Diversity and longitudinal distribution of fishes in the Soto La Marina River basin, Mexico, and relationship with environmental variables

2021 ◽  
Author(s):  
Gorgonio Ruiz-Campos ◽  
Ana Verónica Martínez-Vázquez ◽  
Fernando Contreras-Catala ◽  
Rafael Hernández-Guzmán ◽  
Francisco Javier García-De León
Keyword(s):  
River Basin ◽  
Environmental Variables ◽  
Longitudinal Distribution

scholarly journals Environmental Variables of the Seti Gandaki River Basin Pokhara, Nepal

2018 ◽  
Vol 22 (2) ◽  
pp. 129-139 ◽  
Author(s):  
Kishor Kumar Pokharel ◽  
Khadga Bahadur Basnet ◽  
Trilok Chandra Majupuria ◽  
Chitra Bahadur Baniya
Keyword(s):  
Water Temperature ◽  
River Basin ◽  
Environmental Variables ◽  
Temporal Variations ◽  
Total Alkalinity ◽  
Urban Site ◽  
Water Conductivity ◽  
Free Carbon Dioxide ◽  
Spatio Temporal ◽  
Carbon Dioxide Co2

Present paper focuses on the spatio-temporal variations and correlations among the environmental variables of the Seti Gandaki River basin, Pokhara, Nepal. A total of five sites, three along the river and two in tributaries were selected for this study. Water sampling was done fortnightly for environmental variables following standard methods during July 2011 to June 2012. Mean and standard deviation of the environmental variables revealed that the depth (0.9 ± 0.3), pH (8 ± 0.4), total phosphates (PO4) (0.10 ± 0.03) and nitrates (NO3) (0.13 ± 0.04) were normally variable among the sites. But the discharge (40.00 ± 37.00), width (32.30 ± 13.00), turbidity (81.40 ± 51.00), transparency (29.10 ± 15.00), conductivity (166.00 ± 80.00), water temperature (18.00 ±4.00), dissolved oxygen (DO) (8.00 ± 2.00), free carbon dioxide (CO2) (7.00 ± 2.00) and total alkalinity (98.00 ± 22.00) varied among sites equally. Correlation coefficient between the sites and environmental variables revealed that sites were found significantly correlated with water conductivity (r2 = 0.6), DO (r2 = -0.52), and free CO2 (r2 = 0.6); depth of water with width (r2 = 0.94), discharge (r2 = 0.96), turbidity (r2 = 0.71), transparency (r2 = -0.62), water temperature (r2 = 0.60), pH (r2 = -0.52) and DO (r2 = -0.48); water temperature with pH (r2 = -0.54), DO (r2 = -0.79), free CO2 (r2 = 0.69), total alkalinity (r2 = -0.58), total PO4 (r2 = 0.54) and NO3 (r2 = 0.62), etc. The enhancement of turbidity, conductivity, free CO2, phosphates and nitrates, while, suppression of transparency, pH and DO at the urban site indicated the urban influence. Journal of Institute of Science and TechnologyVolume 22, Issue 2, January 2018, page: 129-139

Benthic Invertebrates Monitoring of the Muling River basin in Northeast China

2020 ◽  
Author(s):  
Xu Sun ◽  
Hongxian Yu
Keyword(s):  
River Basin ◽  
Environmental Variables ◽  
Wiener Index ◽  
Sanjiang Plain ◽  
Functional Feeding Groups ◽  
Heilongjiang Province ◽  
Total Abundance ◽  
Feeding Groups ◽  
Biodiversity Index ◽  
Northeast Of China

Abstract Background: Muling River is the fifth-largest river in Heilongjiang Province, and it is also the main feeding river to the Ussuri River which is the boundary river of China and Russia in Heilongjiang Province northeast of China. Muling River basin located in the south of Sanjiang Plain. Macroinvertebrate samples were collected using a D-frame net and Shannon-Wiener index were calculated in terms of abundance. Results: A total of 158 genera or species macroinvertebrate were collected from the 28 sampling sites and classified into six functional feeding groups including 61 gatherers/collectors, 42 predators, 22 scrapers, 14 shredders, 11 filterers/collectors and 8 omnivores. The correlation and relationship between environmental variables and macroinvertebrate functional feeding groups was explored using Pearson analysis and redundancy analysis. The analysis results displayed that macroinvertebrate functional feeding groups had strong relationships with the environmental variables in the Muling River basin.Conclusions: All FFGs, total abundance and Shannon-Wiener index were not significantly different. Total abundance of macroinvertebrates was higher in summer and biodiversity index was higher in autumn. Environmental factors of natural gradients and nutrition indicator were not significantly different, while others were significantly different.

Factors affecting distribution pattern of dominant macroinvertebrates in Mantovo Reservoir (Republic of Macedonia)

Biologia ◽  
2012 ◽  
Vol 67 (6) ◽  
Author(s):  
Valentina Slavevska-Stamenković ◽  
Momir Paunović ◽  
Stoe Smiljkov ◽  
Trajče Stafilov ◽  
Dana Prelić ◽  
...  
Keyword(s):  
Dissolved Oxygen ◽  
Distribution Pattern ◽  
Environmental Variables ◽  
Longitudinal Distribution ◽  
Factors Affecting ◽  
Republic Of Macedonia ◽  
Spatial And Temporal Heterogeneity ◽  
Oxygen Condition ◽  
The Relationship ◽  
Macroinvertebrate Species

AbstractIn the present study, we analysed spatial and temporal heterogeneity of the limnological characteristics to provide more detailed information about the processes taking place within Mantovo Reservoir (Republic of Macedonia). The relationship between principal macroinvertebrate species and environmental variables was analysed in order to explore factors that dominantly affect community distribution pattern. Unlike the most reservoirs, strong longitudinal gradient for suspended organic matter and nutrients (total phosphorous, nitrates and nitrites) along the reservoir doesn’t exist. However, the process of thermal stratification has a strong influence on the metabolism and structure of the Mantovo ecosystem, which can be demonstrated by the vertical and longitudinal distribution of dissolved oxygen (DO), CO2, pH and metals concentrations. Canonical Correspondence Analysis (CCA) indicated that the main factors controlling spatial distribution of Limnodrilus hoffmeisteri and Chironomus plumosus group were temperature, dissolved oxygen and manganese, including sulphates for C. plumous group. Chaoborus crystallinus showed opposite distribution pattern. Cladotanytarsus mancus group was strongly associated with shallower part (littoral and sublittoral) of Mantovo Reservoir characterized by favourable oxygen condition. None of the environmental variables included in CCA showed any relationship with density of Procladius sp.

Impoundment, environmental variables and temporal scale predict zooplankton beta diversity patterns in an Amazonian river basin

2021 ◽  
Vol 776 ◽  
pp. 145948
Author(s):  
Carla Albuquerque de Souza ◽  
Beatrix E. Beisner ◽  
Luiz Felipe Machado Velho ◽  
Priscilla de Carvalho ◽  
Alfonso Pineda ◽  
...  
Keyword(s):  
River Basin ◽  
Beta Diversity ◽  
Environmental Variables ◽  
Temporal Scale ◽  
Diversity Patterns

scholarly journals Characterizing Groundwater Potential Using GIS-Based Machine Learning Model in Chihe River Basin, China

2021 ◽  
Author(s):  
Dejian Wang ◽  
Jiazhong Qian ◽  
Lei Ma ◽  
Weidong Zhao ◽  
Di Gao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Resources ◽  
River Basin ◽  
Environmental Variables ◽  
Groundwater Potential ◽  
High Potential ◽  
Slope Aspect ◽  
Learning Models ◽  
Topographic Wetness Index ◽  
Machine Learning Models

Abstract Mapping of groundwater potential over space, built by synergizing environmental variables and machine learning models, was of great significance for regional water resources management. Taking the Chihe River basin in Anhui province as an example, thirteen influence factors were used to predict the spatial distribution of groundwater, including elevation, slope, aspect, plan curvature, profile curvature, topographic wetness index (TWI), drainage density, distance to rivers, distance to faults, lithology, soil type, land use, and normalized difference vegetation index (NDVI). The potential of groundwater resource in this region was predicted using GIS-based machine learning models, including logistic regression (LR), deep neural networks (DNN), and random forest (RF) model. Then, the accuracy of prediction results was evaluated by calculating the RMSE, MAE and R evaluation index. The results show that there is no collinearity among the 13 environmental impact factors, which can provide corresponding environmental variables for the evaluation of regional groundwater potential. Machine learning models show that groundwater potential is concentrated in moderate to high potential areas. Among them, the moderate to the high potential of this area accounted for 81.14% in the LR model, 90.36% and 87.55% in the DNN model and the RF model, respectively. According to the result of these evaluation indexes, the three models all have high prediction accuracy, among which the LR model performs more prominently. The good prediction capabilities of these machine learning technologies can provide a reliable scientific basis for spatial prediction of groundwater potential and management of water resources.

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