globalchange  > 全球变化的国际研究计划
DOI: 10.1016/j.tree.2015.09.007
论文题名:
So Many Variables: Joint Modeling in Community Ecology
作者: Warton D.I.; Blanchet F.G.; O'Hara R.B.; Ovaskainen O.; Taskinen S.; Walker S.C.; Hui F.K.C.
刊名: Trends in Ecology and Evolution
ISSN: 1695347
出版年: 2015
卷: 30, 期:12
起始页码: 766
结束页码: 779
语种: 英语
Scopus关键词: biota ; ecosystem ; statistical model ; Biota ; Ecosystem ; Linear Models ; Models, Statistical
英文摘要: Technological advances have enabled a new class of multivariate models for ecology, with the potential now to specify a statistical model for abundances jointly across many taxa, to simultaneously explore interactions across taxa and the response of abundance to environmental variables. Joint models can be used for several purposes of interest to ecologists, including estimating patterns of residual correlation across taxa, ordination, multivariate inference about environmental effects and environment-by-trait interactions, accounting for missing predictors, and improving predictions in situations where one can leverage knowledge of some species to predict others. We demonstrate this by example and discuss recent computation tools and future directions. Many ecological questions require the joint analysis of abundances collected simultaneously across many taxonomic groups, and, if organisms are identified using modern tools such as metabarcoding, their number can be in the thousands.While historically such data have been analyzed using ad hoc algorithms, it is now possible to fully specify joint statistical models for abundance using multivariate extensions of generalized linear mixed models.These modern 'joint modeling' approaches allow the study of correlation patterns across taxa, at the same time as studying environmental response, to tease the two apart.Latent variable models are an especially exciting tool that has recently been used for ordination as well as for studying the factors driving co-occurrence. © 2015 Elsevier Ltd.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/67119
Appears in Collections:全球变化的国际研究计划
气候变化与战略

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作者单位: School of Mathematics and Statistics, Evolution and Ecology Research Centre, The University of New South Wales (UNSW), Sydney, Australia; Department of Mathematics and Statistics, McMaster University, Hamilton, Canada; Biodiversity and Climate Research Centre, Frankfurt, Germany; Metapopulation Research Center, Department of Biosciences, University of Helsinki, Finland; Centre for Biodiversity Dynamics, Department of Biology, Norwegian University of Science and Technology, Norway; Department of Mathematics and Statistics, University of Jyväskylä, Jyväskylä, Finland; Mathematical Sciences Institute, Australian National University, Canberra, Australia

Recommended Citation:
Warton D.I.,Blanchet F.G.,O'Hara R.B.,et al. So Many Variables: Joint Modeling in Community Ecology[J]. Trends in Ecology and Evolution,2015-01-01,30(12)
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