Selection of predictor variables for species distribution models: a case study with an invasive marine bryozoan.

Species distribution models (SDMs) are important tools for predicting the occurrence and abundance of organisms in space and time, with numerous applications in ecology. However, the accuracy and utility of SDMs can be compromised when predictor variables are selected without careful consideration of their ecophysiological relevance to the focal organism. We conducted an in-depth examination of the variable selection process by evaluating predictors to be used in SDMs for Membranipora membranacea, an ecologically significant marine invasive species with a complex lifecycle, as a case study. Using an information-theoretic and multi-model inference approach based on generalized linear mixed models, we assessed multiple environmental variables (depth, kelp density, kelp substrate, temperature, and wave exposure) as predictors of the abundance of multiple life stages of M. membranacea, investigating species-environment relationships and relative and absolute variable importance. We found that the relative importance of a predictor, the metric calculated to represent a predictor, and whether a predictor was proximal or distal were important considerations in the variable selection process. Data constraints (e.g. sample size, characteristics of available predictor data) may inhibit accurate assessment of predictor variables during variable selection. Importantly, our results suggest that species-environment relationships derived from small-scale studies can inform variable selection for SDMs at larger spatiotemporal scales. We developed a conceptual framework for variable selection for SDMs which can be applied to most contexts of species distribution modelling, but particularly those with several candidate predictors and a large dataset.