The DOI matches its Crossref record; 131 of the 132 references it cites were found in a registry or an index.
Environmental controls on the distribution of wildfire at multiple spatial scales
Abstract
Despite its widespread occurrence globally, wildfire preferentially occupies an environmental middle ground and is significantly less prevalent in biomes characterized by environmental extremes (e.g., tundra, rain forests, and deserts). We evaluated the biophysical “environmental space” of wildfire from regional to subcontinental extents, with methods widely used for modeling habitat distributions. This approach is particularly suitable for the biogeographic study of wildfire, because it simultaneously considers patterns in multiple factors controlling wildfire suitability over large areas. We used the Maxent and boosted regression tree algorithms to assess wildfire–environment relationships for three levels of complexity (in terms of inclusion of variables) at three spatial scales: the conterminous United States, the state of California, and five wildfire‐prone ecoregions of California. The resulting models were projected geographically to obtain spatial predictions of wildfire suitability and were also applied to other regions to assess their generality and spatial transferability. Predictions of the potential range of wildfire had high classification accuracy; they also highlighted areas where wildfires had not recently been observed, indicating the potential (or past) suitability of these areas. The models identified several key variables that were not suspected to be important in the large‐scale control of wildfires, but which might indirectly affect control by influencing the presence of flammable vegetation. Models transferred to different areas were useful only when they overlapped appreciably with the target area's environmental space. This approach should allow exploration of the potential shifts in wildfire range in a changing climate, the potential for restoration of wildfire where it has been “extirpated,” and, conversely, the “invasiveness” of wildfire after changes in plant species composition. Our study demonstrates that habitat distribution models and related concepts can be used to characterize environmental controls on a natural disturbance process, but also that future work is needed to refine our understanding of the direct causal factors controlling wildfire at multiple spatial scales.
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“These spatial and temporal resolutions can be considered representative of landscape scale fire regimes for California [8,9].”
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 [8]
Citations
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Self-citationGlobal and Regional Trends and Drivers of Fire Under Climate Change
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Self-citationClimate change and disruptions to global fire activity
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Metadata
- Authors
- Parisien, M.-A., & Moritz, M. A.
- Affiliations
- Canadian Forest Service (CA)
- University of California, Berkeley (US)
- Corresponding author
- Parisien, Marc-André
- Venue
- Ecological Monographs
- Publisher
- Wiley
- Published
- 2009-02
- Volume
- 79
- Issue
- 1
- Pages
- 127-154
- ISSN
- 0012-9615 (ISSN-L)
- 1557-7015
- OpenAlex
- W2065509880
- Cited by
- 383
- References
- 132
- Citations by year
- 2026: 16 · 2025: 22 · 2024: 13 · 2023: 19 · 2022: 28 · 2021: 32 · 2020: 13 · 2019: 39 · 2018: 22 · 2017: 25 · 2016: 29 · 2015: 24 · 2014: 20 · 2013: 26 · 2012: 20
- FWCI
- 15.76
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Ecological Monographs (publishedVersion)
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Fire effects on ecosystems
- Physical Sciences › Environmental Science › Ecological Modeling › Species Distribution and Climate Change
- Physical Sciences › Environmental Science › Ecology › Rangeland and Wildlife Management
- Keywords
- forest fires, habitat suitability, spatial scale, climate change, fire restoration, ecoregions, California, spatial transferability, MaxEnt, boosted regression trees
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