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题名: Predicting Changes in Macrophyte Community Structure from Functional Traits in a Freshwater Lake: A Test of Maximum Entropy Model
作者: Fu, Hui1, 2; Zhong, Jiayou1; Yuan, Guixiang1; Guo, Chunjing1; Lou, Qian1; Zhang, Wei1; Xu, Jun2; Ni, Leyi2; Xie, Ping2; Cao, Te2
刊名: PLOS ONE
发表日期: 2015-07-13
DOI: 10.1371/journal.pone.0131630
卷: 10, 期:7
收录类别: SCI
文章类型: Article
WOS标题词: Science & Technology
类目[WOS]: Multidisciplinary Sciences
研究领域[WOS]: Science & Technology - Other Topics
英文摘要: Trait-based approaches have been widely applied to investigate how community dynamics respond to environmental gradients. In this study, we applied a series of maximum entropy (maxent) models incorporating functional traits to unravel the processes governing macro-phyte community structure along water depth gradient in a freshwater lake. We sampled 42 plots and 1513 individual plants, and measured 16 functional traits and abundance of 17 macrophyte species. Study results showed that maxent model can be highly robust (99.8%) in predicting the species relative abundance of macrophytes with observed community-weighted mean (CWM) traits as the constraints, while relative low (about 30%) with CWM traits fitted from water depth gradient as the constraints. The measured traits showed notably distinct importance in predicting species abundances, with lowest for perennial growth form and highest for leaf dry mass content. For tuber and leaf nitrogen content, there were significant shifts in their effects on species relative abundance from positive in shallow water to negative in deep water. This result suggests that macrophyte species with tuber organ and greater leaf nitrogen content would become more abundant in shallow water, but would become less abundant in deep water. Our study highlights how functional traits distributed across gradients provide a robust path towards predictive community ecology.
关键词[WOS]: AQUATIC MACROPHYTES ; ECOSYSTEM PROCESSES ; COASTAL CALIFORNIA ; PLASTIC RESPONSES ; DEPTH GRADIENT ; PLANT TRAITS ; DIVERSITY ; ECOLOGY ; NUTRIENTS ; ZONATION
语种: 英语
WOS记录号: WOS:000358193100011
ISSN号: 1932-6203
Citation statistics:
内容类型: 期刊论文
URI标识: http://ir.ihb.ac.cn/handle/342005/25955
Appears in Collections:淡水生态学研究中心_期刊论文

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作者单位: 1.Jiangxi Inst Water Sci, Jiangxi Prov Key Lab Water Resources & Environm P, Nanchang, Peoples R China
2.Chinese Acad Sci, Inst Hydrobiol, State Key Lab Freshwater Ecol & Biotechnol, Donghu Expt Stn Lake Ecosyst, Wuhan, Peoples R China
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