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|Title: ||Using generalized estimating equations with regression splines to improve analysis of butterfly transect data|
|Authors: ||Brewer, Ciara|
|Supervisors: ||Buckland, Stephen T.|
|Keywords: ||Butterfly transect data|
Generalized estimating equations
|Issue Date: ||Jun-2008|
|Abstract: ||Surveying animal populations is an important aspect of wildlife
management. Distinguishing trend from random fluctuations and
quantifying trend are key goals in any analysis.
The aim of this thesis is to review analyses of Butterfly Monitoring
Survey (BMS) data and to develop new methods which address some
flaws in previous studies. The BMS was established in 1976 at Monks
Wood, Cambridgeshire and sites were added over time throughout
Britain in order to monitor butterfly population trends. Weekly
counts are made over the monitoring season and the main aims are to
produce annual indices and compare these indices over time for any
Originally, weekly counts were summed to produce relative indices
and missing counts were estimated using linear interpolation. This
thesis discusses the weaknesses of this basic method
and suggests possible improvements.
In recent years, with advancements in statistical methods and
increased computer power, new methods can be applied to accommodate
the longitudinal and flexible nature of ecological data.
Mixed Models, Generalized Estimating Equations and Generalized
Additive Models are used and the relative merits of each modelling
approach discussed. These methods allow for correlation and
non-linearity in data.
Model selection is an important consideration when modelling and
different tests are introduced and compared.
Once a model is selected, site-level indices are estimated, which
can be collated to produce regional and national indices. Different
methods of estimating precision around indices are also contrasted.
Bootstrapping is found to be a convenient and dependable approach.
Abundance is difficult to disentangle from detectability when only
counts of species are carried out. Methods for dealing with this
problem are suggested.
Once reliable annual abundance estimates are found, they can be
compared over time using a variety of statistical techniques. The
chain-ratio method is applied to a subset of real data.|
|Publisher: ||University of St Andrews|
|Appears in Collections:||Statistics Theses|
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