Charlotte Rush

Charlotte Rush

Profile

In 2017, I obtained my BSc (Hons) in Biological Sciences (Zoology) at University of Edinburgh, during which, I completed a meta-analysis study for my thesis and first became interested in computational methods of research and analysis. In 2018, I attained an MSc at Queen’s University Belfast in Ecological Management and Conservation Biology, where I enjoyed refining my skills and knowledge base in general ecology. My MSc thesis involved surveying an abandoned, once flourishing, Victorian garden and lake, to form part of an application  a new outdoor learning environment for future students of the School of Biological Sciences. During the COVID-19 pandemic and consequent lockdowns, I chose to gain new, globally sought after skills in the form of a part-time, remote MSc in Software Development, also at Queen’s University Belfast. I secured important experience in data science and analysis and developed an android application on recording and sharing users perception of safety of public spaces for my final project. Through this, I became passionate about both citizen science in data collection and the potential power of big data. For the last year, I have been working for a FTSE 100 company as a big data engineer, building coding, planning and organisational skills which I know will be useful in future research.

My ultimate goal has always been to join my passions and experiences in both ecology and data, and my PhD project is the perfect cross over of this: using data science to help inform biodiversity conservation and restoration.

Charlotte Rush

Title: "Aiming for Nature Recovery: Data Science to Inform Biodiversity Conservation and Restoration"

Rapid biodiversity loss is degrading the social and economic value of ecosystems. For conservation initiatives to be successful, we need to accurately predict how habitats will change in the future and anticipate the consequences for wildlife and society. This necessitates better understanding of the relationships between climate, land-use, species communities, and people.

In collaboration with Biodiversify, this project completed at University of Essex will utilise the value of biological, socioeconomic and environmental datasets through ecological modelling and big data analytics. Models for forecasting UK biodiversity under different climate and conservation scenarios will be developed, along with estimating baseline populations using millions of records from both monitoring programs and citizen science initiatives. Public discourses on UK biodiversity will also be analysed using natural language processing techniques on social media data – allowing public opinion and potential social conflict to be included in conservation planning.

Ultimately, this project aims to create a toolkit for future policy or decision makers to use when developing and implementing conservation initiatives, avoiding potential public criticism, and choosing what works best for UK biodiversity in the face of an ever changing climate.