Quantifying the effect of predators on endangered species using a bioenergetics approach: Caspian terns and juvenile salmonids in the Columbia River estuary
Bibliographic record
Abstract
We estimated the consumption of juvenile salmonids (Oncorhynchus spp.) and other forage fishes by Caspian terns (Sterna caspia) nesting on Rice Island in the Columbia River estuary in 1997 and 1998 using a bioenergetics modeling approach. The study was prompted by concern that Caspian tern predation might be a substantial source of mortality to out-migrating juvenile salmonids from throughout the Columbia River basin, many populations of which are listed as threatened or endangered under the U.S. Endangered Species Act. The bioenergetics model used estimates of the energy requirements of the tern population and the proportion of tern energy requirements met by various prey types. The resulting estimate of the number of juvenile salmonids consumed by Rice Island Caspian terns was 8.1 million (5.910.4 million) in 1997 and 12.4 million (9.115.7 million) in 1998. Tern predation rates on juvenile salmonids were substantial, representing up to 15% of the juveniles to reach the estuary from some listed populations. Nevertheless, based on simple age-structured models of salmonid populations, it appears unlikely that management of Caspian tern predation alone would reverse salmonid declines. Management to reduce tern predation could, however, contribute to a comprehensive strategy to recover imperiled salmonid populations in the Columbia River basin.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".