Pub Date : 2024-01-30DOI: 10.1186/s42408-024-00247-1
Gregory J. Nowacki, Melissa A. Thomas-Van Gundy
Understanding past fire environments is vitally important for applying silvicultural treatments, which often include prescribed burning to restore fire-dependent ecosystems. We have developed a novel method by which witness trees can be used as pyro-indicators to map past fire environments. The stepwise process first involves partitioning witness trees into two classes, pyrophobic and pyrophilic, based on their known ecophysiological traits. Pyrophilic percentages are then calculated at survey corners by dividing the number of pyrophilic trees by the total number of trees. Next, statistical spatial interpolation is applied to this point-based data set to produce a continuous response surface of pyrophilic percentages. The resultant maps capture gradients of fire importance across the pre-European-settlement landscape, which can be coupled with historic fire regime maps, thus providing additional information for better understanding and explaining past fire environments. We have applied this technique to various available witness-tree databases across the eastern United States. This paper serves as a compendium of our collective work to date.
{"title":"Using witness trees as pyro-indicators to depict past fire environments across the eastern United States","authors":"Gregory J. Nowacki, Melissa A. Thomas-Van Gundy","doi":"10.1186/s42408-024-00247-1","DOIUrl":"https://doi.org/10.1186/s42408-024-00247-1","url":null,"abstract":"Understanding past fire environments is vitally important for applying silvicultural treatments, which often include prescribed burning to restore fire-dependent ecosystems. We have developed a novel method by which witness trees can be used as pyro-indicators to map past fire environments. The stepwise process first involves partitioning witness trees into two classes, pyrophobic and pyrophilic, based on their known ecophysiological traits. Pyrophilic percentages are then calculated at survey corners by dividing the number of pyrophilic trees by the total number of trees. Next, statistical spatial interpolation is applied to this point-based data set to produce a continuous response surface of pyrophilic percentages. The resultant maps capture gradients of fire importance across the pre-European-settlement landscape, which can be coupled with historic fire regime maps, thus providing additional information for better understanding and explaining past fire environments. We have applied this technique to various available witness-tree databases across the eastern United States. This paper serves as a compendium of our collective work to date.","PeriodicalId":12273,"journal":{"name":"Fire Ecology","volume":"50 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139578064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-29DOI: 10.1186/s42408-023-00243-x
Caitlin C. Bloomer, Christopher M. Miller, Robert J. DiStefano, Christopher A. Taylor
Prescribed burning is used to duplicate natural, pre-settlement prairie successional processes. It is an essential and commonly used tool to promote and protect biodiversity and enhance ecosystem function in tallgrass prairie remnants throughout the midwestern United States. The responses to prescribed burns vary widely among faunal groups. We conducted the first study into the response of the prairie crayfish (Procambarus gracilis Bundy) to periodic prescribed burns and other management activities in a tallgrass prairie in Northern Missouri. This species relies on natural and restored prairies across its broad distribution, but little is known on how to actively manage these populations. We found that the density of the prairie crayfish burrows did not vary in response to the burn regime; however, other management activities like the installation of artificial ponds for amphibians and reptiles were directly benefitting this species. Observations indicate that prairie crayfish may also show positive associations with warm-season grass stands and vegetation management should be further explored. The current prairie management practices for vegetation, quail, and herpetofauna are having beneficial or neutral effects on non-target taxa like the prairie crayfish. The value of crayfish and their burrows in prairies is well-established. Conservation biologists should continue to examine how burrowing crayfish are responding to management practices for other taxa to explicitly manage and promote these populations.
{"title":"Midwest prairie management practices benefit the non-target prairie crayfish","authors":"Caitlin C. Bloomer, Christopher M. Miller, Robert J. DiStefano, Christopher A. Taylor","doi":"10.1186/s42408-023-00243-x","DOIUrl":"https://doi.org/10.1186/s42408-023-00243-x","url":null,"abstract":"Prescribed burning is used to duplicate natural, pre-settlement prairie successional processes. It is an essential and commonly used tool to promote and protect biodiversity and enhance ecosystem function in tallgrass prairie remnants throughout the midwestern United States. The responses to prescribed burns vary widely among faunal groups. We conducted the first study into the response of the prairie crayfish (Procambarus gracilis Bundy) to periodic prescribed burns and other management activities in a tallgrass prairie in Northern Missouri. This species relies on natural and restored prairies across its broad distribution, but little is known on how to actively manage these populations. We found that the density of the prairie crayfish burrows did not vary in response to the burn regime; however, other management activities like the installation of artificial ponds for amphibians and reptiles were directly benefitting this species. Observations indicate that prairie crayfish may also show positive associations with warm-season grass stands and vegetation management should be further explored. The current prairie management practices for vegetation, quail, and herpetofauna are having beneficial or neutral effects on non-target taxa like the prairie crayfish. The value of crayfish and their burrows in prairies is well-established. Conservation biologists should continue to examine how burrowing crayfish are responding to management practices for other taxa to explicitly manage and promote these populations.","PeriodicalId":12273,"journal":{"name":"Fire Ecology","volume":"4 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139578067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-29DOI: 10.1186/s42408-023-00244-w
Kelsey Copes-Gerbitz, Ira J. Sutherland, Sarah Dickson-Hoyle, Jennifer N. Baron, Pablo Gonzalez-Moctezuma, Morgan A. Crowley, Katherine A. Kitchens, Tahia Devisscher, Judith Burr
Managing landscape fire is a complex challenge because it is simultaneously necessary for, and increasingly poses a risk to, societies and ecosystems worldwide. This challenge underscores the need for transformative change in the way societies live with and manage fire. While researchers have the potential to act as agents of transformative change, in practice, the ability to affect change is often constrained by siloed and biased expertise, rigid decision-making institutions, and increasingly vulnerable social-ecological systems where urgent rather than long-term solutions are prioritized. Addressing these challenges requires more holistic and equitable approaches to fire research that promote new models of transdisciplinary thinking, collaboration, and practice. To advance transformative solutions to this complex fire challenge, we propose four principles for conducting transdisciplinary fire research: (1) embrace complexity, (2) promote diverse ways of knowing fire, (3) foster transformative learning, and (4) practice problem-centered research. These principles emerged from our experience as a group of early-career researchers who are embedded within and motivated by today’s complex fire challenge within British Columbia (BC), Canada. In this forum piece, we first describe the four principles and then apply the principles to two case studies: (1) BC, a settler-colonial context experiencing increased size, severity, and impacts of wildfires, and (2) our ECR discussion group, a space of collective learning and transformation. In doing so, we present a unique contribution that builds on existing efforts to develop more holistic fire research frameworks and demonstrates how application of these principles can promote transdisciplinary research and transformation towards coexistence with fire, from local to global scales. In this forum piece, we identify and apply four guiding principles for transdisciplinary fire research. Collectively, these principles can foster more inclusive applied fire research that matches the scope and scale of today’s fire challenge and promotes transformative change towards coexisting with fire.
{"title":"Guiding principles for transdisciplinary and transformative fire research","authors":"Kelsey Copes-Gerbitz, Ira J. Sutherland, Sarah Dickson-Hoyle, Jennifer N. Baron, Pablo Gonzalez-Moctezuma, Morgan A. Crowley, Katherine A. Kitchens, Tahia Devisscher, Judith Burr","doi":"10.1186/s42408-023-00244-w","DOIUrl":"https://doi.org/10.1186/s42408-023-00244-w","url":null,"abstract":"Managing landscape fire is a complex challenge because it is simultaneously necessary for, and increasingly poses a risk to, societies and ecosystems worldwide. This challenge underscores the need for transformative change in the way societies live with and manage fire. While researchers have the potential to act as agents of transformative change, in practice, the ability to affect change is often constrained by siloed and biased expertise, rigid decision-making institutions, and increasingly vulnerable social-ecological systems where urgent rather than long-term solutions are prioritized. Addressing these challenges requires more holistic and equitable approaches to fire research that promote new models of transdisciplinary thinking, collaboration, and practice. To advance transformative solutions to this complex fire challenge, we propose four principles for conducting transdisciplinary fire research: (1) embrace complexity, (2) promote diverse ways of knowing fire, (3) foster transformative learning, and (4) practice problem-centered research. These principles emerged from our experience as a group of early-career researchers who are embedded within and motivated by today’s complex fire challenge within British Columbia (BC), Canada. In this forum piece, we first describe the four principles and then apply the principles to two case studies: (1) BC, a settler-colonial context experiencing increased size, severity, and impacts of wildfires, and (2) our ECR discussion group, a space of collective learning and transformation. In doing so, we present a unique contribution that builds on existing efforts to develop more holistic fire research frameworks and demonstrates how application of these principles can promote transdisciplinary research and transformation towards coexistence with fire, from local to global scales. In this forum piece, we identify and apply four guiding principles for transdisciplinary fire research. Collectively, these principles can foster more inclusive applied fire research that matches the scope and scale of today’s fire challenge and promotes transformative change towards coexisting with fire.","PeriodicalId":12273,"journal":{"name":"Fire Ecology","volume":"4 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139578313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-26DOI: 10.1186/s42408-023-00236-w
Faiza Qayyum, Harun Jamil, Tariq Alsboui, Mohammad Hijjawi
Understanding the intricacies of wildfire impact across diverse geographical landscapes necessitates a nuanced comprehension of fire dynamics and areas of vulnerability, particularly in regions prone to high wildfire risks. Machine learning (ML) stands as a formidable ally in addressing the complexities associated with predicting and mapping these risks, offering advanced analytical capabilities. Nevertheless, the reliability of such ML approaches is heavily contingent on the integrity of data and the robustness of training protocols. The scientific community has raised concerns about the transparency and interpretability of ML models in the context of wildfire management, recognizing the need for these models to be both accurate and understandable. The often-opaque nature of complex ML algorithms can obscure the rationale behind their outputs, making it imperative to prioritize clarity and interpretability to ensure that model predictions are not only precise but also actionable. Furthermore, a thorough evaluation of model performance must account for multiple critical factors to ensure the utility and dependability of the results in practical wildfire suppression and management strategies. This study unveils a sophisticated spatial deep learning framework grounded in TabNet technology, tailored specifically for delineating areas susceptible to wildfires. To elucidate the predictive interplay between the model’s outputs and the contributing variables across a spectrum of inputs, we embark on an exhaustive analysis using SHapley Additive exPlanations (SHAP). This approach affords a granular understanding of how individual features sway the model’s predictions. Furthermore, the robustness of the predictive model is rigorously validated through 5-fold cross-validation techniques, ensuring the dependability of the findings. The research meticulously investigates the spatial heterogeneity of wildfire susceptibility within the designated study locale, unearthing pivotal insights into the nuanced fabric of fire risk that is distinctly local in nature. Utilizing SHapley Additive exPlanations (SHAP) visualizations, this research meticulously identifies key variables, quantifies their importance, and demystifies the decision-making mechanics of the model. Critical factors, including temperature, elevation, the Normalized Difference Vegetation Index (NDVI), aspect, and wind speed, are discerned to have significant sway over the predictions of wildfire susceptibility. The findings of this study accentuate the criticality of transparency in modeling, which facilitates a deeper understanding of wildfire risk factors. By shedding light on the significant predictors within the models, this work enhances our ability to interpret complex predictive models and drives forward the field of wildfire risk management, ultimately contributing to the development of more effective prevention and mitigation strategies.
要了解野火在不同地理地貌中产生的错综复杂的影响，就必须对火灾动态和易发地区有细致入微的了解，尤其是在野火风险较高的地区。机器学习（ML）提供了先进的分析能力，是解决与预测和绘制这些风险相关的复杂问题的强大盟友。然而，此类 ML 方法的可靠性在很大程度上取决于数据的完整性和训练协议的稳健性。科学界对野火管理背景下的 ML 模型的透明度和可解释性表示担忧，认为这些模型既要准确又要易于理解。复杂的 ML 算法往往具有模糊性，可能会掩盖其输出结果背后的原理，因此必须优先考虑清晰度和可解释性，以确保模型预测不仅准确，而且可操作。此外，对模型性能的全面评估必须考虑多个关键因素，以确保结果在实际野火扑救和管理策略中的实用性和可靠性。本研究揭示了一个基于 TabNet 技术的复杂空间深度学习框架，该框架专为划定易受野火影响的区域而量身定制。为了阐明该模型的输出结果与各种输入变量之间的预测性相互作用，我们使用 SHapley Additive exPlanations（SHAP）进行了详尽的分析。通过这种方法，我们可以详细了解各个特征是如何影响模型预测的。此外，预测模型的稳健性还通过 5 倍交叉验证技术得到了严格验证，从而确保了研究结果的可靠性。该研究对指定研究区域内野火易发性的空间异质性进行了细致的调查，揭示了具有鲜明地方特色的火灾风险的细微结构。利用 SHapley Additive exPlanations（SHAP）可视化技术，这项研究细致地确定了关键变量，量化了这些变量的重要性，并揭开了模型决策机制的神秘面纱。研究发现，温度、海拔、归一化植被指数 (NDVI)、方位和风速等关键因素对野火易感性的预测具有重要影响。这项研究的结果突出了建模透明度的重要性，有助于加深对野火风险因素的理解。通过揭示模型中的重要预测因素，这项工作提高了我们解释复杂预测模型的能力，推动了野火风险管理领域的发展，最终有助于制定更有效的预防和缓解策略。
{"title":"Wildfire risk exploration: leveraging SHAP and TabNet for precise factor analysis","authors":"Faiza Qayyum, Harun Jamil, Tariq Alsboui, Mohammad Hijjawi","doi":"10.1186/s42408-023-00236-w","DOIUrl":"https://doi.org/10.1186/s42408-023-00236-w","url":null,"abstract":"Understanding the intricacies of wildfire impact across diverse geographical landscapes necessitates a nuanced comprehension of fire dynamics and areas of vulnerability, particularly in regions prone to high wildfire risks. Machine learning (ML) stands as a formidable ally in addressing the complexities associated with predicting and mapping these risks, offering advanced analytical capabilities. Nevertheless, the reliability of such ML approaches is heavily contingent on the integrity of data and the robustness of training protocols. The scientific community has raised concerns about the transparency and interpretability of ML models in the context of wildfire management, recognizing the need for these models to be both accurate and understandable. The often-opaque nature of complex ML algorithms can obscure the rationale behind their outputs, making it imperative to prioritize clarity and interpretability to ensure that model predictions are not only precise but also actionable. Furthermore, a thorough evaluation of model performance must account for multiple critical factors to ensure the utility and dependability of the results in practical wildfire suppression and management strategies. This study unveils a sophisticated spatial deep learning framework grounded in TabNet technology, tailored specifically for delineating areas susceptible to wildfires. To elucidate the predictive interplay between the model’s outputs and the contributing variables across a spectrum of inputs, we embark on an exhaustive analysis using SHapley Additive exPlanations (SHAP). This approach affords a granular understanding of how individual features sway the model’s predictions. Furthermore, the robustness of the predictive model is rigorously validated through 5-fold cross-validation techniques, ensuring the dependability of the findings. The research meticulously investigates the spatial heterogeneity of wildfire susceptibility within the designated study locale, unearthing pivotal insights into the nuanced fabric of fire risk that is distinctly local in nature. Utilizing SHapley Additive exPlanations (SHAP) visualizations, this research meticulously identifies key variables, quantifies their importance, and demystifies the decision-making mechanics of the model. Critical factors, including temperature, elevation, the Normalized Difference Vegetation Index (NDVI), aspect, and wind speed, are discerned to have significant sway over the predictions of wildfire susceptibility. The findings of this study accentuate the criticality of transparency in modeling, which facilitates a deeper understanding of wildfire risk factors. By shedding light on the significant predictors within the models, this work enhances our ability to interpret complex predictive models and drives forward the field of wildfire risk management, ultimately contributing to the development of more effective prevention and mitigation strategies.","PeriodicalId":12273,"journal":{"name":"Fire Ecology","volume":"58 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139578452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-26DOI: 10.1186/s42408-024-00246-2
Maral Bashirzadeh, Mehdi Abedi, Mohammad Farzam
Plant-plant interactions are among the most important factors affecting the natural recovery of vegetation. While the impacts of nurse plants on species composition and biodiversity are well documented, the effects of different nurse’s growth forms on all biodiversity components including taxonomic, functional, and phylogenetic diversity have been less studied and compared, especially for their effects on different times after fire disturbance. This research was focused on comparing the effects of a perennial grass (Elymus hispidens), a perennial herb (Phlomis cancellata), and a high shrub species (Lonicera nummulariifolia) on species composition and the biodiversity components, and how these impacts change across five sites with short-term (1 and 4 years sites), long-term (10 and 20 years sites) times since last fire and a control site where no fire was known in recorded history in semi-arid shrublands of Fereizi Chenaran located in Northeast of Iran. The changes of species composition and taxonomic, functional, and phylogenetic diversity were calculated with respect to the presence/absence of nurse’s growth forms, fire history, and their interactions. Nurse shrubs affected species composition and all biodiversity components, whereas all indices were reduced when considering Elymus grass as nurse plant. On the other hand, the herb Phlomis enhanced species composition and taxonomic diversity, while it had a negative effect on functional and phylogenetic diversity. Such specific effects of nurse types were mostly observed under long timescales (i.e., 10- and 20-year sites). Interestingly, the relative importance of nurse types and time since the last fire largely explained the variation of species composition and biodiversity components, with larger effects of nurse types on all biodiversity components. However, we found a significant contribution of fire explaining variation of species composition and phylogenetic diversity. These results indicated nurse plants can affect the post-fire recovery of vegetation by providing specific mechanisms controlling beneficiary relatedness depending on their growth forms and time scales since the last fire. Therefore, these findings suggest perennial plants in the form of nurse species as a useful factor to develop techniques of active restoration in burned ecosystems.
{"title":"Plant-plant interactions influence post-fire recovery depending on fire history and nurse growth form","authors":"Maral Bashirzadeh, Mehdi Abedi, Mohammad Farzam","doi":"10.1186/s42408-024-00246-2","DOIUrl":"https://doi.org/10.1186/s42408-024-00246-2","url":null,"abstract":"Plant-plant interactions are among the most important factors affecting the natural recovery of vegetation. While the impacts of nurse plants on species composition and biodiversity are well documented, the effects of different nurse’s growth forms on all biodiversity components including taxonomic, functional, and phylogenetic diversity have been less studied and compared, especially for their effects on different times after fire disturbance. This research was focused on comparing the effects of a perennial grass (Elymus hispidens), a perennial herb (Phlomis cancellata), and a high shrub species (Lonicera nummulariifolia) on species composition and the biodiversity components, and how these impacts change across five sites with short-term (1 and 4 years sites), long-term (10 and 20 years sites) times since last fire and a control site where no fire was known in recorded history in semi-arid shrublands of Fereizi Chenaran located in Northeast of Iran. The changes of species composition and taxonomic, functional, and phylogenetic diversity were calculated with respect to the presence/absence of nurse’s growth forms, fire history, and their interactions. Nurse shrubs affected species composition and all biodiversity components, whereas all indices were reduced when considering Elymus grass as nurse plant. On the other hand, the herb Phlomis enhanced species composition and taxonomic diversity, while it had a negative effect on functional and phylogenetic diversity. Such specific effects of nurse types were mostly observed under long timescales (i.e., 10- and 20-year sites). Interestingly, the relative importance of nurse types and time since the last fire largely explained the variation of species composition and biodiversity components, with larger effects of nurse types on all biodiversity components. However, we found a significant contribution of fire explaining variation of species composition and phylogenetic diversity. These results indicated nurse plants can affect the post-fire recovery of vegetation by providing specific mechanisms controlling beneficiary relatedness depending on their growth forms and time scales since the last fire. Therefore, these findings suggest perennial plants in the form of nurse species as a useful factor to develop techniques of active restoration in burned ecosystems.","PeriodicalId":12273,"journal":{"name":"Fire Ecology","volume":"14 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139578445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-25DOI: 10.1186/s42408-023-00242-y
Faiza Qayyum, Nagwan Abdel Samee, Maali Alabdulhafith, Ahmed Aziz, Mohammad Hijjawi
Predicting wildfire progression is vital for countering its detrimental effects. While numerous studies over the years have delved into forecasting various elements of wildfires, many of these complex models are perceived as “black boxes”, making it challenging to produce transparent and easily interpretable outputs. Evaluating such models necessitates a thorough understanding of multiple pivotal factors that influence their performance. This study introduces a deep learning methodology based on transformer to determine wildfire susceptibility. To elucidate the connection between predictor variables and the model across diverse parameters, we employ SHapley Additive exPlanations (SHAP) for a detailed analysis. The model’s predictive robustness is further bolstered through various cross-validation techniques. Upon examining various wildfire spread rate prediction models, transformer stands out, outperforming its peers in terms of accuracy and reliability. Although the models demonstrated a high level of accuracy when applied to the development dataset, their performance deteriorated when evaluated against the separate evaluation dataset. Interestingly, certain models that showed the lowest errors during the development stage exhibited the highest errors in the subsequent evaluation phase. In addition, SHAP outcomes underscore the invaluable role of explainable AI in enriching our comprehension of wildfire spread rate prediction.
{"title":"Shapley-based interpretation of deep learning models for wildfire spread rate prediction","authors":"Faiza Qayyum, Nagwan Abdel Samee, Maali Alabdulhafith, Ahmed Aziz, Mohammad Hijjawi","doi":"10.1186/s42408-023-00242-y","DOIUrl":"https://doi.org/10.1186/s42408-023-00242-y","url":null,"abstract":"Predicting wildfire progression is vital for countering its detrimental effects. While numerous studies over the years have delved into forecasting various elements of wildfires, many of these complex models are perceived as “black boxes”, making it challenging to produce transparent and easily interpretable outputs. Evaluating such models necessitates a thorough understanding of multiple pivotal factors that influence their performance. This study introduces a deep learning methodology based on transformer to determine wildfire susceptibility. To elucidate the connection between predictor variables and the model across diverse parameters, we employ SHapley Additive exPlanations (SHAP) for a detailed analysis. The model’s predictive robustness is further bolstered through various cross-validation techniques. Upon examining various wildfire spread rate prediction models, transformer stands out, outperforming its peers in terms of accuracy and reliability. Although the models demonstrated a high level of accuracy when applied to the development dataset, their performance deteriorated when evaluated against the separate evaluation dataset. Interestingly, certain models that showed the lowest errors during the development stage exhibited the highest errors in the subsequent evaluation phase. In addition, SHAP outcomes underscore the invaluable role of explainable AI in enriching our comprehension of wildfire spread rate prediction.","PeriodicalId":12273,"journal":{"name":"Fire Ecology","volume":"58 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139554550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-22DOI: 10.1186/s42408-023-00239-7
Nicoló Perello, Andrea Trucchia, Francesco Baghino, Bushra Sanira Asif, Lola Palmieri, Nicola Rebora, Paolo Fiorucci
Socio-economic changes in recent decades have resulted in an accumulation of fuel within Mediterranean forests, creating conditions conducive to potential catastrophic wildfires intensified by climate change. Consequently, several wildfire management systems have integrated prescribed fires as a proactive strategy for land management and wildfire risk reduction. The preparation of prescribed fires involves meticulous planning, entailing the identification of specific objectives, verification of prescriptions, and the definition of various scenarios. During the planning phase, simulation models offer a valuable decision-support tool for the qualitative and quantitative assessment of different scenarios. In this study, we harnessed the capabilities of the well-established wildfire simulation tool PROPAGATOR, to identify areas where prescribed fires can be performed, optimizing the wildfire risk mitigation and the costs. We selected a case study in the Liguria region, Italy, where the model is utilized operationally by the regional wildfire risk management system in emergency situations. Initially, we employed the propagation model to simulate a historical wildfire event, showcasing its potential as an emergency response tool. We focused on the most significant fire incident that occurred in the Liguria region in 2022. Subsequently, we employed PROPAGATOR to identify optimal areas for prescribed fires with the dual objectives of maximizing the mitigation of wildfire risk and minimizing treatment costs. The delineation of potential areas for prescribed fires has been established in accordance with regional regulations and expert-based insights. The methodology put forth in this study is capable of discerning the most suitable areas for the implementation of prescribed burns from a preselected set. A Monte Carlo simulation framework was employed to evaluate the efficacy of prescribed burns in mitigating the spread of wildfires. This assessment accounted for a variety of conditions, including fuel loads, ignition points, and meteorological patterns. The PROPAGATOR model was utilized to simulate the progression of wildfire spread. This study underscores the utility of PROPAGATOR in offering both quantitative and qualitative insights that can inform prescribed fire planning. Our methodology has been designed to involve active engagement with subject matter experts throughout the process, to develop scenarios grounded in their expert opinions. The ability to assess diverse scenarios and acquire quantitative information empowers decision-makers to make informed choices, thereby advancing safer and more efficient fire management practices.
{"title":"Cellular automata-based simulators for the design of prescribed fire plans: the case study of Liguria, Italy","authors":"Nicoló Perello, Andrea Trucchia, Francesco Baghino, Bushra Sanira Asif, Lola Palmieri, Nicola Rebora, Paolo Fiorucci","doi":"10.1186/s42408-023-00239-7","DOIUrl":"https://doi.org/10.1186/s42408-023-00239-7","url":null,"abstract":"Socio-economic changes in recent decades have resulted in an accumulation of fuel within Mediterranean forests, creating conditions conducive to potential catastrophic wildfires intensified by climate change. Consequently, several wildfire management systems have integrated prescribed fires as a proactive strategy for land management and wildfire risk reduction. The preparation of prescribed fires involves meticulous planning, entailing the identification of specific objectives, verification of prescriptions, and the definition of various scenarios. During the planning phase, simulation models offer a valuable decision-support tool for the qualitative and quantitative assessment of different scenarios. In this study, we harnessed the capabilities of the well-established wildfire simulation tool PROPAGATOR, to identify areas where prescribed fires can be performed, optimizing the wildfire risk mitigation and the costs. We selected a case study in the Liguria region, Italy, where the model is utilized operationally by the regional wildfire risk management system in emergency situations. Initially, we employed the propagation model to simulate a historical wildfire event, showcasing its potential as an emergency response tool. We focused on the most significant fire incident that occurred in the Liguria region in 2022. Subsequently, we employed PROPAGATOR to identify optimal areas for prescribed fires with the dual objectives of maximizing the mitigation of wildfire risk and minimizing treatment costs. The delineation of potential areas for prescribed fires has been established in accordance with regional regulations and expert-based insights. The methodology put forth in this study is capable of discerning the most suitable areas for the implementation of prescribed burns from a preselected set. A Monte Carlo simulation framework was employed to evaluate the efficacy of prescribed burns in mitigating the spread of wildfires. This assessment accounted for a variety of conditions, including fuel loads, ignition points, and meteorological patterns. The PROPAGATOR model was utilized to simulate the progression of wildfire spread. This study underscores the utility of PROPAGATOR in offering both quantitative and qualitative insights that can inform prescribed fire planning. Our methodology has been designed to involve active engagement with subject matter experts throughout the process, to develop scenarios grounded in their expert opinions. The ability to assess diverse scenarios and acquire quantitative information empowers decision-makers to make informed choices, thereby advancing safer and more efficient fire management practices.","PeriodicalId":12273,"journal":{"name":"Fire Ecology","volume":"20 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139517808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-19DOI: 10.1186/s42408-023-00234-y
Marc Carreras-Sospedra, Shupeng Zhu, Michael MacKinnon, William Lassman, Jeffrey D. Mirocha, Michele Barbato, Donald Dabdub
Wildfires in 2020 ravaged California to set the annual record of area burned to date. Clusters of wildfires in Northern California surrounded the Bay Area covering the skies with smoke and raising the air pollutant concentrations to hazardous levels. This study uses the Fire Inventory from the National Center for Atmospheric Research database and the Community Multiscale Air Quality model to estimate the effects of wildfire emissions on air quality during the period from August 16 to October 28 of 2020. In addition, low-cost sensor data for fine particulate matter (PM2.5) from the PurpleAir network is used to enhance modeled PM2.5 concentrations. The resulting impacts on ozone and PM2.5 are used to quantify the health impacts caused by wildfires using the Benefits Mapping and Analysis Program – Community Edition. Wildfire activity significantly increased direct PM2.5 emissions and emissions of PM2.5 and ozone precursors. Direct PM2.5 emissions surged up to 38 times compared to an average day. Modeling results indicated that wildfires alone led to a rise in ozone daily maximum 8-h average by up to 10 ppb and exceeded PM2.5 air quality standards in numerous locations by up to 10 times. While modeled PM2.5 concentrations were lower than measurements, correcting these with PurpleAir data improved the accuracy. The correction using PurpleAir data increased estimates of wildfire-induced mortality due to PM2.5 exposure by up to 16%. The increased hospital admissions and premature mortality attributed to wildfires were found to be comparable to the health impacts avoided by strategies aimed at meeting ozone and PM2.5 air quality standards. This suggests that widespread wildfire emissions can negate years of efforts dedicated to controlling air pollution. The integration of low-cost sensor data proved invaluable in refining the estimates of health impacts from PM2.5 resulting from wildfires.
{"title":"Air quality and health impacts of the 2020 wildfires in California","authors":"Marc Carreras-Sospedra, Shupeng Zhu, Michael MacKinnon, William Lassman, Jeffrey D. Mirocha, Michele Barbato, Donald Dabdub","doi":"10.1186/s42408-023-00234-y","DOIUrl":"https://doi.org/10.1186/s42408-023-00234-y","url":null,"abstract":"Wildfires in 2020 ravaged California to set the annual record of area burned to date. Clusters of wildfires in Northern California surrounded the Bay Area covering the skies with smoke and raising the air pollutant concentrations to hazardous levels. This study uses the Fire Inventory from the National Center for Atmospheric Research database and the Community Multiscale Air Quality model to estimate the effects of wildfire emissions on air quality during the period from August 16 to October 28 of 2020. In addition, low-cost sensor data for fine particulate matter (PM2.5) from the PurpleAir network is used to enhance modeled PM2.5 concentrations. The resulting impacts on ozone and PM2.5 are used to quantify the health impacts caused by wildfires using the Benefits Mapping and Analysis Program – Community Edition. Wildfire activity significantly increased direct PM2.5 emissions and emissions of PM2.5 and ozone precursors. Direct PM2.5 emissions surged up to 38 times compared to an average day. Modeling results indicated that wildfires alone led to a rise in ozone daily maximum 8-h average by up to 10 ppb and exceeded PM2.5 air quality standards in numerous locations by up to 10 times. While modeled PM2.5 concentrations were lower than measurements, correcting these with PurpleAir data improved the accuracy. The correction using PurpleAir data increased estimates of wildfire-induced mortality due to PM2.5 exposure by up to 16%. The increased hospital admissions and premature mortality attributed to wildfires were found to be comparable to the health impacts avoided by strategies aimed at meeting ozone and PM2.5 air quality standards. This suggests that widespread wildfire emissions can negate years of efforts dedicated to controlling air pollution. The integration of low-cost sensor data proved invaluable in refining the estimates of health impacts from PM2.5 resulting from wildfires.","PeriodicalId":12273,"journal":{"name":"Fire Ecology","volume":"10 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139509838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-18DOI: 10.1186/s42408-023-00240-0
Ashley A. Coble, Brooke E. Penaluna, Laura J. Six, Jake Verschuyl
<p><b>Correction: Fire Ecol 19, 34 (2023)</b></p><p><b>https://doi.org/10.1186/s42408-023–00192-5</b></p><p>When analysing subsequent years of fish and amphibian data, the authors identified an error in some of the reach area calculations that affected vertebrate densities for some sites (density and biomass density for fish and amphibians). Specifically, the formula for reach area in some cells (5 sites) referenced wetted width from an adjacent site instead of the correct site. Because this error did not occur across all cells (sites) and because abundance data were not affected this calculation error was not readily apparent. This error affected densities for fish and amphibians at some sites, including 2 of the most severely burned sites, and therefore affects the individual fish and amphibian responses reported in Fig. 7 a, b. For consistency, Fig. 5 (PCA) has also been updated to reflect these changes.</p><figure><figcaption><b data-test="figure-caption-text">Fig. 5</b></figcaption><picture><source srcset="//media.springernature.com/lw685/springer-static/image/art%3A10.1186%2Fs42408-023-00240-0/MediaObjects/42408_2023_240_Fig1_HTML.png?as=webp" type="image/webp"/><img alt="figure 1" aria-describedby="Fig1" height="1604" loading="lazy" src="//media.springernature.com/lw685/springer-static/image/art%3A10.1186%2Fs42408-023-00240-0/MediaObjects/42408_2023_240_Fig1_HTML.png" width="685"/></picture><p>Principal components analysis (PCA) and relationships of axes with fire severity and pre-fire stand age. <b>a</b> PCA with scores and loadings of physical, chemical, biological, and watershed characteristics. <b>b</b> Principal component 1 (PC1) varied as a function of fire severity as RAVG mean. <b>c</b> Principal component 2 (PC2) varied as a function of pre-fire stand age</p><span>Full size image</span><svg aria-hidden="true" focusable="false" height="16" role="img" width="16"><use xlink:href="#icon-eds-i-chevron-right-small" xmlns:xlink="http://www.w3.org/1999/xlink"></use></svg></figure><p>This correction affects only the fish and amphibian density and biomass density results (Fig. 5, Fig. 7 panel a and b), with minimal edits to the text. However, this small adjustment does not affect the overall conclusions or interpretation of the article, which focuses on the response of in-stream large wood and riparian coarse wood to wildfire.</p><figure><figcaption><b data-test="figure-caption-text">Fig. 7</b></figcaption><picture><source srcset="//media.springernature.com/lw685/springer-static/image/art%3A10.1186%2Fs42408-023-00240-0/MediaObjects/42408_2023_240_Fig2_HTML.png?as=webp" type="image/webp"/><img alt="figure 2" aria-describedby="Fig2" height="551" loading="lazy" src="//media.springernature.com/lw685/springer-static/image/art%3A10.1186%2Fs42408-023-00240-0/MediaObjects/42408_2023_240_Fig2_HTML.png" width="685"/></picture><p>Biological responses that varied as a function of fire severity (RAVG). Biological responses included: <b>a</b> fish densit
图 5b；附加文件 2）火灾严重程度是 PC1 的一个重要预测因子，它表明火灾越严重的流域，其树木死亡率、挽救性采伐、光照可用性、 DOC、DON、NH4+、鱼类密度和溪流温度越高，树冠覆盖率、敏感和不耐受大型无脊椎动物类群、刮削器百分比以及溪流和河岸地区的小直径木材越低（图 5b；附加文件 2）。第五段我们假设溪流生物群会对暴露于更严重火灾的溪流做出负面反应，我们的结果与一些顶级捕食者的假设一致。在顶级捕食者（鱼类或两栖动物）中，我们发现只有鱼类密度和鱼类生物量密度随火灾严重程度和火灾前林木年龄而变化，而两栖动物密度和两栖动物生物量密度则不随任何预测因子而变化（图 5）。我们观察到鱼类密度与火灾严重程度、火灾前林龄以及它们各自的主效应之间存在明显的交互作用。鱼类生物量密度随火灾严重程度而变化，但不随火灾前林龄或它们之间的交互作用而变化。我们的假设是，溪流生物群会对遭受严重火灾的溪流做出负面反应，我们的结果与两栖动物的假设一致，但与鱼类的假设不一致。两栖动物的密度随火灾严重程度和火灾前林木年龄的变化而变化，而鱼类的密度则随火灾严重程度的变化而变化。鱼类生物量密度和两栖动物生物量密度不随任何预测因子的变化而变化（图 5）。我们没有观察到两栖动物密度与火灾严重程度和火灾前林木年龄之间存在明显的交互作用，但它们各自的主效应都很明显，在烧毁程度较轻的流域和火灾前林木年龄较大的地区，两栖动物密度较大。火灾严重程度和火灾前林木年龄对水生生态系统的影响 第一段在火灾严重程度较高的流域，上层林木死亡率、光照可用性、DOM 浓度、抢救性砍伐和溪流温度都有所上升，而树冠覆盖率、LW 直径、DOM 浓度和溪流温度都有所下降、敏感和不耐受大型无脊椎动物类群、刮食者功能摄食群、鱼类密度和鱼类生物量密度降低在燃烧严重程度较高的流域，上层树木死亡率、光照可用性、DOM 浓度、抢救性采伐、溪流温度和鱼类密度增加，而树冠覆盖率、LW 直径、鱼类生物量密度和鱼类生物量密度降低、火灾严重程度和火灾前林木年龄对水生生态系统的影响 第 6 段 我们发现，在整个研究区域内，火灾较严重的流域的鱼类密度和生物量密度都有所下降、这些变化可能共同导致了鱼类密度和鱼类生物量密度的下降。尽管在我们的研究中观察到了直接的下降，但预计这些本地种群将很快恢复（Rieman 和 Clayton，1997 年；Dunham 等人，2003 年；Rieman 等人，2012 年；Gomez Isaza 等人，2022 年）。尽管在我们的研究中观察到的捕食者反应不一，但预计这些本地种群将很快恢复（Rieman 和 Clayton，1997 年；Dunham 等，2003 年；Rieman 等，2012 年；Gomez Isaza 等，2022 年）。结论在喀斯喀特西部特大火灾后的最初 8 到 11 个月内，我们发现更严重的火灾烧毁了更多的上层河岸植被，导致光照、DOM 浓度和大型无脊椎动物密度增加，同时降低了树冠覆盖率、LW 直径、大型无脊椎动物多样性以及鱼类密度、在西卡斯卡特大火灾后的最初 8 到 11 个月内，我们发现更严重的火灾烧毁了更多的上层河岸植被，导致光照、DOM 浓度、大型无脊椎动物和鱼类密度增加，同时树冠覆盖率、LW 直径、大型无脊椎动物多样性和两栖动物密度降低附加文件 5 生物变量与火灾前林木年龄（y）的函数关系。变量包括：a) 无灰干质量（g m-2）；b) 采集器-滤网（%）；c) 碎纸机（%）；d) EPT（%）；e) 两栖动物密度（no. 生物变量与火灾前林龄（y）和火灾严重程度（RAVG）的函数关系。变量包括：a) 无灰干质量（克 m-2）；b) 采集器-滤器（%）；c) 碎纸机（%）；d) EPT（%）；e) 鱼类生物量密度（克 m-2）；f) 两栖动物生物量密度（克 m-2）Coble, A.A., Penaluna, B.E., Six, L.J. et al. 火灾严重程度影响俄勒冈州西部流域的大型木材和溪流生态系统响应。Fire Ecol 19, 34 (2023). https://doi.org/10.1186/s42408-023-00192-5.Download 参考文献作者及单位NCASI, 2438 NW Professional Drive, Corvallis, OR, 97330, USAAshley A. CobleU.S.D.A. Forest Service, Pacific Northwest Research Station, 3200 SW Jefferson Way, Corvallis, OR, 97331, USABrooke E.PenalunaWeyerhaeuser Company, 505 N Pearl St, Centralia, WA, 98531, USALaura J. SixNCASI, 1117 3Rd Street, Anacortes, WA, 98221, USAJake VerschuylAuthorsAshley A. CobleView author publications您也可以在PubMed Google Scholar中搜索该作者Brooke E. PenalunaView author publications您也可以在PubMed Google Scholar中搜索该作者Laura J. Six查看作者发表的文章Six查看作者发表的文章您也可以在PubMed Google Scholar中搜索该作者Jake Verschuyl查看作者发表的文章您也可以在PubMed Google Scholar中搜索该作者通信作者Ashley A. Coble.开放存取本文采用知识共享署名 4.0 国际许可协议进行许可，该协议允许以任何媒介或格式使用、共享、改编、分发和复制，只要您适当注明原作者和来源，提供知识共享许可协议的链接，并注明是否进行了修改。本文中的图片或其他第三方材料均包含在文章的知识共享许可协议中，除非在材料的署名栏中另有说明。如果材料未包含在文章的知识共享许可协议中，且您打算使用的材料不符合法律规定或超出许可使用范围，您需要直接从版权所有者处获得许可。要查看该许可的副本，请访问 http://creativecommons.org/licenses/by/4.0/.Reprints and permissionsCite this articleCoble, A.A., Penaluna, B.E., Six, L.J. et al. Correction：Fire Ecol 20, 5 (2024). https://doi.org/10.1186/s42408-023-00240-0Download citationPublished: 18 January 2024DOI: https://doi.org/10.1186/s42408-023-00240-0Share this articleAnyone you share the following link with will be able to read this cont
{"title":"Correction: Fire severity influences large wood and stream ecosystem responses in western Oregon watersheds","authors":"Ashley A. Coble, Brooke E. Penaluna, Laura J. Six, Jake Verschuyl","doi":"10.1186/s42408-023-00240-0","DOIUrl":"https://doi.org/10.1186/s42408-023-00240-0","url":null,"abstract":"<p><b>Correction: Fire Ecol 19, 34 (2023)</b></p><p><b>https://doi.org/10.1186/s42408-023–00192-5</b></p><p>When analysing subsequent years of fish and amphibian data, the authors identified an error in some of the reach area calculations that affected vertebrate densities for some sites (density and biomass density for fish and amphibians). Specifically, the formula for reach area in some cells (5 sites) referenced wetted width from an adjacent site instead of the correct site. Because this error did not occur across all cells (sites) and because abundance data were not affected this calculation error was not readily apparent. This error affected densities for fish and amphibians at some sites, including 2 of the most severely burned sites, and therefore affects the individual fish and amphibian responses reported in Fig. 7 a, b. For consistency, Fig. 5 (PCA) has also been updated to reflect these changes.</p><figure><figcaption><b data-test=\"figure-caption-text\">Fig. 5</b></figcaption><picture><source srcset=\"//media.springernature.com/lw685/springer-static/image/art%3A10.1186%2Fs42408-023-00240-0/MediaObjects/42408_2023_240_Fig1_HTML.png?as=webp\" type=\"image/webp\"/><img alt=\"figure 1\" aria-describedby=\"Fig1\" height=\"1604\" loading=\"lazy\" src=\"//media.springernature.com/lw685/springer-static/image/art%3A10.1186%2Fs42408-023-00240-0/MediaObjects/42408_2023_240_Fig1_HTML.png\" width=\"685\"/></picture><p>Principal components analysis (PCA) and relationships of axes with fire severity and pre-fire stand age. <b>a</b> PCA with scores and loadings of physical, chemical, biological, and watershed characteristics. <b>b</b> Principal component 1 (PC1) varied as a function of fire severity as RAVG mean. <b>c</b> Principal component 2 (PC2) varied as a function of pre-fire stand age</p><span>Full size image</span><svg aria-hidden=\"true\" focusable=\"false\" height=\"16\" role=\"img\" width=\"16\"><use xlink:href=\"#icon-eds-i-chevron-right-small\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"></use></svg></figure><p>This correction affects only the fish and amphibian density and biomass density results (Fig. 5, Fig. 7 panel a and b), with minimal edits to the text. However, this small adjustment does not affect the overall conclusions or interpretation of the article, which focuses on the response of in-stream large wood and riparian coarse wood to wildfire.</p><figure><figcaption><b data-test=\"figure-caption-text\">Fig. 7</b></figcaption><picture><source srcset=\"//media.springernature.com/lw685/springer-static/image/art%3A10.1186%2Fs42408-023-00240-0/MediaObjects/42408_2023_240_Fig2_HTML.png?as=webp\" type=\"image/webp\"/><img alt=\"figure 2\" aria-describedby=\"Fig2\" height=\"551\" loading=\"lazy\" src=\"//media.springernature.com/lw685/springer-static/image/art%3A10.1186%2Fs42408-023-00240-0/MediaObjects/42408_2023_240_Fig2_HTML.png\" width=\"685\"/></picture><p>Biological responses that varied as a function of fire severity (RAVG). Biological responses included: <b>a</b> fish densit","PeriodicalId":12273,"journal":{"name":"Fire Ecology","volume":"4 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139496593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-17DOI: 10.1186/s42408-023-00233-z
José Manuel Fernández-Guisuraga, Paulo M. Fernandes
Prescribed burning (PB) is becoming relevant in fuel reduction and thus fire hazard abatement in fire-prone ecosystems of southern Europe. Yet, empirical evidence on the effectiveness of this practice to mitigate wildfire severity in Mediterranean shrublands is non-existent, despite being the focus of PB efforts in this region. Here, we intended to quantify the protective effect of PB treatment units (2005–2021) to subsequent wildfire severity in shrublands across mainland Portugal, as well as the relative contribution and complex interactions between drivers of wildfire severity in PB-treated areas and untreated neighboring counterparts through Random Forest regression. We leveraged cloud-computing remote sensing data processing in Google Earth Engine to estimate fire severity (PB and wildfire) as the Relativized Burn Ratio (RBR) using Landsat data catalog. PB treatment was particularly effective at mitigating wildfire severity at the first PB-wildfire encounter in shrublands, with a mean reduction of around 24% in RBR units. Fuel age (i.e., time since prescribed burning) in PB-wildfire intersection areas overwhelmed to a large extent the effect of fire weather, burning probability, and PB severity. The mitigating effect of PB on wildfire severity persisted for a fuel age of around 5 years. However, this effect decreased with increasingly adverse fire weather conditions, such that variation in wildfire severity was somewhat insensitive to fuel age under extreme fire weather. Similarly, the lowest wildfire severity experienced in sites with high burning probability, along with the interaction effect observed between burning probability and fuel age, suggest that repeated PB treatments may be useful in controlling fuel accumulation and mitigating wildfire severity. The relative contribution of fire weather in explaining wildfire severity was exceedingly high in untreated areas, doubling that of the other variables in the model in the absence of PB treatment variables. Our results suggest that the implementation of PB treatments at intervals of less than 5 years is of paramount importance to control fuel build-up and fire hazard under extreme fire weather in productive Mediterranean shrublands. Further research on this topic is warranted in other shrublands worldwide, namely in Mediterranean-type climate regions.
{"title":"Prescribed burning mitigates the severity of subsequent wildfires in Mediterranean shrublands","authors":"José Manuel Fernández-Guisuraga, Paulo M. Fernandes","doi":"10.1186/s42408-023-00233-z","DOIUrl":"https://doi.org/10.1186/s42408-023-00233-z","url":null,"abstract":"Prescribed burning (PB) is becoming relevant in fuel reduction and thus fire hazard abatement in fire-prone ecosystems of southern Europe. Yet, empirical evidence on the effectiveness of this practice to mitigate wildfire severity in Mediterranean shrublands is non-existent, despite being the focus of PB efforts in this region. Here, we intended to quantify the protective effect of PB treatment units (2005–2021) to subsequent wildfire severity in shrublands across mainland Portugal, as well as the relative contribution and complex interactions between drivers of wildfire severity in PB-treated areas and untreated neighboring counterparts through Random Forest regression. We leveraged cloud-computing remote sensing data processing in Google Earth Engine to estimate fire severity (PB and wildfire) as the Relativized Burn Ratio (RBR) using Landsat data catalog. PB treatment was particularly effective at mitigating wildfire severity at the first PB-wildfire encounter in shrublands, with a mean reduction of around 24% in RBR units. Fuel age (i.e., time since prescribed burning) in PB-wildfire intersection areas overwhelmed to a large extent the effect of fire weather, burning probability, and PB severity. The mitigating effect of PB on wildfire severity persisted for a fuel age of around 5 years. However, this effect decreased with increasingly adverse fire weather conditions, such that variation in wildfire severity was somewhat insensitive to fuel age under extreme fire weather. Similarly, the lowest wildfire severity experienced in sites with high burning probability, along with the interaction effect observed between burning probability and fuel age, suggest that repeated PB treatments may be useful in controlling fuel accumulation and mitigating wildfire severity. The relative contribution of fire weather in explaining wildfire severity was exceedingly high in untreated areas, doubling that of the other variables in the model in the absence of PB treatment variables. Our results suggest that the implementation of PB treatments at intervals of less than 5 years is of paramount importance to control fuel build-up and fire hazard under extreme fire weather in productive Mediterranean shrublands. Further research on this topic is warranted in other shrublands worldwide, namely in Mediterranean-type climate regions.","PeriodicalId":12273,"journal":{"name":"Fire Ecology","volume":"2 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139481245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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