Dear colleagues,
We’re happy to announce an upcoming hybrid course that is directly
relevant to*marine mammal research and conservation*:
Introduction to GLMMs and GAMMs with R - With applications to spatial,
temporal, and spatial-temporal marine data
2–6 March 2026, James Cook University, Townsville, Australia (or join
online via Zoom)
This is a hands-on, applied course aimed at researchers working with
marine mammal data who want to use GLMMs and GAMMs in practice, rather
than get lost in theory or heavy maths. The focus is on understanding
what these models are doing, how to fit them in R, and how to interpret
and visualise the results in an ecological and conservation context.
Throughout the course, we use*examples and exercises drawn from marine
mammal research and related marine ecology*, including data structures
commonly encountered in marine mammal studies: repeated observations of
individuals or groups, spatially structured survey data, time series,
and overdispersed count data.
We start with a short refresher on multiple linear regression and
generalised linear models, then move on to generalised additive models
(GAMs), which are widely used in marine mammal science to model
non-linear relationships with space, time, and environmental covariates.
We then introduce mixed-effects models for grouped and repeated-measures
data — for example, multiple observations from the same individual, pod,
site, or survey — before bringing everything together in generalised
additive mixed models (GAMMs).
In the final part of the course, we apply these models to*spatial,
temporal, and spatial-temporal marine data*, using realistic examples
with different response types, including continuous measures and count
data, as commonly used in marine mammal abundance, distribution, and
activity studies.
Across five modules, we cover:
*
How GAMs work, including model selection and smoother interactions,
with marine examples
*
Linear mixed-effects models and Gaussian additive mixed models for
repeated and grouped marine data
*
Hierarchical GAMs (the GAM equivalent of random-slope models),
useful for individual- or site-level variation
*
Poisson and negative binomial GLMMs and GAMMs for count data (e.g.
sightings, detections, events)
*
Applications to spatial and spatial-temporal marine data
*
Guidance on analysing binary, proportional, and continuous data
relevant to marine mammal studies (time allowing)
All analyses are done in*R*, mainly using the*mgcv*and*glmmTMB*packages.
Each module includes practical exercises with real data and fully
annotated R scripts. Before the course starts, participants also receive
access to preparatory materials (short videos and exercises) covering
linear regression, Poisson GLMs, model validation using DHARMa, and
variograms. PDF copies of all theory slides are provided.
As part of the course, we include a*free 1-hour one-to-one video
meeting*, where you can discuss your own marine mammal data or analysis
questions with one or both instructors.
*Venue:*James Cook University, Townsville Campus, Australia (hybrid format)
*Dates & times:*2–6 March 2026, 09.00–16.00 (local time)
*Price:*£500
*Instructors:*Dr. Alain Zuur and Dr. Elena Ieno
Registration: highstat.com
Please feel free to share this with colleagues working in marine mammal
research and conservation who may find it useful.
Kind regards,
Alain Zuur
Highland Statistics Ltd.
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