Evidence map›Paper›PMID 41400311›Full record

ReviewEcology letters2025

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

Jennifer L Williams, Amy L Angert, Aldo Compagnoni, Ali Campbell, Megan L DeMarche, Margaret E K Evans, Joshua C Fowler, Edgar J González, Amy M Iler, Jenna A Loesberg and 8 more

Abstract readReview
In one paragraph

Review in Ecology letters, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors.

Jennifer L WilliamsDepartment of Geography, University of British Columbia, Vancouver, British Columbia, Canada.ORCID https://orcid.org/0000-0002-4497-4961
Amy L AngertDepartment of Botany, University of British Columbia, Vancouver, British Columbia, Canada.
Aldo CompagnoniGerman Centre for Integrative Biodiversity Research (iDiv), Martin Luther University Halle-Wittenberg, Leipzig, Germany.ORCID https://orcid.org/0000-0001-8302-7492
Ali CampbellDepartment of BioSciences, Program in Ecology and Evolutionary Biology, Rice University, Houston, Texas, USA.
Megan L DeMarcheDepartment of Plant Biology, University of Georgia, Athens, Georgia, USA.ORCID https://orcid.org/0000-0002-5010-2721
Margaret E K EvansLaboratory of Tree-Ring Research, University of Arizona, Tucson, Arizona, USA.ORCID https://orcid.org/0000-0003-3220-3382
Joshua C FowlerDepartment of Environmental Studies, University of Colorado, Boulder, Colorado, USA.
Edgar J GonzálezDepartamento de Ecología y Recursos Naturales, Facultad de Ciencias, Universidad Nacional Autónoma de México, Ciudad Universitaria, Mexico City, Mexico.ORCID https://orcid.org/0000-0001-9113-1070
Amy M IlerNegaunee Institute of Plant Conservation Science and Action, Chicago Botanic Garden, Glencoe, Illinois, USA.ORCID https://orcid.org/0000-0002-3354-7593
Jenna A LoesbergDepartment of Geography, University of British Columbia, Vancouver, British Columbia, Canada.
Allison M LouthanDivision of Biology, Kansas State University, Manhattan, Kansas, USA.ORCID https://orcid.org/0000-0002-2735-6539
Alexandra B MartinDepartment of BioSciences, Program in Ecology and Evolutionary Biology, Rice University, Houston, Texas, USA.
Jacob K MoutouamaDepartment of Botany, University of British Columbia, Vancouver, British Columbia, Canada.ORCID https://orcid.org/0000-0003-1599-1671
Scott W NordstromPopulation Research Center, Portland State University, Portland, Oregon, USA.
William K PetryDepartment of Plant & Microbial Biology, North Carolina State University, Raleigh, North Carolina, USA.ORCID https://orcid.org/0000-0002-5230-5987
Bilgecan ŞenAppalachian Laboratory, University of Maryland Center for Environmental Science, Frostburg, Maryland, USA.
Seema N ShethDepartment of Plant & Microbial Biology, North Carolina State University, Raleigh, North Carolina, USA.ORCID https://orcid.org/0000-0001-8284-7608
Tom E X MillerDepartment of BioSciences, Program in Ecology and Evolutionary Biology, Rice University, Houston, Texas, USA.ORCID https://orcid.org/0000-0003-3208-6067

Funding

National Science Foundation, Division of Environmental Biology DEB-1655499National Science Foundation, Division of Environmental Biology DEB-1748133National Science Foundation, Division of Environmental Biology DEB-2208857National Science Foundation, Division of Environmental Biology DEB-2225027National Science Foundation, Division of Environmental Biology DEB-2311414National Science Foundation, Division of Environmental Biology DEB-2335906National Science Foundation, Division of Environmental Biology DEB-2413626Natural Sciences and Engineering Research Council of Canada, Discovery Grant to JLWRice University Creative Ventures FundU.S. Department of Agriculture's National Institute of Food and Agriculture Research Capacity Fund (HATCH) 7002993U.S. Department of Agriculture's National Institute of Food and Agriculture Research Capacity Fund (HATCH) 7004646
6 · The paper itself

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.

Indexed as

ClimateClimate ChangeModels, BiologicalAnimalsBiodiversityDemographyForecastingPopulation Dynamicsclimate changedemographic modelecological forecastingintegral projection modelmatrix modelphysiologypopulation growthstochastic environment

Identifiers

PMID41400311
PMCPMC12707181

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.