Linking crop availability, forest elephant visitation and perceptions of human–elephant interactions in villages bordering Ivindo National Park, Gabon
Bibliographic record
Abstract
Abstract Feeding by Critically Endangered forest elephants Loxodonta cyclotis in rural plantations is a conservation issue in Gabon, but studies characterizing drivers of spatiotemporal patterns of human–elephant interactions remain sparse, hindering mitigation. In this study, we use GPS tracking data from two elephants to characterize temporal patterns of village visitation, and surveys of 101 local farmers across seven villages to determine local patterns of crop planting and harvesting and of human–elephant interactions. Local farmers' perceptions of elephant visitations and empirical data on such visits were positively correlated with local crop availability. However, considering the two elephants separately revealed that the correlations were driven by just one individual, with the second elephant showing weak links between crop availability and visitation, highlighting the challenges in reliably predicting human–wildlife interactions. The most popular local perceptions of the drivers of elephant visitation were the presence of crops (53% of responses) and logging (39%). The most popular proposed interventions were letting the government find a solution (32%), killing problem elephants (30%) and providing compensation for lost crops (22%). We discuss the potential feasibility and efficacy of the proposed solutions in the context of human–elephant interactions. Future research efforts should focus on collaring elephants in zones with high potential for negative human–elephant interaction and expanding perception surveys to villages with contrasting ecological contexts (e.g. with and without logging in their surrounding forests), as these could influence local perceptions of conflicts and conservation initiatives.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".