Bridging natural and social sciences: a framework for identify strategies and actions for the conservation of biodiversity.

The Atlantic Forest is one of the most diverse areas in the world and considered a hotspot. Several actions are needed for its preservation, among them the implementation of the Biodiversity Corridors. The Atlantic Forest has three biodiversity corridors and the Rio de Janeiro State, which harbors huge species diversity, is in the Serra do Mar Corridor. We developed socioeconomic, political and environmental indicators to present conservation strategies supported by a wide database. These indicators complemented the previous surveys of priority areas which emphasized biotic elements, and their integration allowed the elaboration of strategies for the conservation and management, regionally directed, to support actions to be implemented by the Government. The analysis was done considering three subjects: Anthropic Pressure, Physical and Biotic State, and Present Ability of Response. Data analysis followed a synthesis-aggregation schedule and the resulting database was taken to a workshop, where specialists proposed strategies and actions for the conservation. These strategies were discussed considering vegetation remnant distribution, biological relevance, environmental vulnerability, kind of anthropic pressure in the region and potential for success of the actions proposed, based on the ability of response. Rio de Janeiro State is very diverse in biotic, physical, political, socioeconomic and cultural aspects which demand specific actions for each region. So, depending on the present situation of the natural and anthropic environments and on the present and future sources of degradation, regionally directed actions are applicable. This specificity in conservation actions will enable that the State remnants will be more successfully protected.

Digital soil mapping using reference area and artificial neural networks.

Digital soil mapping is an alternative for the recognition of soil classes in areas where pedological surveys are not available. The main aim of this study was to obtain a digital soil map using artificial neural networks (ANN) and environmental variables that express soillandscape relationships. This study was carried out in an area of 11,072 ha located in the Barra Bonita municipality, state of São Paulo, Brazil. A soil survey was obtained from a reference area of approximately 500 ha located in the center of the area studied. With the mapping units identified together with the environmental variables elevation, slope, slope plan, slope profile, convergence index, geology and geomorphic surfaces, a supervised classification by ANN was implemented. The neural network simulator used was the Java NNS with the learning algorithm "back propagation." Reference points were collected for evaluating the performance of the digital map produced. The occurrence of soils in the landscape obtained in the reference area was observed in the following digital classification: medium-textured soils at the highest positions of the landscape, originating from sandstone, and clayey loam soils in the end thirds of the hillsides due to the greater presence of basalt. The variables elevation and slope were the most important factors for discriminating soil class through the ANN. An accuracy level of 82% between the reference points and the digital classification was observed. The methodology proposed allowed for a preliminary soil classification of an area not previously mapped using mapping units obtained in a reference area