Characterizing Ancient Murrelet <i>Synthliboramphus antiquus</i> movement behaviour during breeding‐season foraging trips using hidden Markov models
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Bibliographic record
Abstract
Seabird species are increasingly threatened globally due to a range of anthropogenic impacts affecting at‐sea habitat. Characterizing at‐sea movement patterns and identifying foraging behaviour allows researchers to identify critical habitat but can be challenging for small diving species. The Ancient Murrelet Synthliboramphus antiquus is an alcid species of conservation concern in Canada. At‐sea distribution and movements of this species during the breeding season are currently poorly understood. We mounted GPS tracking devices on breeding Ancient Murrelets from two colonies along the southeastern coast of Haida Gwaii, one colony in 2018 and one in 2019, to collect movement data during foraging trips. We used hidden Markov models (HMMs) to identify three movement behaviour states from track characteristics, which we interpreted as transit, resting and foraging. Immersion data collected from the GPS tags allowed us to interpret the two slow‐moving states and to confidently identify foraging movement behaviour. From the 42 individuals tracked we found that foraging trips encompassed large areas of Hecate Strait, to the east of the colonies. Birds nesting on Ramsay Island in 2018 travelled on average 453 km in 4.3 days, up to a mean of 108 km from the colony. Birds breeding on George Island in 2019 took shorter trips, travelling on average 263 km in 3.0 days, and up to 81 km from the colony. In both years, birds spent approximately one‐third of their time in each behaviour state. Resting often took place throughout a whole night at sea; foraging occurred predominately during the day. Transit took place during the night, when birds were leaving or returning to the colony, or during the day, possibly indicating movement between prey patches. Our results can inform marine conservation of this vulnerable species by differentiating foraging from other at‐sea movement behaviours and is a first step to identifying critical foraging habitat.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it