Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.574765
Title: Diving and depth use in seals : inferences from telemetry data using regression and random walk movement
Author: Photopoulou, Theoni
Awarding Body: University of St Andrews
Current Institution: University of St Andrews
Date of Award: 2012
Availability of Full Text:
Access through EThOS:
Full text unavailable from EThOS. Restricted access.
Access through Institution:
Abstract:
This thesis focuses on methods for using telemetry data to make inferences about the diving behaviour of seals, in terms of their use of depth over time. Three species are considered: grey seals (Halichoerus grypus) and elephant seals (Mirounga leonina and Mirounga angustirostris). Data came from Geographic Positioning System phone tags (GPS phone tags) for grey seals, and Conductivity Temperature Depth Satellite Relay Data Loggers (CTD-SRDLs) for southern elephant seals (M.leonina); both are instruments that transmit Information in abstracted form. Data for northern elephant seals (M.angustirostris) came from anarchival prototype SRDL-type instrument that stored tri-axial acceleration information at high resolution and required recovery to obtain the data. The usefulness of maximum dive depth as a measure of depth use in grey seals, known to forage on the seabed, is explored with a logistic regression analysis using a Generalized Additive Model. Often, maximum dive depth will not be a representative measure of the way seals apportion their time in the water column, so a framework for quantifying depth use is developed for abstracted dive data from southern elephant seals and validated with high resolution time-depth data from northern elephant seals. The implications of using a broken-stick model for abstracting dive data on-board CTD-SRDLs are investigated in terms of its performance and uncertainty. A method for obtaining limits on the time-depth area within which these abstracted dives occurred is developed and used as part of a Bayesian state-space random walk model framework to reconstruct dive trajectories and estimate depth use profiles for abstracted dive data.
Supervisor: Matthiopoulos, Jason; Thomas, Len; Fedak, Mike Sponsor: Sea Mammal Research Unit
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.574765  DOI: Not available
Share: