Predicting the Effect of Land‐Use Policies on Wildlife Habitat Abundance
Bibliographic record
Abstract
Land‐use change is arguably the most pervasive socioeconomic force driving the change and degradation of ecological systems and wildlife habitat. This paper integrates an econometric model of land use with a species‐habitat association matrix to assess the effects of land use policies on land use changes and the resulting impacts on habitat abundance for 763 terrestrial vertebrates in four western states of the United States (California, Oregon, Washington, and Idaho). Results suggest that if the goal is to protect biodiversity through habitat conservation, directly acquiring land and setting it aside for habitat preservation (e.g., by purchasing development rights) is more effective than policies that attempt to change the relative returns from different land uses. On peut dire que le changement d'affectation des terres constitue la force socioéconomique la plus répandue entraînant la modification et la dégradation des écosystèmes et des habitats fauniques. Dans le présent article, nous avons intégré un modèle économétrique d'affectation des terres et une matrice espèces‐habitats pour évaluer l'impact des politiques d'affectation des terres sur les changements d'affectation des terres et leurs répercussions sur l'abondance des habitats pour 763 vertébrés terrestres dans quatre États de l'Ouest des États‐Unis (Californie, Oregon, Washington et Idaho). Si l'objectif visé est la protection de la biodiversité par la conservation des habitats, les résultats semblent indiquer que l'acquisition directe de terres en vue de les réserver pour la préservation des habitats (en faisant l'acquisition de droits d'aménagement par exemple) est plus efficace que les politiques qui tentent de modifier les rendements relatifs tirés des diverses affectations des terres.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".