This applied R course teaches ecologists how to analyse, model, and predict ecological time-series data using practical, reproducible workflows.
The methods are highly relevant to marine mammal research, where long-term monitoring, telemetry, acoustic detections, and environmental covariates generate complex time-series datasets that require robust analytical approaches.
Examples of relevant applications include:
Analysing long-term abundance or sighting records
Modelling behavioural state changes from tagging or biologging data
Forecasting habitat use or distribution shifts under environmental change
Detecting patterns in passive acoustic monitoring time series
Integrating environmental covariates with movement or population data
The course covers:
Preparing ecological time-series datasets for analysis
Supervised and unsupervised machine learning methods
Model validation, forecasting, and interpretation
Building reproducible workflows in R
Delivered online with recordings available afterwards, participants receive course materials, example datasets, and post-course support.
Course details
Dates: 13–17 April 2026
Duration: 5 days (approximately 7 hours per day)
Format: Recorded sessions with live Q&A
Fee: £450
This course is ideal for marine mammal researchers working with telemetry, acoustic monitoring, survey time series, or environmental datasets who want practical skills for analysing complex ecological data using machine learning.
Full details and registration:
https://prstats.org/course/machine-learning-for-ecological-time-series-metr01/