Performance of alternative methods for generating species distribution models for invasive species in the Laurentian Great Lakes

ABSTRACT The Risk Assessment Mapping Program (RAMP) is a user-friendly tool that uses climate data and known occurrences of nonnative species to predict where the species may be able to survive. We compared the performance of RAMP and two machine learning methods, boosted regression trees and maximum entropy, at estimating distributions of 30 aquatic species that are nonnative to the Laurentian Great Lakes Basin. For each species and method, we created models and tested them against subsets of known occurrences to calculate true skill statistics (TSS). This measure ranges between –1 (no better than random) and 1 (perfect assessment). Average TSS values were 0.81 ± 0.09 (boosted regression tree), 0.76 ± 0.12 (maximum entropy), and 0.09 ± 0.06 (RAMP). Despite having high TSS values, our machine learning models generally underestimate potential distributions across the Great Lakes Basin. The RAMP forecasts much greater areas of the basin to be climatically appropriate for each species and may therefore be more suitable for conservative management decisions.