Marbled Murrelets prefer stratified waters close to freshwater inputs in Haida Gwaii, British Columbia, Canada
Bibliographic record
Abstract
Abstract The Marbled Murrelet (Brachyramphus marmoratus) is a small seabird that is currently listed as threatened in Canada. Understanding this species’ marine habitat preferences plays a vital role in our ability to focus conservation planning. We used the longest-running at-sea survey dataset available in British Columbia to examine hotspot persistence and habitat use at Laskeek Bay, Haida Gwaii, BC. The Laskeek Bay Conservation Society has been conducting spring and summer surveys along fixed transect routes in open and shoreline waters from 1997 to 2018. Along with analyzing this long-term dataset, we conducted surveys to measure oceanographic variables (2018–2019) and tested whether Marbled Murrelets in the same area used prey and oceanographic information to select marine habitat in conjunction with physical habitat features. Our hotspot persistence map, defined as areas that repeatedly had counts above a 75% threshold relative to other areas during a given survey, showed that murrelets consistently preferred shoreline transects. Murrelets also preferred shallow marine areas closer to streams, above higher proportions of sandy substrate and closer proximity to abundant nesting habitat. Modeling weather and time variables contributed little additional predictive power. Nonetheless, models that included physical environmental, oceanographic, and prey variables outperformed those with only physical environmental variables. Stratified water was the oceanographic variable most strongly related to higher counts. Our study suggests that stratified waters could work with stream systems to create productive zones for foraging murrelets, and highlights the importance of murrelets having access to marine areas with the preferred physical features.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".