Variability in 20th Century climate change reconstructions and its consequences for predicting geographic responses of California mammals.

Juan L. Parra* & William B. Monahan

Museum of Vertebrate Zoology, 3101 Valley Life Sciences Building, University of California, Berkeley, California, 94720-3160, USA

*author for correspondence juanp@berkeley.edu, 510-642 7782 (ph), 510-643 8238 (fax).

Abstract

Empirical species distribution models are used widely to predict the effects of climate change on biodiversity distribution but rely on multiple assumptions about the certainty of the locality and climate data. Here we consider the potential effects of two climate reconstructions when forecasting geographic responses of California mammals to 20th Century climate change. We use two methods to derive climate surfaces from weather station data (ANUSPLIN and PRISM) representing two sampling eras: historic (1900-1940) and current (1980-2005). We then use a maximum entropy algorithm (MAXENT) to develop distribution models with the historic data and project these models onto the historic and current climate surfaces. Results indicate that levels of disagreement between the two climate datasets are considerably greater in the historical era than in the current era. Disagreement in the bioclimatic variables used for modeling species distributions is variable and more pronounced in precipitation than temperature variables. These discrepancies are reflected in the low agreement between historic geographic range predictions and further propagated when these models are projected to present day. Nonetheless, some common patterns across species and climate estimates are evident. Stability of geographic ranges is the most common prediction during this period, followed by expansion and contraction. Regions with relatively high levels of range stability include parts of the Great Central Valley and Sierra Nevada, while other parts of the Central Valley, the Sonoran desert, and Central- and Southwestern California yield predictions of range shifts. Results stress the importance of incorporating alternative estimates of climate surfaces when modeling species’ distributional responses to climate change. Further research is required to explicitly incorporate alternative climate surfaces and their uncertainty in distribution models.