Integrating hydroacoustic and telemetric surveys to estimate fish abundance: a new approach to an old problem
Bibliographic record
Abstract
Population abundance is a critical metric in fisheries and conservation, but it is very difficult to measure accurately. Existing estimation methods present significant challenges: mark–recapture methods are time- and labour-intensive, and hydroacoustic echo counting methods face issues with target identity and the habitat types where they can be effectively applied. We present a new methodology for abundance estimation that can improve the reliability of echo counting methods. Split beam hydroacoustic survey data are integrated with telemetry data from fish bearing acoustic transponder tags. These tags are counted by a spatially and temporally concurrent multibeam acoustic survey to produce mark–recapture abundance estimates. We assessed this approach on four wild lake trout populations, ranging in abundance from ∼200 to ∼3000 adults. Our abundance estimates were consistent with those derived from conventional Schnabel and Jolly–Seber mark–recapture studies. We show that the precision achievable with this method in 1 year of field work rivals that provided by long-term (>10 years) continuous mark–recapture studies. We also discuss other ecological questions that could be addressed with this approach.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".