Human Influences on Wildfire in Alaska from 1988 through 2005: An Analysis of the Spatial Patterns of Human Impacts
Open accessAbstract
Boreal ecosystems in Alaska are responding to climate change in many ways, including changes in the fire regime. While large-scale wildfires are an essential part of the boreal forest ecosystem, humans are changing fire regimes through ignition and suppression. The authors analyzed the impact humans have on fire ignitions and relative area burned with distance into the forest from human access points such as settlements, highways, and major rivers in Alaska from 1988 to 2005. Additionally, a fire prediction model was created to identify drivers for lightning fires in the boreal forest. Human presence increases the number of ignitions near settlements, roads, and rivers and appears to reduce the area burned within 30–40 km of villages and rivers. In contrast to fires near roads and rivers, human presence may somewhat increase the area burned within 30–40 km of highways. The fire prediction model indicated that the probability of fire increases as distance from human settlements increases. In contrast, the model indicated that the probability of fire decreases as distance from roads increases and that the probability of fire in relation to distance from rivers depends on the year of analysis. While the ecological consequences of these human impacts are still unclear, this research shows that human influences on fire regime clearly affect the pattern of fire within 40 km of settlements, which is an area that represents 31% of interior Alaska. Future research should focus on more completely understanding the role of human presence in the suppression of wildfires in interior Alaska.
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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 [15]
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Driving factors of fire density can spatially vary at the local scale in south-eastern France
Ganteaume, A., & Long-Fournel, M. (2015) · International Journal of Wildland Fire · doi:10.1071/wf13209 · Cited by 33 · see citing works
Global combustion: the connection between fossil fuel and biomass burning emissions (1997–2010)
Balch, J. K., Nagy, R. C., Archibald, S., Bowman, D. M. J. S., Moritz, M. A., Roos, C. I., Scott, A. C., & Williamson, G. J. (2016) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2015.0177 · Cited by 31 · see citing works
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Characterization of wildfire regimes in Canadian boreal terrestrial ecosystems
Jiang, Y., Zhuang, Q., Flannigan, M., & Little, J. M. (2009) · International Journal of Wildland Fire · doi:10.1071/wf08096 · Cited by 25 · see citing works
Sidle, R. C., Gallina, J., & Gomi, T. (2017) · Landscape and Urban Planning · doi:10.1016/j.landurbplan.2017.05.017 · Cited by 24 · see citing works
Calef, M. P., Varvak, A., & McGuire, A. D. (2017) · Forests · doi:10.3390/f8110422 · Cited by 24 · see citing works
Self-citationUnearthing Shifts in Microbial Communities Across a Soil Disturbance Gradient
Seitz, T. J., Schütte, U. M. E., & Drown, D. M. (2022) · Frontiers in Microbiology · doi:10.3389/fmicb.2022.781051 · Cited by 22 · see citing works
Modeling Large Fire Frequency and Burned Area in Canadian Terrestrial Ecosystems with Poisson Models
Jiang, Y., Zhuang, Q., & Mandallaz, D. (2012) · Environmental Modeling & Assessment · doi:10.1007/s10666-012-9307-5 · Cited by 21 · see citing works
Wildfire evacuation patterns and syndromes across Canada's forested regions
Tepley, A. J., Parisien, M., Wang, X., Oliver, J. A., & Flannigan, M. (2022) · Ecosphere · doi:10.1002/ecs2.4255 · Cited by 20 · see citing works
Hansen, W. D., Krawchuk, M. A., Trugman, A. T., & Williams, P. (2022) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2022.105473 · Cited by 18 · see citing works
Rodriguez-Jimenez, F., Lorenzo, H., Acuña-Alonso, C., & Álvarez, X. (2023) · Ecological Informatics · doi:10.1016/j.ecoinf.2023.102010 · Cited by 17 · see citing works
Liang, X., Liu, D., Wang, Z., & Wang, J. (2022) · Landscape and Urban Planning · doi:10.1016/j.landurbplan.2022.104553 · Cited by 11 · see citing works
Gaglioti, B. V., Mann, D. H., Jones, B. M., Wooller, M. J., & Finney, B. P. (2016) · The Holocene · doi:10.1177/0959683616632893 · Cited by 11 · see citing works
Cahoon, S. M. P., Sullivan, P. F., & Gray, A. N. (2022) · Journal of Ecology · doi:10.1111/1365-2745.13963 · Cited by 10 · see citing works
Calef, M. P., Schmidt, J. I., Varvak, A., & Ziel, R. H. (2023) · Forests · doi:10.3390/f14081577 · Cited by 9 · see citing works
Self-citationClimate Changes Consequences from Sun-Earth Connections and Anthropogenic Relationships
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What the Blazes!? A People's History of Fire in Labrador
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Disturbances on Riparian Vegetation: A Comprehensive Review
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Spatial accessibility of anthropogenic fire ignition sources of grassland fire in northeast China
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Recent Climate Change Impacts on the Boreal Forest of Alaska
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Self-citationRoadless rule rescission threatens highest integrity forest ecosystems in the United States
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CanadaFireSat: Towards high-resolution wildfire forecasting with multiple modalities
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Declining anthropogenic wildfires in South American savannas
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Hessilt, T. D., Veraverbeke, S., Mack, M. C., Olsen, R., Turetsky, M. R., Walker, X. J., & Baltzer, J. L. (2026) · International Journal of Wildland Fire · doi:10.1071/wf25286
A Paleoclimate-Compatible Framework for Modeling Lightning-Caused Ignition Probability in Alaska
Uden, C., Clemins, P. J., & Beckage, B. (2026) · Atmosphere · doi:10.3390/atmos17050490
Comparative analysis of natural and anthropogenic wildfire regimes in Mainland Southeast Asia
Jiang, Y., Zhang, Q., Gao, C., Lou, Y., & Shi, C. (2026) · Scandinavian Journal of Forest Research · doi:10.1080/02827581.2026.2682827
Park, H., & Nam, K.-H. (2026) · International Journal of Wildland Fire · doi:10.1071/wf26043
- Authors
- Calef, M. P., McGuire, A. D., & Chapin, F. S.
- Affiliations
- State University of New York (US)
- United States Geological Survey (US)
- University of Alaska Fairbanks (US)
- Venue
- Earth Interactions
- Publisher
- American Meteorological Society
- Published
- 2008-03-01
- Volume
- 12
- Issue
- 1
- Pages
- 1-17
- ISSN
- 1087-3562 (ISSN-L)
- OpenAlex
- W2051188584
- Cited by
- 72
- References
- 52
- Citations by year
- 2026: 7 · 2024: 3 · 2023: 3 · 2022: 10 · 2021: 4 · 2020: 2 · 2017: 7 · 2016: 4 · 2015: 6 · 2014: 2 · 2013: 4 · 2012: 3
- FWCI
- 5.11
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Earth Interactions (publishedVersion)
- Funders
- National Science Foundation (0328282)
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Fire effects on ecosystems
- Physical Sciences › Environmental Science › Global and Planetary Change › Forest Management and Policy
- Physical Sciences › Environmental Science › Ecology › Rangeland and Wildlife Management
- Keywords
- forest fires, human influence on fire, fire regimes, fire ignition, fire suppression, boreal forest, lightning fires, fire prediction model, road proximity, distance to river, interior Alaska, climate change
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.
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