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.