COS 307-6 - Biotic predictors improve species distribution models for invasive plants in Western U.S. forests at high but not low spatial resolutions

Abstract: Invasions by non-native plants threaten forest health and sustainability. The ability to predict areas susceptible to invasion is essential for monitoring and management of invasive species. Species distribution models (SDMs) are often used to identify environmental correlates of a species’ occurrence and predict geographic areas that may be suitable for its presence and are commonly constructed using solely abiotic predictors. However, mounting evidence suggests that not including biotic predictors in SDMs may yield less accurate results at some resolutions typical of landscape-scale models, although this possibility has rarely been evaluated in invasive plants. In this study, we determined whether including descriptors of the biotic environment improved the accuracy of SDMs built at five decreasing spatial resolutions for five common invasive plants in forests of California, Oregon, and Washington, USA and described environmental correlates of each species’ presence.

Predictors of habitat suitability often echoed those identified in previous studies, indicating that our models accurately identified important environmental contributors to occurrence of the focal species. Including biotic predictors in the SDMs consistently improved model accuracy only at the highest resolution examined, which may be due to the spatial scale at which biotic interactions primarily act upon species’ distributions, the particular predictors we used in our models, or correlations between attributes of the abiotic and biotic environment. Our findings suggest that building SDMs using solely abiotic predictors may generally yield models whose accuracy does not differ substantially from those that include biotic predictors, the effects of biotic interactions on the distribution of invasive plants in forests may be detectable at larger scales than previously recognized. This finding supports monitoring and management strategies for invasive species by highlighting potentially important biotic predictors of their occurrence and improving models to identify areas at risk to invasion.