東華大學圖書館 |

Analytical Approaches for Plant Pest Management Across the Biosecurity Continuum.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Analytical Approaches for Plant Pest Management Across the Biosecurity Continuum./
作者:	Montgomery, Kellyn Paige.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	141 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-06, Section: B.
Contained By:	Dissertations Abstracts International83-06B.
標題:	Pathogens. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28847841
ISBN:	9798759924739

Analytical Approaches for Plant Pest Management Across the Biosecurity Continuum.
Montgomery, Kellyn Paige.

Analytical Approaches for Plant Pest Management Across the Biosecurity Continuum. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 141 p.

Source: Dissertations Abstracts International, Volume: 83-06, Section: B.

Thesis (Ph.D.)--North Carolina State University, 2021.

This item must not be sold to any third party vendors.

Non-native plant pests and pathogens threaten biodiversity, ecosystem function, food security, and economic livelihoods. Preventing, minimizing, and mitigating damage from invasive non-native plant pests involve dynamic, multiscale strategies that are agile enough to respond to rapidly changing conditions while balancing economic and environmental trade-offs. Geospatial analytical approaches are uniquely suited to address this challenge by leveraging spatio-temporal data to understand patterns of outbreaks and create predictive models that support management decision-making. In this dissertation, I develop and apply geospatial tools and analytics for plant pest management at global, national, and local scales. In Chapter 2, I present a species-agnostic, spatio-temporal stochastic network model called PoPS (Pest or Pathogen Spread) Global that couples international trade networks with core drivers of biological invasions to forecast the spread of non-native plant pests. The modular design of the framework supports early, proactive responses for emerging pests even when limited data are available and enables forecasts at different spatial and temporal resolutions. I demonstrate the framework using a case study of the invasive planthopper spotted lanternfly, Lycorma delicatula (White) (Hemiptera: Fulgoridae), and test management intervention scenarios. Simulating spotted lanternfly spread through trade networks, the model predicted that Japan, S. Korea, the U.S., and Germany had the highest probabilities of bridgehead population establishment. In Chapter 3, I develop an open-source software package called PoPS (Pest or Pathogen Spread) Border for measuring inspection outcomes for consignments with variations in size, cargo configuration, contamination rates, and contaminant arrangements for designing effective risk-based sampling protocols. I use the tool to estimate contamination rates from historical interception data, quantify trade-offs in effectiveness and workload for inspection strategies, and identify vulnerabilities in sampling protocols as changes in cargo configurations and contamination occur. This work represents first steps toward a decision support tool for creating dynamic inspection protocols that respond to changes in available resources, workload, and commerce trends. In Chapter 4, I investigate the use of 3D crop canopy mapping with unmanned aerial systems (UAS) for field-scale crop scouting and stress detection. Using nutrient stress in fluecured tobacco as a case study, I computed structural metrics and a visible band spectral index from high resolution canopy surface models and orthoimages to assess the relationship between plant nutrient status and UAS-derived metrics. Combining information about canopy structure and spectral reflectance increased model fit for all measured nutrients compared to spectral alone. These results demonstrate that an important relationship exists between relative canopy shape and crop health that can be leveraged to improve the usefulness of low-cost UAS for remote crop stress monitoring. The research presented in this dissertation advances multiscale, interdisciplinary approaches that contribute to reducing global migration of non-native species, improving biosecurity efforts at ports of entry, and detecting field-scale crop stress.

ISBN: 9798759924739Subjects--Topical Terms:

3540520
Pathogens.

Analytical Approaches for Plant Pest Management Across the Biosecurity Continuum.
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520 $a Non-native plant pests and pathogens threaten biodiversity, ecosystem function, food security, and economic livelihoods. Preventing, minimizing, and mitigating damage from invasive non-native plant pests involve dynamic, multiscale strategies that are agile enough to respond to rapidly changing conditions while balancing economic and environmental trade-offs. Geospatial analytical approaches are uniquely suited to address this challenge by leveraging spatio-temporal data to understand patterns of outbreaks and create predictive models that support management decision-making. In this dissertation, I develop and apply geospatial tools and analytics for plant pest management at global, national, and local scales. In Chapter 2, I present a species-agnostic, spatio-temporal stochastic network model called PoPS (Pest or Pathogen Spread) Global that couples international trade networks with core drivers of biological invasions to forecast the spread of non-native plant pests. The modular design of the framework supports early, proactive responses for emerging pests even when limited data are available and enables forecasts at different spatial and temporal resolutions. I demonstrate the framework using a case study of the invasive planthopper spotted lanternfly, Lycorma delicatula (White) (Hemiptera: Fulgoridae), and test management intervention scenarios. Simulating spotted lanternfly spread through trade networks, the model predicted that Japan, S. Korea, the U.S., and Germany had the highest probabilities of bridgehead population establishment. In Chapter 3, I develop an open-source software package called PoPS (Pest or Pathogen Spread) Border for measuring inspection outcomes for consignments with variations in size, cargo configuration, contamination rates, and contaminant arrangements for designing effective risk-based sampling protocols. I use the tool to estimate contamination rates from historical interception data, quantify trade-offs in effectiveness and workload for inspection strategies, and identify vulnerabilities in sampling protocols as changes in cargo configurations and contamination occur. This work represents first steps toward a decision support tool for creating dynamic inspection protocols that respond to changes in available resources, workload, and commerce trends. In Chapter 4, I investigate the use of 3D crop canopy mapping with unmanned aerial systems (UAS) for field-scale crop scouting and stress detection. Using nutrient stress in fluecured tobacco as a case study, I computed structural metrics and a visible band spectral index from high resolution canopy surface models and orthoimages to assess the relationship between plant nutrient status and UAS-derived metrics. Combining information about canopy structure and spectral reflectance increased model fit for all measured nutrients compared to spectral alone. These results demonstrate that an important relationship exists between relative canopy shape and crop health that can be leveraged to improve the usefulness of low-cost UAS for remote crop stress monitoring. The research presented in this dissertation advances multiscale, interdisciplinary approaches that contribute to reducing global migration of non-native species, improving biosecurity efforts at ports of entry, and detecting field-scale crop stress.
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