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. 2020 Oct 7;6(41):eabb8508.
doi: 10.1126/sciadv.abb8508. Print 2020 Oct.

Spatial and temporal variations in global soil respiration and their relationships with climate and land cover

Ni Huang  1 , Li Wang  2 , Xiao-Peng Song  3 , T Andrew Black  4 , Rachhpal S Jassal  4 , Ranga B Myneni  5 , Chaoyang Wu  6 , Lei Wang  1 , Wanjuan Song  1 , Dabin Ji  1 , Shanshan Yu  1 , Zheng Niu  1   7
Affiliations

Spatial and temporal variations in global soil respiration and their relationships with climate and land cover

Ni Huang et al. Sci Adv. .

Abstract

Soil respiration (R s) represents the largest flux of CO2 from terrestrial ecosystems to the atmosphere, but its spatial and temporal changes as well as the driving forces are not well understood. We derived a product of annual global R s from 2000 to 2014 at 1 km by 1 km spatial resolution using remote sensing data and biome-specific statistical models. Different from the existing view that climate change dominated changes in R s, we showed that land-cover change played a more important role in regulating R s changes in temperate and boreal regions during 2000-2014. Significant changes in R s occurred more frequently in areas with significant changes in short vegetation cover (i.e., all vegetation shorter than 5 m in height) than in areas with significant climate change. These results contribute to our understanding of global R s patterns and highlight the importance of land-cover change in driving global and regional R s changes.

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Figures

Fig. 1
Fig. 1. Global distribution of mean annual Rs between 2000 and 2014.
(A) Global map of mean annual Rs at 1 km by 1 km spatial resolution derived using satellite data. (B) Latitudinal distribution of mean annual Rs (blue line) and total annual Rs (orange line). All land grids along a latitudinal row in the global map were averaged to derive mean Rs and summed to derive total Rs.
Fig. 2
Fig. 2. Trends in annual Rs from 2000 to 2014.
(A) Overall trends in annual Rs. To calculate the overall trend at the global scale, global average Rs for each year was first derived. Then, a two-sided Mann-Kendall test and a Theil-Sen median trend analysis were performed. Trends at the regional scales were derived following the same method as the global trend. Gray bars in the upward direction indicate an increasing trend, whereas the hollow bar in the downward direction indicates a decreasing trend, with two asterisks denoting significant trends (P < 0.05). Error bars represent the 95% CIs estimated via a 1000-bootstrap analysis. (B) Spatially explicit trends in annual Rs at 1 km by 1 km spatial resolution. Similar to overall trends in (A), per-pixel trend was characterized using a two-sided Mann-Kendall test and a Theil-Sen median trend analysis. Significant increasing and decreasing trends correspond to positive and negative Theil-Sen estimators, respectively, with significant Mann-Kendall test (P < 0.05). Slightly increasing and decreasing trends correspond to positive and negative Theil-Sen estimators, respectively, with nonsignificant Mann-Kendall test results (P > 0.05). The residual pixels belong to a stable class. (C) Normalized frequency distribution of trends derived using the map presented in (B). The color scheme of the histogram bars matches that of the map legend.
Fig. 3
Fig. 3. Relationships between spatially averaged annual Rs and six driving factors from 2000 to 2014.
The plot shows partial correlation coefficient between spatially averaged annual Rs and each of the six driving factors at global and regional scales. The six driving factors are annual mean air temperature (TEM), annual precipitation (PRE), annual mean standardized precipitation-evapotranspiration (ET) index (SPEI), tree canopy (TC) cover, short vegetation (SV) cover, and bare ground (BG) cover. All variables (i.e., 15-year spatially averaged annual Rs, TEM, PRE, SPEI, TC cover, SV cover, and BG cover at the global and regional scales) were detrended before conducting partial correlation analysis. Two asterisks indicate that the partial correlation is significant at the 0.05 level (two-tailed).
Fig. 4
Fig. 4. Proportion of colocated annual Rs change and each of the six driving factors at global and regional scales from 2000 to 2014.
The plot represents the percentage of the areas with both significant climate (or land-cover) change and significant annual Rs change to the areas with significant annual Rs change. Climate factors include TEM, PRE, and SPEI. Land-cover factors include TC cover, SV cover, and BG cover. This plot indicates that the significantly changed Rs in temperate and boreal regions was more commonly located in areas with significant changes in SV cover than in areas with significant climate change.
Fig. 5
Fig. 5. Contributions of climate change and land-cover change to changes in annual Rs from 2000 to 2014.
The plot indicates that climate change dominates global Rs change, but it does not have consistent influence on Rs change at the regional scale.
Fig. 6
Fig. 6. Spatial patterns of partial correlation coefficient between annual Rs and its six driving factors from 2000 to 2014.
Partial correlation coefficient (R) between detrended annual Rs and detrended driving factors are shown in (A) Rs and TEM, (B) Rs and PRE, (C) Rs and SPEI, (D) Rs and TC cover, (E) Rs and SV cover, and (F) Rs and BG cover. R = ±0.64, R = ±0.51, R = ±0.44, R = ±0.35, and R = ±0.19 correspond to the 0.01, 0.05, 0.1, 0.2, and 0.5 significance levels, respectively. To reduce the effects from the data acquisition error in the land-cover data, only the per-pixel percent cover of TC, SV, and BG greater than 25% (61) was used to conduct the partial correlation analysis.
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