Modeling algorithm influence on the success of predicting new populations of rare species: ground-truthing models for the Pale-Belly Frost Lichen (Physconia subpallida) in Ontario

Conservation of rare species relies on a thorough knowledge of distributions and the locations of populations. Field surveys for new populations of rare species are time consuming and resource intensive. Thus, it is essential to conduct searches as efficiently as possible. Model-based sampling, where species distribution models are used to select sites with high probability of the species presence for surveys, can drastically improve the efficiency and success of field sampling. The vast array of methods to build species distribution models can make it difficult to select one approach to implement for a project. Here we directly compared two methods, Maxent and non-parametric multiplicative regression (NPMR), using the endangered lichen Physconia subpallida as the focal species. We built models using all known localities of P. subpallida in Ontario, Canada, then ground-truthed each of the models for 9 days over a 2-year period, searching only areas predicted to have the highest level of probability of species occurrence. NPMR far outperformed Maxent, with the discovery of six new populations with a total of 36 individuals compared to one new population consisting of a single individual, respectively. The disparity between the two results likely stems from the potentially over-simplified response curves from Maxent when compared to NPMR. If a complex relationship is expected between the species and environmental variables, NPMR may outperform Maxent. However, there is not a single modeling algorithm that works best in every situation, so it is essential to test multiple modeling methods for guiding rare species surveys.