Optimal allocation of resources among general and species‐specific tools for plant pest biosecurity surveillance

Author:

Nguyen Hoa‐Thi‐Minh1ORCID,Chu Long1ORCID,Liebhold Andrew M.23ORCID,Epanchin‐Niell Rebecca4ORCID,Kean John M.5ORCID,Kompas Tom6ORCID,Robinson Andrew P.7ORCID,Brockerhoff Eckehard G.8ORCID,Moore Joslin L.910ORCID

Affiliation:

1. Crawford School of Public Policy Australian National University Canberra Australian Capital Territory Australia

2. USDA Forest Service Northern Research Station Morgantown West Virginia USA

3. Czech University of Life Sciences, Faculty of Forestry and Wood Sciences Prague Czech Republic

4. Department of Agricultural and Resource Economics University of Maryland College Park Maryland USA

5. AgResearch Limited, Ruakura Science Centre Hamilton New Zealand

6. Centre of Excellence for Biosecurity Risk Analysis, School of Biosciences and School of Ecosystem and Forest Sciences University of Melbourne Melbourne Victoria Australia

7. Centre of Excellence for Biosecurity Risk Analysis, Schools of Biosciences and Mathematics and Statistics University of Melbourne Melbourne Victoria Australia

8. Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) Birmensdorf Switzerland

9. Arthur Rylah Institute for Environmental Research, Department of Energy, Environment and Climate Action Heidelberg Victoria Australia

10. School of Biological Sciences Monash University Clayton Victoria Australia

Abstract

AbstractThis paper proposes a surveillance model for plant pests that can optimally allocate resources among survey tools with varying properties. While some survey tools are highly specific for the detection of a single pest species, others are more generalized. There is considerable variation in the cost and sensitivity of these tools, but there are no guidelines or frameworks for identifying which tools are most cost‐effective when used in surveillance programs that target the detection of newly invaded populations. To address this gap, we applied our model to design a trapping surveillance program in New Zealand for bark‐ and wood‐boring insects, some of the most serious forest pests worldwide. Our findings show that exclusively utilizing generalized traps (GTs) proves to be highly cost‐effective across a wide range of scenarios, particularly when they are capable of capturing all pest species. Implementing surveillance programs that only employ specialized traps (ST) is cost‐effective only when these traps can detect highly damaging pests. However, even in such cases, they significantly lag in cost‐effectiveness compared to GT‐only programs due to their restricted coverage. When both GTs and STs are used in an integrated surveillance program, the total expected cost (TEC) generally diminishes when compared to programs relying on a single type of trap. However, this relative reduction in TEC is only marginally larger than that achieved with GT‐only programs, as long as highly damaging species can be detected by GTs. The proportion of STs among the optimal required traps fluctuates based on several factors, including the relative pricing of GTs and STs, pest arrival rates, potential damage, and, more prominently, the coverage capacity of GTs. Our analysis suggests that deploying GTs extensively across landscapes appears to be more cost‐effective in areas with either very high or very low levels of relative risk density, potential damage, and arrival rate. Finally, STs are less likely to be required when the pests that are detected by those tools have a higher likelihood of successful eradication because delaying detection becomes less costly for these species.

Funder

Centre of Excellence for Biosecurity Risk Analysis

Publisher

Wiley

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3