Optimal conservation planning for migratory animals: integrating demographic information across seasons
Bibliographic record
Abstract
Abstract Conservation strategies for migratory animals are typically based on ad‐hoc or simple ranking methods and focus on a single period of the annual cycle. We use a density‐dependent population model to examine one‐time land purchase strategies for a migratory population with a breeding and wintering grounds. Under equal rates of habitat loss, we show that it is optimal to invest more, but never solely, in the habitat with the higher density dependence to habitat cost ratio. When there are two habitats that vary in quality within a season, the best strategy is to invest only in one habitat. Whether to purchase high‐ or low‐quality habitat depends on the general life history of the species and the ratio of habitat quality to habitat cost. When carry‐over effects are incorporated, it is almost always optimal to invest in high‐quality habitat during the season that produces the carry‐over effect. We apply this model to a threatened warbler population and show the optimal strategy is to purchase more breeding than wintering habitat despite the fact that breeding habitat is over ten times more expensive. Our model provides a framework for developing year‐round conservation strategies for migratory animals and has important implications for long‐term planning and management.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".