Avian vomitomics: using seabird regurgitations to assess forage fish size and age distributions as a complement to traditional survey methods
Bibliographic record
Abstract
Abstract Assessing fish size and age distributions is essential for understanding recruitment dynamics, yet traditional survey methods may underrepresent early life stages of pelagic species due to methodological constraints. This study explores the potential of avian vomitomics—the systematic analysis of seabird regurgitations—as a complementary and non-invasive approach for estimating the fork length (FL) and age of Atlantic mackerel (Scomber scombrus). Using partial fish remains collected from northern gannets (Morus bassanus) and a comprehensive reference dataset from Fisheries and Oceans Canada, we developed predictive models that accurately estimate FL from incomplete specimens and infer age from FL. Our results reveal a sequential incorporation of young-of-the-year (YOY) mackerel into gannet diets, with juveniles becoming more prevalent later in the season compared to older age classes (one-year-old [OYO], two years and older [2Y+]). While seabird-derived data are shaped by prey selectivity and localized foraging behavior, these very biases offer a focused lens on early life stages that are typically underrepresented in standard stock assessments. Integrating avian vomitomics with fisheries surveys provides a more nuanced and ecologically grounded view of mackerel population dynamics, supporting ecosystem-based fisheries management. This approach also opens new avenues for characterizing the spatiotemporal distribution of mackerel age classes by linking diet composition with fine-scale dive location and depth data from northern gannets. While the current method is species-specific, the underlying framework can be adapted to other prey species, provided that adequate morphometric references are available.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".