The adaptive value of density-dependent habitat specialization and social network centrality
Bibliographic record
Abstract
Density dependence is a fundamental ecological process. Patterns of animal habitat selection and social behaviour are often density-dependent and density-dependent traits should affect reproduction and survival, and subsequently affect fitness and population dynamics. The Ideal Free Distribution and Optimal Foraging Theory present distinct predictions about how the effect of habitat selection on fitness differs across a population density gradient. Using a social ungulate (Rangifer tarandus) as a model system, we test competing hypotheses about how (co)variance in habitat specialization, social behaviour, and fitness vary across a population density gradient. Within a behavioural reaction norm framework, we estimated repeatability, phenotypic plasticity, and phenotypic covariance among social behaviours and habitat selection to demonstrate the adaptive value of these phenotypes across a population density gradient. In support of Optimal Foraging Theory, but not the Ideal Free Distribution, we found that at high density habitat specialists had higher fitness than generalists, but were also less social than habitat generalists, suggesting the possibility that specialists were inhibited from being social. Our findings illustrate that social strength and habitat specialization varied consistently among individuals across a density gradient, but that habitat specialists maximized fitness at high density. Taken together, our study provides preliminary support for Optimal Foraging Theory as the driving mechanism for density-dependent habitat specialization.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".