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

Psychiatric comorbidity in refractory focal epilepsy: A study of 490 patients

We studied the prevalence and associated factors of psychiatric comorbidities in 490 patients with refractory focal epilepsy. Of these, 198 (40.4%) patients had psychiatric comorbidity. An Axis I diagnosis was made in 154 patients (31.4%) and an Axis II diagnosis (personality disorder) in another 44 (8.97%) patients. After logistic regression, positive family history of psychiatric comorbidities (O.R.=1.98; 95% CI=1.10-3.58; p=0.023), the presence of Axis II psychiatric comorbidities (O.R.=3.25; 95% CI=1.70-6.22; p<0.0001), and the epileptogenic zone located in mesial temporal lobe structures (O.R.=1.94; 95% CI=1.25-3.03; p=0.003) remained associated with Axis I psychiatric comorbidities. We concluded that a combination of clinical variables and selected structural abnormalities of the central nervous system contributes to the development of psychiatric comorbidities in patients with focal epilepsy. (C) 2012 Published by Elsevier Inc.Brazilian Government agenciesBrazilian Government agenciesFAPESP/CINAPCE [04/14004-9]FAPESP/CINAPCECNPq [306644/2010-0, 483108/2010-3]CNPqFAPERGS/PRONEMFAPERGS/PRONE

WUE and CO₂ Estimations by Eddy Covariance and Remote Sensing in Different Tropical Biomes

The analysis of gross primary production (GPP) is crucial to better understand CO2 exchanges between terrestrial ecosystems and the atmosphere, while the quantification of water-use efficiency (WUE) allows for the estimation of the compensation between carbon gained and water lost by the ecosystem. Understanding these dynamics is essential to better comprehend the responses of environments to ongoing climatic changes. The objective of the present study was to analyze, through AMERIFLUX and LBA network measurements, the variability of GPP and WUE in four distinct tropical biomes in Brazil: Pantanal, Amazonia, Caatinga and Cerrado (savanna). Furthermore, data measured by eddy covariance systems were used to assess remotely sensed GPP products (MOD17). We found a distinct seasonality of meteorological variables and energy fluxes with different latent heat controls regarding available energy in each site. Remotely sensed GPP was satisfactorily related with observed data, despite weak correlations in interannual estimates and consistent overestimations and underestimations during certain months. WUE was strongly dependent on water availability, with values of 0.95 gC kg−1 H2O (5.79 gC kg−1 H2O) in the wetter (drier) sites. These values reveal new thresholds that had not been previously reported in the literature. Our findings have crucial implications for ecosystem management and the design of climate policies regarding the conservation of tropical biomes, since WUE is expected to change in the ongoing climate change scenario that indicates an increase in frequency and severity of dry periods

Transpiração pelo método da sonda de dissipação térmica em floresta de transição Amazônica-Cerrado

Com este trabalho objetivou-se analisar o comportamento do fluxo de seiva em espécies da floresta de transição Amazônia Cerrado e caracterizar a dependência do fluxo de seiva, em função do déficit de pressão de vapor da atmosfera (DPV). O fluxo de seiva foi medido utilizando-se sondas de dissipação térmica em 5 espécies diferentes. Os dados foram divididos em quatro estações. No período experimental as curvas de variação sazonal do fluxo de seiva evidenciam a ocorrência de picos no período úmido. Esta tendência sazonal do fluxo de seiva foi evidenciada pela relação entre valores diários de transpiração e do DPV. O valor limite do DPV nessas estações foi de 1 a 1,5 kPa. Com referência ao fluxo de seiva como representativo da taxa transpiratória das plantas, a redução do fluxo no período seco não confirma a hipótese da manutenção do processo de evapotranspiração. O fluxo de seiva nas espécies do estudo é influenciado mais pelas condições atmosféricas do que mesmo pela disponibilidade de água no solo, o que suscita um possível armazenamento da água no caule nos períodos mais secos quando a área foliar diminui mas a taxa transpiratória não é significativamente distinta daquela do período chuvoso