Change of fish composition in the Nong Leng Sai Wetland, Thailand, after newly completed dredging

Abstract. Panprommin D, Sornphui S, Tipdacho P, Chaisan W, Tuncharoen S. 2025. Change of fish composition in the Nong Leng Sai Wetland, Thailand, after newly completed dredging. Biodiversitas 26: 2611-2621. The objective of this study was to explore changes in fish composition and fish species diversity in the Nong Leng Sai Wetland, Mae Chai District, Phayao Province, Northern Thailand, following the completion of dredging in 2023. Fish specimens were collected both in the areas of the Nong Leng Sai non-hunting area and in the Nong Leng Sai common properties area from February to December 2023. The results revealed the presence of 34 species, 31 genera, and 16 families, all belonging to 7 orders. Thirty-one native and three introduced species were found. A total of 561 individual fish were found with a total weight of 40,403.3 grams. The carp group was found in the highest numbers and with the heaviest weight: 325 individual fish, accounting for 57.9%, and a total weight of 29,462.7 grams, accounting for 72.9%, respectively. Fish were found in the miscellaneous group, where 177 fish were identified, accounting for 31.6% of the total, with a total weight of 5,876.4 grams, which represented 14.5% of the total weight. Smith's barb (Puntioplites proctozystron) was the dominant species in terms of numbers. In terms of threatened status, there were 28 species of Least Concern, 4 species of Data Deficient (Systomus rubripinnis, Clarias macrocephalus, Gobiopterus lacustris and Dermogenys pusilla), one species in Vulnerable (Wallago attu) and one species of Endangered (Pangasianodon hypophthalmus). The fish composition and diversity changed from what had previously been reported and were markedly different from those prior to dredging operations. It is necessary to continue monitoring fish diversity and composition after the dredging is complete, to gather information about changes in fish resources over time, leading to informed resource management based on accurate academic data.