globalchange  > 气候减缓与适应
DOI: 10.1111/ele.12757
Scopus记录号: 2-s2.0-85016315095
论文题名:
How to make more out of community data? A conceptual framework and its implementation as models and software
作者: Ovaskainen O.; Tikhonov G.; Norberg A.; Guillaume Blanchet F.; Duan L.; Dunson D.; Roslin T.; Abrego N.
刊名: Ecology Letters
ISSN: 1461023X
EISSN: 1461-0248
出版年: 2017
卷: 20, 期:5
起始页码: 561
结束页码: 576
语种: 英语
英文关键词: Assembly process ; biotic filtering ; community distribution ; community modelling ; community similarity ; environmental filtering ; functional trait ; joint species distribution model ; metacommunity ; phylogenetic signal
Scopus关键词: biotic factor ; community ecology ; conceptual framework ; data set ; phylogenetics ; software ; spatiotemporal analysis ; time series analysis ; Bayes theorem ; biodiversity ; ecosystem ; software ; theoretical model ; Bayes Theorem ; Biodiversity ; Ecosystem ; Models, Theoretical ; Software
英文摘要: Community ecology aims to understand what factors determine the assembly and dynamics of species assemblages at different spatiotemporal scales. To facilitate the integration between conceptual and statistical approaches in community ecology, we propose Hierarchical Modelling of Species Communities (HMSC) as a general, flexible framework for modern analysis of community data. While non-manipulative data allow for only correlative and not causal inference, this framework facilitates the formulation of data-driven hypotheses regarding the processes that structure communities. We model environmental filtering by variation and covariation in the responses of individual species to the characteristics of their environment, with potential contingencies on species traits and phylogenetic relationships. We capture biotic assembly rules by species-to-species association matrices, which may be estimated at multiple spatial or temporal scales. We operationalise the HMSC framework as a hierarchical Bayesian joint species distribution model, and implement it as R- and Matlab-packages which enable computationally efficient analyses of large data sets. Armed with this tool, community ecologists can make sense of many types of data, including spatially explicit data and time-series data. We illustrate the use of this framework through a series of diverse ecological examples. © 2017 The Authors. Ecology Letters published by CNRS and John Wiley & Sons Ltd
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/107623
Appears in Collections:气候减缓与适应

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作者单位: Department of Biosciences, University of Helsinki, P.O. Box 65, Helsinki, Finland; Department of Biology, Centre for Biodiversity Dynamics, Norwegian University of Science and Technology, N-7491, Trondheim, Norway; Department of Mathematics and Statistics, McMaster University, 1280 Main Street West HamiltonON, Canada; Département de biologie, Faculté des sciences, Université de Sherbrooke, 2500 Boulevard Université SherbrookeQC, Canada; Department of Statistical Science, Duke University, P.O. Box 90251, Durham, United States; Department of Ecology, Swedish University of Agricultural Sciences, Box 7044, Uppsala, Sweden; Department of Agricultural Sciences, University of Helsinki, P.O. Box 27, Helsinki, Finland

Recommended Citation:
Ovaskainen O.,Tikhonov G.,Norberg A.,et al. How to make more out of community data? A conceptual framework and its implementation as models and software[J]. Ecology Letters,2017-01-01,20(5)
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