Ecological niche modeling and geographical distribution of pollinator and plants: A case study of Peponapis fervens (Smith, 1879) (Eucerini: Apidae) and Cucurbita species (Cucurbitaceae)

The bees of the Peponapes genus (Eucerini, Apidae) have a Neotropical distribution with the center of species diversity located in Mexico and are specialized in Cucurbita plants. which have many species of economic importance. such as squashes and pumpkins Peponapis fervens is the only species of the genus known from southern South America The Cucurbita species occurring in the same area as P fervens Include four domesticated species (C ficifolia, C maxima maxima, C moschata and C pepo) and one non-domesticated species (Cucurbita maxima andreana) It was suggested that C. in andreana was the original pollen source to P fervens, and this bee expanded its geographical range due to the domestication of Cucurbita The potential geographical areas of these species were determined and compared using ecological niche modeling that was performed with the computational system openModeller and GARP with best subsets algorithm The climatic variables obtained through modeling were compared using Cluster Analysis Results show that the potential areas of domesticated species practically spread all over South America The potential area of P fervens Includes the areas of C m andreana but reaches a larger area, where the domesticated species of Cucurbita also Occur The Cluster Analysis shows a high climatic similarity between P fervens and C. m. andreana Nevertheless. P fervens presents the ability to occupy areas with wider ranges of climatic variables and to exploit resources provided by domesticated species (C) 2009 Elsevier B V All rights reserved

Potamotrygon tigrina, a new species of freshwater stingray from the upper Amazon basin, closely related to Potamotrygon schroederi Fernandez-Yepez, 1958 (Chondrichthyes: Potamotrygonidae)

A new species of Neotropical freshwater stingray, family Potamotrygonidae, is described from the Rio Nanay in the upper Rio Amazonas basin of Peru. Potamotrygon tigrina, n. sp., is easily distinguished from all congeners by its conspicuous dorsal disc coloration, composed of bright yellow to orange vermiculations strongly interwoven with a dark-brown to deep-black background. Additional features that in combination diagnose P. tigrina, n. sp., include the presence of a single angular cartilage, low and not closely grouped dorsal tail spines, and coloration of tail composed of relatively wide and alternating bands of creamy white and dark brown to black. Potamotrygon tigrina is closely related to Potamotrygon schroederi Fernandez-Yepez, 1958, which occurs in the Rio Negro (Brazil) and Rio Orinoco (Venezuela, Colombia). Both species are very similar in proportions and counts, and share features hypothesized to be derived within Potamotrygonidae, related to their specific angular cartilage morphology, distal tail color, dorsal tail-spine pattern, and ventral lateral-line system. To further substantiate the description of P. tigrina, n. sp., we provide a redescription of P. schroederi based on material from the Rio Negro (Brazil) and Rio Orinoco (Venezuela). Specimens from the two basins differ in number of vertebral centra and slightly in size and frequency of rosettes on dorsal disc, distinctions that presently do not warrant their specific separation. Potamotrygon tigrina is frequently commercialized in the international aquarium trade but virtually nothing is known of its biology or conservation status.

U-curve: A branch-and-bound optimization algorithm for U-shaped cost functions on Boolean lattices applied to the feature selection problem

This paper presents the formulation of a combinatorial optimization problem with the following characteristics: (i) the search space is the power set of a finite set structured as a Boolean lattice; (ii) the cost function forms a U-shaped curve when applied to any lattice chain. This formulation applies for feature selection in the context of pattern recognition. The known approaches for this problem are branch-and-bound algorithms and heuristics that explore partially the search space. Branch-and-bound algorithms are equivalent to the full search, while heuristics are not. This paper presents a branch-and-bound algorithm that differs from the others known by exploring the lattice structure and the U-shaped chain curves of the search space. The main contribution of this paper is the architecture of this algorithm that is based on the representation and exploration of the search space by new lattice properties proven here. Several experiments, with well known public data, indicate the superiority of the proposed method to the sequential floating forward selection (SFFS), which is a popular heuristic that gives good results in very short computational time. In all experiments, the proposed method got better or equal results in similar or even smaller computational time. (C) 2009 Elsevier Ltd. All rights reserved.