Model‐based approaches to unconstrained ordination
Francis K. C. Hui,Francis K. C. Hui,Sara Taskinen,Shirley Pledger,Scott D. Foster,David I. Warton +5 more
TLDR
A model‐based approach to unconstrained ordination is proposed based on finite mixture models and latent variable models, capable of handling different data types and different forms of species response to latent gradients.Abstract:
Summary
Unconstrained ordination is commonly used in ecology to visualize multivariate data, in particular, to visualize the main trends between different sites in terms of their species composition or relative abundance.
Methods of unconstrained ordination currently used, such as non-metric multidimensional scaling, are algorithm-based techniques developed and implemented without directly accommodating the statistical properties of the data at hand. Failure to account for these key data properties can lead to misleading results.
A model-based approach to unconstrained ordination can address this issue, and in this study, two types of models for ordination are proposed based on finite mixture models and latent variable models. Each method is capable of handling different data types and different forms of species response to latent gradients. Further strengths of the models are demonstrated via example and simulation.
Advantages of model-based approaches to ordination include the following: residual analysis tools for checking assumptions to ensure the fitted model is appropriate for the data; model selection tools to choose the most appropriate model for ordination; methods for formal statistical inference to draw conclusions from the ordination; and improved efficiency, that is model-based ordination better recovers true relationships between sites, when used appropriately.read more
Citations
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How to make more out of community data? A conceptual framework and its implementation as models and software.
Otso Ovaskainen,Otso Ovaskainen,Gleb Tikhonov,Anna Norberg,F. Guillaume Blanchet,F. Guillaume Blanchet,Leo L. Duan,David B. Dunson,Tomas Roslin,Nerea Abrego,Nerea Abrego +10 more
TL;DR: HMSC is operationalise the HMSC framework as a hierarchical Bayesian joint species distribution model, and is implemented as R- and Matlab-packages which enable computationally efficient analyses of large data sets.
Journal ArticleDOI
So Many Variables: Joint Modeling in Community Ecology.
David I. Warton,F. Guillaume Blanchet,Robert B. O'Hara,Otso Ovaskainen,Otso Ovaskainen,Sara Taskinen,Steven C. Walker,Francis K. C. Hui +7 more
TL;DR: This work demonstrates the potential of a new class of multivariate models for ecology 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, and discusses recent computation tools and future directions.
Journal ArticleDOI
boral – Bayesian Ordination and Regression Analysis of Multivariate Abundance Data in r
TL;DR: Boral as mentioned in this paper is a package available on cran for model-based analysis of multivariate abundance data, with estimation performed using Bayesian Markov chain Monte Carlo methods, incorporating latent variables as a parsimonious method of modelling between species correlation.
Journal ArticleDOI
Generalized joint attribute modeling for biodiversity analysis: median-zero, multivariate, multifarious data
TL;DR: A generalized joint attribute model (GJAM) is developed, a probabilistic framework that readily applies to data that are combinations of presence-absence, ordinal, continuous, discrete, composition, zero-inflated, and censored, and it shows that the environment can be inverse predicted from the joint distribution of species.
Journal ArticleDOI
Millions of reads, thousands of taxa: microbial community structure and associations analyzed via marker genes
Miklós Bálint,Mohammad Bahram,Mohammad Bahram,A. Murat Eren,A. Murat Eren,Karoline Faust,Jed A. Fuhrman,Björn D. Lindahl,Robert B. O'Hara,Maarja Öpik,Mitchell L. Sogin,Martin Unterseher,Leho Tedersoo +12 more
TL;DR: The aim of the review is to critically discuss the advantages and disadvantages of established and emerging statistical methods, and to contribute to the integration of HTS-based marker gene data into community ecology.
References
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