Crop raiders in an ecological trap: optimal foraging individual‐based modeling quantifies the effect of alternate crops
Bibliographic record
Abstract
Crop raiding is an increasing source of human-wildlife conflict that antagonizes humans and can lead to heightened killing of wildlife. Attraction to crops can trigger ecological traps, where animals prefer areas of their range that confer relatively low fitness. Food can be used to draw animals away from problematic areas, but an alternative considered less often is to replace high-quality food with poorer alternatives. In any case, managers often have no means of anticipating by how much such interventions should impact animal use of space. Optimal foraging theory predicts that foragers optimizing their diet should choose food items according to their relative profitability (i.e., digestible energy/ handling time), a theoretical prediction that can orient management actions. Accordingly, we developed an individual-based model (IBM) simulating movement through empirical rules under an optimal foraging framework. Our objective was to quantify the effect size of cultivating alternate crops to reduce crop raiding and the associated human-induced mortality driving an ecological trap for an energy maximizer, plains bison (Bison bison bison). Results showed that almost tripling the area of cultivation of crops of lower profitability (from 24.3% of the bison range outside the protected area in one management scenario to 70.3% in another) only led to a 25% additional decrease in the intensity of crop raiding (from a decrease of 40% in the first scenario to a decrease of 65% in the second). This suggests that localized interventions in the landscape are likely to have a stronger impact in mitigating crop raiding than broad actions ignoring spatial patterns in food distribution. However, we obtained no significant reduction in the number of simulated bison being harvested in the first scenario, and only a small reduction in the second, when the intervention was spatially broad. Our individual-based approach to animal movement informed by optimal foraging demonstrates that linking landscape configuration to mortality rates can help managers anticipate the effectiveness of manipulating food to keep animals away from problematic zones. Yet disarming ecological traps driven by human hunting appears to be a much more challenging undertaking.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".