Species Distribution and Climate Change

Species distribution modeling uses statistical and machine learning methods—most notably MaxEnt—to predict where a species can survive based on environmental conditions that define its ecological niche, then projects those predictions forward under climate change scenarios to estimate range shifts and habitat loss. The work sits at the intersection of ecology, climatology, and conservation biology, and its practical stakes are high: understanding which species will lose suitable habitat, and where, is foundational to setting protection priorities and managing biodiversity under a warming climate. Active research is wrestling with how to make these models more reliable—accounting for biases in observation data, including citizen science records, and improving evaluation methods that can distinguish genuine predictive skill from overfitting. A central open question is how to incorporate species interactions, dispersal limits, and evolutionary adaptation into frameworks that have historically treated habitat suitability as a static snapshot rather than a dynamic process.

Works

1,388,649

Total citations

2,508,562

Keywords

Species Distribution ModelingClimate ChangeEcological NicheMaxEntBiodiversityCitizen Science