Recursive Bayesian computation facilitates adaptive optimal design in ecological studies

• Published in: Ecology
• Main Authors: Leach, Clinton B., Williams, Perry J., Eisaguirre, Joseph M., Womble, Jamie N., Bower, Michael R., Hooten, Mevin B.
• Other Authors: National Science Foundation
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2021
• Subjects: glacier; Alaska
• Online Access: http://dx.doi.org/10.1002/ecy.3573; https://onlinelibrary.wiley.com/doi/pdf/10.1002/ecy.3573; https://onlinelibrary.wiley.com/doi/full-xml/10.1002/ecy.3573; https://esajournals.onlinelibrary.wiley.com/doi/am-pdf/10.1002/ecy.3573; https://esajournals.onlinelibrary.wiley.com/doi/pdf/10.1002/ecy.3573

Abstract Optimal design procedures provide a framework to leverage the learning generated by ecological models to flexibly and efficiently deploy future monitoring efforts. At the same time, Bayesian hierarchical models have become widespread in ecology and offer a rich set of tools for ecological learning and inference. However, coupling these methods with an optimal design framework can become computationally intractable. Recursive Bayesian computation offers a way to substantially reduce this computational burden, making optimal design accessible for modern Bayesian ecological models. We demonstrate the application of so‐called prior‐proposal recursive Bayes to optimal design using a simulated data binary regression and the real‐world example of monitoring and modeling sea otters in Glacier Bay, Alaska. These examples highlight the computational gains offered by recursive Bayesian methods and the tighter fusion of monitoring and science that those computational gains enable.