Can agroforest woodlots work as stepping stones for birds in the Atlantic forest region?

In fragmented landscapes, agroforest woodlots can potentially act as stepping stones facilitating movement between forest fragments. We assessed the influence of agroforest woodlots on bird distribution and diversity in the Atlantic forest region (SE Brazil), and also tested which categories of species can use different types of connection elements, and whether this use is influenced by the distance to large forest patches. We studied two fragmented landscapes, with and without stepping stones linking large fragments, and one forested landscape. Using a point count, a bird survey was undertaken in the fragmented landscapes in five different elements: large remnants (> 400 ha), agroforest woodlots (0.4-1.1 ha), small patches (0.5-7 ha), riparian corridor, and pasture areas (the main matrix). Generalist and open-area species were commonly observed in the agroforest system or other connection elements, whereas only a few forest species were present in these connections. For the latter species, the distance of woodlots to large patches was essential to determine their richness and abundance. Based on our results and data from literature, we suggest that there is an optimal relationship between the permeability of the matrix and the efficiency of stepping stones, which occurs at intermediate degrees of matrix resistance, and is species-dependent. Because the presence of agroforest system favors a higher richness of generalist species, they appeared to be more advantageous for conservation than the monoculture system; for this reason, they should be considered as a management alternative, particularly when the matrix permeability requirement is met.

Importance of interhabitat gaps and stepping-stones for lesser woodcreepers (Xiphorhynchus fuscus) in the Atlantic forest, Brazil

Translocation experiments showed that a woodcreeper bird species is able to move between isolated forest fragments, but this ability is limited by increasing interpatch distances. Larger distances (> 100 m) were overcome by using small stepping-stones (isolated trees), which enhance connectivity and are useful for the species conservation in fragmented landscapes.

Sparse block-Jacobi matrices with arbitrarily accurate Hausdorff dimension

We show that the Hausdorff dimension of the spectral measure of a class of deterministic, i.e. nonrandom, block-Jacobi matrices may be determined with any degree of precision, improving a result of Zlatos [Andrej Zlatos,. Sparse potentials with fractional Hausdorff dimension, J. Funct. Anal. 207 (2004) 216-252]. (C) 2010 Elsevier Inc. 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.

PLANT LEAF IDENTIFICATION BASED ON VOLUMETRIC FRACTAL DIMENSION

Texture is an important visual attribute used to describe the pixel organization in an image. As well as it being easily identified by humans, its analysis process demands a high level of sophistication and computer complexity. This paper presents a novel approach for texture analysis, based on analyzing the complexity of the surface generated from a texture, in order to describe and characterize it. The proposed method produces a texture signature which is able to efficiently characterize different texture classes. The paper also illustrates a novel method performance on an experiment using texture images of leaves. Leaf identification is a difficult and complex task due to the nature of plants, which presents a huge pattern variation. The high classification rate yielded shows the potential of the method, improving on traditional texture techniques, such as Gabor filters and Fourier analysis.

Fractal dimension applied to plant identification

This article discusses methods to identify plants by analysing leaf complexity based on estimating their fractal dimension. Leaves were analyzed according to the complexity of their internal and external shapes. A computational program was developed to process, analyze and extract the features of leaf images, thereby allowing for automatic plant identification. Results are presented from two experiments, the first to identify plant species from the Brazilian Atlantic forest and Brazilian Cerrado scrublands, using fifty leaf samples from ten different species, and the second to identify four different species from genus Passiflora, using twenty leaf samples for each class. A comparison is made of two methods to estimate fractal dimension (box-counting and multiscale Minkowski). The results are discussed to determine the best approach to analyze shape complexity based on the performance of the technique, when estimating fractal dimension and identifying plants. (C) 2008 Elsevier Inc. All rights reserved.