A review of machine learning applications in wildfire science and management

Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the field has rapidly progressed congruently with the wide adoption of machine learning (ML) in the environmental sciences. Here, we present a scoping review of ML in wildfire science and management. Our objective is to improve awareness of ML among wildfire scientists and managers, as well as illustrate the challenging range of problems in wildfire science available to data scientists. We first present an overview of popular ML approaches used in wildfire science to date, and then review their use in wildfire science within six problem domains: 1) fuels characterization, fire detection, and mapping; 2) fire weather and climate change; 3) fire occurrence, susceptibility, and risk; 4) fire behavior prediction; 5) fire effects; and 6) fire management. We also discuss the advantages and limitations of various ML approaches and identify opportunities for future advances in wildfire science and management within a data science context. We identified 298 relevant publications, where the most frequently used ML methods included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. There exists opportunities to apply more current ML methods (e.g., deep learning and agent based learning) in wildfire science. However, despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods requires sophisticated knowledge for their application. Finally, we stress that the wildfire research and management community plays an active role in providing relevant, high quality data for use by practitioners of ML methods.Comment: 83 pages, 4 figures, 3 table

Decreasing brown bear (Ursus arctos) habitat due to climate change in Central Asia and the Asian Highlands

Around the world, climate change has impacted many species. In this study, we used bioclimatic variables and biophysical layers of Central Asia and the Asian Highlands combined with presence data of brown bear (Ursus arctos) to understand their current distribution and predict their future distribution under the current rate of climate change. Our bioclimatic model showed that the current suitable habitat of brown bear encompasses 3,430,493 km2 in the study area, the majority of which (>65%) located in China. Our analyses demonstrated that suitable habitat will be reduced by 11% (378,861.30 km2) across Central Asia and the Asian Highlands by 2,050 due to climate change, predominantly (>90%) due to the changes in temperature and precipitation. The spatially averaged mean annual temperature of brown bear habitat is currently −1.2°C and predicted to increase to 1.6°C by 2,050. Mean annual precipitation in brown bear habitats is predicted to increase by 13% (from 406 to 459 mm) by 2,050. Such changes in two critical climatic variables may significantly affect the brown bear distribution, ethological repertoires, and physiological processes, which may increase their risk of extirpation in some areas. Approximately 32% (1,124,330 km2) of the total suitable habitat falls within protected areas, which was predicted to reduce to 1,103,912 km2 (1.8% loss) by 2,050. Future loss of suitable habitats inside the protected areas may force brown bears to move outside the protected areas thereby increasing their risk of mortality. Therefore, more protected areas should be established in the suitable brown bear habitats in future to sustain populations in this region. Furthermore, development of corridors is needed to connect habitats between protected areas of different countries in Central Asia. Such practices will facilitate climate migration and connectivity among populations and movement between and within countries

From Bears to Birds: Extending the Application of Multidimensional Nutritional Ecology

FROM BEARS TO BIRDS: EXTENDING THE APPLICATION OF MULTIDIMENSIONAL NUTRITIONAL ECOLOGY ABSTRACT Across various disciplines studies of nutrition have traditionally tended to focus on individual nutritional factors. There is abundant evidence, however, that a multidimensional approach is more powerful at explaining and predicting nutritionally related phenomena than examining single variables in isolation, yet is not widely applied. The purpose of this thesis is to extend the application of multidimensional nutrition to a number of new questions, thereby extending its reach across the broad interdisciplinary field of nutritional ecology. I first consider the foraging behavior of obligate carnivores from a nutrient-based perspective in a conceptual review. I then use nutritional geometry to apply the concept of macronutrient balance within a traditional wildlife diet study using data collected from Himalayan blue sheep (Pseudois nayaur). I explore how the macronutrient preferences of an omnivorous carnivore, the grizzly bear (Ursus arctos), is likely to influence food related human-wildlife conflict. I review the nutritional ecology of urban birds, and argue that adopting a multidimensional nutritional approach is likely to advance the field of urban ecology. I then apply this concept in an experiment on free-ranging Australian white ibis (Threskiornis moluccus) in urban Sydney, Australia. Next, I employ phylogenetic meta-analysis of variance and meta-regression to evaluate the degree of intraspecific variance in the macronutrient composition of mammalian milk. Last, I relate monthly trends in online search query data with actual population diet using data from an Australian nutrition survey. Although diverse, each of these studies provides fresh insight into questions which would not be apparent when restricted to single nutritional factors. Altogether, and in conjunction with previous research, the content of this thesis emphasizes the importance of multidimensional thinking as a general approach in nutritional ecology and related fields

Data from: Diet and macronutrient niche of Asiatic black bear (Ursus thibetanus) in two regions of Nepal during summer and autumn

Relatively little is known about the nutritional ecology of omnivorous Asiatic black bears (Ursus thibetanus) in Nepal. We characterized the diet of black bears in two seasons (June–July, “summer”; and October–November “autumn”) and two study areas (Dhorpatan Hunting Reserve [DHR]; and Kailash Sacred Landscape [KSL]). We then conducted nutritional analysis of species consumed by black bears in each study area, in combination with nutritional estimates from the literature, to estimate the proportions of macronutrients (i.e., protein [P], lipid [L], and carbohydrate [C]) in the seasonal bear foods and diets, as well as their macronutrient niche breadth. We found that bamboo (Arundinaria spp.) had the highest relative frequency in both study areas and seasons. Ants and termites were found in DHR diets, but not KSL diets. One anthropogenic crop was found in DHR summer diets (Zea mays) and two were found in KSL summer diets (Z. mays; and Kodo millet [Paspalum scrobiculatum]). Other than insects, no animal prey was found in either diet. The proportions of macronutrients in diets (i.e., realized macronutrient niches) were relatively high in carbohydrate for both study areas and seasons: DHRsummer 24.1P:8.7L:67.2C; KSLsummer 16.7P:8.2L:75.1C; DHRautumn 21.1P:10.5L:68.4C; KSHautumn 19.0P:11.0L:70.0C. Macronutrient niche breadth was 3.1 × greater in the DHR than KSL during summer, and 4.0 × greater in the autumn, primarily due to the higher proportion of lipid in ants and termites relative to plant foods. Within‐study area differences in niche breadth were greater during summer than autumn; in the KSH the macronutrient breadth was 1.4 × greater in summer, while in the DHR it was 1.1 × greater in summer. Similarity in dietary macronutrient proportions despite differences in foods consumed and niche breadth are suggestive of foraging to reach a preferred macronutrient balance

Diet composition of omnivorous Mesopotamian spiny‐tailed lizards (Saara loricata) in arid human‐altered landscapes of Southwest Iran

Abstract The Mesopotamian spiny‐tailed lizard, Saara loricata, is one of the largest lizard species in the Middle East. Here, we report on the diet of the lizard and their potential role in seed dispersal in Southwestern Iran. We analyzed lizard fecal pellet groups (n = 124) for their food item composition and seed content. We calculated the relative frequency of occurrence (FO%), relative volume (V%), and importance value (IV%) for each food item. Moreover, the number of seeds of each plant food item was counted. Our findings reveal the first solid evidence of omnivorous behavior in the lizard. In total, 16 plant food items and 14 animal food items were identified. Herbaceous plants (IV = 110.2%) and invertebrates (4.8%) were the most important food groups. The plant food items with the highest FO% were Poaceae (56.4%), Centaurea sp. (43.5%), and Medicago polymorpha (27.4%); and the V% for these items were 53.6%, 30.9%, and 13.1%, respectively. Most of the seeds that were consumed by lizards were from Poaceae (547 seeds; 47.81%) and Fabaceae (285 seeds; 24.91%). We also found that each individual lizard could play an equal role in the seed dispersal of all plant families identified. Previous studies show that plant species density and richness are important features for the burrow site selection of Mesopotamian spiny‐tailed lizard. This study highlights the potential role of lizards in influencing the vegetation communities around their burrows through seed dispersal

The effects of age, sex and season on the macronutrient composition of the diet of the domestic Asian elephant

Limited data are available on the relationship between seasonal diets and macronutrient and energy intake of domestic Asian elephants. The effects of age, sex and season on the nutrient composition and intake of food were investigated using 16 domesticated Asian elephants of different ages and sexes. There were no significant seasonal differences in the protein content of the major food plants. However, a seasonal variation in the intake of protein was evident. We used geometric modelling of non-protein (NP) neutral detergent fibre (NDF) and protein to examine seasonal nutrient variability within different ages, sexes and physiological states. The model suggested that most individual elephants maintained their recommended metabolizable energy intake from their diet across all seasons. However, we had anticipated less energy intake from poor diet due to less protein and higher NDF in the feeding ground during winter, pre-monsoon and monsoon seasons. Despite eating a lower variety of plants with less protein and higher NDF, elephants maintained a consistent pattern of diet intake in these seasons, suggesting that they acquired the recommended energy intake by regulating their diet, most likely through over-ingesting low-quality, non-complementary food as they did not have the opportunity to select from a variety of plants

Macronutrient Optimization and Seasonal Diet Mixing in a Large Omnivore, the Grizzly Bear: A Geometric Analysis

<div><p>Nutrient balance is a strong determinant of animal fitness and demography. It is therefore important to understand how the compositions of available foods relate to required balance of nutrients and habitat suitability for animals in the wild. These relationships are, however, complex, particularly for omnivores that often need to compose balanced diets by combining their intake from diverse nutritionally complementary foods. Here we apply geometric models to understand how the nutritional compositions of foods available to an omnivorous member of the order Carnivora, the grizzly bear (<i>Ursus arctos</i> L.), relate to optimal macronutrient intake, and assess the seasonal nutritional constraints on the study population in west-central Alberta, Canada. The models examined the proportion of macronutrients that bears could consume by mixing their diet from food available in each season, and assessed the extent to which bears could consume the ratio of protein to non-protein energy previously demonstrated using captive bears to optimize mass gain. We found that non-selective feeding on ungulate carcasses provided a non-optimal macronutrient balance with surplus protein relative to fat and carbohydrate, reflecting adaptation to an omnivorous lifestyle, and that optimization through feeding selectively on different tissues of ungulate carcasses is unlikely. Bears were, however, able to dilute protein intake to an optimal ratio by mixing their otherwise high-protein diet with carbohydrate-rich fruit. Some individual food items were close to optimally balanced in protein to non-protein energy (e.g. <i>Hedysarum alpinum</i> roots), which may help explain their dietary prevalence. Ants may be consumed particularly as a source of lipids. Overall, our analysis showed that most food available to bears in the study area were high in protein relative to lipid or carbohydrate, suggesting the lack of non-protein energy limits the fitness (e.g. body size and reproduction) and population density of grizzly bears in this ecosystem.</p></div

Assessing Nutritional Parameters of Brown Bear Diets among Ecosystems Gives Insight into Differences among Populations

Food habit studies are among the first steps used to understand wildlife-habitat relationships. However, these studies are in themselves insufficient to understand differences in population productivity and life histories, because they do not provide a direct measure of the energetic value or nutritional composition of the complete diet. Here, we developed a dynamic model integrating food habits and nutritional information to assess nutritional parameters of brown bear (Ursus arctos) diets among three interior ecosystems of North America. Specifically, we estimate the average amount of digestible energy and protein (per kilogram fresh diet) content in the diet and across the active season by bears living in western Alberta, the Flathead River (FR) drainage of southeast British Columbia, and the Greater Yellowstone Ecosystem (GYE). As well, we estimate the proportion of energy and protein in the diet contributed by different food items, thereby highlighting important food resources in each ecosystem. Bear diets in Alberta had the lowest levels of digestible protein and energy through all seasons, which might help explain the low reproductive rates of this population. The FR diet had protein levels similar to the recent male diet in the GYE during spring, but energy levels were lower during late summer and fall. Historic and recent diets in GYE had the most energy and protein, which is consistent with their larger body sizes and higher population productivity. However, a recent decrease in consumption of trout (Oncorhynchus clarki), whitebark pine nuts (Pinus albicaulis), and ungulates, particularly elk (Cervus elaphus), in GYE bears has decreased the energy and protein content of their diet. The patterns observed suggest that bear body size and population densities are influenced by seasonal availability of protein an energy, likely due in part to nutritional influences on mass gain and reproductive success

Measures of local grizzly bear abundance and surrounding road density and food supply

Data fields: 1) "DNA_sampling_cell_ID" is the 7x7 km cell used to allocate hair snag sites and used as a random effect in models; 2) "Session_number" is the 2 week session number of sampled site between 25 May and 17 July 2004; 3) "Number_grizzlybears" is the unique bears detected at hair snag site; 4) "Road_density_7440m" is the moving window road density (km/km^2) within a 7440 m radius window calculated in ArcGIS; 5) "Shep_can_fruit_DigE_1690m" is the modeled digestible energy (kcal) of Shepherdia canadensis fruit within a 1690 m radius of the hair snag site summarized in an ArcGIS moving window; 6) "Shep_can_fruit_DigE_7440m" is the modeled digestible energy (kcal) of Shepherdia canadensis fruit within a 7440 m radius of the hair snag site as summarized in an ArcGIS moving window; 7) "Ungulate_DigE_7440m" is the digestible energy (kcal) of ungulate matter within a 7440 m radius of the hair snag site as summarized in an ArcGIS moving window; 8) "NDVI_JulyMax_1690m" is the Natural Difference Vegetation Index (NDVI) maximum value in the month of July from MODIS remote sensing data during the year 2006 with values averaged within a 1690 m radius of the hair snag site as summarized in an ArcGIS moving window; 9) "NDVI_JulyMax_7440m" is the Natural Difference Vegetation Index (NDVI) maximum value in the month of July from MODIS remote sensing data during the year 2006 with values averaged within a 7440 m radius of the hair snag site as summarized in an ArcGIS moving window