The challenge of maintaining Atlantic forest biodiversity: A multi-taxa conservation assessment of specialist and generalist species in an agro-forestry mosaic in southern Bahia

Recent developments have highlighted the importance of forest amount at large spatial scales and of matrix quality for ecological processes in remnants. These developments, in turn, suggest the potential for reducing biodiversity loss through the maintenance of a high percentage of forest combined with sensitive management of anthropogenic areas. We conducted a multi-taxa survey to evaluate the potential for biodiversity maintenance in an Atlantic forest landscape that presented a favorable context from a theoretical perspective (high proportion of mature forest partly surrounded by structurally complex matrices). We sampled ferns, butterflies, frogs, lizards, bats, small mammals and birds in interiors and edges of large and small mature forest remnants and two matrices (second-growth forests and shade cacao plantations), as well as trees in interiors of small and large remnants. By considering richness, abundance and composition of forest specialists and generalists, we investigated the biodiversity value of matrix habitats (comparing them with interiors of large remnants for all groups except tree), and evaluated area (for all groups) and edge effects (for all groups except trees) in mature forest remnants. our results suggest that in landscapes comprising high amounts of mature forest and low contrasting matrices: (1) shade cacao plantations and second-growth forests harbor an appreciable number of forest specialists; (2) most forest specialist assemblages are not affected by area or edge effects, while most generalist assemblages proliferate at edges of small remnants. Nevertheless, differences in tree assemblages, especially among smaller trees, Suggest that observed patterns are unlikely to be stable over time. (C) 2009 Elsevier Ltd. All rights reserved.

Multi-objective clustering ensemble for gene expression data analysis

In this paper, we present an algorithm for cluster analysis that integrates aspects from cluster ensemble and multi-objective clustering. The algorithm is based on a Pareto-based multi-objective genetic algorithm, with a special crossover operator, which uses clustering validation measures as objective functions. The algorithm proposed can deal with data sets presenting different types of clusters, without the need of expertise in cluster analysis. its result is a concise set of partitions representing alternative trade-offs among the objective functions. We compare the results obtained with our algorithm, in the context of gene expression data sets, to those achieved with multi-objective Clustering with automatic K-determination (MOCK). the algorithm most closely related to ours. (C) 2009 Elsevier B.V. All rights reserved.

Topological multi-contour decomposition for image analysis and image retrieval

Successful classification, information retrieval and image analysis tools are intimately related with the quality of the features employed in the process. Pixel intensities, color, texture and shape are, generally, the basis from which most of the features are Computed and used in such fields. This papers presents a novel shape-based feature extraction approach where an image is decomposed into multiple contours, and further characterized by Fourier descriptors. Unlike traditional approaches we make use of topological knowledge to generate well-defined closed contours, which are efficient signatures for image retrieval. The method has been evaluated in the CBIR context and image analysis. The results have shown that the multi-contour decomposition, as opposed to a single shape information, introduced a significant improvement in the discrimination power. (c) 2008 Elsevier B.V. All rights reserved,

Shape classification using complex network and Multi-scale Fractal Dimension

Shape provides one of the most relevant information about an object. This makes shape one of the most important visual attributes used to characterize objects. This paper introduces a novel approach for shape characterization, which combines modeling shape into a complex network and the analysis of its complexity in a dynamic evolution context. Descriptors computed through this approach show to be efficient in shape characterization, incorporating many characteristics, such as scale and rotation invariant. Experiments using two different shape databases (an artificial shapes database and a leaf shape database) are presented in order to evaluate the method. and its results are compared to traditional shape analysis methods found in literature. (C) 2009 Published by Elsevier B.V.