Diversidade Ictiofaunística e Ecologia reprodutiva de uma espécie nativa de peixe da Bacia Piranhas-Assu, RN

The Caatinga biome is rich in endemic fish species fauna. The present study the results of fish faunal surveys conducted in the hydrographic basin of Piranhas-Assu of the Brazilian Caatinga biome. The fish samples collected were distributed in four orders (Characiformes, Perciformes, Siluriformes and Synbranchiformes), 11 families (Characidae, Curimatidae, Auchenipteridae, Anostomidae, Prochilodontidae, Erythrinidae, Cichlidae, Sciaenidae, Heptapteridae, Loricariidae, Synbranchidae) and 22 species, of which 17 are endemic and five have been introduced from other basins. The order Characiformes was the most representative in number of species (46,35% ) followed by Perciformes (35,38%), Siluriformes (17,44%) and Synbranchiformes (0,5%). The Nile tilapia, Oreochomis niloticus, the only exotic species, was most expressive in number of individuals (24.92%) followed by the native species piau preto, Leporinus piau (18,77 %). Considering the relative frequency of occurrence of the 22 species, 13 were constant, five were accessory and four were occasional. This study investigated the reproductive ecology of an endemic fish black piau, Leporinus piau from the Marechal Dutra reservoir, Acari, Rio Grande do Norte. Samplings were done on a monthly basis from January to December 2009, and a total of 211 specimens were captured. The environmental parameters such as rainfall, temperature, pH, electrical conductivity and dissolved oxygen of water were recorded. The sampled population showed a slight predominance of males (55%), however females were larger and heavier. Both sexes of L. piau showed positive allometric growth, indicating a higher increase of weight than length. The first sexual maturation of males occurred at smaller size, with 16.5 cm in total length than females (20.5 cm). During the reproductive period, the condition factor and gonadosomatic index (GSI) of L. piau were negatively correlated. This species has large oocytes with a high mean fecundity of 54.966 with synchronous oocyte development and total spawning

Ecologia reprodutiva do peixe-donzela, Stegastes fuscus cuvier, 1830 (osteichthyes: pomacentridae) em arrecifes rochosos da praia de Búzios, Rio Grande do Norte, Brasil

The reproductive ecology of fish plays a key role both for rational exploitation methods and for protective measures of natural stocks. The purpose of this study was to analyze the reproductive aspects of the damsel-fish, Stegastes fuscus, during October 2004 to September 2005, in the coastal rocky reefs of Búzios Beach, Nísia Floresta, RN. Fish were captured using hooks and hand nets, during low tide. Reproduction was determined using sexual ratio, mean length of first maturation (L50), absolute fecundity and macroscopic characteristics of gonads. The following parameters were related to gonadosomatic index (GSI): condition factor (CF), hepatosomatic index (HSI), rain fall and temperature. In relation to sex distribution, it was observed that 78% were females and 22% were males. The L50 was 6.2 cm for females and 7.0 for males. Average fecundity was 6832 oocytes. Results showed that S. fuscus had better body condition in the months prior to spawning, particularly during initial and intermediate stages of maturation. Five stages of gonadal maturation were identified through macroscopic analysis: immature, in maturation, mature, spent and resting. The HSI was inversely related to the GSI. This was possibly due to the reproductive cycle of this species which was associated to the dry period of this region. During this period, low rain fall and high temperatures provide an propitious reproductive condition for the study species

Previsão do índice bovespa por meio de redes neurais artificiais: uma análise comparada aos métodos tradicionais de séries de tempo

Forecast is the basis for making strategic, tactical and operational business decisions. In financial economics, several techniques have been used to predict the behavior of assets over the past decades.Thus, there are several methods to assist in the task of time series forecasting, however, conventional modeling techniques such as statistical models and those based on theoretical mathematical models have produced unsatisfactory predictions, increasing the number of studies in more advanced methods of prediction. Among these, the Artificial Neural Networks (ANN) are a relatively new and promising method for predicting business that shows a technique that has caused much interest in the financial environment and has been used successfully in a wide variety of financial modeling systems applications, in many cases proving its superiority over the statistical models ARIMA-GARCH. In this context, this study aimed to examine whether the ANNs are a more appropriate method for predicting the behavior of Indices in Capital Markets than the traditional methods of time series analysis. For this purpose we developed an quantitative study, from financial economic indices, and developed two models of RNA-type feedfoward supervised learning, whose structures consisted of 20 data in the input layer, 90 neurons in one hidden layer and one given as the output layer (Ibovespa). These models used backpropagation, an input activation function based on the tangent sigmoid and a linear output function. Since the aim of analyzing the adherence of the Method of Artificial Neural Networks to carry out predictions of the Ibovespa, we chose to perform this analysis by comparing results between this and Time Series Predictive Model GARCH, developing a GARCH model (1.1).Once applied both methods (ANN and GARCH) we conducted the results' analysis by comparing the results of the forecast with the historical data and by studying the forecast errors by the MSE, RMSE, MAE, Standard Deviation, the Theil's U and forecasting encompassing tests. It was found that the models developed by means of ANNs had lower MSE, RMSE and MAE than the GARCH (1,1) model and Theil U test indicated that the three models have smaller errors than those of a naïve forecast. Although the ANN based on returns have lower precision indicator values than those of ANN based on prices, the forecast encompassing test rejected the hypothesis that this model is better than that, indicating that the ANN models have a similar level of accuracy . It was concluded that for the data series studied the ANN models show a more appropriate Ibovespa forecasting than the traditional models of time series, represented by the GARCH model