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Info-gap theory to determine cost-effective eradication of invasive species

Invasive species eradication campaigns often fail due to stochastic arrival events, unpredictable detectability and incorrect resource allocation. Severe uncertainty in model parameter estimates may skew the eradication policy results. Using info-gap decision theory, this research aims to provide ma...

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Autores principales: Liu, Yang, Thomas, Melissa L., Coupland, Grey T., Wang, Penghao, Zheng, Dan, McKirdy, Simon J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9935532/
https://www.ncbi.nlm.nih.gov/pubmed/36797315
http://dx.doi.org/10.1038/s41598-023-29571-3
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author Liu, Yang
Thomas, Melissa L.
Coupland, Grey T.
Wang, Penghao
Zheng, Dan
McKirdy, Simon J.
author_facet Liu, Yang
Thomas, Melissa L.
Coupland, Grey T.
Wang, Penghao
Zheng, Dan
McKirdy, Simon J.
author_sort Liu, Yang
collection PubMed
description Invasive species eradication campaigns often fail due to stochastic arrival events, unpredictable detectability and incorrect resource allocation. Severe uncertainty in model parameter estimates may skew the eradication policy results. Using info-gap decision theory, this research aims to provide managers with a method to quantify their confidence in realizing successful eradication of particular invasive species within their specified eradication budgets (i.e. allowed eradication cost) in face of information-gaps. The potential introduction of the Asian house gecko Hemidactylus frenatus to Barrow Island, Australia is used as a case study to illustrate the model. Results of this research demonstrate that, more robustness to uncertainty in the model parameters can be earnt by (1) increasing the allowed eradication cost (2) investment in pre-border quarantine and border inspection (i.e. prevention) or (3) investment in post-border detection surveillance. The combination of a post-border spatial dispersal model and info-gap decision theory demonstrates a novel and spatially efficient method for managers to evaluate the robustness of eradication policies for incursion of invasive species with unexpected behaviour. These methods can be used to provide insight into the success of management goals, in particular the eradication of invasive species on islands or in broader mainland areas. These insights will assist in avoiding eradication failure and wasteful budget allocation and labour investment.
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spelling pubmed-99355322023-02-18 Info-gap theory to determine cost-effective eradication of invasive species Liu, Yang Thomas, Melissa L. Coupland, Grey T. Wang, Penghao Zheng, Dan McKirdy, Simon J. Sci Rep Article Invasive species eradication campaigns often fail due to stochastic arrival events, unpredictable detectability and incorrect resource allocation. Severe uncertainty in model parameter estimates may skew the eradication policy results. Using info-gap decision theory, this research aims to provide managers with a method to quantify their confidence in realizing successful eradication of particular invasive species within their specified eradication budgets (i.e. allowed eradication cost) in face of information-gaps. The potential introduction of the Asian house gecko Hemidactylus frenatus to Barrow Island, Australia is used as a case study to illustrate the model. Results of this research demonstrate that, more robustness to uncertainty in the model parameters can be earnt by (1) increasing the allowed eradication cost (2) investment in pre-border quarantine and border inspection (i.e. prevention) or (3) investment in post-border detection surveillance. The combination of a post-border spatial dispersal model and info-gap decision theory demonstrates a novel and spatially efficient method for managers to evaluate the robustness of eradication policies for incursion of invasive species with unexpected behaviour. These methods can be used to provide insight into the success of management goals, in particular the eradication of invasive species on islands or in broader mainland areas. These insights will assist in avoiding eradication failure and wasteful budget allocation and labour investment. Nature Publishing Group UK 2023-02-16 /pmc/articles/PMC9935532/ /pubmed/36797315 http://dx.doi.org/10.1038/s41598-023-29571-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Liu, Yang
Thomas, Melissa L.
Coupland, Grey T.
Wang, Penghao
Zheng, Dan
McKirdy, Simon J.
Info-gap theory to determine cost-effective eradication of invasive species
title Info-gap theory to determine cost-effective eradication of invasive species
title_full Info-gap theory to determine cost-effective eradication of invasive species
title_fullStr Info-gap theory to determine cost-effective eradication of invasive species
title_full_unstemmed Info-gap theory to determine cost-effective eradication of invasive species
title_short Info-gap theory to determine cost-effective eradication of invasive species
title_sort info-gap theory to determine cost-effective eradication of invasive species
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9935532/
https://www.ncbi.nlm.nih.gov/pubmed/36797315
http://dx.doi.org/10.1038/s41598-023-29571-3
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