1College of Tourism and Urban-Rural planning, Chengdu University of Technology, Chengdu, 610059, China
2College of Environment and Ecology, Chengdu University of Technology, Chengdu, 610059, China
3State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, 610059, China
4State Environmental Protection Key Laboratory of Synergetic Control and Joint Remediation for Soil & Water Pollution, Chengdu University of Technology, Chengdu, 610059, China
5School of Ecological and Environmental Science, East China Normal University, Shanghai, 200241, China
6Co-Innovation Center for Sustainable Forestry in Southern China, College of Biology and the Environment, Nanjing Forestry University, Nanjing, 210037, China
7Institute of Ecological Resource and Landscape Architecture, Chengdu University of Technology, Chengdu, 610059, China
8College of Earth science, Chengdu University of Technology, Chengdu, 610059, China
9Research Institute of Tropical Forestry, Chinese Academy of Forestry, Guangzhou, 510520, China
1College of Tourism and Urban-Rural planning, Chengdu University of Technology, Chengdu, 610059, China
2College of Environment and Ecology, Chengdu University of Technology, Chengdu, 610059, China
3State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, 610059, China
4State Environmental Protection Key Laboratory of Synergetic Control and Joint Remediation for Soil & Water Pollution, Chengdu University of Technology, Chengdu, 610059, China
5School of Ecological and Environmental Science, East China Normal University, Shanghai, 200241, China
6Co-Innovation Center for Sustainable Forestry in Southern China, College of Biology and the Environment, Nanjing Forestry University, Nanjing, 210037, China
7Institute of Ecological Resource and Landscape Architecture, Chengdu University of Technology, Chengdu, 610059, China
8College of Earth science, Chengdu University of Technology, Chengdu, 610059, China
9Research Institute of Tropical Forestry, Chinese Academy of Forestry, Guangzhou, 510520, China
Received: 12 Sep 2017 – Accepted for review: 12 Jan 2018 – Discussion started: 06 Apr 2018
Abstract. Understanding the drivers of geographic variation in species richness is one of the fundamental goals in ecology and biogeography. Fish is the key element in freshwater ecosystem and the focus of fishery production and biological conservation. Chinese freshwater fish fauna is rich and largely endemic due to variable geography and climate. By compiling the published data on fish richness for 86 nature reserves, and taking environmental predictors into consideration, we aimed to test latitudinal and longitudinal gradients in fish richness and the relative roles of energy availability, physiological tolerance, climatic seasonality and habitat heterogeneity hypotheses in explaining geographic fish richness pattern. Fish richness in China's nature reserves decreases with latitude and showed a hump-shaped relationship with longitude. Latitudinal fish richness is mainly shaped by mean temperature of the coldest month. Mean elevation and associated changes in temperature lead to longitudinal fish richness gradient. Among the four hypotheses tested, physiological tolerance hypothesis performs best and accounts for 55.4 % of the spatial variance in fish richness. Minimum temperature and precipitation are the primary determinants of fish species richness. Habitat heterogeneity is not negligible since adding river density to physiological tolerance model can explain additional 2 % variance in fish richness. Our results can provide useful information for regional fish production and conservation.
- Printer-friendly version
- Supplement