Dispersal Movement and Overwintering Ecology of a Cryptic Migratory Forest Owl
Notice bibliographique
Résumé
Understanding species’ distributions and how a species uses its environment is a vital aspect of ecology and important for the development and implementation of conservation planning. In migratory species, with distinct breeding and nonbreeding ranges, understanding distributions is a complex endeavor. Nevertheless, evaluating movement behavior by linking seasonal distributions and identifying source areas is vital for understanding a species’ full annual cycle ecology. Understanding distributions is particularly important for predators, like birds of prey, that can exert top-down effects on community structure and ecosystem function through their influence on prey populations. Among raptors, owls are severely understudied in large part because their cryptic habits prevent them from being easily detected using widespread, standardized study approaches—something that is especially apparent during the nonbreeding season. In recent decades, concerning trends have been observed in the movement dynamics of diurnal raptors and how this may translate to owls is poorly understood. The Northern Saw-whet Owl (Aegolius acadicus) is a small, migratory forest owl whose widespread distribution and abundance makes it an excellent model for highlighting research priorities in owls and other migratory raptors. In Chapter I, I explored sources of variability for d2Hf in feather samples obtained from juvenile saw-whet owls during the breeding season from 26 nest sites located in three regions: Quebec (CA), South Dakota and Nebraska (USA). I explored the relationship between d2Hf of juveniles and a set of covariates, as well as within-nest d2Hf variation of juveniles and a set of covariates. Results demonstrate total precipitation (year prior to feather growth), latitude, and number of siblings within a nest to be important in predicting d2Hf enrichment, but d2Hf values differed from predicted values commonly used for diurnal raptors. In samples from Quebec, I found unpredicted d2Hf enrichment, as well as temporal variation in d2Hf enrichment in juveniles and their parents. These results demonstrate the complex nature of using d2Hf in studies of migratory raptors and help inform the use of d2Hf in studies of cryptic migratory species. In Chapter II, I used acoustic monitoring to explore nonbreeding season occupancy and detection probabilities of saw-whet owls in the southern Interior Highlands of central North America, a region where they have only recently been recognized as occurring during the nonbreeding season. I used autonomous recording units to sample 56 sites across a large region, and results demonstrated overall high occupancy and low detection probabilities in the region’s pine forest. Occupancy probability was greater at more northerly study sites, where conifer cover was patchier on the landscape, as well as at study sites with greater Barred Owl activity. My study provides valuable information on the poorly-understood nonbreeding distribution of the saw-whet owl and highlights a successful effort to better understand the overwintering ecology of a cryptic, migratory raptor. In Chapter III, I conducted an experiment to better understand the thermal environment experienced by saw-whet owls at diurnal roost sites during the nonbreeding season. Using operative temperature models, I demonstrated that roost sites in conifer crowns—commonly used by saw-whet owls—were more thermally buffered and experienced lower wind speeds than other roost sites likely to be used by owls. My results suggested the species may choose roosting sites to avoid thermally stressful temperatures, particularly at the milder, southern fringe of their nonbreeding distribution. This study provides critical information on the thermal environment experienced by roosting saw-whet owls and could have implications in predicting other novel regions in which the secretive species may occur.
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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) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 tête enseignante, 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 ».