Human activities result in the formation of a mosaic of forest patches within a non-habitat matrix. The response of the local biodiversity to changes in land-use may occur at different scales. It is important to evaluate the effects of the attributes of both the patches and the surrounding landscape on the occupancy of forest patches by animal populations. Here, we assessed the predictive potential of local (basal area, tree density), patch (size, shape) and landscape scale (total area of forest, number of patches, matrix permeability, patch proximity) variables on the occupancy of forest patches by the syntopic primates Alouatta caraya, Sapajus libidinosus and Callithrix penicillata in the city of Goiânia in the Cerrado region of central Brazil. We used playback to survey primate populations in 22 focal patches and assessed the landscape within a 1000 m buffer zone around each site. In A. caraya, occupancy was influenced by the shape of the focal patches, the amount of forest and fragmentation level of the landscape. Focal patch size and the permeability of the matrix were the principal determinants of the occupancy of S. libidinosus. None of the predictors influenced patch occupancy in C. penicillata, and the structure of the vegetation did not influence occupancy in any of the species. The preservation of as many forest patches as possible, both large and small, as well as gallery forests, and the enhancement of matrix permeability will be essential for the long-term conservation of the syntopic primates of the Cerrado of central Brazil.
Resumo -O objetivo deste trabalho foi utilizar as técnicas de reflectância acumulada e mineração de dados, seguidas por classificação orientada a objeto, em imagens do sensor Operational Land Imager (OLI), satélite Landsat 8, para a classificação de vegetação nativa e cobertura agropecuária do Cerrado. Quatro imagens de reflectância foram utilizadas para a discriminação de seis classes -agricultura, pecuária, campo limpo úmido, savana, floresta e campo -, para a classificação do Parque Nacional das Emas, no Estado de Goiás, e adjacências. As imagens foram segmentadas para a extração de atributos espectrais de amostras e a aplicação de combinações de atributos (média + moda, todos os atributos) na mineração de dados. O programa Weka foi utilizado para a construção das árvores de decisão. Essa metodologia indicou que a diferenciação entre alvos aumentou a partir da acumulação temporal da reflectância, em todas as bandas e as classes, e a melhor imagem foi aquela do somatório das quatro datas. A classificação baseada na associação de atributos média + moda não apresentou impedimentos no processamento das regras de decisão, diferentemente da associação de todos os atributos. A classificação média + moda apresentou acurácia satisfatória (exatidão global, 69%; Kappa, 58%; e TAU, 63%). A integração dessas técnicas apresenta potencial para a diferenciação de vegetação nativa e antrópica do Cerrado.Termos para indexação: análise baseada em objeto, análise multitemporal, antropização, classificação supervisionada, mineração, sensoriamento remoto. Object-oriented classification in association with accumulated reflectance and data mining toolsAbstract -The objective of this work was to use the accumulated reflectance technique and data mining application, followed by object-oriented classification, in images of Operational Land Imager (OLI) sensor, Landsat 8, for the classification of native vegetation and agricultural coverage of Cerrado. Four reflectance images were used for the discrimination of six classes -agriculture, livestock, wetland, savannah, forest, and grassland -, for classification of Parque Nacional das Emas and surrounding areas in the state of Goiás, Brazil. The images were segmented for the extraction of sample spectral attributes and application of attribute combinations (mean + mode, all attributes) on data mining. The Weka software was used to construct the decision trees. This methodology indicated that the differentiation among targets increased from the temporal accumulation of the reflectance in all bands and classes, and that the optimal image was that of the sum of the four dates. The classification based on the attribute associations mean + mode showed no restraints in the decision rules processing, unlike the association of all attributes. The mean + mode classification showed a satisfactory accuracy (global accuracy, 69%; Kappa, 58%; and TAU, 63%). The integration of these techniques shows potential to differentiate native and anthropogenic vegetation in the Cerrado.
Environmental determinants of global patterns in species richness are still uncertain. The Metabolic Theory of Ecology (MTE) proposes that species richness patterns can be explained by environmental temperature acting on the metabolism of ectothermic organisms. However, the generality of this theory has been questioned due to its low fit to the geographic variation in species richness of different taxonomic groups. Here, we investigated whether the MTE drives elapid richness, testing the nonstationarity of the relationship between the natural logarithm of species richness (ln S) and the inverse function of temperature (1/kT) using a geographically weighted regression (GWR). The relationship between ln S and 1/kT varied systematically over space and showed non-stationarity. Few tropical locations were consistent with MTE predictions, whereas other regions fitted differently. Although the slope of the GWR model ranged from low to high, the temperature did not predict species richness strongly on average and did not limit the upper values of richness. The response of richness to temperature in some areas might reflect a recent history of colonization and diversification of species across tropical and subtropical regions. In regions not affected by temperature, species richness should be structured by other biotic and abiotic interactions. This scenario reveals that the non-stationarity of the relationship would be linked to idiosyncrasies in the sample sites, which can drift the magnitude or change the relationship between species richness and temperature throughout space.
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