Systematic review of global historical marine ecology reveals geographical and taxonomic research gaps and biases
Bibliographic record
Abstract
The field of historical marine ecology (HME) developed two decades ago to address a lack of knowledge about long-term declines in the ocean. Here, we conduct, to our knowledge, the first global systematic review of HME, analysing 543 peer-reviewed articles to ask: what has been learnt and what gaps remain? The diversity of sources used in HME-from Roman texts to twentieth-century catch records-illustrates the methodological richness of the field. Most articles used documentary sources (68%) and produced quantitative outputs (54%), reflective of HME's origins in marine science. Research focused on economically and culturally valuable taxa like fishes, which account for 41% of articles. Most research found decline (85%), while articles finding increase relied on significantly more recent data, underscoring the need for long-term data to assess decline. Strikingly, we identify geographical gaps and biases that suggest a need for targeted initiatives to support HME in the Global South. For instance, nearly as much research focused on the California Current as the entire Indian Ocean, and 74% of first authors worked in North America and Europe. Understanding the colonial legacy of marine resource extraction and the history of artefact theft that disadvantages Global South researchers should guide the future of HME.This article is part of the theme issue 'Shifting seas: understanding deep-time human impacts on marine 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 | 0.140 | 0.423 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.024 | 0.029 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".