From archives to conservation: why historical data are needed to set baselines for marine animals and ecosystems
Bibliographic record
Abstract
Abstract Intergenerational loss of information about the abundance of exploited species can lead to shifting baselines, which have direct consequences for how species and ecosystems are managed. Historical data provide a means of regaining that information, but they still are not commonly applied in marine conservation and management. Omission of relevant historical information typically results in assessments of conservation status that are more optimistic, recovery targets that are lower, and fisheries quotas that are higher than if long‐term data were considered. Here, we review data and methods that can be used to estimate historical baselines for marine species including bony fishes, sharks, turtles, and mammals, demonstrate how baselines used in management change when historical data are included, and provide specific examples of how data from the past can be applied in management and conservation including extinction risk assessment, recovery target setting, and management of data‐poor fisheries. Incorporating historical data into conservation and management frameworks presents challenges, but the alternative—losing information on past population sizes and ecological variability—represents a greater risk to effective management of marine species and ecosystems.
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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 | 0.072 | 0.192 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.010 | 0.024 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".