文献类型：期刊文献

英文题名：Understanding biodiversity effects on trophic interactions with a robust approach to path analysis

作者：Wang, Yu-Quan[1,2];Shi, Da-Peng[1,2,3];Scherber, Christoph[4,5];Woodcock, Ben A.[6];Hu, Yue-Qing[1,2];Wan, Nian-Feng[1,2,7]

机构：[1]Fudan Univ, Sch Life Sci, Inst Biostat, Shanghai, Peoples R China;[2]East China Univ Sci & Technol, State Key Lab Genet Engn, Shanghai Key Lab Chem Biol, Sch Pharm, Shanghai, Peoples R China;[3]Fudan Univ Shanghai, Shanghai Ctr Math Sci, Shanghai, Peoples R China;[4]Leibniz Inst Anal Biodivers Change, Ctr Biodivers Monitoring & Conservat Sci, Museum Koenig, D-53113 Bonn, Germany;[5]Univ Bonn, Bonn Inst Organism Biol, Bonn, Germany;[6]UK Ctr Ecol & Hydrol, Wallingford OX10 8BB, England;[7]East China Univ Sci & Technol, Inst Pesticides & Pharmaceut, Shanghai, Peoples R China

年份：2025

卷号：2

期号：5

外文期刊名：CELL REPORTS SUSTAINABILITY

收录：WOS:【ESCI(收录号:WOS:001552551300002)】；

基金：We thank Professor Shinichi Nakagawa for providing us with constructive suggestions and thank all researchers whose data and work have been included in this global plant diversity experiments. N.-F.W. was supported by the Shanghai Agriculture Applied Technology Development Program, China (grant no. 2023-02-08-00-12-F04586); Shanghai Science and Technology Innovation Action Plan from Shanghai Municipal Science and Technology Commission of China (22015821000); Natural Science Foundation of Shanghai (22ZR1417200); and National Ten Thousand Plan-Young Top Talents of China. Y.-Q.H. was supported by the National Key R&D Program of China (2023YFF1205101).

语种：英文

摘要：With its facility to assess causal mechanisms among multiple variables, the application of path analysis in medical, natural, and social sciences has become widespread. Of the many types of path analysis, structural equation modeling (SEM), including Bayesian applications of this method, has gained popularity. However, SEM remains constrained by biased estimates in the case of model misspecification, while Bayesian methods are limited by time consumption and computational requirements. Here, we propose a novel estimator utilizing robust estimating equations combined within a Bayesian framework to improve multilevel path analysis. We apply this method to an ecological trophic interaction case study that assessed the path effects of global plant diversity on the interactions of plants, invertebrate herbivores, and their natural enemies. Using a simulation study, we show that this new estimator is unbiased and more robust. Moreover, the computational time cost for the estimating procedure is reduced compared with multivariate Bayesian analysis.