Species traits affect the performance of species distribution models for plants in southern California

Questions: To what extent do plant species traits, including life history, life form, and disturbance response characteristics, affect the degree to which species distributions are determined by physical environmental factors? Is the strength of the relationship between species distribution and environment stronger in some disturbance-response types than in others? Location: California southwest ecoregion, USA. Methods: We developed species distribution models (SDMs) for 45 plant species using three primary modeling methods (GLMs, GAMs, and Random Forests). Using AUC as a performance measure of prediction accuracy, and measure of the strength of species–environment correlations, we used regression analyses to compare the effects of fire disturbance response type, longevity, dispersal mechanism, range size, cover, species prevalence, and model type. Results: Fire disturbance response type explained more variation in model performance than any other variable, but other species and range characteristics were also significant. Differences in prediction accuracy reflected variation in species life history, disturbance response, and rarity. AUC was significantly higher for longer-lived species, found at intermediate levels of abundance, and smaller range sizes. Models performed better for shrubs than subshrubs and perennial herbs. The disturbance response type with the highest SDM accuracy was obligate-seeding shrubs with ballistic dispersal that regenerate via fire-cued germination from a dormant seed bank. Conclusions: The effect of species characteristics on predictability of species distributions overrides any differences in modeling technique. Prediction accuracy may be related to how a suite of species characteristics co-varies along environmental gradients. Including disturbance response was important because SDMs predict the realized niche. Classification of plant species into disturbance response types may provide a strong framework for evaluating performance of SDMs.