DNA analysis of scats reveals spatial and temporal structure in the diversity of harbour seal diet from local haulouts to oceanographic bioregions
Bibliographic record
Abstract
Predation shapes ecosystems, and quantifying the impacts of predation on the distribution and abundance of prey requires substantial effort at appropriate spatial and temporal scales for diet estimation of predators. Here, we present diet estimates of a marine predator (harbour seal Phoca vitulina richards i ) from scat collections (n = 3420) sampled at multiple haulout sites (n = 64) in the Strait of Georgia and other coastal regions around Vancouver Island, British Columbia, Canada, between 2015 and 2019. DNA metabarcoding and hard part analysis were used to identify the proportion and sizes of prey species consumed, respectively. We found that harbour seals consumed 62 primary prey species. Diversity in the diet was highly affected by the number of samples collected and varied at small spatial scales (haulout sites) as well as among broad bioregions. Three to 5 species dominated the diet depending on location, season, and year, including Pacific hake Merluccius productus , Pacific herring Clupea pallasii , and walleye pollock Gadus chalcogrammus . Within the Strait of Georgia, both male and female harbour seals consumed more hake and walleye pollock in areas and seasons in which they were more abundant. Harbour seals consumed a wide size range of prey that also varied by species, season, and region. These results indicate that harbour seal foraging is influenced by both the local abundance and composition of prey. Attempts to model the impact of predation on species of concern risk bias in their estimates and underrepresenting uncertainty if spatial and temporal variation in the diet is not accounted for.
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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 | 0.000 | 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".