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最优入侵物种监测的现实世界:研究带来的实际进展。

Optimal invasive species surveillance in the real world: practical advances from research.

机构信息

USDA Forest Service, Southern Research Station, Eastern Forest Environmental Threat Assessment Center, Research Triangle Park, NC, U.S.A.

Natural Resources Canada, Canadian Forest Service, Great Lakes Forestry Centre, Sault Ste. Marie, ON, Canada.

出版信息

Emerg Top Life Sci. 2020 Dec 15;4(5):513-520. doi: 10.1042/ETLS20200305.

Abstract

When alien species make incursions into novel environments, early detection through surveillance is critical to minimizing their impacts and preserving the possibility of timely eradication. However, incipient populations can be difficult to detect, and usually, there are limited resources for surveillance or other response activities. Modern optimization techniques enable surveillance planning that accounts for the biology and expected behavior of an invasive species while exploring multiple scenarios to identify the most cost-effective options. Nevertheless, most optimization models omit some real-world limitations faced by practitioners during multi-day surveillance campaigns, such as daily working time constraints, the time and cost to access survey sites and personnel work schedules. Consequently, surveillance managers must rely on their own judgments to handle these logistical details, and default to their experience during implementation. This is sensible, but their decisions may fail to address all relevant factors and may not be cost-effective. A better planning strategy is to determine optimal routing to survey sites while accounting for common daily logistical constraints. Adding site access and other logistical constraints imposes restrictions on the scope and extent of the surveillance effort, yielding costlier but more realistic expectations of the surveillance outcomes than in a theoretical planning case.

摘要

当外来物种入侵新环境时,通过监测进行早期检测对于最大限度地减少其影响和保留及时根除的可能性至关重要。然而,初期种群可能难以检测,而且通常用于监测或其他应对活动的资源有限。现代优化技术可以进行监测规划,考虑入侵物种的生物学和预期行为,同时探索多种情况以确定最具成本效益的选择。然而,大多数优化模型忽略了实践者在多日监测活动中面临的一些实际限制,例如每天的工作时间限制、访问调查地点的时间和成本以及人员工作计划。因此,监测管理人员必须依靠自己的判断来处理这些后勤细节,并在实施过程中依靠自己的经验。这是合理的,但他们的决策可能无法解决所有相关因素,也可能无法做到成本效益最大化。更好的规划策略是在考虑常见日常后勤限制的情况下,确定调查地点的最佳路线。增加站点访问和其他后勤限制会限制监测工作的范围和程度,从而产生比理论规划案例更昂贵但更现实的监测结果预期。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2096/7803343/ec91604feb06/ETLS-4-513-g0001.jpg

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