Thursday, August 5, 2010 - 1:50 PM

OOS 44-2: Predicting the present from the past: Modeling plant species distributions over 75 years of measured climate change in California, USA

Solomon Dobrowski1, James H. Thorne Sr.2, Jonathan Greenberg3, Hugh D. Safford4, Alison R. Mynsberge1, and Shawn M. Crimmins1. (1) University of Montana, (2) University of California, Davis, (3) Center for Spatial Technologies and Remote Sensing, (4) USDA-Forest Service, Pacific Southwest Region; University of California-Davis, Department of Environmental Science and Policy


Species distribution model (SDM) forecasts under future climate scenarios are alarming in that they predict range shifts and increased extinction risk for hundreds of species. These projections are largely untested because they lack temporally independent data for validation, thus raising two questions: 1) Are SDM projections transferable in time? and 2) Does temporal transferability relate to species ecological traits? To address these questions we developed SDMs for 133 vascular plant species using data from the mountain ranges of California from two time periods; the 1930s and the present-day. We forecast historical models over 75 years of measured climate change and assessed their projections against current distributions. Similarly, we hindcast contemporary models and compared their projections to historical data. We characterized temporal transferability and related it to species ecological traits including range size, physiognomy, endemism, dispersal distance, fire adaptation, and commonness.


We demonstrate that variability in model performance was driven predominantly by differences between species as compared to model algorithm or time period. The transferability of our forecasts and hindcasts were related to endemism, dispersal strategy, fire adaptation, and the prevalence of the species being modeled. Further, the traits that make a species amenable to making predictions in a single time period may not be useful for achieving transferability between time periods. Our findings provide a priori guidance of the suitability of SDM as an approach for forecasting climate change responses for certain taxa.