Genetic structure of sigmodontine rodents (Cricetidae) along an altitudinal gradient of the Atlantic Rain Forest in southern Brazil

The population genetic structure of two sympatric species of sigmodontine rodents (Oligoryzomys nigripes and Euryoryzomys russatus) was examined for mitochondrial DNA (mtDNA) sequence haplotypes of the control region. Samples were taken from three localities in the Atlantic Rain Forest in southern Brazil, along an altitudinal gradient with different types of habitat. In both species there was no genetic structure throughout their distribution, although levels of genetic variability and gene flow were high

Gap-filling eddy covariance methane fluxes:Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET)

Gap-filling eddy covariance methane fluxes : Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting halfhourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET).Peer reviewe

Global carbon dioxide efflux from rivers enhanced by high nocturnal emissions

Carbon dioxide (CO2) emissions to the atmosphere from running waters are estimated to be four times greater than the total carbon (C) flux to the oceans. However, these fluxes remain poorly constrained because of substantial spatial and temporal variability in dissolved CO2 concentrations. Using a global compilation of high-frequency CO2 measurements, we demonstrate that nocturnal CO2 emissions are on average 27% (0.9 gC m−2 d−1) greater than those estimated from diurnal concentrations alone. Constraints on light availability due to canopy shading or water colour are the principal controls on observed diel (24 hour) variation, suggesting this nocturnal increase arises from daytime fixation of CO2 by photosynthesis. Because current global estimates of CO2 emissions to the atmosphere from running waters (0.65–1.8 PgC yr−1) rely primarily on discrete measurements of dissolved CO2 obtained during the day, they substantially underestimate the magnitude of this flux. Accounting for night-time CO2 emissions may elevate global estimates from running waters to the atmosphere by 0.20–0.55 PgC yr−1

A EXPERIÊNCIA COM OS COMPLEXOS DE ESTUDO NAS ESCOLAS PAULO FREIRE E SEMENTE DA CONQUISTA

Discute a experiência das Escolas de Ensino Médio Paulo Freire e Semente da Conquista, localizadas em Abelardo Luz, oeste catarinense. É resultado de um projeto que articula pesquisa e extensão, o qual envolve a Universidade Pública e as Escolas em questão e se desenvolve há seis anos, tendo por foco a formação de professores e a organização do trabalho pedagógico com base nos complexos de estudo. Os complexos são uma formulação da Pedagogia Socialista Soviética no período inicial da Revolução Russa, e tem sido atualizado em escolas ligadas ao Movimento Sem Terra - MST. Trata-se de uma pesquisa bibliográfica nos chamados pioneiros da pedagogia soviética, favorecida por novas traduções e publicações na última década. Realizaram-se ainda observações em todo o processo, registradas em cadernos próprios e tomamos por base relatórios, avaliações e depoimentos de professores e estudantes. Nosso objetivo é o de registrar e refletir a experiência, identificando possibilidades e limites do trabalho pedagógico que se propõe superador das pedagogias burguesas

CaeAl, NieAl and ZneAl LDH powders as efficient materials to treat synthetic effluents containing o-nitrophenol

Powdered layered double hydroxides (LDH) based on calcium-aluminum (Ca–Al), nickel-aluminum (Ni–Al), and zinc-aluminum (Zn–Al) were synthesized with the purpose to evaluate the removal of o-nitrophenol from synthetic effluents by adsorption. It was verified that Ca–Al, Ni–Al, and Zn–Al LDHs presented a typical layered structure confirming the successful synthesis. o-nitrophenol adsorption on the LDH powders was favored at a pH of 5.0, being attained removal percentages from 70 to 90%, depending on the material. Kinetic experimental data obeyed the general order model, while, Sips represented the experimental equilibrium behavior of the three materials adequately. The maximum adsorption capacities were 135.1 mg g−1,122.1 mg g−1 and 130.3 mg g−1 for Ca–Al, Ni–Al, and Zn–Al LDHs, respectively. For simulated effluent, it was attained a removal of up to 60.3% using Ni–Al LDH. In a general way, the layered double hydroxides based on Ca–Al, Ni–Al, and Zn–Al exhibited an interesting potential as adsorbent materials for the treatment of simulated effluents containing o-nitrophenol. Ni–Al is preferred due to its better performance in the treatment of simulated effluents and higher regeneration potential