#159 Bayesian Occupancy Models, with Matthijs Hollanders

About this episode

Matthijs Hollanders discusses Bayesian occupancy models and their application in species detection using Automated Recording Units.

Support & Resources → Support the show on Patreon → Bayesian Modeling Course (first 2 lessons free) Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work Takeaways : Q: What is a Bayesian occupancy model and what problem does it solve? A: An occupancy model accounts for the fact that you don't always detect a species when surveying for it, especially when the species is rare. A naive count of where you found it underestimates true occupancy. The model adds a repeated-measures component: you visit each site multiple times, and from the pattern of detections vs. non-detections it estimates a detection probability. Matthijs framed it as a zero-inflation structure where the zero-inflation happens at the site level rather than the observation level -- which keeps the model conceptually simple, just a standard GLM with a Bernoulli “is the species here at all?” stacked on top of a detection-rate process. Q: What are Automated Recording Units and why don't traditional occupancy models handle them well? A: ARUs are camera traps and acoustic monitors that record continuously over deployment periods of days, weeks, or months. The…

People in this episode

Host: Alexandre Andorra

Guest: Matthijs Hollanders

Topics covered

• Bayesian occupancy models
• species detection
• Automated Recording Units
• zero-inflation structure
• detection probability

Keywords

• Bayesian occupancy model
• species detection
• Automated Recording Units
• zero-inflation
• detection probability

Mentioned in this episode

Books & works: Good Bayesian

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