Fire severity and fire-induced landscape heterogeneity affect arboreal mammals in fire-prone forests

In fire-prone regions, wildfire influences spatial and temporal patterns of landscape heterogeneity. The likely impacts of climate change on the frequency and intensity of wildfire highlights the importance of understanding how fire-induced heterogeneity may affect different components of the biota. Here, we examine the influence of wildfire, as an agent of landscape heterogeneity, on the distribution of arboreal mammals in fire-prone forests in south-eastern Australia. First, we used a stratified design to examine the role of topography, and the relative influence of fire severity and fire history, on the occurrence of arboreal mammals 2-3 years after wildfire. Second, we investigated the influence of landscape context on the occurrence of arboreal mammals at severely burnt sites. Forested gullies supported a higher abundance of arboreal mammals than slopes. Fire severity was the strongest influence, with abundance lower at severely burnt than unburnt sites. The occurrence of mammals at severely burned sites was influenced by landscape context: abundance increased with increasing amount of unburnt and understorey-only burnt forest within a 1 km radius. These results support the hypothesis that unburnt forest and moist gullies can serve as refuges for fauna in the post-fire environment and assist recolonization of severely burned forest. They highlight the importance of spatial heterogeneity created by wildfire and the need to incorporate spatial aspects of fire regimes (e.g., creation and protection of refuges) for fire management in fire-prone landscapes.

Fighting fire and fatigue: sleep quantity and quality during multi-day wildfire suppression

This study examined firefighters' sleep quantity and quality throughout multi-day wildfire suppression, and assessed the impact of sleep location, shift length, shift start time and incident severity on these variables. For 4 weeks, 40 volunteer firefighters' sleep was assessed using wrist actigraphy. Analyses revealed that the quantity of sleep obtained on fire days was restricted, and pre- and post-sleep fatigue ratings were higher, compared to non-fire days. On fire days, total sleep time was less when: (i) sleep location was in a tent or vehicle, (ii) shifts were greater than 14 h and (iii) shifts started between 05:00 and 06:00 h. This is the first empirical investigation providing objective evidence that firefighters' sleep is restricted during wildfire suppression. Furthermore, sleep location, shift length and shift start time should be targeted when designing appropriate controls to manage fatigue-related risk and preserve firefighters' health and safety during wildfire events. Practitioner Summary: During multi-day wildfire suppression, firefighters' sleep quantity was restricted, and pre- and post-sleep fatigue ratings were higher, compared to non-fire days. Furthermore, total sleep time was less when: (i) sleep occurred in a tent/vehicle, (ii) shifts were >14 h and (iii) shifts started between 05:00 and 06:00 h.

Low-volume high-intensity interval training in a gym setting improves cardio-metabolic and psychological health

Background Within a controlled laboratory environment, high-intensity interval training (HIT) elicits similar cardiovascular and metabolic benefits as traditional moderate-intensity continuous training (MICT). It is currently unclear how HIT can be applied effectively in a real-world environment. Purpose To investigate the hypothesis that 10 weeks of HIT, performed in an instructor-led, groupbased gym setting, elicits improvements in aerobic capacity (VO2max), cardio-metabolic risk and psychological health which are comparable to MICT. Methods Ninety physically inactive volunteers (42±11 y, 27.7±4.8 kg.m-2) were randomly assigned to HIT or MICT group exercise classes. HIT consisted of repeated sprints (15-60 seconds, >90% HRmax) interspersed with periods of recovery cycling (≥25 min.session-1, 3 sessions. week-1). MICT participants performed continuous cycling (70%HRmax, 30-45 min.session-1, 5 sessions.week-1). VO2max, markers of cardio-metabolic risk, and psychological health were assessed pre and post-intervention. Results Mean weekly training time was 55±10 (HIT) and 128±44 min (MICT) (p<0.05), with greater adherence to HIT (83±14% vs. 61±15% prescribed sessions attended, respectively; p<0.05). HIT improved VO2max, insulin sensitivity, reduced abdominal fat mass, and induced favourable changes in blood lipids (p<0.05). HIT also induced beneficial effects on health perceptions, positive and negative affect, and subjective vitality (p<0.05). No difference between HIT and MICT was seen for any of these variables. Conclusions HIT performed in a real-world gym setting improves cardio-metabolic risk factors and psychological health in physically inactive adults. With a reduced time commitment and greater adherence than MICT, HIT offers a viable and effective exercise strategy to target the growing incidence of metabolic disease and psychological ill-being associated with physical inactivity.

A new color space based on K-medoids clustering for fire detection

Pixel color has proven to be a useful and robust cue for detection of most objects of interest like fire. In this paper, a hybrid intelligent algorithm is proposed to detect fire pixels in the background of an image. The proposed algorithm is introduced by the combination of a computational search method based on a swarm intelligence technique and the Kemdoids clustering method in order to form a Fire-based Color Space (FCS), in fact, the new technique converts RGB color system to FCS through a 3*3 matrix. This algorithm consists of five main stages:(1) extracting fire and non-fire pixels manually from the original image. (2) using K-medoids clustering to find a Cost function to minimize the error value. (3) applying Particle Swarm Optimization (PSO) to search and find the best W components in order to minimize the fitness function. (4) reporting the best matrix including feature weights, and utilizing this matrix to convert the all original images in the database to the new color space. (5) using Otsu threshold technique to binarize the final images. As compared with some state-of-the-art techniques, the experimental results show the ability and efficiency of the new method to detect fire pixels in color images.

Evaluating the role of fire disturbance in structuring small reptile communities in temperate forests

Fire is an integral disturbance shaping forest community dynamics over large scales. However, understanding the relationship between fire induced habitat disturbance and biodiversity remain equivocal. Ecological theories including the intermediate disturbance hypothesis (IDH) and the habitat accommodation model (HAM) offer predictive frameworks that could explain faunal responses to fire disturbances. We used an 80 year post-fire chronosequence to investigate small reptile community responses to fires in temperate forests across 74 sites. First, we evaluated if changes in species richness, abundance and evenness post-fire followed trends of prior predictions, including the IDH. Second, using competing models of fine scale habitat elements we evaluated the specific ways which fire influenced small reptiles. Third, we evaluated support for the HAM by examining compositional changes of reptile community post-fire. Relative abundance was positively correlated to age post-fire while richness and evenness showed no associations. The abundance trend was as expected based on the prior prediction of sustained population increase post-disturbance, but the trend for richness contradicted the prediction of highest diversity at intermediate levels of disturbance (according to IDH). Abundance changes were driven mainly by changes in overstorey, ground layer, and shelter, while richness and evenness did not associate with any vegetation parameter. Community composition was not strongly correlated to age since fire, thus support for the HAM was weak. Overall, in this ecosystem, frequent fire disturbances can be detrimental to small reptiles. Future studies utilizing approaches based on species traits could enhance our understanding of biodiversity patterns post-disturbance.

Effects of the fire regime on mammal occurrence after wildfire: site effects vs landscape context in fire-prone forests

Wildfires have major impacts on ecosystems globally. Fire regimes (including fire frequency, intensity, season and type of fire) influence the status of species by altering habitat suitability at the site scale, and by creating heterogeneity at the landscape scale. The relative effects of site and landscape-scale fire attributes on animal species are rarely examined together. Such knowledge is important, given that fire regimes are sensitive to changing land management practices; and that fires are predicted to become larger and more frequent in some regions as a result of climate change. Here, we tested the relative influence of elements of the fire regime (fire severity, fire history) at the site-scale, and the landscape context (extent of surrounding unburnt forest, fire heterogeneity) on the occurrence of native terrestrial mammals after severe wildfire in south-eastern Australia. We conducted surveys by using automatically triggered, infrared cameras at 80 sites in fire-prone eucalypt forests, 2-3. years post-wildfire. Thirteen native mammal species were recorded, eight of which were detected with sufficient frequency for analysis. Most species were widespread (35-90% of sites) and recorded in all fire severity classes. Fire effects at the site-level were more influential than landscape context effects arising from heterogeneity in the fire regime (e.g. extent of surrounding unburnt forest). Fire severity was the most influential of the fire-regime elements investigated, but it affected different species in different ways. This study highlights three main points relevant to conservation of terrestrial mammals after wildfire. First, spatial variation in fire severity associated with wildfire (ranging from unburned to severely burned stands) is an important contributor to the post-fire status of species. Second, post-fire environmental conditions are significant: here, rapid regeneration of vegetation following drought-breaking rains greatly influenced the suitability of post-fire habitats. Third, it is valuable to consider the effects of the fire regime at multiple scales, including both the site (forest stand) and its landscape context. Insights from short-term surveys, such as this, will be enhanced by complementary longitudinal studies, especially where they encompass environmental variation through the post-fire succession.

Fire affects microhabitat selection, movement patterns, and body condition of an Australian rodent (Rattus fuscipes)

Resource selection by animals influences individual fitness, the abundance of local populations, and the distribution of species. Further, the degree to which individuals select particular resources can be altered by numerous factors including competition, predation, and both natural- and human-induced environmental change. Understanding the influence of such factors on the way animals use resources can guide species conservation and management in changing environments. In this study, we investigated the effects of a prescribed fire on small-scale (microhabitat) resource selection, abundance, body condition, and movement pathways of a native Australian rodent, the bush rat (Rattus fuscipes). Using a before-after, control-impact design, we gathered data from 60 individuals fitted with spool and line tracking devices. In unburnt forest, selection of resources by bush rats was positively related to rushes, logs and complex habitat, and negatively related to ferns and litter. Fire caused selection for spreading grass, rushes, and complex habitat to increase relative to an unburnt control location. At the burnt location after the fire, rats selected patches of unburnt vegetation, and no rats were caught at a trapping site where most of the understory had been burnt. The fire also reduced bush rat abundance and body condition and caused movement pathways to become more convoluted. After the fire, some individuals moved through burnt areas but the majority of movements occurred within unburnt patches. The effects of fire on bush rat resource selection, movement, body condition, and abundance were likely driven by several linked factors including limited access to shelter and food due to the loss of understory vegetation and heightened levels of perceived predation risk. Our findings suggest the influence of prescribed fire on small mammals will depend on the resulting mosaic of burnt and unburnt patches and how well this corresponds to the resource requirements of particular species.

Prediction interval-based control of nonlinear systems using neural networks

Prediction interval (PI) is a promising tool for quantifying uncertainties associated with point predictions. Despite its informativeness, the design and deployment of PI-based controller for complex systems is very rare. As a pioneering work, this paper proposes a framework for design and implementation of PI-based controller (PIC) for nonlinear systems. Neural network (NN)-based inverse model within internal model control structure is used to develop the PIC. Firstly, a PI-based model is developed to construct PIs for the system output. This model is then used as an online estimator for PIs. The PIs from this model are fed to the NN inverse model along with other traditional inputs to generate the control signal. The performance of the proposed PIC is examined for two case studies. This includes a nonlinear batch polymerization reactor and a numerical nonlinear plant. Simulation results demonstrated that the proposed PIC tracking performance is better than the traditional NN-based controller.

An efficient hybrid algorithm for fire flame detection

Proposing efficient methods for fire protection is becoming more and more important, because a small flame of fire may cause huge problems in social safety. In this paper, an effective fire flame detection method is investigated. This fire detection method includes four main stages: in the first step, a linear transformation is applied to convert red, green, and blue (RGB) color space through a 3∗3 matrix to a new color space. In the next step, fuzzy c-mean clustering method (FCM) is used to distinguish between fire flame and non-fire flame pixels. Particle Swarm Optimization algorithm (PSO) is also utilized in the last step to decrease the error value measured by FCM after conversion. Finally, we apply Otsu threshold method to the new converted images to make a binary picture. Empirical results show the strength, accuracy and fast-response of the proposed algorithm in detecting fire flames in color images.

Prediction interval-based neural network controller for nonlinear processes

Prediction interval (PI) has been extensively used to predict the forecasts for nonlinear systems as PI-based forecast is superior over point-forecast to quantify the uncertainties and disturbances associated with the real processes. In addition, PIs bear more information than point-forecasts, such as forecast accuracy. The aim of this paper is to integrate the concept of informative PIs in the control applications to improve the tracking performance of the nonlinear controllers. In the present work, a PI-based controller (PIC) is proposed to control the nonlinear processes. Neural network (NN) inverse model is used as a controller in the proposed method. Firstly, a PI-based model is developed to construct PIs for every sample or time instance. The PIs are then fed to the NN inverse model along with other effective process inputs and outputs. The PI-based NN inverse model predicts the plant input to get the desired plant output. The performance of the proposed PIC controller is examined for a nonlinear process. Simulation results indicate that the tracking performance of the PIC is highly acceptable and better than the traditional NN inverse model-based controller.

A new fuzzy-based combined prediction interval for wind power forecasting

This paper makes use of the idea of prediction intervals (PIs) to capture the uncertainty associated with wind power generation in power systems. Since the forecasting errors cannot be appropriately modeled using distribution probability functions, here we employ a powerful nonparametric approach called lower upper bound estimation (LUBE) method to construct the PIs. The proposed LUBE method uses a new framework based on a combination of PIs to overcome the performance instability of neural networks (NNs) used in the LUBE method. Also, a new fuzzy-based cost function is proposed with the purpose of having more freedom and flexibility in adjusting NN parameters used for construction of PIs. In comparison with the other cost functions in the literature, this new formulation allows the decision-makers to apply their preferences for satisfying the PI coverage probability and PI normalized average width individually. As the optimization tool, bat algorithm with a new modification is introduced to solve the problem. The feasibility and satisfying performance of the proposed method are examined using datasets taken from different wind farms in Australia.

Mass spectrometry-based proteomic data for cancer diagnosis using interval type-2 fuzzy system

An interval type-2 fuzzy logic system is introduced for cancer diagnosis using mass spectrometry-based proteomic data. The fuzzy system is incorporated with a feature extraction procedure that combines wavelet transform and Wilcoxon ranking test. The proposed feature extraction generates feature sets that serve as inputs to the type-2 fuzzy classifier. Uncertainty, noise and outliers that are common in the proteomic data motivate the use of type-2 fuzzy system. Tabu search is applied for structure learning of the fuzzy classifier. Experiments are performed using two benchmark proteomic datasets for the prediction of ovarian and pancreatic cancer. The dominance of the suggested feature extraction as well as type-2 fuzzy classifier against their competing methods is showcased through experimental results. The proposed approach therefore is helpful to clinicians and practitioners as it can be implemented as a medical decision support system in practice.

A systematic design of interval type-2 fuzzy logic system using extreme learning machine for electricity load demand forecasting

This paper presents a novel design of interval type-2 fuzzy logic systems (IT2FLS) by utilizing the theory of extreme learning machine (ELM) for electricity load demand forecasting. ELM has become a popular learning algorithm for single hidden layer feed-forward neural networks (SLFN). From the functional equivalence between the SLFN and fuzzy inference system, a hybrid of fuzzy-ELM has gained attention of the researchers. This paper extends the concept of fuzzy-ELM to an IT2FLS based on ELM (IT2FELM). In the proposed design the antecedent membership function parameters of the IT2FLS are generated randomly, whereas the consequent part parameters are determined analytically by the Moore-Penrose pseudo inverse. The ELM strategy ensures fast learning of the IT2FLS as well as optimality of the parameters. Effectiveness of the proposed design of IT2FLS is demonstrated with the application of forecasting nonlinear and chaotic data sets. Nonlinear data of electricity load from the Australian National Electricity Market for the Victoria region and from the Ontario Electricity Market are considered here. The proposed model is also applied to forecast Mackey-glass chaotic time series data. Comparative analysis of the proposed model is conducted with some traditional models such as neural networks (NN) and adaptive neuro fuzzy inference system (ANFIS). In order to verify the structure of the proposed design of IT2FLS an alternate design of IT2FLS based on Kalman filter (KF) is also utilized for the comparison purposes.

Fire Hazard in Coconino County, Arizona

Fire is a major management issue in the southwestern United States. Three spatial models of fire risk for Coconino County, Northern Arizona. These models were generated using thematic data layers depicting vegetation, elevation, wind speed and direction, and precipitation for January (winter), June (summer), and July (start of monsoon season). ArcGIS 9.0 was used to weight attributes in raster layers to reflect their influence on fire risk and to interpolate raster data layers from point data. Final models were generated using the raster calculator in the Spatial Analyst extension of ArcGIS 9.0. Ultimately, the unique combinations of variables resulted in three different models illustrating the change in fire risk during the year.