Incorporating plant phenological responses into species distribution models reduces estimates of future species loss and turnover

• Published in: The New phytologist Vol. 242; no. 5; pp. 2338 - 2352
• Main Authors: Peng, Shijia, Ramirez‐Parada, Tadeo H., Mazer, Susan J., Record, Sydne, Park, Isaac, Ellison, Aaron M., Davis, Charles C.
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 01-06-2024
• Subjects: biodiversity; Climate change; Demography; Ecological distribution; Ecological niches; Environmental gradient; Geographical distribution; Hindcasting; macroecology; Mathematical models; Niches; Phenology; Plant species; Species; species distribution models; species loss; species turnover; trait evolution; biodiversity; species turnover; species distribution models; phenology; species loss; trait evolution; macroecology; climate change
• ISSN: 0028-646X; 1469-8137
• DOI: 10.1111/nph.19698

Summary
Anthropogenetic climate change has caused range shifts among many species. Species distribution models (SDMs) are used to predict how species ranges may change in the future. However, most SDMs rarely consider how climate‐sensitive traits, such as phenology, which affect individuals' demography and fitness, may influence species' ranges.
Using > 120 000 herbarium specimens representing 360 plant species distributed across the eastern United States, we developed a novel ‘phenology‐informed’ SDM that integrates phenological responses to changing climates. We compared the ranges of each species forecast by the phenology‐informed SDM with those from conventional SDMs. We further validated the modeling approach using hindcasting.
When examining the range changes of all species, our phenology‐informed SDMs forecast less species loss and turnover under climate change than conventional SDMs. These results suggest that dynamic phenological responses of species may help them adjust their ecological niches and persist in their habitats as the climate changes.
Plant phenology can modulate species' responses to climate change, mitigating its negative effects on species persistence. Further application of our framework will contribute to a generalized understanding of how traits affect species distributions along environmental gradients and facilitate the use of trait‐based SDMs across spatial and taxonomic scales.