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Dr Nicholas Clark

ARC DECRA

School of Veterinary Science
Faculty of Science
n.clark@uq.edu.au
+61 7 535 15104

Overview

An ecologist by training – I hold a B.Sc. (Hons) in Marine Ecology from the University of North Carolina, Wilmington and a Ph.D. in Ecological Modelling from Griffith University. I am broadly interested in exploring new ways to (1) understand how natural communities are formed and (2) predict how they will change over time. As a DECRA fellow at UQ, my current research focuses on developing computational tools and adapting techniques from statistical forecasting to study how organisms and ecosystems respond to environmental change. This work can be applied to investigate natural dynamics for a range of ecological systems including wild birds, ectoparasites and microbes.

I am an active member of the R community and have written and/or maintain several popular R packages. For example, I’m a lead developer on the MRFcov package for multivariate conditional random fields analyses. I also wrote the mvgam package for fitting dynamic Generalised Additive Models to analyse and forecast multivariate ecological time series.

I am currently seeking PhD candidates with interests and/or skills in data analysis, statistical programming and quantitative ecology to develop multivariate forecast tools and evaluate their abilities to predict how species occurrences, abundances and diversities respond to change.

Research Interests

  • Using forecasts to anticipate how ecosystems respond to environmental change
    I am leading projects to develop novel multivariate forecast models that aim to advance our ability to anticipate ecological change. Expected applications of this work cover a diversity of areas including conservation prioritisation, agriculture and biosecurity.
  • The epidemiology of animal pathogens across the human-wildlife interface
    I am interested in using molecular genetics and epidemiology to improve understanding of how pathogen infection rates and emergence potentials will change as human encroachment alters natural environments
  • The macroecology and biogeography of infectious dieases
    This body of work aims to describe large-scale patterns in the distributions of infectious organisms in order to identify processes governing the spread and invasion potential of pathogens

Research Impacts

My research is geared towards understanding how ecological communities, pathogen infection rates and pathogen emergence will change as climate change and human encroachment continue to alter natural environments. This work has generated translational benefits by helping to provide insights into factors that can be targeted to reduce the spread of pathogens in our animals. Some key media coverage of this body of work includes:

Detecting how ecological communities respond to temperature changes

Understanding parasite spread through wildlife: the crucial role of statistical models

Adapting statistical network models to identify biotic interactions in changing communities

Using evolutionary models to trace the emergence of harmful viruses in pet dogs

Tracing the spread of fleas from pets to wildlife and vice versa

Detecting invasive malaria parasites in Australian birds

Qualifications

  • Doctor of Philosophy, Griffith University

Publications

View all Publications

Grants

View all Grants

Supervision

  • Epidemiology of canine-mediated rabies in Nigeria

    (2023) Doctor Philosophy

  • Epidemiology of feline upper respiratory tract infections in shelter cats in Australia

    Doctor Philosophy

  • Forecasting ecosystem responses to environmental change

    Doctor Philosophy

View all Supervision

Available Projects

  • Forecasting ecosystem responses to environmental change

    This FULLY FUNDED Earmarked Scholarship project is aligned with a recently awarded Category 1 research grant. It offers you the opportunity to work with leading researchers and contribute to large projects of national significance.

    Dr Nicholas Clark is seeking a PhD candidate to work on an exciting new ARC funded DECRA project: forecasting ecosystem responses to environmental change

    There is a growing consensus that using models to anticipate the future is vital to mitigate the impacts of environmental change on ecosystems. Yet most ecological models are one-off attempts to predict what ecosystems might be like in many years or decades. This makes it hard for decision-makers to use these models. It also favours models that are not easily scrutinised and improved. A new international study will use an iterative cycle to 1) forecast how species occurrences and abundances will change over short timescales; 2) use predictions to inspect model failures and 3) improve models so that we can continue to learn. This represents a new way of thinking in ecology that, like weather forecasting, has the power to advance our understanding of ecological processes.

    The candidate will work within a vibrant team of quantitative ecologists and spatio-temporal modellers to tackle two major questions in ecological modelling:

    (1) When can multivariate models improve forecasts of species distributions, abundances and biodiversity compared to simpler models?

    (2) What aspects of models and data control forecast uncertainty across space and time?

    The student will be based at The University of Queensland within the School of Veterinary Science, supervised by Dr. Nicholas Clark and A/Prof Ricardo Soares Magalhães. The candidate will work with a diverse group of international researchers, including Dr Konstans Wells (Swansea University, UK), Prof Ethan White (University of Florida, USA) and A/Prof Wenbiao Hu (Queensland University of Technology). Additional support will be given by partners at the Ecological Forecasting Initiative and the Spatial Epidemiology Laboratory, including assistance in computer-based data analysis, model building and scientific communication. The selected student will have the opportunity to work with all partners on this project but will be based at UQ.

    This project will help develop the candidate’s skills in critical thinking, project management, data management and analysis, writing and communication. Expected applications of the project are incredibly diverse, meaning the student will be well prepared for a future career in research or with government and non-government land management, biosecurity or conservation agencies.

    Preferred educational background

    Applications will be judged on a competitive basis taking into account the applicant's previous academic record, publication record, honours and awards, and employment history.

    A working knowledge of community ecology and mathematical modelling would be of benefit to someone working on this project.

    The applicant will demonstrate academic achievement in the field(s) of ecology or environmental modelling and the potential for scholastic success.

    A background or knowledge of R or Python programming and time series analysis is highly desirable.

    *The successful candidate must commence by Research Quarter 1, 2022. You should apply at least 3 months prior to the research quarter commencement date. International applicants may need to apply much earlier for visa reasons.

  • Ecological forecasting of paralysis tick infestation rates in Australia

    Tick paralysis, caused by neurotoxins contained in the saliva of paralysis ticks, is a life-threatening condition for dogs and cats requiring immediate medical attention. In Australia tick paralysis is a leading cause of emergency admissions, with tens of thousands of tick paralysis cases admitted to veterinary emergency services each year. While preventative treatments and avoidance of tick-prone areas during periods of heightened risk are effective reduction measures, surveillance systems are inadequate to provide timely information to clinicians and pet owners located in areas most at-risk.

    Working as part of a vibrant research team involving a diversity of collaborators, students will benefit in the following ways:

    (1) Experience in data mining and generating critical summaries for time series data

    (2) Quantitative analysis of multistructure datasets

    (3) Contributing to the planning, writing and submission of peer-reviewed publications

    Collaborators involved: A/Prof Ricardo Soares Soares Magalhães

  • Spatial modelling of wildlife hospital admissions

    Wildlife hospitals offer a tremendous service to the local community. One of the key benefits they can provide is gathering information on spatial and temporal patterns in wildlife trauma incidents. Understanding which species are more susceptible to trauma, and uncovering particular areas or times of the year when incidents are more likely to occur, can provide powerful leverage to local planners, conservation groups and policymakers. This project will apply spatial modelling tools to a large dataset of wildlife hospital clinical records to identify factors associated with increased incidence of trauma. Outputs will consist of high-resolution maps of trauma incidence estimates and reports aimed at influencing planning decisions in efforts to reduce these occurrences. Interests in wildlife Health, conservation and spatial data analysis will be appreciated.

    Working as part of a vibrant research team involving a diversity of collaborators, students will benefit in the following ways:

    (1) Quantitative data analysis and spatial modelling

    (2) Interacting with wildlife veterinarians to guide a joint research agenda

    (3) Contributing to the planning, writing and submission of peer-reviewed publications

    This project is funded and has ethics approval

    Collaborators involved: A/Prof Ricardo Soares Magalhães

View all Available Projects

Publications

Featured Publications

Journal Article

Conference Publication

PhD and MPhil Supervision

Current Supervision

Completed Supervision

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.

  • Forecasting ecosystem responses to environmental change

    This FULLY FUNDED Earmarked Scholarship project is aligned with a recently awarded Category 1 research grant. It offers you the opportunity to work with leading researchers and contribute to large projects of national significance.

    Dr Nicholas Clark is seeking a PhD candidate to work on an exciting new ARC funded DECRA project: forecasting ecosystem responses to environmental change

    There is a growing consensus that using models to anticipate the future is vital to mitigate the impacts of environmental change on ecosystems. Yet most ecological models are one-off attempts to predict what ecosystems might be like in many years or decades. This makes it hard for decision-makers to use these models. It also favours models that are not easily scrutinised and improved. A new international study will use an iterative cycle to 1) forecast how species occurrences and abundances will change over short timescales; 2) use predictions to inspect model failures and 3) improve models so that we can continue to learn. This represents a new way of thinking in ecology that, like weather forecasting, has the power to advance our understanding of ecological processes.

    The candidate will work within a vibrant team of quantitative ecologists and spatio-temporal modellers to tackle two major questions in ecological modelling:

    (1) When can multivariate models improve forecasts of species distributions, abundances and biodiversity compared to simpler models?

    (2) What aspects of models and data control forecast uncertainty across space and time?

    The student will be based at The University of Queensland within the School of Veterinary Science, supervised by Dr. Nicholas Clark and A/Prof Ricardo Soares Magalhães. The candidate will work with a diverse group of international researchers, including Dr Konstans Wells (Swansea University, UK), Prof Ethan White (University of Florida, USA) and A/Prof Wenbiao Hu (Queensland University of Technology). Additional support will be given by partners at the Ecological Forecasting Initiative and the Spatial Epidemiology Laboratory, including assistance in computer-based data analysis, model building and scientific communication. The selected student will have the opportunity to work with all partners on this project but will be based at UQ.

    This project will help develop the candidate’s skills in critical thinking, project management, data management and analysis, writing and communication. Expected applications of the project are incredibly diverse, meaning the student will be well prepared for a future career in research or with government and non-government land management, biosecurity or conservation agencies.

    Preferred educational background

    Applications will be judged on a competitive basis taking into account the applicant's previous academic record, publication record, honours and awards, and employment history.

    A working knowledge of community ecology and mathematical modelling would be of benefit to someone working on this project.

    The applicant will demonstrate academic achievement in the field(s) of ecology or environmental modelling and the potential for scholastic success.

    A background or knowledge of R or Python programming and time series analysis is highly desirable.

    *The successful candidate must commence by Research Quarter 1, 2022. You should apply at least 3 months prior to the research quarter commencement date. International applicants may need to apply much earlier for visa reasons.

  • Ecological forecasting of paralysis tick infestation rates in Australia

    Tick paralysis, caused by neurotoxins contained in the saliva of paralysis ticks, is a life-threatening condition for dogs and cats requiring immediate medical attention. In Australia tick paralysis is a leading cause of emergency admissions, with tens of thousands of tick paralysis cases admitted to veterinary emergency services each year. While preventative treatments and avoidance of tick-prone areas during periods of heightened risk are effective reduction measures, surveillance systems are inadequate to provide timely information to clinicians and pet owners located in areas most at-risk.

    Working as part of a vibrant research team involving a diversity of collaborators, students will benefit in the following ways:

    (1) Experience in data mining and generating critical summaries for time series data

    (2) Quantitative analysis of multistructure datasets

    (3) Contributing to the planning, writing and submission of peer-reviewed publications

    Collaborators involved: A/Prof Ricardo Soares Soares Magalhães

  • Spatial modelling of wildlife hospital admissions

    Wildlife hospitals offer a tremendous service to the local community. One of the key benefits they can provide is gathering information on spatial and temporal patterns in wildlife trauma incidents. Understanding which species are more susceptible to trauma, and uncovering particular areas or times of the year when incidents are more likely to occur, can provide powerful leverage to local planners, conservation groups and policymakers. This project will apply spatial modelling tools to a large dataset of wildlife hospital clinical records to identify factors associated with increased incidence of trauma. Outputs will consist of high-resolution maps of trauma incidence estimates and reports aimed at influencing planning decisions in efforts to reduce these occurrences. Interests in wildlife Health, conservation and spatial data analysis will be appreciated.

    Working as part of a vibrant research team involving a diversity of collaborators, students will benefit in the following ways:

    (1) Quantitative data analysis and spatial modelling

    (2) Interacting with wildlife veterinarians to guide a joint research agenda

    (3) Contributing to the planning, writing and submission of peer-reviewed publications

    This project is funded and has ethics approval

    Collaborators involved: A/Prof Ricardo Soares Magalhães

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ORCID: 0000-0001-7131-3301

Scopus ID: 56048761800

Google Scholar ID: eKT6SGcAAAAJ

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