The future of terrestrial mammals in the Mediterranean basin under climate change

The Mediterranean basin is considered a hotspot of biological diversity with a long history of modification of natural ecosystems by human activities, and is one of the regions that will face extensive changes in climate. For 181 terrestrial mammals (68% of all Mediterranean mammals), we used an ensemble forecasting approach to model the future (approx. 2100) potential distribution under climate change considering five climate change model outputs for two climate scenarios. Overall, a substantial number of Mediterranean mammals will be severely threatened by future climate change, particularly endemic species. Moreover, we found important changes in potential species richness owing to climate change, with some areas (e.g. montane region in central Italy) gaining species, while most of the region will be losing species (mainly Spain and North Africa). Existing protected areas (PAs) will probably be strongly influenced by climate change, with most PAs in Africa, the Middle East and Spain losing a substantial number of species, and those PAs gaining species (e.g. central Italy and southern France) will experience a substantial shift in species composition.     

Changes in species interactions across a 2.5 km elevation gradient: effects on plant migration in response to climate change

Predicted climate change in the Andes will require plant species to migrate upslope to avoid extinction. Central to predictions of species responses to climate change is an understanding of species distributions along environmental gradients. Environmental gradients are frequently modelled as abiotic, but biotic interactions can play important roles in setting species distributions, abundances, and life history traits. Biotic interactions also have the potential to influence species responses to climate change, yet they remain mostly unquantified. An important interaction long studied in tropical forests is postdispersal seed predation which has been shown to affect the population dynamics, community structure, and diversity of plant species in time and space. This paper presents a comparative seed predation study of 24 species of tropical trees across a 2.5 km elevation gradient in the Peruvian Andes and quantifies seed predation variation across the elevational gradient. We then use demographic modelling to assess effects of the observed variation in seed predation on population growth rates in response to observed increasing temperatures in the area. We found marked variation among species in total seed predation depending on the major seed predator of the species and consistent changes in seed predation across the gradient. There was a significant increase in seed survival with increasing elevation, a trend that appears to be driven by regulation of seed predators via top–down forces in the lowlands giving way to bottom–up (productivity) regulation at mid‐ to high elevations, resulting in a ninefold increase in effective fecundity for trees at high elevations. This potential increase in seed crop size strongly affects modelled plant population growth and seed dispersal distances, increasing population migration potential in the face of climate change. These results also indicate that species interactions can have effects on par with climate in species responses to global change.     

Climate change at the landscape scale: predicting fine-grained spatial heterogeneity in warming and potential refugia for vegetation

Current predictions of how species will respond to climate change are based on coarse‐grained climate surfaces or idealized scenarios of uniform warming. These predictions may erroneously estimate the risk of extinction because they neglect to consider spatially heterogenous warming at the landscape scale or identify refugia where species can persist despite unfavourable regional climate. To address this issue, we investigated the heterogeneity in warming that has occurred in a 10 km × 10 km area from 1972 to 2007. We developed estimates by combining long‐term daily observations from a limited number of weather stations with a more spatially comprehensive dataset (40 sites) obtained during 2005–2006. We found that the spatial distribution of warming was greater inland, at lower elevations, away from streams, and at sites exposed to the northwest (NW). These differences corresponded with changes in weather patterns, such as an increasing frequency of hot, dry NW winds. As plant species were biased in the topographic and geographic locations they occupied, these differences meant that some species experienced more warming than others, and are at greater risk from climate change. This species bias could not be detected at coarser scales. The uneven seasonal nature of warming (e.g. more warming in winter, minimums increased more than maximums) means that climate change predictions will vary according to which predictors are selected in species distribution models. Models based on a limited set of predictors will produce erroneous predictions when the correct limiting factor is not selected, and this is difficult to avoid when temperature predictors are correlated because they are produced using elevation‐sensitive interpolations. The results reinforce the importance of downscaling coarse‐grained (∼50 km) temperature surfaces, and suggest that the accuracy of this process could be improved by considering regional weather patterns (wind speed, direction, humidity) and topographic exposure to key wind directions.     

The role of climate,habitat, and species co‐occurrence as drivers of change in small mammal distributions over the past century

Species distribution models are commonly used to predict species responses to climate change. However, their usefulness in conservation planning and policy is controversial because they are difficult to validate across time and space. Here we capitalize on small mammal surveys repeated over a century in Yosemite National Park, USA, to assess accuracy of model predictions. Historical (1900–1940) climate, vegetation, and species occurrence data were used to develop single‐ and multi‐species multivariate adaptive regression spline distribution models for three species of chipmunk. Models were projected onto the current (1980–2007) environmental surface and then tested against modern field resurveys of each species. We evaluated models both within and between time periods and found that even with the inclusion of biotic predictors, climate alone is the dominant predictor explaining the distribution of the study species within a time period. However, climate was not consistently an adequate predictor of the distributional change observed in all three species across time. For two of the three species, climate alone or climate and vegetation models showed good predictive performance across time. The stability of the distribution from the past to present observed in the third species, however, was not predicted by our modeling approach. Our results demonstrate that correlative distribution models are useful in understanding species' potential responses to environmental change, but also show how changes in species‐environment correlations through time can limit the predictive performance of models.