Predicting small-mammal responses to climatic warming: autecology, geographic range, and the Holocene fossil record

Forecasting how species will respond to climatic change requires knowledge of past community dynamics. Here we use time-series data from the small-mammal fossil records of two caves in the Great Basin of the American West to evaluate how contrasting and variable local paleoclimates have shaped small-mammal abundance dynamics over the last 7500 years of climatic change. We then predict how species and communities will respond to future scenarios of increased warming and aridity coupled with continued spread of an invasive annual grass (Bromus tectorum). We find that most community-level responses to climatic change occur in the mammalian abundance structure at both sites; the dominance of the community by individuals from species with a southern geographic affinity increases with climatic warming. This suggests that responses occurred in situ rather than by the immigration of new taxa over this time interval. Despite predictability at the community-scale, species-level relationships between abundance and climate are variable and are not necessarily explained by a species’ geographic affinity. Species present at both sites, however, exhibit remarkably similar responses to climate at each site, indicating that species autecology (specifically dietary functional group) is important in determining response to climatic warming. Regression-tree analyses show remarkable concordance between the two cave faunas and highlight the importance of a granivorous dietary strategy in this desert ecosystem. Under projections of increased temperature and decreased precipitation over the next 50 years, our results indicate that granivores should thrive as communities become more dominated by individuals with a southern geographic affinity. Granivores, however, are negatively impacted by the invasion of cheatgrass. The last century of anthropogenic impacts has thus placed granivores at a greater risk of extinction than predicted under climate-only scenarios.