https://github.com/psolymos/point-count-data-analysis
Analysis of avian point-count data in the presence of detection error
https://github.com/psolymos/point-count-data-analysis
birds detectability workshop
Last synced: 17 days ago
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Analysis of avian point-count data in the presence of detection error
- Host: GitHub
- URL: https://github.com/psolymos/point-count-data-analysis
- Owner: psolymos
- License: cc-by-sa-4.0
- Created: 2025-08-17T04:15:31.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-11-13T06:12:55.000Z (7 months ago)
- Last Synced: 2026-01-24T22:47:43.321Z (5 months ago)
- Topics: birds, detectability, workshop
- Language: HTML
- Homepage: https://peter.solymos.org/point-count-data-analysis/
- Size: 36.2 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Analysis of avian point-count data in the presence of detection error
## Instructor
Dr. Péter Sólymos is an ecologist and R programmer. He has worked with continental scale data sets and
developed statistical techniques for estimating population density from messy data sets. He is
the author of numerous well-known R packages, including detect, dclone, vegan, and
ResourceSelection. He works currently as a data scientist helping utility companies
improving their outage and impact prevention practices, and is an adjunct professor at the
University of Alberta in Edmonton, Canada.
## Course overview
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.
By the end of the course, participants should:
- 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
## Intended Audience
- Academics and post-graduate students working on projects related to avian data
- Applied researchers and analysts in public, private or third-sector organizations who
need the reproducibility, speed and flexibility of a programming language such as R
for analysing point count data arising from avian field surveys
## Course details
- Venue: Delivered remotely
- Time zone: UK (GMT)
- Duration: 3 days
- Contact hours: Approx. 12 hours
- ECT’s: Equal to 3 ECT’s
- Language: English
## Course outline
- [Day 0: Getting ready](./day-00/README.md)
- [Day 1: Laying the groundwork](./day-01/README.md)
- [Day 2: Understanding mechanisms](./day-02/README.md)
- [Day 3: Advanced topics](./day-03/README.md)
## Teaching Format
Introductory lectures on the concepts and refreshers on R usage. Intermediate-level lectures
interspersed with hands-on mini practicals and longer projects. Data sets for computer
practicals will be provided by the instructors, but participants are welcome to bring their own
data.
## Prerequisites
### Assumed quantitative knowledge
A basic understanding of statistical, mathematical and physical concepts. Specifically,
generalised linear regression models, including mixed models; basic knowledge of calculus.
### Assumed computer background
Familiarity with R, ability to import/export data, manipulate data frames, fit basic statistical
models (up to GLM) and generate simple exploratory and diagnostic plots.
## References
See publications listed in the [`papers`](./papers/) folder.
## License
© 2025 Péter Sólymos
This work is licensed under CC BY-SA 4.0
