A new approach to adaptive monitoring

Jaksons, Peter

A new approach to adaptive monitoring

dc.contributor.author	Jaksons, Peter
dc.date.accessioned	2015-11-18T23:17:45Z
dc.date.available	2015-11-18T23:17:45Z
dc.date.issued	2014	en
dc.description.abstract	Invasive species are an increasing problem, costing several billion dollars to the global economy. Monitoring methods are needed to provide scientists the necessary information to control populations of invasive species. Adaptive monitoring means that, for each additional survey, the monitoring design is updated based on the information obtained during the previous surveys. In this thesis we introduce a new general framework for adaptive monitoring. This method focuses on increasing the detection rate of invasive species over consecutive surveys. Compared with the existing monitoring methods, the proposed algorithm aims to improve adaptive monitoring by the following three points: (1) The use of a spatially balanced sampling design to select a sample in each survey, (2) by using both spatial and ecological information to update the monitoring strategy, and (3) by incorporating an eradication strategy to an adaptive monitoring design. In Chapter 1, we emphasise the need for the development of adaptive monitoring methods for invasive species. In Chapter 2, we outline the algorithm of our proposed method for adaptive monitoring and discuss the use of spatially balanced sampling designs. In Chapter 3, several sampling designs are introduced and their use for (adaptive) monitoring is evaluated. We give special attention to a new spatially balanced sampling design, named Balanced Acceptance Sampling, and compare it with a selection of existing (spatially balanced) sampling designs such as Generalized Random Tessellation Stratified sampling. In Chapter 4, we illustrate how ecological information can be used to update the monitoring strategy, which is demonstrated using a case study on the Asian tiger mosquito. In addition, several practical issues with probability sampling are discussed. In Chapter 5, the Nearest Unit Tessellation methods are introduced. These methods can model the observed spatial information of the species to update the monitoring strategy. Finally, we demonstrate how to combine ecological and spatial information to adjust a monitoring strategy. Chapter 6 also explores the use of a method to incorporate an eradication strategy to an adaptive monitoring strategy, using the Great White Butterfly data.	en
dc.identifier.uri	http://hdl.handle.net/10092/11363
dc.identifier.uri	http://dx.doi.org/10.26021/3236
dc.language	English
dc.language.iso	en
dc.publisher	University of Canterbury	en
dc.rights	All Right Reserved	en
dc.rights.uri	https://canterbury.libguides.com/rights/theses	en
dc.title	A new approach to adaptive monitoring	en
dc.type	Theses / Dissertations
thesis.degree.discipline	Statistics	en
thesis.degree.grantor	University of Canterbury	en
thesis.degree.level	Doctoral	en
thesis.degree.name	Doctor of Philosophy	en
uc.college	Faculty of Engineering	en