Individual niche trajectories drive fitness variation
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".