干旱区生态环境知识资源中心(Arid): Spatial biases of information influence global estimates of soil respiration: How can we improve global predictions?

Arid

DOI	10.1111/gcb.15666
	Spatial biases of information influence global estimates of soil respiration: How can we improve global predictions?
	Stell, Emma; Warner, Daniel; Jian, Jinshi; Bond-Lamberty, Ben; Vargas, Rodrigo
通讯作者	Vargas, R (corresponding author), Univ Delaware, Dept Plant & Soil Sci, 531 South Coll Ave,152 Townsend Hall, Newark, DE 19716 USA.
来源期刊	GLOBAL CHANGE BIOLOGY
ISSN	1354-1013
EISSN	1365-2486
出版年	2021
英文摘要	Soil respiration (Rs), the efflux of CO2 from soils to the atmosphere, is a major component of the terrestrial carbon cycle, but is poorly constrained from regional to global scales. The global soil respiration database (SRDB) is a compilation of in situ Rs observations from around the globe that has been consistently updated with new measurements over the past decade. It is unclear whether the addition of data to new versions has produced better-constrained global Rs estimates. We compared two versions of the SRDB (v3.0 n = 5173 and v5.0 n = 10,366) to determine how additional data influenced global Rs annual sum, spatial patterns and associated uncertainty (1 km spatial resolution) using a machine learning approach. A quantile regression forest model parameterized using SRDBv3 yielded a global Rs sum of 88.6 Pg C year(-1), and associated uncertainty of 29.9 (mean absolute error) and 57.9 (standard deviation) Pg C year(-1), whereas parameterization using SRDBv5 yielded 96.5 Pg C year(-1) and associated uncertainty of 30.2 (mean average error) and 73.4 (standard deviation) Pg C year(-1). Empirically estimated global heterotrophic respiration (Rh) from v3 and v5 were 49.9-50.2 (mean 50.1) and 53.3-53.5 (mean 53.4) Pg C year(-1), respectively. SRDBv5's inclusion of new data from underrepresented regions (e.g., Asia, Africa, South America) resulted in overall higher model uncertainty. The largest differences between models parameterized with different SRDVB versions were in arid/semi-arid regions. The SRDBv5 is still biased toward northern latitudes and temperate zones, so we tested an optimized global distribution of Rs measurements, which resulted in a global sum of 96.4 +/- 21.4 Pg C year(-1) with an overall lower model uncertainty. These results support current global estimates of Rs but highlight spatial biases that influence model parameterization and interpretation and provide insights for design of environmental networks to improve global-scale Rs estimates.
英文关键词	carbon cycle heterotrophic respiration machine learning network design network representativeness soil CO2 efflux
类型	Article ; Early Access
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000652328400001
WOS关键词	HETEROTROPHIC RESPIRATION ; CO2 EFFLUX ; INTERANNUAL VARIABILITY ; SEMIARID ECOSYSTEMS ; SAMPLING DESIGN ; HOT-MOMENTS ; R PACKAGE ; VEGETATION ; UNCERTAINTY ; TEMPERATURE
WOS类目	Biodiversity Conservation ; Ecology ; Environmental Sciences
WOS研究方向	Biodiversity & Conservation ; Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/352216
作者单位	[Stell, Emma; Vargas, Rodrigo] Univ Delaware, Dept Geog & Spatial Sci, Newark, DE USA; [Warner, Daniel] Univ Delaware, Delaware Geol Survey, Newark, DE USA; [Jian, Jinshi; Bond-Lamberty, Ben] Pacific Northwest Natl Lab, Joint Global Change Res Inst, College Pk, MD USA; [Vargas, Rodrigo] Univ Delaware, Dept Plant & Soil Sci, 531 South Coll Ave,152 Townsend Hall, Newark, DE 19716 USA
推荐引用方式 GB/T 7714	Stell, Emma,Warner, Daniel,Jian, Jinshi,et al. Spatial biases of information influence global estimates of soil respiration: How can we improve global predictions?[J],2021.
APA	Stell, Emma,Warner, Daniel,Jian, Jinshi,Bond-Lamberty, Ben,&Vargas, Rodrigo.(2021).Spatial biases of information influence global estimates of soil respiration: How can we improve global predictions?.GLOBAL CHANGE BIOLOGY.
MLA	Stell, Emma,et al."Spatial biases of information influence global estimates of soil respiration: How can we improve global predictions?".GLOBAL CHANGE BIOLOGY (2021).