#159 Bayesian Occupancy Models, with Matthijs Hollanders

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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 data they produce isn't a sequence of discrete human-led surveys; it's a continuous-time observation stream. Traditional occupancy models were designed for the discrete case -- a human visits a site, records yes or no, goes home. With ARUs, the question becomes how to bin or threshold the continuous data without losing the richer signal it actually contains.

Q: When should you not reach for occARU? A: When your dataset is large and your survey interval is fine-grained. The bottleneck is Stan's fitting speed -- years of daily count data across many sites will fit slowly. The workaround is to bin coarser (weekly or monthly), which doesn't hurt occupancy estimation at all and only loses some detection-rate resolution. If you're only interested in occupancy, big grouping windows are fine.

Full takeaways here

Chapters: 00:12:14 What is an occupancy model and what problem does it solve? 00:16:16 What are Automated Recording Units and why do they need different models? 00:18:45 What is the occARU R package and why does it exist? 00:23:55 Why does occARU model counts directly rather than binary detection? 00:26:38 What does multi-species hierarchical modeling with Gaussian processes look like? 00:32:22 How does occARU implement Gaussian processes efficiently? 00:41:01 Why are Gaussian processes such a powerful but tricky modeling tool? 00:44:11 What is variance decomposition with global-local shrinkage priors? 00:49:02 How does occARU leverage recent Stan features for zero-sum constraints? 00:57:37 When does within-chain parallelization actually help? 01:01:30 How does Monte Carlo integration reduce high Pareto-k values? 01:15:27 When does occARU underperform and what's on the roadmap?

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