What is the evidence that counter‐wildlife crime interventions are effective for conserving African, Asian and Latin American wildlife directly threatened by exploitation? A systematic map
Bibliographic record
Abstract
Abstract Counter‐wildlife crime (CWC) interventions—those that directly protect target wildlife from illegal harvest/persecution, detect and sanction rule‐breakers, and interdict and control illegal wildlife commodities—are widely applied to address biodiversity loss. This systematic map provides an overview of the literature on the effectiveness of CWC interventions for conserving African, Asian and Latin American wildlife directly threatened by exploitation, including human–wildlife conflicts that trigger poaching. Following our systematic map protocol (Rytwinski, Öckerman, et al., 2021), we compiled peer‐reviewed and grey literature and screened articles using pre‐defined inclusion criteria. Included studies were coded for key variables of interest, from which we produced a searchable database, interactive map and structured heatmaps. A total of 530 studies from 477 articles were included in the systematic map. Most studies were from Africa and Asia (81% of studies) and focused on African and Asian elephants (16%), felids (14%) and turtles and tortoises (11%). Most evaluations of CWC interventions targeted wildlife products (rather than species) and the transfer of those products along the wildlife crime continuum (40% of cases). Population/species outcomes were most commonly measured via indicators of threat reduction (65% of cases) and intermediate outcomes (25%). We identified knowledge clusters where studies investigated the links between (1) patrols and other preventative actions to increase detection and population abundance and (2) information analysis and sharing and wildlife crime/trade levels. However, the effectiveness of most interventions was not rigorously evaluated. Most investigations used post‐implementation monitoring only (e.g. lacking a comparator), and no experimental designs were found. We identified several key knowledge gaps including a paucity of studies by geography (Latin America), taxonomy (plants, birds and reptiles), interventions (non‐patrol‐based CWC interventions) and outcomes (biological and the combination of biological and human well‐being outcomes). Our map reveals an opportunity to improve the rigour and documentation of CWC intervention evaluations, which would enable the evidence‐based selection of effective approaches to improve wildlife conservation and national security.
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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 | 0.020 | 0.116 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.050 | 0.040 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".