Predicting marine habitat for marbled murrelets during breeding and nonbreeding seasons in the Salish Sea, British Columbia, Canada
Bibliographic record
Abstract
The marbled murrelet (Brachyramphus marmoratus) is a small seabird inhabiting coastal regions along the Pacific coast of North America, and nests in old-growth forests usually within 80 km from shore. The Canadian population of marbled murrelets is listed as Threatened under the federal Species at Risk Act. To investigate the species' marine distribution, we conducted analyses of the occurrence of marbled murrelets at-sea between 2000 and 2022, utilizing at-sea and marine shoreline surveys in the Canadian portion of the Salish Sea. The data were divided into breeding season (April to August) and non-breeding season (September to March) to examine the relationship between environmental covariates and the species' distribution. We considered terrestrial covariates related to potential nesting habitat, as well as marine covariates related to Pacific sand lance (Ammodytes personatus) quality habitat, slope, depth, streams, tidal currents, shorelines and climate indices (NPGO). We compared marine distribution between breeding and nonbreeding seasons and predicted variations in covariate relationships. Our study focuses on identifying averaged relationships and key spatial areas to gauge habitat quality at a landscape scale. Using a Generalized Additive Modelling approach, we found that both marine and terrestrial covariates contributed to predicting murrelet distribution during both seasons. Notably, Pacific sand lance habitat played a significant role in both the breeding and nonbreeding season, while the overall amount of nesting habitat within an 80 km radius influenced occurrence probability in the nonbreeding season. Our analysis accurately predicted distribution patterns at a resolution of 3 x 3 km with an AUC of 0.89 and AUPRC of 0.52 for the breeding season, and AUC of 0.88 and a AUPRC of 0.28 for non-breeding season. Overall, our study highlights both terrestrial and marine drivers that influence the marine distribution of this threatened species and informs Canadian conservation efforts.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".