Research challenges and opportunities for using big data in global change biology

被引:43
|
作者
Xia, Jianyang [1 ]
Wang, Jing [1 ,2 ]
Niu, Shuli [3 ,4 ]
机构
[1] East China Normal Univ, Sch Ecol & Environm Sci, Res Ctr Global Change & Ecol Forecasting, Zhejiang Tiantong Forest Ecosyst Natl Observat &, Shanghai, Peoples R China
[2] Shanghai Inst Pollut Control & Ecol Secur, Shanghai, Peoples R China
[3] Chinese Acad Sci, Inst Geog Sci, Key Lab Ecosyst Network Observat & Modeling, Beijing, Peoples R China
[4] Chinese Acad Sci, Nat Resources Res, Beijing, Peoples R China
基金
中国国家自然科学基金;
关键词
big data; Earth system model; global change biology; machine learning; model uncertainty; PROGRESSIVE NITROGEN LIMITATION; PLANT TRAIT DATABASE; LAND CARBON STORAGE; MODEL-DATA FUSION; LONG-TERM CARBON; DATA ASSIMILATION; SOIL RESPIRATION; INTERANNUAL VARIABILITY; SPECIES RICHNESS; ELEVATED CO2;
D O I
10.1111/gcb.15317
中图分类号
X176 [生物多样性保护];
学科分类号
090705 ;
摘要
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.
引用
收藏
页码:6040 / 6061
页数:22
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