Sampling for detection, estimation and IPM decision making

Roger D. Moon, L. T.Ted Wilson

Research output: Chapter in Book/Report/Conference proceedingChapter

5 Scopus citations

Abstract

IPM requires information about populations of pest and beneficial organisms in managed and constructed habitats. A recurring question is whether potentially injurious pests are abundant enough to warrant intervention, or whether beneficial organisms or other factors are likely to maintain control. Because most managed habitats are too large to be examined completely, practitioners must sample them and draw an inference about the whole. Sampling plans in IPM can be grouped into three categories, depending on the sampler's goal. First, detection sampling is used in surveillance and regulatory applications, where the critical density of a target organism is effectively zero. Detection plans are designed to control the chance that the organism is erroneously missed. Second, estimation sampling is used where the goal is to quantify abundance, usually with desired levels of precision and reliability. Estimation sampling is used mainly in research, but it can also be used to evaluate IPM implementation and effectiveness. Finally, decision sampling is used where a choice to intervene with one or more management tactics depends on whether abundance has or will soon exceed a threshold density. Rather than estimate density, the goal is more simply to classify the habitat as needing or not needing intervention. In all three situations, the basic process is the same. A sampler selects a set of sample units from the habitat using a defined procedure, assesses each for presence or abundance of the target organism, and then draws a conclusion based on the results.

Original languageEnglish (US)
Title of host publicationIntegrated Pest Management
Subtitle of host publicationConcepts, Tactics, Strategies and Case Studies
PublisherCambridge University Press
Pages75-89
Number of pages15
ISBN (Electronic)9780511626463
ISBN (Print)9780521875950
DOIs
StatePublished - Jan 1 2008

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