Evaluating the benefits of bayesian hierarchical methods for analyzing heterogeneous environmental datasets: a case study of marine organic carbon fluxes

Large compilations of heterogeneous environmental observations are increasingly available as public databases, allowing researchers to test hypotheses across datasets. Statistical complexities arise when analyzing compiled data due to unbalanced spatial sampling, variable environmental context, mixed measurement techniques, and other reasons. Hierarchical Bayesian modeling is increasingly used in environmental science to describe these complexities, however few studies explicitly compare the utility of hierarchical Bayesian models to simpler and more commonly applied methods. Here we demonstrate the utility of the hierarchical Bayesian approach with application to a large compiled environmental dataset consisting of 5,741 marine vertical organic carbon flux observations from 407 sampling locations spanning eight biomes across the global ocean. We fit a global scale Bayesian hierarchical model that describes the vertical profile of organic carbon flux with depth. Profile parameters within a particular biome are assumed to share a common deviation from the global mean profile. Individual station-level parameters are then modeled as deviations from the common biome-level profile. The hierarchical approach is shown to have several benefits over simpler and more common data aggregation methods. First, the hierarchical approach avoids statistical complexities introduced due to unbalanced sampling and allows for flexible incorporation of spatial heterogeneitites in model parameters. Second, the hierarchical approach uses the whole dataset simultaneously to fit the model parameters which shares information across datasets and reduces the uncertainty up to 95% in individual profiles. Third, the Bayesian approach incorporates prior scientific information about model parameters; for example, the non-negativity of chemical concentrations or mass-balance, which we apply here. We explicitly quantify each of these properties in turn. We emphasize the generality of the hierarchical Bayesian approach for diverse environmental applications and its increasing feasibility for large datasets due to recent developments in Markov Chain Monte Carlo algorithms and easy-to-use high-level software implementations

Regional variability in peatland burning at mid- to high-latitudes during the Holocene

Acknowledgements This work developed from the PAGES (Past Global Changes) C-PEAT (Carbon in Peat on EArth through Time) working group. PAGES has been supported by the US National Science Foundation, Swiss National Science Foundation, Swiss Academy of Sciences and Chinese Academy of Sciences. We acknowledge the following financial support: UK Natural Environment Research Council Training Grants NE/L002574/1 (T.G.S.) and NE/S007458/1 (R.E.F.); Dutch Foundation for the Conservation of Irish Bogs, Quaternary Research Association and Leverhulme Trust RPG-2021-354 (G.T.S); the Academy of Finland (M.V); PAI/SIA 80002 and FONDECYT Iniciación 11220705 - ANID, Chile (C.A.M.); R20F0002 (PATSER) ANID Chile (R.D.M.); Swedish Strategic Research Area (SRA) MERGE (ModElling the Regional and Global Earth system) (M.J.G.); Polish National Science Centre Grant number NCN 2018/29/B/ST10/00120 (K.A.); Russian Science Foundation Grant No. 19-14-00102 (Y.A.M.); University of Latvia Grant No. AAp2016/B041/Zd2016/AZ03 and the Estonian Science Council grant PRG323 (TrackLag) (N.S. and A.M.); U.S. Geological Survey Land Change Science/Climate Research & Development Program (M.J., L.A., and D.W.); German Research Foundation (DFG), grant MA 8083/2-1 (P.M.) and grant BL 563/19-1 (K.H.K.); German Academic Exchange Service (DAAD), grant no. 57044554, Faculty of Geosciences, University of Münster, and Bavarian University Centre for Latin America (BAYLAT) (K.H.K). Records from the Global Charcoal Database supplemented this work and therefore we would like to thank the contributors and managers of this open-source resource. We also thank Annica Greisman, Jennifer Shiller, Fredrik Olsson and Simon van Bellen for contributing charcoal data to our analyses. Any use of trade, firm, or product name is for descriptive purposes only and does not imply endorsement by the U.S. Government.Peer reviewedPostprin