Abstract

The distribution of tree species is largely shaped by regional variation in climate and soils. Current models make very simple assumptions about soil water availability with limited inclusion into the predicted distribution of species. Recently, methods have been developed that integrate observations from satellites on maximum leaf area index. These remote sensing estimates, when combined with physiology can provide more detailed maps of available soil water holding capacity (ASWC) and soil fertility. By allowing soil properties as well as climate to vary across western North America, our process-based decision tree models predicted the occurrence of 20 tree species with an average accuracy of 84% (κ=0.79), based on their recorded presence and absence on 43,404 field plots. Changes in productivity and distributions were assessed with varying soil water inputs. ASWC was increased and decreased by 50% from the originally mapped values to evaluate the effects on predicted species distributions. Soil water availability helped explain the variation in the distribution of 75% of the tree species. We found that 30% of the species were very to extremely sensitive to changes in ASWC, while 45% were somewhat sensitive. We conclude that knowledge of soil properties generally improves overall accuracy of species distribution models. Our sensitivity analysis identified the most sensitive species to changes in water availability, and indicated where additional information on soil properties would be most critical to verify.

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