Network Analysis for Ecologists
Learn how to analyse species interactions and ecological networks using R and igraph in this focused 6-hour course.
Duration: 6 hours
Next Date: Available 7 November
Format: Recorded ‘on-demand’ Format
£225Registration Fee
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- Overview
- Instructors
- Schedule
Course Description
This 6 hour course covers concepts, methods, and tools (i.e. R code) that can be used to analyse ecological network data using (but not limited to) the R package IGRAPH.
The course will review concepts on network construction and representation as well as their analysis. We will cover data manipulation and graph formats to represent data as networks. Additionally, we will cover structural analytical techniques commonly used to interrogate networks across different fields of applications. Finally, we will cover mathematical modelling of population dynamics on networks, using ordinary differential equations (ODEs). We will use some real-world empirical datasets to motivate analyses, such as describing network-level and node-level properties. We will complement this with simulated data from numerical simulations performed using the population dynamic models. We will emphasise visualisation and reproducible workflows as well as good programming practices. The modules will consist of introductory lectures, guided computer coding, and participant exercises. The course is intended for intermediate users of R who are interested in ecology, particularly in the areas of network analysis across ecosystem types. You are welcome to use your own data sets.
What You’ll Learn
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During the course will cover the following:
- Fundamentals of network construction and analysis.
- Network metrics.
- Methods to organise data in network format and prepare for analysis.
- Network-level structural properties such as modularity, degree distributions and nestedness, and how to calculate them.
- Node-level structural properties such as degree and centrality measures, and how to calculate them.
- Dynamical ecological modelling using population dynamical equations.
Course Format
Flexible Learning Structure
Learn through a carefully structured mix of lecture recordings and guided exercises that you can pause, revisit, and complete at your own pace—ideal for busy professionals or those balancing multiple commitments.
Access Anytime, Anywhere
All course content is available on-demand, making it accessible across all time zones without the need to attend live sessions or adjust your schedule.
Independent Exploration with Support
Engage deeply with course topics through self-directed study, with the option to reach out to instructors via email for clarification or deeper discussion.
Comprehensive Learning Resources
Gain full access to the same high-quality materials provided in live sessions, including code, datasets, and presentation slides—all available to download and keep. Please note recordings can only be streamed.
Work With Your Own Data, On Your Terms
Apply what you learn directly to your own data projects as you go, allowing for a personalized and immediately practical learning experience.
Continued Guidance and Resource Access
Receive 30 days of post-enrolment email support and unrestricted access to all session recordings during that time, so you can review and reinforce your learning as needed.
Who Should Attend / Intended Audiences
This course is designed for ecologists who are interested in learning the fundamentals of network analysis as applied to ecological communities and species interactions. A basic background in computer coding is assumed, with familiarity in R recommended to support smooth progression through the material. Participants should have a foundational understanding of quantitative concepts, including basic statistical properties of datasets, summary statistics, data distributions, and basic algebraic operations. While not essential, familiarity with calculus and differential equations will be helpful for understanding more advanced topics covered in the latter parts of the course.
Equipment and Software requirements
A laptop or desktop computer with a functioning installation of R and RStudio is required. Both R and RStudio are free, open-source programs compatible with Windows, macOS, and Linux systems.
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.
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.
All necessary R packages will be introduced and installed during the workshop. A comprehensive list of required packages will also be shared with participants ahead of the course to allow for optional pre-installation.
Dr. Miguel Lurgi
Miguel is a computational ecologist and Associate Professor at Swansea University, UK, where he leads the Computational Ecology Lab. His research focuses on the ecological and evolutionary processes shaping complex species interaction networks across spatial scales and ecosystem types. He is particularly interested in how global change drivers impact the structure and function of ecological networks, from microbial communities to large-scale food webs.
Miguel earned his PhD in Terrestrial Ecology from the Autonomous University of Barcelona in 2014, conducting his doctoral research jointly at the Marine Sciences Institute (ICM-CSIC) and the Centre for Ecological Research and Forestry Applications (CREAF). He completed postdoctoral research at the University of Adelaide (Australia) and the Theoretical and Experimental Ecology Station of the CNRS (France) before joining Swansea University in 2019.
Education & Career
- PhD in Terrestrial Ecology, Autonomous University of Barcelona (2014)
- Postdoctoral Researcher, University of Adelaide, Australia (2014–2016)
- Postdoctoral Researcher, CNRS – Theoretical and Experimental Ecology Station, France (2016–2019)
- Associate Professor and Head of the Computational Ecology Lab, Swansea University, UK (since 2019)
Research Focus
Miguel’s work centres on understanding the mechanisms that drive the assembly and stability of ecological communities. He combines empirical data on species distributions and interactions with theoretical models that incorporate ecological and evolutionary dynamics, aiming to predict large-scale biodiversity patterns and their responses to environmental change.
Current Projects
- Modelling the impact of global change on food web dynamics
- Exploring microbial community structure through network theory
- Theoretical frameworks for ecological community assembly across gradients
Professional Consultancy
Miguel contributes expert insight on network modelling and biodiversity responses to global change, collaborating with international partners on conservation planning and ecosystem management projects.
Teaching & Skills
- Teaches courses in computational ecology, ecological networks, and biodiversity modelling
- Skilled in R, Python, and network modelling frameworks
- Strong advocate for reproducibility, open data, and mechanistic ecological understanding
Links
Session 1 – 01:00:00 – Introduction to networks.
Definition of a network and its constituent parts (vertices and edges). Description of their potential uses and their analysis. Conceptual description of network metrics and specific descriptors used to analyse ecological networks.
Session 2 – 01:00:00 – Network analysis methods.
Degree distribution. Modularity. Nestedness. Degree (number of interactions). Heterogeneity of interaction number. Node centrality.
Session 3- 01:00:00 – Practical lab session.
Preparing data for network representation analysis. Representing networks as matrices and as graphs using the igraph package in R. Getting network data from internet sources such as the web of life. Implementing network metrics in R using igraph and other libraries.
Session 4- 01:00:00 – Modelling Network Dynamics.
Introduction to population dynamics in single and multispecies systems. Simulating communities using ecological models. Analysing the outcomes of simulation experiments using networks.
Session 5- 01:00:00 – Methods for data simulation.
Quick introduction to Ordinary Differential Equations and their use in ecology. Developing an ecological question and tackling it using networks and their effects on population dynamics.
Session 6- 01:00:00 – Practical lab session.
Implementing mathematical functions in R. Using an equation solver to develop numerical simulations. Perform the simulation scenario designed during the previous session. Understand and analyse the outcome using the techniques learned the previous day.
Testimonials
PRStats 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 PRStats 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
PRStats 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 PRStats 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
PRStats 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 PRStats 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
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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