Metabarcoding and applied ecology with hyperdiverse organisms: Recommendations for biological control research.

Autor: Lue CH; Department of Biology, Brooklyn College, City University of New York, New York City, New York, USA., Abram PK; Agriculture and Agri-Food Canada, Agassiz Research and Development Centre, Agassiz, British Columbia, Canada., Hrcek J; Biology Centre of the Czech Academy of Sciences, Institute of Entomology, Ceske Budejovice, Czech Republic., Buffington ML; Systematic Entomology Laboratory, ARS/USDA c/o Smithsonian Institution, National Museum of Natural History, Washington, DC, USA., Staniczenko PPA; Department of Biology, Brooklyn College, City University of New York, New York City, New York, USA.
Jazyk: angličtina
Zdroj: Molecular ecology [Mol Ecol] 2023 Dec; Vol. 32 (23), pp. 6461-6473. Date of Electronic Publication: 2022 Sep 12.
DOI: 10.1111/mec.16677
Abstrakt: Metabarcoding is revolutionizing fundamental research in ecology by enabling large-scale detection of species and producing data that are rich with community context. However, the benefits of metabarcoding have yet to be fully realized in fields of applied ecology, especially those such as classical biological control (CBC) research that involve hyperdiverse taxa. Here, we discuss some of the opportunities that metabarcoding provides CBC and solutions to the main methodological challenges that have limited the integration of metabarcoding in existing CBC workflows. We focus on insect parasitoids, which are popular and effective biological control agents (BCAs) of invasive species and agricultural pests. Accurately identifying native, invasive and BCA species is paramount, since misidentification can undermine control efforts and lead to large negative socio-economic impacts. Unfortunately, most existing publicly accessible genetic databases cannot be used to reliably identify parasitoid species, thereby limiting the accuracy of metabarcoding in CBC research. To address this issue, we argue for the establishment of authoritative genetic databases that link metabarcoding data to taxonomically identified specimens. We further suggest using multiple genetic markers to reduce primer bias and increase taxonomic resolution. We also provide suggestions for biological control-specific metabarcoding workflows intended to track the long-term effectiveness of introduced BCAs. Finally, we use the example of an invasive pest, Drosophila suzukii, in a reflective "what if" thought experiment to explore the potential power of community metabarcoding in CBC.
(© 2022 John Wiley & Sons Ltd.)
Databáze: MEDLINE
Externí odkaz: