Quantitative genetics of polar bear (Ursus maritimus) behaviours
Bibliographic record
Abstract
Conflict plays a central role in sexual selection by shaping the behaviours and traits that contribute to reproductive success. It drives competition between individuals, influences mate choice and mating strategies, and ultimately shapes the evolution of sexual dimorphism, courtship displays, and other aspects of reproductive behaviour in many species. Informing conservation management strategies aiming at reducing human-wildlife conflicts involves understanding the interplay of genetic, social, and environmental effects on behavioural phenotypic variation. The objectives of this thesis were to investigate the heritable and non-heritable dynamics shaping complex human-polar bear (Ursus maritimus) conflict risk behaviour observed in Churchill, Manitoba. The study aims to understand the influence of climate change and human settlements on population-level behaviour, and understand the individual-level influences of innate, social, and remaining environmental cues driving polar bear conflict risk behaviour expressed by individuals within the Western Hudson Bay subpopulation. These concepts build on the foundations of quantitative genetics and animal behaviour, using data from an extensively monitored wild polar bear subpopulation near Churchill, Manitoba. The findings suggest that conflict risk behaviour is very closely tied to individuals’ ability to survive and reproduce, and that polar bears have a high affinity for environmental learning and for avoiding serious conflicts. Dynamics in conflict risk behaviour over time underscore the influence of environmental stressors, contrasting with minimal cohort effects. Conservation management strategies focused on reducing human-wildlife conflicts should aim to improve the environmental conditions for wildlife by preserving habitat quality and connectivity.
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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 | 0.000 |
| 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 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".