Climatic thresholds shape northern high‐latitude fire regimes and imply vulnerability to future climate change

Boreal forests and arctic tundra cover 33% of global land area and store an estimated 50% of total soil carbon. Because wildfire is a key driver of terrestrial carbon cycling, increasing fire activity in these ecosystems would likely have global implications. To anticipate potential spatiotemporal variability in fire‐regime shifts, we modeled the spatially explicit 30‐yr probability of fire occurrence as a function of climate and landscape features (i.e. vegetation and topography) across Alaska. Boosted regression tree (BRT) models captured the spatial distribution of fire across boreal forest and tundra ecoregions (AUC from 0.63–0.78 and Pearson correlations between predicted and observed data from 0.54–0.71), highlighting summer temperature and annual moisture availability as the most influential controls of historical fire regimes. Modeled fire–climate relationships revealed distinct thresholds to fire occurrence, with a nonlinear increase in the probability of fire above an average July temperature of 13.4°C and below an annual moisture availability (i.e. P‐PET) of approximately 150 mm. To anticipate potential fire‐regime responses to 21st‐century climate change, we informed our BRTs with Coupled Model Intercomparison Project Phase 5 climate projections under the RCP 6.0 scenario. Based on these projected climatic changes alone (i.e. not accounting for potential changes in vegetation), our results suggest an increasing probability of wildfire in Alaskan boreal forest and tundra ecosystems, but of varying magnitude across space and throughout the 21st century. Regions with historically low flammability, including tundra and the forest–tundra boundary, are particularly vulnerable to climatically induced changes in fire activity, with up to a fourfold increase in the 30‐yr probability of fire occurrence by 2100. Our results underscore the climatic potential for novel fire regimes to develop in these ecosystems, relative to the past 6000–35 000 yr, and spatial variability in the vulnerability of wildfire regimes and associated ecological processes to 21st‐century climate change.     

Wildfire refugia in forests: Severe fire weather and drought mute the influence of topography and fuel age

Wildfire refugia (unburnt patches within large wildfires) are important for the persistence of fire‐sensitive species across forested landscapes globally. A key challenge is to identify the factors that determine the distribution of fire refugia across space and time. In particular, determining the relative influence of climatic and landscape factors is important in order to understand likely changes in the distribution of wildfire refugia under future climates. Here, we examine the relative effect of weather (i.e. fire weather, drought severity) and landscape features (i.e. topography, fuel age, vegetation type) on the occurrence of fire refugia across 26 large wildfires in south‐eastern Australia. Fire weather and drought severity were the primary drivers of the occurrence of fire refugia, moderating the effect of landscape attributes. Unburnt patches rarely occurred under ‘severe’ fire weather, irrespective of drought severity, topography, fuels or vegetation community. The influence of drought severity and landscape factors played out most strongly under ‘moderate’ fire weather. In mesic forests, fire refugia were linked to variables that affect fuel moisture, whereby the occurrence of unburnt patches decreased with increasing drought conditions and were associated with more mesic topographic locations (i.e. gullies, pole‐facing aspects) and vegetation communities (i.e. closed‐forest). In dry forest, the occurrence of refugia was responsive to fuel age, being associated with recently burnt areas (<5 years since fire). Overall, these results show that increased severity of fire weather and increased drought conditions, both predicted under future climate scenarios, are likely to lead to a reduction of wildfire refugia across forests of southern Australia. Protection of topographic areas able to provide long‐term fire refugia will be an important step towards maintaining the ecological integrity of forests under future climate change.     

A PCA-based modelling technique for predicting environmental suitability for organisms from presence records

We present a correlative modelling technique that uses locality records (associated with species presence) and a set of predictor variables to produce a statistically justifiable probability response surface for a target species. The probability response surface indicates the suitability of each grid cell in a map for the target species in terms of the suite of predictor variables. The technique constructs a hyperspace for the target species using principal component axes derived from a principal components analysis performed on a training dataset. The training dataset comprises the values of the predictor variables associated with the localities where the species has been recorded as present. The origin of this hyperspace is taken to characterize the centre of the niche of the organism. All the localities (grid-cells) in the map region are then fitted into this hyperspace using the values of the predictor variables at these localities (the prediction dataset). The Euclidean distance from any locality to the origin of the hyperspace gives a measure of the 'centrality' of that locality in the hyperspace. These distances are used to derive probability values for each grid cell in the map region. The modelling technique was applied to bioclimatic data to predict bioclimatic suitability for three alien invasive plant species ( Lantana camara L., Ricinus communis L. and Solanum mauritianum Scop.) in South Africa, Lesotho and Swaziland. The models were tested against independent test records by calculating area under the curve (AUC) values of receiver operator characteristic (ROC) curves and kappa statistics. There was good agreement between the models and the independent test records. The pre-processing of climatic variable data to reduce the deleterious effects of multicollinearity, and the use of stopping rules to prevent overfitting of the models are important aspects of the modelling process.     

呼中林区火烧点格局分析及影响因素

林火是森林生态系统景观格局、动态和生态过程的重要自然驱动力,理解林火发生空间格局与影响因素对于林火安全管理具有重要的作用。采用点格局分析方法,以黑龙江大兴安岭呼中林区1990-2005年火烧数据为研究案例,分析了火烧点空间格局及其影响因素。结果表明,火烧点在空间上的分布是不均匀的,呈现聚集分布,存在一些火烧高发区和低发区。呼中林区火烧概率是0.004-0.012次/(km² · a),平均火烧概率为0.0077次/(km² · a)。人类活动因子、地形因子和植被因子对林火的发生均具有重要作用。应用空间点格局分析方法表明,距离居民点和道路的距离、高程、坡度和林型是影响林火发生的显著因子。因此在进行森林防火管理时,仅仅通过控制人类活动对于降低林火火险的效果是有限的,地形和林型也是林火防控时重点要考虑的因素。     

Artificial neural networks for assessing forest fire susceptibility in Türkiye

Wildfires often threaten natural and economic resources and human lives. Wildfire susceptibility assessments have become essential for efficient disaster management and increasing resilience. In this study, we assessed the forest fire susceptibility in Istanbul Province and Thrace Region, Türkiye using a well-known machine learning technique, Artificial Neural Networks (ANN). Benefiting from freely available Earth Observation datasets such as Sentinel-2 images, Tree Cover Density from European Union (EU) European Environment Agency (EEA) Copernicus Land Monitoring Service, Shuttle Radar Topography Mission (SRTM) data, etc., and a forest inventory with ignition locations recorded over a period of eight years, we utilized a total of 16 independent and one dependent variables. The variables can be categorized as anthropogenic, topographic, vegetation, and hydrological factors. A ratio of 1:2 was preferred for the fire/non-fire location samples. The results show that the ANN exhibited high prediction performance with Area Under the Receiver Operating Characteristic Curve (AUC) value and F-1 score of 0.94 and 0.80, respectively. Based on feature importance analyses, we found that a human-related factor, proximity to forest roads, was the most predictive input variable. The ANN model trained with openly available data (i.e., without forest database) also yielded a high F-1 score, but produced maps with fewer details. Our results confirm that data-driven machine learning methods are promising for regional forest fire susceptibility assessments and can be extended further for other regions by deriving similar parameters from freely available Earth Observation datasets.     

The Role of Wildfire,Prescribed Fire,and Mountain Pine Beetle Infestations on the Population Dynamics of Black-Backed Woodpeckers in the Black Hills,South Dakota

Wildfire and mountain pine beetle infestations are naturally occurring disturbances in western North American forests. Black-backed woodpeckers (Picoides arcticus) are emblematic of the role these disturbances play in creating wildlife habitat, since they are strongly associated with recently-killed forests. However, management practices aimed at reducing the economic impact of natural disturbances can result in habitat loss for this species. Although black-backed woodpeckers occupy habitats created by wildfire, prescribed fire, and mountain pine beetle infestations, the relative value of these habitats remains unknown. We studied habitat-specific adult and juvenile survival probabilities and reproductive rates between April 2008 and August 2012 in the Black Hills, South Dakota. We estimated habitat-specific adult and juvenile survival probability with Bayesian multi-state models and habitat-specific reproductive success with Bayesian nest survival models. We calculated asymptotic population growth rates from estimated demographic rates with matrix projection models. Adult and juvenile survival and nest success were highest in habitat created by summer wildfire, intermediate in MPB infestations, and lowest in habitat created by fall prescribed fire. Mean posterior distributions of population growth rates indicated growing populations in habitat created by summer wildfire and declining populations in fall prescribed fire and mountain pine beetle infestations. Our finding that population growth rates were positive only in habitat created by summer wildfire underscores the need to maintain early post-wildfire habitat across the landscape. The lower growth rates in fall prescribed fire and MPB infestations may be attributed to differences in predator communities and food resources relative to summer wildfire.     

Previous Fires Moderate Burn Severity of Subsequent Wildland Fires in Two Large Western US Wilderness Areas

Wildland fire is an important natural process in many ecosystems. However, fire exclusion has reduced frequency of fire and area burned in many dry forest types, which may affect vegetation structure and composition, and potential fire behavior. In forests of the western U.S., these effects pose a challenge for fire and land managers who seek to restore the ecological process of fire to ecosystems. Recent research suggests that landscapes with unaltered fire regimes are more “self-regulating” than those that have experienced fire-regime shifts; in self-regulating systems, fire size and severity are moderated by the effect of previous fire. To determine if burn severity is moderated in areas that recently burned, we analyzed 117 wildland fires in 2 wilderness areas in the western U.S. that have experienced substantial recent fire activity. Burn severity was measured using a Landsat satellite-based metric at a 30-m resolution. We evaluated (1) whether pixels that burned at least twice since 1984 experienced lower burn severity than pixels that burned once, (2) the relationship between burn severity and fire history, pre-fire vegetation, and topography, and (3) how the moderating effect of a previous fire decays with time. Results show burn severity is significantly lower in areas that have recently burned compared to areas that have not. This effect is still evident at around 22 years between wildland fire events. Results further indicate that burn severity generally increases with time since and severity of previous wildfire. These findings may assist land managers to anticipate the consequences of allowing fires to burn and provide rationale for using wildfire as a “fuel treatment”.     

Integrating anthropogenic factors into regional‐scale species distribution models—A novel application in the imperiled sagebrush biome

Species distribution models (SDMs) that rely on regional‐scale environmental variables will play a key role in forecasting species occurrence in the face of climate change. However, in the Anthropocene, a number of local‐scale anthropogenic variables, including wildfire history, land‐use change, invasive species, and ecological restoration practices can override regional‐scale variables to drive patterns of species distribution. Incorporating these human‐induced factors into SDMs remains a major research challenge, in part because spatial variability in these factors occurs at fine scales, rendering prediction over regional extents problematic. Here, we used big sagebrush (Artemisia tridentata Nutt.) as a model species to explore whether including human‐induced factors improves the fit of the SDM. We applied a Bayesian hurdle spatial approach using 21,753 data points of field‐sampled vegetation obtained from the LANDFIRE program to model sagebrush occurrence and cover by incorporating fire history metrics and restoration treatments from 1980 to 2015 throughout the Great Basin of North America. Models including fire attributes and restoration treatments performed better than those including only climate and topographic variables. Number of fires and fire occurrence had the strongest relative effects on big sagebrush occurrence and cover, respectively. The models predicted that the probability of big sagebrush occurrence decreases by 1.2% (95% CI: ?6.9%, 0.6%) when one fire occurs and cover decreases by 44.7% (95% CI: ?47.9%, ?41.3%) if at least one fire occurred over the 36 year period of record. Restoration practices increased the probability of big sagebrush occurrence but had minimal effect on cover. Our results demonstrate the potential value of including disturbance and land management along with climate in models to predict species distributions. As an increasing number of datasets representing land‐use history become available, we anticipate that our modeling framework will have broad relevance across a range of biomes and species.     

Integration of Lightning- and Human-Caused Wildfire Occurrence Models

Fire risk indices are useful tools for fire prevention actions by fire managers. A fire ignition is either the result of lightning or human activities. In European Mediterranean countries most forest fires are due to human activities. However, lightning is still an important fire ignition source in some regions. Integration of lightning and human fire occurrence probability into fire risk indices would be necessary to have a complete picture of the causal agents and their relative importance in fire occurrence. We present two methods for the integration of lightning and human fire occurrence probability models at 1 × 1 km grid cell resolution in two regions of Spain: Madrid, which presents a high fire incidence due to human activities; and Aragón, one of the most affected regions in Spain by lightning-fires. For validation, independent fire ignition points were used to compute the Receiver Operating Characteristic (ROC)-Area Under de Curve (AUC) and the Mahalanobis Distance. Results in Madrid are satisfactory for the human fire occurrence probability model (AUC～0.7) but less suitable for the lightning and the integrated models. In Aragón the fit for the human model is reasonable (AUC～0.7) whereas for the integration methods is practically useless (AUC～0.58).     

The carbon costs of mitigating high‐severity wildfire in southwestern ponderosa pine

Forests provide climate change mitigation benefit by sequestering carbon during growth. This benefit can be reversed by both human and natural disturbances. While some disturbances such as hurricanes are beyond the control of humans, extensive research in dry, temperate forests indicates that wildfire severity can be altered as a function of forest fuels and stand structural manipulations. The purpose of this study was to determine if current aboveground forest carbon stocks in fire‐excluded southwestern ponderosa pine forest are higher than prefire exclusion carbon stocks reconstructed from 1876, quantify the carbon costs of thinning treatments to reduce high‐severity wildfire risk, and compare posttreatment (thinning and burning) carbon stocks with reconstructed 1876 carbon stocks. Our findings indicate that prefire exclusion forest carbon stocks ranged from 27.9 to 36.6 Mg C ha^?1 and that the current fire‐excluded forest structure contained on average 2.3 times as much live tree carbon. Posttreatment carbon stocks ranged from 37.9 to 50.6 Mg C ha^?1 as a function of thinning intensity. Previous work found that these thinning and burning treatments substantially increased the 6.1 m wind speed necessary for fire to move from the forest floor to the canopy (torching index) and the wind speed necessary for sustained crown fire (crowning index), thereby reducing potential fire severity. Given the projected drying and increase in fire prevalence in this region as a function of changing climatic conditions, the higher carbon stock in the fire‐excluded forest is unlikely to be sustainable. Treatments to reduce high‐severity wildfire risk require trade‐offs between carbon stock size and carbon stock stability.     

基于MAXENT模型的黑龙江大兴安岭森林雷击火火险预测

黑龙江大兴安岭是森林雷击火的高发地区,急需研发精确的火险预测模型对该区森林火灾进行预测.本文基于大兴安岭地区森林雷击火灾数据及环境变量数据,采用MAXENT模型进行森林雷击火的火险预测.首先对各环境变量进行共线性诊断,再利用累积正则化增益法和Jackknife方法评价了环境变量的重要性,最后采用最大Kappa值和AUC值检测了MAXENT模型的预测精度.结果表明: 闪电能量和中和电荷量的方差膨胀因子（VIF）值分别为5.012和6.230,与其他变量之间存在共线性,不能用于模型训练.日降雨量、云地闪电数量及云地闪回击电流强度是影响森林雷击火发生的3个最重要因素,日平均风速和坡向的影响较小.随着建模数据比例的增加,最大Kappa值和AUC值均有增大趋势.最大Kappa值都大于0.75,平均值为0.772; AUC值都大于0.5,平均值为0.859.MAXENT模型的预测精度达到中等精度,可应用于大兴安岭地区的森林雷击火火险预测.     

Effectiveness of land use and disturbance measures,compared to climate,soil, and topography,for modeling relative abundances of tree species in the eastern United States

Environmental factors control species distributions and abundances, but effectiveness of land use and disturbance variables for modeling species generally is unknown compared to climate, soil, and topography variables. Therefore, I used predictor variables from categories of 1) land use and disturbance, 2) climate, and 3) soil, topography, and wind speed to model the relative abundances (i.e., percentage of all trees) of 65 common tree species in the eastern United States, with a contrast to presence-absence models of species distributions. First, I modeled variables within each category to identify the five most important variables. Then, I combined variables from each category to isolate most important variables, based on five model combinations of input variables from each category, ranging from one (i.e., three total) to five (i.e., 15 total) variables. From the five models of combined categories for each tree species, I identified the model with the greatest R² value. Overall, climate variables were most important for tree species models with one and two input variables from each category, but land use and disturbance variables were most important for models with three to five input variables from each category. Although a range of R² values occurred by species and number of input model variables, 32 species had best models with greatest R² values of 0.50 to 0.81. For all best species models, the most important variables were temperature of the warmest quarter, historical fire return interval for all fires, agricultural area during years 1850 to 1997, and precipitation of the driest month. Current land cover classes, which are accessible and the most commonly modeled land use variables, were not important for modeling tree species abundances or distributions. Climate variables were most important for modeling species distributions. Results support the concept that while climate sets soft boundaries on distributions, relative abundances within distributions are affected by other filters. Future modeling may establish other important land use and disturbance variables, or refinements within the important variables of historical fire return interval and agricultural area over time, advancing integration of both land use and climate variables into studies.     

Adapting to global environmental change in Patagonia: What role for disturbance ecology?

Research from the Patagonian‐Andean region is used to explore challenges and opportunities related to the integration of research on wildfire activity into a broader earth‐system science framework that views the biosphere and atmosphere as a coupled interacting system for understanding the causes and consequences of future wildfire activity. We examine how research in disturbance ecology can inform land‐use and other policy decisions in the context of probable future increases in wildfire activity driven by climate forcing. Climate research has related recent warming and drying trends in much of Patagonia to an upward trend in the Southern Annular Mode which is the leading pattern of extratropical climate variability in the southern hemisphere. Although still limited in spatial extent, tree‐ring fire history studies are beginning to reveal regional patterns of the top‐down climate influences on temporal and spatial pattern of wildfire occurrence in Patagonia. Knowledge of relationships of fire activity to climate variability in the context of predicted future warming leads to the hypothesis that wildfire activity in Patagonia will increase substantially during the first half of the 21st century. In addition to this anticipated increase in extreme fire events due to climate forcing, we further hypothesize that current land‐use trends will increase the extent and/or severity of fire events through bottom‐up (i.e. land surface) influences on wildfire potential. In particular, policy discussions of how to mitigate impacts of climate warming on fire potential need to consider research results from disturbance ecology on the implications of continued planting of flammable non‐native trees and the role of introduced herbivores in favouring vegetation changes that may enhance landscape flammability.     

我国林火发生预测模型研究进展

通过文献回顾,总结了国内林火发生预测模型的研究现状,并从林火发生驱动因子、林火发生概率预测模型、林火发生频次预测模型和模型检验方法等方面进行归纳分析。得出以下结论: 1)气象、地形、植被、可燃物、人类活动等因素是影响林火发生及模型预测精度的主要驱动因子;2)林火发生概率模型中,地理加权逻辑斯蒂回归模型考虑了变量之间的空间相关性,Gompit回归模型适宜非对称结构的林火数据,随机森林模型不需要多重共线性检验,在避免过度拟合的同时提高了预测精度,是林火发生概率预测模型的优选方法之一;3)林火发生频次模型中,负二项回归模型更适合对过度离散数据进行模拟,零膨胀模型和栅栏模型可以处理林火数据中包含大量零值的问题;4)ROC检验、AIC检验、似然比检验和Wald检验方法是林火概率和频次模型的常用检验方法。林火发生预测模型研究仍是我国当前林火管理工作的重点,预测模型的选择需要依据不同地区林火数据特点。此外,构建林火预测模型时需要考虑更多的影响因素,以提高模型预测精度;未来,需要进一步探索其他数学模型在林火发生预测中的应用,不断提高林火发生预测模型的准确度。     

Development and validation of the Durham Risk Score for estimating suicide attempt risk: A prospective cohort analysis

BackgroundWorldwide, nearly 800,000 individuals die by suicide each year; however, longitudinal prediction of suicide attempts remains a major challenge within the field of psychiatry. The objective of the present research was to develop and evaluate an evidence-based suicide attempt risk checklist [i.e., the Durham Risk Score (DRS)] to aid clinicians in the identification of individuals at risk for attempting suicide in the future.Methods and findingsThree prospective cohort studies, including a population-based study from the United States [i.e., the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) study] as well as 2 smaller US veteran cohorts [i.e., the Assessing and Reducing Post-Deployment Violence Risk (REHAB) and the Veterans After-Discharge Longitudinal Registry (VALOR) studies], were used to develop and validate the DRS. From a total sample size of 35,654 participants, 17,630 participants were selected to develop the checklist, whereas the remaining participants (N = 18,024) were used to validate it. The main outcome measure was future suicide attempts (i.e., actual suicide attempts that occurred after the baseline assessment during the 1- to 3-year follow-up period). Measure development began with a review of the extant literature to identify potential variables that had substantial empirical support as longitudinal predictors of suicide attempts and deaths. Next, receiver operating characteristic (ROC) curve analysis was utilized to identify variables from the literature review that uniquely contributed to the longitudinal prediction of suicide attempts in the development cohorts. We observed that the DRS was a robust prospective predictor of future suicide attempts in both the combined development (area under the curve [AUC] = 0.91) and validation (AUC = 0.92) cohorts. A concentration of risk analysis found that across all 35,654 participants, 82% of prospective suicide attempts occurred among individuals in the top 15% of DRS scores, whereas 27% occurred in the top 1%. The DRS also performed well among important subgroups, including women (AUC = 0.91), men (AUC = 0.93), Black (AUC = 0.92), White (AUC = 0.93), Hispanic (AUC = 0.89), veterans (AUC = 0.91), lower-income individuals (AUC = 0.90), younger adults (AUC = 0.88), and lesbian, gay, bisexual, transgender, and queer or questioning (LGBTQ) individuals (AUC = 0.88). The primary limitation of the present study was its its reliance on secondary data analyses to develop and validate the risk score.ConclusionsIn this study, we observed that the DRS was a strong predictor of future suicide attempts in both the combined development (AUC = 0.91) and validation (AUC = 0.92) cohorts. It also demonstrated good utility in many important subgroups, including women, men, Black, White, Hispanic, veterans, lower-income individuals, younger adults, and LGBTQ individuals. We further observed that 82% of prospective suicide attempts occurred among individuals in the top 15% of DRS scores, whereas 27% occurred in the top 1%. Taken together, these findings suggest that the DRS represents a significant advancement in suicide risk prediction over traditional clinical assessment approaches. While more work is needed to independently validate the DRS in prospective studies and to identify the optimal methods to assess the constructs used to calculate the score, our findings suggest that the DRS is a promising new tool that has the potential to significantly enhance clinicians’ ability to identify individuals at risk for attempting suicide in the future.

Using data from three prospective cohorts in the US, Nathan Kimbrel and colleagues report on the development and validation of an evidence-based checklist to identify those at risk of future suicide attempts.     

不同区域森林火灾对生态因子的响应及其概率模型

火灾是影响森林生态系统过程的重要干扰之一,其对森林生态系统内各生态因子的响应各不相同.由于植被状况及生态环境的不同,森林火灾的时空分布特征在中国不同植被气候类型内表现不同,根据植被气候类型分类系统,将中国主要森林火灾地区划分为4个区域:东北(冷温带松林)、华北(落叶阔叶林)、东南(常绿阔叶林)和西南(热带雨林),应用遥感监测数据和地面环境数据,以时空变量、生态因子(植被生长变化指数、湿度等)为可选自变量,应用半参数化Logistic回归模型,就森林火险对不同生态影响因子的响应规律进行了分析,建立了基于生态因子的着火概率模型和大火蔓延概率模型,通过模拟及实际数据散点图、火险概率图,评估了模型应用价值.结果表明,土壤湿度及植被含水量在落叶阔叶林、常绿阔叶林、热带雨林地区对着火概率影响显著.在4个植被气候区内,土壤及凋落物湿度对大火蔓延的作用较小.在冷温带松林、落叶阔叶林、常绿阔叶林地区,植被生长的年内变化对火灾发生的影响显著,在常绿阔叶林地区,年内植被生长变化对大火蔓延的作用较小.森林火险概率与各生态因子的相关关系主要呈现出非线性.不同植被气候区内,火险概率受不同生态因子组合的影响,这与不同区域的植被状况及生态环境不同有关.在不同植被气候类型,应用时空变量、生态因子建立半参数化logistic回归模型,进行着火概率和大火蔓延概率的模拟具有可行性和实际应用能力.为进一步分析森林生态系统与火灾之间的动态关系、展开生态系统火灾干扰研究提供了理论基础.     

Bayesian prediction of wildfire event probability using normalized difference vegetation index data from an Australian forest

Wildfires are impactful natural disasters, creating a significant impact across many rural communities. Predicting wildfire probability provides authorities with invaluable information to take preventive measures at the early stages. This study establishes Bayesian modelling for predicting the wildfire event probability based on a set of environmental predictors and forest vulnerability, represented by the normalized difference vegetation index. Prior information about the impact of these predictors on the likelihood of wildfire is available in the reports on the past major wildfire events. In that sense, the use of prior information in the Bayesian models has the potential to provide accurate predictions for the wildfire probability. Moreover, the relationship between the predictors creates mediating effects on the likelihood of a wildfire event. A multivariate prior distribution in the Bayesian modelling can capture the mediating effects. In this study, Bayesian models with informative and noninformative priors are considered with independent and multivariate prior distributions to utilize the available prior information and handle the mediating effects between the predictors using the normalized difference vegetation index data provided by Google Earth Engine. Nine years of data were gathered across 9841 sampled areas in a forested land of Australia. Modelling results concluded that forest vulnerability is found to be the dominant predictor of wildfire probability. This modelling can help create a Wildfire Warning Index based on climate data and forest vulnerability measurements, enabling preventative actions in high-risk and targeted areas.     

Topographic information improves simulated patterns of post-fire conifer regeneration in the southwest United States

The western United States is projected to experience more frequent and severe wildfires in the future due to drier and hotter climate conditions, exacerbating destructive wildfire impacts on forest ecosystems such as tree mortality and unsuccessful post-fire regeneration. While empirical studies have revealed strong relationships between topographical information and plant regeneration, ecological processes in ecosystem models have either not fully addressed topography-mediated effects on the probability of plant regeneration, or the probability is only controlled by climate-related factors, for example, water and light stresses. In this study, we incorporated seedling survival data based on a planting experiment in the footprint of the 2011 Las Conchas Fire into the Photosynthesis and EvapoTranspiration (PnET) extension of the LANDIS-II model by adding topographic and an additional climatic variable to the probability of regeneration. The modified algorithm included topographic parameters such as heat load index and ground slope and spring precipitation. We ran simulations on the Las Conchas Fire landscape for 2012–2099 using observed and projected climate data (i.e., Representative Concentration Pathway 4.5 and 8.5). Our modification significantly reduced the number of regeneration events of three common southwestern conifer tree species (piñon, ponderosa pine, and Douglas-fir), leading to decreases in aboveground biomass, regardless of climate scenario. The modified algorithm decreased regeneration at higher elevations and increased regeneration at lower elevations relative to the original algorithm. Regenerations of three species also decreased in eastern aspects. Our findings suggest that ecosystem models may overestimate post-fire regeneration events in the southwest United States. To better represent regeneration processes following wildfire, ecosystem models need refinement to better account for the range of factors that influence tree seedling establishment. This will improve model utility for projecting the combined effects of climate and wildfire on tree species distributions.     

Wildfire spatial pattern analysis in the Zagros Mountains,Iran: A comparative study of decision tree based classifiers

Knowledge of wildfire behavior is of key importance for planning and allocating resources to fire suppression efforts. In this study, we analyzed the spatial pattern of wildfires with five decision tree based classifiers, including alternating decision tree (ADT), classification and regression tree (CART), functional tree (FT), logistic model tree (LMT), and Naïve Bayes tree (NBT). The classifiers were trained using historical fire locations in the Zagros Mountains (Iran) from the years 2007–2014 and a set of fifteen explanatory variables (i.e., slope degree, aspect, altitude, plan curvature, topographic position index (TPI), topographic roughness index (TRI), topographic wetness index (TWI), mean annual temperature and rainfall, wind effect, soil type, land use, and proximity to settlements, roads, and rivers) that were first optimized with a twostep process using multicollinearity analysis and the Gain Ratio variable selection method. The classifiers were then validated using the Kappa index and several statistical index-based evaluators (i.e., accuracy, sensitivity, specificity, precision, and F-measure). The global performance of the classifiers was measured using the ROC-AUC method. In this comparative study, the ADT classifier demonstrated the highest performance both in terms of goodness-of-fit with the training dataset (accuracy = 99.8%, AUC = 0.991) and the capability to predict future wildfires (accuracy = 75.7%, AUC = 0.903). This study contributes to the suite of research that evaluates data mining methods for the prediction of natural hazards.     

A fine‐grained spatial prediction model for the red‐listed vascular plant Scorzonera humilis

We explored the applied use of distribution modelling as a tool for making spatial predictions of occurrences of the red‐listed vascular plant species Scorzonera humilis in a study area in southeast Norway. Scorzonera is typical of extensively managed semi‐natural grasslands. A Maxent model was trained on all known records of the species, accurately georeferenced and gridded to fine resolution (grid cells of 25×25 m). Model performance was assessed on the training data by data‐splitting (by which some records were set off for evaluation) and on independent evaluation data collected in the field. Of the eight predictor variables used in the modelling, distance to roads and to arable land were most important followed by land‐cover class and altitude. Judged from the area under curve (AUC), the model was good to excellent and a significant, positive relationship was found between relative probabilities of occurrence predicted by the model and true probability of presence provided by the independently collected evaluation data. The model was used together with the evaluation data to estimate presence of Scorzonera humilis in 0.7% of the grid cells in the study area. The grid cells in which the model predicted highest probability for Scorzonera to be present had a true probability of presence of ca 12%, i.e. 17×higher than in an average cell. The present study demonstrates that, even when only simple predictor variables are available, spatial prediction modelling contributes important knowledge about rare species such as prevalence estimates, spatial prediction maps and insights into the species’ autecology. Spatial prediction modelling also makes cost‐efficient monitoring of rare species possible. However, it is pointed out that these benefits require evaluation of the model on independently sampled evaluation data.