Sobrevivência e tamanho populacional do Tiê-preto Tachyphonus Rufus (aves: thraupidae) em fragmento de restinga no extremo Norte da Mata Atlântica

For a long time, we believed in the pattern that tropical and south hemisphere species have high survival. Nowadays results began to contradict this pattern, indicating the need for further studies. Despite the advanced state of the study of bird population parameters, little is known about their variation throughout the year and the factors affecting them. Reproduction, for example, is one factor that may alter adult survival rates, because during this process the breeding pair allocates resources to maintain itself to maintain offspring, making itself more susceptible to diseases and predation. The aim of this study was to estimate survival and population size of a Central and South America passerine, Tachyphonus rufus (Boddaert, 1783), testing hypotheses about the factors that define these parameters. We performed data collection between Nov/2010 and ago/2012 in 12 ha plot, in a fragment of Atlantic Forest in northeastern Brazil. We used capture-mark-recapture methods to generate estimates using Closed Design Robust model in the program MARK. We generated Multi-state models to test some assumptions inherent to Closed Robust Design. The influence of co-variables (time, rain and reproductive cycle) and the effect of transient individuals were measured. Capture, recapture and apparent survival parameters were defined by reproductive cycle, while temporary dispersal was influence by rain. The estimates showed a higher apparent survival during the non-breeding period (92% ± 1%) than during breeding (40% ± 9%), revealing a cost of reproduction and suggesting a trade-off between surviving and reproducing. The low annual survival observed (34%) did not corroborate the pattern of high rates expected for a tropical bird. The largest population size was estimated to be 56 individuals in Nov/11, explained by high recruitment of juveniles, while the lowest observed in May/12: 10 individuals, probably as a result of massive influx of competitor species. Results from this study add to the growing literature on life history of Neotropical species. We encourage studies like this especially in Brazil, where there are few information, and suggest that covariates related to habitat quality and environmental changes should be tested, so that we can generate increasingly reliable models

Estratégia de navegação com planejamento dinâmico e algoritmo genético aplicada a robôs móveis terrestres

This work proposes a new autonomous navigation strategy assisted by genetic algorithm with dynamic planning for terrestrial mobile robots, called DPNA-GA (Dynamic Planning Navigation Algorithm optimized with Genetic Algorithm). The strategy was applied in environments - both static and dynamic - in which the location and shape of the obstacles is not known in advance. In each shift event, a control algorithm minimizes the distance between the robot and the object and maximizes the distance from the obstacles, rescheduling the route. Using a spatial location sensor and a set of distance sensors, the proposed navigation strategy is able to dynamically plan optimal collision-free paths. Simulations performed in different environments demonstrated that the technique provides a high degree of flexibility and robustness. For this, there were applied several variations of genetic parameters such as: crossing rate, population size, among others. Finally, the simulation results successfully demonstrate the effectiveness and robustness of DPNA-GA technique, validating it for real applications in terrestrial mobile robots.

Estratégia de navegação com planejamento dinâmico e algoritmo genético aplicada a robôs móveis terrestres

This work proposes a new autonomous navigation strategy assisted by genetic algorithm with dynamic planning for terrestrial mobile robots, called DPNA-GA (Dynamic Planning Navigation Algorithm optimized with Genetic Algorithm). The strategy was applied in environments - both static and dynamic - in which the location and shape of the obstacles is not known in advance. In each shift event, a control algorithm minimizes the distance between the robot and the object and maximizes the distance from the obstacles, rescheduling the route. Using a spatial location sensor and a set of distance sensors, the proposed navigation strategy is able to dynamically plan optimal collision-free paths. Simulations performed in different environments demonstrated that the technique provides a high degree of flexibility and robustness. For this, there were applied several variations of genetic parameters such as: crossing rate, population size, among others. Finally, the simulation results successfully demonstrate the effectiveness and robustness of DPNA-GA technique, validating it for real applications in terrestrial mobile robots.