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Quantitative Modeling Tools for Inva...

Thompson, Brielle Kwarta,

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Quantitative Modeling Tools for Invasive Species Management Decisions /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Quantitative Modeling Tools for Invasive Species Management Decisions // Brielle Kwarta Thompson.
Author:	Thompson, Brielle Kwarta,
Description:	1 electronic resource (236 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-01, Section: B.
Contained By:	Dissertations Abstracts International86-01B.
Subject:	Natural resource management. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31301629
ISBN:	9798383226506

Quantitative Modeling Tools for Invasive Species Management Decisions /
Thompson, Brielle Kwarta,

Quantitative Modeling Tools for Invasive Species Management Decisions /Brielle Kwarta Thompson. - 1 electronic resource (236 pages)

Source: Dissertations Abstracts International, Volume: 86-01, Section: B.

Invasive species threaten biodiversity and cause harm to the environment, economies, and human health. Natural resource managers tasked with determining management plans for controlling or eradicating invasive species often grapple with challenges such as ecosystem complexity, uncertainty about the effectiveness of management actions, limited budgets, and conflict with the public regarding management practices. Quantitative population models applied to invasive species management provide a cost-effective tool for evaluating management outcomes in a virtual environment before management is implemented. In particular, simulation models provide insight into the performance of alternatives under varying ecological states and management assumptions prior to substantial time investment or expensive on-the-ground experiments.Here, I demonstrate how quantitative models can be harnessed to effectively inform invasive species management decisions. First, I provide an extensive review of mechanistic models that are used for invasive species management to address the gap between those who build models and those who are tasked with actual management implementation (Chapter 2). Second, I provide a simulation study to assess different spatial strategies for invasive rusty crayfish (Faxonius rusticus) removal in the complex riverine environment of the John Day River, USA (Chapter 3). The model indicated that to minimize overall population abundance, crayfish should be removed in locations where their abundance is highest, and removal at the most downstream extent of their invasion is key for preventing invasion to new areas, i.e., the Columbia River, USA. Third, I provide an adaptive management framework for invasive flowering rush (Butomus umbellatus) management in the Columbia River to support decisions regarding allocation of resources towards monitoring and control under two invasion conditions (Chapter 4). The model revealed that for an established invasion, it was beneficial to conduct monitoring and removal at spatially fixed areas, whereas for an emerging invasion, effort can be more effectively allocated in highly invaded areas. The model also indicated that for an emerging invasion, managers may benefit by integrating community science data into their monitoring to help track the emerging invasion. Finally, I examined how to identify optimal invasive species management actions involving multiple decision makers, each with their own management objectives (Chapter 5). To do so, I compared multiple-criteria decision analysis (MCDA), used for decisions involving multiple objectives, and game theory, used in circumstances with multiple decision makers. I showed that MCDA sometimes failed to reveal invasive species harvest actions that were identified in game-theoretic analyses as providing improved outcomes, but MCDA provided better insight into the preferred actions of each individual decision maker. Overall, my research demonstrates ways in which quantitative models can be used to help decision makers identify promising solutions to invasive species management. Broadly, my research demonstrates ways in which quantitative modeling tools can be used to help inform decision making in natural resource management.

English

ISBN: 9798383226506Subjects--Topical Terms:

589570
Natural resource management.
Subjects--Index Terms:

Decision analysis

Quantitative Modeling Tools for Invasive Species Management Decisions /
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300 $a 1 electronic resource (236 pages)
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500 $a Source: Dissertations Abstracts International, Volume: 86-01, Section: B.
500 $a Advisors: Converse, Sarah J.; Olden, Julian D. Committee members: Berdahl, Andrew M.
502 $b Ph.D. $c University of Washington $d 2024.
520 $a Invasive species threaten biodiversity and cause harm to the environment, economies, and human health. Natural resource managers tasked with determining management plans for controlling or eradicating invasive species often grapple with challenges such as ecosystem complexity, uncertainty about the effectiveness of management actions, limited budgets, and conflict with the public regarding management practices. Quantitative population models applied to invasive species management provide a cost-effective tool for evaluating management outcomes in a virtual environment before management is implemented. In particular, simulation models provide insight into the performance of alternatives under varying ecological states and management assumptions prior to substantial time investment or expensive on-the-ground experiments.Here, I demonstrate how quantitative models can be harnessed to effectively inform invasive species management decisions. First, I provide an extensive review of mechanistic models that are used for invasive species management to address the gap between those who build models and those who are tasked with actual management implementation (Chapter 2). Second, I provide a simulation study to assess different spatial strategies for invasive rusty crayfish (Faxonius rusticus) removal in the complex riverine environment of the John Day River, USA (Chapter 3). The model indicated that to minimize overall population abundance, crayfish should be removed in locations where their abundance is highest, and removal at the most downstream extent of their invasion is key for preventing invasion to new areas, i.e., the Columbia River, USA. Third, I provide an adaptive management framework for invasive flowering rush (Butomus umbellatus) management in the Columbia River to support decisions regarding allocation of resources towards monitoring and control under two invasion conditions (Chapter 4). The model revealed that for an established invasion, it was beneficial to conduct monitoring and removal at spatially fixed areas, whereas for an emerging invasion, effort can be more effectively allocated in highly invaded areas. The model also indicated that for an emerging invasion, managers may benefit by integrating community science data into their monitoring to help track the emerging invasion. Finally, I examined how to identify optimal invasive species management actions involving multiple decision makers, each with their own management objectives (Chapter 5). To do so, I compared multiple-criteria decision analysis (MCDA), used for decisions involving multiple objectives, and game theory, used in circumstances with multiple decision makers. I showed that MCDA sometimes failed to reveal invasive species harvest actions that were identified in game-theoretic analyses as providing improved outcomes, but MCDA provided better insight into the preferred actions of each individual decision maker. Overall, my research demonstrates ways in which quantitative models can be used to help decision makers identify promising solutions to invasive species management. Broadly, my research demonstrates ways in which quantitative modeling tools can be used to help inform decision making in natural resource management.
546 $a English
590 $a School code: 0250
650 4 $a Natural resource management. $3 589570
650 4 $a Aquatic sciences. $3 3174300
650 4 $a Applied mathematics. $3 2122814
653 $a Decision analysis
653 $a Invasive species
653 $a Population modeling
653 $a Resource management
653 $a Human health
690 $a 0528
690 $a 0792
690 $a 0364
710 2 $a University of Washington. $b Quantitative Ecology and Resource Management. $e degree granting institution. $3 3816872
720 1 $a Converse, Sarah J. $e degree supervisor.
720 1 $a Olden, Julian D. $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-01B.
790 $a 0250
791 $a Ph.D.
792 $a 2024
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31301629