Can foraging behaviour reveal the eco-evolutionary dynamics of habitat selection?
Bibliographic record
Abstract
Rationale: Adaptive behaviours, particularly those related to resource harvest and the time available for fitness-enhancing activities, may serve as suitable surrogates for fitness. Methods: I explore this potential link between behaviour and eco-evolutionary dynamics with controlled field experiments. The experiments manipulated densities of meadow voles foraging in large replicated enclosures. I used the lock-step connection between resource harvest and fitness to generate three fitness surrogates: giving-up densities from artificial resource patches, quitting-harvest rates, and time available for non-foraging behaviours that enhance fitness. Results: Per capita consumption from food trays did not change with population size. Time allocated to foraging increased with population density. Quitting-harvest rates in both safe and risky patches declined linearly with population density. The total amount of time necessary for a new individual to acquire sufficient energy for maintenance increased hyperbolically. Invasion landscapes based on the three fitness surrogates yielded the same behaviourally and evolutionarily stable strategy (ESS) of habitat selection. But the fitness benefits, subsequent convergence towards the ESS, and potential variation about the ESS, varied. Conclusions: Adaptive foraging behaviour is a reliable and rapid metric for assessing the evolutionary stability of habitat selection. This proof of concept suggests that behavioural metrics may play a prominent role in assessments of other strategies. We may even be able to use behavioural metrics to forecast ecological and evolutionary futures associated with ecological change.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".