青藏高原草地地上生物量和理论载畜量

doi:10.5814/j.issn.1674-764x.2022.01.015

资源与生态学报 ›› 2022, Vol. 13 ›› Issue (1): 129-141.DOI: 10.5814/j.issn.1674-764x.2022.01.015

青藏高原草地地上生物量和理论载畜量

张宪洲¹^,²(), 李猛³, 武建双⁴, 何永涛¹^,², 牛犇¹^,^*()

1.中国科学院地理科学与资源研究所生态系统网络观测与模拟重点实验室拉萨高原生态试验站,北京 100101
2.中国科学院大学资源与环境学院,北京 100190
3.南通大学地理科学学院,江苏南通 226007
4.中国农业科学院农业环境与可持续发展研究所,北京 100081

收稿日期:2021-09-13 接受日期:2021-10-13 出版日期:2022-01-30 发布日期:2022-01-08
通讯作者: 牛犇

Alpine Grassland Aboveground Biomass and Theoretical Livestock Carrying Capacity on the Tibetan Plateau

ZHANG Xianzhou¹^,²(), LI Meng³, WU Jianshuang⁴, HE Yongtao¹^,², NIU Ben¹^,^*()

1. Lhasa Plateau Ecosystem Research Station, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, China
3. School of Geographic Sciences, Nantong University, Nantong, Jiangsu 226007, China
4. Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, China

Received:2021-09-13 Accepted:2021-10-13 Online:2022-01-30 Published:2022-01-08
Contact: NIU Ben
About author:ZHANG Xianzhou, E-mail: zhangxz@igsnrr.ac.cn
Supported by:
The Second Tibetan Plateau Scientific Expedition and Research Program (STEP)(2019QZKK1002);The National Natural Sciences Foundation of China(41807331);The West Light Foundation of the Chinese Academy of Sciences(2018)

摘要/Abstract

摘要：

准确模拟和预测草地地上生物量 (Aboveground biomass, AGB)和理论载畜量对于维持草地生态系统平衡、优化放牧管理至关重要。当前很多研究以围栏外草地AGB为基础,估算了青藏高原草地AGB的现存量。但是,牛羊啃食后的草地AGB现存量无法准确评估草地理论载畜量。围栏内草地不受家畜采食影响,其年际变率由环境因子驱动,可视为草地潜在AGB (potential AGB, AGB_p),更适用于草地理论载畜量的评估。本研究以青藏高原345个围栏内AGB观测数据为基础,结合气候、土壤和地形数据,利用随机森林算法构建草地潜在地上生物量估算模型,并对当前气候条件（2000-2018年）和未来20年（2021-2040年）4种气候变化情景（SSP1-2.6、SSP2-4.5、SSP3-7.0和SSP5-8.5）下的草地AGB_p和高寒草地理论载畜量进行模拟与预测。结果表明：（1）随机森林算法可准确模拟当前气候条件下的青藏高寒草地AGB_p (R² = 0.76, P < 0.001);2000-2018年青藏高寒草地AGB_p平均值为102.4 g m^?2,时间上增加趋势不明显 (P > 0.05);AGB_p年际波动和生长季降水显著正相关（R²= 0.57, P < 0.001）,和生长季温度日较差显著负相关（R²= 0.51, P < 0.001）。（2）当前气候条件下,青藏高寒草地平均理论载畜量为0.94 SSU ha^?1（standardized sheep unit ha^?1）;在过去20年约有54.1%草地理论载畜量呈提升状态。（3）和当前相比,未来20年青藏高原中部和北部草地AGB_p和理论载畜量呈下降态势。因此,建议未来在厘清气候变化影响下草畜关系的基础上进行有针对性的草牧业规划和管理,以缓解区域气候变化引起的草畜矛盾。

关键词: 高寒草地, 承载力, 地上生物量, 气候变化, 随机森林, 青藏高原

Abstract:

The accurate simulation and prediction of grassland aboveground biomass (AGB) and theoretical livestock carrying capacity are key steps for maintaining ecosystem balance and sustainable grassland management. The AGB in fenced grassland is not affected by grazing and its variability is only driven by climate change, which can be regarded as the grassland potential AGB (AGB_p). In this study, we compiled the data for 345 AGB field observations in fenced grasslands and their corresponding climate data, soil data, and topographical data on the Qinghai-Tibetan Plateau (TP). We further simulated and predicted grassland AGB_p and theoretical livestock carrying capacity under the climate conditions of the past (2000-2018) and future two decades (2021-2040) based on a random forest (RF) algorithm. The results showed that simulated AGB_p matched well with observed values in the field (R² = 0.76, P < 0.001) in the past two decades. The average grassland AGB_p on the Tibetan Plateau was 102.4 g m^-2, and the inter-annual changes in AGB_p during this period showed a non-significant increasing trend. AGB_p fluctuation was positively correlated with growing season precipitation (R² = 0.57, P < 0.001), and negatively correlated with the growing season diurnal temperature range (R² = 0.51, P < 0.001). The average theoretical livestock carrying capacity was 0.94 standardized sheep units (SSU) ha^-1 on the TP, in which about 54.1% of the areas showed an increasing trend during the past two decades. Compared with the past two decades, the theoretical livestock carrying capacity showed a decreasing trend in the future, which was mainly distributed in the central and northern TP. This study suggested that targeted planning and management should be carried out to alleviate the forage-livestock contradiction in grazing systems on the Tibetan Plateau.

Key words: alpine grassland, aboveground biomass, carrying capacity, climate change, random forest, Tibetan Plateau

张宪洲, 李猛, 武建双, 何永涛, 牛犇. 青藏高原草地地上生物量和理论载畜量[J]. 资源与生态学报, 2022, 13(1): 129-141.

ZHANG Xianzhou, LI Meng, WU Jianshuang, HE Yongtao, NIU Ben. Alpine Grassland Aboveground Biomass and Theoretical Livestock Carrying Capacity on the Tibetan Plateau[J]. Journal of Resources and Ecology, 2022, 13(1): 129-141.

图/表 12

参考文献 73

[1]	Bai Y F, Han X G, Wu J G, et al. 2004. Ecosystem stability and compensatory effects in the Inner Mongolia grassland. Nature, 431: 181-184. DOI URL
[2]	Breiman L. 2001. Random forests. Machine Learning, 45(1): 5-32. DOI URL
[3]	Cao Y N, Wu J S, Zhang X Z, et al. 2019. Dynamic forage-livestock balance analysis in alpine grasslands on the Northern Tibetan Plateau. Journal of Environmental Management, 238: 352-359. DOI URL
[4]	Cao Y N, Wu J S, Zhang X Z, et al. 2020. Comparison of methods for evaluating the forage-livestock balance of alpine grasslands on the Northern Tibetan Plateau. Journal of Resources and Ecology, 11(3): 272-282. DOI URL
[5]	Chen B X, Zhang X Z, Tao J, et al. 2014. The impact of climate change and anthropogenic activities on alpine grassland over the Qinghai-Tibet Plateau. Agricultural and Forest Meteorology, 189-190: 11-18. DOI URL
[6]	Chen H, Zhu Q A, Peng C H, et al. 2013. The impacts of climate change and human activities on biogeochemical cycles on the Qinghai-Tibetan Plateau. Global Change Biology, 19(10): 2940-2955. DOI URL
[7]	Chen T X, Chen X. 2007. Variation of diurnal temperature range in China in the past 50 years. Plateau Meteorology, 26(1): 150-157. (in Chinese)
[8]	Dai A G, Trenberth K E, Karl T R. 1999. Effects of clouds, soil moisture, precipitation, and water vapor on diurnal temperature range. Journal of Climate, 12(8): 2451-2473. DOI URL
[9]	Dong S K, Li J P, Li X Y, et al. 2010. Application of design theory for restoring the “black beach” degraded rangeland at the headwater areas of the Qinghai-Tibetan Plateau. African Journal of Agricultural Research, 5(25): 3542-3552.
[10]	Fan J W, Shao Q Q, Liu J Y, et al. 2010. Assessment of effects of climate change and grazing activity on grassland yield in the Three Rivers Headwaters Region of Qinghai-Tibet Plateau, China. Environmental Monitoring and Assessment, 170(1-4): 571-584. DOI URL
[11]	Frankenberg C, Yin Y, Byrne B, et al. 2021. Comment on “Recent global decline of CO₂ fertilization effects on vegetation photosynthesis”. Science, 373(6562): eabg2947. DOI: 10.1126/science.abg2947. DOI URL
[12]	Fu G, Shen Z X, Zhang X Z. 2018. Increased precipitation has stronger effects on plant production of an alpine meadow than does experimental warming in the Northern Tibetan Plateau. Agricultural and Forest Meteorology, 249: 11-21. DOI URL
[13]	Fu G, Sun W, Li S W, et al. 2017. Modeling aboveground biomass using MODIS images and climatic data in grasslands on the Tibetan Plateau. Journal of Resources and Ecology, 8(1): 42-49. DOI URL
[14]	Gao X X, Dong S K, Li S, et al. 2020. Using the random forest model and validated MODIS with the field spectrometer measurement promote the accuracy of estimating aboveground biomass and coverage of alpine grasslands on the Qinghai-Tibetan Plateau. Ecological Indicators, 112: 106114. DOI: 10.1016/j.ecolind.2020.106114. DOI URL
[15]	Harris R B. 2010. Rangeland degradation on the Qinghai-Tibetan Plateau: A review of the evidence of its magnitude and causes. Journal of Arid Environments, 74(1): 1-12. DOI URL
[16]	He Y T, Zhang X Z, Yu C. 2016. Coupling crop farming and pastoral system for regional development and their ecological effects on the Tibetan Plateau. Bulletin of Chinese Academy of Sciences, 31(1): 112-117. (in Chinese)
[17]	Hou X Y. 2001. Vegetation atlas of China. Beijing, China: Science Press. (in Chinese)
[18]	Jiao C C, Yu G R, Ge J P, et al. 2017. Analysis of spatial and temporal patterns of aboveground net primary productivity in the Eurasian steppe region from 1982 to 2013. Ecology and Evolution, 7(14): 5149-5162. DOI URL
[19]	Karhu K, Fritze H, Tuomi M, et al. 2010. Temperature sensitivity of organic matter decomposition in two boreal forest soil profiles. Soil Biology and Biochemistry, 42(1): 72-82. DOI URL
[20]	Knapp A K, Smith M D. 2001. Variation among biomes in temporal dynamics of aboveground primary production. Science, 291(5503): 481-484. PMID
[21]	Li M, He Y T, Fu G, et al. 2016. Livestock-forage balance in the Three River Headwater Region based on the terrestrial ecosystem model. Ecology and Environmental Sciences, 25(12): 1915-1921. (in Chinese)
[22]	Li M, Wu J S, Song C Q, et al. 2019. Temporal variability of precipitation and biomass of alpine grasslands on the Northern Tibetan Plateau. Remote Sensing, 11(3): 360. DOI: 10.3390/rs11030360. DOI URL
[23]	Li X H. 2013. Using “random forest” for classification and regression. Chinese Journal of Applied Entomology, 50(4): 1190-1197. (in Chinese)
[24]	Liu Y W, Piao S L, Gasser T, et al. 2019. Field-experiment constraints on the enhancement of the terrestrial carbon sink by CO₂ fertilization. Nature Geoscience, 12(10): 809-814. DOI URL
[25]	Liu Y X, Zhao W W, Hua T, et al. 2019. Slower vegetation greening faced faster social development on the landscape of the Belt and Road Region. Science of the Total Environment, 697: 134103. DOI: 10.1016/j.scitotenv.2019.134103. DOI URL
[26]	Long R J, Apori S O, Castro F B, et al. 1999. Feed value of native forages of the Tibetan Plateau of China. Animal Feed Science and Technology, 80(2): 101-113. DOI URL
[27]	Long R J, Shang Z H, Guo X S, et al. 2009. Case study 7: Qinghai-Tibetan Plateau rangelands. Rangeland Degradation & Recovery in China’s Pastoral Lands, 25(4): 29-36.
[28]	Lopatin J, Dolos K, Hernández H J, et al. 2016. Comparing generalized linear models and random forest to model vascular plant species richness using LiDAR data in a natural forest in central Chile. Remote Sensing of Environment, 173(315): 200-210. DOI URL
[29]	Luo T X, Li W H, Leng Y F, et al. 1998. Estimation of total biomass and potential distribution of net primary productivity in the Tibetan Plateau. Geographical Research, 17(4): 2-9. (in Chinese)
[30]	Luo T X, Li W H, Zhu H Z. 2002. Estimated biomass and productivity of natural vegetation on the Tibetan Plateau. Ecological Applications, 12(4): 980-997. DOI URL
[31]	Ma W H, Fang J Y, Yang Y H, et al. 2010. Biomass carbon stocks and their changes in Northern China’s grasslands during 1982-2006. Science China (Life Sciences), 53(7): 841-850.
[32]	Ni J. 2004. Forage yield-based carbon storage in grasslands of China. Climatic Change, 67(2-3): 237-246. DOI URL
[33]	Niu B, He Y T, Zhang X Z, et al. 2016. Tower-based validation and improvement of MODIS gross primary production in an alpine swamp meadow on the Tibetan Plateau. Remote Sensing, 8(7): 592. DOI: 10.3390/rs8070592. DOI URL
[34]	Niu B, He Y T, Zhang X Z, et al. 2020. Satellite-based estimates of canopy photosynthetic parameters for an alpine meadow in Northern Tibet. Journal of Resources and Ecology, 11(3): 253-262. DOI URL
[35]	Niu B, Zeng C X, Zhang X Z, et al. 2019. High below-ground productivity allocation of alpine grasslands on the Northern Tibet. Plants, 8(12): 535. DOI: 10.3390/plants8120535 DOI URL
[36]	Niu B, Zhang X Z, He Y T, et al. 2017. Satellite-based estimation of gross primary production in an alpine swamp meadow on the Tibetan Plateau: A multi-model comparison. Journal of Resources and Ecology, 8(1): 57-66. DOI URL
[37]	Niu B, Zhang X Z, Piao S L, et al. 2021. Warming homogenizes apparent temperature sensitivity of ecosystem respiration. Science Advances, 7(15): eabc7358. DOI: 10.1126/sciadv.abc7358. DOI URL
[38]	Phillips C L, Gregg J W, Wilson J K. 2011. Reduced diurnal temperature range does not change warming impacts on ecosystem carbon balance of Mediterranean grassland mesocosms. Global Change Biology, 17(11): 3263-3273. DOI URL
[39]	Piao S L, Fang J Y, He J S, et al. 2004. Spatial distribution of grassland biomass in China. Acta Phytoecologica Sinica, 4: 491-498. (in Chinese)
[40]	Piao S L, Zhang X Z, Wang T, et al. 2019. Responses and feedback of the Tibetan Plateau’s alpine ecosystem to climate change. Chinese Science Bulletin, 64(27): 2842-2855. (in Chinese)
[41]	Sang Y X, Huang L, Wang X H, et al. 2021. Comment on “Recent global decline of CO₂ fertilization effects on vegetation photosynthesis”. Science, 373(6562): eabg4420. DOI: 10.1126/science.abg4420. DOI URL
[42]	Scurlock J M O, Hall D O. 1998. The global carbon sink: A grassland perspective. Global Change Biology, 4(2): 229-233. DOI URL
[43]	Shang Z H, Gibb M J, Leiber F, et al. 2014. The sustainable development of grassland-livestock systems on the Tibetan Plateau: Problems, strategies and prospects. The Rangeland Journal, 36(3): 267-296.
[44]	Shen M G, Piao S L, Cong N, et al. 2015. Precipitation impacts on vegetation spring phenology on the Tibetan Plateau. Global Change Biology, 21(10): 3647-3656. DOI URL
[45]	Shi P L, Zhang X Z. 2020. Towards regional synergy: Reconciling rangeland ecological functioning with forage production of cultivated pasture. Journal of Resources and Ecology, 11(3): 247-252. DOI URL
[46]	Si S, Bi X, Kong X, et al. 2020. Spatial-temporal characteristics of the emission intensities of several major greenhouse gases and aerosols under CMIP6 scenarios. Climatic and Environmental Research, 25(4): 366-384. (in Chinese)
[47]	Song J, Wan S Q, Piao S L, et al. Elevated CO₂ does not stimulate carbon sink in a semi-arid grassland. Ecology Letters, 22(3): 458-468. DOI URL
[48]	Tan K, Ciais P, Piao S L, et al. 2010. Application of the ORCHIDEE global vegetation model to evaluate biomass and soil carbon stocks of Qinghai-Tibetan grasslands. Global Biogeochemical Cycles, 24(1): GB1013. DOI: 10.1029/2009GB003530. DOI
[49]	Tin K H. 1998. The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8): 832-844. DOI URL
[50]	Wang S H, Zhang Y G, Ju W M, et al. 2020. Recent global decline of CO₂ fertilization effects on vegetation photosynthesis. Science, 370(6522): 1295-1300. DOI URL
[51]	Wang S H, Zhang Y G, Ju W M, et al. 2021. Response to comments on “Recent global decline of CO₂ fertilization effects on vegetation photosynthesis”. Science, 373(6562): eabg7484. DOI: 10.1126/science.abg7484. DOI URL
[52]	Wang S Y, Zhang B, Yang Q C, et al. 2017. Responses of net primary productivity to phenological dynamics in the Tibetan Plateau, China. Agricultural and Forest Meteorology, 232: 235-246. DOI URL
[53]	Winkler A J, Myneni R B, Hannart A, et al. 2021. Slowdown of the greening trend in natural vegetation with further rise in atmospheric CO₂. Biogeosciences, 18(17): 4985-5010. DOI URL
[54]	Wu J S, Feng Y F, Zhang X Z, et al. 2017. Grazing exclusion by fencing non-linearly restored the degraded alpine grasslands on the Tibetan Plateau. Scientific Reports, 7(1): 15202. DOI: 10.1038/s41598-017-15530-2. DOI
[55]	Wu J S, Fu G. 2018. Modelling aboveground biomass using MODIS FPAR/LAI data in alpine grasslands of the Northern Tibetan Plateau. Remote Sensing Letters, 9(2): 150-159. DOI URL
[56]	Wu J S, Wurst S, Zhang X Z. 2016. Plant functional trait diversity regulates the nonlinear response of productivity to regional climate change in a)Tibetan alpine grasslands. Scientific Reports, 6: 35649. DOI: 10.1038/s41598-017-15530-2. DOI
[57]	Xia J Z, Ma M N, Liang T G, et al. 2018. Estimates of grassland biomass and turnover time on the Tibetan Plateau. Environmental Research Letters, 13(1): 014020. DOI: 10.1038/s41598-017-15530-2. DOI URL
[58]	Xu L L, Niu B, Zhang X Z, et al. 2021. Dynamic threshold of carbon phenology in two cold temperate grasslands in China. Remote Sensing, 13(4): 574. DOI: 10.3390/rs13040574. DOI URL
[59]	Xu W H, Xiao Y, Zhang J J, et al. 2017. Strengthening protected areas for biodiversity and ecosystem services in China. Proceedings of the National Academy of Sciences of the USA, 114(7): 1601-1606. DOI URL
[60]	Yang G, Peng C H, Chen H, et al. 2017. Qinghai-Tibetan Plateau peatland sustainable utilization under anthropogenic disturbances and climate change. Ecosystem Health and Sustainability, 3(3): e01263. DOI: 10.1002/ehs2.1263. DOI URL
[61]	Yang Y H, Fang J Y, Ma W H, et al. 2010. Large-scale pattern of biomass partitioning across China’s grasslands. Global Ecology and Biogeography, 19(2): 268-277. DOI URL
[62]	Yang Y H, Fang J Y, Pan Y D, et al. 2009. Aboveground biomass in Tibetan grasslands. Journal of Arid Environments, 73(1): 91-95. DOI URL
[63]	Yao T D, Thompson L G, Mosbrugger V, et al. 2012. Third pole environment (TPE). Environmental Development, 3: 52-64. DOI URL
[64]	Yao Y T, Wang X H, Li Y, et al. 2018. Spatiotemporal pattern of gross primary productivity and its covariation with climate in China over the last thirty years. Global Change Biology, 24(1): 184-196. DOI URL
[65]	Zeng N, Ren X L, He H L, et al. 2019. Estimating grassland aboveground biomass on the Tibetan Plateau using a random forest algorithm. Ecological Indicators, 102: 479-487. DOI
[66]	Zhang X Z, He Y T, Shen Z X, et al. 2015a. Frontier of the ecological construction support the sustainable development in Tibet Autonomous Region. Bulletin of the Chinese Academy of Sciences, 30(3): 306-312. (in Chinese)
[67]	Zhang X Z, Wang X, Gao Q, et al. 2016. Research in ecological restoration and reconstruction technology for degraded alpine ecosystem, boosting the protection and construction of ecological security barrier in Tibet. Acta Ecologica Sinica, 36(22): 7083-7087. (in Chinese)
[68]	Zhang X Z, Yang Y, Piao S, et al. 2015b. Ecological change on the Tibetan Plateau. Chinese Science Bulletin, 60(32): 3048-3056. (in Chinese)
[69]	Zhang Y, Gao Q Z, Dong S K, et al. 2015c. Effects of grazing and climate warming on plant diversity, productivity and living state in the alpine rangelands and cultivated grasslands of the Qinghai-Tibetan Plateau. The Rangeland Journal, 37(1): 57-65. DOI URL
[70]	Zhang Y, Li B, Zheng D. 2002. A discussion on the boundary and area of the Tibetan Plateau in China. Geographical Research, 21(1): 1-8. (in Chinese)
[71]	Zhang Y Q, Tang Y H, Jiang J, et al. 2007. Characterizing the dynamics of soil organic carbon in grasslands on the Qinghai-Tibetan Plateau. Science in China (Series D), 50(1): 113-120. DOI URL
[72]	Zheng D. 1996. Natural region system research of Tibetan Plateau. Science in China (Series D), 26(4): 336-341. (in Chinese)
[73]	Zhu Z C, Zeng H, Myneni R B, et al. 2021. Comment on “Recent global decline of CO₂ fertilization effects on vegetation photosynthesis”. Science, 373(6562): eabg5673. DOI: 10.1126/science.abg5673. DOI URL

Time period	Study area (10⁴ km²)	Methods	Variables considered	AGB (g m^-2)	References
1960-2002	147.74	Century	Climate and soil data	70.00	Zhang et al., 2007
2002-2004	139.00	Orchidee	Climate, soil and LAI data	119.78	Tan et al., 2010
1980-1990	113.60	Area-weighted average	-	58.11	Ni, 2004
-	101.10	Area-weighted average	-	61.15	Luo et al., 1998
2001-2004	-	Filed observations	-	59.30	Yang et al., 2010
2001-2004	112.80	Linear regression	EVI	68.80	Yang et al., 2009
1982-2006	129.50	Exponential regression	NDVI	74.11	Ma et al., 2010
2005	122.80	Exponential regression	NDVI	43.33	Xu et al., 2017
-	124.00	Exponential regression	NDVI	78.02	Piao et al., 2004
1982-2013	154.48	Exponential regression	NDVI	104.40	Jiao et al., 2017
2000-2014	151.11	Random forest	Climate, terrain and NDVI	77.12	Zeng et al., 2019
-	132.00	Random forest	Climate and NDVI	78.40	Xia et al., 2018
2000-2017	-	Random forest	Climate, terrain and NDVI	59.63	Gao et al., 2020

Time period	Study area (10⁴ km²)	Methods	Variables considered	AGB (g m^-2)	References
1960-2002	147.74	Century	Climate and soil data	70.00	Zhang et al., 2007
2002-2004	139.00	Orchidee	Climate, soil and LAI data	119.78	Tan et al., 2010
1980-1990	113.60	Area-weighted average	-	58.11	Ni, 2004
-	101.10	Area-weighted average	-	61.15	Luo et al., 1998
2001-2004	-	Filed observations	-	59.30	Yang et al., 2010
2001-2004	112.80	Linear regression	EVI	68.80	Yang et al., 2009
1982-2006	129.50	Exponential regression	NDVI	74.11	Ma et al., 2010
2005	122.80	Exponential regression	NDVI	43.33	Xu et al., 2017
-	124.00	Exponential regression	NDVI	78.02	Piao et al., 2004
1982-2013	154.48	Exponential regression	NDVI	104.40	Jiao et al., 2017
2000-2014	151.11	Random forest	Climate, terrain and NDVI	77.12	Zeng et al., 2019
-	132.00	Random forest	Climate and NDVI	78.40	Xia et al., 2018
2000-2017	-	Random forest	Climate, terrain and NDVI	59.63	Gao et al., 2020

Classification	Abbreviation	Meaning
Eco-geographical regions	TP	Tibetan Plateau
	IB1	Golog-Nagqu high-cold shrub-meadow zone
	IIAB1	Western Sichuan-eastern Tibet montane coniferous forest zone
	IC1	Southern Qinghai high-cold meadow steppe zone
	IC2	Qiangtang high-cold steppe zone
	ID1	Kunlun high-cold desert zone
	IIC1	Southern Tibet montane shrub-steppe zone
	IIC2	Eastern Qinghai-Qilian montane steppe zone
	IID1	Nagri montane desert-steppe and desert zone
	IID2	Qaidam montane desert zone
	IID3	Northern slopes of Kunlun montane desert zone
	OA1	Southern slopes of Himalaya montane evergreen broad-leaved forest zone
Grass and livestock	AGB_p	(Potential, only climate-derived) Aboveground biomass of the grassland
	LCC_T	Theoretical livestock carrying capacity
	SSU	The standardized sheep unit (daily feed of 1.33 kg hay in this study)
Climate and soil	GSDR	Growing season (May to September in each year) diurnal temperature range
	GSP	Growing season precipitation
	GST	Growing season temperature
	NGSDR	Non-growing season diurnal temperature range
	NGSP	Non-growing season precipitation
	NGST	Non-growing season temperature
	SOM	Soil organic matter

Classification	Abbreviation	Meaning
Eco-geographical regions	TP	Tibetan Plateau
	IB1	Golog-Nagqu high-cold shrub-meadow zone
	IIAB1	Western Sichuan-eastern Tibet montane coniferous forest zone
	IC1	Southern Qinghai high-cold meadow steppe zone
	IC2	Qiangtang high-cold steppe zone
	ID1	Kunlun high-cold desert zone
	IIC1	Southern Tibet montane shrub-steppe zone
	IIC2	Eastern Qinghai-Qilian montane steppe zone
	IID1	Nagri montane desert-steppe and desert zone
	IID2	Qaidam montane desert zone
	IID3	Northern slopes of Kunlun montane desert zone
	OA1	Southern slopes of Himalaya montane evergreen broad-leaved forest zone
Grass and livestock	AGB_p	(Potential, only climate-derived) Aboveground biomass of the grassland
	LCC_T	Theoretical livestock carrying capacity
	SSU	The standardized sheep unit (daily feed of 1.33 kg hay in this study)
Climate and soil	GSDR	Growing season (May to September in each year) diurnal temperature range
	GSP	Growing season precipitation
	GST	Growing season temperature
	NGSDR	Non-growing season diurnal temperature range
	NGSP	Non-growing season precipitation
	NGST	Non-growing season temperature
	SOM	Soil organic matter

Eco-geographical regions*	AGB_p mean (g m^-2)		AGB_p trend (g m^-2 yr^-1)
Eco-geographical regions*	Mean	Standard deviation (SD)	Mean	Standard deviation (SD)
IB1	181.64	52.87	0.47	0.80
ICI	93.68	32.03	0.31	0.60
IC2	53.81	24.90	-0.11	0.30
ID1	55.90	7.70	0.02	0.08
IIAB1	196.47	46.29	-0.23	0.69
IIC2	167.11	44.29	1.04	0.59
IIC1	80.47	25.17	-0.16	0.29
IID2	109.63	24.70	0.09	0.22
OA1	228.15	35.00	-0.53	0.48
IID1	56.42	24.77	0.36	0.60
IID3	92.39	23.35	-0.02	0.11
TP	102.40	63.47	0.14	0.61

青藏高原草地地上生物量和理论载畜量

Alpine Grassland Aboveground Biomass and Theoretical Livestock Carrying Capacity on the Tibetan Plateau

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参考文献 73

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Eco-geographical regions*	SSP1-2.6	SSP2-4.5	SSP3-7.0	SSP5-8.5
IB1	-2.70±9.54	-2.24±9.31	-2.16±9.60	-2.40±10.10
IC1	-16.33±10.27	-14.05±10.32	-14.37±10.69	-16.29±11.05
IC2	-0.67±18.05	0.81±18.09	0.63±18.76	-11.51±17.43
ID1	-7.79±8.29	-7.74±8.37	-8.59±8.73	-17.47±7.44
IIAB1	2.75±5.73	2.48±5.64	2.84±6.07	2.28±6.07
IIC2	-7.11±8.8	-8.14±8.54	-8.43±8.28	-6.01±9.10
IIC1	-0.98±9.29	-0.72±9.31	-0.22±9.45	-1.24±8.96
IID2	-4.66±7.11	-4.71±6.85	-4.76±6.81	-4.33±7.11
OA1	1.94±2.69	1.30±1.93	1.61±2.28	0.93±2.01
IID1	4.73±17.9	4.21±18.01	1.39±17.27	-1.07±21.35
IID3	-5.03±8.21	-4.18±7.24	-4.11±7.46	-5.27±8.00
Tibetan Plateau	-3.75±13.82	-3.05±13.7	-3.25±14.05	-7.96±14.14

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