Modelling the spatial dependence of the rainfall erosivity index in the Brazilian semiarid region

O objetivo deste trabalho foi modelar a dependência espacial e mapear o índice de erosividade das chuvas (EI30) na região semiárida do Brasil. Foram utilizados registros de erosividade mensal de 210 postos pluviométricos, com série temporal diária igual ou superior a 15 anos. Com base nos valores do EI30, a modelagem da dependência espacial foi realizada pelo ajuste do semivariograma. A partir dos modelos de semivariograma, foram gerados mapas de isolinhas de erosividade com interpolador da krigagem. De acordo com a série histórica de dados, o valor máximo mensal médio do EI30 foi observado em março, e o valor anual variou de 1.439 a 5.864 MJ mm ha-1 por ano, classificado como baixo e moderado, respectivamente. Os maiores valores do EI30 foram obtidos nos extremos norte e sul da região semiárida. Foi observada dependência espacial média para erosividade da chuva, para a maioria dos meses, principalmente com o modelo de semivariograma esférico. O alcance da erosividade variou entre 62 e 1.508 km, para o EI30 mensal, e foi de, aproximadamente, 1.046 km para o anual. A modelagem aplicada, com a validação dos semivariogramas pelo teste de jackknife, permite a espacialização do EI30 para a região semiárida do Brasil.The objective of this work was to model the spatial dependence and to map the rainfall erosivity index (EI30) in the semiarid region of Brazil. Registers of monthly erosivity from 210 rainfall stations were used, with daily time series equal to or greater than 15 years. Based on the values of the EI30, a spatial dependence model was made by adjusting the semivariogram. From the semivariogram models, erosivity isoline maps were generated with a kriging interpolator. According to the historical data series, the maximum monthly average value of the EI30 was observed in March, and the annual value ranged from 1,439 to 5,864 MJ mm ha-1 per year, classified as low and moderate, respectively. The highest EI30 values were obtained in the northern and southern extremes of the semiarid region. Average spatial dependence was observed for rainfall erosivity, in most months, especially with the spherical semivariogram model. The range of erosivity varied from 62 to 1,508 km for the monthly EI30 and was of approximately 1,046 km for the annual one. The applied model, with the validation of the semivariograms using the jackknife test, allows the spatialization of the EI30 for the semiarid region of Brazil

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Compostagem e vermicompostagem de resíduos domiciliares com esterco bovino para a produção de insumo orgânico Composting and vermicomposting of home residues with bovine manure for organic amendment production

O objetivo deste trabalho foi avaliar a produção de adubo orgânico de resíduos domiciliares, por meio da integração de compostagem e vermicompostagem, com e sem esterco bovino nesses processos. Foram realizadas duas etapas experimentais: na primeira, testou-se a termoestabilização dos resíduos e estabeleceram-se condições propícias para a introdução das minhocas; na segunda, aos 27 dias de termoestabilização, os resíduos resultantes da primeira etapa foram colocados em um recipiente de 9 dm³, com e sem minhocas, em delineamento inteiramente casualizado, com quatro repetições. Aos 19, 55 e 69 dias, avaliaram-se as taxas de reprodução e sobrevivência das minhocas adultas e os atributos químicos do composto com e sem minhoca. As minhocas sobreviveram e se reproduziram nos substratos, com maior taxa de multiplicação no substrato com esterco. A utilização dos resíduos orgânicos de origem domiciliar, para a produção de insumo, é tecnicamente viável, tanto por meio da compostagem, quanto da vermicompostagem. A adição de esterco não reduziu o tempo de maturação do composto. À exceção do K e Mg, que tiveram seus teores alterados, a integração dos processos de compostagem e vermicompostagem, com e sem esterco, produziu adubos com características químicas similares.The objective of this work was to evaluate the production of organic amendment from home residues by integration of composting and vermicomposting processes with and without bovine manure. Two experimental steps were adopted: in the first one, thermal stabilization of the residues and adequate conditions were set up for earthworm introduction; in the second step, at 27 days of thermal stabilization, residues remained from the first step were placed in a 9-dm³ container, with and without earthworms, in a completed randomized experimental design of four replicates. Evaluations were made at 19, 55, 69-day period for reproduction and survival rate of adult worms, and chemical attributes of the compost with and without earthworms. Earthworms survived and reproduced in the substrates, and manure gave the highest reproduction rate. The utilization of organic home residues for production of amendment is technically viable by means of composting or vermicomposting. Addition of doses of manure did not decreased the time for compost maturation. Except for the alteration in the levels of K and Mg, the integration of composting and vermicomposting processes yielded products with similar chemical composition

Modelling the spatial dependence of the rainfall erosivity index in the Brazilian semiarid region

The objective of this work was to model the spatial dependence and to map the rainfall erosivity index (EI30) in the semiarid region of Brazil. Registers of monthly erosivity from 210 rainfall stations were used, with daily time series equal to or greater than 15 years. Based on the values of the EI30, a spatial dependence model was made by adjusting the semivariogram. From the semivariogram models, erosivity isoline maps were generated with a kriging interpolator. According to the historical data series, the maximum monthly average value of the EI30 was observed in March, and the annual value ranged from 1,439 to 5,864 MJ mm ha-1 per year, classified as low and moderate, respectively. The highest EI30 values were obtained in the northern and southern extremes of the semiarid region. Average spatial dependence was observed for rainfall erosivity, in most months, especially with the spherical semivariogram model. The range of erosivity varied from 62 to 1,508 km for the monthly EI30 and was of approximately 1,046 km for the annual one. The applied model, with the validation of the semivariograms using the jackknife test, allows the spatialization of the EI30 for the semiarid region of Brazil.O objetivo deste trabalho foi modelar a dependência espacial e mapear o índice de erosividade das chuvas (EI 30 ) na região semiárida do Brasil. Foram utilizados registros de erosividade mensal de 210 postos pluviométricos, com série temporal diária igual ou superior a 15 anos. Com base nos valores do EI 30, a modelagem da dependência espacial foi realizada pelo ajuste do semivariograma. A partir dos modelos de semivariograma, foram gerados mapas de isolinhas de erosividade com interpolador da krigagem. De acordo com a série histórica de dados, o valor máximo mensal médio do EI 30 foi observado em março, e o valor anual variou de 1.439 a 5.864 MJ mm ha -1 por ano, classificado como baixo e moderado, respectivamente. Os maiores valores do EI 30 foram obtidos nos extremos norte e sul da região semiárida. Foi observada dependência espacial média para erosividade da chuva, para a maioria dos meses, principalmente com o modelo de semivariograma esférico. O alcance da erosividade variou entre 62 e 1.508 km, para o EI 30 mensal, e foi de, aproximadamente, 1.046 km para o anual. A modelagem aplicada, com a validação dos semivariogramas pelo teste de jackknife, permite a espacialização do EI 30 para a região semiárida do Brasil

Measurement of the isolated diphoton cross-section in pp collisions at sqrt(s) = 7 TeV with the ATLAS detector

The ATLAS experiment has measured the production cross-section of events with two isolated photons in the final state, in proton-proton collisions at sqrt(s) = 7 TeV. The full data set acquired in 2010 is used, corresponding to an integrated luminosity of 37 pb^-1. The background, consisting of hadronic jets and isolated electrons, is estimated with fully data-driven techniques and subtracted. The differential cross-sections, as functions of the di-photon mass, total transverse momentum and azimuthal separation, are presented and compared to the predictions of next-to-leading-order QCD.Comment: 15 pages plus author list (27 pages total), 9 figures, 2 tables, final version to appear in Physical Review