Human and biophysical drivers of fires in Semiarid Chaco mountains of Central Argentina

Estimation of potential wildfire behavior characteristics to assess wildfire danger in southwest China using deep learning schemes

Accurate estimation of potential wildfire behavior characteristics (PWBC) can improve wildfire danger assessment. However, wildfire behavior has been estimated by most fire spread models with immeasurable uncertainties and difficulties in large-scale applications. In this study, a PWBC estimation model (named PWBC-QR-BiLSTM) was proposed by coupling the Bi-directional Long Short-Term Memory (BiLSTM) and quantile regression (QR) methods. Multi-source data, including fuel, weather, topography, infrastructure, and landscape variables, were input into the PWBC-QR-BiLSTM model to estimate the potential rate of spread (ROS) and fire radiative power (FRP) over western Sichuan of China, and then to estimate the probability density of ROS and FRP. Daily ROS and FRP were extracted from the Global Fire Atlas and the MOD14A1/MYD14A1 product. The optimal PWBC-QR-BiLSTM model was determined using the Non-dominated Sorting Genetic Algorithm Ⅱ (NAGA-Ⅱ). Results showed that the PWBC-QR-BiLSTM performed well in estimating potential ROS and FRP with high accuracy (ROS: R2 > 0.7 and MAPE<30%, FRP: R2 > 0.8 and MAPE<25%). The modal PWBC values extracted from the estimated probability density were closer to the observed values, which can be regarded as a good indicator for wildfire danger assessment. The variable importance analysis also verified that fuel and infrastructure variables played an important role in driving wildfire behavior. This study suggests the potential of utilizing artificial intelligence to estimate PWBC and its probability density to improve the guidance on wildfire management.

Global change impacts on cacti (Cactaceae): current threats, challenges and conservation solutions

BACKGROUND

The plant family Cactaceae provides some of the most striking examples of adaptive evolution, expressing undeniably the most spectacular New World radiation of succulent plants distributed across arid and semi-arid regions of the Americas. Cacti are widely regarded for their cultural, economic and ecological value, yet they are also recognized as one of the most threatened and endangered taxonomic groups on the planet.

SCOPE

This paper reviews current threats to species of cacti that have distributions in arid to semi-arid subtropical regions. Our review focuses primarily on four global change forces: (1) increases in atmospheric CO2 concentrations; (2) increases in mean annual temperatures and heat waves; (3) increases in the duration, frequency and intensity of droughts; and (4) and increases in competition and wildfire frequency from invasion by non-native species. We provide a broad range of potential priorities and solutions for stemming the extinction risk of cacti species and populations.

CONCLUSIONS

Mitigating ongoing and emerging threats to cacti will require not only strong policy initiatives and international cooperation, but also new and creative approaches to conservation. These approaches include determining species at risk from climate extremes, enhancing habitat quality after disturbance, approaches and opportunities for ex situ conservation and restoration, and the potential use of forensic tools for identifying plants that have been removed illegally from the wild and sold on open markets.

Use of machine learning as a tool for determining fire management units in the brazilian atlantic forest

Geoprocessing techniques are generally applied in natural disaster risk management due to their ability to integrate and visualize different sets of geographic data. The objective of this study was to evaluate the capacity of classification and regression tree (CART) to assess fire risk. MCD45A1 product of the burnt area, relative to a 16-year period (2000-2015) was used to obtain a fire occurrence map, from center points of the raster, using a kernel density approach. The resulting map was then used as a response variable for CART analysis with fire influence variables used as predictors. A total of 12 predictors were determined from several databases, including environmental, physical, and socioeconomic aspects. Rules generated by the regression process allowed to of define different risk levels, expressed in 35 management units, and used to produce a fire prediction map. Results of the regression process (r = 0.94 and r² = 0.88) demonstrate the capability of the CART algorithm in highlighting hierarchical relationships among predictors, while the model's easy interpretability provides a solid basis for decision making. This methodology can be expanded in other environmental risk analysis studies and applied to any area of the globe on a regional scale.

Scientists' warning of the impacts of climate change on mountains

Mountains are highly diverse in areal extent, geological and climatic context, ecosystems and human activity. As such, mountain environments worldwide are particularly sensitive to the effects of anthropogenic climate change (global warming) as a result of their unique heat balance properties and the presence of climatically-sensitive snow, ice, permafrost and ecosystems. Consequently, mountain systems-in particular cryospheric ones-are currently undergoing unprecedented changes in the Anthropocene. This study identifies and discusses four of the major properties of mountains upon which anthropogenic climate change can impact, and indeed is already doing so. These properties are: the changing mountain cryosphere of glaciers and permafrost; mountain hazards and risk; mountain ecosystems and their services; and mountain communities and infrastructure. It is notable that changes in these different mountain properties do not follow a predictable trajectory of evolution in response to anthropogenic climate change. This demonstrates that different elements of mountain systems exhibit different sensitivities to forcing. The interconnections between these different properties highlight that mountains should be considered as integrated biophysical systems, of which human activity is part. Interrelationships between these mountain properties are discussed through a model of mountain socio-biophysical systems, which provides a framework for examining climate impacts and vulnerabilities. Managing the risks associated with ongoing climate change in mountains requires an integrated approach to climate change impacts monitoring and management.

Remote Sensing and Meteorological Data Fusion in Predicting Bushfire Severity: A Case Study from Victoria, Australia

The extent and severity of bushfires in a landscape are largely governed by meteorological conditions. An accurate understanding of the interactions of meteorological variables and fire behaviour in the landscape is very complex, yet possible. In exploring such understanding, we used 2693 high-confidence active fire points recorded by a Moderate Resolution Imaging Spectroradiometer (MODIS) sensor for nine different bushfires that occurred in Victoria between 1 January 2009 and 31 March 2009. These fires include the Black Saturday Bushfires of 7 February 2009, one of the worst bushfires in Australian history. For each fire point, 62 different meteorological parameters of bushfire time were extracted from Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) data. These remote sensing and meteorological datasets were fused and further processed in assessing their relative importance using four different tree-based ensemble machine learning models, namely, Random Forest (RF), Fuzzy Forest (FF), Boosted Regression Tree (BRT), and Extreme Gradient Boosting (XGBoost). Google Earth Engine (GEE) and Landsat images were used in deriving the response variable–Relative Difference Normalised Burn Ratio (RdNBR), which was selected by comparing its performance against Difference Normalised Burn Ratio (dNBR). Our findings demonstrate that the FF algorithm utilising the Weighted Gene Coexpression Network Analysis (WGCNA) method has the best predictive performance of 96.50%, assessed against 10-fold cross-validation. The result shows that the relative influence of the variables on bushfire severity is in the following order: (1) soil moisture, (2) soil temperature, (3) air pressure, (4) air temperature, (5) vertical wind, and (6) relative humidity. This highlights the importance of soil meteorology in bushfire severity analysis, often excluded in bushfire severity research. Further, this study provides a scientific basis for choosing a subset of meteorological variables for bushfire severity prediction depending on their relative importance. The optimal subset of high-ranked variables is extremely useful in constructing simplified and computationally efficient surrogate models, which can be particularly useful for the rapid assessment of bushfire severity for operational bushfire management and effective mitigation efforts.

Assessing the socio-economic and land-cover drivers of wildfire activity and its spatiotemporal distribution in south-central Chile

Sustained human pressures on the environment have significantly increased the frequency, extent, and severity of wildfires, globally. This is particularly the case in Mediterranean regions, in which human-caused wildfires represent up to 90% of all recorded wildfire ignitions. In Chile, it has been estimated that nearly 90% of wildfires are related to human activities, and that their frequency and distribution have steadily increased over the last decade. Despite this, the role of socio-economic factors in driving wildfire activity and its spatiotemporal distribution remains unclear. In this study, we assess the association between socio-economic drivers and spatiotemporal patterns of wildfires in the Mediterranean region of south-central Chile over the period 2010-2018. Our results show that 98.5% of wildfires are related to human activities, either accidentally (58.2%) or intentionally (36.6%). Wildfires occurred primarily during the summer months and their density at the commune-level was associated with increased road access, as well as with the percentage of land covered by agriculture, exotic tree plantations, and native forest. Wildfire activity at the commune-level was also related to socio-economic variables such as population density, proportion of indigenous population, and unemployment rate, although such associations varied considerably depending on the region and on whether the wildfire was started accidentally or intentionally. Our study provides a comprehensive and interdisciplinary assessment of the complex ways in which land-cover and socio-economic factors drive the distribution of wildfire activity in south-central Chile. It represents an important guide for policy-making, as well a baseline for research into strategies aimed at predicting and mitigating wildfire activity at both local and national levels.

Gis and fuzzy logic applied to modelling forest fire risk

Fire risk mapping is a basic planning and protection element. This study presents the application of fuzzy logic in a geographic information system (GIS) as an alternative multi-criteria analysis for determining the areas of highest risk of forest fire in natural forest remnants in the Brazil. In the decision-making process, a set of factors that are relevant to fire safety were identified in the study area. For each input variable chosen for the model, a pertinence function was defined that best described its influence on fire risk. Subsequently, the variables were combined for the presentation of the final fire risk map. Concluded in the study that an increased risk of fire occurs at the wildland - urban interface. A strong relationship was observed between the fire ignition points and proximity to roads and urban areas. The proposed model was efficient to integrate the variables and determine areas of greatest risk.

Chironomidae assemblages at different altitudes in Northwest Argentina: the role of local factors

This study aimed to examine spatiotemporal variations in chironomid assemblages and to detect how environmental variables affect their structure. We sampled seven streams at low and high altitudes in Northwest Argentina under contrasting climate conditions (Puna and Chaco Serrano) during high- and low-water periods. The environmental variables that affected Chironomidae community structure were water temperature, conductivity, hardness, current velocity and type of substrate. Fine substrates, gravel and low water temperature favoured cold stenothermal fauna, composed of Orthocladiinae, Diamesinae and Podonominae specimens in the high-altitude streams, whereas warm waters with low conductivity and higher velocity favoured increased species diversity in lowland streams, where there was greater abundance of Chironominae (which corresponds to warm eurythermal fauna). The studied environments belong to a transition zone that should be preserved where cold stenothermic and warm eurythermal Chironomidae overlap.

Application of learning vector quantization and different machine learning techniques to assessing forest fire influence factors and spatial modelling

This study assesses forest-fire susceptibility (FFS) in Fars Province, Iran using three geographic information system (GIS)-based machine-learning algorithms: boosted regression tree (BRT), general linear model (GLM), and mixture discriminant analysis (MDA). Recently, BRT, GLM, and MDA have become important machine-learning algorithms and their use has been enriched by application to various fields of research. A database of historical FFs identified using Landsat-8 OLI and MODIS satellite images (at 358 locations) and ten influencing factors (elevation, slope, topographical wetness index, aspect, distance from urban areas, annual mean temperature, land use, distance from road, annual mean rainfall, and distance from river) were input into a GIS. The 358 sites were divided into two sets for training (70%) and validation (30%). BRT, GLM, and MDA models were used to analyze the spatial relationships between the factors influencing FFs and the locations of fires to generate an FFS map. The prediction success of each modelled FFS map was determined with the help of the ROC curve, accuracy, overall accuracy, True-skill statistic (TSS), F-measures, corrected classify instances (CCI), and K-fold cross-validation (4-fold). The accuracy results of training and validation dataset in the BRT (AUC = 88.90% and 88.2%) and MDA (AUC = 86.4% and 85.6%) models are more effective than the GLM (AUC = 86.6% and 82.5%) model. Also, the outcome of the 4-fold measure confirmed the results from the other accuracy measures. Therefore, the accuracies of the BRT and MDA models are satisfactory and are suitable for FFS mapping in Fars Province. Finally, the well-accepted neural network application of learning-vector quantization (LVQ) reveals that land use, annual mean rainfall, and slope angle were the most useful determinants of FFS. The resulting FFS maps can enhance the effectiveness of planning and management of forest resources and ecological balances in this province.

A Comparison of the Qualitative Analytic Hierarchy Process and the Quantitative Frequency Ratio Techniques in Predicting Forest Fire-Prone Areas in Bhutan Using GIS

Forest fire is an environmental disaster that poses immense threat to public safety, infrastructure, and biodiversity. Therefore, it is essential to have a rapid and robust method to produce reliable forest fire maps, especially in a data-poor country or region. In this study, the knowledge-based qualitative Analytic Hierarchy Process (AHP) and the statistical-based quantitative Frequency Ratio (FR) techniques were utilized to model forest fire-prone areas in the Himalayan Kingdom of Bhutan. Seven forest fire conditioning factors were used: land-use land cover, distance from human settlement, distance from road, distance from international border, aspect, elevation, and slope. The fire-prone maps generated by both models were validated using the Area Under Curve assessment method. The FR-based model yielded a fire-prone map with higher accuracy (87% success rate; 82% prediction rate) than the AHP-based model (71% success rate; 63% prediction rate). However, both the models showed almost similar extent of ‘very high’ prone areas in Bhutan, which corresponded to coniferous-dominated areas, lower elevations, steeper slopes, and areas close to human settlements, roads, and the southern international border. Moderate Resolution Imaging Spectroradiometer (MODIS) fire points were overlaid on the model generated maps to assess their reliability in predicting forest fires. They were found to be not reliable in Bhutan, as most of them overlapped with fire-prone classes, such as ‘moderate’, ‘low’, and ‘very low’. The fire-prone map derived from the FR model will assist Bhutan’s Department of Forests and Park Services to update its current National Forest Fire Management Strategy.

Does fire disturbance affect ant community structure? Insights from spatial co-occurrence networks

The coexistence of several species involves a complex mix of positive and negative interactions that can be represented as networks. As much as other ecological features, patterns of multispecies co-occurrence are susceptible to anthropogenic disturbance. In ant communities, wildfires may enhance competitive interactions by benefiting active, aggressive species, and by increasing encounter probabilities through decreased space availability. We explored ant co-occurrence patterns by analysing the macro and microscopic structure of their interaction networks in burned and unburned habitats. We built co-occurrence networks using significant aggregations and segregations between species pairs as positive and negative interactions, respectively. We described aggregate network properties and microscopic structural changes by comparing species and interactions turnover between burned and unburned sites. We found no differences in the macroscopic structure of co-occurrence networks between different fire regimes. However, we detected changes in the composition of both species and negative interactions. Interaction turnover between networks of different habitats was mostly explained by rewiring of interactions between shared species rather than by species replacement. Our results reflected changes in ant communities in response to fire although there were no changes in global structural patterns. These changes in species and negative interactions suggest modifications in species roles translated into changes in the spatial distribution of ant species. The analysis of species co-occurrence networks is a useful tool to detect and visualize patterns in ant communities and to understand the mechanisms underlying the effects of disturbance on biodiversity.

Urbanization, environmental stabilization and temporal persistence of bird species: a view from Latin America

Background

A scarcely studied consequence of urbanization is the effect of temporal stabilization of the environment on bird communities. This alteration is thought to dampen environmental variations between day and night, seasons and years, promoting a temporal persistence of bird composition in urban areas. The aim of this study was to review current evidence of temporal stabilization of biotic and abiotic factors in urban environments and the potential effects of such stabilization on temporal variation of bird species presence at different temporal scales.

Methods

I selected the literature by searching published articles and book chapters using Scopus and Google scholar. I only included articles that compared the temporal variation of bird composition or resources between different levels of urbanization.

Results

In general, there is evidence of temporal stabilization of abiotic and biotic factors at the three time scales considered. At the diurnal scale, the main factor considered was artificial light in the context of light pollution. At the seasonal and interannual scales, several case studies found a smaller temporal variation of primary productivity in urban than in natural and rural areas. Bird species composition showed more stabilization in urban environments at the three temporal scales: (1) several case studies reported bird activity at night, associated with artificial light; (2) studies in urban parks and along urbanization gradients showed smaller seasonal variation of bird composition in the more urbanized areas; and (3) in general, case studies along urbanization gradients showed smaller interannual variation of bird composition in the more urbanized areas, although some studies showed no relationships or opposite trends than expected.

Discussion

The published evidence suggests that urban areas dampen the natural cycles at several temporal scales. The stabilization of biotic and abiotic factors, such as light, temperature, food and habitat structure, is desynchronized from natural diurnal, seasonal and interannual cycles. However, there is a dearth of long-term comparisons of bird composition and studies that simultaneously analyze the relationship between resources and bird composition stabilization at the seasonal and interannual scales. More research is needed in the Southern hemisphere, where there is a lack of studies dealing with the seasonal and interannual variations of primary productivity along urbanization gradients and nocturnal activity of bird species. A future research agenda should include differentiation of spatial and temporal homogenization of avifaunas.

Determining fuel moisture thresholds to assess wildfire hazard: A contribution to an operational early warning system

Fuel moisture content (FMC) is an important fuel property for assessing wildfire hazard, since it influences fuel flammability and fire behavior. The relationship between FMC and fire activity differs among land covers and seems to be a property of each ecosystem. Our objectives were to analyze pre-fire FMC among different land covers and to propose a wildfire hazard classification for the Sierras Chicas in the Chaco Serrano subregion (Argentina), by analyzing pre-fire FMC distributions observed for grasslands, shrublands and forests and using percentiles to establish thresholds. For this purpose, we used a fire database derived from Landsat imagery (30 m) and derived FMC maps every 8 days from 2002 to 2016 using MODIS reflectance products and empirical equations of FMC. Our results indicated that higher FMC constrains the extent of wildfires, whereas at lower FMC there are other factors affecting their size. Extreme and high fire hazard thresholds for grasslands were established at FMC of 55% and 67% respectively, at 72% and 105% for forests and at 106% and 121% for shrublands. Our FMC thresholds were sensitive to detect extreme fire hazard conditions during years with high fire activity in comparison to average conditions. The differences in the distributions of pre-fire FMC among land covers and between ecosystems highlighted the need to locally determine land cover-specific FMC thresholds to assess wildfire hazard. Our wildfire hazard classification applied to FMC maps in an operational framework will contribute to improving early warning systems in the Sierras Chicas. However, moisture alone is not sufficient to represent true fire hazard in Chaco forests and the combination with other variables would provide better hazard assessments. These operational wildfire hazard maps will help to better allocation of fire protective resources to minimize negative impact on people, property and ecosystems. To the best of our knowledge, this is the first study analyzing pre-fire FMC over several fire seasons in a non-Mediterranean ecosystem, aiming at assessing wildfire hazard.

Assessing wildfire exposure in the Wildland-Urban Interface area of the mountains of central Argentina

Wildfires are a major threat to people and property in Wildland Urban Interface (WUI) communities worldwide, but while the patterns of the WUI in North America, Europe and Oceania have been studied before, this is not the case in Latin America. Our goals were to a) map WUI areas in central Argentina, and b) assess wildfire exposure for WUI communities in relation to historic fires, with special emphasis on large fires and estimated burn probability based on an empirical model. We mapped the WUI in the mountains of central Argentina (810,000 ha), after digitizing the location of 276,700 buildings and deriving vegetation maps from satellite imagery. The areas where houses and wildland vegetation intermingle were classified as Intermix WUI (housing density > 6.17 hu/km² and wildland vegetation cover > 50%), and the areas where wildland vegetation abuts settlements were classified as Interface WUI (housing density > 6.17 hu/km², wildland vegetation cover < 50%, but within 600 m of a vegetated patch larger than 5 km²). We generated burn probability maps based on historical fire data from 1999 to 2011; as well as from an empirical model of fire frequency. WUI areas occupied 15% of our study area and contained 144,000 buildings (52%). Most WUI area was Intermix WUI, but most WUI buildings were in the Interface WUI. Our findings suggest that central Argentina has a WUI fire problem. WUI areas included most of the buildings exposed to wildfires and most of the buildings located in areas of higher burn probability. Our findings can help focus fire management activities in areas of higher risk, and ultimately provide support for landscape management and planning aimed at reducing wildfire risk in WUI communities.

Quantifying the human influence on fire ignition across the western USA

Humans have a profound effect on fire regimes by increasing the frequency of ignitions. Although ignition is an integral component of understanding and predicting fire, to date fire models have not been able to isolate the ignition location, leading to inconsistent use of anthropogenic ignition proxies. Here, we identified fire ignitions from the Moderate Resolution Imaging Spectrometer (MODIS) Burned Area Product (2000-2012) to create the first remotely sensed, consistently derived, and regionally comprehensive fire ignition data set for the western United States. We quantified the spatial relationships between several anthropogenic land-use/disturbance features and ignition for ecoregions within the study area and used hierarchical partitioning to test how the anthropogenic predictors of fire ignition vary among ecoregions. The degree to which anthropogenic features predicted ignition varied considerably by ecoregion, with the strongest relationships found in the Marine West Coast Forest and North American Desert ecoregions. Similarly, the contribution of individual anthropogenic predictors varied greatly among ecoregions. Railroad corridors and agricultural presence tended to be the most important predictors of anthropogenic ignition, while population density and roads were generally poor predictors. Although human population has often been used as a proxy for ignitions at global scales, it is less important at regional scales when more specific land uses (e.g., agriculture) can be identified. The variability of ignition predictors among ecoregions suggests that human activities have heterogeneous impacts in altering fire regimes within different vegetation types and geographies.

Reintroducing Guanaco in the Upper Belt of Central Argentina: Using Population Viability Analysis to Evaluate Extinction Risk and Management Priorities

Wildlife reintroduction is an increasingly used strategy to reverse anthropocene defaunation. For the purpose of ecosystem restoration, in 2007 the guanaco (Lama guanicoe) was reintroduced to the Quebrada del Condorito National Park, situated in the mountains of central Argentina. With the aim of developing management recommendations, the project included permanently monitoring the population to evaluate its dynamics and the ecological response of the individuals released into the area. Nine years later and after two releases of guanacos (113 individuals in 2007 without and 25 in 2011 with a pre-adaptation period), only 24 individuals, which conform three reproductive groups, and one group of solitary males were settled in the Park. Here I modeled a population viability analysis to evaluate extinction risk, using VORTEX software. Initial population structure, specified age distribution, mortality and reproductive rates, and mate monopolization recorded during field work were used in the model, whereas the remaining used demographic parameters, such as age of first offspring, maximum number of broods per year, mean foaling rate, and length of fecundity period, were taken from the literature. Each of the three different scenarios (without supplementation of individuals, and with a realistic and optimistic supplementation) and two possible catastrophic events (fires and food shortage) covering 100 years was repeated 1000 times. Even though the guanaco reintroduction project can be considered to have been partially successful since its start, the model predicts that the current reintroduced population could be extinct in the next few decades if no reinforcements occur, and that only a continuous supplementation can reach the probability that the population survives over the next 100 years. I conclude that, so far, the current population is at a high risk of extinction if further supplementation of individuals is discontinued.

Predicting environmental mitigation requirements for hydropower projects through the integration of biophysical and socio-political geographies

Uncertainty about environmental mitigation needs at existing and proposed hydropower projects makes it difficult for stakeholders to minimize environmental impacts. Hydropower developers and operators desire tools to better anticipate mitigation requirements, while natural resource managers and regulators need tools to evaluate different mitigation scenarios and order effective mitigation. Here we sought to examine the feasibility of using a suite of multi-faceted explanatory variables within a spatially explicit modeling framework to fit predictive models for future environmental mitigation requirements at hydropower projects across the conterminous U.S. Using a database comprised of mitigation requirements from more than 300 hydropower project licenses, we were able to successfully fit models for nearly 50 types of environmental mitigation and to apply the predictive models to a set of more than 500 non-powered dams identified as having hydropower potential. The results demonstrate that mitigation requirements are functions of a range of factors, from biophysical to socio-political. Project developers can use these models to inform cost projections and design considerations, while regulators can use the models to more quickly identify likely environmental issues and potential solutions, hopefully resulting in more timely and more effective decisions on environmental mitigation.

GIS applied to location of fires detection towers in domain area of tropical forest

In most countries, the loss of biodiversity caused by the fires is worrying. In this sense, the fires detection towers are crucial for rapid identification of fire outbreaks and can also be used in environmental inspection, biodiversity monitoring, telecommunications mechanisms, telemetry and others. Currently the methodologies for allocating fire detection towers over large areas are numerous, complex and non-standardized by government supervisory agencies. Therefore, this study proposes and evaluates different methodologies to best location of points to install fire detection towers considering the topography, risk areas, conservation units and heat spots. Were used Geographic Information Systems (GIS) techniques and unaligned stratified systematic sampling for implementing and evaluating 9 methods for allocating fire detection towers. Among the methods evaluated, the C3 method was chosen, represented by 140 fire detection towers, with coverage of: a) 67% of the study area, b) 73.97% of the areas with high risk, c) 70.41% of the areas with very high risk, d) 70.42% of the conservation units and e) 84.95% of the heat spots in 2014. The proposed methodology can be adapted to areas of other countries.