A hierarchical, multivariate meta‐analysis approach to synthesising global change experiments
Kiona Ogle, Yao Liu, Sara Vicca, Michael Bahn
Abstract
Summary Meta‐analyses enable synthesis of results from globally distributed experiments to draw general conclusions about the impacts of global change factors on ecosystem function. Traditional meta‐analyses, however, are challenged by the complexity and diversity of experimental results. We illustrate how several key issues can be addressed by a multivariate, hierarchical Bayesian meta‐analysis (MHBM) approach applied to information extracted from published studies. We applied an MHBM to log‐response ratios for aboveground biomass (AB, n = 300), belowground biomass (BB, n = 205) and soil CO 2 exchange (SCE, n = 544), representing 100 studies. The MHBM accounted for study duration, climate effects and covariation among the AB, BB and SCE responses to elevated CO 2 (eCO 2 ) and/or warming. The MHBM revealed significant among‐study covariation in the AB and BB responses to experimental treatments. The MHBM imputed missing duration (4.2%) and climate (6%) data, and revealed that climate context governs how eCO 2 and warming impact ecosystem function. Predictions identified biomes that may be particularly sensitive to eCO 2 or warming, but that are under‐represented in global change experiments. The MHBM approach offers a flexible and powerful tool for synthesising disparate experimental results reported across multiple studies, sites and response variables.