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dc.contributor.advisorBuckland, S. T. (Stephen T.)
dc.contributor.advisorPestana, Dinis D.
dc.contributor.authorMarques, Tiago Andre Lamas Oliveira
dc.coverage.spatial204en
dc.date.accessioned2007-11-02T12:21:16Z
dc.date.available2007-11-02T12:21:16Z
dc.date.issued2007-11
dc.identifier.urihttp://hdl.handle.net/10023/391
dc.description.abstractDistance sampling is one of the most commonly used methods for estimating density and abundance. Conventional methods are based on the distances of detected animals from the center of point transects or the center line of line transects. These distances are used to model a detection function: the probability of detecting an animal, given its distance from the line or point. The probability of detecting an animal in the covered area is given by the mean value of the detection function with respect to the available distances to be detected. Given this probability, a Horvitz-Thompson- like estimator of abundance for the covered area follows, hence using a model-based framework. Inferences for the wider survey region are justified using the survey design. Conventional distance sampling methods are based on a set of assumptions. In this thesis I present results that extend distance sampling on two fronts. Firstly, estimators are derived for situations in which there is measurement error in the distances. These estimators use information about the measurement error in two ways: (1) a biased estimator based on the contaminated distances is multiplied by an appropriate correction factor, which is a function of the errors (PDF approach), and (2) cast into a likelihood framework that allows parameter estimation in the presence of measurement error (likelihood approach). Secondly, methods are developed that relax the conventional assumption that the distribution of animals is independent of distance from the lines or points (usually guaranteed by appropriate survey design). In particular, the new methods deal with the case where animal density gradients are caused by the use of non-random sampler allocation, for example transects placed along linear features such as roads or streams. This is dealt with separately for line and point transects, and at a later stage an approach for combining the two is presented. A considerable number of simulations and example analysis illustrate the performance of the proposed methods.en
dc.description.sponsorshipGrant number SFRH/BD/10812/200 received from Fundo Social Europeu (European Social Fund)
dc.format.extent1209875 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherUniversity of St Andrews
dc.subjectDistance samplingen
dc.subjectMeasurement erroren
dc.subjectAssumption violationen
dc.subjectDensity gradienten
dc.subjectAnimal surveysen
dc.subjectTransectsen
dc.subject.lccQA276.6M28
dc.subject.lcshSampling (Statistics)en
dc.subject.lcshAnimal populations (Estimates)en
dc.subject.lcshError analysis (Mathematics)en
dc.titleIncorporating measurement error and density gradients in distance sampling surveysen
dc.typeThesisen
dc.contributor.sponsorFundação para a Ciência e Tecnologia (Portugal)en
dc.contributor.sponsorFundo Social Europeu, III quadro comunitário de apoio
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD Doctor of Philosophyen
dc.publisher.institutionThe University of St Andrewsen
dc.publisher.departmentCenter For Research into Ecological and Environmental Modellingen


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