Bioinformatic methods for estimating animal abundance from eDNA
Effective decision making in environmental management needs to come from the ability to identify deviations from the norm. Such experience is only built by insights gained over time from constant monitoring of our biodiversity. This allows us to understand the patterns or trends that are present in an ecosystem.
While we have become accustomed to the unit of reporting from traditional capture and observation methods, they are invasive and resource consuming. Environmental DNA (eDNA) on the other hand, is an ideal method for ongoing monitoring, as it is a non-invasive method of detection and can also detect cryptic organisms. However, the unit of detection, being genomic material, comes with its own challenges, primarily the interpretation of the data in the ecological context.
This project examines the scope and types of ecological questions that can be addressed by eDNA data. More specifically, the project focuses on analytical methods that address organism abundance; trend analysis over time and methods for cross-study comparisons. By being better aware of the scope and types of questions that can be addressed using eDNA data, this will increase our understanding and insight into how ecosystems function and how they can be better managed.
Project Lead: Dr Xin-Yi Chua (Environomics FSP Postdoctoral Fellow)
Supervisor: Annette McGrath.
This project forms a component of The Bioinformatics for Environomics Project.
A complimentary project led by Elise Furlan ‘Estimating abundance of species by improving eDNA techniques’ will investigate and seek solutions to the sampling and molecular laboratory procedures that lead to biases in DNA amplification from eDNA samples.”
Image credit: Concentration.01, Fogg Dam Conservation Reserve, Middle Point, Northern Territory, Australia by Geoff Whalan at https://www.flickr.com/photos/geoffwhalan/19628068701/in/photolist under a Creative Commons Attribution 2.0. Full terms at https://creativecommons.org/licenses/by-nc-nd/2.0/