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arXiv Statistics ML By Emilia Arens, Nina van Tiel, Robin Zbinden, Damien Robert, Lukas Drees, Chiara Vanalli, Benjamin Kellenberger, Niklaus E. Zimmermann, Lo\"ic Pellissier, Devis Tuia, Jan Dirk Wegner

SAGE: A sampling-aware global evaluation benchmark for species distribution modeling

Read the original on arXiv Statistics ML →

The paper introduces SAGE, a Sampling‑Aware Global Evaluation benchmark for species distribution modeling that uses GBIF records for training and sPlotOpen vegetation plots for presence‑absence evaluation across 5,771 plant species. It groups species by sampling effort and relative prevalence to assess how well single‑species and multi‑species deep‑learning SDMs perform under different data conditions. The study finds that Random Forests and DeepSDMs perform best overall, with DeepSDMs excelling for infrequently recorded species only when bias‑correction techniques are applied.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Statistics ML.

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