Temporal Diversity Patterns and Phenology in Fruit-feeding Butterflies in the Atlantic Forest

The Atlantic Forest deserves special attention due to its high level of species endemism and degree of threat. As in other tropical biomes, there is little information about the ecology of the organisms that occur there. The objectives of this study were to verify how fruit-feeding butterflies are distributed through time, and the relation with meteorological conditions. Species richness and Shannon index were partitioned additively at the monthly level, and beta diversity, used as a hierarchical measure of temporal species turnover, was calculated among months, trimesters, and semesters. Circular analysis was used to verify how butterflies are distributed along seasons and its relation with meteorological conditions. We sampled 6488 individuals of 73 species. Temporal diversity of butterflies was more grouped than expected by chance among the months of each trimester. Circular analyses revealed that diversity is concentrated in hot months (September-March), with the subfamily Brassolinae strongly concentrated in February-March. Average temperature was correlated with total abundance of butterflies, abundance of Biblidinae, Brassolinae and Morphinae, and richness of Satyrinae. The present results show that 3mo of sampling between September and March is enough to produce a nonbiased sample of the local assemblage of butterflies, containing at least 70 percent of the richness and 25 percent of abundance. The influence of temperature on sampling is probably related to butterfly physiology. Moreover, temperature affects resource availability for larvae and adults, which is higher in hot months. The difference in seasonality patterns among subfamilies is probably a consequence of different evolutionary pressures through time.

Slicing the metric space to provide quick indexing of complex data in the main memory

Searching in a dataset for elements that are similar to a given query element is a core problem in applications that manage complex data, and has been aided by metric access methods (MAMs). A growing number of applications require indices that must be built faster and repeatedly, also providing faster response for similarity queries. The increase in the main memory capacity and its lowering costs also motivate using memory-based MAMs. In this paper. we propose the Onion-tree, a new and robust dynamic memory-based MAM that slices the metric space into disjoint subspaces to provide quick indexing of complex data. It introduces three major characteristics: (i) a partitioning method that controls the number of disjoint subspaces generated at each node; (ii) a replacement technique that can change the leaf node pivots in insertion operations; and (iii) range and k-NN extended query algorithms to support the new partitioning method, including a new visit order of the subspaces in k-NN queries. Performance tests with both real-world and synthetic datasets showed that the Onion-tree is very compact. Comparisons of the Onion-tree with the MM-tree and a memory-based version of the Slim-tree showed that the Onion-tree was always faster to build the index. The experiments also showed that the Onion-tree significantly improved range and k-NN query processing performance and was the most efficient MAM, followed by the MM-tree, which in turn outperformed the Slim-tree in almost all the tests. (C) 2010 Elsevier B.V. All rights reserved.