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Title
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A hierarchical, multivariate meta‐analysis approach to synthesising global change experiments
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Author
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Abstract
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| 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 CO2 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 CO2 (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. |
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Language
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English
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Source (journal)
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New phytologist. - Oxford
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Publication
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Oxford
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2021
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ISSN
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0028-646X
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DOI
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10.1111/NPH.17562
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Volume/pages
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231
:6
(2021)
, p. 2382-2394
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Article Reference
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nph.17562
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ISI
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000672881300001
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Pubmed ID
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34137037
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Medium
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E-only publicatie
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Full text (Publisher's DOI)
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Full text (open access)
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Full text (publisher's version - intranet only)
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