Estimates of dispersal are vital for any enterprise that seeks to manage populations. Information on dispersal helps identify locations to target and actions to use. Whether we are looking to control or eradicate an invasive pest species, or to assist a threatened native species, an understanding of dispersal patterns is extremely valuable. For exapmle, The rate at which invasive populations spread into new areas is determined by their rates of dispersal and population growth. Of these, the rate of dispersal is almost always the harder to estimate, with long-distance dispersal being particularly difficult to quantify. Dispersal can, in principle, be estimated from genetic data, and this project looks to apply and develop methods for estimating dispersal from genomics data.


This project aims to investigate and quantify methods for estimating dispersal and gene flow directionality using a combination of landscape genomic and spatial occurrence data. It looks to clarify and extend recent developments in this field of research, and evaluate the functionality and accuracy of the new methods against both real and simulated data. To do this, the project will make use of both real and simulated data.


  1. Use ecologically realistic simulations to test the robustness of existing population genetic inference tools.
  2. Investigate and develop approaches to quantifying directionality of gene flow in populations.
  3. Extend kinship approaches for dispersal kernel estimation to incorporate environmental heterogeneity and continuous measures of relatedness


The cost of acquiring large amounts of DNA sequencing data is now so low that it is feasible to rapidly obtain and analyse tens of thousands of loci for many hundreds of individuals. Data of this kind can be used to estimate not just the amount of gene flow and a mean dispersal rate, but the direction of gene flow and the entire distribution of dispersal distances that occurs in a population. The direction of gene flow is vital for predicting the spread of a species, and the the distribution of dispersal distances – the dispersal kernel – is a keystone piece of information required for spatially explicit population models. An ability to estimate the direction and range of dispersal distances with genomic data is an enabling technology that rapidly opens up a huge range of applications across environment, health, and agriculture.

Candidate growth and outputs

This candidate will gain highly valuable and transferable skills in scientific research, population genomics, statistical inference, simulation modelling, and ecology. High quality, peer-reviewed publications will include internationally relevant manuscripts covering such topics, with research findings presented at relevant local and international conferences as well as more targeted seminars/workshops. The candidate will be engaged in outreach through stakeholder meetings to share data and research highlights as well as hone communication skills and develop relationships with industry partners.

The project is supported by a tax free stipend of $32,500 p.a. with a top up of $7,000 p.a. conditional on performance.

How to apply

Candidates must have:

  • A strong track record in undergraduate studies;
  • Honours or Masters by research
  • A background or keen interest in ecology, evolution, genetics, statistics, or related discipline.

See here for details about scholarship (ignore the bit about the scholarship being closed): https://scholarships.curtin.edu.au/Scholarship/?id=6856

If interested, please submit an Expression of Interest via the Curtin website

Applications are open now and will close when a suitable candidate is found.

Send enquiries to Prof Ben Phillips.