Global research priorities for historical ecology to inform conservation
Bibliographic record
Abstract
Historical ecology draws on a broad range of information sources and methods to provide insight into ecological and social change, especially over the past ~12,000 years. While its results are often relevant to conservation and restoration, insights from its diverse disciplines, environments, and geographies have frequently remained siloed or underrepresented, restricting their full potential. Here, we synthesize knowledge from the fields of history, anthropology, paleontology, and ecology from scholars and practitioners working in marine, freshwater, and terrestrial environments on six continents and various archipelagoes to identify global research priorities for historical ecology to influence conservation. Specifically, we identify and address questions within four key priority areas: (i) methods and concepts, (ii) knowledge co-production and community engagement, (iii) policy and management, and (iv) climate change impacts. This work highlights the ways that historical ecology has developed and matured in its use of novel information sources, its efforts to move beyond extractive research practices and toward knowledge co-production, and its potential use in addressing management challenges, including climate change. Together, we demonstrate the ways that this field has brought together researchers across disciplines, connected academics to practitioners, and engaged communities to create and apply knowledge of the past to addressing the challenges of our shared future.
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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 | 0.053 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.023 | 0.034 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.022 | 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".