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Title: Incorporating measurement error and density gradients in distance sampling surveys
Author: Marques, Tiago Andre Lamas Oliveira
ISNI:       0000 0004 2716 2800
Awarding Body: University of St Andrews
Current Institution: University of St Andrews
Date of Award: 2007
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Distance 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.
Supervisor: Buckland, Stephen T.; Pestana, Dinis D.; Fundo Social Europeu, III quadro comunitário de apoio Sponsor: Fundação para a Ciência e Tecnologia
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Distance sampling ; Measurement error ; Assumption violation ; Density gradient ; Animal surveys ; Transects ; QA276.6M28 ; Sampling (Statistics) ; Animal populations (Estimates) ; Error analysis (Mathematics)