Zhou, Ming (2018) Statistical Development of Ecological Removal Models. Doctor of Philosophy (PhD) thesis, University of Kent,. (KAR id:70240)
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Abstract
Removal sampling is commonly used to estimate abundance of populations in which captured individuals are permanently removed from a study area. The classic removal model (Moran, 1951) assumes a constant capture probability and all animals are available for detection throughout the study, which results in a simple geometric decline of counts of removed individuals over time. However, the real data collected from some species exhibit unexpected fluctuations in the number of captured animals. The work in this thesis is driven by real data on common lizards, Zootoca vivipara and great crested newts, Triturus cristatus, where existing approaches may give rise to misleading conclusions.
When modelling removal data it is crucial to account for imperfect availability in the population, as individuals could sometimes temporarily become undetectable at
study area, or emerge from an area outside the study. This thesis deals with three
aspects of removal modelling: (i) We develop a robust design multievent removal
modelling (RMER framework) which allows considerable flexibility in estimating temporary emigration as well as capture probability and the size of populations. We
also consider the effect of sparse data and investigate the use of modelling different
sources of data in conjunction with the removal data (Besbeas et al, 2002). (ii) The
estimation of temporary emigration or population renewal for removal data relies on
the use of the robust design (Zhou et al. 2018). However, there are many removal
data which lack the robust design structure. Motivated by the analysis of a data
set of common lizards collected under standard sampling protocol, we develop and
evaluate the use of penalised maximum likelihood estimation to allow populations to be open to new individuals via birth/arrival for data sets without the robust design. (iii) We use four criteria to explore study design aspects of removal data with the robust design, including the trade-off in survey effort allocation between primary periods and secondary periods for a fixed level of total sampling effort. The models we propose can account for temporary emigration or new arrivals of individuals during removal sampling and represent a step forward with respect to current modelling approaches and will guide wildlife management.
Item Type: | Thesis (Doctor of Philosophy (PhD)) |
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Thesis advisor: | McCrea, Rachel |
Thesis advisor: | Matechou, Eleni |
Thesis advisor: | Cole, Diana |
Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science |
SWORD Depositor: | System Moodle |
Depositing User: | System Moodle |
Date Deposited: | 21 Nov 2018 13:10 UTC |
Last Modified: | 05 Nov 2024 12:32 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/70240 (The current URI for this page, for reference purposes) |
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