Predicting the Impact of Climate Change on Freshwater Fish Distribution by Incorporating Water Flow Rate and Quality Variables

Abstract

In this study, water flow rate and quality variables that restrict freshwater fish distribution were incorporated in species distribution modeling to evaluate the impacts of climate change. A maximum entropy model (MaxEnt) was used to predict the distribution of 76 fish species in the present (2012-2014) and in the future (2025-2035 and 2045-2055) based on representative concentration pathway (RCP) 4.5 and RCP 8.5 scenarios for five major river basins (Han, Nakdong, Geum, Seomjin, and Yeongsan) in South Korea. The accuracy of MaxEnt performance was improved from 0.905 to 0.933, and from 0.843 to 0.864 in the model training and test, respectively, by introducing flow rate, total nitrogen, total phosphorus (TP), and total suspended solids (TSS). TSS and TP were ranked as the second and fourth contributing parameters, respectively, among the 17 variables considered in this study. There was a greater decline in species richness index under scenario RCP 8.5 than under scenario RCP 4.5, and in 2050 compared with 2030. However, the tolerance guild index (TGI) was predicted to improve in the future. The increase in TGI coupled with the decrease in species richness index (SRI), indicated that climate change is likely to have adverse effects on freshwater fish. Notably, the habitat of Korean spotted barbel (Hemibarbus mylodon), an endemic species of South Korea, is expected to contract largely in 2050 based on the RCP 8.5 scenario. These findings demonstrate that the incorporation of flow rate and water quality parameters into climatic variables can improve the prediction of freshwater fish distribution under climate change.