Evidence map›Paper›PMID 39221784›Full record

ArticleThe Journal of animal ecology2025

Detecting context dependence in the expression of life history trade-offs.

Louis Bliard, Jordan S Martin, Maria Paniw, Daniel T Blumstein, Julien G A Martin, Josephine M Pemberton, Daniel H Nussey, Dylan Z Childs, Arpat Ozgul

Abstract read
In one paragraph

Article in The Journal of animal ecology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Unmeasured prior viability selection resolves the paradox of stasis for body size in wild Soay sheep.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  3. Article
  4. Article
  5. Article
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

9 authors.

Louis BliardDepartment of Evolutionary Biology and Environmental Studies, Zurich University, Zurich, Switzerland.ORCID 0000-0002-2349-8513
Jordan S MartinInstitute of Evolutionary Medicine, Zurich University, Zurich, Switzerland.ORCID 0000-0001-8704-6076
Maria PaniwDepartment of Evolutionary Biology and Environmental Studies, Zurich University, Zurich, Switzerland.ORCID 0000-0002-1949-4448
Daniel T BlumsteinDepartment of Ecology and Evolutionary Biology, University of California Los Angeles, California, Los Angeles, USA.ORCID 0000-0001-5793-9244
Julien G A MartinDepartment of Biology, University of Ottawa, Ottawa, Ontario, Canada.ORCID 0000-0001-7726-6809
Josephine M PembertonInstitute of Ecology and Evolution, University of Edinburgh, Edinburgh, UK.
Daniel H NusseyInstitute of Ecology and Evolution, University of Edinburgh, Edinburgh, UK.ORCID 0000-0002-9985-0317
Dylan Z ChildsDepartment of Animal and Plant Sciences, University of Sheffield, Sheffield, UK.ORCID 0000-0002-0675-4933
Arpat OzgulDepartment of Evolutionary Biology and Environmental Studies, Zurich University, Zurich, Switzerland.ORCID 0000-0001-7477-2642

Funding

Natural Environment Research CouncilSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung 31003A_182286US National Science Foundation 1557130US National Science Foundation D.E.B.-1119660
6 · The paper itself

Abstract

Life history trade-offs are one of the central tenets of evolutionary demography. Trade-offs, depicting negative covariances between individuals' life history traits, can arise from genetic constraints, or from a finite amount of resources that each individual has to allocate in a zero-sum game between somatic and reproductive functions. While theory predicts that trade-offs are ubiquitous, empirical studies have often failed to detect such negative covariances in wild populations. One way to improve the detection of trade-offs is by accounting for the environmental context, as trade-off expression may depend on environmental conditions. However, current methodologies usually search for fixed covariances between traits, thereby ignoring their context dependence. Here, we present a hierarchical multivariate 'covariance reaction norm' model, adapted from Martin (2023), to help detect context dependence in the expression of life-history trade-offs using demographic data. The method allows continuous variation in the phenotypic correlation between traits. We validate the model on simulated data for both intraindividual and intergenerational trade-offs. We then apply it to empirical datasets of yellow-bellied marmots (Marmota flaviventer) and Soay sheep (Ovis aries) as a proof-of-concept showing that new insights can be gained by applying our methodology, such as detecting trade-offs only in specific environments. We discuss its potential for application to many of the existing long-term demographic datasets and how it could improve our understanding of trade-off expression in particular, and life history theory in general.

Indexed as

Life History TraitsMarmotaModels, BiologicalAnimalsBiological EvolutionFemaleSheepcovariance reaction normdemographyheterogeneitylife‐historymixed effectsmultivariate modelphenotypic correlationtrade‐off

Identifiers

PMID39221784
PMCPMC11880661

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Registered trials

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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.