CAN WE MEASURE CARRYING CAPACITY WITH FORAGING BEHAVIOR?
Bibliographic record
Abstract
Carrying capacity is one of the most important, yet least understood and rarely estimated, parameters in population management and modeling. A simple behavioral metric of carrying capacity would advance theory, conservation, and management of biological populations. Such a metric should be possible because behavior is finely attuned to variation in environment including population density. We connect optimal foraging theory with population dynamics and life history to develop a simple model that predicts this sort of adaptive density-dependent change in food consumption. We then confirm the model's unexpected and manifold predictions with field experiments. The theory predicts reproductive thresholds that alter the marginal value of energy as well as the value of time. Both effects cause a pronounced discontinuity in quitting-harvest rate that we revealed with foraging experiments. Red-backed voles maintained across a range of high densities foraged at a lower density-dependent rate than the same animals exposed to low-density treatments. The change in harvest rate is diagnostic of populations that exceed their carrying capacity. Ecologists, conservation biologists, and wildlife managers may thus be able to use simple and efficient foraging experiments to estimate carrying capacity and habitat quality.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".