From archives to conservation: why historical data are needed to set baselines for marine animals and ecosystems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 it