Dr Ane Pastor2, Dr Craig Sherman2, Dr Morgan Ellis2, Ms Kate Tuohey2, Dr Ross Venell3, Dr Cian Foster-Thorpe4, Dr Eric Treml1,2
1Australian Institute of Marine Science, Perth, Australia, 2Deakin University, Queenscliff, Australia, 3Cawthron Institute, Nelson, New Zealand, 4Department of Agriculture, Fisheries and Forestry, Canberra, Australia
Biography:
Dr Eric Treml is a Marine Scientist at the Australian Institute of Marine Science in Perth, WA, Australia. His background is in marine biology, landscape ecology, coastal management, biosecurity, and the geospatial sciences. He has 20 years of experience leading research projects involving multi-disciplinary and international teams that seek to support local to regional management of coastal marine resources. Currently, Treml’s research portfolio is focusing on understanding marine population connectivity, climate adaptation, the epidemiology of marine pest incursions, and the flow of blue carbon.
Abstract:
Marine pest introductions continue to occur and increase at accelerated rates, threatening out marine environment and blue economy. Although eDNA approaches have gained widespread use in the detection of both indigenous and non-indigenous species, fundamental knowledge gaps remain around understanding what influences the probability of detection, and how to optimise eDNA sampling in marine environments. Here, we partition eDNA research into four major research themes: eDNA concentration (production and decay), transport (advection and mixing), sampling design strategies, and the modelling of these dynamics. We review current developments and challenges in each theme with a focus on field sampling strategies and the biophysical modelling of eDNA. In this presentation, we highlight three case studies in Port Philip Bay, Australia, where we 1) quantify the spatial and temporal variability of eDNA dispersion and detection likelihoods, 2) use biophysical models to inform a field sampling strategy to minimise false negatives, and 3) demonstrate a backtracking modelling technique to identify upstream DNA sources to existing sample (or monitoring) sites. We conclude by identifying specific recommendations to help improve future eDNA studies. This work highlights how predictive models can help minimise false negatives and improve early detection, together improving response and management decisions.