Relating Environmental Factors to the Similarity of Fish Communities Using the Artificial Neural Networks Approach
Date Issued
2016
Date
2016
Author(s)
Yu, Tzu-Chun
Abstract
Being located in the subtropical monsoon zone along with the topographically steep gradients, Taiwan suffers from extremely unevenly distributed rainfall in both space and time. This condition makes the use and management of water resources particularly difficult. The fast economic growth and urbanization has led to dramatically increased water demands in recent years. Large-scale reservoirs were therefore constructed to satisfy water needs of agricultural, industrial and public sectors, and effective reservoir operations were managed to maximize the use of limited water resources. However, the natural physical environments have been altered by reservoirs and hydraulic facilities. Additionally, the lack of wastewater monitoring programs has deteriorated water quality in rivers. Being concerned with ecological impacts made by the changes of physical and chemical conditions in rivers, this study aims to explore the complicated relationships of river flow, water quality and fish community, examine the impacts of extreme events on riverine fishes, and investigate the influence of hydraulic facilities on eco-hydrological environments for making an environmental- and ecological-friendly water resources management. In this study, the Shindien River was chosen as the investigative area because long-term (2005-2012) records of river flow, water quality and fish distribution in the river were available. The construction and operational histories of reservoirs and the records of extreme events (typhoons and droughts) were also available. Daily river flows were converted into a flow regime based on Taiwan Ecohydrologic Indicator System (TEIS). Then statistical analyses were conducted to explore the impacts of extreme events on fish species composition. Following that, Gamma Test (GT) was used to determine key factors affecting fish species similarity with respect to water quality parameters and flow regime variables, and the Self-Organizing Map (SOM) was applied to exploring the non-linear relationships among key factors and fish species similarity. Finally, two predicting models were constructed to estimate the fish species similarity by using the Back Propagation Neural Network (BPNN) and the Adapted Network-Based Fuzzy Inference System (ANFIS). The results of statistical analyses indicated that extreme events did not show statistically detectible changes in the surveyed fish species composition, which might be because the time interval of two consecutive surveys was too long. Such phenomena therefore could not clarify the impacts of extreme events on fish species composition. The results of the GT indicated that differences in suspended sediments, electrical conductivity, 10-day maximum flow and 10-day minimum flow of the surveyed month between two sampling sites were obvious and therefore these four variables were identified as key factors affecting fish species similarity. The clustering results of the SOM indicated that two sampling sites with similar water quality showed high fish species similarity, so as two sampling sites with similar flow regimes. Nevertheless, the flow regimes of two sampling sites with low fish species similarity did not make significant difference. The ANFIS model provided advantages of using membership functions to display the characteristics of input data, and when linking with the SOM, it was flexible in developing knowledge-based rules to rationally suggest the effects of environmental factors on fish species similarity. The BPNN model showed better prediction accuracy than the ANFIS. In sum, this study systematically explored the complex influences of flow regime, water quality and reservoirs on fish species composition and well constructed fish similarity prediction models, which can provide useful information for effective and sustainable river ecosystem management.
Subjects
Eco-hydrology
Fish species composition
Fish Species Similarity
Gamma Test (GT)
Artificial Neural Network (ANN)
Type
thesis
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