Predicting assemblages and species richness of endemic fish in the upper Yangtze River.
The present work describes the ability of two modeling methods, Classification and Regression Tree (CART) and Random Forest (RF), to predict endemic fish assemblages and species richness in the upper Yangtze River, and then to identify the determinant environmental factors contributing to the models. The models included 24 predictor variables and 2 response variables (fish assemblage and species richness) for a total of 46 site units. The predictive quality of the modeling approaches was judged with a leave-one-out validation procedure. There was an average success of 60.9% and 71.7% to assign each site unit to the correct assemblage of fish, and 73% and 84% to explain the variance in species richness, by using CART and RF models, respectively. RF proved to be better than CART in terms of accuracy and efficiency in ecological applications. In any case, the mixed models including both land cover and river characteristic variables were more powerful than either individual one in explaining the endemic fish distribution pattern in the upper Yangtze River. For instance, altitude, slope, length, discharge, runoff, farmland and alpine and sub-alpine meadow played important roles in driving the observed endemic fish assemblage structure, while farmland, slope grassland, discharge, runoff, altitude and drainage area in explaining the observed patterns of endemic species richness. Therefore, the various effects of human activity on natural aquatic ecosystems, in particular, the flow modification of the river and the land use changes may have a considerable effect on the endemic fish distribution patterns on a regional scale.
He
Yongfeng
Y
Lab. Evolution & Diversité Biologique, UMR 5174, CNRS, Université Toulouse, 118, route de Narbonne, 31062 Toulouse, Cedex 4, France. yongfeng83@gmail.com
Wang
Jianwei
J
Lek-Ang
Sithan
S
Lek
Sovan
S
D000818:Animals; D044822:Biodiversity; D002681:China; D004784:Environmental Monitoring; D005399:Fishes / Q000145:classification*; D005544:Forecasting; D055593:Geological Phenomena; D008962:Models, Theoretical; D045483:Rivers