Poisson distribution

Ecological niche

Environmental niche modelling

Statistics

Bayesian probability

Markov chain Monte Carlo

Covariate

Machine learning

Internal medicine

Species distribution

Paleontology

Artificial intelligence

Composite material

Random effects model

Habitat

Ecology

Materials science

Range (aeronautics)

Environmental data

Context (archaeology)

Count data

Mathematics

Computer science

Community

Medicine

Biology

Meta-analysis

Methods in Ecology and Evolution

Joint Species Distribution Modelling (JSDM) is becoming an increasingly popular statistical method for analysing data in community ecology. Hierarchical Modelling of Species Communities (HMSC) is a general and flexible framework for fitting JSDMs. HMSC allows the integration of community ecology data with data on environmental covariates, species traits, phylogenetic relationships and the spatio-temporal context of the study, providing predictive insights into community assembly processes from non-manipulative observational data of species communities.The full range of functionality of HMSC has remained restricted to Matlab users only. To make HMSC accessible to the wider community of ecologists, we introduce Hmsc 3.0, a user-friendly r implementation.We illustrate the use of the package by applying Hmsc 3.0 to a range of case studies on real and simulated data. The real data consist of bird counts in a spatio-temporally structured dataset, environmental covariates, species traits and phylogenetic relationships. Vignettes on simulated data involve single-species models, models of small communities, models of large species communities and models for large spatial data. We demonstrate the estimation of species responses to environmental covariates and how these depend on species traits, as well as the estimation of residual species associations. We demonstrate how to construct and fit models with different types of random effects, how to examine MCMC convergence, how to examine the explanatory and predictive powers of the models, how to assess parameter estimates and how to make predictions. We further demonstrate how Hmsc 3.0 can be applied to normally distributed data, count data and presence-absence data.The package, along with the extended vignettes, makes JSDM fitting and post-processing easily accessible to ecologists familiar with r.

Joint species distribution modelling with the <scp>r</scp> ‐package H <scp>msc</scp>

Joint species distribution modelling with the r ‐package H msc