Predictive performance of presence‐only species distribution models: a benchmark study with reproducible code

Abstract Species distribution modeling (SDM) is widely used in ecology and conservation. We find that, in general, nonparametric techniques with the capability of controlling for model complexity outperformed traditional regression methods, with MaxEnt and boosted regression trees still among the top performing models.

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• 1 Currently, the most available data for SDM are species presence‐only records (available through digital databases).
• 2 There have been many studies comparing the performance of alternative algorithms for modeling presence‐only data.
• 3 Among these, a 2006 paper from Elith and colleagues has been particularly influential in the field, partly because they used several novel methods (at the time) on a global data set that included independent presence–absence records for model evaluation.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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