Continental‐scale acoustic telemetry and network analysis reveal new insights into stock structure
Bibliographic record
Abstract
Abstract Delineation of population structure (i.e. stocks) is crucial to successfully manage exploited species and to address conservation concerns for threatened species. Fish migration and associated movements are key mechanisms through which discrete populations mix and are thus important determinants of population structure. Detailed information on fish migration and movements is becoming more accessible through advances in telemetry and analysis methods however such information is not yet used systematically in stock structure assessment. Here, we described how detections of acoustically tagged fish across a continental‐scale array of underwater acoustic receivers were used to assess stock structure and connectivity in seven teleost and seven shark species and compared to findings from genetic and conventional tagging. Network analysis revealed previously unknown population connections in some species, and in others bolstered support for existing stock discrimination by identifying nodes and routes important for connectivity. Species with less variability in their movements required smaller sample sizes (45–50 individuals) to reveal useful stock structure information. Our study shows the power of continental‐scale acoustic telemetry networks to detect movements among fishery jurisdictions. We highlight methodological issues that need to be considered in the design of acoustic telemetry studies for investigating stock structure and the interpretation of the resulting data. The advent of broad‐scale acoustic telemetry networks across the globe provides new avenues to understand how movement informs population structure and can lead to improved management.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".