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Record W7046448805

Dispersal Movement and Overwintering Ecology of a Cryptic Migratory Forest Owl

2024· article· en· W7046448805 on OpenAlexaboutno aff

Bibliographic record

VenueJournal of the Arkansas Academy of Science · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsOverwinteringBiological dispersalPredationNest (protein structural motif)Abundance (ecology)JuvenileEcosystemFunctional ecology
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.637
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.011
GPT teacher head0.282
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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