Point-count Data Analysis

Analysis of point-count data in the presence of variable survey methodologies and detection error offered by Péter Sólymos to BIOS2 Fellows in March 2021.

Péter Sólymos

University of Alberta


March 25, 2021

This course is aimed towards researchers analyzing 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.


Dr. Peter Solymos
Boreal Avian Modelling Project and the Alberta Biodiversity Monitoring Institute
Department of Biological Sciences, University of Alberta


Each day will consist of 3 sessions, roughly one hour each, with short breaks in between.

The video recordings from the workshop can be found on YouTube.

Session Topic Files Videos
Day 1 Naive techniques
1. Introductions Slides Video
2. Organizing point count data Notes Part 1, Part 2
3. Regression techniques Notes Part 1, Part 2
Day 2 Behavioral complexities
1. Statistical assumptions and nuisance variables Slides Video
2. Behavioral complexities Notes bSims, Video
3. Removal modeling techniques Notes Video
4. Finite mixture models and testing assumptions Notes Mixtures, Testing
Day 3 The detection process
1. The detection process Slides Video
2. Distance sampling and density Notes Video
3. Estimating population density Notes Video
4. Assumptions Notes Video
Day 4 Coming full circle
1. QPAD overview Slides Video
2. Models with detectability offsets Notes Offsets, Models
3. Model validation and error propagation Notes Validation, Error
4. Recordings, roadsides, closing remarks Notes Video

Get course materials

Install required software

Follow the instructions at the R website to download and install the most up-to-date base R version suitable for your operating system (the latest R version at the time of writing these instructions is 4.0.4).

Then run the following script in R:


Having RStudio is not absolutely necessary, but it will make life easier. RStudio is also available for different operating systems. Pick the open source desktop edition from here (the latest RStudio Desktop version at the time of writing these instructions is 1.4.1106).

Prior exposure to R programming is not necessary, but knowledge of basic R object types and their manipulation (arrays, data frames, indexing) is useful for following hands-on exercises. Software Carpentry’s Data types and structures in R is a good resource to brush up your R skills.

Get the notes

If you don’t want to use git:

  1. Download the workshop archive release into a folder
  2. Extract the zip archive
  3. Open the workshop.Rproj file in RStudio (or open any other R GUI/console and setwd() to the directory where you downloaded the file)
  4. (You can delete the archive)

If you want to use git: fork or clone the repository

cd into/your/dir
git clone https://github.com/psolymos/qpad-workshop.git

Useful resources


Sólymos, P., Toms, J. D., Matsuoka, S. M., Cumming, S. G., Barker, N. K. S., Thogmartin, W. E., Stralberg, D., Crosby, A. D., Dénes, F. V., Haché, S., Mahon, C. L., Schmiegelow, F. K. A., and Bayne, E. M., 2020. Lessons learned from comparing spatially explicit models and the Partners in Flight approach to estimate population sizes of boreal birds in Alberta, Canada. Condor, 122: 1-22. PDF

Sólymos, P., Matsuoka, S. M., Cumming, S. G., Stralberg, D., Fontaine, P., Schmiegelow, F. K. A., Song, S. J., and Bayne, E. M., 2018. Evaluating time-removal models for estimating availability of boreal birds during point-count surveys: sample size requirements and model complexity. Condor, 120: 765-786. PDF

Sólymos, P., Matsuoka, S. M., Stralberg, D., Barker, N. K. S., and Bayne, E. M., 2018. Phylogeny and species traits predict bird detectability. Ecography, 41: 1595-1603. PDF

Van Wilgenburg, S. L., Sólymos, P., Kardynal, K. J. and Frey, M. D., 2017. Paired sampling standardizes point count data from humans and acoustic recorders. Avian Conservation and Ecology, 12(1):13. PDF

Yip, D. A., Leston, L., Bayne, E. M., Sólymos, P. and Grover, A., 2017. Experimentally derived detection distances from audio recordings and human observers enable integrated analysis of point count data. Avian Conservation and Ecology, 12(1):11. PDF

Sólymos, P., and Lele, S. R., 2016. Revisiting resource selection probability functions and single-visit methods: clarification and extensions. Methods in Ecology and Evolution, 7:196-205. PDF

Matsuoka, S. M., Mahon, C. L., Handel, C. M., Sólymos, P., Bayne, E. M., Fontaine, P. C., and Ralph, C. J., 2014. Reviving common standards in point-count surveys for broad inference across studies. Condor 116:599-608. PDF

Sólymos, P., Matsuoka, S. M., Bayne, E. M., Lele, S. R., Fontaine, P., Cumming, S. G., Stralberg, D., Schmiegelow, F. K. A. & Song, S. J., 2013. Calibrating indices of avian density from non-standardized survey data: making the most of a messy situation. Methods in Ecology and Evolution 4:1047-1058. PDF

Matsuoka, S. M., Bayne, E. M., Sólymos, P., Fontaine, P., Cumming, S. G., Schmiegelow, F. K. A., & Song, S. A., 2012. Using binomial distance-sampling models to estimate the effective detection radius of point-counts surveys across boreal Canada. Auk 129:268-282. PDF


The course material is licensed under Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license. Source code is under MIT license.

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BibTeX citation:
  author = {Péter Sólymos},
  title = {Point-Count {Data} {Analysis}},
  date = {2021-03-25},
  url = {https://bios2.github.io/posts/2021-03-25-point-count-data-analysis},
  langid = {en}
For attribution, please cite this work as:
Péter Sólymos. 2021. “Point-Count Data Analysis.” BIOS2 Education Resources. March 25, 2021. https://bios2.github.io/posts/2021-03-25-point-count-data-analysis.