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 jmattern@ucsc.edu
Explore Related Media
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)
Publications
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 (2021), Evaluating the benefits of Bayesian hierarchical methods for analyzing heterogeneous environmental datasets: a case study of marine organic carbon fluxes, Frontiers in Environmental Science, doi: 10.3389/fenvs.2021.491636
Jann Paul Mattern, Kristof Glauninger, Gregory L. Britten, John R. Casey, Sangwon Hyun, Zhen Wu, E. Virginia Armbrust, Zaid Harchaoui, Francois Ribalet (2022), A Bayesian approach to modeling phytoplankton population dynamics from size distribution time series, PLoS Computational Biology, doi: 10.1371/journal.pcbi.1009733
(CBIOMES investigators highlighted in bold)