Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.593918
Title: Species richness estimation for benthic data
Author: Norris, Beth J.
Awarding Body: University of Kent
Current Institution: University of Kent
Date of Award: 2012
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Abstract:
This thesis addresses species richness estimation for benthic data by describing the clustering of individuals within a species using a Neyman Type A distribution, and incorporating this into species richness estimates. A review of current species richness estimation methods is included . The maximumlikelihood approach to species richness estimation is extended to incorporate the Neyman Type A model, with a gamma mixing distribution on the mean abundance of individuals within a species. Species richness estimates of this model are compared •1 to those of the simpler negative binomial and Poisson models. The use of a penalisedlikelihood is applied to avoid sp uriously large estimates of species richness that can be associated with the 'boundary problem'. The Bayesian approach to species richness is considered, using uninformative and informative priors. Informative priors are elicited using expert opinion obtained from a number of benthic ecologists at the Centre for Environment, Fisheries and Aquaculture Science. These are iDcorporated into species richness estimation in the form of priors, and also converted into penalties for use in the frequentist approach. Several benthic data sets aTe anaJysed throughout, along with a Lepidoptera data set, and a data set from a common bird census carried out in the USA. In addition, several simulation studies are undertaken to illustrate the performance of the estimators. The research culminates in the application of species richness estimators to estimate species mortality due to dredging carried out off the Norfolk coast. Several estimators can be considered to gain a picture of the effect of dredging, and I recommend that species richness estimators should reflect the underlying distribution of t he data. I also recommend that a precautionary approach should be taken when using these estimators in practical applications.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.593918  DOI: Not available
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