Designing biology informed anthropogenically driven invasive forest pests risk models
department or school: Forestry, Faculty of
data provider: DSpace
degree campus: UBCV
creator: Srivastava, Vivek
issued: 2020-07-28T21:39:26Z
issued: 2020
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: Forest invasive species (FIS) pose significant threats to the economic and ecological stability of our forests. Given the potential impact of invasive species, tools are needed that will help prevent invasions, or enable effective early responses through robust interception and surveillance frameworks. Spatial models aim at identifying meaningful spatial relationships between different geographical features. Climate based spatial models have the ability to provide information on current and future potential for invasion and spread. Species distribution modelling (SDM) is a valuable tool for predicting the potential distribution of invasive species across space and time. While recent developments in modeling approaches and wider availability of environmental datasets have create advanced and more accurate SDM, in many cases the developed models ignore the associated underlying ecological processes. Moreover, bioclimatic variables usually included in the SDMs do not account for anthropogenic impacts on the response variable and the methods to integrate species traits as predicted by genetics into projections of species distributions are not generally used. In this PhD dissertation I address these key issues in particular reference to two pests and two pathogens that represent urgent threats. I explored the sensitivity of SDM modelling in two of the major ports in Canada and produced dispersal restricted projections of individual FIS distributions in various climate change scenarios. I also analyzed the methodology of determining climatic niches and compared the native and post-invasion niches of chosen FIS. I found that if I ignored the underlying FIS biology such as use of biologically relevant predictors, appropriate feature selection and inclusion of dispersal and biotic interactions when I developed SDMs, I obtained complex SDMs that provided an incomplete picture of the potential FIS invasion. Most of the representative invasives were far away from reaching niche…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/75304
note: Designing biology informed anthropogenically driven invasive forest pests risk models by Vivek Srivastava B.Sc., HNB Garhwal University, India, 2011 M.Tech., Indian Institute of Remote Sensing, India, 2013 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Forestry) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) July 2020 © Vivek Srivastava, 2020 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate And Postdoctoral Studies for acceptance, the dissertation entitled: Designing biology informed anthropogenically driven invasive forest pests risk models. Submitted by Vivek Srivastava in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Forestry Examining Committee: Dr. Verena C. Griess, Forestry Supervisor Dr. Richard C Hamelin, Forestry Supervisory Committee Member Dr. Nicholas C Coops, Forestry Supervisory Committee Member Dr. Allan Carroll, Forestry University Examiner Dr. Chris Harley, Zoology University Examiner Dr. Thomas J. Stohlgren, Dept. of Ecosystem Science and Sustainability, Colorado State University External Examiner iii Abstract Forest invasive species (FIS) pose significant threats to the economic and ecological stability of our forests. Given the potential impact of invasive species, tools are needed that will help prevent invasions, or enable effective early responses through robust interception and surveillance frameworks. Spatial models aim at identifying meaningful spatial relationships between different geographical features. Climate based spatial models have the ability to provide information on current and future potential for invasion and spread. Species distribution modelling (SDM) is a valuable tool for predicting the potential distribution of invasive species across space and time. While recent developments in modeling approaches and wider availability of environme…
has type: Thesis/Dissertation
date issued: 2020-11
is shown at: 10.14288/1.0392577
language: eng
degree discipline: Forestry
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/75304