Varying coefficient analysis for indeterminate species interactions with non-parametric estimation, exemplifying with a fig-fig wasp system  

作　　者：SHI Lei WANG RuiWu ZHU LiXing ZEN WeiMing XU WangLi ZHENG Qi 

机构地区：[1]Statistics and Mathematics School, Yunnan University of Finance and Economics, Kunming 650221, China [2]Ecology, Conservation, and Environment Center (ECEC), State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoo-logy, Chinese Academy of Sciences, Kunming 650223, China [3]Department of Mathematics, Hong Kong Baptist University, Hong Kong, China [4]Scientific and Technical Department, Yunnan University of Nationalities, Kunming 650031, China [5]Statistics College, RenMing University of China, Beijing 100080, China

出　　处：《Chinese Science Bulletin》2011年第24期2545-2552,共8页

基　　金：supported by the National Basic Research Program of China (2007CB411600);Na-tional Natural Science Foundation of China (30670272, 30770500 and 10761010);the Natural Science Foundation of Yunnan Province (2009- CD104);the West Light Foundation of the Chinese Academy of Sciences;Special Fund for the Excellent Youth of the Chinese Academy of Sciences (KSCX2-EW-Q-9);State Key Laboratory of Genetic Resources and Evolu-tion;the National Social Science Foundation of China (08XTJ001);Research Grants Council of Hong Kong (HKBU2030/07P)

摘　　要：Research on species interactions has generally assumed that species have a fixed interaction and therefore linear or non-linear parametric regression models (e.g. exponential, logistic) have been widely used to describe the species interaction. However, these models that describe the relationship between interacting species as a specific functional response might not be appropriate for real biological communities, for instance, in a chaotic system, when the species relationship varies among different situations. To allow a more accurate description of the relationship, we developed a species correlation model with varying coefficient analysis, in which a non-parametric estimation is applied to identify, as a function of related factors, variation in the correlation coefficient. This was applied to a fig-fig wasp model system. When the effect of the factors on the relationship can be described with parameters, the new method reduces to traditional parametric correlation analysis. In this way, the new method is more general and flexible for empirical data analyses, but different by allowing investigation of whether a species interaction varies with respect to factors, and of the factors that maintain or change the species interaction. This method will have important applications in both theoretical and applied research (e.g. epidemiology, community management).Research on species interactions has generally assumed that species have a fixed interaction and therefore linear or non-linear parametric regression models （e.g. exponential, logistic） have been widely used to describe the species interaction. However, these models that describe the relationship between interacting species as a specific functional response might not be appropriate for real biological communities, for instance, in a chaotic system, when the species relationship varies among different situations. To allow a more accurate description of the relationship, we developed a species correlation model with varying coefficient analysis, in which a non-parametric estimation is applied to identify, as a function of related factors, variation in the correlation coefficient. This was applied to a fig-fig wasp model system. When the effect of the factors on the relationship can be described with parame- ters, the new method reduces to traditional parametric correlation analysis. In this way, the new method is more general and flexi- ble for empirical data analyses, but different by allowing investigation of whether a species interaction varies with respect to fac- tors, and of the factors that maintain or change the species interaction. This method will have important applications in both theo- retical and applied research （e.g. epidemiology, community management）.

关 键 词：相互作用系数 非参数估计 混沌系统 无花果 物种 非线性参数 例证 小蜂 

分 类 号：Q968[生物学—昆虫学]