Developing a novel approach to fill gaps in vital rates for sea duck management: breeding propensity of American common eider (Somateria molissima dresseri) hens determined from satellite telemetry movement patterns
Notice bibliographique
Résumé
Waterfowl play key roles both ecologically, acting as biomonitors of ecosystem health, and anthropogenically, where most species are hunted and harvested. Sea ducks in particular are of great importance, specifically within Indigenous harvest and cultural practices, however, sea ducks generally tend to spend their time in habitats difficult to access and thus are understudied compared to other waterfowl. Population declines of several North American sea duck species were observed starting in the 1980s without known causes, and the Sea Duck Joint Venture (SDJV) was formed in response to concerns for those populations. The SDJV works in conjunction with partners to close knowledge gaps for better management and conservation of sea duck populations and habitats within Canada and the United States of America. To work towards a better understanding of population dynamics via population modelling, vital rates must be quantified. The American common eider (<em>Somateria mollisima dresseri</em>) is one such sea duck with recent concern for changing population trends, with population decreases in central areas of the range and increases or stability in northern areas. This subspecies of eider is long-lived and an intermittent breeder, such that females do not breed every year. Thus, a key vital rate to examine for a better understanding of current population trends for this species is breeding propensity, the proportion of sexually mature females that nest in a given year. This thesis is part of a large collaborative project that used satellite Platform Terminal Transmitter (PTT) devices deployed in female eiders across their range that collected data for up to three years. One hundred and thirteen hens transmitted locations over one to three years, resulting in movement data over 164 individual breeding seasons, which I used to develop a three-state hidden Markov model with a covariate for overlap with breeding locations. I assigned a breeding status to each hen (i.e., successful nesting attempt, failed nesting attempt, potential prospecting, skipped breeding) from which I determined overall breeding propensity and breeding propensity by region. While my findings fill an important knowledge gap for the American common eider, continued monitoring efforts are necessary for this species given conflicting changes in population trends across their range. Sea ducks in general are poorly studied, however, the methodology which I have developed for this well investigated species can be adapted and applied to other less studied sea duck species and contribute towards closing information gaps for conservation and management efforts.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».