Standardization of commercial catch data from multiple gears in mixed fisheries accounting for preferential sampling, catchability, and fishing effort
Bibliographic record
Abstract
Commercial fisheries constitute a valuable source of high-resolution information that can aid in assessing stocks and establishing management schemes. Especially, multi-gear and multi-species fisheries can provide fine-scale insights in space and time with regards to the patterns in species distribution and abundance as well as to the comparative behavior of the fishing gears deployed. In this work we propose a Generalized Additive Modeling framework to standardize catch data collected through observer monitoring programs using a 2018–2021 dataset from the eastern Ionian (Mediterranean Sea, FAO GFCM GSA20) as a case study of data-poor mixed fisheries. Our framework extends the standardization procedures by accounting for preferential sampling, integrating effort from multiple gears and jointly modeling species. We show that such an integration leads to more robust estimations of abundance for both target and by-catch species as well as decreases inference uncertainty. Regarding single stocks, the identification of the independent effect of factors (e.g. spatial, temporal, fishing effort, gear, skipper effect) can aid in monitoring and management decisions; furthermore, an objective index of abundance is estimated that can be used to infer inter-annual trends from more extended time-series useful for stock assessments . Using standardized catch values, we have generated seasonal maps of species distribution and multiple-species persistence hotspots that are useful for designing spatiotemporal management restrictions and also informative of species ecology. We also address the effect of the technical (selectivity) and behavioral aspects of the fishing gears to inform gear-based management. Finally, we demonstrate how this broad inferential process can be condensed to form species assemblages (based on their shared responses on drivers of catch and abundance) as well as fishing gear assemblages (based on their catch profiles and the apparent heterogeneity between vessels deploying common gears) that can act as units of reference for management. Apart from an objective estimation of stock abundance in time and space, our standardization framework illustrates how ecological, technical and behavioral aspects of mixed fisheries can be collectively evaluated to inform stock assessment and management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".