Composition of the Ant Fauna (Hymenoptera: Formicidae) in Public Squares in Southern Brazil

The composition of the ant fauna was examined in public squares of three municipalities that compose the hydrographic basin of the Upper Tiete River: Biritiba Mirim, Salesopolis, and Mogi das Cruzes. Richness, frequency of occurrence, similarity, and influence of seasons on the species composition were examined. The method was standardized as sampling units consisted of a set of three baits arranged in a triangle with vertices two meters apart. Sardines in oil were used as attractant. A total of 86 species was collected. Myrmicinae and Pheidole were the richest subfamily and genus, respectively. Eighty species were collected in Mogi das Cruzes, 49 in Salesopolis, and 45 in Biritiba Mirim, with 34 species common to the three areas. The ordination analysis (NMDS) revealed the presence of two distinct communities: one in Mogi das Cruzes and another in Biritiba Mirim-Salesopolis. These data were supported by the dendogram based on the Bray-Curtis dissimilarity index. This result might be associated with the distinct geographic and demographic characteristics of the areas. Regarding seasonality, the composition of the fauna of Mogi das Cruzes is independent of the season of the year, unlike the observed in Biritiba Mirim and Salesopolis.

Semi-supervised dimensionality reduction based on partial least squares for visual analysis of high dimensional data

Dimensionality reduction is employed for visual data analysis as a way to obtaining reduced spaces for high dimensional data or to mapping data directly into 2D or 3D spaces. Although techniques have evolved to improve data segregation on reduced or visual spaces, they have limited capabilities for adjusting the results according to user's knowledge. In this paper, we propose a novel approach to handling both dimensionality reduction and visualization of high dimensional data, taking into account user's input. It employs Partial Least Squares (PLS), a statistical tool to perform retrieval of latent spaces focusing on the discriminability of the data. The method employs a training set for building a highly precise model that can then be applied to a much larger data set very effectively. The reduced data set can be exhibited using various existing visualization techniques. The training data is important to code user's knowledge into the loop. However, this work also devises a strategy for calculating PLS reduced spaces when no training data is available. The approach produces increasingly precise visual mappings as the user feeds back his or her knowledge and is capable of working with small and unbalanced training sets.