Optimal allocation of resources between control and surveillance for complex eradication scenarios
Abstract To ensure the success of complex invasive‐species eradication programs across large areas, efficient and effective resource allocation is crucial. This study incorporates analytical Bayesian solutions and measures of uncertainty into a framework of progressive management to guide optimal re...
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| Format: | Article |
| Language: | English |
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Wiley
2025-02-01
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| Series: | Methods in Ecology and Evolution |
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| Online Access: | https://doi.org/10.1111/2041-210X.14473 |
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| author | Mahdi Parsa David Ramsey Belinda Barnes |
| author_facet | Mahdi Parsa David Ramsey Belinda Barnes |
| author_sort | Mahdi Parsa |
| collection | DOAJ |
| description | Abstract To ensure the success of complex invasive‐species eradication programs across large areas, efficient and effective resource allocation is crucial. This study incorporates analytical Bayesian solutions and measures of uncertainty into a framework of progressive management to guide optimal resource allocation between control (mop‐ups) and surveillance programs. Shannon entropy is used to quantify uncertainty, accounting for often highly skewed and bimodal distributions, and the expected value of perfect information (EVPI) is incorporated to assess the potential benefits of reducing uncertainty in key model parameters. Results demonstrate that strategies that hedge against uncertainty can improve the robustness of management outcomes substantially with only marginal increases in expected costs, and EVPI analysis identifies conditions under which investment into control or surveillance becomes worthwhile. By systematically integrating uncertainty measures into the decision‐making process, this study provides a framework that leads to more reliable outcomes from eradication programs implemented progressively over large areas. |
| format | Article |
| id | doaj-art-ca5b67a7579f434884b095b2451895d2 |
| institution | Kabale University |
| issn | 2041-210X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | Methods in Ecology and Evolution |
| spelling | doaj-art-ca5b67a7579f434884b095b2451895d22025-02-05T05:43:20ZengWileyMethods in Ecology and Evolution2041-210X2025-02-0116238839910.1111/2041-210X.14473Optimal allocation of resources between control and surveillance for complex eradication scenariosMahdi Parsa0David Ramsey1Belinda Barnes2Australian Bureau of Agricultural and Resource Economics and Sciences Canberra Australian Capital Territory AustraliaDepartment of Energy, Environment and Climate Action Arthur Rylah Institute Heidelberg Victoria AustraliaAustralian Bureau of Agricultural and Resource Economics and Sciences Canberra Australian Capital Territory AustraliaAbstract To ensure the success of complex invasive‐species eradication programs across large areas, efficient and effective resource allocation is crucial. This study incorporates analytical Bayesian solutions and measures of uncertainty into a framework of progressive management to guide optimal resource allocation between control (mop‐ups) and surveillance programs. Shannon entropy is used to quantify uncertainty, accounting for often highly skewed and bimodal distributions, and the expected value of perfect information (EVPI) is incorporated to assess the potential benefits of reducing uncertainty in key model parameters. Results demonstrate that strategies that hedge against uncertainty can improve the robustness of management outcomes substantially with only marginal increases in expected costs, and EVPI analysis identifies conditions under which investment into control or surveillance becomes worthwhile. By systematically integrating uncertainty measures into the decision‐making process, this study provides a framework that leads to more reliable outcomes from eradication programs implemented progressively over large areas.https://doi.org/10.1111/2041-210X.14473Bayesian modelsinvasive species eradicationresource allocationShannon entropyvalue of perfect information |
| spellingShingle | Mahdi Parsa David Ramsey Belinda Barnes Optimal allocation of resources between control and surveillance for complex eradication scenarios Methods in Ecology and Evolution Bayesian models invasive species eradication resource allocation Shannon entropy value of perfect information |
| title | Optimal allocation of resources between control and surveillance for complex eradication scenarios |
| title_full | Optimal allocation of resources between control and surveillance for complex eradication scenarios |
| title_fullStr | Optimal allocation of resources between control and surveillance for complex eradication scenarios |
| title_full_unstemmed | Optimal allocation of resources between control and surveillance for complex eradication scenarios |
| title_short | Optimal allocation of resources between control and surveillance for complex eradication scenarios |
| title_sort | optimal allocation of resources between control and surveillance for complex eradication scenarios |
| topic | Bayesian models invasive species eradication resource allocation Shannon entropy value of perfect information |
| url | https://doi.org/10.1111/2041-210X.14473 |
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