Generalized linear mixed models for phylogenetic analyses of community structure

By Anthony R. Ives, Matthew R. Helmus. 2011

247 citations recorded by the source corpus.

Subfields: Ecological Modeling, Nature and Landscape Conservation, Paleontology

View Paper: https://www.semanticscholar.org/paper/8f49f9111a005b4ead26ec635619c07a255a0427

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In Ecological Modeling

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In Paleontology

Citations in the corpus

Selecting traits that explain species–environment relationships: a generalized linear mixed model approach (2013, unknown): “In a recent paper, Ives & Helmus (2011) investigated the phylogenetic structure in community data in combination with either a single environmental variable or a single trait variable or no external data.”
Fast likelihood calculations for comparative analyses (2012, methodology): “For instance, hierarchical models with non-normal errors have been developed for phylogenetic analysis that are based on linear predictors with an essentially Brownian structure (Hadfield & Nakagawa 2010; Ives & Helmus 2011).”
A linear-time algorithm for Gaussian and non-Gaussian trait evolution models. (2014, background): “For example, Ives and Helmus (2011) investigate the presence/absence of n species at m sites to determine whether closely related species are more likely to co-occur at the same sites, and to quantify the phylogenetic signal in species co-occurrence.”
Rethinking Community Assembly through the Lens of Coexistence Theory (2012, background): “Despite these disadvantages, advanced statistical techniques are continually improving our ability to infer processes from patterns (Pillar & Duarte 2010, Chase & Myers 2011, Ives & Helmus 2011, Pavoine et al. 2011).”
The fourth‐corner solution – using predictive models to understand how species traits interact with the environment (2014, methodology): “There has been a recent trend towards the development of model-based approaches to analysing multispecies data (Dunstan, Foster & Darnell 2011; Ives & Helmus 2011; Ovaskainen & Soininen 2011; Warton, Wright &Wang 2012), and in this article, we have showed that such a method can be used to solve the…”
Plant Facilitation and Phylogenetics (2013, methodology): “…statistical methods are now available to separate the opposing phylogenetic effects of mechanisms occurring simultaneously, like phylogenetic clustering driven by environmental filtering and phylogenetic overdispersion caused by facilitation or competition (Helmus et al. 2007, Ives & Helmus 2011).”
mvabund– an R package for model‐based analysis of multivariate abundance data (2012, methodology): “…parameter / j is constant across sites but can vary across species, and the mean of Y ij is l jkl , a log-linear function of block and treatment: An important feature of using a model-based analysis framework is that the model that is ﬁtted can be used for predictive purposes (Ives & Helmus 2011).”
Phylogenetic Regression for Binary Dependent Variables (2014, methodology): “Here, we use the approach presented in Ives and Helmus (2011) for solving a more general formulation of PGLMM models designed for data sets containing the presence/ absence of species from ecological communities as dependent variables.”
Measuring and predicting the influence of traits on the assembly processes of wood‐inhabiting fungi (2017, methodology): “To account for possible phylogenetic correlations in the species’ responses to the environmental covariates, we followed Ives & Helmus (2011), by including a phylogenetic correlation matrix in the model’s covariance structure (Appendix S5).”
Climate, soil and plant functional types as drivers of global fine‐root trait variation (2017, methodology): “Given the lack of within-species variation for most traits in the dataset and to avoid overfitting, we were unable to fit a general linear mixed model with phylogeny as a random effect (e.g. Ives & Helmus 2011).”
So Many Variables: Joint Modeling in Community Ecology. (2015, methodology): “The phylogenetic GLMM models correlations as a function of phylogenetic relatedness [50,51], although including latent variables as well might be advisable, to detect correlations not related to phylogeny, as in model V of [50].”