Tokyo Metropolitan University

Predicting where a species occurs from presence records alone has long relied on models such as MaxEnt that must be tuned for each dataset. On a standard benchmark of 225 species in six regions, TabPFN 3.5 reached a mean AUC of 0.732 without tuning, matching the best published five-model ensemble; fine-tuned on presence–background tasks from other regions it rose to 0.736, the best single model on the benchmark and significantly above MaxEnt's 0.728. The authors caution that six regions cannot establish the advantage in new regions, and that the model is sensitive to environmentally biased records.

Predicting where a species occurs from presence records alone has long relied on models such as MaxEnt that must be tuned for each dataset. On a standard benchmark of 225 species in six regions, TabPFN 3.5 reached a mean AUC of 0.732 without tuning, matching the best published five-model ensemble; fine-tuned on presence–background tasks from other regions it rose to 0.736, the best single model on the benchmark and significantly above MaxEnt's 0.728. The authors caution that six regions cannot establish the advantage in new regions, and that the model is sensitive to environmentally biased records.