Timothy Staples

Measuring marine climate anomalies

There are dire predictions for the ecological consequences of ocean warming. However, climate change models predict increases in environmental volatility, temperature extremes and potentially changes to global currents.

Much work has been done on the local change of climatic conditions via “climate velocity”, and the emergence of global “marine novelty”. Both methods attempt to measure “climatic anomalies”, deviations from the past, but with restrictions and assumptions that do not capture the multifaceted nature of climate nor how taxa experience local environmental conditions.

This project attempts to answer a seemingly simple question: “Where (and how) have ocean environments changed from the past, and where might they change in the future?”

To answer this question, the successful applicant will adapt anomaly detection techniques applied in video surveillance and cancer diagnosis to spatiotemporal climate grids, creating consistent and robust measurements of climate anomalies that extend beyond annual means and global comparisons.

This project forms part of an ARC Discovery Early Career Research Award DE240100398: Advancing detection and understanding of anomalous ecological change, commencing 1st September 2024.

Successful applicant will have:

• Competence in handling, processing, and analysing large data products.
• Proficiency in a statistical programming language (e.g., R, Julia, Python).
• Strong ecological, biological, or climatological expertise. Marine-specific knowledge is advantageous but not required.
• Broad curiosity and be motivated by problem-solving.

Reframing biodiversity and conservation indicators via ecological anomalies

Biological conservation and ecological restoration are hampered by the need to make complex inferences from limited data. Nowhere is this more evident than in comparisons between communities or ecosystems. It is a challenge to distinguish uniquely pristine or degraded systems within and across geographic and political entities.

Comparing multidimensional observations is an old practice, expansively studied under the frame of “anomaly detection”, applied in fields as varied as cybersecurity, banking fraud detection and cancer diagnosis. Ecological monitoring and management have not, as yet, explored whether these techniques have application for wicked environmental problems.

This project will explore the utility of anomaly detection methods as (1) advancing the concept of complementarity in conservation planning, (2) whole-of-system monitoring to measure ecological health and monitor for early warning signs of degradation and (3) potential development of national and international reporting indicators.

This project forms part of an ARC Discovery Early Career Research Award DE240100398: Advancing detection and understanding of anomalous ecological change, commencing 1st September 2024.

Successful applicant will have:

• Strong ecological or environmental expertise.
• Proficiency in a statistical programming language (e.g., R, Julia, Python).
• Broad curiosity and be motivated by problem-solving.
• A strong interest in conservation and biodiversity management.