Mr Lachlan J. Gretgrix1,2, Dr Jack L. Scanlan1, Dr Francesco Martoni1, Dr Mark J. Blacket1, Dr Brendan C. Rodoni1,2, Dr Paul Cunningham1,2, Dr Alexander M. Piper1

1Agriculture Victoria Research, Agribio Centre, 5 Ring Road, Bundoora, Australia, 2School of Applied Systems Biology, La Trobe University, Bundoora, Australia

Biography:

Lachlan Gretgrix is a PhD candidate with La Trobe University and Agriculture Victoria, where his research focuses on the application of high-throughput DNA metabarcoding to improve the monitoring of insect populations in agricultural ecosystems. He holds a Master of Science degree from La Trobe University with the thesis title “The Hidden Population Structure of Australian Terrestrial Invertebrates in a Fire Prone Landscape”, a chapter of which he recently published, entitled “Genetic diversity of a short-ranged endemic terrestrial snail”. Lachlan has a keen interest in the applications of metabarcoding for increasing our understanding of the population dynamics of both exotic and native invertebrate species within Australia.

Abstract:

As entomological studies increasingly transition from morphological identification of individual specimens to high-throughput DNA metabarcoding of unsorted trap samples, producing quantitative data that is comparable with results from traditional methods has become an important goal. This is particularly relevant for monitoring pests and beneficial insects in agroecosystems, where estimates of insect abundance are necessary for decision-making, such as timely spraying of pesticides and releases of biocontrol agents. Current metabarcoding protocols for analysing bulk insect samples only provide an estimate of relative abundance of species present and introduce a number of biases that distort the proportion of metabarcoding reads produced for each species relative to the true proportion of individuals/DNA molecules in the community. Here, I will present our findings on detecting and quantifying taxonomic bias present in the metabarcoding pipeline using mock communities composed of several insect orders typically observed on grain farms. I will also outline possible avenues to account for these biases, including Unique Molecular Identifiers (UMIs) and taxon-specific correction factors, which will be explored further throughout my PhD project. This work is part of a larger Grains Research and Development Corporation funded project focusing on the detection and monitoring of endemic and exotic grain pests. Through the development of quantitative metabarcoding approaches for monitoring the abundance of pest, beneficial, and native species collected from bulk insect traps, we aim to provide more reliable recommendations for grain growers and broaden our understanding of insect diversity in agroecosystems.