Imperial College London
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ProfessorJamesRosindell

Faculty of Natural Sciences, Department of Life Sciences (Silwood Park)

Professor of Biodiversity Theory
 
 
 
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Contact

 

+44 (0)20 7594 2242j.rosindell

 
 
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Location

 

W1.5KennedySilwood Park

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Summary

 

Publications

Citation

BibTex format

@article{Overcast:2021:10.1111/1755-0998.13514,
author = {Overcast, I and Ruffley, M and Rosindell, J and Harmon, L and Borges, PA and Emerson, BC and Etienne, RS and Gillespie, R and Krehenwinkel, H and Mahler, DL and Massol, F and Parent, CE and Patino, J and Peter, B and Week, B and Wagner, C and Hickerson, MJ and Rominger, A},
doi = {10.1111/1755-0998.13514},
journal = {Molecular Ecology Resources},
pages = {2782--2800},
title = {A unified model of species abundance, genetic diversity, and functional diversity reveals the mechanisms structuring ecological communities},
url = {http://dx.doi.org/10.1111/1755-0998.13514},
volume = {21},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Biodiversity accumulates hierarchically by means of ecological and evolutionary processes and feedbacks. Within ecological communities drift, dispersal, speciation, and selection operate simultaneously to shape patterns of biodiversity. Reconciling the relative importance of these is hindered by current models and inference methods, which tend to focus on a subset of processes and their resulting predictions. Here we introduce massive ecoevolutionary synthesis simulations (MESS), a unified mechanistic model of community assembly, rooted in classic island biogeography theory, which makes temporally explicit joint predictions across three biodiversity data axes: (i) species richness and abundances, (ii) population genetic diversities, and (iii) trait variation in a phylogenetic context. Using simulations we demonstrate that each data axis captures information at different timescales, and that integrating these axes enables discriminating among previously unidentifiable community assembly models. MESS is unique in generating predictions of community-scale genetic diversity, and in characterizing joint patterns of genetic diversity, abundance, and trait values. MESS unlocks the full potential for investigation of biodiversity processes using multidimensional community data including a genetic component, such as might be produced by contemporary eDNA or metabarcoding studies. We combine MESS with supervised machine learning to fit the parameters of the model to real data and infer processes underlying how biodiversity accumulates, using communities of tropical trees, arthropods, and gastropods as case studies that span a range of data availability scenarios, and spatial and taxonomic scales.
AU  - Overcast,I
AU  - Ruffley,M
AU  - Rosindell,J
AU  - Harmon,L
AU  - Borges,PA
AU  - Emerson,BC
AU  - Etienne,RS
AU  - Gillespie,R
AU  - Krehenwinkel,H
AU  - Mahler,DL
AU  - Massol,F
AU  - Parent,CE
AU  - Patino,J
AU  - Peter,B
AU  - Week,B
AU  - Wagner,C
AU  - Hickerson,MJ
AU  - Rominger,A
DO  - 10.1111/1755-0998.13514
EP  - 2800
PY  - 2021///
SN  - 1471-8278
SP  - 2782
TI  - A unified model of species abundance, genetic diversity, and functional diversity reveals the mechanisms structuring ecological communities
T2  - Molecular Ecology Resources
UR  - http://dx.doi.org/10.1111/1755-0998.13514
UR  - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000710080900001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://onlinelibrary.wiley.com/doi/10.1111/1755-0998.13514
UR  - http://hdl.handle.net/10044/1/105656
VL  - 21
ER  -
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