Mr Lachlan J. Gretgrix1,2, Dr Jack L. Scanlan1, Dr Mark J. Blacket1,2, Assoc. Prof. Brendan C. Rodoni1,2, Dr Alexander M. Piper1,2
1Agriculture Victoria, Bundoora, Australia, 2La 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 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:
DNA metabarcoding is rapidly supplanting morphological identifications in ecological studies of invertebrates, yet deriving meaningful, quantitative estimates of abundance using this method remains a major challenge. Standard metabarcoding protocols applied to bulk samples are biased by species-specific morphological and molecular characteristics that distort estimated relative abundance. As mitochondrial loci are the most commonly used barcodes, mitochondrial copy-number (MCN) may be an under-addressed source of bias. MCN has the potential to vary significantly between taxa and life-stages, due to differences in morphology, tissue composition, and energy requirements. However, it is currently unclear how much MCN varies between taxa, life-stages and individuals, and how that may bias relative abundance estimates from metabarcoding.
Here, we present our work towards characterising bias caused by MCN variation, focusing on aphids. We applied two methods of estimating MCN — qPCR and whole genome sequencing (WGS) — to winged and wingless specimens from 10 aphid species, to test the hypothesis that MCN differs significantly both across species and between morphs. We also outline the complementary strategy of developing taxon-specific correction factors to improve the quantitative outputs and interpretability of metabarcoding datasets.
Our results have shown that MCN varies between aphid species and morphs, which likely serves as an additional source of bias for mitochondrial-based metabarcoding. We have developed two potential methods to estimate MCN from single specimens that can be used to calculate species-specific correction factors, which we aim to use to improve quantitative applications of invertebrate metabarcoding.