Análise da estrutura genética populacional da piraúna (cephalopholis fulva: serranidae) ao longo da costa e ilhas oceânicas brasileiras

Brazil has about 8,500 km of coastline and on this scale, fishing is a historically important source of animal protein for human consumption. The national fishing background shows a growth of marine fishery production until 1985 and within this period it was recorded a steady decline. From the year 2003 fishing statistics aim to some "recovery" of the total fisheries production, which probably is related to a change in industry practice. The target of commercial fishing became smaller species with low commercial value, but very abundants. The coney, Cephalopholis fulva (Serranidae), is one of these species that have been suffering a greater fishing pressure in recent years. In order to provide data about the current situation of the genetic diversity of these populations, several molecular markers have been being used for this purpose. The prior knowledge of genetic variability is crucial for management and biodiversity conservation. To this end, the control region sequences (dloop) of mtDNA from Cephalopholis fulva (Serranidae) from five geographical points of the coast of Brazil (Ceará, Rio Grande do Norte, Bahia and Espírito Santo) and the Archipelago of Fernando de Noronha (FN) were sequenced and their genetic diversity analyzed. The FST values were very low (0.0246 to 0.000), indicating high gene flow between the sampled spots. The indices h and indicate a secondary contact between previously allopatric lineages differentiated or large and stable populations with long evolutionary history. Tests of Tajima and Fu showed expansion for all populations. In contrast, the mismatch distribution and SSD indicated expansion just for coastal populations. Unlike other species of the Atlantic which have been deeply affected by events on later Pleistocene, the population-genetic patterns of C. fulva may be related to recent events occurred approximately 130,000 years ago. Moreover, the data presented by geographical samples of the specie C. fulva showed high genetic diversity, also indicating the absence of deleterious effects of over-exploitation on this specie, as well as evidence of complete panmixia between all sampled populations

Aplicação do Sensoriamento Remoto e do Sistema de Informações Geográficas na Detecção de Manchas de Óleo na Região do Pólo de Exploração de Guamaré, R.N.

Objective to establish a methodology for the oil spill monitoring on the sea surface, located at the Submerged Exploration Area of the Polo Region of Guamaré, in the State of Rio Grande do Norte, using orbital images of Synthetic Aperture Radar (SAR integrated with meteoceanographycs products. This methodology was applied in the following stages: (1) the creation of a base map of the Exploration Area; (2) the processing of NOAA/AVHRR and ERS-2 images for generation of meteoceanographycs products; (3) the processing of RADARSAT-1 images for monitoring of oil spills; (4) the integration of RADARSAT-1 images with NOAA/AVHRR and ERS-2 image products; and (5) the structuring of a data base. The Integration of RADARSAT-1 image of the Potiguar Basin of day 21.05.99 with the base map of the Exploration Area of the Polo Region of Guamaré for the identification of the probable sources of the oil spots, was used successfully in the detention of the probable spot of oil detected next to the exit to the submarine emissary in the Exploration Area of the Polo Region of Guamaré. To support the integration of RADARSAT-1 images with NOAA/AVHRR and ERS-2 image products, a methodology was developed for the classification of oil spills identified by RADARSAT-1 images. For this, the following algorithms of classification not supervised were tested: K-means, Fuzzy k-means and Isodata. These algorithms are part of the PCI Geomatics software, which was used for the filtering of RADARSAT-1 images. For validation of the results, the oil spills submitted to the unsupervised classification were compared to the results of the Semivariogram Textural Classifier (STC). The mentioned classifier was developed especially for oil spill classification purposes and requires PCI software for the whole processing of RADARSAT-1 images. After all, the results of the classifications were analyzed through Visual Analysis; Calculation of Proportionality of Largeness and Analysis Statistics. Amongst the three algorithms of classifications tested, it was noted that there were no significant alterations in relation to the spills classified with the STC, in all of the analyses taken into consideration. Therefore, considering all the procedures, it has been shown that the described methodology can be successfully applied using the unsupervised classifiers tested, resulting in a decrease of time in the identification and classification processing of oil spills, if compared with the utilization of the STC classifier