Exploration and diet specialization in eastern chipmunks - Québec - Canada
Bibliographic record
Abstract
Individual diet specialization (IDS) is widespread and can affect the ecological and evolutionary dynamics of populations in significant ways. Extrinsic factors (e.g., food abundance) and individual variation in energetic needs, morphology, or physiology, have been suggested as drivers of IDS. Behavioral traits like exploration and boldness can also impact foraging decisions, although their effects on IDS have not yet been investigated. Specifically, variation among individuals in exploratory behavior and their position along the exploration/exploitation trade-off may affect their foraging behavior, acquisition of food items and home-range size, which may in turn influence the diversity of their diet. Here we analyzed stable carbon and nitrogen isotopes in hair of wild eastern chipmunks, Tamias striatus, to investigate the influence of individual differences in exploration on IDS. We found that exploration profile, sex, and yearly fluctuations in food availability explained differences in the degree of dietary specialization and in plasticity in stable carbon and stable nitrogen over time. Thus, consistent individual differences in exploration can be an important driver of within-population niche specialization and could therefore affect within-species competition. Our results highlight the need for a more thorough investigation of the mechanisms underlying the link between individual behavioral differences and diet specialization in wild animal populations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".