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...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-02-01
|
| Series: | Methods in Ecology and Evolution |
| Subjects: | |
| Online Access: | https://doi.org/10.1111/2041-210X.14473 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|---|
| ISSN: | 2041-210X |