Southwestern ponderosa pine forest patterns following wildland fires managed for resource benefit differ from reference landscapes

Donager, Jonathon; Meador, Andrew J. Sánchez; Huffman, David W.

doi:10.1007/s10980-021-01352-1

Cited by 5 publications

(1 citation statement)

References 110 publications

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“…Prescribed fires can exceed 10,000 ha (Weir & Scasta, 2022), as well as wildfires managed for resource objectives, e.g. Donager et al (2022). Fire size does not equate to human impacts -in Australia, house loss, human fatalities and economic loss are predicted from the combination of energy release rate and exposure to the fire (Harris et al, 2012), and the deadliest single wildfire event in Europe was the 1431-ha Mati fire in Greece, 2018 (102 fatalities; Xanthopoulos & Athanasiou, 2019).…”

Section: Size and The Extreme Attributes Of A Wildfire Can Differ Sub...mentioning

confidence: 99%

Megafire: An ambiguous and emotive term best avoided by science

Stoof,

de Vries,

Castellnou Ribau

et al. 2023

Global Ecol Biogeogr

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BackgroundAs fire regimes are changing and wildfire disasters are becoming more frequent, the term megafire is increasingly used to describe impactful wildfires, under multiple meanings, both in academia and popular media. This has resulted in a highly ambiguous concept.ApproachWe analysed the use of the term ‘megafire’ in popular media to determine its origin, its developments over time, and its meaning in the public sphere. We subsequently discuss how relative the term ‘mega’ is, and put this in the context of an analysis of Portuguese and global data on fire size distribution.ResultsWe found that ‘megafire’ originated in the popular news media over 20 years before it appeared in science. Megafire is used in a diversity of languages, considers landscape fires as well as urban fires, and has a variety of meanings in addition to size. What constitutes ‘mega’ is relative and highly context‐dependent in space and time, given variation in landscape, climate, and anthropogenic controls, and as revealed in examples from the Netherlands, Portugal and the Global Fire Atlas. Moreover, fire size does not equate to fire impact.ConclusionGiven the diverse meanings of megafire in the popular media, we argue that redefining megafire in science potentially leads to greater disparity between science and practice. Megafire is widely used as an emotive term that is best left for popular media. For those wanting to use it in science, what constitutes a megafire should be defined by the context in which it is used, not by a metric of one‐size‐fits‐all.

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Section: Size and The Extreme Attributes Of A Wildfire Can Differ Sub...mentioning

confidence: 99%

Megafire: An ambiguous and emotive term best avoided by science

Stoof,

de Vries,

Castellnou Ribau

et al. 2023

Global Ecol Biogeogr

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Resetting the baseline: using machine learning to find lost meadows

Cummings,

Pope,

Mak

2023

Landsc Ecol

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Context Mountain meadows occur in specific geomorphological conditions where low-gradient topography promotes fine sediment accumulation and high groundwater tables. Over 150 years of human-caused hydrological degradation of meadows along with fire suppression has resulted in decreased groundwater elevations and encroachment of upland vegetation, greatly diminishing the ecological value of meadows for water storage, baseflow, sediment capture, wildfire resistance, wildlife habitat, and carbon storage. Objectives We aimed to understand where and how frequently meadows historically occurred to reset the baseline condition and provide insight into their restoration potential. We trained machine learning algorithms to identify potential meadow areas with similar hydrogeomorphic conditions to extant meadows while ignoring their unique vegetative characteristics because we hypothesized that vegetation would change but geomorphology would remain. Methods We used a publicly available dataset of over 11,000 hand-digitized meadow polygons occurring within a 25,300 km2, 60-watershed region in the Sierra Nevada, California USA to train random forest models to detect meadow-like hydrogeomorphic conditions. Predictor variables represented topographical position, flow accumulation, snowpack, and topographical relief at differing spatial scales. We assessed model performance and produced maps delineating high probability meadow polygons. Results Our findings showed that there is nearly three times more potential meadow habitat than currently documented. The predicted area includes a mixture of existing but undocumented meadows, non-meadowlands that may have converted from meadows due to lost function and forest encroachment, and areas with meadow-like geomorphology that may never have been meadow. The polygons encompassing predicted meadows often expanded existing meadows habitats into adjacent areas with continuous topography, but with upland vegetation and incised channels. Conclusions Using readily available data and accessible statistical techniques, we demonstrated the accuracy of a tool to detect about three times more historical meadows than currently recognized within a complex, mountainous landscape. This “found” area greatly increased the potential area that could be subject to meadow restoration with benefits for biodiversity, wildfire management, carbon sequestration, and water storage.

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