About Me

I am interested in a broad range of fundamental and applied questions on the interface between ecology, epidemiology and evolution. I use a combination of mathematical modelling, ecological experiments with model species, and field observations from natural ecosystems, to understand the processes and mechanisms that allow population persistence and species coexistence. I like to base my theoretical research in the context of important applied problems such as food security, biodiversity loss and invasive species. This has led me to work on species as diverse as baculoviruses, honey bees, parasitoid wasps, porpoises, seagrass and triggerfish.

Areas of Expertise

  • Disease ecology
  • Ecological genomics
  • Metapopulations
  • Population biology
  • Spatial dynamics

Publications

  1. & Aggregation dynamics explain vegetation patch-size distributions. Theoretical Population Biology 108, 70-74.
  2. & Fractal measures of spatial pattern as a heuristic for return rate in vegetative systems. Royal Society Open Science 3(3), 150519
  3. & Disease profiles of juvenile edible crabs (Cancer pagurus L.) differ at two geographically-close intertidal sites. Journal of Invertebrate Pathology 128, 1-5.
  4. & Zebrafish Rab5 proteins and a role for Rab5ab in nodal signalling. Developmental Biology 397(2), 212-224.
  5. & A virulent strain of deformed wing virus (DWV) of honeybees (Apis mellifera) prevails after Varroa destructor-mediated, or in vitro, transmission. PLoS Pathogens 10(6), e1004230

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Teaching

  • BIB214 Ecological data analysis

    This module introduces students to the basics of analyzing ecological data, using the R Software Environment for Statistical Computing. The topics covered will be also broad enough to be equally applicable to most basic data analysis in biology. Students will receive 8 computer-based workshops/practicals, complemented by short introductory lectures to each workshop. These workshops will cover 5 key themes: 1). Scientific computing, reproducibility and the R Software Environment; 2). Data management; 3). Data visualization; 4). Data analysis - The general linear model; 5). Data analysis - Outline of more advanced methods. The module will be subject to continuous assessment consisting of 8 pieces of computer-based work, which will require the students to carefully complete all course work assigned on a weekly basis ('independent learning'), in order to be able to complete the assignments. There will also be a weekly 1 hour feedback session/lecture.

  • BIO250 Introduction to field ecology

    This residential field course comprises practical work employing techniques appropriate to sample biodiversity and environmental parameters from a range of terrestrial and freshwater habitats (woodlands, grasslands, freshwater systems). Students will learn techniques for the identification of species, practice recording accurate field notes, and gain experience in the analysis and presentation of ecological data. Furthermore students will be able to recognise different temperate habitats and the indicator species associated with them.

  • BIO334 Advanced Data Analysis

    This module extends core knowledge of statistical computing to cover a range of more specialized topics of particular importance to the analysis of real world biological datasets, such as those collected for final year undergraduate research dissertations. We use the R software environment; building on experience of this gained during the core Second Year module, BIB214 – Ecological Data Analysis. Students will be guided through 5 computer-based workshops / practicals, including brief introductory lectures to each topic. Further help will be provided through a series of drop-in sessions and a dedicated module Facebook group. The workshops, and associated additional guidance, will cover 5 key themes: 1) Linear modelling refresher, 2) Generalised Linear Modelling A - Count data, 3) Generalised Linear Modelling B - Proportion data, 4) Non-parametric analysis, 5) Introduction to grouped data. The module will be subject to continuous assessment, consisting of 5 pieces of computer-based work, throughout the course. In addition, students will complete a coursework assignment after the course, where they will gain additional experience of analysis and interpreting biological data.

Supervision

  • The role of PCBs on parasitism in harbour porpoise using UK strandings data (current)

    Student name:
    MRes
    Other supervisor: Dr Luca Borger
  • The role of phenology on grey seal pup production and survival (current)

    Student name:
    MRes
    Other supervisor: Dr Luca Borger
  • 'A comparison of two popular pollinator planting initiatives and associated insect pollinator species diversity and abundance within major urban areas of South Wales' (current)

    Student name:
    MRes
    Other supervisor: Dr Daniel Forman
  • Modelling the cross-ecosystem impacts of fisheries discards (current)

    Student name:
    MRes
    Other supervisor: Dr Mike Fowler
  • The role of detection probability in understanding the population dynamics of grey seals. (current)

    Student name:
    MRes
    Other supervisor: Dr Luca Borger
  • The role of ecological trade-offs in insect populations (current)

    Student name:
    MRes
    Other supervisor: Dr Wendy Harris
  • Advanced Telemetry and Bio-logging for Investigating Grey Seal Interactions with Marine Renewable Energy (MRE) Installations. (current)

    Student name:
    PhD
    Other supervisor: Dr Luca Borger
  • Linking plant demography, ecological dynamics and population genetics across space and time. (current)

    Student name:
    PhD
    Other supervisor: Dr Luca Borger
  • Ecology and Conservation of Sabellaria alveolata reefs. (current)

    Student name:
    PhD
    Other supervisor: Dr Ruth Callaway
    Other supervisor: Dr John Griffin
  • Untitled (current)

    Student name:
    PhD
    Other supervisor: Dr John Griffin
  • Development of biopesticides (semiochemicals and fungi) for mosquito control (current)

    Student name:
    PhD
    Other supervisor: Professor Tariq Butt
  • 'A New Paradigm for Deriving Animal Behaviour: Tri-Axial Magnetometry' (awarded 2016)

    Student name:
    MRes
    Other supervisor: Professor Rory Wilson