Joint species distribution modelling with the r-package Hmsc.
community ecology
community modelling
community similarity
hierarchical modelling of species communities
joint species distribution modelling
multivariate data
species distribution modelling
Journal
Methods in ecology and evolution
ISSN: 2041-210X
Titre abrégé: Methods Ecol Evol
Pays: United States
ID NLM: 101539246
Informations de publication
Date de publication:
Mar 2020
Mar 2020
Historique:
received:
23
09
2019
accepted:
16
12
2019
entrez:
21
3
2020
pubmed:
21
3
2020
medline:
21
3
2020
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 32194928
doi: 10.1111/2041-210X.13345
pii: MEE313345
pmc: PMC7074067
doi:
Types de publication
Journal Article
Langues
eng
Pagination
442-447Informations de copyright
© 2019 The Authors. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society.
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