Novel methods to select environmental variables in MaxEnt: A case study using invasive crayfish

•This study identifies the need for variable selection when forming species distribution models via MaxEnt.•Two potential methods are proposed—one involves selecting from a priori determined environmental variable sets, while the other utilizes a reiterative process of model formation and stepwise removal of least contributing variables.•Both methods were tested on eight known species of invasive crayfish, with results reinforcing the need for species-specific environmental variable sets.

The popularity of MaxEnt in species distribution modeling has been driven by several factors including its high degree of accuracy, and flexibility to tailor efforts to species-specific situations. Although many recent studies have identified the importance of adjusting mathematical transformation (feature class) and regularization of coefficient values, collectively known as tuning, few studies have addressed the need to customize the variables used in species distribution modeling, and use unselected variable sets. This study presents two novel methods to select for environmental variables in MaxEnt. The first involves selecting from a priori determined environmental variable sets (pre-selected based on ecological or biological knowledge), and the second utilizes a reiterative process of model formation and stepwise removal of least contributing variables. Both methods were tested on eight known species of invasive crayfish, with results reinforcing the need for species-specific environmental variable sets. While the reiterative process generally performs better than the a priori selected variables, selection of method can be based on information availability. These techniques appear to outperform the current practice of utilizing unselected variable sets and is especially important considering the increasing application of species distribution modeling (across spatial and temporal barriers) in conservation and management efforts whereby inaccurate predictions might have adverse effects.