The Black Oystercatcher as a Sentinel Species in the Recovery of the Northern Abalone
Bibliographic record
Abstract
We documented the diet of the Black Oystercatcher (Haematopus bachmani) on Haida Gwaii, British Columbia, Canada, (2004–2010) with prey remains from 99 territories in Gwaii Haanas National Park Reserve, National Marine Conservation Area Reserve and Haida Heritage Site. The ranking of its four main prey types did not vary annually. Numerically, the prey comprised 50–75% limpets, 14–34% mussels, 8–18% chitons, 1–2% Northern Abalone (Haliotis kamtschatkana), and <1% other species. In 2009, we estimated prey availability and prey preference by Ivlev's electivity index. Northern Abalone, limpets, and chitons were highly preferred; mussels, turban snails, and barnacles were taken in proportion to their occurrence or avoided. Black Oystercatchers preferred abalone of 50 mm, smaller than the mean size available, in contrast to the selection of larger-than-average prey, typical for other prey species. In 2010, 52% of nesting territories sampled contained remains of Northern Abalone, despite that species' small contribution to the diet. The Northern Abalone has never been reported as prey of the Black Oystercatcher despite its high vulnerability to predation at low tides. We speculate that the recent inclusion of the Northern Abalone in the Black Oystercatcher diet on Haida Gwaii may indicate a greater abundance of Northern Abalone than in other regions of its distribution. The frequency of the oystercatcher's feeding on abalone was unexpected because under Canada's Species at Risk Act, the Northern Abalone was listed as “endangered” in 2010, after the population continued to decline after legal protection from harvest in 1990.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".