BIOS² is hosting a training workshop about analysis of point-count data in the presence of variable survey methodologies and detection error. This online training will be led by Dr. Péter Sólymos (University of Alberta), starting on March 16, 2021.
This training workshop 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.
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.
The training will include practical exercises using the R statistical programming environment. Prior exposure to R programming is not necessary, but knowledge of R object types and their manipulation (arrays, data frames, indexing) is recommended.
This 12-hour online training will be conducted in 4 sessions: March 16, 18, 23 & 25 (2021) from 12pm to 3pm EDT (9am -12pm Pacific / 10am – 1pm MT). The training will be held in English.
Registration here1by March 14th, 2021.
The training will be led by Péter Sólymos, adjunct professor and statistical ecologist at the Alberta Biodiversity Monitoring Institute, University of Alberta and the Boreal Avian Modelling Project. His research focuses on developing data analytics and novel statistical methods for biodiversity conservation, natural resource management, and environmental protection over large spatial scales.