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Record W3216768289 · doi:10.1111/ele.13934

Rapid evolution of life‐history traits in response to warming, predation and competition: A meta‐analysis

2021· review· en· W3216768289 on OpenAlexafffund
Tess Nahanni Grainger, Jonathan M. Levine

Bibliographic record

VenueEcology Letters · 2021
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaBanting Research Foundation
KeywordsPredationBiologyTraitEcologyCompetition (biology)Evolutionary ecologyFecundityNatural selectionExperimental evolutionMeta-analysisClimate changeEvolutionary biologyPopulationDemography

Abstract

fetched live from OpenAlex

Although studies quantifying evolutionary change in response to the selective pressures that organisms face in the wild have demonstrated that organisms can evolve rapidly, we lack a systematic assessment of the frequency, magnitude and direction of rapid evolutionary change across taxa. To address this gap, we conducted a meta-analysis of 58 studies that document the effects of warming, predation or competition on the evolution of body size, development rate or fecundity in natural or experimental animal populations. We tested whether there was a consistent effect of any selective agent on any trait, whether the direction of these effects align with theoretical predictions, and whether the three agents select in opposing directions on any trait. Overall, we found weak effects of all three selective agents on trait evolution: none of our nine traits by selective agent combinations had an overall effect that differed from zero, only 31% of studies had a significant within-study effect, and attributes of the included studies generally did not account for between-study variation in results. One notable exception was that predation targeting adults consistently resulted in the evolution of smaller prey body size. We discuss potential causes of these generally weak responses and consider how our results inform the ongoing development of eco-evolutionary research.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.863
Threshold uncertainty score0.799

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.075
GPT teacher head0.275
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations29
Published2021
Admission routes2
Has abstractyes

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