Accounting for spatial heterogeneity in trapping pressure and its impact on population dynamics of sympatric pine and stone martens
Bibliographic record
Abstract
Abstract Context The consequences of human exploitation of animal populations remain poorly understood, particularly for populations experiencing spatially-varying harvesting intensity and exposure. If unaccounted for, this heterogeneity may lead to inappropriate harvesting policies. Methods We developed a spatially-explicit individual-based model to evaluate the effects of such spatially heterogeneous harvesting on the population dynamics of pine and stone martens in France. Objectives By comparing scenarios of spatially heterogeneous and homogeneous trapping pressure, we investigated the sustainability of current voluntary trapping practices in the absence of dedicated population monitoring. Results We show that spatially heterogeneous trapping pressure, despite being lower pressure on average than our simulated homogenous trapping scenarios, has a stronger, more negative impact on population size and density of both species. Conclusions Our results highlight the need to account for the spatial heterogeneity when assessing the effectiveness of management practices. Neglecting spatial heterogeneity when assessing or formatting harvesting practices may unintentionally generate management strategies that yield unforeseen consequences for the target populations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".