mvabund– an R package for model‐based analysis of multivariate abundance data

By Yi Alice Wang, Ulrike Naumann, Stephen T. Wright, David I. Warton. 2012

1,409 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Ecological Modeling, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/df46ecb07600b5fe802ce4e8eef889776f35f11d

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Citations in the corpus

So Many Variables: Joint Modeling in Community Ecology. (2015, background): “When there are many taxa, currently the only viable alternative approach for making valid multivariate inferences is to resample sites to make design-based inferences about environmental effects [58].”
Nitrogen addition alters ectomycorrhizal fungal communities and soil enzyme activities in a tropical montane forest (2017, methodology): “The univariate ANOVA also revealed changes in abundance of eight species associated with different plant developmental stages.”
Modelling Palaeoecological Time Series Using Generalised Additive Models (2018, methodology): “One currently available avenue for fitting a multivariate GAM is via regularised sandwich estimators and GLMs (Warton, 2011), which involves fitting separate GLMs (or GAMs) to each response variable and subsequently using resamplingbased hypothesis tests to determine which covariates are related to ”
An Economical Custom-Built Drone for Assessing Whale Health (2017, methodology): “Significant differences in communities sampled in seawater, UAV-captured air or whale blow samples were defined using generalized linear models within mvabund (Wang et al., 2012).”
Variational Approximations for Generalized Linear Latent Variable Models (2017, neutral): “The R code for performing VA estimation of GLLVMs is available in the supplementary material of this article, and in future work we plan to integrate (even faster versions of) these functions into the mvabund package (Wang et al., 2012).”
Comparative analysis of the microbial communities in agricultural soil amended with enhanced biochars or traditional fertilisers (2014, methodology): “MVAbund fits a generalised linear model (GLM) to each OTU separately and uses resampling-based (to control for the family-wise error rate) hypothesis testing to make inferences about abundance differences of each OTU due to treatments.”
How to make more out of community data? A conceptual framework and its implementation as models and software. (2017, background): “…species, these models allow one to seek community-level patterns in how species respond to their environment (e.g. Ovaskainen & Soininen 2011; Wang et al. 2012; Hui et al. 2015; Ovaskainen et al. 2016b), to relate such patterns to species traits and phylogenies (Pollock et al. 2012; Brown et…”
Using metabarcoding to ask if easily collected soil and leaf-litter samples can be used as a general biodiversity indicator (2014, methodology): “mvabund is a multivariate implementation of generalized linear models, and, unlike dissimilarity-matrix-based methods, mvabund does not confound location with dispersion effects, which can inflate type 1 and 2 errors (Wang et al., 2012; Warton et al., 2011).”
Food habits of the world's grey wolves (2016, methodology): “As a second test, we ran a multivariate linear model using the R package mvabund (Wang et al. 2012).”