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
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

442-447

Informations de copyright

© 2019 The Authors. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society.

Références

Proc Biol Sci. 2017 May 31;284(1855):
pubmed: 28539525
Ecol Lett. 2017 May;20(5):561-576
pubmed: 28317296
Trends Ecol Evol. 2015 Dec;30(12):766-779
pubmed: 26519235
Nature. 2012 Nov 15;491(7424):444-8
pubmed: 23123857
Biol Rev Camb Philos Soc. 2017 Feb;92(1):169-187
pubmed: 26426308
Ecology. 2020 Feb;101(2):e02929
pubmed: 31725922

Auteurs

Gleb Tikhonov (G)

Department of Computer Science Aalto University Espoo Finland.
Organismal and Evolutionary Biology Research Programme University of Helsinki Helsinki Finland.

Øystein H Opedal (ØH)

Organismal and Evolutionary Biology Research Programme University of Helsinki Helsinki Finland.
Centre for Biodiversity Dynamics Department of Biology Norwegian University of Science and Technology Trondheim Norway.

Nerea Abrego (N)

Department of Agricultural Sciences University of Helsinki Helsinki Finland.

Aleksi Lehikoinen (A)

The Helsinki Lab of Ornithology Finnish Museum of Natural History University of Helsinki Helsinki Finland.

Melinda M J de Jonge (MMJ)

Department of Environmental Science Institute for Water and Wetland Research Radboud University Nijmegen The Netherlands.

Jari Oksanen (J)

Botany Unit Finnish Museum of Natural History University of Helsinki Helsinki Finland.

Otso Ovaskainen (O)

Organismal and Evolutionary Biology Research Programme University of Helsinki Helsinki Finland.
Centre for Biodiversity Dynamics Department of Biology Norwegian University of Science and Technology Trondheim Norway.

Classifications MeSH