Morphometric Otolith Analysis

Mapp, J (2015) Morphometric Otolith Analysis. Doctoral thesis, University of East Anglia.

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Abstract

Abstract
Fish otoliths have long played an important role in sustainable �sheries management.
Stock assessment models currently used rely on species speci�c age pro�les obtained
from the seasonal patterns of growth marks that otoliths exhibit. We compare methods
widely used in �sheries science (elliptical Fourier) with an industry standardised
encoding method (MPEG7 - Curvature-Scale-Space) and with a recent addition to
shape modelling techniques (time-series shapelets) to determine which performs best.
An investigation is carried out into transform methods that retain size-information,
and whether the boundary encoding method is impacted be otolith age, performing
tests over three 2-class otolith datasets across six discrete and concurrent age groups.
Impact of segmentation methods are assessed to determine whether automated or expert
segmented methods of boundary extraction are more advantageous, and whether
constructed classi�ers can be used at di�erent institutions.
Tests show that neither time-series shaplets nor Curvature-Scale-Space methods
o�er any real advantage over Fourier transform methods given mixed age datasets.
However, we show that size indices are most indicative of �sheries stock in younger
single-age datasets, with shape holding more discriminatory potential in older samples.
Whilst commonly used Fourier transform methods generally return best results;
we show that classi�cation of otolith boundaries is impacted by the method of boundary
segmentation. Hand traced boundaries produce classi�ers more robust to test data
segmentation methods and are more suited to distributed classi�ers.
Additionally we present a proof of concept study showing that high energy synchrotron
scans are a new, non-invasive method of modelling internal otolith structure,
allowing comparison of slices along near in�nite numbers of virtual complex planes.

Item Type:	Thesis (Doctoral)
Faculty \ School:	Faculty of Science > School of Computing Sciences
Depositing User:	Users 7376 not found.
Date Deposited:	17 Jun 2016 10:03
Last Modified:	17 Jun 2016 10:03
URI:	https://ueaeprints.uea.ac.uk/id/eprint/59388
DOI:

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