A comparative methodological approach to studying the diet of a recovering marine predator, the grey seal (<i>Halichoerus grypus</i>)
Bibliographic record
Abstract
Anthropogenic influences caused depletion and subsequent recovery of marine predators, but ecological consequences of altered predator abundance are not well understood. Although many methods are used to study predator diets, methodological biases and logistical challenges preclude robust sampling schemes. We aimed to compare two non-invasive methods: metabarcoding scat-derived deoxyribonucleic acid and hard parts analysis of scat for the Northwest Atlantic grey seal ( Halichoerus grypus (Fabricius, 1791)), a species that rebounded after near extirpation. We hypothesized that metabarcoding would detect a greater diversity and frequency of prey, and that notable differences in diet will be detected since prior studies. Grey seal scat samples ( N = 247) were collected between 2018 and 2019 from Monomoy Island, Massachusetts, USA. Metabarcoding detected greater prey richness on average, with more frequent detections of clupeids (Clupeidae) and flatfish (Pleuronectiformes), whereas hard parts analysis more frequently detected phycid hakes ( Urophycis spp. Gill, 1863). Combining methods increased detections of 13 prey taxa, with 32 prey taxa identified overall. Skates (Rajidae), flatfish, clupeids, and sand lance ( Ammodytes spp. Linnaeus, 1758) were top-occurring prey. Our study highlights the importance of using multiple methods to characterize generalist predator diets using non-invasive techniques and suggests grey seal diet has changed since the early 2000s.
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 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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".