Fisheries decision-makers’ perspectives on the use of historical data to inform assessment and management
Bibliographic record
Abstract
The lack of historical perspectives has hindered effective fisheries management. Historical data can help address shifted baselines in fisheries, but the process of integrating historical data into fisheries decision-making has not been clear in part because the data needs of decision-makers have not been assessed. Using Mexico as a case study, we conducted interviews with fisheries decision-makers to identify the current use of historical data, data needs, and pathways for integrating historical data into fisheries decision-making. We found that some historical data are currently used for decision-making, with the earliest archival data in use deriving from the 1850s. However, we also found that additional historical data existed for many fisheries that were not used, with an average of almost 30 years of data gap between the oldest data that exist and the oldest data used. Fisheries decision-makers described a need for six different types of historical information: catch, socioeconomic, biological, ecological, spatial and technological. Together respondents describe nine interrelated pathways to integrate historical data into fisheries decision-making both within and outside of the management structure, and identify 43 specific fisheries management contexts in which historical data would benefit decision-making. This research uses experts’ knowledge to illustrate how historical data can be used to improve fisheries management beyond the theoretical pathways and highlights the need for collaborative research to identify, collect, and apply the best available information in fisheries decision-making.
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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 | 0.074 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| 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 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".