Methods to mitigate human–wildlife conflicts involving common mesopredators: a meta‐analysis
Bibliographic record
Abstract
Abstract Conflicts between humans and mesopredators are frequent and widespread. Over the last decades, conflicts have led to the development and application of different mitigation methods to diminish the costs and damage caused by such conflicts. We conducted a systematic literature search and meta‐analysis to assess the influence of different mitigation methods on 3 common nuisance species: raccoons ( Procyon lotor ), red foxes ( Vulpes vulpes ), and striped skunks ( Mephitis mephitis ). A majority of the studies, from 1963‒2022, were conducted in North America, followed by Australia and Europe. The predation of wildlife species of conservation concern by nuisance species is the main reported source of conflict in the published literature. Lethal control is the most commonly tested method and is generally effective at reducing conflicts based on the calculated effect size. Barriers have mixed effects, with electric fences and nest exclosures both being effective, whereas conventional fences seem to be less effective. Repellents mimicking predators (e.g., guard animal, predator smell) are also effective. Conditioned taste aversion is a promising approach, but no precise product or chemical has proven to be effective. Many interventions suffered from a lack of validation through experimental approach. Research on human–mesopredator conflict mitigation would benefit from repeated studies using the same methods in similar contexts, thus reducing heterogeneity in the results, and by testing new and innovative methods.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.041 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".