Abstract Arctic and subarctic (i.e., [sub]arctic) ecosystems are predicted to be particularly susceptible to climate change. The area of tundra is expected to decrease and temperate climates will extend further north, affecting species inhabiting northern environments. Consequently, species at high latitudes should be especially susceptible to climate change, likely experiencing significant range contractions. Contrary to these expectations, our modelling of species distributions suggests that predicted climate change up to 2080 will favour most mammals presently inhabiting (sub)arctic Europe. Assuming full dispersal ability, most species will benefit from climate change, except for a few cold-climate specialists. However, most resident species will contract their ranges if they are not able to track their climatic niches, but no species is predicted to go extinct. If climate would change far beyond current predictions, however, species might disappear. The reason for the relative stability of mammalian presence might be that arctic regions have experienced large climatic shifts in the past, filtering out sensitive and range-restricted taxa. We also provide evidence that for most (sub)arctic mammals it is not climate change per se that will threaten them, but possible constraints on their dispersal ability and changes in community composition. Such impacts of future changes in species communities should receive more attention in literature.

Citation: Hof AR, Jansson R, Nilsson C (2012) Future Climate Change Will Favour Non-Specialist Mammals in the (Sub)Arctics. PLoS ONE 7(12): e52574. https://doi.org/10.1371/journal.pone.0052574 Editor: Tamara Natasha Romanuk, Dalhousie University, Canada Received: July 16, 2012; Accepted: November 20, 2012; Published: December 20, 2012 Copyright: © 2012 Hof 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 project was funded by the Nordic Council of Ministers (http://www.norden.org/en/nordic-council-of-ministers), Project number: 91228. 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.

Introduction Evidence shows that species respond to climate change by adjusting their geographic ranges [1], and such changes are envisaged to increase in the future [2]. Indeed, changing climates have been recognized as one of the main drivers behind shifts in species distributions, and species extinctions, range contractions and expansions driven by climate change currently occur at a continental scale [3], [4]. Biological impacts are expected to be greater in those regions where the rate and magnitude of climate change are greater [5]. It is predicted that arctic and subarctic ecosystems are particularly susceptible to climate change [6], [7], with amongst others an expected decrease in the extent of tundra ecosystems and a northward expansion of temperate climate types [8]. It is supposed that the large expected climate change at high northern latitudes therefore makes species in (sub)arctic regions particularly susceptible [9]–[11], especially the European part of the (Sub)arctics, since this region is the most geographically complex with the most infrastructure and great cultural, social, and political heterogeneity [12]. In addition, (sub)arctic species, such as the arctic fox (Alopex lagopus), are physiologically adapted to current (cold) climates, which could make them vulnerable to warming [13]. However, northward range expansions to compensate for southern range losses are limited by lack of land further north, the region being situated at the northern edge of the continent. In order to preserve current biodiversity in the face of climate change, reliable predictions of expected changes in species geographic distributions are of fundamental importance, especially in regions, like (sub)arctic Europe, that are expected to experience pronounced changes. Besides understanding the direction and the magnitude of predicted changes in species geographic ranges, it is essential to consider whether species are able to disperse to potential future ranges. In addition, communities are likely going to change considerably in the future, calling for assessments of climate change effects on all constituent species before community level predictions can be made. Although Levinski et al. [4] studied the impact of future climate change on mammals in Europe, their work was at a much courser scale (10′×10′ resolution vs. our 1′×1′ resolution) and based upon the fuzzy envelop model, which is less advanced than the well established MaxEnt algorithm used by us. In addition, they did not study community level impacts. In response to the large projected climate change in northern Europe, and expected subsequent effects on biodiversity and communities, we assessed potential changes in the geographic distribution of all terrestrial mammal species currently present in (sub)arctic Europe along with species that might colonize. We used species distribution modelling, incorporating projections of future climate and vegetation, in order to provide a better insight into the magnitude of the risk mammal species are facing, and the potential community level changes they have to endure due to climate change.

Materials and Methods Although the study site was limited to sub(arctic) Europe, the area that we modelled included an additional zone of approximately 1000 km south of the study site (indicated in Figure 1), since many species are expected to shift or expand their geographic ranges to higher latitudes [1]. Thus, many possible colonizers were included. We collected occurrence data for 61 mammal species (Table 1) for the period 2000−2010 from national and global databases (http://www.artsobservasjoner.no, http://www.artportalen.se, http://www.hatikka.fi, and http://data.gbif.org). On average 426 occurrences (se = 69) were obtained per species. Data were limited (<30) for three species (Apodemus agrarius, n = 20; Castor canadensis, n = 8; and Sorex minutissimus, n = 16). Data were limiting also for the arctic fox, and since the International Union for Conservation of Nature (IUCN) reports the species to have critically low levels in Fennoscandia, additional data were sought [14] (total n = 33). PPT PowerPoint slide

PowerPoint slide PNG larger image

larger image TIFF original image Download: Figure 1. Predicted species richness in (sub)arctic Europe. a) 2000, b) CGCM2 A2 scenario 2080; species are able to fully utilize their potential future range, c) CGCM2 B2 scenario 2080; species are able to fully utilize their potential future range, d) CGCM2 A2 scenario 2080; species are limited to areas where their current range and potential future range overlap, e) CGCM2 B2 scenario 2080; species are limited to areas where their current range and potential future range overlap. The maps are displayed in the Albers Equal Area projection for Europe. The inset shows the study region in red and the additional zone to include possible colonizers in the study in dark grey. https://doi.org/10.1371/journal.pone.0052574.g001 PPT PowerPoint slide

PowerPoint slide PNG larger image

larger image TIFF original image Download: Table 1. Effects of future climate change (CGCM2 A2, B2 scenario) on (sub)arctic mammals. https://doi.org/10.1371/journal.pone.0052574.t001 Occurrence data are often biased due to differences in sampling intensity. Detailed occurrence data from north-western Russia were highly limited and often non-existent in comparison to Fennoscandia. As a remedy to clumped occurrence data reflecting variation in sampling intensity we randomly deleted excessive data from Fennoscandia by using a raster (grid size 10 km2) with the aim to have not more than one randomly chosen observation per grid-cell. This approach does not deal with under-sampled areas (i.e., north-western Russia), implying that the full environmental ranges of the species were not captured. Setting the extent of the environmental variables to the entire study area (including north-western Russia) in the distribution models led to conservative predictions of current species distributions in comparison to the ranges suggested by the IUCN, since the model assumed that environmental conditions in north-western Russia were not suitable for the species. Severe under-prediction is a grave error in the context of climate change, and excluding north-western Russia in the extent of the environmental variables led to more accurate current predictions in comparison to the IUCN-ranges. Therefore, predictions for current situations in north-western Russia and for 2080 were based on the extent of the environmental variables and the occurrence data from Fennoscandia [15]. We used MaxEnt [16] to predict species distributions. We used the default convergence threshold (10−6) and maximum number of iterations (500) values. Hinge features were applied when the number of presence records exceeded 15, which was the case for all species, except for C. canadensis. Climate projections for 2080 used in this study were the downscaled general circulation model CGCM2, developed by the Canadian Centre for Climate Modelling and Analysis, under emission scenarios A2 and B2 (http://www.worldclim.org/futdown.htm). Nineteen bioclimatic variables derived from monthly temperature and rainfall values during 1950−2000, described and available at http://www.worldclim.org/futdown.htm, were used in the models. Since species distributions can largely be determined by habitat type in addition to climatic conditions [17], we included habitat related variables in the modelling. We used projections of the main vegetation zones (boreal needle leaved forests, grasslands, shrub areas, and shade intolerant broadleaved forests) for 1990 and 2080 [18]. A dynamic vegetation model (LPJ-GUESS) was used to project transient impacts of changes in climate on vegetation of northern Europe; the resulting vegetation projection provided continuous data of biomass of the main vegetation zones. The climate data from WorldClim were available at the 30 arc-seconds (∼1 km2) scale. The vegetation data were available at the 25 arc-minutes scale and interpolated to the 30 arc-seconds scale in ArcGis (9.3.1 by ESRI) by means of the natural neighbour method. Unfortunately, some degree of spatial autocorrelation between climatic variables is unavoidable and testing for spatial autocorrelation for presence-only data is not possible according to Dormann et al. [19]. We did not pre-select variables, judging all included variables to be biologically meaningful and taking advantage of the regularization application of MaxEnt which reduces potential overfitting of large numbers of autocorrelated variables [16]. Regularization deals with the selection of environmental variables (regulating some to zero) and has shown to perform well [20]. In addition, its regularization parameter is said to be more stable than stepwise regression when correlated variables are present, which reduces the need to remove correlated variables or to use PCA to select a few dominant axes [21]. Furthermore, MaxEnt minimizes autocorrelation between variables, as it gives more weight to variables exhibiting high correlation with the occurrence data [21]. In addition to creating predictions for species using all above mentioned variables, we created models based upon the variable that explained most (relative strongest contributor to the AUC when used by itself) of the variation in species occurrences for all species. We created these models both for the current and the future distribution of each species, and we doubled the change in a climatic variable between the current and the future situation as a simplified test of the sensitivity of the species to a more severe climate change scenario. The continuous suitability predicted by MaxEnt was transformed into binary suitable/unsuitable area by applying cut-off thresholds where the difference between sensitivity and specificity was minimized [22]. This method was chosen since it has shown to be one of the superior methods to transform continuous probabilities of species occurrence to binary presence/absence occurrence [23]. The extent of, and overlap between, the predicted current and potential future ranges were calculated. Species richness was based upon the total number of species present per 30 arc-second grid cell. The future species richness was estimated for (1) worst case scenario (WCS): no dispersal ability; the species concerned is only able to persist in areas where its predicted current and potential future ranges overlap, and (2) best case scenario (BCS): full dispersal ability; the species concerned is able to reach its full potential future extent of occurrence. Average range shift and direction of the shift were based upon the centroids of predicted current and potential future ranges. The Area Under the Curve (AUC) of a Receiver Operating Characteristic (ROC) plot [16] was used to assess the accuracy of the predictions of species distribution models. By means of randomized partition, 30% of the occurrence data were set aside as ‘test’ data, comparing the AUC of these models with the AUC from ‘train’ models. We also assessed how closely the predicted current distribution ranges matched the species geographic ranges as defined by the IUCN for all native species (n = 54). The accuracy was expressed as the percentage of the predicted current range that lay within the published range and the percentage of the published range that was covered by the predicted current range.

Discussion In contrast to the general belief that species inhabiting the (sub)arctics will face increased levels of stress due to climate change [9], [11], our work suggests that the climate in sub(arctic) Europe will ameliorate the future conditions for most of its mammalian species. Warmer and wetter conditions favour more species. However, alterations in landscapes and ecosystem management caused by socioeconomic activities can severely impact species distribution and migration. It is thus uncertain if species will be able to reach areas that we expect to meet their climatic requirements in the future. While species diversity will increase to a large extent according to our full dispersal scenario, the no-dispersal scenario shows that species richness will decrease in many areas instead. Although highly dispersive species are likely well-adapted to colonizing small isolated patches of habitat, even if their habitat requirements are restrictive, species like the hazel dormouse are highly dependent on continuous habitat in order to migrate into new areas [26]. Moreover, as many as ten non-volant species would have to exceed a colonization rate of ∼7.9 km yr−1 set by Fløjgaard et al. [24] as a maximum dispersal rate for a large range of non-volant terrestrial European mammals, based upon the average rate with which two highly invasive mammal species, the grey squirrel and the muskrat, colonized large parts of Europe. It is therefore highly unlikely that small bodied species will be able to colonize all patches that become suitable to their needs according to our full dispersal ability scenario. It would be highly worthwhile to obtain accurate species specific estimates of colonization rates that could be incorporated in future range maps of species; this would increase the value of future species distribution scenarios beyond our full dispersal and no dispersal scenarios. Species that have no or hardly any overlap between their current and their predicted future realized niches, and that are poor dispersers or habitat specialists, like the Siberian flying squirrel, are particularly vulnerable to future climate change, risking local extinction in sub(arctic) Europe. The IUCN currently states that the Siberian flying squirrel continuously declines in many parts of its range, owing to loss of old-growth mixed forests. Other anthropogenic factors that further affect species dispersal directly, such as hunting, poaching and road mortality, or indirectly, such as increased habitat fragmentation caused by forestry, industrialization and other socioeconomic development, are thus likely to pose an additional threat to the success of species to trace their climatic envelopes. Besides that, we did not study how climate change might affect species’ ranges to the south of our study region. Species which are predicted to expand their range in our study region might not necessarily experience an increase in total range size when their entire world distribution is regarded. It is not surprising that we predict that most species in the (sub)arctics that contract their ranges in the future are confined to alpine conditions. These species are associated with conditions that are increasingly disappearing under the pressure of climate change, and we thus expect them to experience increasing habitat loss and fragmentation. Nonetheless, we did not predict any species to go extinct; even increasing the severity of climate change only led to the predicted extinction of one species although one other would not be able to colonize and several others would lose over 90% of their current range. Although these latter models are undoubtedly over-simplified, they do suggest that the severity of climate change needs to be large before species go extinct due to climate change per se. The reason for the predicted tolerance of distribution ranges of mammal species in (sub)arctic Europe to new climatic conditions may be that large climatic swings in the past [6] have already filtered out taxa with narrow climatic tolerance [27], or prevented such species from evolving. In accordance with this, arctic regions harbour few range-restricted mammal species [28]. Our predictions do not account for the increased pressure from other species due to expansions or shifts in species ranges. Although many arctic species are capable of coping with direct effects of climate change such as increased temperature and UV-B radiation, the impact of indirect effects, such as increased competition and predation is likely going to be stronger in many cases and should therefore not be underestimated [29]. Although most species already coexist with a number of predators and competitors to the south of the study region, which might shed some light on how future potential impacts might be [24], [30], new communities are likely to form due to expanding geographic ranges of species and colonization of newcomers, increasing the abundance of certain species and lowering that of others. Therefore, several species will have to cope with more predator or competitor species in addition to environmental changes. As the abundance and distribution of species have a tendency to be linked, where widespread species tend to be more abundant [31], a species like the mountain hare may receive more competition from the European hare, with negative consequences for the first [32]. We further predict that species like the tundra vole and the European roe deer may experience increased predation in parts of their future range. However, positive indirect effects of climate change for these species, such as increased distributions of prey species and increased competition between predators might lessen these effects to some extent. Especially top-predators may have a beneficial impact on prey-populations due to their limiting effect on smaller predators [33]. In addition, species turnover will likely have socioeconomic consequences as also (domesticated) reindeer (Rangifer tarandus) may suffer from increased predation by the grey wolf and the brown bear [34]. However, both the distribution and abundance of the grey wolf and the brown bear have since long been influenced by humans [35], [36], a situation which is likely to continue in the future. The projected increase in geographic overlap between predators and prey in the future suggests that further studies are needed to predict community-level effects of climate change [37]. Especially since climate change can lead to different outcomes of altered species interactions, species may become rare or highly abundant [38], and the importance of biotic-interactions in predicting species’ future ranges has already been shown [37], [39]. Such studies should involve mechanistic modelling of species interactions, observations of interactions in different climatic settings, or experiments. We conclude that large magnitudes of climate change do not necessarily equate to substantial loss of species, provided that dispersal ability is not hampered, but suggest that changes in species interactions, limitations to successful colonization and human impacts related to climate change may threaten species, even when areas are predicted to still be largely suitable to their environmental needs under new climatic conditions. Our study has clear implications regarding the necessity to include future climate change and concurrent changes in community composition in conservation planning. Current protected areas may not provide species with their future requirements [40]. Although none of the species assessed is predicted to go regionally extinct based upon our models, we provide evidence that the vulnerability of already threatened species may increase due to the introduction of new competing/predatory species in their geographic range. We also stress the importance of habitat connectivity and of the existence of sufficient and appropriate corridors to allow dispersal between suitable habitats for the future persistence of various species. The results are likely applicable to other regions as well, particularly to other polar and alpine regions.

Acknowledgments We thank Terry Chapin and an anonymous reviewer for their useful suggestions on the manuscript.

Author Contributions Conceived and designed the experiments: ARH RJ CN. Performed the experiments: ARH. Analyzed the data: ARH. Contributed reagents/materials/analysis tools: ARH RJ CN. Wrote the paper: ARH RJ CN.