Contemporary wildfires further degrade resistance and resilience of fire-excluded forests

Hessburg, Paul F.; Salter, R. Brion; Merschel, Andrew G.; Reilly, Matthew J.

doi:10.1016/j.foreco.2021.119975

Cited by 14 publications

(13 citation statements)

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“…A relatively narrow range of moderate fire severities, roughly 365–560 RdNBR, were most likely to restore stand density and basal area, aligning with findings from several smaller-scale field-based studies of fire effects [ 18 , 47 , 53 , 59 , 96 ]. However, stand composition, which is a critical component of forest restoration and resistance to future disturbance, remained departed from historical conditions even at high fire severities ( Fig 4 ) [ 1 , 89 , 97 ]. Composition of dry mixed-conifer stands was more departed from historical composition than ponderosa pine stands, but even at very high fire severities ponderosa pine stands still contained substantially more fire-intolerant species than were historically present ( Fig 4 ; Fig 4 in S1 Appendix ).…”

Section: Discussionmentioning

confidence: 99%

“…Historically, low-severity fire was extensive in dry forests, but small patches of high-severity fire (typically <0.5 ha and rarely >10 ha) occurred as well and this heterogeneity should be not be discounted [ 19 , 21 , 89 , 102 , 132 ]. Early seral and non-forest conditions also play an important role in the landscape ecology of fire and are a key ecological resource [ 101 , 133 ].…”

Section: Discussionmentioning

confidence: 99%

“…For the Fremont-Winema National Forest, we used historical forest conditions from a timber inventory collected between 1914 and 1922 on the former Klamath Reservation [ 86 , 87 ]. For the Ochoco National Forest we used historical forest conditions from a timber inventory collected between 1922 and 1925 on the Warm Springs Indian Reservation [ 88 , 89 ]. For stands on the Deschutes National Forest, we averaged estimates from the historical timber inventories on the Warm Springs Indian Reservation and former Klamath Reservation because plots used in this analysis on the Deschutes National Forest were geographically situated between the historical reconstructions [ 86 ].…”

Section: Methodsmentioning

confidence: 99%

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Too hot, too cold, or just right: Can wildfire restore dry forests of the interior Pacific Northwest?

et al. 2023

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As contemporary wildfire activity intensifies across the western United States, there is increasing recognition that a variety of forest management activities are necessary to restore ecosystem function and reduce wildfire hazard in dry forests. However, the pace and scale of current, active forest management is insufficient to address restoration needs. Managed wildfire and landscape-scale prescribed burns hold potential to achieve broad-scale goals but may not achieve desired outcomes where fire severity is too high or too low. To explore the potential for fire alone to restore dry forests, we developed a novel method to predict the range of fire severities most likely to restore historical forest basal area, density, and species composition in forests across eastern Oregon. First, we developed probabilistic tree mortality models for 24 species based on tree characteristics and remotely sensed fire severity from burned field plots. We applied these estimates to unburned stands in four national forests to predict post-fire conditions using multi-scale modeling in a Monte Carlo framework. We compared these results to historical reconstructions to identify fire severities with the highest restoration potential. Generally, we found basal area and density targets could be achieved by a relatively narrow range of moderate-severity fire (roughly 365–560 RdNBR). However, single fire events did not restore species composition in forests that were historically maintained by frequent, low-severity fire. Restorative fire severity ranges for stand basal area and density were strikingly similar for ponderosa pine (Pinus ponderosa) and dry mixed-conifer forests across a broad geographic range, in part due to relatively high fire tolerance of large grand (Abies grandis) and white fir (Abies concolor). Our results suggest historical forest conditions created by recurrent fire are not readily restored by single fires and landscapes have likely passed thresholds that preclude the effectiveness of managed wildfire alone as a restoration tool.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Too hot, too cold, or just right: Can wildfire restore dry forests of the interior Pacific Northwest?

et al. 2023

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“…Climate change will present multiple challenges and changes for the wildland fire and land management community and calls for more proactive and adaptive management strategies (Hagmann et al , 2022Hessburg et al 2021;Prichard et al 2021). Society is likely to see growing emphasis on fire management as part of a broader portfolio of nature-based climate solutions, in large part through increasing forest resilience to catastrophic fire and managing for forest carbon in a manner that accounts for fire risk dynamics (Fargione et al 2018;Griscom et al 2017).…”

Section: Rationalementioning

confidence: 99%

Potential operational delineations: new horizons for proactive, risk-informed strategic land and fire management

et al. 2022

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Background The PODs (potential operational delineations) concept is an adaptive framework for cross-boundary and collaborative land and fire management planning. Use of PODs is increasingly recognized as a best practice, and PODs are seeing growing interest from federal, state, local, tribal, and non-governmental organizations. Early evidence suggests PODs provide utility for planning, communication, coordination, prioritization, incident response strategy development, and fuels mitigation and forest restoration. Recent legislative action codifies the importance of PODs by devoting substantial financial resources to their expansion. The intent of this paper is to explore new horizons that would help land and fire management organizations better address risks and capitalize on opportunities. Specifically, we focus on how PODs are a natural platform for improvement related to two core elements of risk management: how we leverage preparation and foresight to better prepare for the future; and how we learn from the past to better understand and improve performance and its alignment with strategy. Results We organize our exploration of new horizons around three key areas, suggesting that PODs can enable climate-smart forest and fire management and planning, inform more agile and adaptive allocation of suppression resources, and enable risk-informed performance measurement. These efforts can be synergistic and self-reinforcing, and we argue that expanded application of PODs at local levels could enhance the performance of the broader wildland fire system. We provide rationales for each problem area and offer growth opportunities with attendant explanations and illustrations. Conclusions With commitment and careful effort, PODs can provide rich opportunities for innovation in both backward-looking evaluative and forward-looking anticipatory frameworks. In addition to continued improvement of core PODs elements, attention must be paid to being more inclusive and participatory in PODs planning, to building sufficient capacity to expand PODs applications in meaningful boundary spanning ways, to ensure their continuity and relevance over time through maintenance and updating, and to deliver necessary information to responders to inform the effective management of wildfires. Lastly, ongoing monitoring and evaluation of PODs and related initiatives is essential to support organizational learning and continual improvement.

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“…The limitations of these methods are (i) they are based on shallow machine learning models that require the selection of useful features as the input, and (ii) they do not consider the imbalanced problem in the historical data, e.g., the number of large-scale forest fire is much less than that of small-scale ones [19], making the prediction models neglect information of large-scale forest fires, which is, in fact, more important to prevent serious consequences [20].…”

Section: Introductionmentioning

confidence: 99%

Forest Fire Prediction with Imbalanced Data Using a Deep Neural Network Method

Lai

Zeng

Guo

et al. 2022

Forests

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Forests suffer from heavy losses due to the occurrence of fires. A prediction model based on environmental condition, such as meteorological and vegetation indexes, is considered a promising tool to control forest fires. The construction of prediction models can be challenging due to (i) the requirement of selection of features most relevant to the prediction task, and (ii) heavily imbalanced data distribution where the number of large-scale forest fires is much less than that of small-scale ones. In this paper, we propose a forest fire prediction method that employs a sparse autoencoder-based deep neural network and a novel data balancing procedure. The method was tested on a forest fire dataset collected from the Montesinho Natural Park of Portugal. Compared to the best prediction results of other state-of-the-art methods, the proposed method could predict large-scale forest fires more accurately, and reduces the mean absolute error by 3–19.3 and root mean squared error by 0.95–19.3. The proposed method can better benefit the management of wildland fires in advance and the prevention of serious fire accidents. It is expected that the prediction performance could be further improved if additional information and more data are available.

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Contemporary wildfires further degrade resistance and resilience of fire-excluded forests

Cited by 14 publications

References 91 publications

Too hot, too cold, or just right: Can wildfire restore dry forests of the interior Pacific Northwest?

Too hot, too cold, or just right: Can wildfire restore dry forests of the interior Pacific Northwest?

Potential operational delineations: new horizons for proactive, risk-informed strategic land and fire management

Forest Fire Prediction with Imbalanced Data Using a Deep Neural Network Method

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