Groundtruthing of pelagic forage fish detected by hydroacoustics in a whale feeding area using environmental DNA
Bibliographic record
Abstract
Abstract Conservation of whales, considered as umbrella species in marine environments, requires to be able to understand their relationships with ecosystem components such as prey species, including pelagic fish. However, studying such relationships in nature is a technical challenge. In this study, we used two noninvasive methods in combination, namely hydroacoustics and environmental DNA (eDNA), to detect five pelagic or semipelagic fish species in the Saguenay–St. Lawrence Marine Park (Québec, Canada): the sandlance Ammodytes sp., the Atlantic herring Clupea harengus , the capelin Mallotus villosus , the rainbow smelt Osmerus mordax, and the redfish Sebastes sp. The Marine Park is a major summer feeding ground for a wide diversity of marine wildlife species, including the endangered St. Lawrence beluga whale population. Up to now, scarce research efforts have been dedicated to the estimation of pelagic fish abundance and diversity in this area. Hydroacoustics allowed to easily discriminate the classification of echoes from fish, and with certain limitations to distinguish swim bladder fish from fish without swim bladder. We used eDNA to groundtruth acoustics data and to improve species identification. eDNA analyses especially demonstrated that the capelin was the most predominant species, while the abundance of the redfish and the sandlance was strongly variable over the 2 years of the study. Our results also suggest that there are annual fluctuations in prey availability that marine mammals encounter in this area. Although the approach we used is not without constraints that should be addressed in future studies, we hope that this study will contribute to science‐based conservation and fisheries management policies.
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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".