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Joint species distribution modelling with the r-package Hmsc

Tikhonov, Gleb ; Opedal, Øystein H. LU ; Abrego, Nerea ; Lehikoinen, Aleksi LU ; de Jonge, Melinda M.J. ; Oksanen, Jari and Ovaskainen, Otso (2020) In Methods in Ecology and Evolution 11(3). p.442-447
Abstract

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... (More)

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.

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author
; ; ; ; ; and
publishing date
type
Contribution to journal
publication status
published
subject
keywords
community ecology, community modelling, community similarity, hierarchical modelling of species communities, joint species distribution modelling, multivariate data, species distribution modelling
in
Methods in Ecology and Evolution
volume
11
issue
3
pages
442 - 447
publisher
British Ecology Society / John Wiley & Sons, Inc.
external identifiers
  • scopus:85078810449
  • pmid:32194928
ISSN
2041-210X
DOI
10.1111/2041-210X.13345
language
English
LU publication?
no
id
ea7153e5-004e-456d-8309-3a008c1e4164
date added to LUP
2020-03-17 08:30:06
date last changed
2020-12-01 02:02:27
@article{ea7153e5-004e-456d-8309-3a008c1e4164,
  abstract     = {<p>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.</p>},
  author       = {Tikhonov, Gleb and Opedal, Øystein H. and Abrego, Nerea and Lehikoinen, Aleksi and de Jonge, Melinda M.J. and Oksanen, Jari and Ovaskainen, Otso},
  issn         = {2041-210X},
  language     = {eng},
  month        = {03},
  number       = {3},
  pages        = {442--447},
  publisher    = {British Ecology Society / John Wiley & Sons, Inc.},
  series       = {Methods in Ecology and Evolution},
  title        = {Joint species distribution modelling with the r-package Hmsc},
  url          = {http://dx.doi.org/10.1111/2041-210X.13345},
  doi          = {10.1111/2041-210X.13345},
  volume       = {11},
  year         = {2020},
}