Habitat, limnological signatures and spatial modeling: a zoning proposal for the Curuá-Una hydroelectric reservoir, Pará, Brazil

• Autor(a) principal: Santos,Juciley de Almeida
• Data de Publicação: 2019
• Outros Autores: Sousa,Keid Nolan Silva, Santos,Paulo Roberto Brasil, Lima,Joelson Leal de, Bacelar,Rivolo de Jesus
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Acta Limnologica Brasiliensia (Online)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-975X2019000100319
• topic: Amazon; geostatistical; anthropic action; limnology; landscape

Abstract Aim The objective of this work is to characterize, spatially model and to perform the zoning of the aquatic environment in the Curuá-Una HPP reservoir, in the state of Pará, in the Brazilian Amazon. Methods The data were collected from 77 sampling points distributed over 20 transects in the Curuá-Una reservoir, in November 2016. The data were obtained through descriptive templates of the landscape, and assessment of limnological, bathymetry and georeferencing variables. To describe and model spatial patterns for the limnological Proxies, geostatistical analysis was used with semivariogram fitting, and interpolation using Ordinary Kriging to generate the maps. To determine the degree of association of the landscape Proxies, Correspondence Analysis (CA) was chosen, and to relate the landscape Proxies with the limnological Proxies, Canonical Correspondence Analysis (CCA) was carried out. Results The results of the analysis of the limnological Proxies showed that the variables presented normal distribution according to the Shapiro-Wilk test (5%) except for transparency and temperature. Most of the variables obtained well-defined, level and good geostatistical analysis. There was a prevalence of gaussian and spherical adjustment models. Different zones in the distribution of the limnological variables in the longitudinal axis of the reservoir were observed. The CA showed a short local gradient in the variables, which effectively characterizes the interface of landscape and human. In Figure 5, the first two axes of the CCA showed 61.17% of the data variability. The limnological signatures showed 42.3% of variability, with high correlation between the landscape Proxies and the environmental Proxies in both axes. Conclusions This type of approach should be useful in managing Brazilian river basins, especially in the Amazon, a focus for the construction of numerous hydroelectric dams, as it can indicate the limnological and environmental state and provide a clearer view of these environments.