Exploring Environmental Coverages of Species: A New Variable Selection Methodology for Rulesets from the Genetic Algorithm for Ruleset Prediction

Variable selection for, and determination of variable importance within, species distribution models (SDMs) remain an important area of research with continuing challenges. Most SDM algorithms provide normally exhaustive searches through variable space, however, selecting variables to include in models is a first challenge. The estimation of the explanatory power of variables and the selection of the most appropriate variable set within models can be a second challenge. Although some SDMs incorporate the variable selection rubric inside the algorithms, there is no integrated rubric to evaluate the variable importance in the Genetic Algorithm for Ruleset Production (GARP). Here, we designed a novel variable selection methodology based on the rulesets generated from a GARP experiment. The importance of the variables in a GARP experiment can be estimated based on the consideration of the prevalence of each environmental variable in the dominant presence rules of the best subset of models and its coverage. We tested the performance of this variable selection method based on simulated species with both weak and strong responses to simulated environmental covariates. The variable selection method generally performed well during the simulations with over 2/3 of the trials correctly identifying most covariates. We then predict the distribution of Bacillus anthracis (the bacterium that causes anthrax) in the continental United States (US) and apply our variable selection procedure as a real-world example. We found that the distribution of B. anthracis was primarily determined by organic content, soil pH, calcic vertisols, vegetation, sand fraction, elevation, and seasonality in temperature and moisture.