Methods for accurate evaluation of population abundance from ecological data

Alqhtani, Manal (2018). Methods for accurate evaluation of population abundance from ecological data. University of Birmingham. Ph.D.

[img]
Preview
Alqhtani18PhD.pdf
Text - Accepted Version
Available under License All rights reserved.

Download (3MB) | Preview

Abstract

An accurate evaluation of total population density is required in many ecological and biological field. To protect crops from pest attacks, the population density of pests must be evaluated adequately. Accurate information obtained as a result of trapping in ecological monitoring is beneficial for decision-making purposes when implementing a control action. In pest monitoring, a classic technique of evaluating density based on a statistical method may result in poor accuracy. Accuracy can be optimised by applying alternative numerical integration methods to the problem. We explain how insufficient information regarding population density negatively affects the accuracy of estimation. Consequently, a coarse grid problem arises where the numerical integration methods are no longer valid. The evaluation of integration error is now a random variable and the probabilistic approach is used, due to the uncertainty in sampling data. In this thesis several population models have been considered to explain that the value of correlation coefficient on a coarse sampling grid is lost even if the true value is close to one. Phenomenon of ghost synchronization has been observed when the value of correlation coefficient on a coarse sampling grid is close to one but in reality the dynamics are not correlated.

Type of Work: Thesis (Doctorates > Ph.D.)
Award Type: Doctorates > Ph.D.
Supervisor(s):
Supervisor(s)EmailORCID
Petrovskaya, NataliaUNSPECIFIEDUNSPECIFIED
Licence: All rights reserved
College/Faculty: Colleges (2008 onwards) > College of Engineering & Physical Sciences
School or Department: School of Mathematics
Funders: None/not applicable
Subjects: Q Science > QA Mathematics
Q Science > QH Natural history
URI: http://etheses.bham.ac.uk/id/eprint/8740

Actions

Request a Correction Request a Correction
View Item View Item

Downloads

Downloads per month over past year