Learnings from collaborative monitoring of remote wildlife populations: factors affecting changes in number and distribution of nesting Hudson Bay eiders ducks in the Belcher Islands
Notice bibliographique
Résumé
Anthropogenic pressures are causing a global decline in biodiversity that, in turn, impacts the human communities depending on it.In the conservation effort, efficient management requires up-to-date and accurate information about the population dynamics, habitat requirements, and distribution of organisms.There is an increasing appreciation of the benefits of coproduction and the combination of multiple knowledge systems to increase our understanding of the rapidly changing ecosystems.In this thesis, I used data from a long-term collaborative monitoring program involving Inuit and federal government researchers to study the factors affecting changes in population size and nesting distribution of a harvested sea duck in south-eastern Hudson Bay, the common eider (Somateria mollissima).I also highlight practical challenges and propose solutions related to cultural and institutional barriers that impede the delivery of respectful approaches and best practices in collaborative research programs involving large and regulated institutions and remote Indigenous communities.I would first like to thank my two co-supervisors, Vivian Nguyen and Grant Gilchrist, who provided constant support, guidance, and thoughtful advice throughout this research project.I have learned immensely through numerous conversations with both of them.They helped me develop and constantly adapt my research project.Their constant optimism has helped me move forward and kept the process fun even during the toughest periods.Many thanks to Greg Robertson who provide clear and detailed answers to my numerous questions regarding survey design and statistical approaches.Greg really helped me push my scientific rigor and helped me better understand the application of some statistical methods to ecological questions.I also want to thank Joel Heath, Lucassie Arragutainaq, and Johnny Kudluarok who initiated the 2021-2022 survey project in the Belcher Islands and organized most of the fieldwork logistics in Sanikiluaq.It has been a pleasure to collaborate with them.Special thanks to Joel who continuously helped me navigate remote communication with Inuit collaborators in Sanikiluaq and who often played the intermediary to overcome language, cultural, and physical distance barriers.I'm thankful to the whole Sanikiluaq field crew for their devotion to rigorous data collection, the knowledge they shared with me about their territory, the good time spent on the land, the laughter, and all the country food shared under the tent or around the campfires.Thank you to Lisa Pirie-Dominix for securing the funding for the realization of the survey fieldwork and the gathering of the previous survey data.iv Many thanks to Holly Hennin who provided essential logistical support for the realization of the fieldwork and who consistently helped me navigate Environment Canada and Carleton University's various administrative procedures.Thank you to all my student colleagues who, despite the COVID-19 pandemic, strived to create a pleasant social dynamic within our lab.Particularly, thanks to Adam Perkovic and Allison Drake for numerous fun and enriching conversation about Arctic research.Thankfully to generous funding from
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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 | 0,003 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».