Environmental conditions modulate compensatory effects of site dependence in a food‐caching passerine
Bibliographic record
Abstract
Although density regulates the abundance of most wild animal populations by influencing vital rates, such as fecundity and survival, the mechanisms responsible for generating negative density dependence are unclear for many species. Site dependence occurs when there is preferential filling of high-quality territories, which results in higher per capita vital rates at low densities because a larger proportion of occupied territories are of high quality. Using 41 yr of territory occupancy and demographic data, we investigated whether site dependence was a mechanism acting to influence fecundity and, by extension, regulate a population of Canada Jays in Algonquin Provincial Park, Ontario, Canada. As predicted by site dependence, the proportion of occupied territories that were of high quality was negatively correlated with population density and periods of vacancy were shorter for high-quality territories than for low-quality territories. We also found evidence that per capita fecundity was positively related to the proportion of occupied territories that were of high quality, but only when environmental conditions, which influence the entire population, were otherwise poor for breeding. Our results suggest that site dependence likely plays a role in regulating this population but that environmental conditions can modulate the strength of density dependence.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".