Conservation of Earth’s biodiversity is embedded in Indigenous fire stewardship
Bibliographic record
Abstract
117, 12904-12914 (2020)]. With the aim of assessing the impacts of Indigenous fire stewardship on biodiversity and species function across Earth's major terrestrial biomes, we conducted a review of relevant primary data papers published from 1900 to present. We examined how the frequency, seasonality, and severity of human-ignited fires can improve or reduce reported metrics of biodiversity and habitat heterogeneity as well as changes to species composition across a range of taxa and spatial and temporal scales. A total of 79% of applicable studies reported increases in biodiversity as a result of fire stewardship, and 63% concluded that habitat heterogeneity was increased by the use of fire. All studies reported that fire stewardship occurred outside of the window of uncontrollable fire activity, and plants (woody and nonwoody vegetation) were the most intensively studied life forms. Three studies reported declines in biodiversity associated with increases in the use of high-severity fire as a result of the disruption of Indigenous-controlled fire regimes with the onset of colonization. Supporting Indigenous-led fire stewardship can assist with reviving important cultural practices while protecting human communities from increasingly severe wildfires, enhancing biodiversity, and increasing ecosystem heterogeneity.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".