Model-based hypervolumes for complex ecological data

Jarvis, Susan G. ORCID: https://orcid.org/0000-0002-6770-2002; Henrys, Peter A. ORCID: https://orcid.org/0000-0003-4758-1482; Keith, Aidan M. ORCID: https://orcid.org/0000-0001-9619-1320; Mackay, Ellie ORCID: https://orcid.org/0000-0001-5697-7062; Ward, Susan E.; Smart, Simon M. ORCID: https://orcid.org/0000-0003-2750-7832. 2019 Model-based hypervolumes for complex ecological data. Ecology, 100 (5), e02676. 7, pp. 10.1002/ecy.2676

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Abstract/Summary

Developing a holistic understanding of the ecosystem impacts of global change requires methods that can quantify the interactions among multiple response variables. One approach is to generate high dimensional spaces, or hypervolumes, to answer ecological questions in a multivariate context. A range of statistical methods has been applied to construct hypervolumes but have not yet been applied in the context of ecological datasets with spatial or temporal structure, for example where the data are nested or demonstrate temporal autocorrelation. We outline an approach to account for data structure in quantifying hypervolumes based on the multivariate normal distribution by including random effects. Using simulated data we show that failing to account for structure in data can lead to biased estimates of hypervolume properties in certain contexts. We then illustrate the utility of these ‘model‐based hypervolumes’ in providing new insights into a case study of afforestation effects on ecosystem properties where the data has a nested structure. We demonstrate that the model‐based generalisation allows hypervolumes to be applied to a wide range of ecological datasets and questions.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1002/ecy.2676
UKCEH and CEH Sections/Science Areas:	Soils and Land Use (Science Area 2017-) Water Resources (Science Area 2017-)
ISSN:	0012-9658
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
Additional Keywords:	afforestation, Countryside Survey, Gaussian distribution, high‐dimensional, multivariate, niche
NORA Subject Terms:	Ecology and Environment
Date made live:	05 Mar 2019 15:47 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/522418

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