We calculated how the climatically suitable ranges of 62 European mammals will be affected by climate change (“exposure”), using SDM predictions. We calculated species’ capacity to undergo range shifts using predictive traits that correspond to ecological generalization and reproductive strategy. These traits correlate with several proxies for range shift capacity, including recent range shifts (Estrada et al., 2016 ), suggesting that they correspond to species ability to establish new populations and proliferate (Angert et al., 2011 ; Sol et al., 2012 ). We ranked each species according to several metrics of exposure and range‐shift capacity. We synthesized this information using radar plots and identified four “risk syndromes” into which species could fall. Furthermore, we investigated the sensitivity of results to the methods used to rank species. European mammals are very well characterized by SDMs (Mitchell‐Jones et al., 1999 ), have a large amount of trait data available (Jones et al., 2009 ), and have been extensively utilized to develop our understanding of the effects of climate change on wildlife (Schloss et al., 2012 ). Therefore, European mammals are an ideal system with which to examine both changes to climatically suitable ranges and species range‐shift capacity simultaneously.

Our main objectives were (1) to ask whether species whose climatically suitable ranges will be greatly altered by climate change are those most or least likely to shift their ranges to new areas, (2) to ask whether trait‐based estimates of range‐shift capacity cause more or less uncertainty than other, more frequently studied elements of climate‐related species risk, and (3) to demonstrate how risk based on exposure and range‐shift capacity can be synthesized and visualized.

Previous studies investigating the relationship between predictive traits and species response to climate change have selected traits based on expert opinion (Foden et al., 2013 ; Garcia et al., 2014 ; Triviño et al., 2013 ). However, a recent review of available evidence suggests that many expert‐selected traits in these papers might not correspond to range‐shift capacities (Estrada et al., 2016 ). This causes disagreement as to which traits should be selected for range‐shift studies and could result in studies that are not comparable (Estrada et al., 2016 ). We therefore used the trait framework defined by Estrada et al. ( 2016 ) to investigate how 62 European mammals might respond to climate change.

The most commonly used predictive traits in range‐shift predictions are traits associated with movement ability, such as dispersal distance (Engler & Guisan, 2009 ; Estrada et al., 2016 ). However, data on movement ability are often limited, preventing simulations of species‐specific dispersal scenarios (Bateman, Mruphy, Reside, Mokany, & VanDerWal, 2013 ). Additionally, the contribution of movement ability to range shifts has received contradictory evidence in the literature (Lester, Ruttenberg, Gaines, & Kinlan, 2007 ; Schloss, Nunez, & Lawler, 2012 ). This suggests additional traits need to be incorporated that correspond to other elements of range‐shift (Estrada et al., 2016 ). To this end, Estrada et al. ( 2016 ) proposed that the range‐shift process be split into four key stages: (1) emigration: individuals leave natal location; (2) movement: transfer of individuals away from original location; (3) establishment: the ability of individuals to form new populations; and (4) proliferation: the growth of established populations to cause the population to spread. The predictive traits most relevant to each stage can then be identified.

Anthropogenic climate change is increasingly threatening global biodiversity (Dawson, Jackson, House, Prentice, & Mace, 2011 ). Species’ extinction likelihood is lessened if they can colonize newly climatically suitable areas (“range shift”), which could reduce extinction risk (Parmesan & Yohe, 2003 ). Identifying areas that will be climatically suitable in the future and understanding species ability to colonize these areas would help us prioritize conservation to alleviate climate‐driven threats (Estrada, Morales‐Castilla, Caplat, & Early, 2016 ). The most frequently used method for forecasting species exposure to climate change is to use species distribution models (SDMs) (e.g., Thuiller, Lavorel, Araujo, Sykes, & Prentice, 2005 ). These models correlate species’ current distributions with climatic variables and estimate the geographic areas that will be suitable for species in the future (Pearson & Dawson, 2003 ). However, SDMs cannot in themselves account for species’ ability to reach climatically suitable locations (Early & Sax, 2011 ), and thus fail to accurately inform species extinction risk (Pearson et al., 2014 ). Understanding the likelihood that species will undergo range shifts (i.e., species’ range‐shift capacity, Estrada et al., 2016 ) is vital to identify the species most vulnerable to climate change (Maggini et al., 2014 ) and to aid in conservation efforts (Willis et al., 2015 ).

We defined four “synthetic” risk syndromes: (1) high exposure and low range‐shift capacity; (2) high exposure and high range‐shift capacity; (3) low exposure and low range‐shift capacity; and (4) low exposure and high range‐shift capacity. Species with high exposure were those for which the average of the three exposure metrics was ≥5.5. Species with low range‐shift capacity were those for which the average of the two range‐shift capacity metrics was ≥5.5. All other species were given low exposure or high range‐shift capacity. For both exposure and range‐shift capacity metrics, risk syndromes were calculated separately for the best‐, average‐ and worst‐case scenarios (Appendix S2 ). We used a multinomial logistic regression (using the “nnet” r package) to ask whether the synthetic risk syndrome was related to any of the exposure and range‐shift metrics in each of the three scenarios (i.e., if one or more metrics were strongly driving the risk levels). We also used Pearson's correlation to ask whether highly exposed species tended to have low range‐shift capacities, and vice versa .

Radar charts of idealized risk syndromes (a–d) and example species (e–h) which represent each risk syndrome and were chosen by visually assessing the species which best matched the idealized risk syndromes. Each axis represents the species’ score for exposure metrics (area lost, area gained and range‐margin shift) or range‐shift metrics (EG [ecological generalization] traits and RS [reproductive strategy] traits). Values for all axes are identical to the labelled axes, and high numeric values indicate a high exposure or low range‐shift capacity, and low values represent a low exposure or high range‐shift capacity. Black solid line represents the average scenario for each metric, and the dotted and dashed lines represent the best‐case scenario and worst‐case scenario that resulted from each climate projection or trait. Radar charts for all species are available in the supplementary material

All exposure and range‐shift capacity metrics were used to assess the overall risk faced by each species, using each metric as an axis on a radar diagram (Figure 1 , Table 1 ). We plotted values for each species on these axes using the decile classification scheme, as the scale of 1–10 is easily interpretable, and illustrates the variation in metric values more than a scheme with fewer categories. We plotted the lowest, average and highest decile metric levels, to illustrate the best‐, average‐ and worst‐case risk assessment scenario, respectively, for each species (Appendix S1 ). We used the r statistical package “fmsb” (Nakazawa, 2015 ) to make radar plots.

Finally, we investigated whether species’ current range size could suggest particular levels of exposure or range‐shift capacities using Pearson's product–moment correlation. Range size is thought to correspond to range‐shift capacity, in that widespread species are easily able to colonize new locations (Estrada et al., 2015 ). Range size is also suggested to correspond to exposure, in that narrow‐ranged species are climate specialists and climate change could substantially relocate suitable habitat (Franklin, Wejnert, Hathaway, Rochester, & Fisher, 2009 ).

To assess whether the exposure resulting from different climate projections were consistent, we calculated Pearson's product moment correlation between the exposures resulting from the four climate projections within each exposure metric. To test whether the range‐shift capacities resulting from different traits were consistent, we calculated Pearson's product moment correlation between the range‐shift capacities resulting from the three traits within each range‐shift metric (Appendix S1 ).

We calculated three exposure metrics for each species (range loss, range gain, and range‐margin shift) under each of the four future climate projections (Table 1 and Appendix S1 ). We used the range changes obtained for each climate projection to calculate best‐, average‐ and worst‐case scenario values for each exposure metric. We calculated two range‐shift capacity metrics for each species (ecological generalization and reproductive strategy) using the three traits in each metric (Table 1 and Appendix S1 ). We used each trait measurement to calculate best‐, average‐ and worst‐case scenario values for each range‐shift capacity metric. The best‐case scenario for climate projections is the one that produced most range gain, least range loss and the shortest range‐margin shift. The best‐case scenario for range‐shift capacity is the one that produced highest ecological generalization and fastest reproductive strategy. The inverse of these conditions was used as the worst‐case scenario.

For 62 of the native European mammals with SDMs, there were data available for at least one trait corresponding to each of the range‐shift capacity indicators considered. Trait data were obtained from Estrada et al. ( 2017 ). Each trait was only recorded once or a few times for each species, despite substantial evidence for intraspecific trait variation (Jongejans, Shea, Skarpaas, Kelly, & Ellner, 2011 ). Using the upper and lower trait values for each species would better represent species range‐shift capacity under climate change. However, due to a lack of intraspecific trait information, we had to assign species a single value for each trait, either the mean or the single value available.

To quantify species capacity to range shift, we considered traits related to different stages of range‐shift (Estrada et al., 2016 ). Traits were chosen based on their correlation with several proxies for range‐shift capacity: range filling, species’ recent range shifts, range size, naturalization potential, population declines and endangerment (Estrada et al., 2016 ). Traits relating to movement ability and ecological generalization have the most studied and consistent relationships with the above proxies, followed by reproductive strategy (Estrada et al., 2016 ). Lack of data prohibits analysis of movement ability for European mammals, however, ecological generalization and reproductive strategy have a strong relationship with how much of its climatically suitable area a species occupies (“range filling,” Estrada, Morales‐Castilla, Meireles, Caplat, & Early, 2017 ). Range filling may be a particularly important proxy for range‐shift capacity, as species’ absence from climatically suitable areas is due to non‐climatic range limitations (e.g., dispersal limitation, lack of available habitat, susceptibility to or dependence on biotic interactions). Range filling is also sufficiently easy to calculate that it can be used as a proxy for range‐shift capacity for many species simultaneously. Also, in Europe the current ranges of many mammals are restricted by their ability to range‐shift from climatic refugia occupied during the last glacial maximum (Dullinger et al., 2012 ; Svenning & Skov, 2004 ). The factors that limited post‐glacial range shifts are likely to continue to limit modern‐day range shifts and therefore traits that correspond to range filling inform limitations on modern range shifts (Estrada et al., 2016 ). We therefore used ecological generalization (residual brain size, diet breadth, habitat breadth) and reproductive strategy traits (interbirth interval, litter size, age at sexual maturity) to indicate species range‐shift capacity. Thus, we used traits related to the range‐shift stages of establishment and proliferation. Details of the traits and the rationale for their relationship with population establishment and proliferation are as follows:

Species baseline and future projected distributions were compared to calculate range area lost, range area gained and range‐margin shift (Table 1 ). “Range area lost” was defined as the percentage area of the distribution projected by the SDM for the baseline period that is unsuitable in the future. “Area gained” is the percentage area that SDMs calculate to be climatically unsuitable during the baseline period, but suitable in the future. “Range‐margin shift” was calculated by comparing the centroid of the baseline and future distributions. We first categorized the direction the centroid moved (i.e., north, east, south and west). The median latitude and longitude of the 10% most marginal 10′ grid cells (e.g., most northerly grid cells for species moving north) were determined for both the baseline distribution and each future distribution, and the distance between them calculated (following Brommer, 2004 ). This metric is analogous to “backward velocity,” which describes the maximum distance a species is required to move in order to colonize climatically suitable areas in the future (Hamann, Roberts, Barber, Carroll, & Nielsen, 2015 ). Calculations were performed in ArcGIS 10.2.2 (ESRI, 2013 ).

Presence data of native, extant, European mammals (141 species) were obtained from The Atlas of European Mammals (Mitchell‐Jones et al., 1999 ) on 50 km × 50 km UTM grid cells within Europe from −10°9′23″ to 30°43′10″E and from 34°59′30″ to 70°58′33″N. The distribution of each species was modelled using four climatic variables averaged across the baseline period 1961–1990: mean annual growing degree days, mean temperature of the coldest month, annual precipitation and a moisture summer index. These variables have been previously used to model plant and vertebrates in Europe and were chosen to reflect two primary properties of climate (energy and water) that have known roles in imposing constraints upon species distributions (Araújo, Alagador, Cabeza, Nogués‐Bravo, & Thuiller, 2011 ; Whittaker, Nogués‐Bravo, & Araújo, 2007 ). Climatic variables were derived from the Climatic Research Unit TS 3.0 dataset (Mitchell & Jones, 2005 ), at 10′ resolution. We calculated the average climatic conditions in each 50‐km grid cell and used these data to construct SDMs to calculate climate suitability for the baseline period. The relationships between baseline climatic variables and species’ distributions were modelled using seven SDM techniques: generalized linear models (GLM), generalized additive models (GAM), generalized boosting models (GBM), classification tree analysis (CTA), artificial neural networks (ANN), flexible discriminant analysis (FDA) and surface range envelope (SRE). Each model was then evaluated and included in an ensemble model if AUC > 0.8 and TSS > 0.6 (similar to Araújo et al., 2011 ). Results are presented for species whose distributions could be accurately modelled (i.e., the ensemble model produced a mean AUC > 0.8 and TSS > 0.6; 70 out of 141 species). Ensemble models of selected species calibrated at 50‐km resolution were then predicted to each 10′ grid cell taking into account the values of the variables that formed part of the different models. We also predicted ensemble models to future climatic conditions at 10′ resolution. Future climatic variables for the period 2035–2064 were obtained from two general circulation models (GCMs: CNRM‐CM5 and ICHEC‐EC‐EARTH) and two representative concentration pathways (RCPs: 4.5 and 8.5), at 10′ resolution from EURO‐CORDEX (Jacob et al., 2014 ). Thus, we had four future climatic scenarios (combination of GCMs and RCPs).

Figure 3 shows future distributions predicted for the time period 2035–2064, to highlight exposure metrics for species that correspond to one of four risk syndromes we defined (Figure 1 ). Sorex coronatus is forecast to lose up to 43% of its current range and may be required to move 558 km to occupy future areas (Figure 3 a). This species is classed as highly exposed with low range‐shift capacity and is most at risk to climate change (Figure 1 e). Clethrionomys rufocanus is predicted to lose up to 53% of its current range and gain only 13% (Figure 3 b) and is subsequently classed as highly exposed, but has a relatively high range‐shift capacity (Figure 1 f). Capra ibex could increase the area it can occupy by 916% and lose only 8% of its current range (Figure 3 c) and hence is not threatened by exposure but has a low range‐shift capacity and so is unlikely to colonize the newly available habitat (Figure 1 g). Finally, Mus spretus loses 33% of its current range and gains 207% (Figure 3 d). This species has high range‐shift capacity so may well be able to move the 138 km needed to occupy all of this future potential range, and thus faces relatively low exposure and has high range‐shift capacity (Figure 1 h). See Appendix S2 and Table S5 in the supplementary material for risk syndromes of all 62 species.

Under the worst‐case scenario, 62% of species were regarded as being highly exposed to future climate change. 15% had high exposure but high range‐shift capacity, and 47% were both highly exposed and vulnerable, that is, at most risk from climate change (Figure 2 ). Under the worst‐case scenario, 24% of species had low exposure to climate change and low range‐shift capacity and only 14% of species had low exposure and high range‐shift capacity. The percentage of species falling into each risk syndrome under the best and average scenarios is shown in Figure 2 . There was no correlation between exposure and range‐shift metric values when calculated under each of the best, average and worst scenarios (Pearson's correlations, p > .1 under all scenarios). Multinomial logistic regression revealed that the synthetic risk syndrome was not significantly affected by any of the exposure and range‐shift metrics for either of the three scenarios, indicating that no particular metric drives the risk syndromes ( p > .1 for all explanatory variables).

For each of the two range‐shift metrics, the capacities derived from the four numerical classification schemes were significantly and very closely correlated (mean ± SD correlations among ecological generalization traits 0.82 ± 0.12; among reproductive strategy traits 0.93 ± 0.02, Table S2 ). There was some consistency between the traits within the reproductive strategy range‐shift metric; however, correlations between other traits were not statistically significant (Table 2 ). There was no correlation between the two range‐shift metrics (Table S4 ).

For each of the three exposure metrics, the exposures derived from the four climate scenarios and four numerical classification schemes were significantly and very closely correlated (mean ± SD correlations among climate scenarios: area gained 0.97 ± 0.02; area lost 0.88 ± 0.06; range‐margin shift 0.80 ± 0.08; among numerical classifications schemes: area gained 0.86 ± 0.13; area lost 0.97 ± 0.02; range‐margin shift 0.92 ± 0.07; Tables S1 and S2 ). Thus, the choice of climate projection and numerical classification scheme had little effect on the exposure assessments.

4 DISCUSSION

We assess the risk climate change poses to European mammals based on its effects on climatically suitable ranges (range area lost, gained and distance moved) and groups of traits that indicate species capacity to colonize new areas during range shifts (ecological generalization and reproductive strategy). Up to 47% of species were found to be both highly threatened by climate change and to possess traits that limit their ability to establish and proliferate in new climatically suitable areas (Figure 2). There is a large difference in species risk syndromes between best‐ and worst‐case scenarios (Figure 2). When the worst climate scenario and most range‐limiting traits are considered, many more species fall into the highest risk syndrome than the lowest.

Traditional risk assessments, such as the IUCN Red List, could change starkly under climate change if trait‐based vulnerability assessments are included (Foden et al., 2013). Twenty one species identified as being of least concern under the European IUCN Red List (Temple & Terry, 2007) were classified here as being the most at risk under the worst‐case scenario (see Appendix S2 for all species). For example, the Eurasian elk (Alces alces), Iberian wild goat (Capra pyrenaica) and Pyrenean chamois (Rupicapra pyrenaica) were all classified as highly exposed with low range‐shift capacity under all three scenarios, yet are classed as Least Concern by the European IUCN Red List (Temple & Terry, 2007) (Table S5). However, IUCN Red List does not appear to underestimate climate change risk for any species, and our results suggest that the conservation situation could worsen for some already‐threatened species. Cabrera's vole (Microtus cabrerae) and the wolverine (Gulo gulo) were classified as vulnerable by the European IUCN Red List (Temple & Terry, 2007). We identified both as highly exposed with low range‐shift capacity under the worst‐ and average‐case scenarios (Table S5). These findings are in line with Pacifici et al. (2017), who found that 47% of terrestrial mammals have already been negatively impacted by climate change but only 7% of those were classed as threatened by climate change and severe weather by the IUCN. The IUCN has recently developed best‐practice guidelines to improve Climate Change Vulnerability Assessments (Huntley et al., 2016). The guidelines recommend the use of traits and stress the importance of collecting improved trait data. We further recommend trait‐based assessments, but emphasize the importance of using traits for which there is evidence that they indicate range‐shift capacity, for example using methods proposed by Estrada et al. (2016) and Pacifici et al. (2017).

Species with a small current range had the lowest range‐shift capacities under the best‐case scenario (but not worst or average scenarios). This provides mixed support for the hypothesis that range‐restricted species might be less able to establish and proliferate in future climatically suitable areas (Williams, Shoo, Isaac, Hoffmann, & Langham, 2008).

We caution that our risk rankings are relative and depend on the other species included in the analysis, so should not be interpreted as absolute risk. Absolute risk ranking procedures, such as the IUCN Red List, are preferable, as risks will not change when new species are added, and risks can be compared between taxa, time periods or regions. However, absolute risk can only be calculated using a very detailed model for every species. The necessary data are available for very few species, and even the most detailed models are not necessarily highly accurate (Fordham et al., 2017). Relative rankings, on the other hand, could be calculated for a large number of European mammal species and can potentially be applied to other groups of species and study areas, allowing a preliminary risk analysis to be undertaken. Our study species are a representative range of European mammals, and we consider the proportion of species falling into each of the four risk syndromes to be representative of the exposure and range‐shift capacities of European mammals as a whole. Relative rankings have been very useful in other branches of conservation biology. For example, the Zoological Society of London's 10 year EDGE (Evolutionary Distinct and Globally Endangered) of existence programme uses relative values of evolutionary distinctiveness to highlight one‐of‐a‐kind species on the verge of extinction (Isaac, Turvey, Collen, Waterman, & Baillie, 2007). We therefore consider that the use of evidence‐based traits to calculate relative risk is a step forward for conservation prioritization, and our highly ranked species should be prioritized for modelling to produce an absolute threat value.

We found very high correlations between the four numerical classification schemes we used; any of the schemes would have produced very similar risk assessments (Table S2). Thus, our results suggest that studies that use different classification schemes could be comparable for conservation or scientific purposes. We also found that uncertainty due to the choice of climate change scenarios (RCPs 4.5 and 85) was low (Table S1). However, there was high disagreement in the threat rankings derived from the three metrics of range change (i.e., exposure, Table 2). Thus, few species will simultaneously experience high habitat loss, little habitat gain and long required range‐shift distances, which is a positive outcome for conservation. When prioritizing conservation, we suggest that it is more important to choose the exposure metric that is most appropriate to the specific conservation context than it is to calculate results using multiple climate change scenarios or ranking schemes.

Within the traits used to compile the range‐shift capacity metrics, there was no congruency between the ecological generalization traits, but we obtained significant relationships between three reproductive strategy traits (Table 2). The relationships are likely due to trade‐offs between “r” and “K” reproductive strategies (Trakimas, Whittaker, & Borregaard, 2016). For example, r‐strategy species that reproduce at an earlier age also tend to produce large litters and have fewer days between reproductive events (Estrada et al., 2016). We note that while species with a slow reproductive rate may be poorly able to colonize new areas, trait trade‐offs could mean that these species are protected from climatic changes in their current range (Capellini, Baker, Allen, Street, & Venditti, 2015; Estrada et al., 2017; Sol et al., 2012). For example, these K‐strategy species often have large brains, which can help species adjust behaviour, adopt novel resources and avoid new predators, and provide a survival advantage in novel environments (Sol et al., 2008). K‐strategy species could also prioritize future reproduction over current reproduction on an event of unsuitable climatic conditions, which makes populations less vulnerable to environmental fluctuations in situ (Sol et al., 2012).

We considered each trait belonging to just one of the range‐shift capacity trait groups, that is, ecological generalization or reproductive strategy. As stated above, relative brain size was considered as an indicator of ecological generalization, as species with larger brains have greater behavioural flexibility that can act as a buffer against environmental challenges, improving survival and conferring higher ability to persist in novel environments (Sol, 2009; Sol et al., 2008). However, having larger brains can have also some costs, for example, on reproduction, with higher sexual maturity age or fewer offspring (Isler & van Schaik, 2009). For this reason, an indirect relationship between brain size and vulnerability to extinction has been found, with species with larger brains being more threatened (Gonzalez‐Voyer, González‐Suárez, Vilà, & Revilla, 2016). In our analyses, species were categorized by both types of traits, so this trade‐off was taken into account. Interestingly, we found no relationship between species rankings in both of these trait categories; species with fast reproductive strategies did not tend to be ecological generalists. The lack of congruency between different trait categories, the expectation of trade‐offs and the greater uncertainty between range‐shift metrics than between climate change scenarios indicate that choosing the most appropriate traits to indicate range‐shift capacity is more important for risk assessments than choosing the climate forecast. We recommend the use of traits that indicate different elements of the range‐shift process in risk assessments. Investigating the traits that determine species range‐shift capacities could help target conservation planning. For example, species’ ecological generalization or reproductive strategy traits could identify scenarios where improving habitat quality or increasing reproductive success (e.g., through captive breeding programs) could help populations to range shift and survive climate change. We also found much more uncertainty between the exposure and range‐shift metrics than between the climate change scenarios (Table 2). This suggests that synthesizing the results from several exposure and range‐shift metrics is important.

The ecological generalization and reproductive capacity trait categories we used contribute towards establishment and proliferation during range shifts and were selected from seven possible trait categories (Estrada et al., 2016). For European mammals, strong support is given for reproductive and generalization traits (Estrada et al., 2017), as these traits explain a large proportion of the variance (24.6%) in range filling (Estrada et al., 2017). We did not use movement ability, which has been shown to correspond to range shifts in plants (Cadotte et al., 2006; Estrada et al., 2015), mammals (Santini et al., 2016) and birds (Pacifici et al., 2017) although the relationship is far from universal (Estrada et al., 2016). Unfortunately, a direct metric (i.e., distance an individual can travel from the natal location in a lifetime) is not available for most species. Instead, indirect traits are used, which are much less informative (Angert et al., 2011). Therefore, although we could not study it, movement ability is likely to affect range shifts (Estrada et al., 2016) and our risk assessment should be interpreted with caution.

The trait‐based framework utilized here enables comparative studies across multiple taxonomic groups and ensures that traits used are evidenced as important for species range‐shift capacity. Large‐scale quantitative analyses of the relationship between a broad range of traits and range shifts are now emerging (Pacifici et al., 2017), and we recommend that future studies explicitly consider traits relating to species ability to move, establish and proliferate in climatically suitable areas (Estrada et al., 2015, 2017). By considering climate change effects on species ranges and species’ capacity to colonize new locations simultaneously, we find many European mammal species for which the predominant form of climate change threat assessments (i.e., SDM forecasts) may well be inaccurate. The large proportion of species that are both relatively highly exposed to climate change and have low range‐shift capacities suggest that concerted action needs to be taken to assist many species to colonize new habitat.