St Andrews Research Repository

St Andrews University Home
View Item 
  •   St Andrews Research Repository
  • Research Centres and Institutes
  • Centre for Research into Ecological & Environmental Modelling (CREEM)
  • Centre for Research into Ecological & Environmental Modelling (CREEM) Theses
  • View Item
  •   St Andrews Research Repository
  • Research Centres and Institutes
  • Centre for Research into Ecological & Environmental Modelling (CREEM)
  • Centre for Research into Ecological & Environmental Modelling (CREEM) Theses
  • View Item
  •   St Andrews Research Repository
  • Research Centres and Institutes
  • Centre for Research into Ecological & Environmental Modelling (CREEM)
  • Centre for Research into Ecological & Environmental Modelling (CREEM) Theses
  • View Item
  • Login
JavaScript is disabled for your browser. Some features of this site may not work without it.

Estimating wildlife distribution and abundance from line transect surveys conducted from platforms of opportunity

Thumbnail
View/Open
FernandaFCMarquesPhDThesis.pdf (22.46Mb)
Date
2001
Author
Marques, Fernanda F. C.
Supervisor
Buckland, S. T. (Stephen T.)
Metadata
Show full item record
Altmetrics Handle Statistics
Abstract
Line transect data obtained from 'platforms of opportunity' are useful for the monitoring of long term trends in dolphin populations which occur over vast areas, yet analyses of such data axe problematic due to violation of fundamental assumptions of line transect methodology. In this thesis we develop methods which allow estimates of dolphin relative abundance to be obtained when certain assumptions of line transect sampling are violated. Generalised additive models are used to model encounter rate and mean school size as a function of spatially and temporally referenced covariates. The estimated relationship between the response and the environmental and locational covariates is then used to obtain a predicted surface for the response over the entire survey region. Given those predicted surfaces, a density surface can then be obtained and an estimate of abundance computed by numerically integrating over the entire survey region. This approach is particularly useful when search effort is not random, in which case standard line transect methods would yield biased estimates. Estimates of f (0) (the inverse of the effective strip (half-)width), an essential component of the line transect estimator, may also be biased due to heterogeneity in detection probabilities. We developed a conditional likelihood approach in which covariate effects are directly incorporated into the estimation procedure. Simulation results indicated that the method performs well in the presence of size-bias. When multiple covariates are used, it is important that covariate selection be carried out. As an example we applied the methods described above to eastern tropical Pacific dolphin stocks. However, uncertainty in stock identification has never been directly incorporated into methods used to obtain estimates of relative or absolute abundance. Therefore we illustrate an approach in which trends in dolphin relative abundance axe monitored by small areas, rather than stocks.
Type
Thesis, PhD Doctor of Philosophy
Collections
  • Centre for Research into Ecological & Environmental Modelling (CREEM) Theses
  • Statistics Theses
URI
http://hdl.handle.net/10023/3727

Items in the St Andrews Research Repository are protected by copyright, with all rights reserved, unless otherwise indicated.

Advanced Search

Browse

All of RepositoryCommunities & CollectionsBy Issue DateNamesTitlesSubjectsClassificationTypeFunderThis CollectionBy Issue DateNamesTitlesSubjectsClassificationTypeFunder

My Account

Login

Open Access

To find out how you can benefit from open access to research, see our library web pages and Open Access blog. For open access help contact: openaccess@st-andrews.ac.uk.

Accessibility

Read our Accessibility statement.

How to submit research papers

The full text of research papers can be submitted to the repository via Pure, the University's research information system. For help see our guide: How to deposit in Pure.

Electronic thesis deposit

Help with deposit.

Repository help

For repository help contact: Digital-Repository@st-andrews.ac.uk.

Give Feedback

Cookie policy

This site may use cookies. Please see Terms and Conditions.

Usage statistics

COUNTER-compliant statistics on downloads from the repository are available from the IRUS-UK Service. Contact us for information.

© University of St Andrews Library

University of St Andrews is a charity registered in Scotland, No SC013532.

  • Facebook
  • Twitter