Microchemical provenancing of prey remains in cormorant pellets reveals the use of diverse foraging grounds
Bibliographic record
Abstract
Abstract Piscivorous birds in aquatic ecosystems exert predation pressure on fish populations. But the site‐specific impact on fish populations, including stocked and commercially used fish species, remains disputed. One of the key questions for the management of piscivorous birds and fish is determining the origin of prey and thus which fish populations are targeted by the birds. We addressed this question by provenancing otoliths (earstones) of fish obtained from regurgitated pellets of piscivorous birds by otolith microchemistry analysis. We retrieved otoliths from regurgitated pellets of great cormorants (Phalacrocorax carbo sinensis) collected every 2 weeks for 2 years from breeding and roosting colonies at Chiemsee in Bavaria, Germany, and classified them according to family or species. We collected water samples from Chiemsee and potential surrounding foraging grounds. We measured the strontium (Sr) 87Sr/86Sr isotope ratio and Sr mass fraction of water and otoliths using (laser ablation) inductively coupled plasma‐mass spectrometry. We assigned otoliths from regurgitated pellets to habitat clusters of origin by comparing the Sr isotopic and elemental composition of otoliths and waterbodies. In 36% of cormorant pellets collected at Chiemsee, prey was assigned to waterbodies distinct from Chiemsee. Furthermore, cormorants used different foraging sites during 1 day. Microchemical provenancing of prey remains can contribute to identifying foraging sites of piscivorous birds and to what extend the birds switched among foraging sites.
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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) | 0.001 | 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".