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Mr Timothy Staples Jones

Postdoctoral Research Fellow

School of the Environment
Faculty of Science
t.staples@uq.edu.au
+61 7 336 59154

Overview

I'm a quantitative community ecologist with broad experience across terrestrial and marine systems, modern and geological time, local and global scales with both theoretical and practical focus. I'm driven by discovery, interested in myriad topics on how communities form and function, but particularly how we measure and make comparisons between communities.

  • Translating anomaly detection to ecology: In a world experiencing climate change, biodiversity loss and other human impacts, detecting anomalous ecological systems accurately and early potentially offers vast benefits to conservation and ecosystem management. Anomaly detection is a fast-moving area of research applied in fields as varied as banking fraud detection, cybersecurity and cancer diagnosis. These fields deal with data as complex and incomplete as ecology, but we haven't plumbed this expertise for benefits to ecology. This is the primary focus of my DECRA.
  • Grounding ecological novelty for practical use: Ecologists have been talking about novel systems for twenty years, and related topics (such as "no-analog systems") for even longer. Despite well-cited work on how "novel" can be a useful label for ecological restoration, there's a mismatch between management frameworks, which often use ad-hoc qualitative criteria, and quantitative novelty research, which has been mostly performed at global scales. Understanding how to measure novelty, how analytic choices affect measurements, and how to downscale our inferences to be practicable, has been a focus for me and my colleagues.
  • The linguistic evolution of programming languages: The use of scientific programming languages like R, Python and Julia are becoming not only popular, but mandatory skills for researchers. The utility of these languages has been improved by new versions and a plethora of community-created addon packages. This approximates features of natural language evolution, where lexicon changes over time. Understanding the speed and direction of how programming languages evolve can give us a unique insight into how humans learn and alter languages, and how we might ensure they remain understandable into the future. I am currently using GitHub as a vast repository of time-stamped programming “texts”, ripe for linguistic analysis.
  • Community ecology through the lens of functional traits: It doesn’t matter who you are, it matters what you do. That applies to organisms too. Despite decades of research into how physiology and life history strategy, often proxied through easy-to-measure “functional traits”, functional ecology is still more niche than it should be. Currently my colleagues and I are exploring how a functional lens alters ecological novelty, but I am always thinking about ecology in the light of how organisms live.

Qualifications

  • Doctor of Philosophy, The University of Queensland

Publications

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Grants

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Available Projects

  • 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.

View all Available Projects

Publications

Featured Publications

Journal Article

Other Outputs

  • Ancient DNA from Pandora Reef, GBR

    Gomez-Cabrera, Maria, Young, J M, Roff, G, Staples, T, Ortiz, J C, Pandolfi, John M and Cooper, A (2019). Ancient DNA from Pandora Reef, GBR. The University of Queensland. (Dataset) doi: 10.14264/uql.2019.2

Possible Research Projects

Note for students: The possible research projects listed on this page may not be comprehensive or up to date. Always feel free to contact the staff for more information, and also with your own research ideas.

  • 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.

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ORCID: 0000-0002-8550-2661

Scopus ID: 57192155609

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