Relationships between Human Population Density and Burned Area at Continental and Global Scales
Open accessAbstract
We explore the large spatial variation in the relationship between population density and burned area, using continental-scale Geographically Weighted Regression (GWR) based on 13 years of satellite-derived burned area maps from the global fire emissions database (GFED) and the human population density from the gridded population of the world (GPW 2005). Significant relationships are observed over 51.5% of the global land area, and the area affected varies from continent to continent: population density has a significant impact on fire over most of Asia and Africa but is important in explaining fire over < 22% of Europe and Australia. Increasing population density is associated with both increased and decreased in fire. The nature of the relationship depends on land-use: increasing population density is associated with increased burned are in rangelands but with decreased burned area in croplands. Overall, the relationship between population density and burned area is non-monotonic: burned area initially increases with population density and then decreases when population density exceeds a threshold. These thresholds vary regionally. Our study contributes to improved understanding of how human activities relate to burned area, and should contribute to a better estimate of atmospheric emissions from biomass burning.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the modern period, the link between people and fire seems strongest at middle levels of development, with fires becoming less likely beyond certain population density, urbanization, or fossil fuel dependence.
“The human–fire connection in the modern era appears strongest at intermediate levels of development, as fires become less likely in the landscape beyond a certain population density, level of urbanization, or dependence on fossil fuels (11, 13, 24).”
From Human-started wildfires expand the fire niche across the United States (2017) · cites this paper as [11]
Geographic variation in regional and continental-scale fire activity in the US is strongly associated with proxies for human-caused changes such as population density, road density, and land-use and development patterns.
“Geographic variability in regional and continental-scale fire activity in the United States is strongly tied to proxies for these human-caused changes, including population and road density, and different land-use and development patterns (10–15).”
From Human-started wildfires expand the fire niche across the United States (2017) · cites this paper as [11]
The anthropogenic influence on fire activity is well known but still a matter of debate, as the nature of this influence is complicated.
“The anthropogenic influence (AI) on fire activity is well known, but is still a matter of debate, as the nature of this influence is complicated [33-35].”
From A Global Index for Mapping the Exposure of Water Resources to Wildfire (2016) · cites this paper as [34]
Relationships between fire and global population patterns have been identified.
“Relationships between fire and global population patterns have been identified [16–18]”
From Incorporating Anthropogenic Influences into Fire Probability Models: Effects of Human Activity and Climate Change on Fire Activity in California (2016) · cites this paper as [17]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Koenker, R. W., & Bassett, G. W. (1978) · Econometrica · doi:10.2307/1913643 · Cited by 13,479 · see citing works
Determinants of woody cover in African savannas.
Sankaran, M., Hanan, N. P., Scholes, R. J., Ratnam, J., Augustine, D. J., Cade, B. S., Gignoux, J., Higgins, S. I., Le Roux, X., Ludwig, F., et al. (2005) · Nature · doi:10.1038/nature04070 · Cited by 1,963 · see citing works
Putting people in the map: anthropogenic biomes of the world
Ellis, E. C., & Ramankutty, N. (2007) · Frontiers in Ecology and the Environment · doi:10.1890/070062 · Cited by 1,858 · see citing works
GLC2000: a new approach to global land cover mapping from Earth observation data
claude, B. E. M., & Belward, A. S. (2005) · International Journal of Remote Sensing · doi:10.1080/01431160412331291297 · Cited by 1,799 · see citing works
Anthropogenic transformation of the biomes, 1700 to 2000
Ellis, E. C., Klein Goldewijk, K., Siebert, S., Lightman, D., & Ramankutty, N. (2010) · Global Ecology and Biogeography · doi:10.1111/j.1466-8238.2010.00540.x · Cited by 1,543 · see citing works
Likelihood of a model and information criteria
Akaike, H. (1981) · Journal of Econometrics · doi:10.1016/0304-4076(81)90071-3 · Cited by 1,305 · see citing works
PIECEWISE REGRESSION: A TOOL FOR IDENTIFYING ECOLOGICAL THRESHOLDS
Toms, J. D., & Lesperance, M. L. (2003) · Ecology · doi:10.1890/02-0472 · Cited by 1,038 · see citing works
Global Pyrogeography: the Current and Future Distribution of Wildfire
Krawchuk, M. A., Moritz, M. A., Parisien, M.-A., Van Dorn, J., & Hayhoe, K. (2009) · PLoS ONE · doi:10.1371/journal.pone.0005102 · Cited by 1,000 · see citing works
Climate change and forest fires
Flannigan, M., Stocks, B. J., & Wotton, B. M. (2000) · The Science of The Total Environment · doi:10.1016/s0048-9697(00)00524-6 · Cited by 987 · see citing works
Climate and human influences on global biomass burning over the past two millennia
Marlon, J. R., Bartlein, P. J., Carcaillet, C., Gavin, D. G., Harrison, S. P., Higuera, P. E., Joos, F., Power, M. J., & Prentice, I. C. (2008) · Nature Geoscience · doi:10.1038/ngeo313 · Cited by 823 · see citing works
Self-citationWhat limits fire? An examination of drivers of burnt area in Southern Africa
ARCHIBALD, S., ROY, D. P., Van WILGEN, B. W., & SCHOLES, R. J. (2009) · Global Change Biology · doi:10.1111/j.1365-2486.2008.01754.x · Cited by 773 · see citing works
A climate-change risk analysis for world ecosystems
Scholze, M., Knorr, W., Arnell, N. W., & Prentice, I. C. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0601816103 · Cited by 756 · see citing works
Self-citationHuman influence on California fire regimes
Syphard, A. D., Radeloff, V. C., Keeley, J. E., Hawbaker, T. J., Clayton, M. K., Stewart, S. I., & Hammer, R. B. (2007) · Ecological Applications · doi:10.1890/06-1128.1 · Cited by 661 · see citing works
Evolution of human-driven fire regimes in Africa
Archibald, S., Staver, A. C., & Levin, S. A. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1118648109 · Cited by 376 · see citing works
Global and regional analysis of climate and human drivers of wildfire
Aldersley, A., Murray, S. J., & Cornell, S. E. (2011) · Science of The Total Environment · doi:10.1016/j.scitotenv.2011.05.032 · Cited by 311 · see citing works
Barbosa, P. M., Stroppiana, D., Grégoire, J., & Pereira, J. M. C. (1999) · Global Biogeochemical Cycles · doi:10.1029/1999gb900042 · Cited by 311 · see citing works
Self-citationBurning the seasonal mosaic: preventative burning strategies in the wooded savanna of southern Mali
Laris, P. (2002) · Human Ecology · doi:10.1023/a:1015685529180 · Cited by 251 · cited 1× · see citing works
“The impact of landscape fragmentation on reducing fire in agricultural area has also been documented [16,39,40].”
BackgroundVegetation burning in the year 2000: Global burned area estimates from SPOT VEGETATION data
Tansey, K. J., Grégoire, J., Stroppiana, D., Sousa, A., Silva, J. M. N., Pereira, J. M. C., Boschetti, L., Maggi, M., Brivio, P. A., Fraser, R. H., et al. (2004) · Journal of Geophysical Research Atmospheres · doi:10.1029/2003jd003598 · Cited by 241 · see citing works
Self-citationLenihan, J. M., Bachelet, D., Neilson, R. P., & Drapek, R. (2008) · Climatic Change · doi:10.1007/s10584-007-9362-0 · Cited by 217 · see citing works
How does fire intensity and frequency affect miombo woodland tree populations and biomass?
Ryan, C. M., & Williams, M. (2010) · Ecological Applications · doi:10.1890/09-1489.1 · Cited by 208 · see citing works
Simulating fire regimes in human-dominated ecosystems: Iberian Peninsula case study
Venevsky, S., Thonicke, K., Sitch, S., & Cramer, W. (2002) · Global Change Biology · doi:10.1046/j.1365-2486.2002.00528.x · Cited by 190 · cited 1× · see citing works
“However, those models that explicitly include anthropogenic fire generally focus on human impacts on ignitions and furthermore employ either a universal population density value as a threshold for anthropogenic fire ignitions [47], or single-peaked global function of population density [48].”
BackgroundModeling fire and the terrestrial carbon balance
Prentice, I. C., Kelley, D. I., Foster, P. N., Friedlingstein, P., Harrison, S. P., & Bartlein, P. J. (2011) · Global Biogeochemical Cycles · doi:10.1029/2010gb003906 · Cited by 170 · see citing works
Self-citationSilva, J. M. N., Carreiras, J., Rosa, I. M. D., & Pereira, J. (2011) · Journal of Geophysical Research Atmospheres · doi:10.1029/2011jd016056 · Cited by 61 · see citing works
Self-citationExploratory spatial data analysis of global MODIS active fire data
Oom, D., & Pereira, J. M. C. (2012) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2012.07.018 · Cited by 48 · see citing works
Self-citationSá, A. C. L., Pereira, J. M. C., Charlton, M. E., Mota, B., Barbosa, P., & Fotheringham, A. S. (2010) · Journal of Geographical Systems · doi:10.1007/s10109-010-0123-7 · Cited by 47 · see citing works
Self-citationLoboda, T. V., Giglio, L., Boschetti, L., & Justice, C. O. (2012) · Frontiers of Earth Science · doi:10.1007/s11707-012-0313-3 · Cited by 41 · see citing works
The human geography of fire: a research agenda
Pyne, S. J. (2009) · Progress in Human Geography · doi:10.1177/0309132508101598 · Cited by 35 · see citing works
An Assessment of the Effectiveness of Multiple Hypothesis Testing for Geographical Anomaly Detection
Brunsdon, C., & Charlton, M. E. (2011) · Environment and Planning B Planning and Design · doi:10.1068/b36093 · Cited by 28 · see citing works
Flagged first, then by citations
Research works printed but not linked: 14
- [2]Johnson E (1996) Fire and Vegetation Dynamics: Studies from the North American Boreal Forest. Cambridge University Press Cambridge.
- [3]Pyne SJ, Andrews PL, Laven RD (1996) Introduction to wildland fire. New York, John Wiley and Sons p. 769.
- [6]IPCC (2007) Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [ Solomon S Qin D Manning M Chen Z Marquis M Cambridge University Press, Cambridge, UK and New York, NY, USA.
- [7]Sanderson EW, Jaiteh M, Levy MA, Redford KH, Wannebo AV et al. (2002) The Human Footprint and the Last of the Wild. BioScience, 52(10): 891- 904.
- [9]Guyette RP, Muzika RM, Dey DC (2002) Dynamics of an anthropogenic fire regime. Ecosystems 5: 472- 486. doi: 10.1007/s10021-002-0115-7. · DOI
- [19]Brunsdon CA, Fotheringham AS, Charlton ME (1998) Geographically weighted regression–modelling spatial non-stationarity. Stat 47(3): 431– 443.
- [20]Fotheringham S, Brunsdon C, Charlton M (2002) Geographically Weighted Regression: the analysis of spatially varying relationships. John Wiley & Sons, UK.
- [21]Openshaw S, Taylor PJ (1981) The modifiable areal unit problem. in: Quantitative geography: a British View, (eds) N. Wrigley and R.J. Bennett, (Routledge and Kegan Paul: London), p. 60-70
- [23]Byrne G, Charlton M, Fotheringham S (2009) Multiple dependent hypothesis tests in geographically weighted regression. In: Lees BG Laffan SW 10th International conference on geocomputation. UNSW, Sydney November-December
- [27]Koenker R (2005) Quantile Regression. Cambridge University Press Cambridge. UK.
- [29]Chiu GS (2003) Bent cable Regression for Assesing Abruptness of Change. PhD Thesis, University of British Columbia: Canada.
- [30]Cade SB, Noon BR (2003) A gentle introduction to quantile regression for ecologists. Front Ecol Environment 1(8): 412- 420. Available online at: doi:10.1890/1540-9295(2003)001[0412:AGITQR]2.0.CO; 2 · DOI
- [37]Gralewicz NJ, Nelson TA, Wulder MA (2011) Spatial and temporal patterns of wildfire ignitions in Canada from 1980 to 2006. International Journal of Wildland Fire 21(3): 230- 242.
- [41]Page Le Y, van der Werf GR, Morton DC, Pereira JMC (2010) Modelling fire-driven deforestation potential in Amazonia under current and projected climate conditions. Journal of Geophysical Research 115: 11
Cited by
Sorted by citations
Human-started wildfires expand the fire niche across the United States
Balch, J. K., Bradley, B. A., Abatzoglou, J. T., Nagy, R. C., Fusco, E. J., & Mahood, A. L. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1617394114 · Cited by 1,043 · see citing works
The spatially varying influence of humans on fire probability in North America
Parisien, M.-A., Miller, C., Parks, S. A., DeLancey, E. R., Robinne, F.-N., & Flannigan, M. D. (2016) · Environmental Research Letters · doi:10.1088/1748-9326/11/7/075005 · Cited by 166 · see citing works
Mann, M. L., Batllori, E., Moritz, M. A., Waller, E. K., Berck, P., Flint, A. L., Flint, L. E., & Dolfi, E. (2016) · PLOS ONE · doi:10.1371/journal.pone.0153589 · Cited by 151 · see citing works
Fire Disturbance in Tropical Forests of Myanmar—Analysis Using MODIS Satellite Datasets
Biswas, S., Lasko, K. D., & Vadrevu, K. P. (2015) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2015.2423681 · Cited by 41 · see citing works
A Global Index for Mapping the Exposure of Water Resources to Wildfire
Robinne, F.-N., Miller, C., Parisien, M.-A., Emelko, M., Bladon, K., Silins, U., & Flannigan, M. (2016) · Forests · doi:10.3390/f7010022 · Cited by 41 · see citing works
- Authors
- Bistinas, I., Oom, D., Sá, A. C. L., Harrison, S. P., Prentice, I. C., & Pereira, J. M. C.
- Affiliations
- University of Lisbon (PT)
- University of Reading (GB)
- Imperial College London (GB)
- Corresponding author
- Prentice, I. Colin
- ORCID
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2013-12-16
- Volume
- 8
- Issue
- 12
- Pages
- e81188
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC3865302
- OpenAlex
- W2018994427
- Cited by
- 110
- References
- 30
- Citations by year
- 2026: 8 · 2025: 9 · 2024: 6 · 2023: 18 · 2022: 12 · 2021: 11 · 2020: 8 · 2019: 7 · 2018: 8 · 2017: 7 · 2016: 7 · 2015: 5 · 2014: 4
- FWCI
- 4.07
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- CentAUR (University of Reading) (acceptedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Spiral (Imperial College London) (submittedVersion, cc-by)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Fire effects on ecosystems
- Physical Sciences › Environmental Science › Global and Planetary Change › Land Use and Ecosystem Services
- Physical Sciences › Environmental Science › Global and Planetary Change › Impact of Light on Environment and Health
- Keywords
- population density, burned area, fire emissions, biomass burning, land use, rangeland, cropland, non-monotonic relationship, population thresholds, Global Fire Emissions Database, spatial heterogeneity
- MeSH terms
- Climate, Fires, Humans, Population Density, Ecosystem, Biomass
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 ›
Are you an author, or do you see a problem with this page? Request a review