Soil chemical variables improve models of understorey plant species distributions

Abstract

Aim: Species distribution models (SDMs) assume that all ecologically relevant predictor variables are included. This assumption is frequently violated in SDMs of plant species, as soil variables are rarely included. Here, we used in-situ, soil geochemical variables along with more commonly used topo-climatic and remotely sensed variables to create SDMs of understorey plant species. We evaluated whether the potential importance of soil variables is greater than generally described in SDM literature, which predictors are most important, and whether a standard subset of predictors can be used to effectively model all species. Location: Northern Appalachian Mountains. Taxon: Vascular, forest understorey plants. Methods: We fit models for the presence of 41 forest understorey plant species across 158 plots using soil, topographic and spectral predictors. Results: Models containing all three predictor types performed best. Soil and topographic variables had comparable importance; spectral variables were of lesser importance. The best predictor variable was B horizon carbon to nitrogen ratio (B C:N), followed by topographic position index, elevation and B horizon exchangeable calcium (B Ca). A standard subset of variables did not effectively model all species. Main conclusions: Our results and those of other SDMs that include in-situ soil geochemical data suggest that soil variables are increasingly important with more detailed descriptions of soils. Soil fertility data, such as B C:N and B Ca, are particularly important in acidic, forest soils where pH is a poor indicator of fertility. Commonly used topo-climatic variables provide meaningful predictions but are limited by their use of indirect predictor variables, inhibiting transferability and interpretability. The poor performance of a standard subset of variables highlights the uniqueness of each species' niche and the need to have a variety of predictor variables.