Nutrient enrichment is related to two facets of beta diversity for stream invertebrates across the united states

Beta diversity, the spatial or temporal variability of species composition, is a key concept in community ecology. However, our ability to predict the relative importance of the main drivers of beta diversity (e. g., environmental heterogeneity, dispersal limitation, and environmental productivity) remains limited. Using a comprehensive data set on stream invertebrate assemblages across the continental United States, we found a hump-shaped relationship between beta diversity and within-ecoregion nutrient concentrations. Within-ecoregion compositional dissimilarity matrices were mainly related to environmental distances in most of the 30 ecoregions analyzed, suggesting a stronger role for species-sorting than for spatial processes. The strength of these relationships varied considerably among ecoregions, but they were unrelated to within-ecoregion environmental heterogeneity or spatial extent. Instead, we detected a negative correlation between the strength of species sorting and nutrient concentrations. We suggest that eutrophication is a major mechanism disassembling invertebrate assemblages in streams at a continental scale.

Semi-supervised learning with concept drift using particle dynamics applied to network intrusion detection data

Concept drift, which refers to non stationary learning problems over time, has increasing importance in machine learning and data mining. Many concept drift applications require fast response, which means an algorithm must always be (re)trained with the latest available data. But the process of data labeling is usually expensive and/or time consuming when compared to acquisition of unlabeled data, thus usually only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are based on assumptions that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenging task in machine learning. Recently, a particle competition and cooperation approach has been developed to realize graph-based semi-supervised learning from static data. We have extend that approach to handle data streams and concept drift. The result is a passive algorithm which uses a single classifier approach, naturally adapted to concept changes without any explicit drift detection mechanism. It has built-in mechanisms that provide a natural way of learning from new data, gradually "forgetting" older knowledge as older data items are no longer useful for the classification of newer data items. The proposed algorithm is applied to the KDD Cup 1999 Data of network intrusion, showing its effectiveness.

Auxílio do geoprocessamento para a geração do mapa de suscetibilidade à erosão: o caso do córrego Mandu, Rio Claro/SP

The mining of sand, currently, is essential for urban growth, by providing input for the building industry. The consequences of this mining activity to environmental triggers may be severe and irreversible. Among the major impacts caused by sand mining the riparian vegetation removal is detached. The riparian vegetation is essential for balance and maintaining the local ecosystem. For all that had been shown, is possible to verify the importance of environmental studies in areas wich there are mining. This study aimed specially to assess environmental impacts triggered by a mining, located near the headwaters of the stream Mandu, situated in Ajapi, District of Rio Claro-SP. For this purpose, we used remote sensing techniques and GIS to produce thematic maps of slope, pedology, geology, land use and occupation of the soil, and riparian vegetation, using the capabilities of GIS / ArcGIS. The slope map was based on data from the Cartographic IGC 1979, scale 1:10,000. For the production of pedological and geological maps were used Semi-Detailed soil survey of the state of São Paulo, 1981 (1:100,000) and the Geological Map of Zaine (1994), scale 1:50,000, respectively. Since the maps of Use and Land Occupation and Riparian Forest were obtained by visual interpretation of the image of CBERS 2010 following the merger between the HRC and CCD images. From these mappings, and through multi-criteria analysis, map of susceptibility to erosion was made, which supported the environmental assessment of the studied area, indicating susceptible and unsuitable areas for the deployment of economic activities and urban sprawl. This study serves as a model can be replicated in other watersheds, assisting in the proper use planning and land use, aiming at the rational use of natural resources

Should phylogenetic and functional diversity metrics compose macroinvertebrate multimetric indices for stream biomonitoring?

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