Pathways for integrating historical information into fisheries decision‐making
Bibliographic record
Abstract
Abstract Historical information has provided key insights into long‐term ecological change to marine species and ecosystems, with value to fisheries. Yet, pathways to integrate these diverse data sources into fisheries decision‐making have not been clear. Here, we identify an array of biological, ecological, and social information suitable for contemporary science‐based decision‐making, derived from local ecological knowledge, historical archives, archaeological middens and palaeoecological material. We outline two broad pathways to integrate these historical data into fisheries decision‐making, demonstrating that data‐driven use of historical information is relevant across a range of management contexts. First, historical information can inform fisheries assessments that range from simple to complex, affecting indicators of stock status. Second, it can inform estimates of biological potential and social preference, affecting the choice of fisheries reference points. Using the Caribbean Sea as an example, we illustrate these ideas with case studies representing diverse species and historical data types. Integrating historical data can improve indicators of the current state of fish populations and result in management decisions based on a more complete understanding of a potential range of variation, avoiding shifted baselines. The urgency of this work is underscored by accelerating environmental changes and the rapid loss of invaluable historical information sources. By illuminating pathways, our goal is to increase the accessibility of these types of information and to underscore that scientists, managers, and resource users have roles to play in identifying and integrating relevant long‐term data at various spatial and temporal scales to sustainably manage marine fisheries.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".