Open Access Te Herenga Waka-Victoria University of Wellington
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Clustering and Classification in Fisheries

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posted on 2021-11-15, 22:00 authored by Fujita, Yuki

This goal of this research is to investigate associations between presences of fish species, space, and time in a selected set of areas in New Zealand waters. In particular we use fish abundance indices on the Chatham Rise from scientific surveys in 2002, 2011, 2012, and 2013. The data are collected in annual bottom trawl surveys carried out by the National Institute of Water and Atmospheric Research (NIWA). This research applies clustering via finite mixture models that gives a likelihood-based foundation for the analysis. We use the methods developed by Pledger and Arnold (2014) to cluster species into common groups, conditional on the measured covariates (body size, depth, and water temperature). The project for the first time applies these methods incorporating covariates, and we use simple binary presence/absence data rather than abundances. The models are fitted using the Expectation-Maximization (EM) algorithm. The performance of the models is evaluated by a simulation study. We discuss the advantages and the disadvantages of the EM algorithm. We then introduce a newly developed function clustglm (Pledger et al., 2015) in R, which implements this clustering methodology, and perform our analysis using this function on the real-life presence/absence data. The results are analysed and interpreted from a biological point of view. We present a variety of visualisations of the models to assist in their interpretation. We found that depth is the most important factor to explain the data.


Copyright Date


Date of Award



Te Herenga Waka—Victoria University of Wellington

Rights License

Author Retains Copyright

Degree Discipline


Degree Grantor

Te Herenga Waka—Victoria University of Wellington

Degree Level


Degree Name

Master of Science

ANZSRC Type Of Activity code

970101 Expanding Knowledge in the Mathematical Sciences

Victoria University of Wellington Item Type

Awarded Research Masters Thesis



Victoria University of Wellington School

School of Mathematics, Statistics and Operations Research


Arnold, Richard