Analysing Ecological Data with Detection Error
Learn to analyse ecological field data with detection error using R. Work with point counts, ARU data, N-mixture models, distance sampling and time-removal methods.
Duration: 4 Days, 4 hours per day
Next Date: April 20-23, 2026
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.
£350Registration Fee
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- Overview
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Course Description
This course is aimed towards researchers analysing field observations, who are often faced by data heterogeneities due to field sampling protocols changing from one project to another, or through time over the lifespan of projects, or trying to combine legacy data sets with new data collected by recording units.
Such heterogeneities can bias analyses when data sets are integrated inadequately or can lead to information loss when filtered and standardized to common standards. Accounting for these issues is important for better inference regarding status and trend of species and communities.
Analysts of such ‘messy’ data sets need to feel comfortable with manipulating the data, need a full understanding the mechanics of the models being used (i.e. critically interpreting the results and acknowledging assumptions and limitations), and should be able to make informed choices when faced with methodological challenges.
The course emphasizes critical thinking and active learning through hands on programming exercises. We will use publicly available data sets to demonstrate the data manipulation and analysis. We will use freely available and open-source R packages.
The expected outcome of the course is a solid foundation for further professional development via increased confidence in applying these methods for field observations.
What You’ll Learn
During the course we will cover the following:
- Understand basic statistical concepts related to detection error
- Work with field collected data and data from automated recording units (ARU)
- Know packages such as unmarked, detect, bSims
- Critically evaluate modelling options and assumptions using simulations
- Fit N-mixture, distance sampling, and time-removal models to data
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 avian data, as well as applied researchers and analysts in public, private, or third-sector organizations who require the reproducibility, speed, and flexibility of a programming language like R for analyzing point count data from avian field surveys. Users should have a basic understanding of statistical, mathematical, and physical concepts—particularly generalized linear regression models (including mixed models) and basic calculus. Familiarity with R is expected, including the ability to import and export data, manipulate data frames, fit basic statistical models up to GLMs, and generate simple exploratory and diagnostic plots.
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. Péter Solymos
Péter is an ecologist and scientific programmer whose work integrates large-scale ecological data analysis with advanced statistical modelling in R. His research focuses on developing flexible, reproducible tools for estimating species population densities and understanding ecological patterns across continental-scale datasets. Péter’s methodological contributions are widely recognized, particularly in handling complex and “messy” ecological data arising from imperfect surveys.
He is the author and maintainer of several well-known R packages used across ecological and statistical communities, including detect, dclone, vegan, and ResourceSelection, which support everything from hierarchical modelling to data cloning and resource use analysis. Beyond academic research, Péter applies his statistical expertise in industry, currently working as a data scientist helping utility companies improve outage prediction and impact mitigation strategies.
Péter also holds an adjunct faculty position at the University of Alberta in Edmonton, Canada, where he contributes to ecological research and mentorship in statistical computing and environmental modelling.
Education & Career
• Adjunct Professor, University of Alberta
• Data Scientist, Applied Predictive Analytics in the Energy Sector
• Author and maintainer of open-source R packages for ecological analysis
Research Focus
Péter’s work centers on scalable methods for ecological inference, particularly in the context of abundance estimation, imperfect detection, and model-based survey analysis. He is passionate about open science, reproducibility, and building tools that bridge ecological theory with practical data challenges.
Current Projects
• Developing R tools for estimating species density from imperfect survey data
• Applying predictive analytics to improve outage prevention in utility services
• Contributing to statistical and ecological workflows for large-scale biodiversity monitoring
Software & Tools
• detect – Tools for estimating detection probabilities and abundance
• dclone – Data cloning methods for Bayesian model calibration
• vegan – A foundational package for community ecology analysis
• ResourceSelection – Tools for analyzing habitat and resource use
Teaching & Skills
• Expert in R programming, hierarchical modelling, and Bayesian inference
• Active mentor in ecological statistics and open-source software development
• Promotes reproducible and efficient data analysis workflows across sectors
Links
• Google Scholar
• Work Homepage
• Personal Homepage
Session 1 – 01:00:00 – Introduction and background
Session 2 – 01:00:00 – Field data collection and data compilation
Session 3 – 01:00:00 – Overview of regression techniques
Session 4 – 01:00:00 – Naïve estimates of occupancy and abundance
Session 5 – 00:45:00 – Understanding observation error
Session 6 – 00:45:00 – Fitting occupancy models
Session 7 – 00:45:00 – Fitting N-mixture models
Session 8 – 00:45:00 – Dynamic occupancy and abundance models
Session 9 – 00:45:00 – Single visit based approaches
Session 10 – 00:45:00 – Introduction to data simulations
Session 11 – 00:45:00 – Time-removal models
Session 12 – 00:45:00 – Distance sampling
Session 13 – 00:45:00 – Estimating and applying detectability offsets
Session 14 – 00:45:00 – Applications of variable-effort survey design
Session 15 – 01:00:00 – Analysing data from audio recordings and camera traps
Session 16 – 01:00:00 – Multi-species models and using species traits and phylogeny
Session 17 – 01:00:00 – Dealing with roadside and other biases
Session 18 – 01:00:00 – Closing remarks
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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