Optimally grouping species to improve parsimony of species interaction models.

dc.contributor.authorBrown, Christopher R. P.
dc.date.accessioned2023-04-21T01:46:39Z
dc.date.available2023-04-21T01:46:39Z
dc.date.issued2023en
dc.description.abstractSpecies interaction models are an important tool in community ecology, but fitting a model to a particular community requires a lot of data, as there are a lot of interactions to quantify. The aggregation of species into groups is a common tool to reduce the number of parameters in these models. Current methods require choosing groupings a priori, and reliable methods of selecting appropriate groupings are not available. Herein, I describe a method for finding optimal groupings a posteriori, yielding a data-informed approach for using grouping to improve the parsimony of species interaction models. Applying this method to empirical data, I find that optimal species groupings are difficult to derive from information available a priori. Further, different groupings are needed to describe different components of species interactions within the same community species group differently in how they affect others than in how they respond to interactions from others. This violates the common assumption in species interaction models that a single grouping can be used to aggregate species in all regards.en
dc.identifier.urihttps://hdl.handle.net/10092/105367
dc.identifier.urihttp://dx.doi.org/10.26021/14462
dc.languageEnglish
dc.language.isoenen
dc.rightsAll Rights Reserveden
dc.rights.urihttps://canterbury.libguides.com/rights/thesesen
dc.titleOptimally grouping species to improve parsimony of species interaction models.en
dc.typeTheses / Dissertationsen
thesis.degree.disciplineBiological Sciencesen
thesis.degree.grantorUniversity of Canterburyen
thesis.degree.levelMastersen
thesis.degree.nameMaster of Scienceen
uc.bibnumber3268986
uc.collegeFaculty of Scienceen
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