Collection of relevant results obtained with the ERTS-1 satellite images by the Institute for Space Research (INPE), volume 1

There are no author-identified significant results in this report

Anthropogenic Habitats Facilitate Dispersal of an Early Successional Obligate: Implications for Restoration of an Endangered Ecosystem

Landscape modification and habitat fragmentation disrupt the connectivity of natural landscapes, with major consequences for biodiversity. Species that require patchily distributed habitats, such as those that specialize on early successional ecosystems, must disperse through a landscape matrix with unsuitable habitat types. We evaluated landscape effects on dispersal of an early successional obligate, the New England cottontail (Sylvilagus transitionalis). Using a landscape genetics approach, we identified barriers and facilitators of gene flow and connectivity corridors for a population of cottontails in the northeastern United States. We modeled dispersal in relation to landscape structure and composition and tested hypotheses about the influence of habitat fragmentation on gene flow. Anthropogenic and natural shrubland habitats facilitated gene flow, while the remainder of the matrix, particularly development and forest, impeded gene flow. The relative influence of matrix habitats differed between study areas in relation to a fragmentation gradient. Barrier features had higher explanatory power in the more fragmented site, while facilitating features were important in the less fragmented site. Landscape models that included a simultaneous barrier and facilitating effect of roads had higher explanatory power than models that considered either effect separately, supporting the hypothesis that roads act as both barriers and facilitators at all spatial scales. The inclusion of LiDAR-identified shrubland habitat improved the fit of our facilitator models. Corridor analyses using circuit and least cost path approaches revealed the importance of anthropogenic, linear features for restoring connectivity between the study areas. In fragmented landscapes, human-modified habitats may enhance functional connectivity by providing suitable dispersal conduits for early successional specialists

A simple model for predicting snow albedo decay using observations from the Community Collaborative Rain, Hail, and Snow-Albedo (CoCoRAHS-Albedo) Network

The albedo of seasonal snow cover plays an important role in the global climate system due to its influence on Earth’s radiation budget and energy balance. Volunteer CoCoRaHS-Albedo observers collected 3,249 individual daily albedo, snow depth, and density measurements using standardized techniques at dozens of sites across New Hampshire, USA over four winter seasons. The data show that albedo increases rapidly with snow depth up to ~ 0.14 m. Multiple linear regression models using snowpack age, snow depth or density, and air temperature provide reasonable approximations of surface snow albedo during times of albedo decay. However, the linear models also reveal systematic biases that highlight an important non-linearity in snow albedo decay. Modeled albedo values are reasonably accurate within the range of 0.6 to 0.9, but exhibit a tendency to over-estimate lower albedo values and under-estimate higher albedo values. We hypothesize that rapid reduction in high albedo fresh snow results from a decrease in snow specific surface area, while during melt-events the presence of liquid water in the snowpack accelerates metamorphism and grain growth. We conclude that the CoCoRaHS-Albedo volunteer observer network provides useful snow albedo, depth, and density measurements and serves as an effective model for future measurement campaigns

The victory of Jair Bolsonaro according to the brazilian electoral study of 2018

The 2018 elections surprised most analysts and political scientists in Brazil and abroad. The competitive dynamic that opposed the PT and the PSDB in presidential elections since 1994 has been disrupted, resulting in a novelty for the Brazilian party system. The purpose of this research article is to identify the determinants of votes for Jair Bolsonaro in the two rounds of the 2018 presidential election from data collected by the Brazilian Electoral Study. By means of multivariate analyses of the two rounds of the presidential election, we found that flourishing 'antipetismo' (i.e. anti-Workers' Party sentiment), growing numbers of voters self-identifying as right wing and increased importance of variables linked to voters' political identification all underpinned the victory of Jair Bolsonaro141CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQ307503/2018-