Modeling Wildfire Dynamics in Latin America Using the FLAM Framework

The increasing frequency of wildfires caused by climate change poses a significant threat globally, particularly in Latin America – a region known for its critical ecosystems. Its vulnerability to climate change-induced wildfire threats, resulting from increasing temperatures and changing precipitation patterns, is uncertain, highlighting the need for comprehensive strategies such as incorporating advanced modeling and proactive measures to understand, manage, and conserve its ecological state in the face of threats posed by climate change, such as wildfires. This study utilizes the wildFire cLimate impacts and Adaptation Model (FLAM) by IIASA to provide a comprehensive analysis of past and projected wildfire dynamics in Latin America. FLAM is a process-based fire parameterization algorithm used to assess the impacts of climate, fuel availability, topography, and anthropogenic factors on wildfire characteristics. It is highly adjustable and adaptable, making it suitable to analyze past and future wildfire trends in diverse regions such as Latin America. We analyzed spatial and temporal wildfire patterns using MODIS satellite data alongside historical climate and anthropogenic data to calibrate FLAM. We generated projections of burned areas until 2100 under 3 RCP scenarios for Latin American as a whole, as well as for distinct sub-regions to better assess regional wildfire dynamics and climate change impacts. Moreover, we developed a scenario to explore the impacts of increased fire suppression efficiency on projected burned area and highlight the impacts of focusing mitigation and management efforts on areas identified as hotspots (high risk of wildfire). The study shows FLAM’s effectiveness in modeling historical wildfires and its sensitivity to the RCP scenarios in predicting wildfire trends in Latin America. Our analysis and results show how FLAM helps in evaluating the potential future changes in wildfire intensity, and geographic spread under various climatic scenarios. FLAM projected a dramatic rise in burned area until the end of the century across Latin America in line with observed trends, especially under severe climate change scenarios. Regions with the highest temperature rises are also prone to reduced precipitation, which further increase wildfire risks. The spatially-explicit projections highlight areas at higher risk of wildfire, enabling targeted and efficient fire management and mitigation strategies. Our study further showed the potential impact of adaptive measures, such as enhanced fire suppression efficiency in identified hotspots, in reducing annual mean burned area. Overall, this study provides critical insights into the relationship between climate change and wildfire dynamics using a state of the art model. It sets the foundation for further research on fires in Latin America and efficient management strategies which can be modelled by FLAM

Anticipating Future Risks of Climate-Driven Wildfires in Boreal Forests

Extreme forest fires have historically been a significant concern in Canada, the Russian Federation, the USA, and now pose an increasing threat in boreal Europe. This paper deals with application of the wildFire cLimate impacts and Adaptation Model (FLAM) in boreal forests. FLAM operates on a daily time step and utilizes mechanistic algorithms to quantify the impact of climate, human activities, and fuel availability on wildfire probabilities, frequencies, and burned areas. In our paper, we calibrate the model using historical remote sensing data and explore future projections of burned areas under different climate change scenarios. The study consists of the following steps: (i) analysis of the historical burned areas over 2001–2020; (ii) analysis of temperature and precipitation changes in the future projections as compared to the historical period; (iii) analysis of the future burned areas projected by FLAM and driven by climate change scenarios until the year 2100; (iv) simulation of adaptation options under the worst-case scenario. The modeling results show an increase in burned areas under all Representative Concentration Pathway (RCP) scenarios. Maintaining current temperatures (RCP 2.6) will still result in an increase in burned area (total and forest), but in the worst-case scenario (RCP 8.5), projected burned forest area will more than triple by 2100. Based on FLAM calibration, we identify hotspots for wildland fires in the boreal forest and suggest adaptation options such as increasing suppression efficiency at the hotspots. We model two scenarios of improved reaction times—stopping a fire within 4 days and within 24 h—which could reduce average burned forest areas by 48.6% and 79.2%, respectively, compared to projected burned areas without adaptation from 2021–2099

Integrating Human Domain Knowledge into Artificial Intelligence for Hybrid Forest Fire Prediction: Case Studies from South Korea and Italy

Forest fires pose a growing global threat, exacerbated by climate change-induced heat waves. The intricate interplay between changing climate, biophysical, and anthropogenic factors emphasizes the urgent need for sophisticated predictive models. Existing models, whether process-based for interpretability or machine learning-based for automatic feature identification, have distinct strengths and weaknesses. This study addresses these gaps by integrating human domain knowledge, crucial for interpreting forest fire dynamics, into a machine learning framework. We introduce FLAM-Net, a neural network derived from IIASA's wildfire Climate impacts and Adaptation Model (FLAM), melding process-based insights of FLAM with machine learning capabilities. In optimizing FLAM-Net for South Korea, new algorithms interpret national-specific forest fire patterns, and multi-scale applications, facilitated by U-Net-based deep neural networks (DN-FLAM), yield downscaled predictions. Successfully tailored to South Korea's context, FLAM-Net and DN-FLAM reveal spatial concentration near metropolitan areas and the east coastal region, with temporal concentration in spring. Performance evaluation yields Pearson's r values of 0.943, 0.840, and 0.641 for temporal, spatial, and spatio-temporal dimensions. Projections based on Shared Socioeconomic Pathways (SSP) indicate an increasing trend in forest fires until 2050, followed by a decrease due to increased precipitation. During the optimization process of FLAM-Net for Italy, optimal parameters for sub-areas are identified. This involves considering biophysical and anthropogenic factors at each grid, contributing to improved localized projection optimization by utilizing various sets of optimal parameters. There by, this process illuminates the intricate connections between environmental factors and their interpretation in the dynamics of forest fires. This study demonstrates the advantages of hybrid models like FLAM-Net and DN-FLAM, seamlessly combining process-based insights and artificial intelligence for interpretability, accuracy, and efficient optimization. The findings contribute scientific evidence for developing context-specific climate resilience strategies, with global applicability to enhance climate resilience

Evidence of antineutrinos from distant reactors using pure water at SNO

The SNO+ Collaboration reports the first evidence of reactor antineutrinos in a Cherenkov detector. The nearest nuclear reactors are located 240 km away in Ontario, Canada. This analysis uses events with energies lower than in any previous analysis with a large water Cherenkov detector. Two analytical methods are used to distinguish reactor antineutrinos from background events in 190 days of data and yield consistent evidence for antineutrinos with a combined significance of 3.5σ

A Second-Generation Device for Automated Training and Quantitative Behavior Analyses of Molecularly-Tractable Model Organisms

A deep understanding of cognitive processes requires functional, quantitative analyses of the steps leading from genetics and the development of nervous system structure to behavior. Molecularly-tractable model systems such as Xenopus laevis and planaria offer an unprecedented opportunity to dissect the mechanisms determining the complex structure of the brain and CNS. A standardized platform that facilitated quantitative analysis of behavior would make a significant impact on evolutionary ethology, neuropharmacology, and cognitive science. While some animal tracking systems exist, the available systems do not allow automated training (feedback to individual subjects in real time, which is necessary for operant conditioning assays). The lack of standardization in the field, and the numerous technical challenges that face the development of a versatile system with the necessary capabilities, comprise a significant barrier keeping molecular developmental biology labs from integrating behavior analysis endpoints into their pharmacological and genetic perturbations. Here we report the development of a second-generation system that is a highly flexible, powerful machine vision and environmental control platform. In order to enable multidisciplinary studies aimed at understanding the roles of genes in brain function and behavior, and aid other laboratories that do not have the facilities to undergo complex engineering development, we describe the device and the problems that it overcomes. We also present sample data using frog tadpoles and flatworms to illustrate its use. Having solved significant engineering challenges in its construction, the resulting design is a relatively inexpensive instrument of wide relevance for several fields, and will accelerate interdisciplinary discovery in pharmacology, neurobiology, regenerative medicine, and cognitive science

Energy Reallocation to Breeding Performance through Improved Nest Building in Laboratory Mice.

Mice are housed at temperatures (20-26°C) that increase their basal metabolic rates and impose high energy demands to maintain core temperatures. Therefore, energy must be reallocated from other biological processes to increase heat production to offset heat loss. Supplying laboratory mice with nesting material may provide sufficient insulation to reduce heat loss and improve both feed conversion and breeding performance. Naïve C57BL/6, BALB/c, and CD-1breeding pairs were provided with bedding alone, or bedding supplemented with either 8g of Enviro-Dri, 8g of Nestlets, for 6 months. Mice provided with either nesting material built more dome-like nests than controls. Nesting material improved feed efficiency per pup weaned as well as pup weaning weight. The breeding index (pups weaned/dam/week) was higher when either nesting material was provided. Thus, the sparing of energy for thermoregulation of mice given additional nesting material may have been responsible for the improved breeding and growth of offspring

Development and Evolution of the Muscles of the Pelvic Fin

Locomotor strategies in terrestrial tetrapods have evolved from the utilisation of sinusoidal contractions of axial musculature, evident in ancestral fish species, to the reliance on powerful and complex limb muscles to provide propulsive force. Within tetrapods, a hindlimb-dominant locomotor strategy predominates, and its evolution is considered critical for the evident success of the tetrapod transition onto land. Here, we determine the developmental mechanisms of pelvic fin muscle formation in living fish species at critical points within the vertebrate phylogeny and reveal a stepwise modification from a primitive to a more derived mode of pelvic fin muscle formation. A distinct process generates pelvic fin muscle in bony fishes that incorporates both primitive and derived characteristics of vertebrate appendicular muscle formation. We propose that the adoption of the fully derived mode of hindlimb muscle formation from this bimodal character state is an evolutionary innovation that was critical to the success of the tetrapod transition

The liquid-argon scintillation pulseshape in DEAP-3600

DEAP-3600 is a liquid-argon scintillation detector looking for dark matter. Scintillation events in the liquid argon (LAr) are registered by 255 photomultiplier tubes (PMTs), and pulseshape discrimination (PSD) is used to suppress electromagnetic background events. The excellent PSD performance of LAr makes it a viable target for dark matter searches, and the LAr scintillation pulseshape discussed here is the basis of PSD. The observed pulseshape is a combination of LAr scintillation physics with detector effects. We present a model for the pulseshape of electromagnetic background events in the energy region of interest for dark matter searches. The model is composed of (a) LAr scintillation physics, including the so-called intermediate component, (b) the time response of the TPB wavelength shifter, including delayed TPB emission at O(ms) time-scales, and c) PMT response. TPB is the wavelength shifter of choice in most LAr detectors. We find that approximately 10% of the intensity of the wavelength-shifted light is in a long-lived state of TPB. This causes light from an event to spill into subsequent events to an extent not usually accounted for in the design and data analysis of LAr-based detectors