Detection of an insect pest, the Green vegetable bug, using eDNA

Dr Justine Larrouy1, Dr Tom Moore1, Mr Simon Bulman1

1Plant And Food Research, Lincoln, New Zealand

Biography:

Justine has been hired as a scientist at PFR in the last 1.5 years. She is working on a range of projects from eDNA to the plant microbiome. Justine recently completed her PhD on the floral microbiome of Leptospermum scoparium (mānuka) [with Manpreet Dhami, Eirian Jones, Hayley Ridgway]

Abstract:

Environmental DNA methods offer new opportunities for detecting invasive pests, especially species where current methods are expensive or of low efficacy. While substantial advances in eDNA detections have occurred from aquatic DNA sources, there has been less pest detection from terrestrial environments. Some of the earliest terrestrial work focused on a pentatomid insect pest, the Brown Marmorated Stink Bug (BMSB), Halyomorpha halys. BMSB was detectable in the field from fruit rinsed with deionised water followed by filtration, or from eDNA collected from plants with paint rollers.

We aimed to adapt BMSB eDNA detection for operation in New Zealand, where the insect is a high incursion risk. A particular problem for border detections is that BMSB are attracted to aggregation pheromone traps but often stop on vegetation nearby and remain undetected. In our project, we worked with the model pentatomid species, the Green Vegetable bug (GVB), Nezara viridula. We tested the limits of GVB detection from leaf and fruit surfaces exposed to insects under laboratory conditions. eDNA from leaves was captured using washing and filtration on 0.45 micron polyethersulfone (PES) filters. Detection of insect DNA was carried out using qPCR assays targeting sequences from the GVB mitochondrial cytochrome oxidase 1 (COI) gene or the ribosomal rRNA internal transcribed spacer regions. Assessment of selected samples was also performed with COI DNA metabarcoding.

Results of insect detections over various times and at different densities will be presented, together with statistical assessment of detection probabilities.