The structure of global invasive assemblages and their relationship to regional habitat variables: converting scientifically relevant data into decision relevant information
type: CreativeWork
description: Quantitative methods for pest risk assessment combine sound statistical tools with sound ecological theory to convert scientifically relevant data into decision-relevant information. This thesis investigated a quantitative method for pest risk assessment called pest profile analysis (PPA). PPA is a new methodology that is based on the premise that the risk of invasion by crop pests into new areas can be predicted by analysing regional insect pest assemblages (also known as pest profiles). Regional pest assemblages comprise the presence or absence of recognised pest species in each region of the world. The analysis involves clustering these regions based on similarities between their pest profiles. PPA assumes that co-occurrence of pest species in a region is the outcome of a non-random structured process driven by biotic and abiotic characteristics of the region. The most commonly used clustering technique for grouping regional pest assemblages is a self-organizing map (SOM), which is an artificial neural network algorithm. Two other clustering methods that have also been used for PPA are hierarchical clustering (HC) and k-means. The main aim of this thesis was to perform a thorough validation test of the PPA approach. To do so, I first analysed the sensitivity of SOM PPA to changes in the number of species used as input data. The results showed that SOM PPA outputs (weight values that are interpreted as risk indices) were quite sensitive to changes in the input data. However, when the risk indices were transformed into ranked lists of species, the ranks were significantly less sensitive and hence potentially more useful for pest risk assessment. I assessed the validity of the groups (clusters) of regions obtained from a SOM PPA by applying an external validation measure, the ζ diversity metric. The ζ metric was used to quantify similarities between pest profiles within clusters. The results showed it can be used for assessing the uncertainty associated with PPA out…
abstract: Quantitative methods for pest risk assessment combine sound statistical tools with sound ecological theory to convert scientifically relevant data into decision-relevant information. This thesis investigated a quantitative method for pest risk assessment called pest profile analysis (PPA). PPA is a new methodology that is based on the premise that the risk of invasion by crop pests into new areas can be predicted by analysing regional insect pest assemblages (also known as pest profiles). Regional pest assemblages comprise the presence or absence of recognised pest species in each region of the world. The analysis involves clustering these regions based on similarities between their pest profiles. PPA assumes that co-occurrence of pest species in a region is the outcome of a non-random structured process driven by biotic and abiotic characteristics of the region. The most commonly used clustering technique for grouping regional pest assemblages is a self-organizing map (SOM), which is an artificial neural network algorithm. Two other clustering methods that have also been used for PPA are hierarchical clustering (HC) and k-means. The main aim of this thesis was to perform a thorough validation test of the PPA approach. To do so, I first analysed the sensitivity of SOM PPA to changes in the number of species used as input data. The results showed that SOM PPA outputs (weight values that are interpreted as risk indices) were quite sensitive to changes in the input data. However, when the risk indices were transformed into ranked lists of species, the ranks were significantly less sensitive and hence potentially more useful for pest risk assessment. I assessed the validity of the groups (clusters) of regions obtained from a SOM PPA by applying an external validation measure, the ζ diversity metric. The ζ metric was used to quantify similarities between pest profiles within clusters. The results showed it can be used for assessing the uncertainty associated with PPA out…
author: Roigé Valiente, Mariona
language: en
keywords: SOM PPA, macroecology, invasive assemblages, zeta diversity, quantitative methods for pest risk assessment, pest profile analysis, biotic homogenization, clustering, invasive ecology, ANZSRC::070308 Crop and Pasture Protection (Pests, Diseases and Weeds), ANZSRC::0501 Ecological Applications, ANZSRC::0602 Ecology
license: https://creativecommons.org/licenses/by-nc-nd/4.0/
date published: 2017-03-31
date modified: 2026-07-24T02:51:05Z
provider: Lincoln University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Lincoln University

PropertyValue
property ID: Handle
value: https://hdl.handle.net/10182/8388