Using Dynamic, Fuels-Based Fire Probability Maps to Reduce Large Wildfires in the Great Basin

Maestas, Jeremy D.; Smith, Joseph T.; Allred, Brady W.; Naugle, David E.; Jones, Matthew O.; O'Connor, Casey; Boyd, Chad S.; Davies, Kirk W.; Crist, Michele R.; Olsen, Andrew C.

doi:10.1016/j.rama.2022.08.002

Cited by 7 publications

(2 citation statements)

References 53 publications

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“…In this way, our model complements previous models-often more complex ones based on machine learning-that incorporate more near-term antecedent conditions such as, for example, total precipitation the preceding season or month (Abatzoglou and Kolden 2013; Pastick et al 2021;Smith et al 2022). Such models have sometimes been created with the explicit goal of helping predict fire risk in the upcoming year (Maestas et al 2022;Smith et al 2022).…”

Section: Model Overviewmentioning

confidence: 97%

Wildfire probability estimated from recent climate and fine fuels across the big sagebrush region

Holdrege,

Schlaepfer,

Palmquist

et al. 2024

fire ecol

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Background Wildfire is a major proximate cause of historical and ongoing losses of intact big sagebrush (Artemisia tridentata Nutt.) plant communities and declines in sagebrush obligate wildlife species. In recent decades, fire return intervals have shortened and area burned has increased in some areas, and habitat degradation is occurring where post-fire re-establishment of sagebrush is hindered by invasive annual grasses. In coming decades, the changing climate may accelerate these wildfire and invasive feedbacks, although projecting future wildfire dynamics requires a better understanding of long-term wildfire drivers across the big sagebrush region. Here, we integrated wildfire observations with climate and vegetation data to derive a statistical model for the entire big sagebrush region that represents how annual wildfire probability is influenced by climate and fine fuel characteristics. Results Wildfire frequency varied significantly across the sagebrush region, and our statistical model represented much of that variation. Biomass of annual and perennial grasses and forbs, which we used as proxies for fine fuels, influenced wildfire probability. Wildfire probability was highest in areas with high annual forb and grass biomass, which is consistent with the well-documented phenomenon of increased wildfire following annual grass invasion. The effects of annuals on wildfire probability were strongest in places with dry summers. Wildfire probability varied with the biomass of perennial grasses and forbs and was highest at intermediate biomass levels. Climate, which varies substantially across the sagebrush region, was also predictive of wildfire probability, and predictions were highest in areas with a low proportion of precipitation received in summer, intermediate precipitation, and high temperature. Conclusions We developed a carefully validated model that contains relatively simple and biologically plausible relationships, with the goal of adequate performance under novel conditions so that useful projections of average annual wildfire probability can be made given general changes in conditions. Previous studies on the impacts of vegetation and climate on wildfire probability in sagebrush ecosystems have generally used more complex machine learning approaches and have usually been applicable to only portions of the sagebrush region. Therefore, our model complements existing work and forms an additional tool for understanding future wildfire and ecological dynamics across the sagebrush region.

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Section: Model Overviewmentioning

confidence: 97%

Wildfire probability estimated from recent climate and fine fuels across the big sagebrush region

Holdrege,

Schlaepfer,

Palmquist

et al. 2024

fire ecol

Self Cite

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“…By targeting intact landscapes this approach is inherently threat‐based, and assumes that managing biome‐level threats (e.g., conifer expansion among rangelands) propagates benefits to species of conservation concern (Roberts et al, 2022). In practice, a defend the core strategy has been adopted by practitioners to manage the detrimental effects of invasive annual grass expansion (Allred et al, 2022; Creutzburg et al, 2022) and large wildfires (Maestas, Smith, et al, 2022), yet remains largely untested as an effective surrogate for encompassing the requirements of species underlying biome‐specific strategies.…”

Section: Introductionmentioning

confidence: 99%

Grassland intactness outcompetes species as a more efficient surrogate in conservation design

Tack,

Jakes,

Jones

et al. 2023

Conservat Sci and Prac

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Mapped representations of species−habitat relationships often underlie approaches to prioritize area‐based conservation strategies to meet conservation goals for biodiversity. Generally a single surrogate species is used to inform conservation design, with the assumption that conservation actions for an appropriately selected species will confer benefits to a broader community of organisms. Emerging conservation frameworks across western North America are now relying on derived measures of intactness from remotely sensed vegetation data, wholly independent from species data. Understanding the efficacy of species‐agnostic planning approaches is a critical step to ensuring the robustness of emerging conservation designs. We developed an approach to quantify ‘strength of surrogacy’, by applying prioritization algorithms to previously developed species models, and measuring their coverage provided to a broader wildlife community. We used this inference to test the relative surrogacy among a suite of species models used for conservation targeting in the endangered grasslands of the Northern Sagebrush Steppe, where careful planning can help stem the loss of private grazing lands to cultivation. In this test, we also derived a simpler surrogate of intact rangelands without species data for conservation targeting, along with a measure of combined migration representative of key areas for connectivity. Our measure of intactness vastly outperformed any species model as a surrogate for conservation, followed by that of combined migration, highlighting the efficacy of strategies that target large and intact rangeland cores for wildlife conservation and restoration efforts.

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Effects of Climate Change on Forests

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Using Dynamic, Fuels-Based Fire Probability Maps to Reduce Large Wildfires in the Great Basin

Cited by 7 publications

References 53 publications

Wildfire probability estimated from recent climate and fine fuels across the big sagebrush region

Wildfire probability estimated from recent climate and fine fuels across the big sagebrush region

Grassland intactness outcompetes species as a more efficient surrogate in conservation design

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