Predictive modelling of burn probability and burn severity in a desert spring ecosystem

2012 ◽  
Vol 21 (8) ◽  
pp. 1014 ◽  
Author(s):  
Stephanie O. Sunderman ◽  
Peter J. Weisberg
Keyword(s):  
Fire Ecology ◽  
Bayesian Model Averaging ◽  
Riparian Forest ◽  
Model Averaging ◽  
Predictive Modelling ◽  
Fire Regimes ◽  
Burn Severity ◽  
High Severity ◽  
Burn Probability ◽  
Desert Spring

Little is known about the fire ecology of desert springs, despite their importance for biodiversity and for provision of ecosystem services. Desert spring ecosystems are characterised by high and continuous fuel loads compared with surrounding uplands, suggesting that fire may play a significant ecological role. For the Ash Meadows spring complex in the south-western USA, we used ecological-niche factor analysis and a Bayesian model averaging regression technique to characterise the environmental conditions associated with spatially explicit burn probability and burn severity over a 24-year period. Burn probability and burn severity were both more strongly associated with fuel availability than with proximity to anthropogenic ignition sources; however, areas with more homogeneous vegetation cover were positively associated with high-severity burns but were negatively associated with burn probability. Burn probability was greater near areas of high anthropogenic influence, whereas areas further from anthropogenic alteration were more likely to experience high-severity fire. Riparian forest and emergent wetland vegetation were most likely to burn although they were among the rarest vegetation types. Human activities may strongly influence fire regimes in desert spring wetlands through groundwater pumping and introductions of exotic plants that alter fuelbed heterogeneity and shift the balance among woody and herbaceous vegetation.

scholarly journals Extreme Winds Flip Influence of Fuels and Topography on Megafire Burn Severity in Mesic Conifer Forests Under Record Fuel Aridity

2021 ◽  
Author(s):  
Cody Evers ◽  
Sebastian Busby ◽  
Max Nielsen-Pincus ◽  
Andrés Holz
Keyword(s):  
Land Use ◽  
Tree Mortality ◽  
Fire Regimes ◽  
Relative Influence ◽  
Burn Severity ◽  
Fire Season ◽  
Cascade Mountains ◽  
Private Lands ◽  
High Severity ◽  
Extreme Winds

Abstract The coupling of unusually hot and dry weather have led to global increases in the occurrence of megafires. Despite the conventional wisdom that extreme heat and aridity overwhelm the controls on burn severity patterns (i.e., vegetation mortality), we hypothesize that wind is the main driver of megafire events in temperate mesic forests with climate-restricted fire regimes, yet that fuels and topography remain important influences on burn severity patterns. The infrequent occurrence of large high-severity wildfire in these forests means that contemporary empirical data (e.g., remote sensing) from past megafires are largely missing. During the extraordinary 2020 fire season, ca. 0.8 million ha burned in the North American Pacific Northwest (PNW) over two weeks under record-breaking fuel aridity and winds, representing the first modern example of megafires that characterize disturbance regimes west of the region’s Cascade Mountains. Considering increasing concern and uncertainty surrounding the drivers of megafire events in temperate mesic forests, our objective was to understand the relative influence of, and potential interactions between, weather, fuels, and topography on high-severity (> 75% tree mortality) fire probability among five synchronous megafires in the western Cascade Mountains. To assess the influence of several potential drivers of high-severity fire and whether these relationships varied with land use and ownership, we developed remotely sensed fire extent and burn severity maps for two periods of the explosive 2020 PNW fire season: (1) during extreme winds and (2) after the extreme winds subsided. The area burned during the windstorm accounted for 90% of the total fire sizes and saw a 2.5-times greater proportion of high-severity fire than during the period without winds. Our results suggest that wind is the major driver of megafires in forests with climate-limited fire regimes, yet that fuels and topography shape burn severity patterns even under extreme fuel aridity and winds. The relative influence of topography on burn severity outweighed fuels during the windstorm, while fuels outweighed the influence of topography after winds subsided. Early-seral forests primarily concentrated on private lands, burned more severely than their older and taller counterparts, regardless of topography, over the entire megafire event. Meanwhile, mature stands burned severely only under extreme winds and especially on steeper slopes. Although climate change and land-use legacies may prime mesic temperate forests to burn more frequently and at higher severities than historically observed, and especially among early-seral forests, our work suggests that future high-severity megafires are only likely to occur during coinciding periods of heat, fuel aridity, and extreme winds.

scholarly journals Walking through a 'phoenix landscape': hiker surveys reveal nuanced perceptions of wildfire effects

2020 ◽  
Vol 29 (7) ◽  
pp. 561
Author(s):  
Alexandra M. Weill ◽  
Lauren M. Watson ◽  
Andrew M. Latimer
Keyword(s):  
Prescribed Fire ◽  
Fire Ecology ◽  
Fire Suppression ◽  
Recovery Period ◽  
Fire Regimes ◽  
The Past ◽  
High Severity ◽  
Before And After ◽  
The Usa ◽  
Local Ecology

Public opinion of wildfire is often perceived to be negative and in support of fire suppression, even though research suggests public opinions have become more positive over the past few decades. However, most prior work on this topic has focused on homeowners in forested regions. In this study, we shift the lens to hikers in a chaparral- and oak-savannah-dominated landscape that burned at high severity in 2015. We surveyed hikers before and after their hike about their familiarity and perceptions of local fire, and wildfire in the nation at large. We found hikers were familiar with topics such as prescribed fire and basic fire ecology, but knew little about local ecology or fire regimes. Post-hike perceptions of fire and feelings about wildfire in the USA were complex and heterogeneous, not predominantly negative. Contrary to frequent media descriptions of post-fire landscapes as ‘devastated’ or ‘moonscapes,’ many participants described the burned landscape with awe and admiration. These results suggest that residents of fire-prone landscapes may benefit from programming that emphasises benefits and challenges of fire in the local landscape and incorporates visits to local burned sites throughout the recovery period.

Selecting Predictors by Using Bayesian Model Averaging in Bridge Models

2012 ◽  
Author(s):  
Lorenzo Bencivelli ◽  
Massimiliano Giuseppe Marcellino ◽  
Gianluca Moretti
Keyword(s):  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Bridge Models

scholarly journals KBoost: a new method to infer gene regulatory networks from gene expression data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Luis F. Iglesias-Martinez ◽  
Barbara De Kegel ◽  
Walter Kolch
Keyword(s):  
Breast Cancer ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
R Package ◽  
Breast Cancer Patients ◽  
Sources Of Information ◽  
Cancer Subtypes ◽  
Gene Regulatory

AbstractReconstructing gene regulatory networks is crucial to understand biological processes and holds potential for developing personalized treatment. Yet, it is still an open problem as state-of-the-art algorithms are often not able to process large amounts of data within reasonable time. Furthermore, many of the existing methods predict numerous false positives and have limited capabilities to integrate other sources of information, such as previously known interactions. Here we introduce KBoost, an algorithm that uses kernel PCA regression, boosting and Bayesian model averaging for fast and accurate reconstruction of gene regulatory networks. We have benchmarked KBoost against other high performing algorithms using three different datasets. The results show that our method compares favorably to other methods across datasets. We have also applied KBoost to a large cohort of close to 2000 breast cancer patients and 24,000 genes in less than 2 h on standard hardware. Our results show that molecularly defined breast cancer subtypes also feature differences in their GRNs. An implementation of KBoost in the form of an R package is available at: https://github.com/Luisiglm/KBoost and as a Bioconductor software package.

scholarly journals Identification of Mortality Risks in the Advancement of Old Age: Application of Proportional Hazard Models Based on the Stepwise Variable Selection and the Bayesian Model Averaging Approach

Nutrients ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 1098
Author(s):  
Ewelina Łukaszyk ◽  
Katarzyna Bień-Barkowska ◽  
Barbara Bień
Keyword(s):  
Vitamin D ◽  
Mortality Rate ◽  
Plasma Level ◽  
Variable Selection ◽  
Lymphocyte Count ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Multivariable Analysis ◽  
Model Averaging ◽  
Nutritional Risk Index

Identifying factors that affect mortality requires a robust statistical approach. This study’s objective is to assess an optimal set of variables that are independently associated with the mortality risk of 433 older comorbid adults that have been discharged from the geriatric ward. We used both the stepwise backward variable selection and the iterative Bayesian model averaging (BMA) approaches to the Cox proportional hazards models. Potential predictors of the mortality rate were based on a broad range of clinical data; functional and laboratory tests, including geriatric nutritional risk index (GNRI); lymphocyte count; vitamin D, and the age-weighted Charlson comorbidity index. The results of the multivariable analysis identified seven explanatory variables that are independently associated with the length of survival. The mortality rate was higher in males than in females; it increased with the comorbidity level and C-reactive proteins plasma level but was negatively affected by a person’s mobility, GNRI and lymphocyte count, as well as the vitamin D plasma level.

Fire regime shift linked to increased forest density in a piñon–juniper savanna landscape

2014 ◽  
Vol 23 (2) ◽  
pp. 234 ◽  
Author(s):  
Ellis Q. Margolis
Keyword(s):  
Regime Shift ◽  
Fire History ◽  
Fire Regime ◽  
Fire Regimes ◽  
Fire Frequency ◽  
Climatic Conditions ◽  
Tree Density ◽  
High Severity ◽  
Wide Range ◽  
In Fire

Piñon–juniper (PJ) fire regimes are generally characterised as infrequent high-severity. However, PJ ecosystems vary across a large geographic and bio-climatic range and little is known about one of the principal PJ functional types, PJ savannas. It is logical that (1) grass in PJ savannas could support frequent, low-severity fire and (2) exclusion of frequent fire could explain increased tree density in PJ savannas. To assess these hypotheses I used dendroecological methods to reconstruct fire history and forest structure in a PJ-dominated savanna. Evidence of high-severity fire was not observed. From 112 fire-scarred trees I reconstructed 87 fire years (1547–1899). Mean fire interval was 7.8 years for fires recorded at ≥2 sites. Tree establishment was negatively correlated with fire frequency (r=–0.74) and peak PJ establishment was synchronous with dry (unfavourable) conditions and a regime shift (decline) in fire frequency in the late 1800s. The collapse of the grass-fuelled, frequent, surface fire regime in this PJ savanna was likely the primary driver of current high tree density (mean=881treesha–1) that is >600% of the historical estimate. Variability in bio-climatic conditions likely drive variability in fire regimes across the wide range of PJ ecosystems.

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