Compound-specific isotope analysis of amino acids (CSIA-AA) is regarded as a more advanced method of unraveling food web connections than the traditional bulk isotope approach. One of the most important assumptions of the CSIA-AA approach is that the initial offset (beta) between glutamate and phenylalanine in primary producers should remain unchanged. However, the considerable difference in beta values between algae (+3.4 parts per thousand) and vascular plants (-8.0 parts per thousand) raises concern regarding the application of the CSIA-AA approach for complex ecosystems that depend on both resources. In the present study, a Bayesian mixing model was used to estimate the individual contribution of basal dietary resources to each consumer of Poyang Lake, and then, the beta was reevaluated to adapt to the assimilation of the individual primary producer for each consumer specimen. In general, the contributions of vascular plant-derived resources to most consumers are consistent with their feeding behaviors. The smaller contribution of vascular plants to planktivorous fish (usually less than 20%) is biologically expected because of their feeding ecology. Using this strategy, we successfully obtained realistic trophic positions (TP) of consumers in Poyang Lake, which are significantly different from traditional TPalgae or TPvascular. For instance, the TP value of grass carp (2.04) was consistent with the feeding behavior of this fish, i.e., they primarily feed on vascular hydrophytes. Therefore, the combination of the CSIA-AA and Bayesian mixing model provides a better understanding of food web structures, even in a complex freshwater ecosystem. Publication name | LIMNOLOGY AND OCEANOGRAPHY-METHODS, 10.1002/lom3.10332 | Author(s) | Zhang, Zhongyi; Tian, Jing; Cao, Yansheng; Zheng, Nengjian; Zhao, Jingling; Xiao, Hongwei; Guo, Wei; Zhu, Renguo; Xiao, Huayun | Corresponding author(s) | XIAO Huayun xiaohuayun@ecit.cn East China Univ Technol, Jiangxi Prov Key Lab Causes & Control Atmospher P, Nanchang 330013, Jiangxi, Peoples R China. | Author(s) from IGCAS | TIAN Jing | View here for the details
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