A species-specific catchability model to partition hydroacoustic total salmon counts
Bibliographic record
Abstract
ABSTRACT Objective Fisheries sonar systems can yield accurate and precise total fish counts but cannot unambiguously differentiate fish species with similar or overlapping size and target-strength distributions. To obtain species-specific abundances for the management of fisheries of individual species, the total fish count produced by an acoustic system must be partitioned into abundances of individual species by test-fishing-based species composition methods. However, species composition estimates based on traditional models for catch-per-unit-effort (CPUE) data can be biased due to differences in swimming speed, body size, and spatial distributions between fish. The objective of this study is to establish a generalized CPUE model linking the catch data with the underlying compositions of fish species that display unequal catchabilities and saturation levels to fishing nets. Methods We propose a relationship between CPUE and fish abundance through a multispecies disc function model that is linear at low abundances but predicts gear saturation, with initial slope (catchability) that varies with fish species. Fitting this model to historical CPUE data yields maximum-likelihood estimates of species-specific catchabilities and saturation factors for the model. The CPUE-based species estimates (likely biased) are treated as an input to the derived model to obtained corrected species estimates. Results The generalized CPUE model was applied to acoustic counts of Pacific salmon returning to the Fraser River in British Columbia from 2008–2018 seasons acquired at two acoustic fish-counting sites on the lower river. The model resulted in substantial corrections for abundance estimates for Sockeye Salmon Oncorhynchus nerka, Pink Salmon O. gorbuscha, and Chinook Salmon O. tshawytscha. Chinook Salmon showed severe gear saturation that led to downward bias in abundance estimates even after correcting for saturation effects. Conclusions Our study showed that for a gill-net-based test fishery operation, Pink Salmon had a lower catchability estimate than Sockeye Salmon with a relative catchability of 40−50%, whereas Chinook Salmon’s relative catchability was in the range of 300−400%. These unequal catchabilities must be taken into account when partitioning total acoustic salmon count using CPUE data to avoid the deflation of abundances of Pink Salmon and the inflation of Chinook Salmon.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".