New SAGE benchmark evaluates AI models for species distribution accuracy

Researchers have introduced SAGE, a new benchmark designed to evaluate species distribution models (SDMs) by accounting for sampling biases and species prevalence. The benchmark utilizes data from GBIF and sPlotOpen, combining community science records with vegetation plot data to assess model performance across 5771 plant species. Initial findings indicate that while Random Forests and deep-learning-based SDMs (DeepSDMs) perform well overall, DeepSDMs show an advantage primarily for infrequently recorded species, especially when bias-correction techniques are applied. AI

IMPACT This benchmark could improve the reliability and ecological credibility of AI models used in biodiversity research and conservation.

RANK_REASON The cluster describes a new benchmark for evaluating species distribution models, which is a research contribution. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

• DeepSDMs
• Global Biodiversity Information Facility
• random forest
• Sage

• paper
• other

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