ABSTRACT. Faunistic analysis of Braconidae (Hymenoptera) in three areas of native woods of Paraná State, Brazil. The material studied comes from three of the several natural environments which still occur in Paraná State. The samples were collected during the project PROFAUPAR, developed by Departamento de Zoologia, Universidade Federal do Paraná. The selected sites were: Antonina (in the coastal plain), São José dos Pinhais (in the coastal mountain range) and Telêmaco Borba (in the second plateau). During one year of the project, 2,106 specimens of Braconidae were collected with light trap. These wasps represented 21 subfamilies and 1,966 specimens were identified in 85 genera. Hormiinae, Rogadinae, Microgastrinae, Alysiinae, Doryctinae and Meteorinae were the most frequent subfamilies. Indices of diversity and evenness were used to discuss richness and dominance of genera in each locality. Statistical analyses indicated that the asymptote of actual genera richness have been approached only to Antonina and Telêmaco Borba.
A redefinition of the genus Tarasco Marsh is provided along with description of two new species found in Brazil (T. granulata Barbalho and Penteado-Dias, new species and T. costata Barbalho and Scatolini, new species). This redefinition of the genus considers variation in characters in wing venation, such as the presence of vein r-m in the fore wing and vein 1-SC+R being complete and tubular in the hind wing.
The decision-making of complex problems, such as epidemics monitoring and control, involves multiple heterogeneous data and spatial and temporal aspects. Most problems cannot be reduced to one objective, characterized as multi-criteria decision-making (MCDM) problems. Adding temporal and spatial aspects further increases the complexity of addressing those problems. This paper proposed a framework that uses evolutionary algorithms and map algebra for addressing spatial and temporal multidimensional complex problems. It was evaluated in a case study of dengue and tuberculosis diseases in an urban environment, considering multi-resolution data and a genetic algorithm. Several analyses were conducted, generating maps and information essential to generate insights into the problem and a better understanding of the spatial relations between the variables. The framework and the code implemented could be applied to different problems, spatial resolutions, and objectives.
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