $$ * )+ $ 1 1 * . $$ * * * 1 3 3 $ $ 3 3 $ $ 3 $ * / * * 3 3 ( $ * * 3 ( * )< ' / 0 * 3 * )< )# , ) * ! ! C ! ! D! D! 4 C ! - * 3 $ $ 3 $ * $ * ' ' % & 4 4 4 4 ! ! 7 ! ! 4 4! 4 ' ' ' C! ! ' ' ' ! ' ' ' ' ' ' ' ' ' ' ' ' ' 0 0 0 ' ' ' ' :' ' ' ' 0 0 0: 4 D 4 4 4 S 4 ! 4 ! 4 4 4 8 ! > 4 $ % D 4 * % ' ! 8 % ' )A ' ! ' 0." - > ./" - , $ $ C 77 4! !! 4 7 D ! 4 4 ! 4 7 >C 4 4 4 4 7! 4 ! D! > 4 ! 4 8 4 4 ' ! ' ' ' ' ! ' > ! C 4! 7! ! S S ! S ! ' ' ' ' ' ' ' ' ' ' 0 ' 0: ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' 0 ' ' ' ' ' ' ' ' ' ' ' : :' ' ' :' ' ' : ' ' ' ' ' ' ' ' ' ' ' ( ( ( >8 * 3 3 ( & 3 ( & )& 3 ( % )0 * + )3 ) 4) 1 ) ) )' 3 $ * $ * $ $ $ $ $ ' ' * ; ' , :; * 3 $ $ $ $ * ) ; * < .( * $ * :$ ; * ) ' 3 $ $ $ * ); 3 ). $ )% )/ * ) ); * / * $ * : < ,< )0 . * * * 3 3 3 3 ' 3 ' 1 ' & % S S 4 4 S ' ' ' ' ' ' 0: ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' :' ' ' ' ' ' ' ' ' ' ' ' ! ! 4 C D! > 8 ! ! $ 4 ! 4 4 4 S S 4 ! ! ! D! ! 4! ' ' > 8 ' 4 4 4 4 ! 4 4! S D ! >! 7! > ! ! ! ' ' ' ' ' ' ' ' ' ! C ! ' > 8! ! 7 ! 7! ! ! ! ! ' ' ' ' ' D! 4 4 7 4! 8 > 4 7 > 7! S 4 D $ $ $ $ $ % ' 4 4 4 * * )% ; * )# 4 4 $ * )& 4 > 4 4 * & 4 ! ' ' ' ' ' ' ' 8 $ * : ! 3 ' * ! * < : "4) 3 ( )3 ( ) < )B )B & & & & & & & & & & ' ' ' B )< 0." ) ! % ' # # $ $ $ % % % & - ./" ' - , ! ! 4 ! C ! ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' : : ' ' ' 0 ' ' ' ' ' ' ' ' ) 3 $ * 3 $ * ,3 $ * ) 3 $ * ); / $ * ) * ). * ) ; * )3 ,% : ( * )' 1 ) & * )@ % * ) $& * ' ' ' ' ) ) ' ' 3 3 * $ $ $ $ $ $ $ $ $ ' ! 4 8 ! 4 $ $ 4 ! ! 7 ! 4 ! * * * * ). )& * & & * ( ,/ ) ) )3 ) )3 * * * )F )3 * * )< )& >! B 3 ' 3 3 * * % )3 ' 1 % ' % ' ! ! ! ! ! ! ! 4 ! D! D 4 4 7 > ! 4 4! 4D 4 C 8 ! S ! ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' 0: :' :' :' :' :' ' 0 ' ' ' :' ' $ * ) 4 C ! 4 > ! D * * * * * ' & ! ! C ! - % ' ) ) ) ) ) * * * * * * * * 0." ./" ' - , $ $ $ $ * % $ $ $ * * % & )A .( * 3 < $ * * 4 4 4! ! D! ! * 3 3 $ $ 3 ' $ ! ! $ ! ' 4 ' 4 4 ' ' 4 4 ' ! ! 4 ! ' ' 4 4 ' ! 4 ' 4 ! ' ' ' ' 4 4 $ * & ! ! ! ! D * @ C! C! ! ! ! 8! ! ' ' ' ' 0: 0: 0 :' 0: ' ' :' ' ' ' ' ' ' ' ' ' ' ' ' ' ' : ': 1 ' ' ' ' ' :' 0 :' ' ' ' ' ' 0: ' ' ' ' ' ' : ' ' ' 1 ); 3 * $ )A . > 7! ! 3 ( * )+ )1 3 ( ; 3 ( / * 4 )0 1 ( 3 ( % )% , , , , , , , >> ) )# )A )0 HH )< B * )& B * )3 B ). B * ); : ) $ A ; < ) )3 ( , & ) ' 3 3 3 3 3 3 * * )' * < $ $ $ 3 ' * * * 3 3 $ $ $ $ $ $ $ $ $ $ $ * * ' ' & % 4 4 S ' ' ' ' S ' ' ' ' 4 S S S S S S ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' 4 S ' ' ' ' 4 S ' ' ' ' 4 4 4 4 4 4 ! ! 4 * ) ' * * A ' 4 $ * * ,% ' 4 4 ' $ ' ' 4 % ' S 4 $ ' 4 4 * ' 4 4 ' * * * * * + + + + * * ) 3 ( ; * 4) 3 ( ; * ) 3 ( ; * 7) 3 ( ; * )0 3 3 ( * )0 3 ( % ) ; 3 ( % ' ! ! % ' * * ; * * ; * * * * * * * % * * ' ! ' 0." - ! ./" - , ! ! 4 D! 4! ' ' ! ' ' ' ' ' ' ' ' ' ' ' ' ' > ' D! 4 4 ' ' ' ' ' ' 4 ! ! ! ! ! ! ! ! ! ! ! ! ! 4 4 4 D 4 D D C! ! 8 4! 4 ! D 4 4 D! 4 4 D 4 ! ! S ' ' 0: ' ' ' ' ' ' ' :' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' :' ' ' ' ' ' :' ' :' ' ' ); 3 )3 ,3 ( )3 ( , 3 ( )& 3 ( )& 3 ( )& $ * ) ,A * )% & * )1 / * ). ; $$ 3 ( * 3 * * * $ $ $ $ $ ' ' * > C! - )@ A ) ) % 3 ( * A 3 3 * : D C $ $ : / >C ); B ); * A )0 )0 )% * ' 3 $ $ 3 $ $ * : ' 1 % ' % ' ! ! 4 4 4 4 4 4 8 4 4 ! C 4 7 4 D! 4 4 4 4 4! D! ! 4 S S S S ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' :' ' ' 0: : ' ' ' ' ' ' ' :' ' ' ' ' ' ' ' ' ' ' ' :' :' ' $ < * * $ > 8 4 ' ' ' ' ' :' ' ! ! ' 2 :' ' 4! ' ' ' ' ' ' ' ' :' ' ' ' ' ' ' ' C 7 4 * . / 7 $ * @ )/ ' & C 4 4 $ * - % ' , , , , , , , - 0." ./" ' - , 7 > 4 ! C 8 ! D 4 4 D ! ! 7 ! ' : Appendix E. Sample Vulnerability Assessment Form The NatureServe Climate Change Vulnerability lf'IdEX Release 2.01 10 May 2010; Bruce Young, Eiizabeth Byers. Keiiy Grayuer. Kim Hati. Geoff Hammerson. Atari Redder With input from: Jay Cordeirol. Kristin Szabo Funding for Reiease 2.0 generousiy provided by the Duke Energy Corporation. Resp-red fie-'5! I Geographic Area Assessed:I West Virginia Clear Form I Assessor: Sam Norris I Species Scienti?c Name:I Aiasmidohta rnaiyihafa I English Name:I Elktoe I Major Taxonomic Group:I Invert-Mollusk I Relation of Species' Range to ?Area:I Eastiwest edge of range ICheck it species is an obligate of caves or groundwater aquatic systems: I I fittest be -nar'tred with an for accwate scorn-.9 of these spec-es] Assessment Notes [to document special methods and data sources} database: F'armalee and Elogan 1998'. 1Lhu'atters et al. 2009. Natural barriers: watershed change immediatelyto north. Habitat is small shallow rivers and creeks. cold water. Glochidia dispersed on ?sh hosts. Section Exp-bate to Loco Irate Charge .Cs. 5:5 'ge Temperature Hamon Moisture luletric of range} Severity of range} Severity >55" C}warmer 5.1-5.5? C}warmer -0.1?l 009? - -0.11 4.5-5.0? C}warmer -0.0?4 - -0. 3.9-4.4? C}warmer 0051 - -0.0 3.9" [2.2l C}warmer 0.028 - -0. {Must sum to too; Totai.? Total: 100 (Must sum to too; Section B: Indirect Exposure to Climate Change {Evaluate to." geogi'aoivcs.? area under Mark an in boxes that appiy. Effect on Vulnerability Factors that influence vulnerabili a: less: three regs-red] Greatly Somewhat Somewhat increase Increase increase Neutral decrease Decrease Unknown 1} Exposure to sea level rise 2} Distribution relative to barriers a} Natural barriers b} Anthropogenic barriers 3} Predicted impact of land use changes resulting from human responsesto climate change Section C: Sensitivity Mark an in boxes that appiy. Effect on Vulnerability Factors that in?uence vulnerability Greatly Somewhat Somewhat increase Increase increase Neutral decrease Decrease Unknown :4 1} Dispersal and movements 2} Predicted sensitivity to temperature and moisture changes a} F'redicted sensitivity to changes in temperature i} historical thermal niche ii} physiological thermal niche b} F'redicted sensitivity to changes in precipitation, hydrology, or moisture regime i} historical hydrological niche ii} physiological hydrological niche c} Dependence on a speci?c disturbance regime likelyto be impacted by climate change d} Dependence on ice, ice?edge, or snow?cover habitats 3} Restriction to uncommon geological features or derivatives 4} Reliance on interspeci?c interactions a} Dependence on other species to generate habitat b} Dietary versatility (animals only} c} Pollinator versatility (plants only} at Dependence on other species for propagule dispersal }Forms part of an interspeci?c interaction not covered by 4a-d 5} Genetic factors a} Measured genetic variation b} Occurrence of bottlenecks in recent evolutionary history {use oniy it 5a is "unknown?l 6} Phonological response to changing seasonal temperature and precipitation dynamics Section D: Documented or Modeled Response to Climate Change ales-3' across his tar-t Mark an in all boxes that Effect on {Options-U increase Increase increase Neutral decrease Decrease Unknown 1} Documented response to recent climate change 2} Modeled future [2050} change in population or range size 3} Overlap of modeled future [2050} range with current range 4} Occurrence of protected areas in modeled future {2050} distribution Climate Change Vulnerability Index Copy Data to for Alasmidonta mar-inata in West Virinia Results Table Con?dence in Species Extremel Vulnerable Information Moderate Notes: Histogram below De?nitions oflndex Values Extremely Vulnerable 5EV1: Abundance andfor range extent within geographical area assessed extremely likely to substantially decrease or disappear by 2050. Highly Vulnerable tHVi: Abundance andtor range extent within geographical area assessed likely to decrease significantly by 2050. Moderately Vulnerable Abundance andfor range extent 1.vithin geographical area assessed likely to decrease by 2050. Not VulnerablelPresurned Stable Available evidence does not suggest that abundance andtor range extent within the geographical area assessed will change (increasetdecrease) substantially by 2050. Actual range boundaries may change. Not Likely ilLi: Available evidence suggests that abundance andtor range extent within geographical area assessed is likely to increase by 2050. Insufficient Evidence tlE}: Available information about a species' vulnerability is inadequate to calculate an Index score. Con?dence in Species Info 100 40 20 Frequency EV PS I. Results ot a Monte Carlo simulation {1000 runs) of the data entered in the Index. 69