Do individuals in better condition survive for longer? Field survival estimates according to male alternative reproductive tactics and sex

There is a gap in terms of the supposed survival differences recorded in the field according to individual condition. This is partly due to our inability to assess survival in the wild. Here we applied modern statistical techniques to field-gathered data in two damselfly species whose males practice alternative reproductive tactics (ARTs) and whose indicators of condition in both sexes are known. In Paraphlebia zoe, there are two ART: a larger black-winged (BW) male which defends mating territories and a smaller hyaline-winged (HW) male that usually acts as a satellite. In this species, condition in both morphs is correlated with body size. In Calopteryx haemorrhoidalis, males follow tactics according to their condition with males in better condition practicing a territorial ART. In addition, in this species, condition correlates positively with wing pigmentation in both sexes. Our prediction for both species was that males practicing the territorial tactic will survive less longer than males using a nonterritorial tactic, and larger or more pigmented animals will survive for longer. In P. zoe, BW males survived less than females but did not differ from HW males, and not necessarily larger individuals survived for longer. In fact, size affected survival but only when group identity was analysed, showing a positive relationship in females and a slightly negative relationship in both male morphs. For C. haemorrhoidalis, survival was larger for more pigmented males and females, but size was not a good survival predictor. Our results partially confirm assumptions based on the maintenance of ARTs. Our results also indicate that female pigmentation, correlates with a fitness component - survival - as proposed by recent sexual selection ideas applied to females.

Image indexing and retrieval using an ART-2A neural network architecture

Traditional content-based image retrieval (CBIR) systems use low-level features such as colors, shapes, and textures of images. Although, users make queries based on semantics, which are not easily related to such low-level characteristics. Recent works on CBIR confirm that researchers have been trying to map visual low-level characteristics and high-level semantics. The relation between low-level characteristics and image textual information has motivated this article which proposes a model for automatic classification and categorization of words associated to images. This proposal considers a self-organizing neural network architecture, which classifies textual information without previous learning. Experimental results compare the performance results of the text-based approach to an image retrieval system based on low-level features. (c) 2008 Wiley Periodicals, Inc.