How to make more out of community data? A conceptual framework and its implementation as models and software

Otso Ovaskainen; Gleb Tikhonov; Anna Norberg; F. Guillaume Blanchet; Leo Duan; David Dunson; Tomas Roslin; Nerea Abrego

doi:10.1111/ele.12757

journal article Open Access Mar 20, 2017

How to make more out of community data? A conceptual framework and its implementation as models and software

Otso Ovaskainen

Gleb Tikhonov Anna Norberg

F. Guillaume Blanchet

Leo Duan David Dunson Tomas Roslin

Nerea Abrego

Ecology Letters Vol. 20 No. 5 pp. 561-576 · Wiley

View at Publisher Save 10.1111/ele.12757

Abstract

AbstractCommunity ecology aims to understand what factors determine the assembly and dynamics of species assemblages at different spatiotemporal scales. To facilitate the integration between conceptual and statistical approaches in community ecology, we propose Hierarchical Modelling of Species Communities (HMSC) as a general, flexible framework for modern analysis of community data. While non‐manipulative data allow for only correlative and not causal inference, this framework facilitates the formulation of data‐driven hypotheses regarding the processes that structure communities. We model environmental filtering by variation and covariation in the responses of individual species to the characteristics of their environment, with potential contingencies on species traits and phylogenetic relationships. We capture biotic assembly rules by species‐to‐species association matrices, which may be estimated at multiple spatial or temporal scales. We operationalise theHMSCframework as a hierarchical Bayesian joint species distribution model, and implement it as R‐ and Matlab‐packages which enable computationally efficient analyses of large data sets. Armed with this tool, community ecologists can make sense of many types of data, including spatially explicit data and time‐series data. We illustrate the use of this framework through a series of diverse ecological examples.

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References

99

[1]

10.1111/1365‐2745.12722

[2]

10.1111/oik.03674

[3]

10.1890/1540-9295(2007)5[145:fkgipa]2.0.co;2

[4]

Specialization and Rarity Predict Nonrandom Loss of Interactions from Mutualist Networks

Marcelo A. Aizen, Malena Sabatino, Jason M. Tylianakis

Science 10.1126/science.1215320

[5]

10.1111/j.1600-0587.2013.00643.x

[6]

10.2307/3546962

[7]

Begon M. (1986)

[8]

10.1016/j.tree.2016.07.009

[9]

10.1089/cmb.2016.0061

[10]

10.1890/0012-9658(2003)084[1101:ctaeso]2.0.co;2

[11]

10.1016/j.tree.2011.01.009

[12]

10.1016/s0065-2504(08)60183-x

[13]

10.1111/2041-210x.12163

[14]

10.1111/geb.12102

[15]

10.1890/0012-9658(1997)078[1958:cafasa]2.0.co;2

[16]

Caro T.M. (2010)

[17]

10.1098/rstb.2011.0063

[18]

10.1002/ecm.1241

[19]

10.2307/2256278

[20]

10.1146/annurev-ecolsys-120213-091759

[21]

10.2307/5503

[22]

10.1111/j.1461-0248.2011.01722.x

[23]

10.1890/11-1183.1

[24]

Species Distribution Models: Ecological Explanation and Prediction Across Space and Time

Jane Elith, John R. Leathwick

Annual Review of Ecology, Evolution, and Systemati... 10.1146/annurev.ecolsys.110308.120159

[25]

Elton C. (1966)

[26]

Habitat loss and the structure of plant–animal mutualistic networks

Miguel A. Fortuna, Jordi Bascompte

Ecology Letters 10.1111/j.1461-0248.2005.00868.x

[27]

10.1111/j.1365-2656.2010.01688.x

[28]

10.1002/env.2245

[29]

Inferring Correlation Networks from Genomic Survey Data

Jonathan Friedman, Eric J. Alm

PLOS Computational Biology 10.1371/journal.pcbi.1002687

[30]

Garnier E. (2016)

[31]

10.1017/cbo9780511623332

[32]

Ecological assembly rules in plant communities—approaches, patterns and prospects

Lars Götzenberger, Francesco de Bello, Kari Anne Bråthen et al.

Biological Reviews 10.1111/j.1469-185x.2011.00187.x

[33]

10.1111/j.1461-0248.2006.00884.x

[34]

Plankton networks driving carbon export in the oligotrophic ocean

Lionel Guidi, Samuel Chaffron, Lucie Bittner et al.

Nature 10.1038/nature16942

[35]

10.1111/ecog.02445

[36]

10.1111/j.1365-2699.2011.02550.x

[37]

Predicting species distribution: offering more than simple habitat models

Antoine Guisan, Wilfried Thuiller

Ecology Letters 10.1111/j.1461-0248.2005.00792.x

[38]

10.1111/ele.12189

[39]

Hanski I. "Extinction debt and species credit in boreal forests: modelling the consequences of different approaches to biodiversity conservation" Ann. Zool. Fenn. (2000)

[40]

Harvey P.H. (1991) 10.1093/oso/9780198546412.001.0001

[41]

10.1086/285705

[42]

Holyoak M. (2005)

[43]

10.1111/2041-210x.12236

[44]

10.1016/j.tree.2007.01.014

[45]

10.1111/een.12048

[46]

10.1111/j.1365-2699.2011.02663.x

[47]

Community assembly, coexistence and the environmental filtering metaphor

Nathan J. B. Kraft, Peter B. Adler, Oscar Godoy et al.

Functional Ecology 10.1111/1365-2435.12345

[48]

Predicting changes in community composition and ecosystem functioning from plant traits: revisiting the Holy Grail

S. LAVOREL, E. GARNIER

Functional Ecology 10.1046/j.1365-2435.2002.00664.x

[49]

10.1098/rspb.2013.2728

[50]

Legendre P. (2012)

Showing 50 of 99 references

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Details

Published: Mar 20, 2017
Vol/Issue: 20(5)
Pages: 561-576
License: View

Authors

O

Otso Ovaskainen

Department of Biosciences University of Helsinki P.O. Box 65 Helsinki FI‐00014 Finland; Department of Biology Centre for Biodiversity Dynamics Norwegian University of Science and Technology N‐7491 Trondheim Norway

G

Gleb Tikhonov

Department of Biosciences University of Helsinki P.O. Box 65 Helsinki FI‐00014 Finland

A

Anna Norberg

Department of Biosciences University of Helsinki P.O. Box 65 Helsinki FI‐00014 Finland

F

F. Guillaume Blanchet

Department of Mathematics and Statistics McMaster University 1280 Main Street West Hamilton Ontario L8S 4K1 Canada; Département de biologie Faculté des sciences Université de Sherbrooke 2500 Boulevard Université Sherbrooke Québec J1K 2R1 Canada

L

Leo Duan

Department of Statistical Science Duke University P.O. Box 90251 Durham USA

D

David Dunson

Department of Statistical Science Duke University P.O. Box 90251 Durham USA

T

Tomas Roslin

Department of Ecology Swedish University of Agricultural Sciences Box 7044 Uppsala 75651 Sweden

N

Nerea Abrego

Department of Biology Centre for Biodiversity Dynamics Norwegian University of Science and Technology N‐7491 Trondheim Norway; Department of Agricultural Sciences University of Helsinki P.O. Box 27 Helsinki FI‐00014 Finland

Funding

Suomen Akatemia Award: 284601

Norges Forskningsråd Award: 223257

Helsingin Yliopiston Tiedesäätiö

Cite This Article

Otso Ovaskainen, Gleb Tikhonov, Anna Norberg, et al. (2017). How to make more out of community data? A conceptual framework and its implementation as models and software. Ecology Letters, 20(5), 561-576. https://doi.org/10.1111/ele.12757

How to make more out of community data? A conceptual framework and its implementation as models and software

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