Using the prey captured by breeding Crested Terns to assess the availability of forage fish for other coastal meso-predators
Bibliographic record
Abstract
Context The diets of seabirds are an effective indicator of changes in forage fish abundance and availability providing insight into how changing fish stocks impact the meso-predators that consume them. Non-invasive methods for monitoring seabird diets are a valuable tool in conservation. Aims We aimed to assess the availability of forage fish that were carried by Crested Terns for the threatened Little Penguins (Eudyptula minor) and other meso-predators on Penguin Island, Western Australia. Methods We used digital photography with 400–500 mm telephoto lenses to identify prey carried to Crested Tern (Thalasseus bergii) colonies on Penguin Island during the 2021, 2022 and 2023. Results Crested Terns breeding on Penguin Island captured a wider range of prey (62 species) than recorded in other diet studies at other colonies in southern Australia and South Africa. Blue Sprat (Spratelloides robustus) and Sandy Sprat (Hyperlophus vittatus) dominated the forage fish taken by the terns in 2021 and 2022 breeding seasons with Sardines (Sardinops vagax) and Anchovies (Engraulis australis) becoming more common in 2023. Conclusions A recruitment event was recorded in Sandy Sprats in 2021 after a near record winter rainfall in the region. This recruitment event was significant as Sandy Sprats, a critical resource for Little Penguins breeding on Penguin Island, were thought to have been unavailable in local waters since a marine heatwave event in 2011. Implications Early indications were consistent with Crested Tern diet influencing Penguin breeding performance; however, this can only be confirmed with a longer time series. Ongoing monitoring of forage fish using bill-loading Crested Terns may have an important role in the future management of the Little Penguin colony on Penguin Island.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".