Analyzing Pellets and Feces of African Royal Terns (<i>Thalasseus maximus albididorsalis</i>) Results in Different Estimates of Diet Composition
Bibliographic record
Abstract
A frequently used method to estimate diet composition is based on the identification of fish otoliths present in pellets and feces. However, whether pellets and feces provide similar unbiased estimates of the diet remains poorly understood. The diet of African Royal Terns (Thalasseus maximus albididorsalis) breeding in the Parc National du Delta du Saloum, Senegal, was studied. Prey species composition based on otoliths in freshly regurgitated pellets and a mixture of pellets and feces (excrement) accumulated near nests during the incubation period were compared. Altogether, 59 fish species were identified. Pellets contained far less prey species than excrement. Maximum diet overlap between excrement and pellets varied between 0.34 and 0.43 (mean = 0.36). Differences between minimum and maximum overlap between both sample types were small in all years. Pellets contained almost exclusively large otoliths (widths 3.0–8.5 mm), whereas excrement contained two fractions: large sized ones, identical to those present in the pellets and smaller-sized ones (0.5–3.0 mm) originating from feces. It is hypothesized that large otoliths cannot pass the intestinal tracts of the birds and are therefore regurgitated. Differences in prey species composition in pellets and excrement could potentially be explained by a combination of seasonal changes in availability of prey species and size of otoliths. Neither pellets nor feces alone give an unbiased picture of the diet of African Royal Terns.
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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".