Use of Geospatial Methods to Characterize Dispersion of the Emerald Ash Borer in Southern Ontario, Canada

Simple item page
dc.contributor.advisorHu, Baoxin
dc.creatorTasneem, Farah
dc.date.accessioned2019-07-02T16:16:45Z
dc.date.available2019-07-02T16:16:45Z
dc.date.copyright2019-03-01
dc.date.issued2019-07-02
dc.date.updated2019-07-02T16:16:44Z
dc.degree.disciplineEarth & Space Science
dc.degree.levelMaster's
dc.degree.nameMSc - Master of Science
dc.description.abstractSince the introduction of the Asian Emerald Ash Borer beetle (EAB, Agrilus planipennis) to Southern Ontario in 2002, the condition of all species of Ash trees (Fraxinus) in the province is currently at risk. In this research, the effects of positive spatial autocorrelation on the EAB data as a result of sampling bias was addressed by applying a filtering distance threshold. To analyze the impact of environmental and anthropogenic predictors on the distribution of the EAB, logistic regression, Random Forest (RF) and a hybrid of Random Forest and GLM known as the Random Generalized Linear Model (RGLM) were applied to EAB data from 2006-2012 across Ontario. Ultimately, three risk maps were created from the 2006-2012 EAB data to validate the prediction dataset from 2013. In terms of model transferability, RGLM had the best extrapolation accuracy (84%), followed by stepwise backward logistic regression (70%), and Random Forest (52%).
dc.identifier.urihttp://hdl.handle.net/10315/36285
dc.language.isoen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectEcology
dc.subject.keywordsPredictive modelling
dc.subject.keywordsSpecies distribution modelling
dc.subject.keywordsGIS modelling
dc.subject.keywordsGIS
dc.subject.keywordsGeospatial analysis
dc.subject.keywordsEAB modelling
dc.subject.keywordsEAB
dc.subject.keywordsRisk maps
dc.subject.keywordsRandom Forest
dc.subject.keywordsRGLM
dc.subject.keywordsData analysis
dc.subject.keywordsMulticollinearity
dc.subject.keywordsSpatial autocorrelation
dc.subject.keywordsSpecies data
dc.titleUse of Geospatial Methods to Characterize Dispersion of the Emerald Ash Borer in Southern Ontario, Canada
dc.typeElectronic Thesis or Dissertation

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Tasneem_Farah_2019_Msc.pdf
Size:
2.42 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 2 of 2
No Thumbnail Available
Name:
license.txt
Size:
1.83 KB
Format:
Plain Text
Description:
Download
No Thumbnail Available
Name:
YorkU_ETDlicense.txt
Size:
3.38 KB
Format:
Plain Text
Description:
Download

Collections

Earth & Space Science

All items in the YorkSpace institutional repository are protected by copyright, with all rights reserved except where explicitly noted.