Conditioned Taste Aversion as a Tool for Mitigating Human-Wildlife Conflicts
Bibliographic record
Abstract
Modern wildlife management has dual mandates to reduce human-wildlife conflict (HWC) for burgeoning populations of people while supporting conservation of biodiversity and the ecosystem functions it affords. These opposing goals can sometimes be achieved with non-lethal intervention tools that promote coexistence between people and wildlife. One such tool is conditioned taste aversion (CTA), the application of an evolutionary relevant learning paradigm in which an animal associates a transitory illness to the taste, odor or other characteristic of a particular food item, resulting in a long-term change in its perception of palatability. Despite extensive support for the power of CTA in laboratory studies, field studies have exhibited mixed results, which erodes manager confidence in using this tool. Here we review the literature on CTA in the context of wildlife conservation and management and discuss how success could be increased with more use of learning theory related to CTA, particularly selective association, stimulus salience, stimulus generalization, and extinction of behavior. We apply learning theory to the chronological stages of CTA application in the field and illustrate them by synthesizing and reviewing past applications of CTA in HWC situations. Specifically, we discuss (1) when CTA is suitable, (2) how aversion can be most effectively (and safely) established, (3) how generalization of aversion from treated to untreated food can be stimulated and (4) how extinction of aversion can be avoided. For each question, we offer specific implementation suggestions and methods for achieving them, which we summarize in a decision-support table that might be used by managers to guide their use of CTA across a range of contexts. Additionally, we highlight promising ideas that may further improve the effectiveness of CTA field applications in the future. With this review, we aspire to demonstrate the diverse past applications of CTA as a non-lethal tool in wildlife management and conservation and facilitate greater application and efficacy in the future.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".