Analysis of Avian Point-Count Data in the Presence of Detection Error
Learn to analyse avian point-count data with R, accounting for detection error using models like N-mixture, distance sampling, and time-removal.
Duration: 12 hours
Next Date: Available 24 November
Format: Recorded ‘on-demand’ Format
£450Registration 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. Analysis 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 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
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 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 – 00:40:00 – Introduction and background
Session 2 – 00:40:00 – Review of field sampling techniques
Session 3 – 00:40:00 – Introduction to agent-based simulations
Session 4 – 00:40:00 – Overview of regression techniques
Session 5 – 00:40:00 – Naïve estimates of occupancy and abundance
Session 6 – 00:40:00 – Multiple visits and N-mixture models
Session 7 – 00:50:00 – Bird behaviour
Session 8 – 00:50:00 – Time-removal models
Session 9 – 00:50:00 – Observation process
Session 10 – 00:50:00 – Distance sampling
Session 11 – 00:50:00 – Combining removal and distance sampling (QPAD)
Session 12 – 00:50:00 – Single visit-based approaches (N-mixture and SQPAD)
Session 13 – 00:50:00 – Analysing data from recording units
Session 14 – 00:50:00 – Multi-species models and using species traits and phylogeny
Session 15 – 00:50:00 – Dealing with roadside and other biases
Session 16 – 00:50:00 – 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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