Inferring Movements and Staging Locations for Canvasbacks Using Light-Level Geolocators
Bibliographic record
Abstract
Abstract Understanding the geographic extent and timing of wildlife movements enables resource managers to inform habitat needs of target species efficiently and effectively. We use light-level geolocators—which enable researchers to estimate individual locations from light-level data—to build a more complete understanding of the geography and timing of migratory movements for canvasback Aythya valisineria in the Pacific Flyway. During the springs of 2015–2017, we placed 151 geolocators on canvasbacks using two alternative attachment methods (leg-band vs. nasal-saddle mounts) during spring migration (February–March) near Reno, Nevada. Eight of these geolocators (five males and three females) were successfully retrieved, representing 10 near-complete annual migration cycles (two geolocators contained data for two migration years). Eight of the 10 estimated spring canvasback migrations (five male and three female) ended at breeding sites in the Prairie Pothole Region of southern Canada and northern United States (often via stopover sites in Utah and Montana), whereas one male and one female migrated to breeding sites in Alaska. Notably, one female settled on nesting grounds in southern Saskatchewan and then in central Alaska in successive years. During spring migration, canvasbacks made an average of 3.3 ± 0.5 stopovers, with an average duration of 14.8 ± 2.2 d. Three canvasbacks made a distinct molt migration after breeding. For fall migration, canvasback made an average of 2.7 ± 0.3 stopovers, lasting an average of 12.3 ± 2.5 d, on their way to wintering sites in California's Central Valley and coastal regions near San Francisco Bay. Retrieval rate for nasal-saddle-mounted geolocators was significantly lower than leg band-mounted devices because of failure of nasal-saddle attachment. This study demonstrates the value of geolocators for assessing year-round habitat use for waterfowl species that have negative behavioral reactions to traditional backpack devices. This information complements standard band-recovery approaches and enables waterfowl managers to ensure that the spatial and temporal distributions of individuals are identified so that habitat conservation efforts can reflect the full annual habitat use cycle.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".