Wildfire danger prediction and understanding with deep learning

The authors thank Fabian Gans who provided the instructions to deploy the data cube in a cloud‐optimized format. Publisher Copyright: © 2022 The Authors.Climate change exacerbates the occurence of extreme droughts and heatwaves, increasing the frequency and intensity of large wildfires across the globe. Forecasting wildfire danger and uncovering the drivers behind fire events become central for understanding relevant climate-land surface feedback and aiding wildfire management. In this work, we leverage Deep Learning (DL) to predict the next day's wildfire danger in a fire-prone part of the Eastern Mediterranean and explainable Artificial Intelligence (xAI) to diagnose model attributions. We implement DL models that capture the temporal and spatio-temporal context, generalize well for extreme wildfires, and demonstrate improved performance over the traditional Fire Weather Index. Leveraging xAI, we identify the substantial contribution of wetness-related variables and unveil the temporal focus of the models. The variability of the contribution of the input variables across wildfire events hints into different wildfire mechanisms. The presented methodology paves the way to more robust, accurate, and trustworthy data-driven anticipation of wildfires.publishersversionpublishe

Ex vivo exposure to titanium dioxide and silver nanoparticles mildly affect sperm of gilthead seabream (Sparus aurata) - A multiparameter spermiotoxicity approach

Nanoparticles (NP) are potentially repmtoxic, which may compromise the success of populations. However, the reprotoxicity of NP is still scarcely addressed in marine fish. Therefore, we evaluated the impacts of environmentally relevant and supra environmental concentrations of titanium dioxide (TiO2: 10 to 10,000 mu g.L-1) and silver NP (Ag: 0.25 to 250 mu g.L-1) on the sperm of gilthead seabream (Sparus aurata). We performed short-term direct exposures (ex vivo) and evaluated sperm motility, head morphometry, mitochondrial function, antioxidant responses and DNA integrity. No alteration in sperm motility (except for supra environmental Ag NP concentration), head morphometry, mitochondrial function, and DNA integrity occurred. However, depletion of all antioxidants occurred after exposure to TiO2 NP, whereas SOD decreased after exposure to Ag NP (lowest and intermediate concentration). Considering our results, the decrease in antioxidants did not indicate vulnerability towards oxidative stress. TiO2 NP and Ag NP induced low spermiotoxicity, without proven relevant ecological impacts.info:eu-repo/semantics/publishedVersio

Rhizobium strains competitiveness on bean nodulation in Cerrado soils.

ABSTRACT: The objective of this work was to identify the most competitive and effective Rhizobium strains in order to increase common bean yield by nitrogen fixation as alternative or complementation to the nitrogen fertilization. Competitiveness tests were lead in axenic conditions, in Cerrado soil pots and in three field experiments, with native Rhizobium strains that were previously identified, according to their effectiveness and genetic variability. The identification of strains in nodules was performed using serological tests (axenic conditions) ? agglutination and enzyme linked immunosorbent (Elisa) assays ? and random amplified polymorfic DNA (RAPD) (Cerrado soil). Plant yield was determined using the dry weight (greenhouse conditions), total N and grain yield (field experiments). Among the analyzed Rhizobium strains, native strain SLA 2.2 and commercial strain CIAT 899 were the dominant nodules in plants of the most productive plots, presenting yield productivity similar or higher to those obtained in treatments where 20 kg ha-1 of N were applied. RESUMO: O objetivo deste trabalho foi identificar as estirpes de Rhizobium mais efetivas e competitivas, a fim de maximizar a produtividade do feijoeiro por meio da fixação de nitrogênio, como alternativa à adubação nitrogenada. Foram conduzidos testes de competitividade em condições axênicas, em vasos com solo do Cerrado e em três experimentos de campo, com estirpes de Rhizobium nativas, previamente selecionadas quanto à efetividade e à variabilidade genética. A identificação das estirpes nos nódulos foi efetuada por meio das técnicas de aglutinação e ensaio imunoabsorvente de ligação de enzimas (Elisa), em condições de casa de vegetação, e pela técnica de DNA polimórfico amplificado ao acaso (RAPD), em solo de Cerrado. A produtividade das plantas foi determinada pela produção de matéria seca, teor de N e produção de grãos (condições de campo). A estirpe nativa SLA 2.2 e a estirpe comercial CIAT 899 foram dominantes nos nódulos das plantas das parcelas mais produtivas, com índices de produtividade iguais ou superiores aos obtidos nos tratamentos em que foram aplicados 20 kg ha-1 de N

Reverse engineering model structures for soil and ecosystem respiration: the potential of gene expression programming

Accurate model representation of land-atmosphere carbon fluxes is essential for climate projections. However, the exact responses of carbon cycle processes to climatic drivers often remain uncertain. Presently, knowledge derived from experiments, complemented with a steadily evolving body of mechanistic theory provides the main basis for developing such models. The strongly increasing availability of measurements may facilitate new ways of identifying suitable model structures using machine learning. Here, we explore the potential of gene expression programming (GEP) to derive relevant model formulations based solely on the signals present in data by automatically applying various mathematical transformations to potential predictors and repeatedly evolving the resulting model structures. In contrast to most other machine learning regression techniques, the GEP approach generates "readable" models that allow for prediction and possibly for interpretation. Our study is based on two cases: artificially generated data and real observations. Simulations based on artificial data show that GEP is successful in identifying prescribed functions with the prediction capacity of the models comparable to four state-of-the-art machine learning methods (Random Forests, Support Vector Machines, Artificial Neural Networks, and Kernel Ridge Regressions). Based on real observations we explore the responses of the different components of terrestrial respiration at an oak forest in south-east England. We find that the GEP retrieved models are often better in prediction than some established respiration models. Based on their structures, we find previously unconsidered exponential dependencies of respiration on seasonal ecosystem carbon assimilation and water dynamics. We noticed that the GEP models are only partly portable across respiration components; the identification of a "general" terrestrial respiration model possibly prevented by equifinality issues. Overall, GEP is a promising tool for uncovering new model structures for terrestrial ecology in the data rich era, complementing more traditional modelling approaches

Diverse soil carbon dynamics expressed at the molecular level

The stability and potential vulnerability of soil organic matter (SOM) to global change remains incompletely understood due to the complex processes involved in its formation and turnover. Here we combine compound-specific radiocarbon analysis with fraction-specific and bulk-level radiocarbon measurements in order to further elucidate controls on SOM dynamics in a temperate and sub-alpine forested ecosystem. Radiocarbon contents of individual organic compounds isolated from the same soil interval generally exhibit greater variation than those among corresponding operationally-defined fractions. Notably, markedly older ages of long-chain plant leaf wax lipids (n-alkanoic acids) imply that they reflect a highly stable carbon pool. Furthermore, marked 14C variations among shorter- and longer-chain n-alkanoic acid homologues suggest that they track different SOM pools. Extremes in SOM dynamics thus manifest themselves within a single compound class. This exploratory study highlights the potential of compound-specific radiocarbon analysis for understanding SOM dynamics in ecosystems potentially vulnerable to global change

Efeito da implantação de uma floresta mista sobre a população de microrganismos celulolíticos em solos do semi-árido mineiro.

Na recuperacao e manutencao da fertilidade dos solos degradados, a decomposicao apresenta-se particularmente importante sendo a razao celulose/N um dos principais fatores que influenciam a efetividade desses processo. Neste trabalho avaliou-se a ocorrencia e a dinamica da populacao de microorganismos celuloliticos nos solos de diferentes modelos de reflorestamento de uma area degradada do Projeto Jaiba/MG. Esses modelos incluiram especies arboreas de eucalipto, leguminosas e nao leguminosas nativas. Observaram-se diferencas significativas no numero de celuloliticos entre as estacoes e os locais estudados. Nas areas impactadas ocorreu elevacao menos intensa dos microrganismos evidenciando o efeito limitante do impacto sobre essa populacao microbiana. Nos modelos de plantio avaliados o numero de celuloliticos diferiu significativamente, sendo o mais elevado no modelo representado pelo plantio do maior numero de especies. em todos os experimentos, a maior populacao de celuloliticos ocorreu nos consorcios entre leguminosas e outras especies vegetais nativas e a menor nas areas de plantio de eucalipto, sugerindo uma acao limitante dessa planta sobre o crescimento desses microrganismos. Esses resultados evidenciaram o papel dos celuloliticos como bioindicadores da qualidade do solo e da resposta as diferentes praticas de manejo

Microbial carbon use efficiency promotes global soil carbon storage

Soils store more carbon than other terrestrial ecosystems$^{1,2}$. How soil organic carbon (SOC) forms and persists remains uncertain$^{1,3}$, which makes it challenging to understand how it will respond to climatic change$^{3,4}$. It has been suggested that soil microorganisms play an important role in SOC formation, preservation and loss$^{5–7}$. Although microorganisms affect the accumulation and loss of soil organic matter through many pathways$^{4,6,8–11}$, microbial carbon use efficiency (CUE) is an integrative metric that can capture the balance of these processes$^{12,13}$. Although CUE has the potential to act as a predictor of variation in SOC storage, the role of CUE in SOC persistence remains unresolved$^{7,14,15}$. Here we examine the relationship between CUE and the preservation of SOC, and interactions with climate, vegetation and edaphic properties, using a combination of global-scale datasets, a microbial-process explicit model, data assimilation, deep learning and meta-analysis. We find that CUE is at least four times as important as other evaluated factors, such as carbon input, decomposition or vertical transport, in determining SOC storage and its spatial variation across the globe. In addition, CUE shows a positive correlation with SOC content. Our findings point to microbial CUE as a major determinant of global SOC storage. Understanding the microbial processes underlying CUE and their environmental dependence may help the prediction of SOC feedback to a changing climate

Earth System Model Evaluation Tool (ESMValTool) v2.0 - An extended set of large-scale diagnostics for quasi-operational and comprehensive evaluation of Earth system models in CMIP

The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool designed to improve comprehensive and routine evaluation of Earth system models (ESMs) participating in the Coupled Model Intercomparison Project (CMIP). It has undergone rapid development since the first release in 2016 and is now a well-tested tool that provides end-to-end provenance tracking to ensure reproducibility. It consists of (1) an easy-to-install, well-documented Python package providing the core functionalities (ESMValCore) that performs common preprocessing operations and (2) a diagnostic part that includes tailored diagnostics and performance metrics for specific scientific applications. Here we describe large-scale diagnostics of the second major release of the tool that supports the evaluation of ESMs participating in CMIP Phase 6 (CMIP6). ESMValTool v2.0 includes a large collection of diagnostics and performance metrics for atmospheric, oceanic, and terrestrial variables for the mean state, trends, and variability. ESMValTool v2.0 also successfully reproduces figures from the evaluation and projections chapters of the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) and incorporates updates from targeted analysis packages, such as the NCAR Climate Variability Diagnostics Package for the evaluation of modes of variability, the Thermodynamic Diagnostic Tool (TheDiaTo) to evaluate the energetics of the climate system, as well as parts of AutoAssess that contains a mix of top-down performance metrics. The tool has been fully integrated into the Earth System Grid Federation (ESGF) infrastructure at the Deutsches Klimarechenzentrum (DKRZ) to provide evaluation results from CMIP6 model simulations shortly after the output is published to the CMIP archive. A result browser has been implemented that enables advanced monitoring of the evaluation results by a broad user community at much faster timescales than what was possible in CMIP5

Dormancy within Staphylococcus epidermidis biofilms : a transcriptomic analysis by RNA-seq

The proportion of dormant bacteria within Staphylococcus epidermidis biofilms may determine its inflammatory profile. Previously, we have shown that S. epidermidis biofilms with higher proportions of dormant bacteria have reduced activation of murine macrophages. RNA-sequencing was used to identify the major transcriptomic differences between S. epidermidis biofilms with different proportions of dormant bacteria. To accomplish this goal, we used an in vitro model where magnesium allowed modulation of the proportion of dormant bacteria within S. epidermidis biofilms. Significant differences were found in the expression of 147 genes. A detailed analysis of the results was performed based on direct and functional gene interactions. Biological processes among the differentially expressed genes were mainly related to oxidation-reduction processes and acetyl-CoA metabolic processes. Gene set enrichment revealed that the translation process is related to the proportion of dormant bacteria. Transcription of mRNAs involved in oxidation-reduction processes was associated with higher proportions of dormant bacteria within S. epidermidis biofilm. Moreover, the pH of the culture medium did not change after the addition of magnesium, and genes related to magnesium transport did not seem to impact entrance of bacterial cells into dormancy.The authors thank Stephen Lorry at Harvard Medical School for providing CLC Genomics software. This work was funded by Fundacao para a Ciencia e a Tecnologia (FCT) and COMPETE grants PTDC/BIA-MIC/113450/2009, FCOMP-01-0124-FEDER-014309, FCOMP-01-0124-FEDER-022718 (FCT PEst-C/SAU/LA0002/2011), QOPNA research unit (project PEst-C/QUI/UI0062/2011), and CENTRO-07-ST24-FEDER-002034. The following authors had an individual FCT fellowship: VC (SFRH/BD/78235/2011) and AF (2SFRH/BD/62359/2009)