Employing inferences across scales: Integrating spatial data with different resolutions to enhance Maxent models

Abstract

Challenges associated with developing species distribution models (SDMs) with high-resolution data (including from lidar) prompted our investigation into a complementary approach to enhance the performance of SDMs using spatial data with different resolutions. In our experiment we developed a model with Maxent (a presence-background SDM) with variables that had a 30-m resolution, and then used the output of the model to restrict the background sampling area for models developed with variables that had a 10-m resolution. According to common measures of model quality, this approach produced better models than both a model developed with the default Maxent background sampling area and a model developed using the conventional approach of resampling environmental data to a common spatial resolution. We then reviewed the ecological meaning of this approach and observed how model mechanics were impacted as restricting the background sampling areas led to background points that had a greater contrast with the presence points, and therefore different environmental characteristics than background points sampled from the default background sampling area.