Distribution, composition and seasonality of aquatic birds in the Nhecolandia sub-region of South Pantanal, Brazil

Despite remarkable significance of Pantanal for the conservation of aquatic birds, the status of their populations, the spatiotemporal patterns of distribution and habitat use and structure of communities are little known. Thus, we studied three aquatic environments (Negro river, bays and salines) from 2007 to 2009 in the Nhecolandia Pantanal to verify the distribution and composition of aquatic birds and also if there is significant seasonal influence on these aspects. We adopted the transect method (288 hours of sampling) and recorded 135 species (7.834 individuals). The Negro river showed the highest diversity, while the salines the lowest. The similarity of aquatic bird communities was higher between bays and salines, followed by Negro river and bays and lower between salines and Negro river. The equidistribution is more variable in the salines and more stable in the Negro river. The environments strongly differ from each other in aquatic bird composition in space (habitat use and distribution) and time (seasonal water fluctuations). The diversity of bird community in the dry season varies significantly in the salines, followed by the bays and more stable in the Negro river. The Negro river, regardless of large annual amplitude of flow, is more seasonally stable since its riparian vegetation is continuous (not isolated) and constant. These aspects provide better conditions to stay all year, contributing to decrease the seasonal nomadic tendencies of aquatic birds. Finally, all these data provide strong arguments to the preservation of all phytophysiognomies in the Nhecolandia sub-region of Pantanal, but with special attention to the salines widely used by many flocks of aquatic birds (mainly in the dry season) and migrant and/or rare species restricted to this habitat.

Population ecology of the planktonic shrimp lucifer faxoni borradaile, 1915 (crustacea, sergestoidea, luciferidae) of the southeastern coast of brazil

Luciferid shrimps have short life spans and a rapid turnover of generations, engage in sequential spawning, and protect their eggs during incubation. This study investigates the ecology of Lucifer faxoni Borradaile, 1915 in the littoral zone, Ubatuba region, São Paulo. Sampling was conducted monthly from July 2005 to December 2006 using a Renfro net trawled over a distance of 50 m for a total sampling effort of 50 m² at each station. Nine stations were sampled, ranging from 1 to 15 m deep. Three stations each were grouped into zones 1, 2 and 3 (Z1, Z2 and Z3). Monthly values of salinity, temperature and rainfall were recorded at each station. The pre-buccal somite length (SL) of each specimen was measured. The results showed that in shallower zones (Z1 and Z2), 6306 individuals were captured, whereas in the deeper zone (Z3), 3808 specimens were captured, but no significant differences in SL was detected between the specimens from Z1 and Z2 and those from Z3 (ANOVA, p=0.25). The abundance of shrimps did not differ significantly between seasons (Tukey’s test, p=0.02) except in the spring. The sex ratio differed significantly over the seasons (χ², p<0.05). The results were closely associated with environmental factors with respect to the spatial and seasonal distribution of L. faxoni. Rainfall affected salinity directly, and contributed to the displacement of these shrimps to deeper areas.

The use of artificial neural networks in analysing the nutritional ecology of Chrysomya megacephala (F.) (Diptera: Calliphoridae), compared with a statistical model

Artificial neural networks (ANNs) have been widely applied to the resolution of complex biological problems. An important feature of neural models is that their implementation is not precluded by the theoretical distribution shape of the data used. Frequently, the performance of ANNs over linear or non-linear regression-based statistical methods is deemed to be significantly superior if suitable sample sizes are provided, especially in multidimensional and non-linear processes. The current work was aimed at utilising three well-known neural network methods in order to evaluate whether these models would be able to provide more accurate outcomes in relation to a conventional regression method in pupal weight predictions of Chrysomya megacephala, a species of blowfly (Diptera: Calliphoridae), using larval density (i.e. the initial number of larvae), amount of available food and pupal size as input data. It was possible to notice that the neural networks yielded more accurate performances in comparison with the statistical model (multiple regression). Assessing the three types of networks utilised (Multi-layer Perceptron, Radial Basis Function and Generalised Regression Neural Network), no considerable differences between these models were detected. The superiority of these neural models over a classical statistical method represents an important fact, because more accurate models may clarify several intricate aspects concerning the nutritional ecology of blowflies.