What limits fire? An examination of drivers of burnt area in Southern Africa
Open accessAbstract
The factors controlling the extent of fire in Africa south of the equator were investigated using moderate resolution (500 m) satellite‐derived burned area maps and spatial data on the environmental factors thought to affect burnt area. A random forest regression tree procedure was used to determine the relative importance of each factor in explaining the burned area fraction and to address hypotheses concerned with human and climatic influences on the drivers of burnt area. The model explained 68% of the variance in burnt area. Tree cover, rainfall in the previous 2 years, and rainfall seasonality were the most important predictors. Human activities – represented by grazing, roads per unit area, population density, and cultivation fraction – were also shown to affect burnt area, but only in parts of the continent with specific climatic conditions, and often in ways counter to the prevailing wisdom that more human activity leads to more fire. The analysis found no indication that ignitions were limiting total burnt area on the continent, and most of the spatial variation was due to variation in fuel load and moisture. Split conditions from the regression tree identified (i) low rainfall regions, where fire is rare; (ii) regions where fire is under human control; and (iii) higher rainfall regions where burnt area is determined by rainfall seasonality. This study provides insights into the physical, climatic, and human drivers of fire and their relative importance across southern Africa, and represents the beginnings of a predictive framework for burnt area.
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In southern Africa, burn area decreased with increased grazing, road density and human population density.
“In southern Africa, Archibald et al. (2009) showed that burn area decreased with increased grazing, road density and human population density.”
From The human dimension of fire regimes on Earth (2011) · cites this paper as Archibald et al., 2009
The ‘Split conditions’ section says human activities can decrease burnt area and that, in certain areas, the area burned depends on grazing and road-network density. ‘The effect of people’ reports a negative association with population density, but its regression analysis finds that association only above about 10 people per km². The citing sentence omits these limits, and the cited passages do not establish a general decrease with each increase in grazing or road density. (LLM, conf=0.84)
Experimental — accuracy not yet measuredAt large enough spatial scales, fuel loads rather than ignition events determine fire occurrence in savannas.
“This finding is consistent with the observation that at large enough spatial scales, it is fuel loads rather than ignition events that determine fire occurrence in savannas [26].”
From A Disease-Mediated Trophic Cascade in the Serengeti and its Implications for Ecosystem C (2009) · cites this paper as [26]
In “Drivers of fire: fuel and weather,” the paper says that, at a subcontinental scale, annual burnt area is controlled mainly by fuel; “Drivers of fire: ignitions” finds no indication that ignition frequency limits burnt area. The claim substitutes fire occurrence for burnt area, but the paper also reports fewer detected fires in sparsely populated regions, without a reduction in burnt area. It therefore supports the comparison for area burned, not unequivocally for fire occurrence. (LLM, conf=0.86)
Experimental — accuracy not yet measuredIn the tropical savanna biome, humans influence fire regimes but to a much lower degree than in tropical rain forests.
“Here, humans influence fire regimes but to a much lower degree than in tropical rain forests (Archibald et al., 2009).”
From The human dimension of fire regimes on Earth (2011) · cites this paper as Archibald et al., 2009
The ‘Relative importance of environmental variables’ section reports that human activity influences burnt area in southern Africa, while tree cover, rainfall and dry-season length are more important predictors. The paper does not compare that influence with human influence on fire regimes in tropical rain forests, so it does not support the claimed difference in degree. (LLM, conf=0.86)
Experimental — accuracy not yet measured
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- Authors
- ARCHIBALD, S., ROY, D. P., Van WILGEN, B. W., & SCHOLES, R. J.
- Affiliations
- Council for Scientific and Industrial Research (ZA)
- South Dakota State University (US)
- Corresponding author
- ARCHIBALD, SALLY
- Venue
- Global Change Biology
- Publisher
- Wiley
- Published
- 2009-02-06
- Volume
- 15
- Issue
- 3
- Pages
- 613-630
- ISSN
- 1354-1013 (ISSN-L)
- 1365-2486
- OpenAlex
- W2157206590
- Cited by
- 773
- References
- 64
- Citations by year
- 2026: 42 · 2025: 41 · 2024: 56 · 2023: 56 · 2022: 58 · 2021: 43 · 2020: 47 · 2019: 42 · 2018: 61 · 2017: 37 · 2016: 47 · 2015: 38 · 2014: 39 · 2013: 39 · 2012: 45
- FWCI
- 35.23
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- Global Change Biology (publishedVersion)
- SUNScholar (Stellenbosch University) (submittedVersion)
- Open PRAIRIE (South Dakota State University) (submittedVersion)
- Funders
- National Aeronautics and Space Administration
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Fire effects on ecosystems
- Physical Sciences › Environmental Science › Ecology › Rangeland and Wildlife Management
- Physical Sciences › Environmental Science › Global and Planetary Change › Land Use and Ecosystem Services
- Keywords
- fire regimes, burned area, fire drivers, human influence on fire, precipitation seasonality, tree cover, fuel load, moisture, grazing, roads, population density, cultivation, ignition, fire prediction, Southern Africa
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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