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DOI | 10.1016/j.tpb.2012.02.003 |
Estimating covariation between vital rates: A simulation study of connected vs. separate generalized linear mixed models (GLMMs) | |
Evans, Margaret E. K.1; Holsinger, Kent E.2 | |
通讯作者 | Evans, Margaret E. K. |
来源期刊 | THEORETICAL POPULATION BIOLOGY
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ISSN | 0040-5809 |
出版年 | 2012 |
卷号 | 82期号:4页码:299-306 |
英文摘要 | Covariation between vital rates is recognized as an important pattern to be accounted for in demographic modeling. We recently introduced a model for estimating vital rates and their covariation as a function of known and unknown effects, using generalized linear mixed models (GLMM’s) implemented in a hierarchical Bayesian framework (Evans et al., 2010) In particular, this model included a model-wide year effect (YEAR) influencing all vital rates, which we used to estimate covariation between vital rates due to exogenous factors not directly included in the model. This YEAR effect connected the GLMMs of vital rates into one large model; we refer to this as the "connected GLMMs" approach. Here we used a simulation study to evaluate the performance of a simplified version of this model, compared to separate GLMMs of vital rates, in terms of their ability to estimate correlations between vital rates. We simulated data from known relationships between vital rates and a covariate, inducing correlations among the vital rates. We then estimated those correlations from the simulated data using connected vs. separate GLMMs with year random effects. We compared precision and accuracy of estimated vital rates and their correlations under three scenarios of the pervasiveness of the exogenous effect (and thus true correlations). The two approaches provide equally good point estimates of vital rate parameters, but connected GLMMs provide better estimates of covariation between vital rates than separate GLMMs, both in terms of accuracy and precision, when the common influence on vital rates is pervasive. We discuss the situations where connected GLMMs might be best used, as well as further areas of investigation for this approach. (C) 2012 Elsevier Inc. All rights reserved. |
英文关键词 | Vital rate covariation Generalized linear mixed models Year effects Hierarchical Bayesian model Demography Transition matrix model |
类型 | Article |
语种 | 英语 |
国家 | France ; USA |
收录类别 | SCI-E |
WOS记录号 | WOS:000311981400007 |
WOS关键词 | POPULATION-GROWTH RATE ; FLORIDA SCRUB MINT ; VARIABLE ENVIRONMENTS ; DESERT ANNUALS ; VIABILITY ; DYNAMICS ; AGE ; DEMOGRAPHY ; FITNESS ; FIRE |
WOS类目 | Ecology ; Evolutionary Biology ; Genetics & Heredity ; Mathematical & Computational Biology |
WOS研究方向 | Environmental Sciences & Ecology ; Evolutionary Biology ; Genetics & Heredity ; Mathematical & Computational Biology |
资源类型 | 期刊论文 |
条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/175176 |
作者单位 | 1.Museum Natl Hist Nat, Origin Struct & Evolut Biodivers UMR 7205, F-75231 Paris 05, France; 2.Univ Connecticut, Dept Ecol & Evolutionary Biol, Storrs, CT 06269 USA |
推荐引用方式 GB/T 7714 | Evans, Margaret E. K.,Holsinger, Kent E.. Estimating covariation between vital rates: A simulation study of connected vs. separate generalized linear mixed models (GLMMs)[J],2012,82(4):299-306. |
APA | Evans, Margaret E. K.,&Holsinger, Kent E..(2012).Estimating covariation between vital rates: A simulation study of connected vs. separate generalized linear mixed models (GLMMs).THEORETICAL POPULATION BIOLOGY,82(4),299-306. |
MLA | Evans, Margaret E. K.,et al."Estimating covariation between vital rates: A simulation study of connected vs. separate generalized linear mixed models (GLMMs)".THEORETICAL POPULATION BIOLOGY 82.4(2012):299-306. |
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