Influence of spatiotemporal dynamics of sea ice and individual effects on the population ecology of polar bears in Hudson Bay
Notice bibliographique
Résumé
Understanding factors that influence the population ecology of species is challenging but essential to provide insights into life history and critical for their conservation and management, particularly for species affected by climate change. Climate change is rapidly altering ecosystems throughout the world; however, the Arctic region is warming 4x faster than anywhere else on the planet. Ice obligate species such as polar bears (Ursus maritimus) rely on sea ice for their survival and are thus sensitive to environmental change and can provide important insights into the Arctic marine ecosystem. In this dissertation I investigated the influence of spatiotemporal dynamics of sea ice and individual effects on the population ecology of polar bears in Hudson Bay, Canada. To assess the spatiotemporal dynamics of sea ice in Hudson Bay, I evaluated trends in the spatial distribution of remnant ice over time from 1980-2019 and assessed temporal patterns of ice duration at two different spatial scales. I then assessed the influence of these ice metrics on survival of male polar bears using a long-term dataset from western Hudson Bay for three age classes, subadult (1-4 years old), prime (5-19 years old), and senescent (20+ years old). I found that the interannual variation in the spatial distribution of remnant ice was high in Hudson Bay but that there was no trend over time. Sea ice duration declined significantly over time resulting in a longer ice-free period. Prime-aged adult males had lower survival during the earliest ice retreat dates in Hudson Bay. Polar bears occupy a remote and harsh environment throughout their range and intraspecific interactions and their consequences for population dynamics are poorly understood. I documented an observation of infanticide by an adult male polar bear on the sea ice in Hudson Bay followed by a second observation of an adult female that was lactating and in breeding condition without the presence of cubs. I discuss theoretical motivations for such behaviour and explore why the sexually selected infanticide hypothesis may be applicable to polar bears because of their life history and impacts of climate change on populations. Next, I evaluated changes in pregnancy rates for polar bears over time and used a generalized linear model to assess the effects of age, mass, and sea ice conditions on this key reproductive parameter. I found that pregnancy rates declined through time and increased in their interannual variation. The most important predictors for pregnancy rate were mass and age, where heavier bears and females in their prime (5-19 years old) were more likely to be pregnant. The age of first breeding increased after 1990 with fewer 4-year-old bears classified as pregnant compared to the previous decade. I assessed the costs of reproduction for female bears, temporal changes in sex ratios, and factors influencing offspring survival using mark-recapture data from 1980-2019. I found a cost of reproduction for female polar bears, which resulted in lower survival for females with cubs and was compounded by environmental effects. The adult sex ratio in the study area was female-biased with a nonlinear relationship through time. The operational sex ratio followed a similar pattern except it was male-biased and associated with higher variance. Cub survival was influenced by sea ice conditions, maternal age and body mass indicating a complex interaction from multiple factors influencing the population dynamics of polar bears. Lastly, I assessed the movement, survival and abundance of polar bears across Hudson Bay using genetic biopsy sampling in relation to both sea ice and harvest from 2017-2024 using multistate mark-recapture models where 3 distinct geographic areas represented states in the model. I found that the spatial distribution of remnant sea ice in Hudson Bay influenced movement between states for adult males, with higher rates occurring for adjacent areas. I documented higher movement rates between subpopulations than previously reported with possible higher survival rates for bears in a geographic state with a lower vulnerability of harvest. This research provides insights into factors that influence the population ecology of polar bears and demonstrates the value of long-term research for understanding both environmental and individual effects on the population dynamics of an apex predator in the Arctic marine ecosystem.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».