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基于水理化参数和沉积物金属含量的秘鲁阿萨纳-图米拉卡盆地水生生物多样性预测模型

The predictive model of hydrobiological diversity in the Asana-Tumilaca basin, Peru based on water physicochemical parameters and sediment metal content.

作者信息

Flores Del Pino Lisveth, Carrasco Apaza Nancy Marisol, Caro Sánchez Benites Víctor, Téllez Monzón Lena Asunción, Visitación Bustamante Kimberly Karime, Arana-Maestre Jerry, Suárez Ramos Diego, Wetzell Canales-Springett Ayling, Dioses Morales Jacqueline Jannet, Jaco Rivera Evilson, Uriarte Ortiz Alex, Jorge-Montalvo Paola, Visitación-Figueroa Lizardo

机构信息

Center for Research in Chemistry, Toxicology, and Environmental Biotechnology, Department of Chemistry, Faculty of Science, Universidad Nacional Agraria La Molina, 15024, Lima, Peru.

Museum of Natural History, Department of Limnology, Universidad Nacional Mayor de San Marcos, 15072, Lima, Peru.

出版信息

Heliyon. 2024 Mar 14;10(6):e27916. doi: 10.1016/j.heliyon.2024.e27916. eCollection 2024 Mar 30.

DOI:10.1016/j.heliyon.2024.e27916
PMID:38524626
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10958436/
Abstract

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.

摘要

该流域的水生生物多样性取决于生物和非生物因素。基于阿萨纳 - 图米拉卡流域八个监测站在旱季和雨季的水体理化参数(PPW)和沉积物金属含量(MCS),通过多元线性回归分析(MLRA)建立了针对周丛生物和大型底栖动物的水生生物多样性预测模型。电导率值在47.9至3617μS/cm之间,由于地热水的影响,卡皮卢内河的电导率值最高。根据派珀图,该流域的水具有硫酸钙和碳酸氢钙 - 硫酸盐的成分。根据威尔科克斯图,除卡皮卢内河外,该流域的水质被判定为良好到非常良好。周丛生物的香农 - 维纳多样性指数(H')在旱季和雨季分别为2.62和2.88,大型底栖动物的为2.10和2.44,表明多样性中等;皮洛均匀度指数(J'),周丛生物在旱季和雨季分别为0.68和0.70,大型底栖动物为0.68和0.59,表明均匀度相似。在模型中有三种情况,前两种情况仅使用PPW或MCS,情况3同时使用PPW和MCS。对于情况3,周丛生物和大型底栖动物的H'和J'预测值与观测值的相关系数,周丛生物分别为0.7437和0.6523,大型底栖动物分别为0.9321和0.8570,均优于情况1和情况2。此外,主成分分析表明,沉积物中的砷、铅和锌含量对大型底栖动物的多样性、均匀性和丰富度有负面影响。相比之下,铜和铬由于适应过程而产生积极影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d3cb/10958436/29c80d9f0a62/gr10.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d3cb/10958436/29c80d9f0a62/gr10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d3cb/10958436/e5bc15e3ba65/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d3cb/10958436/e59e4bd2c296/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d3cb/10958436/ab5ede51eaaa/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d3cb/10958436/0a036681ed11/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d3cb/10958436/f76a41cc6a88/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d3cb/10958436/675a6ec5fac1/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d3cb/10958436/97afe69084bd/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d3cb/10958436/b80e499bc84a/gr8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d3cb/10958436/bb2b29bbe588/gr9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d3cb/10958436/29c80d9f0a62/gr10.jpg

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