Dr Kathryn Wiltshire1,2, Dr Daniele Giblot-Ducray2,3, Dr Thomas Prowse2, Dr Marty Deveney1,2
1PIRSA SARDI Aquatic Sciences, West Beach, Australia, 2University of Adelaide, Adelaide, Australia, 3PIRSA SARDI Crop Sciences, Waite, Australia
There is a need for molecular survey methods to be validated before being applied in management frameworks, but guidelines and validation studies have largely focussed on laboratory validation and standardisation of molecular techniques. These are important but need to be followed by operational validation to understand field performance of molecular surveillance systems to permit adequate survey design and appropriately interpret results. We operationally validated a molecular surveillance system, comprising plankton tows and qPCR testing, using parallel molecular and traditional surveys for priority marine pests. To quantify survey performance, we used an extension of species occupancy-detection models (SODMs) in a Bayesian framework to assess the likelihood of both false negatives and false positives, and to infer seasonal abundance of the target pests in each location. We further assessed diagnostic performance of the qPCR assays, informing where false negatives were due to sampling rather than assay performance. The molecular survey method achieves equivalent confidence of detection for populations 2-3 orders of magnitude smaller than appropriate traditional methods for each pest, but detectability of pests in plankton was seasonal, and timing of sampling is important to maximise the likelihood of detection. A similar Bayesian SODM approach was developed to interpret survey results using priors informed by the data from these investigations. Here we will present the modelling approach and summarise key findings.
Biography:
Kathryn Wiltshire is a senior research officer with the marine biosecurity subprogram at SARDI Aquatic Sciences. Her background is in ecology, especially with regards to marine invasive species, but she is also experienced in ecological modelling, including Bayesian methods to assess the performance of molecular and surveillance methods.