Linking Climate and Demography to Predict Population Dynamics and Persistence Under Global Change

Predicting the effects of climate change on plant and animal populations is an urgent challenge for understanding the fate of biodiversity under global change. At the surface, quantifying how climate drives the vital rates that underlie population dynamics appears simple, yet many decisions are required to connect climate to demographic data. Competing approaches have emerged in the literature with little consensus around best practices. Here we provide a practical guide for how to best link vital rates to climate for the purposes of inference and projection of population dynamics. We first describe the sources of demographic and climate data underlying population models. We then focus on best practices to model the relationships between vital rates and climate, highlighting what we can learn from mechanistic and phenomenological models. Finally, we discuss the challenges of prediction and forecasting in the face of uncertainty about climate-demographic relationships as well as future climate. We conclude by suggesting ways forward to build this field of research into one that makes robust forecasts of population persistence, with opportunities for synthesis across species.

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Publication Details

Journal
Ecology Letters
Published
2025-12-01
DOI
https://doi.org/10.1111/ele.70283
Primary Topic
Species Distribution and Climate Change
Type
article
Field-Weighted Citation Impact
0.00

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article

Linking Climate and Demography to Predict Population Dynamics and Persistence Under Global Change

Seema N. Sheth, Alexandra Martin, Tom E. X. Miller, Jacob K. Moutouama et al.
Ecology Letters
Species Distribution and Climate Change
article

Linking Climate and Demography to Predict Population Dynamics and Persistence Under Global Change

Seema N. Sheth, Alexandra Martin, Tom E. X. Miller, Jacob K. Moutouama, Aldo Compagnoni, Jennifer L. Williams, Megan L. DeMarche, Joshua C. Fowler, William K. Petry, Allison M. Louthan, Amy M. Iler, Edgar J. González, Amy L. Angert, Bilgecan Şen, Scott W. Nordstrom, Margaret E. K. Evans, Jenna A. Loesberg, Ali Campbell
article en

Abstract

Predicting the effects of climate change on plant and animal populations is an urgent challenge for understanding the fate of biodiversity under global change. At the surface, quantifying how climate drives the vital rates that underlie population dynamics appears simple, yet many decisions are required to connect climate to demographic data. Competing approaches have emerged in the literature with little consensus around best practices. Here we provide a practical guide for how to best link vital rates to climate for the purposes of inference and projection of population dynamics. We first describe the sources of demographic and climate data underlying population models. We then focus on best practices to model the relationships between vital rates and climate, highlighting what we can learn from mechanistic and phenomenological models. Finally, we discuss the challenges of prediction and forecasting in the face of uncertainty about climate-demographic relationships as well as future climate. We conclude by suggesting ways forward to build this field of research into one that makes robust forecasts of population persistence, with opportunities for synthesis across species.

Ecology LettersVol. 28(12)
Portland State University (US), North Carolina State University (US), University of Arizona (US), University of British Columbia (CA), University of Maryland Center for Environmental Science (US), University of Georgia (US), University of Colorado Boulder (US), Kansas State University (US), Center for Plant Conservation (US), German Centre for Integrative Biodiversity Research (DE), Rice University (US), Universidad Nacional Autónoma de México (MX)
National Science Foundation, U.S. Department of Agriculture, Rice University, National Institute of Food and Agriculture, Natural Sciences and Engineering Research Council of Canada
Openalex Percentile: Top 78%
Species Distribution and Climate Change
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