The focus of the environment theme is to seek methodological innovations that can transform our understanding and management of the natural environment. This is a major cross-disciplinary challenge requiring a close collaboration between environmental scientists, computer sciences, statisticians, social scientists, and many others.
The Environment theme aims to develop new understanding and innovative solutions to the dual crises of climate change and biodiversity loss, which are inextricably linked. This time-critical mission requires close cross-disciplinary collaboration between ecologists, environmental scientists, computer sciences, statisticians, social scientists, and many others.
Biodiversity is under unprecedented threat due to habitat destruction, climate change, pollution, and other human-driven pressures. Understanding and addressing these challenges requires innovative approaches that can make sense of complex ecological systems, large yet fragmented datasets, and rapid environmental changes.
At the intersection of data science, artificial intelligence, and environmental research, we seek to develop and apply cutting-edge methods that:
- Capture and analyse the complexity, interconnectedness, and uncertainty inherent in ecosystems.
- Overcome challenges posed by sparse and irregular data from extreme environmental events and biological organisms.
- Integrate and interpret heterogeneous biodiversity and environmental datasets over space, time and taxa.
- Detect critical shifts in ecosystems, such as species population declines or habitat degradation, across scales.
Emerging technologies, including acoustic telemetry, photogrammetry, remote sensing, bioacoustics, automated species identification, and cloud and exascale computing, have the potential to revolutionise biodiversity monitoring. These technologies enable real-time, high-resolution data collection on species movements, environmental conditions, and ecosystem dynamics, while cloud and exascale computing provides the computational power to integrate and analyse vast datasets across disciplines and regions.
AI and machine learning further enhance our ability to process and interpret these complex datasets, offering predictive models for biodiversity loss, early warning systems for ecosystem collapse, and data-driven strategies for conservation and restoration.
By leveraging expertise across environmental sciences, computer science, statistics, and social sciences, we aim to position data science and AI at the forefront of tackling biodiversity loss and broader environmental challenges. Our goal is to establish the Data Science Institute as a global leader in harnessing digital innovation for a nature positive future.
