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Record W2950855153 · doi:10.1111/1365-2435.13389

Individual niche trajectories drive fitness variation

2019· article· en· W2950855153 on OpenAlexaff
Raul Costa‐Pereira, Benjamin J. Toscano, Franco L. Souza, Travis Ingram, Márcio S. Araújo

Bibliographic record

VenueFunctional Ecology · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsMcMaster University
Fundersnot available
KeywordsBiologyForagingNicheEcologyNatural selectionPopulationRange (aeronautics)Optimal foraging theoryEcological nicheEvolutionary biologyHabitatDemography

Abstract

fetched live from OpenAlex

Abstract Variation in fitness across individuals is central to population growth, species coexistence and evolution by natural selection. Fitness variation associated with resource use is hugely consequential, but how this variation is generated and maintained within natural populations remains unclear. In particular, individual fitness may depend on many cumulative foraging decisions over time, but this hypothesis remains untested. We used multi‐tissue stable isotope analysis to determine isotopic niche trajectories within species, populations and sexes of thin‐toed frogs and explored how this temporal dimension of diet affects individual reproductive investment, body condition and parasite load. We found that individual frogs shifted their diets less than expected under a null model, likely due to functional trade‐offs that limit the incorporation of new prey types over time. However, within the observed range of diet shifts, individuals that modified their diet to a greater degree exhibited higher fitness, although this effect was sex‐dependent. We suggest that these different relationships between isotopic niche trajectory length and fitness within thin‐toed frogs are driven by variability in the resource environment, negative density dependence and allometric constraints. These strong fitness effects suggest that the temporal dimension of diet change is a potential target of natural selection and, therefore, could drive correlated evolution in phenotypic traits underlying diet flexibility. Our findings add a new level of complexity to the understanding of ecological and evolutionary consequences of niche variation by demonstrating that temporal variation in foraging consistency within populations leads to different fitness pay‐offs. A free Plain Language Summary can be found within the Supporting Information of this article.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0770.011

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 head0.009
GPT teacher head0.206
Teacher spread0.197 · 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; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations31
Published2019
Admission routes1
Has abstractyes

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