Dispersal Movement and Overwintering Ecology of a Cryptic Migratory Forest Owl
Bibliographic record
Abstract
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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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".