Behaving in accordance with natural cycles is essential for survival. Birds in the temperate regions use the changes of day length to time their behavior. However, at equatorial latitudes the photoperiod remains almost constant throughout the year, and it is unclear which cues songbirds use to regulate behaviors, such as singing. Here, we investigated the timing of dawn-song of male silver-beaked tanagers in the equatorial lowland Amazonas over two years. In this region, birds experience around nine minutes of annual day length variation, with sunrise times varying by 32 minutes over the year. We show that the seasonal timing of dawn-song was highly regular between years, and was strongly correlated with slight increases in day length. During the singing season the daily dawn-song onset was precisely aligned to variations in twilight time. Thus, although photoperiodic changes near the equator are minimal, songbirds can use day length variation to time singing.
Resumo Estudos na área da hidrologia mostraram que podemos evitar desastres naturais através de previsões hidrológicas. Nesse trabalho foi utilizada a metodologia de Box-Jenkins de séries temporais multivariadas para previsão diária de nível fluviométrico do rio Tocantins para o município de Marabá-PA, que sofre anualmente com eventos de enchentes, ocasionado pelo aumento periódico do rio Tocantins e pela situação de vulnerabilidade da população que residem em áreas de riscos. Foram utilizados dados de níveis diários observados nas estações fluviométricas de Marabá e Carolina e Conceição do Araguaia da Agência Nacional de Águas (ANA), do período de 01/12/2008 a 31/03/2011. Evidenciou-se que o modelo ajustado conseguiu capturar a dinâmica das séries temporais, com bons prognósticos para o período de sete dias, com erro absoluto máximo de 0,08m, e com precisão na previsão acima de 99,00%. Assim, a pesquisa mostrou que o modelo de previsão teve um bom ajuste apresentando bons resultados, podendo ser utilizado como ferramentas de apoio para Defesa Civil, auxiliando no planejamento e preparo de ações preventivas para o município de Marabá.
Kriging is a geostatistical interpolation technique that performs the prediction of observations in unknown locations through previously collected data. The modelling of the variogram is an essential step of the kriging process because it drives the accuracy of the interpolation model. The conventional method of variogram modelling consists of using specialized knowledge and in-depth study to determine which parameters are suitable for the theoretical variogram. However, this situation is not always possible, and, in this case, it becomes interesting to use an automatic process. Thus, this work aims to propose a new methodology to automate the estimation of theoretical variogram parameters of the kriging process. The proposed methodology is based on preprocessing techniques, data clustering, genetic algorithms, and the K-Nearest Neighbor classifier (KNN). The performance of the methodology was evaluated using two databases, and it was compared to other optimization techniques widely used in the literature. The impacts of the clustering step on the stationary hypothesis were also investigated with and without trend removal techniques. The results showed that, in this automated proposal, the clustering process increases the accuracy of the kriging prediction. However, it generates groups that might not be stationary. Genetic algorithms are easily configurable with the proposed heuristic when setting the variable ranges in comparison to other optimization techniques, and the KNN method is satisfactory in solving some problems caused by the clustering task and allocating unknown points into previously determined clusters.
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