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|Title: ||Incorporating measurement error and density gradients in distance sampling surveys|
|Authors: ||Marques, Tiago Andre Lamas Oliveira|
|Supervisors: ||Buckland, Stephen T.|
Pestana, Dinis D.
|Keywords: ||Distance sampling|
|Issue Date: ||Nov-2007|
|Abstract: ||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.|
|Publisher: ||University of St Andrews|
|Appears in Collections:||Statistics Theses|
Centre for Research into Ecological & Environmental Modelling (CREEM) Theses
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