Search Results (3)

The aim of this study was to observe how abiotic and biotic factors in a tropical region influence the rate of monogenean parasitism in Nile tilapia (Oreochromis niloticus) that are farmed in net cages. A total of 240 sexually reversed fish were analyzed, and 20 from each culture stage were collected during each sampling month. Overall, 60 fish were sampled in April (autumn), 60 in August (winter), 60 in November (spring), and 60 in February (summer). Fish were collected from a commercial fish farm located in Capivara Reservoir in the lower Paranapanema River region of Paraná, Brazil. In total, 3290 monogenean parasites were collected from fish gills of the following species: Cichlidogyrushalli, C. thurstonae, Scutogyruslongicornis, C. cirratus, C. sclerosus, and C. tilapiae. Higher parasitological indices were observed in colder seasons with lower precipitation. Autumn had the highest parasitic infection values compared to the other seasons. The occurrence of monogenean parasites showed a negative correlation with season, in contrast to the culture stage, in which there was a positive correlation. These results may provide a means for establishing adequate fish farm management to predict periods of high monogenean infestation. Full article

Riparian zones consist of important environmental regions, specifically to maintain the quality of water resources. Accurately mapping forest vegetation in riparian zones is an important issue, since it may provide information about numerous surface processes that occur in these areas. Recently, machine learning algorithms have gained attention as an innovative approach to extract information from remote sensing imagery, including to support the mapping task of vegetation areas. Nonetheless, studies related to machine learning application for forest vegetation mapping in the riparian zones exclusively is still limited. Therefore, this paper presents a framework for forest vegetation mapping in riparian zones based on machine learning models using orbital multispectral images. A total of 14 Sentinel-2 images registered throughout the year, covering a large riparian zone of a portion of a wide river in the Pontal do Paranapanema region, São Paulo state, Brazil, was adopted as the dataset. This area is mainly composed of the Atlantic Biome vegetation, and it is near to the last primary fragment of its biome, being an important region from the environmental planning point of view. We compared the performance of multiple machine learning algorithms like decision tree (DT), random forest (RF), support vector machine (SVM), and normal Bayes (NB). We evaluated different dates and locations with all models. Our results demonstrated that the DT learner has, overall, the highest accuracy in this task. The DT algorithm also showed high accuracy when applied on different dates and in the riparian zone of another river. We conclude that the proposed approach is appropriated to accurately map forest vegetation in riparian zones, including temporal context. Full article

In this study, we used moderate resolution imaging spectroradiometer (MODIS) satellite images to quantify the sedimentation processes in a cascade of six hydropower dams along a 700-km transect in the Paranapanema River in Brazil. Turbidity field measurement acquired over 10 years were used to calibrate a turbidity retrieval algorithm based on MODIS surface reflectance products. An independent field dataset was used to validate the remote sensing estimates showing fine accuracy (RMSE of 9.5 NTU, r = 0.75, N = 138). By processing 13 years of MODIS images since 2000, we showed that satellite data can provide robust turbidity monitoring over the entire transect and can identify extreme sediment discharge events occurring on daily to annual scales. We retrieved the decrease in the water turbidity as a function of distance within each reservoir that is related to sedimentation processes. The remote sensing-retrieved turbidity decrease within the reservoirs ranged from 2 to 62% making possible to infer the reservoir type and operation (storage versus run-of-river reservoirs). The reduction in turbidity assessed from space presented a good relationship with conventional sediment trapping efficiency calculations, demonstrating the potential use of this technology for monitoring the intensity of sedimentation processes within reservoirs and at large scale. Full article