The predictive model of hydrobiological diversity in the Asana-Tumilaca basin, Peru based on water physicochemical parameters and sediment metal content
Lisveth Flores del Pino,
Nancy Marisol Carrasco Apaza,
Víctor Caro Sánchez Benites,
Lena Asunción Téllez Monzón,
Kimberly Karime Visitación Bustamante,
Jerry Arana-Maestre,
Diego Suárez Ramos,
Ayling Wetzell Canales-Springett,
Jacqueline Jannet Dioses Morales,
Evilson Jaco Rivera,
Alex Uriarte Ortiz,
Paola Jorge-Montalvo,
Lizardo Visitación-Figueroa
Affiliations
Lisveth Flores del Pino
Center for Research in Chemistry, Toxicology, and Environmental Biotechnology, Department of Chemistry, Faculty of Science, Universidad Nacional Agraria La Molina, 15024, Lima, Peru
Nancy Marisol Carrasco Apaza
Center for Research in Chemistry, Toxicology, and Environmental Biotechnology, Department of Chemistry, Faculty of Science, Universidad Nacional Agraria La Molina, 15024, Lima, Peru
Víctor Caro Sánchez Benites
Center for Research in Chemistry, Toxicology, and Environmental Biotechnology, Department of Chemistry, Faculty of Science, Universidad Nacional Agraria La Molina, 15024, Lima, Peru
Lena Asunción Téllez Monzón
Center for Research in Chemistry, Toxicology, and Environmental Biotechnology, Department of Chemistry, Faculty of Science, Universidad Nacional Agraria La Molina, 15024, Lima, Peru
Kimberly Karime Visitación Bustamante
Center for Research in Chemistry, Toxicology, and Environmental Biotechnology, Department of Chemistry, Faculty of Science, Universidad Nacional Agraria La Molina, 15024, Lima, Peru
Jerry Arana-Maestre
Museum of Natural History, Department of Limnology, Universidad Nacional Mayor de San Marcos, 15072, Lima, Peru
Diego Suárez Ramos
Center for Research in Chemistry, Toxicology, and Environmental Biotechnology, Department of Chemistry, Faculty of Science, Universidad Nacional Agraria La Molina, 15024, Lima, Peru
Ayling Wetzell Canales-Springett
Center for Research in Chemistry, Toxicology, and Environmental Biotechnology, Department of Chemistry, Faculty of Science, Universidad Nacional Agraria La Molina, 15024, Lima, Peru
Jacqueline Jannet Dioses Morales
Center for Research in Chemistry, Toxicology, and Environmental Biotechnology, Department of Chemistry, Faculty of Science, Universidad Nacional Agraria La Molina, 15024, Lima, Peru
Evilson Jaco Rivera
Universidad Politécnica de Cataluña, 08034, Barcelona, Spain
Alex Uriarte Ortiz
Organismo de Evaluación y Fiscalización Ambiental (OEFA), Ministerio Del Ambiente, 15076, Lima, Peru
Paola Jorge-Montalvo
Center for Research in Chemistry, Toxicology, and Environmental Biotechnology, Department of Chemistry, Faculty of Science, Universidad Nacional Agraria La Molina, 15024, Lima, Peru; Corresponding author.
Lizardo Visitación-Figueroa
Center for Research in Chemistry, Toxicology, and Environmental Biotechnology, Department of Chemistry, Faculty of Science, Universidad Nacional Agraria La Molina, 15024, Lima, Peru
The hydrobiological diversity in the basin depends on biotic and abiotic factors. A predictive model of hydrobiological diversity for periphyton and macrobenthos was developed through multiple linear regression analysis (MLRA) based on the physicochemical parameters of water (PPW) and metal content in sediments (MCS) from eight monitoring stations in the Asana-Tumilaca Basin during the dry and wet seasons. The electrical conductivity presented values between 47.9 and 3617 μS/cm, showing the highest value in the Capillune River due to the influence of geothermal waters. According to Piper's diagram, the water in the basin had a composition of calcium sulfate and calcium bicarbonate-sulfate. According to the Wilcox diagram, the water was found to be between good and very good quality, except for in the Capillune River. The Shannon–Wiener diversity indices (H′) were 2.62 and 2.88 for periphyton, and 2.10 and 2.44 for macrobenthos, indicating moderate diversity; for the Pielou's evenness index (J′), they were 0.68 and 0.70 for periphyton, and 0.68 and 0.59 for macrobenthos, indicating similar equity, in the dry and wet seasons, respectively, for both indices. In the model there were three cases, where the first two cases only worked with PPW or MCS, and case 3 worked with PPW and MCS. For case 3, the predicted values for H′ and J′ of periphyton and macrobenthos concerning those observed presented correlation coefficients of 0.7437 and 0.6523 for periphyton and 0.9321 and 0.8570 for macrobenthos, respectively, which were better than those of cases 1 and 2. In addition, principal component analysis revealed that the As, Pb, and Zn contents in the sediments negatively influenced the diversity, uniformity, and richness of the macrobenthos. In contrast, Cu and Cr had positive impacts because of the adaptation processes.
