Dr Joshua Kestel1,2, A/Prof Bill Bateman1,2,3, A/Prof David Field4, Dr Nicole White1, A/Prof. Paul Nevill1,2
1Trace and Environmental DNA (TrEnD) Laboratory, School of Molecular and Life Sciences, Curtin University, Perth, Australia, 2Minesite Biodiversity Monitoring with eDNA (MBioMe) Research Group, School of Molecular and Life Sciences, Curtin University, Perth, Australia, 3Behavioural Ecology Laboratory, School of Molecular and Life Sciences, Curtin University, Perth, Australia, 4Applied Biosciences, Macquarie University, Sydney, Australia
Biography:
I am a molecular ecologist based at Curtin University in Perth, Western Australia, and I work on everything from soil microbes to mammals. My main interest is eDNA based monitoring of terrestrial biodiversity and I work closely with the resources and agriculture sectors.
Abstract:
Collating data about natural capital and the ecosystem services that underpin agricultural productivity, such as the activity of beneficial (e.g. pollinators) and antagonistic (e.g. plant pests) native and introduced arthropod taxa, is critical for timely management strategies. To date, these monitoring efforts have largely relied upon conventional survey and monitoringmethods (e.g. sweep netting and morphological identifications), which are difficult to implement at the large scale of agriculture. Environmental DNA (eDNA) metabarcoding is a molecular method that amplifies trace amounts of DNA deposited by organisms from diverse substrates including soil, plant tissue and even air. In this study, we used eDNA metabarcoding of tree flowers, complemented with digital video recording (DVR) devices, to detect temporal, fine- and large-scale arthropod community changes across two Persea americana (‘Hass’ avocado) orchards. In total, we detected 42 arthropod families with eDNA metabarcoding, representing 66 unique taxa, nearly all of which are unmanaged native species. The number of arthropod eDNA detections increased by 14% during peak flowering and included species from different functional groups including known arthropod pollinators, pests, parasites and predators. At fine-spatial scales, inflorescence samples collected in the upper and lower canopy show that Hymenoptera taxa were 13% more likely to be detected in the upper canopy. While at large-spatial scales, eDNA metabarcoding showed that the arthropod communities in both orchards shared less than 50% similarity at low flowering and became more similar towards peak flowering. By comparing eDNA detections with those from DVRs, we determined that arthropods that visited flowers more frequently had a higher eDNA detection probability, emphasising the need to complement this molecular method with additional survey approaches. Our findings highlight the value of eDNA-based monitoring and illustrate that agroecosystem management requires a growing awareness that the production boundary has expanded, and that the goods and services that umanaged arthropod species provide need to be included on the balance sheet.