Using hidden Markov models to identify Ancient Murrelet foraging behaviour and habitat during the breeding season
Bibliographic record
Abstract
Abstract: Understanding where seabirds travel and the behaviours they exhibit while on foraging trips is an important step in understanding their at-sea habitat requirements. Investigating movements of individuals from specific breeding colonies has become easier with the advent of tracking devices that can be mounted directly on individual birds. Foraging areas are often of most interest for conservation management, and one of the first steps to identifying important foraging habitat is to differentiate foraging behaviour from the record of movement captured by tracking devices. The Ancient Murrelet (Synthliboramphus antiquus) is a seabird species of conservation interest in Canada, due to the high proportion of the global population nesting in a relatively concentrated area of the British Columbia coast. In 2018 and 2019 we collected GPS tracks from Ancient Murrelets nesting on two colonies within the Haida Gwaii archipelago. We calculated trip metrics such as foraging range, total trip length, and trip duration. We successfully used hidden Markov models to classify movement exhibited by murrelets into three behaviour states (foraging, resting, and transit). We found that immersion data from GPS tags were essential for differentiating slow-moving behaviours. Logistic regression models suggested that depth, seafloor slope, tidal speed, and distance from the colony were negatively associated with foraging probability, while foraging intensity was greater in deeper areas. The combination of individual movement analysis and habitat analysis provides an important first step to identifying priority at-sea habitats, including critical breeding-season foraging areas, for murrelets in the waters around Haida Gwaii. Results will be used by the Canadian Government in support of the Ocean Protection Plan and successful management of this species under the Species at Risk Act. Authors: Vivian Pattison¹, Laurie Wilson¹, Patrick O'Hara¹, Christopher Bone², Laura Cowen² ¹Environment and Climate Change Canada, ²University of Victoria
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".