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GLS-corrected RLQ analysis: a new multivariate method for incorporating spatial and phylogenetic information into trait-environment analyses

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dc.contributor.advisor Newmaster, Steven
dc.contributor.author Henry, Thomas
dc.date.accessioned 2016-05-20T19:21:57Z
dc.date.available 2016-05-20T19:21:57Z
dc.date.copyright 2016-05
dc.date.created 2016-05-18
dc.date.issued 2016-05-20
dc.identifier.uri http://hdl.handle.net/10214/9726
dc.description.abstract A key goal in community ecology is to understand factors that shape the distribution of species on the landscape. One relevant factor is the relationship between the environment and species traits (environmental filtering). RLQ analysis is a multivariate method that measures the relationship between species traits and the environment. However, it ignores spatial and phylogenetic patterns in the data. In this thesis, I develop a method for incorporating spatial and phylogenetic information into RLQ analysis. In the first chapter, I demonstrate that phylogenetic and spatial effects bias multivariate statistical methods, and that methods based on the generalized least squares (GLS) framework can remove this bias. In the second chapter, I develop a new variant of RLQ analysis based on the GLS framework. In the third chapter, I use an existing dataset on herbaceous plant communities in Ontario forests as a case study for the new GLS-based RLQ method. en_US
dc.language.iso en en_US
dc.subject Multivariate statistics en_US
dc.subject Ecology en_US
dc.subject Evolution en_US
dc.subject Environmental filtering en_US
dc.subject RLQ analysis en_US
dc.title GLS-corrected RLQ analysis: a new multivariate method for incorporating spatial and phylogenetic information into trait-environment analyses en_US
dc.type Thesis en_US
dc.degree.programme Integrative Biology en_US
dc.degree.name Master of Science en_US
dc.degree.department Department of Integrative Biology en_US
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