Association of two new Coryphasia species (Araneae, Salticidae) with tank-bromeliads in southeastern Brazil: Habitats and patterns of host plant use

Two allopatric species of Coryphasia (Araneae: Salticidae), both described for the first time in this study, have been found associated with Bromeliaceae in distinct phytophysiognomies (e.g., inselbergs, highland forests and restingas) from southeastern Brazil. In this study, we investigated whether these salticids were associated specifically with bromeliads, and whether they used bromeliads of different species and sizes in distinct geographic regions. The Coryphasia species were rarely found outside bromeliads, occupied larger bromeliad species among those available, and were generally more frequent on bromeliads in open areas, such as inselbergs on mountain tops. The two Coophasia species were observed submerging in phytotelmata, possibly as an anti-predatory behavior. The patterns of spatial and microspatial distribution and the submergence behavior of these species were similar to those of other bromeliad-dwelling salticids, which suggests a convergence among spiders that live on bromeliads.

The role of spatial and temporal descriptors for neotropical tadpole communities in southern Brazil

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Contemporary pollen flow, mating patterns and effective population size inferred from paternity analysis in a small fragmented population of the Neotropical tree Copaifera langsdorffii Desf. (Leguminosae-Caesalpinioideae)

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Passive and active dispersers respond similarly to environmental and spatial processes: an example from metacommunity dynamics of tree hole invertebrates

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Sustainable development: changing production patterns, social equity and the environment

Includes bibliography

Spatial clustering applied to health area

The significant volume of work accidents in the cities causes an expressive loss to society. The development of Spatial Data Mining technologies presents a new perspective for the extraction of knowledge from the correlation between conventional and spatial attributes. One of the most important techniques of the Spatial Data Mining is the Spatial Clustering, which clusters similar spatial objects to find a distribution of patterns, taking into account the geographical position of the objects. Applying this technique to the health area, will provide information that can contribute towards the planning of more adequate strategies for the prevention of work accidents. The original contribution of this work is to present an application of tools developed for Spatial Clustering which supply a set of graphic resources that have helped to discover knowledge and support for management in the work accidents area. © 2011 IEEE.

How individual movement response to habitat edges affects population persistence and spatial spread

How individual-level movement decisions in response to habitat edges influence population-level patterns of persistence and spread of a species is a major challenge in spatial ecology and conservation biology. Here, we integrate novel insights into edge behavior, based on habitat preference and movement rates, into spatially explicit growth-dispersal models. We demonstrate how crucial ecological quantities (e.g., minimal patch size, spread rate) depend critically on these individual-level decisions. In particular, we find that including edge behavior properly in these models gives qualitatively different and intuitively more reasonable results than those of some previous studies that did not consider this level of detail. Our results highlight the importance of new empirical work on individual movement response to habitat edges. © 2013 by The University of Chicago.

Applying machine learning based on multiscale classifiers to detect remote phenology patterns in Cerrado savanna trees

Plant phenology is one of the most reliable indicators of species responses to global climate change, motivating the development of new technologies for phenological monitoring. Digital cameras or near remote systems have been efficiently applied as multi-channel imaging sensors, where leaf color information is extracted from the RGB (Red, Green, and Blue) color channels, and the changes in green levels are used to infer leafing patterns of plant species. In this scenario, texture information is a great ally for image analysis that has been little used in phenology studies. We monitored leaf-changing patterns of Cerrado savanna vegetation by taking daily digital images. We extract RGB channels from the digital images and correlate them with phenological changes. Additionally, we benefit from the inclusion of textural metrics for quantifying spatial heterogeneity. Our first goals are: (1) to test if color change information is able to characterize the phenological pattern of a group of species; (2) to test if the temporal variation in image texture is useful to distinguish plant species; and (3) to test if individuals from the same species may be automatically identified using digital images. In this paper, we present a machine learning approach based on multiscale classifiers to detect phenological patterns in the digital images. Our results indicate that: (1) extreme hours (morning and afternoon) are the best for identifying plant species; (2) different plant species present a different behavior with respect to the color change information; and (3) texture variation along temporal images is promising information for capturing phenological patterns. Based on those results, we suggest that individuals from the same species and functional group might be identified using digital images, and introduce a new tool to help phenology experts in the identification of new individuals from the same species in the image and their location on the ground. © 2013 Elsevier B.V. All rights reserved.

3D geovisualisation techniques applied in spatial data mining

The increase in the number of spatial data collected has motivated the development of geovisualisation techniques, aiming to provide an important resource to support the extraction of knowledge and decision making. One of these techniques are 3D graphs, which provides a dynamic and flexible increase of the results analysis obtained by the spatial data mining algorithms, principally when there are incidences of georeferenced objects in a same local. This work presented as an original contribution the potentialisation of visual resources in a computational environment of spatial data mining and, afterwards, the efficiency of these techniques is demonstrated with the use of a real database. The application has shown to be very interesting in interpreting obtained results, such as patterns that occurred in a same locality and to provide support for activities which could be done as from the visualisation of results. © 2013 Springer-Verlag.

Population, social equity and changing production patterns

Includes bibliography

Longitudinal patterns of fish assemblages in mountain streams from tropical forest biome

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Spatial Variation in Morphometry in Vanzosaura rubricauda (Squamata, Gymnophthalmidae) from Open Habitats of South America and its Environmental Correlates

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

The influence of landscape on the spatial and temporal distribution of stream macroalgal communities of two types of subtropical biomes

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Spatial-temporal variation of parasites in Cnemidophorus ocellifer (Teiidae) and Tropidurus hispidus and Tropidurus semitaeniatus (Tropiduridae) from Caatinga areas in northeastern Brazil

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Temporal, spatial and spatiotemporal analysis of the occurrence of visceral leishmaniasis in humans in the City of Birigui, State of São Paulo, from 1999 to 2012

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)