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Write a reflection on the data we found and the attached article regarding Land Use practices in China. Please discuss why you think the land use in China discussed in this article is supported (or not) by the data you collected.

Minimum 2 paragraphs.

In-text citations - any format is OK

Citation at the end.

these are the data i found

Trees - 3310.1 kg Carbon

Mixed - 4514.2 kg Carbon

Herbaceous - 148.4 kg Carbon

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SPECIAL FEATURE Carbon pools in China’s terrestrial ecosystems: New estimates based on an intensive field survey Xuli Tanga,1, Xia Zhaob,1, Yongfei Baib,1, Zhiyao Tangc,1, Wantong Wanga,d, Yongcun Zhaoe, Hongwei Wanb, Zongqiang Xieb, Xuezheng Shie, Bingfang Wuf, Gengxu Wangg, Junhua Yana, Keping Mab, Sheng Duh, Shenggong Lii, Shijie Hanj, Youxin Mak, Huifeng Hub, Nianpeng Hei, Yuanhe Yangb, Wenxuan Hanl, Hongling Hei, Guirui Yui, Jingyun Fangb,c,2, and Guoyi Zhoua,2 a South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China; bInstitute of Botany, Chinese Academy of Sciences, Beijing 100093, China; cKey Laboratory for Earth Surface Processes of the Ministry of Education, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China; dCollege of Tourism, Henan Normal University, Xinxiang 453007, China; eState Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China; fInstitute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China; gInstitute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China; hState Key Laboratory of Soil Erosion and Dryland Farming on Loess Plateau, Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling 712100, China; iInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; jInstitute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China; kXishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla 666303, China; and lCollege of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China | carbon stock climatic influences terrestrial ecosystems change of carbon pools has occurred in China’s croplands and grasslands over the past three decades (8, 9). Although there have been several studies of the carbon pools of China’s terrestrial ecosystems, the estimates of these pools have varied by more than 100 Pg C (SI Appendix, Table S1), suggesting an inconsistency among these estimates. This inconsistency is likely due to the limitation of sample size and data representativeness, multiplicity of data sources, and inconsistency of methodologies. In addition, previous estimates at both regional and national scales were primarily obtained based on summarized data of the regional or national censuses (e.g., China’s forest inventory and China’s grassland resource survey) (7, 10, 11) and not from original observations (2). Our knowledge of the driving forces causing the changes in terrestrial ecosystem carbon pools is also very limited and has impeded the application of management measures. To fill this knowledge gap, we conducted a nationwide field campaign between 2011 and 2015 to investigate the carbon stocks of terrestrial ecosystems in China. A reviewable, consistent inventory system, independent of the routine surveys Significance Previous estimations of carbon budgets in China’s terrestrial ecosystems varied greatly because of the multiplicity of data sources and the inconsistency of methodologies. By conducting a methodologically consistent field campaign across the country, we estimated that the total carbon pool in China’s forests, shrublands, grasslands, and croplands was 79.24 ± 2.42 Pg C. The carbon density exhibited a strong dependence on climate regime: it decreased with temperature but increased with precipitation. The country’s forests have a large potential of biomass carbon sequestration of 1.9–3.4 Pg C in the next 10 to 20 years assuming no removals. Our findings provide a benchmark to identify the effectiveness of the government’s natural protection policies. | human influences | spatial variations | T errestrial ecosystems are a significant carbon sink on Earth, accounting for ∼20–30% of the total anthropogenic carbon dioxide (CO2) emissions to the atmosphere. Compared with oceans, terrestrial ecosystems can be readily managed to either increase or decrease carbon sequestration by restoring or degrading vegetation (1). China is a good example of this interaction between human-driven vegetation change and terrestrial carbon exchange (2, 3). For example, China’s forest coverage decreased from 30 to 40% in the early 1950s to ∼14% in the early 1980s because of excessive exploitation of forest resources. However, since then, nationwide vegetation restoration practices, including several key ecological restoration programs, have been implemented (4), resulting in a significant increase in forest coverage—from 13.9% in the early 1990s to 21% in the 2010s (5, 6). Corresponding to the changes in forest area and the growth of established forests, the carbon pools of China’s forest ecosystems have significantly increased during these decades (7–9). Compared with forests, biomass production of grasslands and croplands is quite low, varying from 0.01 to 0.02 Pg C per year, and thus limited Author contributions: J.F. and G.Z. designed research; X.T., Y.B., Z.T., Y.Z., H.W., Z.X., X.S., G.W., J.Y., K.M., S.D., S.L., S.H., Y.M., H. He, G.Y., and G.Z. performed research; X.T., X.Z., W.W., Y.Z., and B.W. analyzed data; and X.T., X.Z., W.W., H. Hu, N.H., Y.Y., W.H., J.F., and G.Z. wrote the paper. The authors declare no conflict of interest. This article is a PNAS Direct Submission. Published under the PNAS license. 1 X.T., X.Z., Y.B., and Z.T. contributed equally to this work. 2 To whom correspondence may be addressed. Email: or jyfang@urban. This article contains supporting information online at 1073/pnas.1700291115/-/DCSupplemental. Published online April 16, 2018. PNAS | April 17, 2018 | vol. 115 | no. 16 | 4021–4026 SUSTAINABILITY SCIENCE China’s terrestrial ecosystems have functioned as important carbon sinks. However, previous estimates of carbon budgets have included large uncertainties owing to the limitations of sample size, multiple data sources, and inconsistent methodologies. In this study, we conducted an intensive field campaign involving 14,371 field plots to investigate all sectors of carbon stocks in China’s forests, shrublands, grasslands, and croplands to better estimate the regional and national carbon pools and to explore the biogeographical patterns and potential drivers of these pools. The total carbon pool in these four ecosystems was 79.24 ± 2.42 Pg C, of which 82.9% was stored in soil (to a depth of 1 m), 16.5% in biomass, and 0.60% in litter. Forests, shrublands, grasslands, and croplands contained 30.83 ± 1.57 Pg C, 6.69 ± 0.32 Pg C, 25.40 ± 1.49 Pg C, and 16.32 ± 0.41 Pg C, respectively. When all terrestrial ecosystems are taken into account, the country’s total carbon pool is 89.27 ± 1.05 Pg C. The carbon density of the forests, shrublands, and grasslands exhibited a strong correlation with climate: it decreased with increasing temperature but increased with increasing precipitation. Our analysis also suggests a significant sequestration potential of 1.9–3.4 Pg C in forest biomass in the next 10–20 years assuming no removals, mainly because of forest growth. Our results update the estimates of carbon pools in China’s terrestrial ecosystems based on direct field measurements, and these estimates are essential to the validation and parameterization of carbon models in China and globally. ECOLOGY Edited by Susan E. Trumbore, Max Planck Institute for Biogeochemistry, Jena, Germany, and approved November 21, 2017 (received for review February 15, 2017) conducted for forests and shrublands by the Chinese Ministry of Forestry (6) and for grasslands by the Chinese Ministry of Agriculture (12) was developed based on the spatial distributions of China’s terrestrial ecosystems (SI Appendix, Texts S1–S3). In total, 13,030 field plots were investigated across forests, shrublands, and grasslands in mainland China using consistent methodology (Materials and Methods and SI Appendix, Fig. S1). We also conducted a systematic field investigation for croplands, with 1,341 field plots from 58 counties that represent typical cropping systems used in China (SI Appendix, Text S4, and Fig. S1). Here we defined “forest” as the land with an area of ≥0.067 ha dominated by trees and with a tree crown coverage of ≥20%; “shrubland” as the land dominated by shrubs with a canopy height of <5 m and canopy coverage of >30–40%; “grassland” as the land dominated by herbaceous plants; and “cropland” as the land surface covered by crops with a minimum area of 5,400 m2 that is seeded at least once per year (SI Appendix, Texts S1–S4). Our field campaign investigated all carbon components of an entire ecosystem in these four vegetation groups, including aboveand belowground biomass, understory plants, litter, and soils. The major purposes of this study are to estimate the carbon pools of these ecosystems and to elucidate the possible climatic and anthropogenic drivers of the spatial distributions of these carbon pools by using direct field measurements collected in this study. Note that we did not investigate the carbon pools in Taiwan, Hong Kong, Macao, and the South China Sea Islands because of the unavailability of fieldwork and the small land areas in these islands. Our study focused on forests, shrublands, and grasslands when exploring the drivers shaping the distribution of carbon stocks because croplands are intensively human managed. Results Carbon Stocks and Their Spatial Variations. Ecosystem carbon density (carbon stock per hectare) of forests, shrublands, and grasslands exhibited large spatial variations at the national scale (Fig. 1). Both biomass and litter carbon densities decreased from the northeastern, southern, southeastern, and southwestern regions to the northern and northwestern regions and to the Tibetan Fig. 1. Spatial distribution of ecosystem carbon density (Mg C ha−1) in forests, shrublands, grasslands, and croplands in China. (A) biomass carbon. (B) Soil organic carbon (up to 1 m in depth, where applicable). (C) Litter carbon. (D) Total ecosystem carbon. The site-averaged carbon density of each biome in each province was assigned to the corresponding polygons of the ChinaCover map. (For details on the ChinaCover map and associated vegetation biomes, see ref. 5. Please note that we did not investigate the carbon pools in Taiwan, Hong Kong, Macao, and the South China Sea Islands.) 4022 | Fig. 2. Distribution of provincial-level total ecosystem carbon pools (Pg C) in China’s forests, shrublands, grasslands, and croplands and their histograms by region. In each histogram, the carbon pools of biomass, litterfall, and soil in forests (F), shrublands (S), grasslands (G), and croplands (C) are shown for the six regions (Northeast, North, Northwest, East, South Central, and Southwest). Plateau (Fig. 1 A and C). However, the soil carbon density displayed complex variations: the maximum density occurred on Mount Xing’an in the northeastern region, Mounts Qilian and Bayan Har in Qinghai, and Mounts Tianshan and Alta in northern Xinjiang, followed by the southern and southeastern regions. The lowest soil carbon densities were in the lower basins in Xinjiang, the Hexi Corridor in Gansu, and on part of the Loess Plateau (Fig. 1B). The mean ecosystem carbon density showed the highest value in forest ecosystems (163.8 ± 8.4 Mg C ha−1), which is ∼1.8 times higher than that in shrublands (89.9 ± 4.4 Mg C ha−1) and grasslands (90.3 ± 5.3 Mg C ha−1) (see SI Appendix, Table S2 for details). Overall, the area-weighted average ecosystem carbon density of all three vegetation groups was 115.7 ± 6.2 Mg C ha−1, with 23.1 ± 5.7, 0.8 ± 0.9, and 91.8 ± 9.2 Mg C ha−1 stored in biomass, litter, and soil. The total carbon pool of these three ecosystems was 62.93 ± 3.39 Pg C, of which biomass, litter, and soil organic carbon [(SOC) at a 1-m depth, where applicable] were 12.55 ± 3.07 (20%), 0.46 ± 0.48 (0.7%), and 49.92 ± 4.98 Pg C (79.3%), respectively (SI Appendix, Table S3). The largest carbon pool was in forests (30.83 ± 1.57 Pg C, 49%), followed by grasslands (25.40 ± 1.49 Pg C, 40.4%), and shrublands (6.69 ± 0.32 Pg C, 10.6%). Geographically, 19.53 ± 0.54 Pg C (31%) was stored in southwestern China (Fig. 2) because of its large area and high carbon densities in vegetation biomass and soils. By contrast, only 4.55 ± 0.11 Pg C (7%) was stored in eastern China (Fig. 2), where carbon densities were quite low (Fig. 1D). In addition, we used the Random Forest simulation (a machinelearning approach) to elucidate the detailed spatial patterns of carbon density and then estimated the national total carbon pools (for details, see SI Appendix, Text S2). The biome-scale mean carbon densities based on the Random Forest simulation showed a good coincidence with those based on the area-weighted average approach (SI Appendix, Fig. S2). The overall carbon stock of forests, shrublands, and grasslands totaled 64.17 ± 1.92 Pg C, which is highly consistent with our estimate using the area-weighted average approach (62.93 ± 3.39 Pg C) (SI Appendix, Table S3). Compared with these three ecosystems, the cropland ecosystem had lower biomass carbon density (3.06 ± 0.87 Mg C ha−1), Tang et al. SPECIAL FEATURE but similar soil carbon density (92.04 ± 4.06 Mg C ha−1) (SI Appendix, Table S2). Higher values occurred in the northeastern regions, followed by the southwestern regions, while lower values were found in the dry areas in northern China. Overall, the total carbon pool of China’s croplands was estimated as 16.32 ± 0.41 Pg C (SI Appendix, Table S3). Carbon Allocation Between Below- and Aboveground Biomass and Between Soil and Vegetation. Both above- and belowground bio- Fig. 3. Frequency distribution of carbon densities of different carbon sectors in China’s forests, shrublands, and grasslands. (A, D, and G) Aboveground biomass (AGB). (B, E, and H) Belowground biomass (BGB). (C, F, and I) Soil organic carbon (SOC). Line in A–C: forests; line in D–F: shrublands; line in G–I: grasslands. Tang et al. Fig. 4. Relationships between carbon density and MAT and MAP in forests, shrublands, and grasslands in China for two MAT groups (≤10 °C and >10 °C) and two MAP groups (≤400 mm and >400 mm). (A and B) Vegetation biomass carbon. (C and D) Litter carbon. (E and F) Soil organic carbon. (G and H) Whole-ecosystem carbon. Each dot shows the average carbon density within each 1 °C MAT and 100 mm MAP. and soil carbon) respond to climatic regimes under different climatic conditions, as these two climatic thresholds are important in China’s climatic classification (13). As a result, the spatial pattern of the carbon density showed a strong correlation with the climate variables (Fig. 4 and SI Appendix, Fig. S6). In general, the total carbon density and all carbon sectors (biomass, litter, and soil) decreased with increasing MAT but had a lower decreasing rate in the regions where the MAP exceeded 400 mm. By contrast, they increased with increasing MAP and showed a higher increasing rate in the regions in which MAT < 10 °C (Fig. 4). Furthermore, we found a close relationship between ecosystem carbon density and the wetness index (P/PET, a surrogate of the moisture index that indicates the ratio of precipitation to potential evapotranspiration) (r2 = 0.92, P < 0.0001) (SI Appendix, Fig. S7) (14). Interestingly, an annual P/PET value of 1.0 strongly corresponded to the ecosystem carbon density value of 100 Mg C ha−1 and to the threshold to segment the linear relationship between carbon density and the wetness index. Specifically, the carbon density showed a strong correlation with P/PET when the density was ≤1.0 (r2 = 0.96, P < 0.0001); otherwise, the correlation was poor when the density was >1 (r2 = 0.16, P = 0.154). These results suggest that carbon density exhibits various feedbacks to climate under different moisture conditions. Effects of Human Activities on Carbon Stocks. To examine the effects of human activities on different carbon sectors in forests, shrublands, and grasslands, we divided all field sites into two PNAS | April 17, 2018 | vol. 115 | no. 16 | 4023 SUSTAINABILITY SCIENCE Effects of Climatic Factors on Carbon Stocks. To illustrate relationships between ecosystem carbon stocks and climatic variables, we divided all of the field data into two groups according to a mean annual precipitation (MAP) of 400 mm (i.e., the threshold of an arid climate) and a mean annual temperature (MAT) of 10 °C (i.e., the threshold of a warm temperate climate) to detect how ecosystem carbon sectors (total, biomass, litter, ECOLOGY mass carbon densities varied among forests, shrublands, and grasslands (Fig. 3). The site-averaged aboveground biomass carbon densities were 42.5 ± 4.6 (mean ± 1 SD) Mg C ha−1 in forests, 3.3 ± 4.6 Mg C ha−1 in shrublands, and 0.4 ± 0.6 Mg C ha−1 in grasslands, respectively. Their site-averaged belowground biomass carbon densities were 10.7 ± 7.1, 3.1 ± 4.6, and 3.5 ± 4.8 Mg C ha−1, respectively. The allocation of below- to aboveground biomass carbon (root to shoot ratio, or RS ratio) differed markedly among forests and shrublands (SI Appendix, Fig. S4), and the biomes in each vegetation group (SI Appendix, Fig. S5A). The site-averaged soil carbon densities showed greater variations than did biomass carbon densities (Fig. 3). The mean SOC densities were 126 ± 98.1 Mg C ha−1 in forests, 60.2 ± 83.2 Mg C ha−1 in shrublands, and 58.4 ± 69.3 Mg C ha−1 in grasslands. The ratio of soil to biomass carbon density showed large variation across sites within vegetation groups (SI Appendix, Fig. S4). Compared with forests, shrublands showed much larger ratios of soil carbon to vegetation biomass carbon because of the relatively smaller vegetation biomass densities in shrublands (SI Appendix, Figs. S4 and S5B). groups based on the degree of human disturbance: sites with intensive human influences, which included forest plantations and intensively grazed grasslands, and other sites with fewer human influences, which included natural forests, primary shrublands, and natural or less-grazed grasslands (SI Appendix, Text S5). Our results indicate that intensive human activities have reduced both the above- and belowground biomass of most vegetation types (SI Appendix, Fig. S5A) with overall reductions of 21% (r2 = 0.96, P < 0.001) for aboveground biomass and 24% (r2 = 0.61, P < 0.01) for belowground biomass (SI Appendix, Fig. S8). Interestingly, the reduction in belowground biomass was almost proportional to the reduction in aboveground biomass, thus resulting in insignificant changes in the RS ratio. In contrast to forests and shrublands, human activities have significantly reduced aboveground biomass in two of the four grassland types, but they have not consistently decreased the belowground biomass, leading to an elevated RS ratio in heavily influenced grassland sites. However, human disturbance did not exert significant effects on soil carbon stocks for all biome types; the overall SOC density of 14 vegetation types with intensive disturbances was approximately equivalent to that with fewer intensive effects (slope of linear regression = 0.97, r2 = 0.61, P < 0.01) (SI Appendix, Fig. S8D). Discussion Comparison of Carbon Pools with Previous Estimates. The extensive field survey in the present study has provided a full picture of the ecosystem carbon stocks in the forests, shrublands, grasslands, and croplands of China. Our estimate of China’s forest biomass carbon density was higher than that in previous studies (55.7 vs ...
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Running head: ASSIGNMENT


Assignment: Environment Science



Assignment: Environment Science
Rapid development in recent decades in China has led to large-scale changes in land use,
and as a result, massive carbon emission has been created. Tang et al. (2018), steered a national
field survey between in China to examine carbon terrestrial ecosystem. In this survey, more than
13,030 field posts were investigated in fo...

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