Continental‐Scale Biodiversity Predictions Are Influenced by Climatic Variability and Extreme Weather

ABSTRACT

Increasingly variable and extreme weather can push organisms beyond their physiological thermal or hydric tolerances and limit where they can persist, affecting their geographic distributions and biodiversity patterns. For example, organisms often live near their physiological limits at range edges and may be unable to persist in these regions under variable and extreme conditions. However, it remains unclear how climatic variability and extreme weather define continental‐scale species distributions and biodiversity patterns. Here we link hundreds of millions of citizen science bird observations from 2004 to 2024 to high‐resolution maps of within‐year climatic variability and extreme weather risk, exploring how these factors define both summer and winter distributions and biodiversity patterns for 535 North American species across 2901 individual models. We find that species distribution models accounting for climatic variability or extreme weather risk performed better than others at predicting richness and individual species presences across 220 well‐surveyed sites. Such models predicted narrower geographic distributions than models relying on only climatic means, especially at the range edges, resulting in species' ranges that were truncated on average by 6% in summer and 10% in winter when climatic variability and extreme weather variables were included. These effects were observed in both seasons but were particularly strong in winter, a time with greater short‐term weather variability than summer. Richness estimates were substantially lower when climatic variability or extreme weather were accounted for, especially in the US southwest and central plains (up to 35 fewer species), regions highly prone to extreme heat, cold, and drought. Our results suggest that more mechanistically informed biodiversity predictions that account for climatic variability and extreme weather are critical for reliably predicting distributional and biodiversity patterns.