Abstract Climate change will have far-reaching impacts on biodiversity, including increasing extinction rates. Current approaches to quantifying such impacts focus on measuring exposure to climatic change and largely ignore the biological differences between species that may significantly increase or reduce their vulnerability. To address this, we present a framework for assessing three dimensions of climate change vulnerability, namely sensitivity, exposure and adaptive capacity; this draws on species’ biological traits and their modeled exposure to projected climatic changes. In the largest such assessment to date, we applied this approach to each of the world’s birds, amphibians and corals (16,857 species). The resulting assessments identify the species with greatest relative vulnerability to climate change and the geographic areas in which they are concentrated, including the Amazon basin for amphibians and birds, and the central Indo-west Pacific (Coral Triangle) for corals. We found that high concentration areas for species with traits conferring highest sensitivity and lowest adaptive capacity differ from those of highly exposed species, and we identify areas where exposure-based assessments alone may over or under-estimate climate change impacts. We found that 608–851 bird (6–9%), 670–933 amphibian (11–15%), and 47–73 coral species (6–9%) are both highly climate change vulnerable and already threatened with extinction on the IUCN Red List. The remaining highly climate change vulnerable species represent new priorities for conservation. Fewer species are highly climate change vulnerable under lower IPCC SRES emissions scenarios, indicating that reducing greenhouse emissions will reduce climate change driven extinctions. Our study answers the growing call for a more biologically and ecologically inclusive approach to assessing climate change vulnerability. By facilitating independent assessment of the three dimensions of climate change vulnerability, our approach can be used to devise species and area-specific conservation interventions and indices. The priorities we identify will strengthen global strategies to mitigate climate change impacts.

Citation: Foden WB, Butchart SHM, Stuart SN, Vié J-C, Akçakaya HR, Angulo A, et al. (2013) Identifying the World's Most Climate Change Vulnerable Species: A Systematic Trait-Based Assessment of all Birds, Amphibians and Corals. PLoS ONE 8(6): e65427. https://doi.org/10.1371/journal.pone.0065427 Editor: Sebastien Lavergne, CNRS/Université Joseph-Fourier, France Received: January 2, 2013; Accepted: April 24, 2013; Published: June 12, 2013 Copyright: © 2013 Foden et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This study was funded by the John D. and Catherine T. MacArthur Foundation. Grant number: 06-87945-000-GSS (http://www.macfound.org/). In kind contributions were also made by Imperial College London and Conservation International. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist.

Discussion This analysis highlights the importance of broadening climate change vulnerability assessment methods, and introduces a new approach that is comparable to that used by the IUCN Red List to identify species at elevated risk of extinction. By considering biological traits that contribute significantly to species’ sensitivity and low adaptive capacity, alongside climate change projections, we assess climate change vulnerability for all species in three taxonomic groups. Since the results are relative rather than absolute climate change vulnerability measures, they cannot meaningfully be compared with statistics presented in previous global assessments (e.g., Thomas et al. [5] and IPCC [6]), but the considerable refinements our approach introduces provide several important contributions to climate change adaptation strategies. Our findings identify species and regions for which assessments based on climate change exposure alone may need to be moderated. The species and regions we highlight as having high climate change sensitivity and low adaptive capacity should be considered as more vulnerable than exposure-based assessments alone may suggest. Conversely, there are also species for which climatic changes are projected to be substantial, but our assessments suggest that they may be able to cope with these better than other species, and so while monitoring and other conservation interventions might continue to be necessary, they represent a lower priority for climate change related conservation interventions in the immediate future. Trait-based climate change vulnerability assessments may be particularly valuable for species whose distributions are not reliably predicted by climate alone. Comparisons of the results of this study with those from other approaches are needed. However, given the difficulties associated with empirical validation of all methods of climate change vulnerability assessment, and the urgency for conservation response to climate change, the safest practical way ahead is to diversify the range and number of methods employed. Case-by-case assessment of species’ climate change sensitivity, exposure and adaptive capacity also provides relevant information to tailor conservation interventions. We identify the species and regions of highest climate change vulnerability, as well as ‘potential persisters’, ‘potential adapters’ and species of ‘high latent risk’, and recommend generalised conservation interventions for each vulnerability class. Vulnerability traits prevalent in particular species, species groups or regions may also provide valuable information for informing more detailed management plans. As species’ traits will change little over assessment timeframes, while exposure estimates, which depend on human actions and model predictions, will be more frequently updated, climate change vulnerability assessments can be updated based primarily on changes in exposure, making them useful both as indices of change and for continually adapting management strategies. There are some important caveats to our results that also indicate priority areas for new research (see Supporting Discussion in Supporting Information S1 for full discussion). Empirical validation that the framework and assessments are ecologically robust is of high priority [21]. The current paucity of investigations into the mechanisms of climate change impacts also hampers quantification of the extinction risk attributable to each selected trait. Our approach of highlighting the worst affected species where such evidence is lacking means that the relative climate change vulnerability measures we present cannot be meaningfully compared between birds, amphibians and corals (although comparisons should be robust within each of these groups). For corals, where bleaching frequency tolerance thresholds are established (i.e., a maximum of once per 5 years; p≥0.2 year−1 [22]), we find that these are far exceeded by our top 25% threshold (p≥0.85 year−1), underscoring the importance of interpreting scores as relative measures and supporting other findings that corals are at extremely high risk from climate change [22], [23]. Trait values are likely to be correlated among species and to be linked to environmental change in many different ways, resulting in thresholds and abrupt state changes [24]; detailed field studies will be required to disentangle the causes and effects and to make reliable attributions to climate change versus other pressures [25]. We also note that climate change may benefit a proportion of species. Sensitivity analyses carried out by adjusting the thresholds for the climate change vulnerability dimensions (see Supporting Methods in Supporting Information S1; Figures S10, S11, S12; Tables S13, S14, S15, S16, S17, S18, S19, S20, S21) show that the geographic focal areas we identify for each taxonomic group are fairly robust to these caveats and uncertainties. Finally, since previous global-scale climate change vulnerability assessments [5], [6] were based on inferences made from ad hoc or geographically restricted studies of samples of species, our results provide the first global maps of climate change vulnerability for entire taxonomic groups. By comparing regions of highest climate change vulnerability with those of greatest threat from largely non-climate change related stressors, we identify areas of greatest concern overall, as well as those newly emerging as at risk due to climate change. This information is vital for large-scale conservation planning exercises, and highlights where more detailed assessment is needed. The approach we describe, as well as the priorities identified through this study, will strengthen global strategies to reduce climate change impacts.

Materials and Methods Assessing Sensitivity and Low Adaptive Capacity Within each of the seven trait sets, outlined as (a) to (g) in the main text, we selected traits appropriate for birds, amphibians and corals, and gathered trait data for each species using published and grey literature, online databases (e.g., [26]–[28]) and experts’ knowledge. For birds (see Table S1), we estimated habitat specialization based on the number of IUCN Red List defined habitats in which each species is known to occur, its dependence on microhabitats, and its ability to tolerate disturbance (for forest species). Environmental tolerance breadths were estimated using spatial and seasonal variability in temperature and precipitation across species’ ranges as proxies. This was calculated as average absolute deviations in historical mean temperature and mean precipitation across a species’ range and for each month, based on WorldClim’s [29] interpolated observational data for 1975 (mean 1950–2000) (see Supporting Methods in Supporting Information S1). We identified species with high dependence on fewer than 5 species (typically invertebrates) as well as those with small total or effective population sizes. We estimated intrinsic dispersal abilities using data on known mean maximum dispersal distances, and identified species with extrinsic barriers to dispersal, specifically those restricted to mountains, islands and/or polar edges of land masses. We recorded low genetic diversity where known, and used measures of generation length and reproductive output to estimate potential relative rates of evolvability. For amphibians (see Table S2), habitat specialization was assessed based on number of IUCN Red List habitats occupied and dependence on microhabitats. Environmental tolerance ranges were estimated using spatial and seasonal variability in temperature and precipitation across species’ ranges as proxies, as for birds. We identified species that are dependent on a rainfall or increased water availability cues for their mass breeding, as well as those known or suspected to be susceptible to non-benign infection from Chytrid fungus. Species that are not known to have become established outside their natural ranges, are not associated with flowing water and have very small ranges were regarded as having relatively low intrinsic dispersal capacities. Exclusively montane and island species, and those at the polar edges of land masses or suitable natural habitats were assessed as having extrinsic dispersal barriers. Species known to have very low annual reproductive output were regarded as of lower evolvability. For corals (see Table S3), we identified habitat specialists as species occurring exclusively in few habitats, as well as those with narrow depth ranges. Species with larvae that are likely to be particularly exposed to sea surface warming (i.e., obligatory broadcast spawners and/or brooders) were regarded as having lower tolerance to warming, and we used evidence of past mass high temperature mortality as a proxy for measuring adult colonies’ tolerances. Exclusively shallow-water species, for which impacts of rising temperatures, irradiance and storms will be unattenuated by depth, were also highlighted. We identified species not known to be associated with thermally tolerant algal Symbiodinium symbionts from clades D, C1 and C15, as well as those not known to be able to change or ‘shuffle’ clades and/or types over time. Particularly slow-growing and long-lived species were also highlighted. Maximum time for larval settlement was used as a proxy for species’ intrinsic dispersal capacities, and species where currents and/or cold water could present extrinsic barriers to larval dispersal were also identified. Assessing Exposure Habitat and elevation suitability modelling to refine species’ distribution ranges. Since distribution maps for many of our focal species are only available as generalised range polygons, they often include unoccupied and potentially unsuitable areas which may be unrepresentative of the species’ climatic requirements and tolerances. To improve the accuracy of our exposure and environmental tolerance assessments, we refined species’ distribution maps (from the IUCN Red List) by excluding areas of known unsuitable habitat and elevation. Habitat suitability modelling was carried out by rasterizing the IUCN Red List maps to 10 minute resolution and cross-referencing habitat affiliations recorded in the IUCN Red List (2009) with the spatially explicit Global Land Cover 2000 habitat types [30]. The 1×1 km Global Land Cover 2000 was rasterized into twenty-three 10 minute grids, each representing one of the 23 Global Land Cover 2000 types. For each grid, cells’ values represented the percentage of the underlying 1×1 km vector covered by the land cover type in question. The probability of the presence of suitable habitat in each cell of a species’ range was calculated as the sum of the percentage presence of all suitable habitat types; following a conservative approach, we excluded only cells with zero probability of suitable habitat (see Supporting Methods in Supporting Information S1 for full details). To exclude areas with unsuitable elevations, we used the IUCN Red List and literature to estimate species’ individual elevational limits. The 1×1 km GTOPO30 elevation dataset was rasterized to two 10 minute grids, one containing the maximum elevation and one the minimum value in the underlying vector data. Elevation suitability of the cell was calculated as the extent to which each species’ elevation range lies between the minimum and maximum elevation for the cell; again, following a conservative approach, we excluded from species’ ranges only cells with no overlap between the species’ and cell’s elevation ranges. For corals, IUCN Red List distribution polygons (rasterized to 10 minutes) were refined by excluding areas that did not intersect with a coral reef, as defined by ReefBase’s global dataset of coral reef locations [31]. Calculating exposure parameters. For birds and amphibians, we considered exposure to five components of climate change, namely changes in mean temperature, temperature variability, mean precipitation, precipitation variability and sea level rise. Climate change projections were based on an ensemble of four General Circulation Models (UKMO HadCM3, MPIM ECHAM5, CSIRO MK3.5 and GFDL CM2.1), downscaled to 10 minutes [32], considering three emissions scenarios (B2, A1B and A2) for 1975 (mean 1961–1990), 2050 (mean 2041–2060) and 2090 (mean 2081–2100). The paper’s main results are based on the mid-range A1B emission scenario for projected changes from 1975 to 2050. To determine the potential role of alternative emissions pathways and longer timeframes, we then compared these results with those for A2 (high) and B1 (low) scenarios, and extended assessment timeframes to 2090 (Figure 4; Figures S7, S8, S9; Tables S19, S20, S21). Mean temperature change was modelled as the absolute change in projected mean annual temperature across each species’ current distribution range, and change in temperature variability was calculated as the absolute difference in projected average absolute deviation in mean monthly temperatures between each month and all cells in a species’ range. To assess mean precipitation changes we calculated the absolute ratio of change in projected mean annual precipitation and measured change in precipitation variability as the absolute ratio of change in projected average absolute deviation in mean monthly precipitation between each month and all cells in a species’ range. Species were assessed as highly exposed if they were among the 25% of species with greatest projected changes for any of these four measures. They were considered to be highly exposed to sea level rise impacts if they are known to occur exclusively or primarily in one or more climate change vulnerable coastal habitats (as listed in the Supporting Methods in Supporting Information S1). Coral exposure estimates were based on two measures. Risk of mortality due to bleaching was estimated by calculating the mean probability of severe bleaching across a species’ range (severe bleaching is projected to occur due to thermal stress resulting from degree heating month values exceeding 2°C-month) [22], [33]. Global spatial projections of maximum annual degree heating months were calculated using output from simulations of the Geophysical Fluid Dynamics Laboratory CM2.0 and CM2.1 climate models [22]. Secondly, we calculated the proportion of coral species’ ranges exposed to ‘extremely marginal’ ocean acidification levels (i.e., aragonite saturation states <3 [34]), using projections by Cao and Caldeira [35] based on the University of Victoria Earth System Climate Model version 2.8 [36]. Species were assessed as highly exposed if they were among the 25% of species with highest probability of bleaching and/or the greatest proportions of their ranges deemed unsuitable due to ocean acidification. As for birds and amphibians, the paper’s main coral results are based on changes projected by the mid-range A1B emission scenario from 1975 to 2050, and potential variation due to alternative emissions pathways (i.e., A2 and B1) and longer timeframes (i.e., 1975–2090) is explored in Figure 4, Figures S7, S8, S9 and Tables S19, S20, S21. Assigning Climate Change Vulnerability Scores Species were assigned scores of ‘high’, ‘low/lower’ or ‘unknown’ risk for each trait or exposure measure. While data for some traits were qualitative or thresholds for the ‘high’ category were clear (e.g., ‘occurs only on mountain tops’), in ∼66% of traits, there was no a priori basis for setting a particular threshold (e.g., for projected mean precipitation change). In such cases we scored the worst affected 25% of species as ‘high’. We explored the sensitivity of our results to shifting this threshold to include the worst affected 35% and 15% of species, as well as to stricter and more lenient expert-defined thresholds (Figures S10, S11, S12; Tables S16, S17, S18, S19, S20, S21), as well as to the choice of the individual traits included (Tables S13, S14, S15, S16). A species that scored ‘high’ under any trait or exposure measure triggered a score of ‘high’ for the vulnerability dimension to which it belonged (e.g., a species with a ‘high’ score under habitat specialisation was then considered to have a ‘high’ sensitivity score). To qualify as highly climate change vulnerable overall, species required ‘high’ scores for all three of sensitivity, low adaptive capacity and exposure (see Figure S13). We repeat the important caveat that, due to the scarcity of direct evidence to support trait scoring thresholds, climate change vulnerability scores must be interpreted as relative measures, and comparison of percentages of climate change vulnerable species between taxonomic groups is not meaningful (See Supporting Discussion in Supporting Information S1). We document the regions and families containing highest numbers of climate change vulnerable species, and compare results with assessments of non-climatic threat from the IUCN Red List. To reflect uncertainty due to unknown values for some species-trait combinations, we repeated our analyses treating unknowns as either ‘high’ (pessimistic scenario) or ‘low/lower’ (optimistic scenario) and present results as ranges of plausible values between these extremes. Full Methods and associated references are available in Supporting Information S1.

Acknowledgments Trait Selection Workshops The following people contributed to trait data selection via workshops held at Silwood Park, Ascot, UK in 2007 and at the IUCN World Conservation Congress in Barcelona in 2008: Rob Alkemade, Andrew Baker, Jon Bielby, Neil Brummitt, Simon Butler, Mar Cabeza, Kent Carpenter, Ben Collen, Keith Crandall, Nick Dulvy, Robert Ewers, Richard Grenyer, Gabriel Grimsditch, Craig Hilton-Taylor, Sarah Holbrook, Joaquin Hortal, Kate Jones, David Keith, Suzanne Livingstone, Zoe Macavoy, Rob Marchant, Tom Meagher, JB Mihoub, David Obura, Shyama Pagad, Paul Pearce-Kelly, Beth Pollidoro, John Reynolds, Ana Rodrigues, Alex Rogers, Andy Sheppard, Charles Sheppard and Stephen Williams. Trait Data Collection Corals: We thank A. Baker, A. Romanski and T. LaJeunesse for provision of detailed coral species and zooxanthellae clade data; P. Harrison and C.C. Wallace for provision of coral reproduction publications; R. Berkelmans for provision of coral temperature tolerance and bleaching publications and discussions; D. Obura and other colleagues for discussions concerning trait selection; T. Murdoch and R. Aronson for information on Atlantic species; and K. Carpenter and colleagues from the Global Marine Species Assessment for the provision of trait data. We also thank J.E.N. Veron and C.C. Wallace for their guidance on taxonomic considerations. Birds: For assistance with data collection we thank Joe Wood, Tris Allinson, Lotty Packman, Pete Newton, Louisa Richmond-Coggan, Sally Fisher, Simon Mitchell, Phil Martin, Joe Taylor, Andy Symes, Gill Bunting, Richard Johnson, Jemma Able, Dan Omolo, Simon Mahood, and Jez Bird. We also thank the many thousands of other individuals and organisations who have provided information, data, support and/or other contributions to BirdLife’s Red List assessments for the world’s birds upon which the current analyses draw heavily. Amphibians: We thank all experts involved in the global assessment of amphibians, published by IUCN in 2004. For general discussions on trait selection and data we thank members of the IUCN Species Survival Commission’s Amphibian Specialist Group; in particular we thank Diego Cisneros-Heredia and Marco Rada for discussions on glass frogs. Exposure Modelling Deborah Hemming from the UK Met Office provided UKMO HadCM3 outputs and the UNEP-World Conservation Monitoring Centre provided ReefBase datasets. Inputs on various aspects of exposure modelling were provided by Paul Roberts, David Orme, Jonathan Burchard, Deepak Katariya, James Jardine, Karen Tabor, Simon Tokumine and Maria Dickinson. Red List Assessments The IUCN Red List of Threatened Species consists of data and assessments generated by thousands of scientists, without whose often voluntary contributions, neither the Red List nor this study would exist. We thank these scientists, Red List partners, especially BirdLife International, the Species Survival Commission and the IUCN Global Species Programme for their enormous contributions. Drafting the Manuscript We thank our reviewers for suggestions that have greatly improved this paper. Bob Scholes and Jamie Carr also provided valuable comments. We thank Holly Dublin for project guidance, Ben Collen for help with workshop coordination and Amy Burden, Maureen Martindale and Craig Hilton-Taylor for project support.

Author Contributions Conceived and designed the experiments: WBF GMM SHMB JCV SNS HRA. Analyzed the data: WBF GMM SHMB. Wrote the paper: WBF GMM SHMB JCV. Gathered species trait data: AA AG ET LMD SHMB STG CHS. Conducted climate change exposure modeling: WBF LC SDD VK RB RAH AFH SEO.