Prof. Simon Jarman1, Dr Shaun Wilkinson2, Dr Tim Cooper3, Dr Michael Marnane4, Dr Bruce Deagle5, Dr Travis Elsdon4, Dr Shane Herbert6, Prof Euan Harvey1
1Curtin University, Bentley, Australia, 2Wilderlab, Miramar, New Zealand, 3BHP, Perth, Australia, 4Chevron Technical Group, Perth, Australia, 5CSIRO, Hobart, Australia, 6eDNA Frontiers, Perth, Australia
Biography:
Simon Jarman is interested in genomic approaches for estimating the ecological parameters that describe populations and communities of organisms in the wild. He has worked extensively of community structure, trophic interactions, population age structure, lifespan and age at maturity estimation, and identification of invasive or high value species in eDNA.
Abstract:
The relative abundance of species in any ecosystem is one of the most important determinants of ecological community structure, function, resilience, and conservation value. Estimating the abundance of species detected in eDNA datasets has many applications, and a range of papers have been published that indicate quantitative estimates can be generated from metabarcoding data. However, most of these quantify by making direct calculations from read counts. These approaches may be improved by using established ecological theory on species abundance distributions (SADs). SADs are mathematical models based on the observations that in any community a small number of species are abundant, and most species are rare. eDNA metabarcoding produces data that can be used to estimate the parameters of SAD models. This will provide relative species abundance estimates for groups of interacting species taken from natural environments. I will describe how to estimate SAD parameters from eDNA metabarcoding data, software for doing this, how sample sizes are important for making this work, and the sorts of eDNA metabarcoding datasets that can be used to estimate SADs.