Analyzing Ecological Data with Detection Error

This course is for researchers who analyze field observations. These data are often inconsistent because sampling protocols can change across projects, over time, or when older data are combined with new recordings.

If these differences are not handled well, results can be biased. If data are over-filtered to force standardization, useful information can be lost. Addressing these issues is essential for better estimates of species status and trends.

To work with these messy data sets, analysts need practical data skills, a clear understanding of model mechanics, and the ability to interpret results critically, including assumptions and limits.

The course focuses on critical thinking and active learning through hands-on programming exercises. We use public data sets for examples and analysis, and we use free, open-source R packages.

By the end of the course, participants will have a strong foundation and more confidence in applying these methods to field observation data.

The online workshop is organized by PR Stats.

Find R code used in the course on the GitHub site: https://github.com/psolymos/analysing-ecological-data-with-detection-error.