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Research challenges and opportunities for using big data in global change biology
Author(s) -
Xia Jianyang,
Wang Jing,
Niu Shuli
Publication year - 2020
Publication title -
global change biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.146
H-Index - 255
eISSN - 1365-2486
pISSN - 1354-1013
DOI - 10.1111/gcb.15317
Subject(s) - big data , data science , cyberinfrastructure , computer science , bottleneck , global change , biological data , pace , biosphere , earth system science , data assimilation , climate change , ecology , data mining , biology , geography , meteorology , genetics , geodesy , embedded system
Abstract Global change biology has been entering a big data era due to the vast increase in availability of both environmental and biological data. Big data refers to large data volume, complex data sets, and multiple data sources. The recent use of such big data is improving our understanding of interactions between biological systems and global environmental changes. In this review, we first explore how big data has been analyzed to identify the general patterns of biological responses to global changes at scales from gene to ecosystem. After that, we investigate how observational networks and space‐based big data have facilitated the discovery of emergent mechanisms and phenomena on the regional and global scales. Then, we evaluate the predictions of terrestrial biosphere under global changes by big modeling data. Finally, we introduce some methods to extract knowledge from big data, such as meta‐analysis, machine learning, traceability analysis, and data assimilation. The big data has opened new research opportunities, especially for developing new data‐driven theories for improving biological predictions in Earth system models, tracing global change impacts across different organismic levels, and constructing cyberinfrastructure tools to accelerate the pace of model‐data integrations. These efforts will uncork the bottleneck of using big data to understand biological responses and adaptations to future global changes.