Megan Morton
Student e-mail: 2191979L@student.gla.ac.uk
Staff e-mail: Megan.Laxton@glasgow.ac.uk
ResearchGate: https://www.researchgate.net/profile/Megan-Laxton
Web of Science: https://www.webofscience.com/wos/author/record/M-7732-2015
https://orcid.org/0000-0002-5017-7395
Research title: Developing a nested, multiscale framework for modelling animal movement processes from long-term tracking data
Research Summary
Thesis
My research is focused on developing a nested, multiscale framework for modelling animal movement processes from long-term tracking data.
Research Interests
I am interested in spatial and spatiotemporal modelling of ecological processes; particularly animal movement and species distribution modelling. Much of my work centers around point process and geostatistical modelling using Integrated Nested Laplace Approximation (INLA).
Publications
2023
Laxton, Megan Ruth 
ORCID: https://orcid.org/0000-0002-5017-7395, Nightingale, Glenna, Lindgren, Finn, Sivakumaran, Arjuna and Othieno, Richard
(2023)
Extending the R number by applying hyperparameters of Log Gaussian Cox process models in an epidemiological context to provide insights into COVID-19 positivity in the City of Edinburgh and in students residing at Edinburgh University.
PLoS ONE, 18(11),
e0291348.
(doi: 10.1371/journal.pone.0291348)
(PMID:37988358)
(PMCID:PMC10662770)
Articles
Laxton, Megan Ruth ORCID: https://orcid.org/0000-0002-5017-7395, Nightingale, Glenna, Lindgren, Finn, Sivakumaran, Arjuna and Othieno, Richard
(2023)
Extending the R number by applying hyperparameters of Log Gaussian Cox process models in an epidemiological context to provide insights into COVID-19 positivity in the City of Edinburgh and in students residing at Edinburgh University.
PLoS ONE, 18(11),
e0291348.
(doi: 10.1371/journal.pone.0291348)
(PMID:37988358)
(PMCID:PMC10662770)
Grants
My PhD research is funded by the UKRI Engineering and Physical Sciences Research Council (EPSRC).
