Predicting fish assemblages and diversity in shallow lakes in the Yangtze River basin

doi:10.1016/j.limno.2011.09.007

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	Predicting fish assemblages and diversity in shallow lakes in the Yangtze River basin
	Cheng, Lin 1,2,3; Lek, Sovan 3; Lek-Ang, Sithan 3; Li, Zhongjie 1; Li, ZJ (reprint author), Chinese Acad Sci, Inst Hydrobiol, State Key Lab Freshwater Ecol & Biotechnol, Wuhan 430072, Peoples R China.
刊名	LIMNOLOGICA
	2012-05-01
卷号	42 期号:2 页码:127-136
关键词	Floodplain Basin Self-organizing Map Cart Model Rf Model Indicator Species
ISSN号	0075-9511
DOI	10.1016/j.limno.2011.09.007
文献子类	Article
英文摘要	Habitat modifications induced by humans severely impact biotic components of freshwater ecosystems. In China, shallow lakes in the Yangtze River basin are facing severe habitat degradation induced by pollution, habitat losing, macrophytes disappearing and fishery activities. Effectively modeling the fish communities on the basis of biotic and abiotic environmental descriptors would be helpful to understand the relationships between fish and their environment, and to develop suitable conservation strategies to sustain the biodiversity in these ecosystems. From 2007 to 2009, investigations were carried out on fish and their environment in 6 lakes distributed in the mid-reach of the Yangtze River basin. According to the CPUE values of each fish species from each sampling, 117 datasets were ordinated using self-organizing map (SOM). Fish communities were classified into three clusters of species assemblages, spatial and temporal distributions were showing in it. Seasonal changes in fish community were more obvious in vegetated habitats than in unvegetated areas. The total CPUE, fish diversity and species richness were significantly different among the assemblages (p < 0.01). Based on the indicative value of each species in each cluster calculated by Indval method, 16 species were identified as indicators: 13 indicators in cluster G1 are pelagic or benthopelagic fish, the only one indicator species in G2 is a tolerant species (Culter dabry B.), while the other two indicator species in G3 are demersal fish (Rhinogobius giurinus R. and Odontobutis obscurus T. & G.). These results are in agreement with the contributions of different ecological groups of fish in each assemblage in the trained SOM, pelagic and benthopelagic fish were found having more activities in spring and winter, while more activities of demersal fish were found in summer and autumn. Fish community assemblages, the total fish CPUE, diversity and species richness in those lakes were then predicted by 15 abiotic and biotic factors using random forest (RF) and classification and regression tree (CART) predictive models. The predicted assignment of each site unit to the correct assemblage had an average success of 74.4% and 60.7% in RF and CART models, respectively. The dominant variables for discriminating three fish assemblages were water depth, distance to the bank and total phosphorus. While the two important variables in prediction fish CPUE, diversity and species richness were lake surface area and water depth, density of rotifer and water depth, water depth and water temperature, respectively. The overall percentages of successful prediction varied from 56.5% to 67% utilizing leave-one-out for cross-validation tests. (C) 2011 Elsevier GmbH. All rights reserved.; Habitat modifications induced by humans severely impact biotic components of freshwater ecosystems. In China, shallow lakes in the Yangtze River basin are facing severe habitat degradation induced by pollution, habitat losing, macrophytes disappearing and fishery activities. Effectively modeling the fish communities on the basis of biotic and abiotic environmental descriptors would be helpful to understand the relationships between fish and their environment, and to develop suitable conservation strategies to sustain the biodiversity in these ecosystems. From 2007 to 2009, investigations were carried out on fish and their environment in 6 lakes distributed in the mid-reach of the Yangtze River basin. According to the CPUE values of each fish species from each sampling, 117 datasets were ordinated using self-organizing map (SOM). Fish communities were classified into three clusters of species assemblages, spatial and temporal distributions were showing in it. Seasonal changes in fish community were more obvious in vegetated habitats than in unvegetated areas. The total CPUE, fish diversity and species richness were significantly different among the assemblages (p < 0.01). Based on the indicative value of each species in each cluster calculated by Indval method, 16 species were identified as indicators: 13 indicators in cluster G1 are pelagic or benthopelagic fish, the only one indicator species in G2 is a tolerant species (Culter dabry B.), while the other two indicator species in G3 are demersal fish (Rhinogobius giurinus R. and Odontobutis obscurus T. & G.). These results are in agreement with the contributions of different ecological groups of fish in each assemblage in the trained SOM, pelagic and benthopelagic fish were found having more activities in spring and winter, while more activities of demersal fish were found in summer and autumn. Fish community assemblages, the total fish CPUE, diversity and species richness in those lakes were then predicted by 15 abiotic and biotic factors using random forest (RF) and classification and regression tree (CART) predictive models. The predicted assignment of each site unit to the correct assemblage had an average success of 74.4% and 60.7% in RF and CART models, respectively. The dominant variables for discriminating three fish assemblages were water depth, distance to the bank and total phosphorus. While the two important variables in prediction fish CPUE, diversity and species richness were lake surface area and water depth, density of rotifer and water depth, water depth and water temperature, respectively. The overall percentages of successful prediction varied from 56.5% to 67% utilizing leave-one-out for cross-validation tests. (C) 2011 Elsevier GmbH. All rights reserved.
WOS关键词	BENTHIC COMMUNITY STRUCTURE ; ARTIFICIAL NEURAL-NETWORKS ; ORGANIZING MAP ALGORITHM ; FRESH-WATER FISH ; SPECIES RICHNESS ; BIOTIC INTEGRITY ; RANDOM FORESTS ; SUBMERGED MACROPHYTES ; AQUATIC MACROPHYTES ; CONSERVATION
WOS研究方向	Marine & Freshwater Biology
语种	英语
WOS记录号	WOS:000303176100005
资助机构	National Natural Science Foundation of China[30830025, 30900182]; Ministry of Agriculture of China[200903048-04] ; National Natural Science Foundation of China[30830025, 30900182]; Ministry of Agriculture of China[200903048-04] ; National Natural Science Foundation of China[30830025, 30900182]; Ministry of Agriculture of China[200903048-04] ; National Natural Science Foundation of China[30830025, 30900182]; Ministry of Agriculture of China[200903048-04]
公开日期	2012-09-25
内容类型	期刊论文
源URL	[http://ir.ihb.ac.cn/handle/342005/16865]
专题	水生生物研究所_淡水生态学研究中心_期刊论文
通讯作者	Li, ZJ (reprint author), Chinese Acad Sci, Inst Hydrobiol, State Key Lab Freshwater Ecol & Biotechnol, Wuhan 430072, Peoples R China.
作者单位	1.Chinese Acad Sci, Inst Hydrobiol, State Key Lab Freshwater Ecol & Biotechnol, Wuhan 430072, Peoples R China 2.Chinese Acad Sci, Grad Sch, Beijing 100039, Peoples R China 3.Univ Toulouse, UMR EDB 5174, CNRS, F-31062 Toulouse 09, France
推荐引用方式 GB/T 7714	Cheng, Lin,Lek, Sovan,Lek-Ang, Sithan,et al. Predicting fish assemblages and diversity in shallow lakes in the Yangtze River basin[J]. LIMNOLOGICA,2012,42(2):127-136.
APA	Cheng, Lin,Lek, Sovan,Lek-Ang, Sithan,Li, Zhongjie,&Li, ZJ .(2012).Predicting fish assemblages and diversity in shallow lakes in the Yangtze River basin.LIMNOLOGICA,42(2),127-136.
MLA	Cheng, Lin,et al."Predicting fish assemblages and diversity in shallow lakes in the Yangtze River basin".LIMNOLOGICA 42.2(2012):127-136.