There is evidence that the distribution and abundance of harmful blue green algae is changing in Australia. However, the evidence of this is generally limited to a number of reports and a limited number of scientific articles. Therefore, over the past years I have been collecting water quality data sets and blue-green algae data from various state and local government organisations in order to start to piece together a national database that can be used to map harmful algae biovolume across Australia. While this database is already substantial and has already yielded interesting patterns such as the southward shift in Raphidiopsis raciborskii and even substitution of species, it is far from complete and therefore has many more interesting stories to yield.
This project is suited to someone who is mad about DATA and the secrets it may hold, has a passion for investigating environmental issues, is prepared to learn about, or already knows about aquatic ecology and in particular cyanobacteria aka blue green algae. You'll be happy to go down to the vaults to dig up old data, look through popular news articles, meet custodians of data and negotiating how you can obtain data and use it in a way that is sensitive to the organisations requirements, speak to experts to clarify patters and anomalies you are finding, and be willing to go out and sample water to collect your own data. Finally, you will investigate new data streams such as environmental DNA (NGS) data and how this data can support, add to, or supplant existing data.
Harmful algae blooms are an increasing problem that are causing human, animal and plant health issues on large scales. However, surveillance of the blooms is often opportunistic and managed by different state or local organisations and there is no national reporting body. Imagine piecing together all of this rich data and making all sorts of discoveries such as how these populations are changing in time and space and forecasting how these populations may further change in a changing climate, under changing water and land management regimes! This is truly powerful data.
The ideal candidate should have a strong strong skills in R and/or Python.
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HDR Inherent Requirements.
In addition to the academic requirements set out in the Science Postgraduate Handbook, you may be required to satisfy a number of inherent requirements to complete this degree. Example of inherent requirement may include:
The opportunity ID for this research opportunity is 2665