DImodelsMulti: An R package for fitting Diversity‐Interactions models to repeated measures or multivariate data
Year: 2026
Type: article
Abstract: Diversity‐Interactions (DI) modelling is a regression‐based approach used for modelling the biodiversity and ecosystem function (BEF) relationship. This methodology assesses the effects of manipulating (initial) species richness, composition (species identities) and species proportions on ecosystem function responses measured at a later point in time. The models separate the effects of each individual species and test the strength of potential species interactions. Univariate DI models for a continuous response measured at a singular time point can be fit using the DImodels R package. This package contains highly customisable fitting options but cannot handle datasets where multiple ecosystem functions (multivariate) or multiple time points (repeated measures) have been recorded. While multiple ecosystem functions or time points can be analysed separately, it is often desirable to formally compare across the functions or time points. This requires covariances between the multiple measurements from the same study unit to be explicitly captured in the modelling framework. We present DImodelsMulti , an R package that complements and extends DImodels . It enables the fitting of multivariate or repeated measures DI models in a user‐friendly way. For multivariate data, fitted models can be used to predict for each ecosystem function, and the relative importance of model predictors can be assessed by comparing across the multiple responses. For repeated measures data, fitted models can be used to predict at each time point, and temporal patterns can be formally assessed and tested.
Source: Methods in Ecology and Evolution
Authors: Laura Byrne, Rishabh Vishwakarma, Rafael A. Moral, Caroline Brophy
Language: English
Subfield: Nature and Landscape Conservation
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,500
OpenAlex ID: W7127417432