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Model-based simultaneous clustering and ordination of multivariate abundance data in ecology

Hui, Francis

Description

When studying multivariate abundance data, one of the main patterns ecologists are often interested in is whether the sites exhibit clustering on the low-dimensional, ordination space representing species composition. A new model-based approach called CORAL (Clustering and Ordination Regression AnaLysis) is developed for tackling this question, based on performing simultaneous clustering and ordination using latent variable regression. By drawing the latent variables from a finite mixture...[Show more]

dc.contributor.authorHui, Francis
dc.date.accessioned2021-08-10T01:53:05Z
dc.identifier.issn0167-9473
dc.identifier.urihttp://hdl.handle.net/1885/243422
dc.description.abstractWhen studying multivariate abundance data, one of the main patterns ecologists are often interested in is whether the sites exhibit clustering on the low-dimensional, ordination space representing species composition. A new model-based approach called CORAL (Clustering and Ordination Regression AnaLysis) is developed for tackling this question, based on performing simultaneous clustering and ordination using latent variable regression. By drawing the latent variables from a finite mixture density, CORAL probabilistically classifies sites based on their positions on an underlying signal space. This is similar to mixtures of factor analyzers, except CORAL is designed for non-normal responses and uses species-specific rather than cluster-specific factor loadings (regression coefficients). Estimation is performed via Bayesian MCMC sampling, with code provided in the Supplementary Material. Simulations demonstrate that, by utilizing the joint information available in the data for both classification and dimension reduction, CORAL outperforms several popular, algorithm-based methods for clustering and ordination in ecology. CORAL is applied to a dataset of presence–absence records collected at sites along the Doubs River near the France–Switzerland border, with results revealing two clusters or ecological regions partly resembling the spatial separation of upstream and downstream sites.
dc.format.mimetypeapplication/pdf
dc.language.isoen_AU
dc.publisherElsevier
dc.rights© 2016 Elsevier
dc.sourceComputational Statistics and Data Analysis
dc.subjectDimension reduction
dc.subjectFinite mixture models
dc.subjectHierarchical Bayesian model
dc.subjectMixtures of factor analyzers
dc.subjectLatent variable model
dc.titleModel-based simultaneous clustering and ordination of multivariate abundance data in ecology
dc.typeJournal article
local.description.notesImported from ARIES
local.identifier.citationvolume105
dc.date.issued2017
local.identifier.absfor010401 - Applied Statistics
local.identifier.absfor010405 - Statistical Theory
local.identifier.absfor050101 - Ecological Impacts of Climate Change
local.identifier.ariespublicationU3488905xPUB24981
local.publisher.urlhttps://www.elsevier.com/en-au
local.type.statusPublished Version
local.contributor.affiliationHui, Francis, College of Science, ANU
local.description.embargo2099-12-31
local.bibliographicCitation.startpage1
local.bibliographicCitation.lastpage10
local.identifier.doi10.1016/j.csda.2016.07.008
dc.date.updated2020-11-23T10:50:09Z
local.identifier.scopusID2-s2.0-84982793444
local.identifier.thomsonID000385604500001
CollectionsANU Research Publications

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