Human–wildlife coexistence needs more evidence‐based interventions to reduce the losses of crops, livestock and fishery catches
Bibliographic record
Abstract
Abstract Evidence‐based interventions designed to reduce wildlife‐caused losses are essential for human–wildlife coexistence. The lack of systematic summarization of research effort and evidence makes it challenging for researchers, managers and policymakers to prioritize interventions for evaluation and implementation. Here, we compiled experimental case studies of nonlethal technical interventions designed to reduce the losses of crops, livestock and fishery catches caused by terrestrial carnivores, elephants, farmland birds and marine fauna worldwide. Then, we summarized the research effort and the performance of interventions by their sensory stimuli and target animals. We found that: (i) 54 of 88 interventions included in this study had statistically effective evidence, where only 39% (21/54) were evaluated with more than three experiments; (ii) physical‐, sound‐, chemical‐ and light (or visual) ‐based interventions were the most in numbers and their performance varied greatly; (iii) farmland birds, seabirds and cetaceans were the most studied animal groups while there are only a few experiments for elephants; and (iv) the interventions for marine fauna generally had no impact on the target catch of fisheries. Syntheses and applications: Our results indicated that collective effort is needed to further evaluate interventions using various sensory stimuli and launch incentive programs to motivate the implementation of interventions, particularly related to marine fauna conservation. Our synthesis could be helpful for stakeholders to tackle the negative human‐wildlife interactions outlined as Target 4 of the Kunming–Montreal Global Biodiversity Framework.
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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 | 0.027 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".