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The ash produced by forest fires is a complex mixture of organic and inorganic particles with many properties. Amounts of ash and char are used to roughly evaluate the impacts of a fire on nutrient cycling and ecosystem recovery. Numerous studies have suggested that fire severity can be assessed by measuring changes in ash characteristics. Traditional methods to determine fire severity are based on in situ observations, and visual approximation of changes in the forest floor and soil which are both laborious and subjective. These measures primarily reflect the level of consumption of organic layers, the deposition of ash, particularly its depth and color, and fire-induced changes in the soil. Recent studies suggested adding remote sensing techniques to the field observations and using machine learning and spectral indices to assess the effects of fires on ecosystems. While index thresholding can be easily implemented, its effectiveness over large areas is limited to pattern coverage of forest type and fire regimes. Machine learning algorithms, on the other hand, allow multivariate classifications, but learning is complex and time-consuming when analyzing space-time series. Therefore, there is currently no consensus regarding a quantitative index of fire severity. Considering that wildfires play a major role in controlling forest carbon storage and cycling in fire-suppressed forests, this study examines the use of low-cost multispectral imagery across visible and near-infrared regions collected by unmanned aerial systems to determine fire severity according to the color and chemical properties of vegetation ash. The use of multispectral imagery data might reduce the lack of precision that is part of manual color matching and produce a vast and accurate spatio-temporal severity map. The suggested severity map is based on spectral information used to evaluate chemical/mineralogical changes by deep learning algorithms. These methods quantify total carbon content and assess the corresponding fire intensity that is required to form a particular residue. By designing three learning algorithms (PLS-DA, ANN, and 1-D CNN) for two datasets (RGB images and Munsell color versus Unmanned Aerial System (UAS)-based multispectral imagery) the multispectral prediction results were excellent. Therefore, deep network-based near-infrared remote sensing technology has the potential to become an alternative reliable method to assess fire severity. [ABSTRACT FROM AUTHOR]

Premise: Although fire cues (high temperatures and smoke) influence seed germination in numerous species from fire‐prone environments, their effects on seed germination of species from neotropical savannas are poorly understood. Methods: We exposed seeds of eight grass species from the Cerrado, the Brazilian savanna to heat‐shock (80°C or 110°C for 5 min) and/or smoke water, and then set them to germinate in light or dark, at either summer (28°C/18°C) or winter (27°C/14°C) temperature regimes in an incubator. In addition, we evaluated the effects of smoke water on seedling root and shoot growth for four of the species. Results: Smoke interacted with the dark treatment to increase germination from 28% to 93% in Aristida recurvata and 77% to 95% in Aristida riparia. Smoke had no effect on germination of either of these species in the light. Heat‐shock alone also promoted seed germination in A. recurvata. For Digitaria lehmanniana, smoke interacted with heat‐shock to improve germination from 5% to 16%. In contrast, the fire treatments did not have any effect on the seed germination of the remaining five species. Smoke water stimulated root growth for A. riparia, A. recurvata, and Ctenium cirrosum but had no effect on their shoot growth. Conclusions: The strong promotive effect of smoke on Aristida germination suggests that these species are fire‐adapted. Aristida species have an active awn system, which facilitates seed burial, and the smoke and dark interaction would ensure buried seeds germinated post‐fire. The species that showed no response to fire cues may either have adapted via alternative strategies or require different concentrations of smoke or levels of heat. This study is one of very few examples showing a positive germination and seedling growth response to smoke for species from neotropical savannas. [ABSTRACT FROM AUTHOR]

Metabolic physiology and animal behaviour are often considered to be linked, positively or negatively, according to either the performance or allocation models. Performance seems to predominate over allocation in natural systems, but the constraining environmental context may reveal allocation limitations to energetically expensive behaviours. Habitat disturbance, such as the large-scale fire that burnt wetlands of Biebrza National Park (NE Poland), degrades natural ecosystems. It arguably reduces food and shelter availability, modifies predator-prey interactions, and poses a direct threat for animal survival, such as that of the wetland specialist root vole Microtus oeconomus. We hypothesized that fire disturbance induces physiology-behaviour co-expression, as a consequence of changed environmental context. We repeatedly measured maintenance and exercise metabolism, and behavioural responses to the open field, in a root voles from post-fire and unburnt locations. Highly repeatable maintenance metabolism and distance moved during behavioural tests correlated positively, but relatively labile exercise metabolism did not covary with behaviour. At the same time, voles from a post-fire habitat had higher maintenance metabolism and moved shorter distances than voles from unburnt areas. We conclude there is a prevalence of the performance mechanism, but simultaneous manifestation of context-dependent allocation constraints of the physiology-behaviour covariation after disturbance. The last occurs at the within-individual level, indicating the significance of behavioural plasticity in the context of environmental disturbance., Competing Interests: Competing interests The authors declare no competing or financial interests., (© 2024. Published by The Company of Biologists Ltd.)

Post-fire environmental footprint is expected at varying scales in space and in time and demands development of multi-scale monitoring approaches. In this paper, a spatially and temporally explicit multi-scale model that reveals the physical and morphological indicators affecting hillslope susceptibility at varying scales, is explained and demonstrated. The qualitative and quantitative suitability classification procedures are adapted to translate the large-scale space-borne data supplied by satellite systems (Landsat OLS8 and Sentinel 2 and 3) to local scale produced by a regional airborne survey performed by unmanned aerial vehicle (UAV). At the smallest spatial and temporal resolution, a daily airborne imagery collection by UAV is linked to micro-topography model, using statistical and mathematical approaches. [ABSTRACT FROM AUTHOR]

Many studies have claimed that fire acts as the chief ecological factor cueing dormancy break in seeds with a water-impermeable seed coat, i.e. physical dormancy (PY), in Mediterranean ecosystems. However, a proposal is made that seasonal temperature changes must be viewed as more meaningful dormancy-breaking cues because: (1) fire is erratic and may break PY in seasons during which seedlings cannot complete their life cycle; (2) fire may not occur for long periods, thereby only providing an opportunity for dormancy break and germination once in every several years; and (3) if fire opens the specialized anatomical structures called ‘water gaps’, in seconds, their evolutionary role of detecting environmental conditions becomes irrational. Although fire breaks dormancy in a proportion of seeds, given the risk of seed mortality and the post-fire environment providing cues for dormancy break, it is suggested that fire might possibly be an exaptation. [ABSTRACT FROM AUTHOR]