Sex‐specific diet differences in harbor seals (<i>Phoca vitulina</i>) via spatial assortment
Bibliographic record
Abstract
). Previous work at a limited number of locations has shown that male seal diet contains more salmon than that of female seals and that sex ratios at haul-out sites differ spatiotemporally. This intrapopulation variation in predation may result in greater effects on salmon than suggested by models assuming equal spatial distribution and diet proportion. To address the generality of these patterns, we examined the sex ratios and diet of male and female harbor seals from 13 haul-out sites in the inland waters of Washington State and the province of British Columbia during 2012-2018. DNA metabarcoding was conducted to determine prey species proportions of individual scat samples. The sex of harbor seals was then determined from each scat matrix sample with the use of quantitative polymerase chain reaction (qPCR). We analyzed 2405 harbor seal scat samples using generalized linear mixed models (GLMMs) to examine the factors influencing harbor seal sex ratio at haul-out sites and permutational multivariate analysis of variance (PERMANOVA) to examine the influence of sex and haul-out site on harbor seal diet composition. We found that the overall sex ratio was 1:1.02 (female:male) with notable spatiotemporal variation. Salmoniformes were about 2.6 times more abundant in the diet of males than in the diet of females, and Chinook salmon comprised ca. three times more of the average male harbor seal's diet than the average female's diet. Based on site-specific sex ratios and diet data, we identified three haul-out sites where Chinook salmon appear to be under high predation pressure by male harbor seals: Cowichan Bay, Cutts Area, and Fraser River. Our study indicates that combining sex-specific pinniped diet data with the sex ratio of haul-out sites can help identify priority sites of conservation concern.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".