CBIOMES Bayesian Modeling Working Group

Bayesian CBIOMES: Statistical and software tools for integrating data and models

Contact: Greg Britten, Paul Mattern

Bayesian inference is a powerful mathematical framework that can be used to integrate mechanistic ecosystem models with diverse observational datasets. In January 2020 we held our first CBIOMES workshop to bring together modelers and observationalists to share methods and build collaboration via hands-on case studies. We have maintained these collaborations since the workshop, resulting in several presentations and scientific manuscripts (see below). Below you will also find materials and recordings from the workshop, including step-by-step instructions to get started with Bayesian analysis and associated software, along with a library of code to try the case studies.

To find out more or to get involved, email gbritten@mit.edu or mattern@ucsc.edu

Explore Related Media

Overview of workshop

GitHub repository of workshop case studies and associated code

Recordings from the 2020 workshop (login required)

Bayesian Modeling Plenary Session” (2020 Annual Meeting) (login required)

“A Bayesian Approach to Size-structured Matrix Population Model” (2020 Annual Meeting e-poster) (login required)

Why size-dependence alone cannot explain observed Prochlorococcus division rates” (2020 e-poster) (login required)

Stan’s New Friends! Probabilistic programming packages to integrate data and models while balancing uncertainties” (2020 e-poster) (login required)



B.B. Cael, Emma L. Cavan, and Gregory L. Britten (2021), Reconciling the size-dependence of marine particle sinking speed, (accepted at Geophysical Research Letters), doi: 10.1029/2020GL091771

Britten G.L., Y. Mohajerani, L. Primeau, M. Aydin, C. Garcia, W. Wang, B. Pasquier, B.B. Cael, F.W. Primeau, Evaluating the benefits of Bayesian hierarchical methods for analyzing heterogeneous environmental datasets: a case study of marine organic carbon fluxes. (Accepted at Frontiers in Environmental Science)

Mattern J.P., K. Glauninger, G.L. Britten, J. Casey, S. Hyun, Z. Wu, E.V. Armbrust, M.J. Follows, Z. Harchaoui, F. Ribalet, A flexible Bayesian formulation of size-structured matrix population models. (To submit to PLoS Computational Biology)

(CBIOMES investigators highlighted in bold)