Non-destructive insect metabarcoding as a surveillance tool for the Australian grains industry: a first trial for the iMapPESTS smart trap

By

Martoni, Francesco
Smith, Reannon L.
Piper, Alexander M.
Nancarrow, Narelle
Aftab, Mohammad
Trebicki, Piotr
Kimber, Rohan B. E.
Rodoni, Brendan C.
Blacket, Mark J

Type

Article

Date of Publication

2023-2-2

Original Publication

Metabarcoding and metagenomics

Volume

7

Pages

1--17

Subjects

Aphids , biosecurity , diversity , environmental data , Morphology

Contributed by

Pensoft Publishers

Language

English

Published by

Bulgaria Pensoft Publishers 2017-

Identifiers

DOI: https://doi.org/10.3897/mbmg.7.95650

 

Abstract

Surveillance and long-term monitoring of insect pest populations are of paramount importance to limit dispersal and inform pest management. Molecular methods have been employed in diagnostics, surveillance and monitoring for the past few decades, often paired with more traditional techniques relying on morphological examinations. Within this context, the ‘iMapPESTS: Sentinel Surveillance for Agriculture’ project was conceptualised to enhance on-farm pest management decision-making via development and deployment of smart traps, able to collect insects, as well as recording associated environmental data. Here, we compared an iMapPESTS ‘Sentinel’ smart trap to an alternative suction trap over a 10-week period. We used a non-destructive insect metabarcoding approach complemented by insect morphological diagnostics to assess and compare aphid species presence and diversity across trap samples and time. Furthermore, we paired this with environmental data recorded throughout the sampling period. This methodology recorded a total of 497 different taxa from 70 traps over a 10-week period in the grain-growing region in western Victoria. This included not only the 14 aphid target species, but an additional 12 aphid species, including a new record for Victoria. Ultimately, with more than 450 bycatch species detected, this highlighted the value of insect metabarcoding, not only for pest surveillance, but also at a broader ecosystem level, with potential applications in integrated pest management and biocontrol.