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Updated: May 5, 2026

A Method for Quantifying Foliage-Dwelling Arthropods
Published on: October 20, 2019
The detectability half-life in arthropod predator-prey research: what it is, why we need it, how to measure it, and
Matthew H Greenstone1, Mark E Payton, Donald C Weber
1U.S.D.A. - Agricultural Research Service, Invasive Insect Biocontrol and Behavior Laboratory, 10300 Baltimore Avenue, Beltsville, MD, 20705, USA.
Abstract:
Molecular gut-content analysis enables detection of arthropod predation with minimal disruption of ecosystem processes. Most assays produce only qualitative results, with each predator testing either positive or negative for target prey remains. Nevertheless, they have yielded important insights into community processes. For example, they have confirmed the long-hypothesized role of generalist predators in retarding early-season build-up of pest populations prior to the arrival of more specialized predators and parasitoids and documented the ubiquity of secondary and intraguild predation. However, raw qualitative gut-content data cannot be used to assess the relative impact of different predator taxa on prey population dynamics: they must first be weighted by the relative detectability periods for molecular prey remains for each predator-prey combination. If this is not carried out, interpretations of predator impact will be biased towards those with the longest detectabilities. We review the challenges in determining detectability half-lives, including unstated assumptions that have often been ignored in the performance of feeding trials. We also show how detectability half-lives can be used to properly weight assay data to rank predators by their importance in prey population suppression, and how sets of half-lives can be used to test hypotheses concerning predator ecology and physiology. We use data from 32 publications, comprising 97 half-lives, to generate and test hypotheses on taxonomic differences in detectability half-lives and discuss the possible role of the detectability half-life in interpreting qPCR and next-generation sequencing data.
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