A 31-year Time Series of At-sea Counts Shows a Non-significant Decline of Marbled Murrelets at Laskeek Bay, Haida Gwaii, 1990–2020
Bibliographic record
Abstract
The Marbled Murrelet Brachyramphus marmoratus breeds and overwinters along the coast of British Columbia, Canada, and is listed as Threatened under the Canadian Species at Risk Act. Understanding population trends for this seabird species is important for management and recovery, yet long-term time-series data for Marbled Murrelet abundance are rare. We update trends and annual fluctuations of Marbled Murrelet numbers derived from at-sea counts in Laskeek Bay, Haida Gwaii, on the north coast of British Columbia, 1990–2020. We found a non-significant negative trend (−1.55% per year). Counts varied seasonally and peaked in early June; counts also varied with distance from shore, with the highest numbers occurring within 1 km of shore. Importantly, a change in survey protocol after 1996, which reduced the transect width from 400 m to 100 m, resulted in lower counts, and we found that counts were 2.7 times greater when wider transects were surveyed. Inter-annual fluctuations in counts were high, but we found no significant relationships between bird counts and either large-scale oceanographic cycles or more localized indicators of ocean productivity. Compared to previous analyses of this dataset, which showed strong declines, the absence of a trend in at-sea counts is more in line with trends derived from systematic radar counts conducted within the Haida Gwaii conservation region over a similar period (−2.8% per year). Our study emphasizes the need to investigate fluctuations in at-sea counts more closely to understand what may be driving peaks in at-sea counts, including possible movement of birds between regions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.047 | 0.006 |
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; both teacher heads agree on what is shown here.
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".