Deep Learning for Animal Movement with deepSSF (DSSF01)
Learn to model animal movement with deepSSF in Python. Apply deep learning to GPS tracking data to predict movement, space use and landscape connectivity.
Duration: 4 Days, 4 hours per day
Next Date: Feb 15-18, 2027
Format: Live Online Format
UK (GMT) local time - All sessions will be recorded and made available to ensure accessibility for attendees across different time zones.
£325Registration Fee
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- Overview
- Instructors
- Schedule
Course Description
This course is aimed towards researchers that want to analyse animal GPS-tracking data using deep learning, particularly for when the goal is to generate movement predictions and downstream outputs such space use and landscape connectivity.
Animal movement behaviour is a sophisticated process that varies across multiple timescales, often with complex interactions to the surrounding environment. Modellers are then faced with many choices about how to specify these dynamic relationships and interactions. Deep learning provides tools that naturally handle these complexities and interactions, with minimal a priori specification required of the models. However, although they are flexible and powerful, deep learning architectures and the code required to implement and fit them can be intimidating.
Here, we will cover the deep learning components (convolutional layers, multilayer perceptions (neural networks), mixture density networks) that comprise the deepSSF approach, showing that they are often simple and straightforward to fit to data. Our aim is then to combine these components into bespoke and customised models for a range of movement data and applications. We will focus on active learning through hands-on programming exercises.
We will use publicly available data sets to demonstrate model training and predictions and use freely available and open-source Python packages. The expected outcome of the course is a solid foundation in certain areas of deep learning, which can be directly applied to animal telemetry data and used to generate predictions.
What You’ll Learn
- A background of predictive animal movement modelling, in particular of step-selection functions
- Understanding deep learning components such as convolutional layers, multilayer perceptrons (neural networks), and mixture density networks
- Backpropagation, loss functions and training procedures
- Using the deepSSF Python package to train deep learning models on different datasets
- Assessing model performance using withheld data
- Generating movement trajectories from fitted models, which can be used for downstream predictions such as animal space use and landscape connectivity
Course Format
Interactive Learning Format
Each day features a well-balanced combination of lectures and hands-on practical exercises, with dedicated time for discussing participants’ own data, time permitting.
Global Accessibility
All live sessions are recorded and made available on the same day, ensuring accessibility for participants across different time zones.
Collaborative Discussions
Open discussion sessions provide an opportunity for participants to explore specific research questions and engage with instructors and peers.
Comprehensive Course Materials
All code, datasets, and presentation slides used during the course will be shared with participants by the instructor.
Personalized Data Engagement
Participants are encouraged to bring their own data for discussion and practical application during the course.
Post-Course Support
Participants will receive continued support via email for 30 days following the course, along with on-demand access to session recordings for the same period.
Who Should Attend / Intended Audiences
This course is intended for academics and postgraduate students working on projects involving the analysis and/or prediction of animal telemetry data, as well as applied researchers and analysts in public, private, or third-sector organizations. Users should have a basic understanding of statistical concepts such as regression, and some experience with data analysis and manipulation including the ability to import and export data, manipulate data frames, fit basic statistical models, and generate exploratory and diagnostic plots. Although the language used will be Python, and familiarity with Python will help, an understanding of similar programming languages (e.g. R) will also be beneficial.
Equipment and Software requirements
A laptop or desktop computer with a functioning installation of Python, and usually an Integrated Development Environment (IDE) such as VSCode is required. Both Python and VSCode are free, open-source programs compatible with Windows, macOS, and Linux systems.
While not essential, using a large monitor—or ideally a dual-monitor setup—can significantly enhance your learning experience by allowing you to view course materials and work in R simultaneously.
A working webcam is recommended to support interactive elements of the course. We encourage participants to keep their cameras on during live Zoom sessions to foster a more engaging and collaborative environment.
All necessary R packages will be introduced and installed during the workshop.
Prof. Garrett M. Street
Garrett is a quantitative ecologist working at the interface of habitat selection, animal movement, and landscape ecology. His research focuses on how fine-scale behavioral processes—such as thermoregulation, foraging, and habitat preference—shape broad-scale patterns in species distribution, abundance, and population dynamics. He utilizes a range of methods including statistical and simulation modeling, biotelemetry, geographic information systems (GIS), and laboratory microcosms to explore ecological processes from both theoretical and empirical perspectives. His recent work further examines animal fitness and body condition under energetic constraints, behavioral influences on habitat selection and species distribution, and the role of animal movement in the global carbon budget.
Garrett earned his PhD in Integrative Biology from the University of Guelph (supervised by Dr. John Fryxell), following a Master’s degree in Population & Conservation Biology from Texas State University (under Dr. Floyd “Butch” Weckerly). He then completed postdoctoral research at the University of Minnesota with Drs. John Fieberg and James Forester. In August 2015, he joined the Department of Wildlife, Fisheries, and Aquaculture at Mississippi State University, where he co-founded the Quantitative Ecology and Spatial Technologies (QuEST) Laboratory.
Education & Career
• PhD in Integrative Biology, University of Guelph
• M.Sc. in Population & Conservation Biology, Texas State University
• Postdoctoral Researcher, University of Minnesota
• Associate Professor, Department of Wildlife, Fisheries & Aquaculture, Mississippi State University (since 2015)
Research Focus
Garrett’s work centers on elucidating how micro-scale behavioral and physiological processes translate into macro-scale ecological patterns. He is particularly interested in the energetics of movement and foraging, habitat selection dynamics, and ecological forecasting across climatic and spatial gradients. His lab, QuEST, combines cutting-edge quantitative tools and empirical data to address key questions in movement ecology and ecosystem health across diverse taxa and landscapes.
Current Projects
• Energetic constraints on animal movement and body condition
• Behavioral drivers of habitat preference and species distribution
• Modeling animal movement across spatiotemporal and bioclimatic gradients
• Exploring wildlife contributions to carbon fluxes in ecosystems
• Feral swine and white-tailed deer behavior in managed landscapes, moose habitat selection, and avian population dynamics
Teaching & Skills
• Delivers courses in wildlife biostatistics, landscape ecology, GIS applications, and movement ecology
• Expert in applying statistical modeling, telemetry data, GIS, and simulation approaches to movement ecology
• Experienced in training students and professionals in quantitative ecological methods
Links
• University Profile
• Research Gate
Session 1 – 02:00:00 – Introduction to SSF and deepSSF
Overview of step selection functions as the foundation for deepSSF. Combining animal movement and habitat selection to learn about behaviour and generate predictions. Covers some introductory deep learning concepts in the context of animal movement.
- SSF concepts
- Create selection surfaces using SSFs
- Increasing flexibility in SSFs
- deepSSF
- Movement process (neural networks)
- Habitat selection process (convolution layers)
- How these combine into the next-step probability surface
Break – 00:30:00
Session 2 – 01:00:00 – Introduction to the deepSSF package
- Overview of the deepSSF package and its functions.
Session 3 – 01:30:00 – Data preparation – from CSV to Dataloader
- Movement data (CSV) and spatial environmental data (raster/tif) preparation. Creating Dataset and Dataloader objects using PyTorch.
Break – 00:30:00
Session 4 – 01:30:00 – deepSSF model architecture
- Creating the deepSSF model in PyTorch. Combining the model components, and selecting the number of neural network layers and weights and the number of convolution layers and kernels. How this affects training and performance.
Session 5 – 01:30:00 – Training models – loss functions, optimisers, schedulers and early stopping
- Overview of the loss function, optimiser, schedulers and early stopping functions required for training deepSSF models, and how these combine into a training loop.
Break – 00:30:00
- Session 6 – 01:30:00 – Assessing and validating model performance
- Testing predictive performance of both the movement and habitat selection processes against with-held data. Extracting probability weights and diagnostic plots to determine what the model has learned.
Session 7 – 02:00:00 – Simulating animal movement from deepSSF models
- Using the trained deepSSF model to simulate animal movement trajectories. How these might be used to generate space use estimates, landscape connectivity and scenario testing.
Break – 00:30:00
Session 8 – 01:00:00 – Conclusions and future possibilities
A recap of deepSSF models and how they’re trained, and how they might be further developing into the future.
Session 13 – 02:00:00 – Conceptual component: Introduction to step selection, decision-making processes, null and alternative models for definitions of availability within SSF
Session 14 – 02:00:00 – Conceptual component: movement-integrated step-selection analysis.
Session 15 – 04:00:00 – Practical component: Creation of available step data, estimation of SSF using multiple
packages and approaches, simulation of utilization and occurrence distributions.
Testimonials
PR Stats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PR Stats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.
Rolando O. Santos
PhD Assistant Professor, Florida International University
Courses attended
SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07
Testimonials
PR Stats provided excellent training in stable isotope analysis through the SIMMPR course, which was incredibly valuable for my research. I was fortunate to attend the course through a generous fee waiver, which directly supported my work and enabled me to develop skills that contributed to my recent publication on reservoir food webs in Sri Lanka. I’m very grateful for the opportunity and support, and would highly recommend their courses to others working in ecological research.
Subodha Silva
Aquatic Ecology Researcher
Courses attended
SIMMPR
Testimonials
PR Stats has become an invaluable part of developing my skills in advanced statistical and spatial analysis. Through training in areas such as Bayesian statistics and Species Distribution Modelling, I’ve gained both practical expertise and exposure to leading experts in the field. The impact on my research has been significant with at least four of my published papers have been directly influenced by PR Stats courses. My most recent work benefitted from modelling advice on sample design and model accuracy evaluation and can be seen here.
Carlos P.E. Bedson
Quantitative Spatial Ecology, Ecology and Environment Research Centre, Manchester Metropolitan University, United Kingdom
Courses attended
ADVR08, ENMR03, BMIN02, ISBD01, BADA01, SDMB06
Frequently asked questions
Everything you need to know about the product and billing.
When will I receive instructions on how to join?
You’ll receive an email on the Friday before the course begins, with full instructions on how to join via Zoom. Please ensure you have Zoom installed in advance.
Do I need administrator rights on my computer?
I’m attending the course live — will I also get access to the session recordings?
I can’t attend every live session — can I join some sessions live and catch up on others later?
I’m in a different time zone and plan to follow the course via recordings. When will these be available?
I can’t attend live — how can I ask questions?
Will I receive a certificate?
When will I receive instructions on how to join?
You’ll receive an email on the Friday before the course begins, with full instructions on how to join via Zoom. Please ensure you have Zoom installed in advance.
Do I need administrator rights on my computer?
I’m attending the course live — will I also get access to the session recordings?
I can’t attend every live session — can I join some sessions live and catch up on others later?
I’m in a different time zone and plan to follow the course via recordings. When will these be available?
I can’t attend live — how can I ask questions?
Will I receive a certificate?
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