Cognition of common mammal mesopredators and implications for their management
Bibliographic record
Abstract
An animal’s cognitive abilities can modulate its interaction with humans and exacerbate conflicts. Mesopredator mammals demonstrate innovation and learning through their behaviour, especially in a generalist and widespread species like the common raccoon (Procyon lotor). The aim of this thesis is to combine wildlife management with the study of cognition to provide better coexisting conditions between humans and mesopredators. I first conducted a narrative synthesis to characterize the contexts in which conflicts occur with the raccoon, the red fox (Vulpes vulpes) and the striped skunk (Mephitis mephitis), and a meta-analysis to rigorously evaluate the efficacy of the mitigation techniques in reducing the intensity of conflicts. Although lethal interventions are regularly applied with relatively high efficacy, many nonlethal options are also effective. Many methods are based on a profound understanding of animal behaviour and cognition. Shifting toward cognitive studies, I experimentally tested problem-solving and learning performances of wild raccoons in three Québec national parks. I demonstrated innovative problem-solving in raccoons, and that task difficulty level has a clear effect on success probability and time to solve the problem. Higher exploratory diversity was linked to success, but not persistence. I also found evidence of learning, by an improved performance in term of success probability over consecutive trials. Raccoons living in a zone of the park more affected by the human presence also present more pronounced learning performance, which likely relates to their strong propensity to forage on human food. There are also indications that the improved performance gained through learning is retained over the winter season. Indeed, we found the success rates of the last trial from a summer to be similar to that of the first trial of the following summer. Basing mitigation interventions on scientifically proven methods and better integration of animal behaviour, may improve mesopredators management. Expanding our knowledge of cognition in common species contributes to our appreciation and tolerance toward wildlife. Overall, my findings could facilitate reaching a balanced coexistence between humans and mesopredators.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".