3.2.2. Background points

Elith et al. (2020) stated that the 10,000 randomly distributed background points across each region in the original NCEAS dataset, which is the default number in the Maxent software, might be insufficient for some of the regions. Consequently, previous studies used 50,000 randomly distributed background points for each region (Valavi et al., 2022). The issue of optimal sampling size and distribution of background points remains a major challenge in SDM which will not be discussed in this present study. As our study focuses on a comparison between modeling approaches and not on calculating context‐specific ecologically meaningful SDMs, we argue that a comparison is justified as long as modeling conditions are held constant between different approaches. Thus, we also used 10,000 background points (default setting and recommended by Merow et al., 2013) for each region in the NCEAS dataset but did not sample them randomly over the entire study area. Instead, we used conditioned Latin hypercube sampling (Minasny & McBratney, 2006) as implemented in the R package “clhs” (version 0.9.0; Roudier, 2011) to distribute the background points over the study area whereby all variables of the environmental data were represented as well as possible. Background points within the same pixel as the environmental layers as presence records were removed.