I was a Research Fellow at the University of St Andrews for four years, based in the Centre for Research into Ecological and Environmental Modelling. I won the Young Biometrician Award 2021 sponsored by the British and Irish Region of the International Biometric Society and the Fisher Memorial Trust, read more here.

My research focussed on stochastic processes that are latent or partially observed. This usually involved stochastic differential equations, hidden Markov models, smoothing techniques, and high-dimensional integrals. Below are some of the methods I worked on.

Statistical Methods
  • Stochastic (partial) differential equations
  • Hidden Markov models
  • Path integration: computing functional integrals efficiently
  • Stochastic processes: Wiener process and extensions
  • Smoothing by penalization
  • Multimodel inference
Applications in Statistical Ecology
Computer Science
  • Software development in R and C++
  • Solving PDEs, matrix methods: Krylov subspaces, fast Fourier transform, sparse matrices
  • Object-orientated programming

My PhD was about Incorporating Animal Movement with Distance Sampling and Spatial Capture-Recapture and was supervised by Stephen T. Buckland and Roland Langrock.


  • 2022 – present
    PDGE Secondary Mathematics,
    University of Dundee
  • 2014 – 2018
    PhD Statistics, University of St Andrews
  • 2010 – 2014
    MMath Mathematics,
    University of Andrews, First Class


  • 2018 – 2022
    Research Fellow, University of St Andrews
  • 2017 – 2018
    School co-coordinator for Academic Skills Project, CAPOD


Careers in Statistics

I organised a one-day online conference on Careers in Statistics for MSc and PhD students in Statistics. The idea was to give people a taste of what statisticians do in their day-to-day work across a range of different careers. All talks were recorded and you can watch them on youtube (just click the pic or title above!)

Varying-Coefficient SDEs in Ecology

Stochastic differential equations are one way to model processes that unfold over time, e.g. animal movement. These equations are controlled by parameters thay may change over time, space, or other covariates. This work provides a fast, simple framework for fitting these models with flexible, non-parametric covariate effects. 

Uncovering ecological state dynamics with hidden Markov models

Hidden Markov models are widely used in Ecology as they provide an attractive correspondence between their statistical framework and how ecological systems can be conceptualized. This review paper is useful for ecologists who want to see the variety of applications HMMs could be used for. I was lucky enough to work with these experts in HMMs and contributed the sections on population-level processes


Here is a brief summary for some of the projects I worked on:

  • Modelling whale movement and diving behaviour and how it is affected by Navy Sonar

    This was part of work with the Atlantic Behavioural Response study and within the Double Mocha project. There are many challenges in this work: how to model three-dimensional animal movement, how to quantitatively identify if and how sonar exposure affects movement, and how to integrate inference across different tags and spatio-temporal scales.

  • Estimating survival and density over time and space from photo ID in Barataria Bay

    The common bottlenose dolphin population in Barataria Bay was affected by the Deepwater Horizon Spill. McDonald et al. (2017) provided estimates of the survival probability and abundance in this population from photo ID surveys using open population spatial capture-recapture (openpopscr). Since then, more data has been collected and a more computationally efficient way to fit openpopscr models, allowing for inference to be extended in time and complexity.

  • Developing a robust way to make inference on biodiversity trends from the UK Hoverfly recording scheme, citizen science data

    The UK Hoverfly recording scheme is an opportunistic, citizen-science scheme that is growing in membership over the years and in the amount of data available over space and time. The challenge with drawing inference from these data is that species presence and sampling bias (in space and by observer ability) are confounded. In this project, I was working on developing a method that can make robust conclusions from these data and clearly defining what "robustness" refers to in this context.

  • Wanting it all: developing integrated animal movement models for behaviour, continuous-time, measurement error, and step selection

    There are many methods to fit animal movement models: HMMs can capture behavior-switching, crawl can efficiently handle continuous-time irregular location records and measurement error, MCMC methods can be used to capture step selection. One approach to combining these is an MCMC with multiple efficiencies made (e.g. Kalman filter), but I was working on an alternative approach that can be more efficient but still retain all these desirable components.


Submitted for Review

Published Papers


Software available from my GitHub page (software is unsupported).

  • occuR: R package to fit multi-season occupancy models with non-parametric smooths on occupancy and detection probability, combining Template Model Builder and mgcv
  • CTMCdive: R package to fit continuous-time Markov chain model with temporaly-varying smooth transition intensities (intended to model cetacean dive and surface durations).
  • openpopscr: R package to fit open population Jolly-Seber spatial capture-recapture models by maximum likelihood.
  • SimDs: source code to simulation line transect distance sampling surveys with animals moving in 2D.


  • Stochastic differential equations in statistical ecology: a discussion of three research goals (Jun 2021). Invited Plenary. Virtual National Centre for Statistical Ecology Conference
  • Modelling latent animal movement in distance sampling and spatial capture-recapture. (Mar 2021) 8th Channel Conference by International Biometric Society, Online
  • Modelling latent animal movement and behaviour in population abundance surveys using hidden Markov models. (Oct 2020) École polytechnique fédérale de Lausanne.
  • Understanding the SPDE approach to smoothing. (Sept 2020) Royal Statistical Society Conference, Online.
  • Continuous-time behaviour-switching animal movement by maximum likelihood. (June 2020) International Statistical Ecology Conference, Online.
  • Open population spatial capture-recapture. (Feb 2020) Kent University and Glasgow University Statistics Seminars.
  • Modelling latent animal movement and behaviour in population abundance surveys using hidden Markov models. (Sept 2019) Royal Statistical Society Conference, Belfast.
  • Incorporating point process and occupancy modelling of citizen science multispecies records. (Jul 2019) Addressing statistical challenges of modern technological advances (NCSE) , Edinburgh.
  • Integrating continuous-time animal movement with spatial capture-recapture. (Oct 2018) The Wildlife Society Conference, Cleveland.
  • Continuous-time spatial capture-recapture with animal movement. (July 2018) International Statistical Ecology Conference, St Andrews.
  • Encounters between animals and detectors in wildlife abundance surveys. (Feb 2018) Royal Statistical Society Local Group, Liverpool.
  • Accounting for animal movement in distance sampling. (Aug 2017) Intermediate Distance Sampling Workshop, St Andrews.
  • Behaviour-switching animal movement with distance sampling. (Jul 2017). EURING conference, Barcelona.
  • Incoporating animal movement with distance sampling and spatial capture-recapture. (Jun 2017). Summer Meeting for the National Centre for Statistical Ecology, Canterbury.
  • Counting animals in Statistical Ecology. (Nov 2016).St Andrews Mathematics Undergraduate Society, St Andrews.
  • Modelling movement in continuous-time spatial capture-recapture. (Oct 2016). Centre for Research into Ecological and Environmental Modelling, St Andrews.
  • The effect of animal movement on distance sampling. (Aug 2016)Advanced Distance Sampling Workshop, St Andrews.
  • Incorporating animal movement into distance sampling. (Jul 2016). International Statistical Ecology Conference , Seattle.
  • Path integrals in Ecology. (Jan 2016). Mathematics and Statistics School Research Day, St Andrews.
  • Encounters in Ecology. (Aug 2015) 38th Research Students' Conference in Probability and Statistics
  • The Effect of Animal Movement on Line Transect Estimates of Abundance. (Jul 2013). Summer meeting for the National Centre for Statistical Ecology, Lowestoft.