Compensatory conservation measures for an endangered caribou population under climate change
Bibliographic record
Abstract
Future human land use and climate change may disrupt movement behaviors of terrestrial animals, thereby altering the ability of individuals to move across a landscape. Some of the expected changes result from processes whose effects will be difficult to alter, such as global climate change. We present a novel framework in which we use models to (1) identify the ecological changes from these difficult-to-alter processes, as well as (2) the potential conservation measures that are best able to compensate for these changes. We illustrated this framework with the case of an endangered caribou population in Québec, Canada. We coupled a spatially explicit individual-based movement model with a range of landscape scenarios to assess the impacts of varying degrees of climate change, and the ability of conservation actions to compensate for such impacts on caribou movement behaviors. We found that (1) climate change impacts reduced movement potential, and that (2) the complete restoration of secondary roads inside protected areas was able to fully offset this reduction, suggesting that road restoration would be an effective compensatory conservation action. By evaluating conservation actions via landscape use simulated by an individual-based model, we were able to identify compensatory conservation options for an endangered species facing climate 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 | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".