Characterization of Fish Assemblages and Standard Length Distributions among Different Sampling Gears Using an Artificial Neural Network

Abstract

Several sampling gears are used to collect fish in the lentic ecosystem. The collected fish differ in their characteristics and community structure depending on the sampling gear. The objectives of this study were to 1) compare the community structure of fish assemblages sampled using four sampling gears (kick net, cast net, gill net, and fyke net) in the Singal (SG), Yedang (YD), and Juam (JA) reservoirs, and 2) to understand the characteristics of fishes collected by each sampling gear. A total of 1887 individuals of 14 species, 9113 individuals of 15 species, and 9294 individuals of 27 species were collected, respectively, from the SG, YD, and JA reservoirs. Among the four sampling gears tested, the fyke net collected the largest numbers of species and individuals, while the gill net collections had the highest diversity index. The results obtained with the self-organizing map (SOM) provided a more detailed characterization of the sampled fish than the metrics that are typically used to evaluate sampling gears. In particular, SOM analysis showed a similar pattern of the standard length of fish and sampling gear. Since each sampling gear has unique characteristics, the selection of an appropriate sampling gear should be based on the study objectives and features of the sampling sites.