https://orcid.org/0000-0002-0458-5953
Figures
Abstract
Regional differences and regulatory mechanisms of vegetation productivity response to changing environmental conditions constitute a core issue in macroecological researches. To verify the main limiting factors of different macrosystems [temperature-limited Tibetan Plateau (TP), precipitation-limited Mongolian Plateau (MP), and nutrient-limited Loess Plateau (LP)], we conducted a comparative survey of the east-west grassland transects on the three plateaus and explored the factors limiting regional productivity and their underlying mechanisms. The results showed that aboveground net primary productivity (ANPP) of LP (109.10 ± 16.76 g m−2 yr−1) was significantly higher than that of MP (66.71 ± 11.11 g m−2 yr−1) and TP (57.02 ± 10.59 g m−2 yr−1). The response rate of ANPP with environmental changes was different among different plateaus, being closely related to the main limiting factors. On MP, this was precipitation, on LP it was temperature and nutrients, and on TP, it was non-specific, reflecting restriction by the extremely low temperature. After autocorrelation screening of environmental factors, different regions exhibited different productivity response mechanisms. MP was mainly influenced by temperature and precipitation, LP was influenced by temperature and nutrient, and TP was influenced by nutrient, reflecting the modifying effect of the main limiting factors. The effect of each regional environment on ANPP was 72.56% on average and only 27.18% after simple regional integration. The regional model could optimize the simulation error of the integrated model, and the relative deviations in MP, LP, and TP were reduced by 31.76%, 17.22%, and 2.23%, respectively. These findings indicate that the grasslands on the three plateaus may have different or even the opposite mechanisms to control productivity.
Citation: Li Q, Hou J, Yan P, Xu L, Chen Z, Yang H, et al. (2020) Regional response of grassland productivity to changing environment conditions influenced by limiting factors. PLoS ONE 15(10): e0240238. https://doi.org/10.1371/journal.pone.0240238
Editor: Dafeng Hui, Tennessee State University, UNITED STATES
Received: June 13, 2020; Accepted: September 22, 2020; Published: October 16, 2020
Copyright: © 2020 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are available from Dryad (DOI: 10.5061/dryad.8sf7m0ckg).
Funding: We received support for this research from National Natural Science Foundation of China, 31961143022, 31870437, National Key R&D Program of China, 2017YFA0604803. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Vegetation productivity, the productive capacity of plant communities under natural environmental conditions, is a research hotspot in terrestrial ecosystems [1]. The most direct manifestation of vegetation productivity is food and fuel, which are closely related to human survival. It is estimated that approximately 40% of vegetation productivity in terrestrial ecosystem can be directly or indirectly utilized by humans [2]. Therefore, improving the simulation accuracy and forecasting ability of vegetation productivity models is of great significance for evaluating for ecosystem carrying capacity and sustainable development of the ecological environment [3].
Widespread regional variation is one of the major challenges for estimating large-scale vegetation productivity, and it is a common problem faced by ecologists. For estimating vegetation productivity at regional and global scales, model simulation is the most informative method, while field surveys and observations often verify the simulation accuracy of the model [4]. In previous studies, the estimations of vegetation productivity mainly focused on improving the universality of a model, making the relationship model show trends in parameter enrichment and structural complexity [5, 6]. As a combination of different geographic plates or biota, Earth’s surface is influenced by factors such as topography, altitude, and distance from the ocean. Therefore, each region should have different environmental regulatory mechanisms and show completely different characteristics at different spatial scales or in different seasons [7, 8]. Therefore, in a unified empirical model obtained from certain biota or global data, the accuracy error of regional simulation needs to be further explored or quantified.
On different plateaus (or macrosystems), the role of major limiting factors in the response mechanism of vegetation productivity to changing environmental conditions may be an important theoretical basis for solving such problems. In natural ecosystems, based on Liebig’s law of “minimal factors” [9], there is always one factor that reaches a state of insufficiency first, leading to the stability in entire system. Thus, limiting factors at the regional scale may be considered as relatively insufficient ecological factors after regional comparison. In addition to the regulation of various environmental factors, the level of vegetation productivity is also closely related to plant attributes [10, 11]. However, considering the interaction and co-evolution between plant attributes and main regional ecological factors [12], the concept of regional limiting factors is worth paying more attention to. At this level, these factors differ from the limiting factors at individual or population levels and are based on the overall control of the biota and large-scale environment. Therefore, we can compare regions with different main limiting factors to compare the response intensity at which vegetation productivity responds to environmental changes and the regional variation in response mechanisms to assess the status of regional limiting factors in the general promotion of large-scale productivity models.
Generally, research based on limiting factors has mostly been carried out under controlled experiments, but research using natural global change transects with evidently different limiting factors are rare. The global change terrestrial transect [13] is arranged along the direction of change for the main or secondary driving factors, such as temperature, precipitation, land use intensity, and nutrient status [14]. The transect has the characteristic of "replace time with space", in that regional spatial changes of the gradient can be regarded as a long-term ecological change. To some extent, these transects can be understood as long-term control experiments preset by earth; the biggest difference between them and control experiments is that the former reflect long-term adaptations in plants, whereas the latter focus on short-term responses. Therefore, experimental design on the concept of terrestrial transects is ideal for exploring the response mechanism of vegetation productivity under different regional limiting factors.
Temperature, precipitation and nutrients are important drivers of global change. Many control experiments have shown that extreme temperatures [15] and droughts [16] will significantly reduce aboveground net primary productivity (ANPP), and the synergy of N and P [17] will promote ANPP. The grasslands of the Tibetan Plateau (TP), Loess Plateau (LP) and Mongolian Plateau (MP), as the specific macrosystems in the Northern Hemisphere, may be ideal locations for verifying the effects of regional limiting factors. On the TP, owing to high altitude and widespread glaciers, low average temperature may be the main factor limiting plant growth [18, 19]. On the LP, owing to its unique geological structure and topography formed by aeolian soil, soil erosion is serious, meaning that a lack of nutrients may be the main factor limiting vegetation growth [20, 21]. The MP is an arid and semi-arid region, and the grassland vegetation dynamics are related to the variability in precipitation, and thus, insufficient precipitation is the main limiting factor [22].
Based on the new idea of comparative transect, the present study focuses on the grasslands of the TP, LP, and MP to explore the regional response of productivity to environmental changes and to verify the modification effects of the main limiting factors. Our research was intended to determine the following: 1) distribution patterns of vegetation productivity in three typical grassland macrosystems; 2) environmental response characteristics and specific expression of vegetation productivity in different macrosystems; and 3) main mechanisms underlying grassland productivity in different macrosystems. Further verify the hypothesis that the regional limiting factor plays a leading role in the productivity response mechanism.
Methods
Study area
Typical grassland ecosystems on three plateaus in the Northern Hemisphere were selected (31–45°N, 80–123°W; S1 Table). The three plateau transects were intended to represent regions restricted by temperature, precipitation, and nutrients, and the measured data for mean annual temperature (MAT) on the TP, mean annual precipitation (MAP) on the MP, and soil N content on the LP support this inference (Fig 1). The map data illustrated in Fig 1 was derived from Land Cover Climate Modeling Grid product (MCD12C1) (https://lpdaac.usgs.gov/products/mcd12c1v006/).
After comparing the three regions, the main limiting factors of each region were obtained. Grasslands on the Tibetan Plateau have a relatively low mean annual temperature (MAT), which is considered to lead to temperature limitation; grasslands on the Mongolian Plateau have a relatively low mean annual precipitation (MAP), which is considered to lead to precipitation limitations; grasslands on the Loess Plateau have relatively low total soil N leading to nutrient limitation.
The average altitude of TP is >4000m, and MAT is <0°C, the highest monthly average temperature in <10°C, and the MAP is 20–487mm [23]. There are three grassland types, namely, alpine meadow, alpine grassland, and alpine desert from southeast to northwest [24]. For LP, the altitude is 300–3000 m, the MAT is 3.7–14.0°C, and the MAP is ~110–860 mm, and this plateau belongs to the dry with cold semi-arid climate (Bsk) and snow with dry winter climate (Dwa) [25–27]. The vegetation types were distributed from the southeast to northwest with warm forest, warm forest grassland, warm typical grassland, and warm desert grassland [28]. The MP, in a cold semi-arid climate (Bsk) [27, 29], is within a typical temperate continental climate (Dwb), with an MAT of −1.7–5.6°C and an MAP of 90–433 mm [30]. From east to west, there are forests, forest grasslands, meadow grasslands, typical grasslands, desert grasslands and deserts [31].
Transects setup
Grassland transects across the TP, LP, and MP were spread out along the precipitation gradient. There were 10 sites set up from east to west on each plateau. Sites 1–3 were in meadows, sites 4–7 were in steppes, and sites 8–10 were in deserts (Fig 1, S1 Table). All sites for grassland investigation were selected from natural grasslands with little human activity or grazing. To enhance the comparability among the three transects, the classification of grassland vegetation types was relatively simple (S1 Fig), which differed from the professional vegetation grassland classification system that emphasizes differences among vegetation groups in different regions [32, 33]. Two 50-m paralleled splines within the site were setup as repetitions, with four plots evenly arranged within each spline.
The transect on the TP spanned ~1600 km at an altitude of 4104–4938 m. The transect on the LP spanned >800 km at an altitude of 804–1714 m. The transect on the MP spanned >900 km at an altitude of 144–1272 m. (No specific permission was requirement for these locations to conduct field investigation for the aim of natural science in China, because these lands are public and these investigations did not involve endangered species.).
Field survey
The field investigation was carried out in the peak plant growth period from July to August. In each plot (1 m × 1 m), we first collected litter and standing litter. Then we estimated the total coverage and average height and measured the plant height, sub-coverage, and abundance of species. Finally, we collected the aboveground parts of different plant species. A total of 260 species, 152 genera, and 48 families were collected. The samples were oven dried at 85°C and to a constant weight to calculate the aboveground biomass (AGB). Soil samples were also collected using soil drills from each plot. After air-drying at 25°C, we removed plant roots, gravel, and other debris, passed the samples through a 2-mm soil sieve, and ground them using a ball mill (MM400, Retsch, Haan, Germany).
Data sources
Aboveground net primary productivity.
AGB was obtained through plot survey in the late growing season of the grassland. For herb plants, AGB was considered as ANPP, and for shrub plants, we use the linear equation from Chen et al. [34]:
(1)
(2)
where both a and b are constants, and the constant values are different in different regions or shrub communities. This series of equations did not consider the shrub age, which will lead to the underestimation of ANPP.
Climate data.
The climate data was extracted from online datasets based on the longitude and latitude. The MAT and MAP data were derived from the National Earth System Science Data Center, National Science & Technology Infrastructure of China (http://www.geodata.cn). The Aridity data came from the Global-Aridity and Global-PET Database [35] of Consortium for Spatial Information of the Consultative Group on International Agricultural Research (https://cgiarcsi.community/data/global-aridity-and-pet-database/).
Nutrient data.
Soil nutrient data are obtained from the actual measurement of soil samples. The soil total N was measured by elemental analyzer (Vario MAX CN, Elementar, Germany), and the soil total P and soil total K were measured by a microwave digestion system (MARS Xpress, CEM, Matthews, USA) and an inductively coupled plasma emission spectrometer (ICP-OES, Optima 5300 DV, Perkin Elmer, Waltham, MA, USA).
Data analysis
For the geographical distribution of ANPP, multiple comparisons (Duncan’s test, α = 0.05) were used to test the significance of ANPP differences in different regions and grassland types. Ordinary least squares was used to check the response rate (intensity) of ANPP to environmental factors in various regions. Furthermore, standardized major axis analysis was used to test the significance of the difference in response rates between different regions. We used stepwise regression to obtain the main master model of ANPP, and then compared the simulation deviation caused by simple integration. The initial model included MAT, MAP, Aridity, N, P, K parameters. On the basis of the species productivity matrix of sites, canonical correspondence analysis was used to interpret the ANPP based on environmental factors. Data analysis was performed by R-3.5.2 [36−38], and charts were drawn in PowerPoint 2010 and R-3.5.2. The significance test level was P < 0.05.
Results
Spatial variation in grassland ANPP
The grassland ANPP of three plateaus ranged from 14.44 ± 2.96 g m–2 yr–1 to 175.16 ± 99.87 g m–2 yr–1. The ANPP of each transect showed a significant upward trend with longitude from west to east (Fig 2a). The average of ANPP on LP was 109.10 ± 16.76 g m–2 yr–1, which was significantly higher than that on the MP (66.71 ± 11.11 g m–2 yr–1) and TP (57.02 ± 10.59 g m–2 yr–1) (Fig 2b). Among different grassland types, the ANPP on all plateaus showed the following similar trend: meadow > grassland > desert (Fig 2c).
The error line is one times the standard error. Different letters (a, b, c, d, e) indicate significant difference (p < 0.05).
Regional specificity of grassland productivity response to climate and nutrient changes
The response of ANPP to changes in climate (Fig 3a) and nutrients (Fig 3b) were mostly positively correlated, reflecting that high temperature and humid promote productivity. Furthermore, there were significant differences in the intensity of vegetation productivity response to environmental factors in different regions (Fig 4), and the response was closely related to the main limiting factors in each region.
Solid line shows that ANPP was significantly correlated with environmental factors (p < 0.05). MAT, mean annual temperature; MAP, mean annual precipitation.
The response intensity is the slope of the linear fitting equation between ANPP and environmental factors; the error line represents one times of standard error; * represents the significance of the two regions, *p < 0.05; **: p<0.01; ***: p < 0.001. MAT, mean annual temperature; MAP, mean annual precipitation.
The response intensity of ANPP with MAP on the MP (0.431) was significantly higher than that on the LP (0.235) and TP (0.197). Therefore, MP had a stronger precipitation response specificity, corresponding to precipitation limitation in this region. Owing to the limitation of excessively low temperature, the response of ANPP on the TP to the environment was overall low, which in turn leads to its non-specificity temperature response. The LP showed a strong response specificity in MAT, N and P, and especially N, indicating that the changes in ANPP were closely related to nutrient limitation.
Comprehensive regulation of environmental factors on grassland productivity modified by limiting factors
After filtering the autocorrelation factors, each regional environmental factor presented different models to controlling ANPP (Table 1). When the total data from the three regions were considered, ANPP showed a master model of MAT, MAP, and N, reflecting the overall regulation of temperature, precipitation, and nutrients. After the regions were screened, the main master model of each region was different and was closely related to the main limiting factors on each plateau. The ANPP of the MP was comprehensively regulated by MAT and MAP, but on the LP, ANPP was regulated by MAT, N, and P, and on the TP, ANPP was regulated by N.
Difference between regional specificity and simple regional integration
In each region, the contribution of climate factors and soil nutrients to grassland ANPP was 72.56% on average. This wad 75.22% on the MP (Fig 5a), 71.53% on the LP (Fig 5b), and 70.93% on the TP (Fig 5c). After simply integrating the data of the three regions, the contribution of climate and soil nutrient factors was only 27.18% (Fig 5d), indicating that the three plateaus may have different or even divergent productivity response to changing environment conditions.
Climate factors were mean annual temperature (MAT), mean annual precipitation (MAP), and Aridity, and soil nutrients were N, P, and K. The response variable was the biomass matrix of the species, and the explanatory variable was the environmental factor matrix.
Comparing the fitting errors of the integrated model and the regional model (Fig 6a), the absolute and relative deviations of the regional model were considerably lower than those of the integrated model (Fig 6b), especially on the LP. Among them, the relative deviation of the MP, LP and TP decreased by 31.76%, 17.22%, and 2.23%, respectively.
Dotted line ± 20%, indicating the range of ANPP measured value fluctuation by 20%; * represents the significance of the two regions, *p < 0.05; **: p<0.01; ***: p < 0.001; ns, no significant difference. MP, Mongolian Plateau; LP, Loess Plateau; TP, Tibetan Plateau.
Discussion
Regional variation in grassland ANPP
Within each plateau, ANPP showed a gradual upward trend as the longitude increased (Fig 2). Comparatively, ANPP first increased and then decreased as TP–LP–MP with the increase in latitude. These results agreed with those reported by Jiao et al. [39] in Europe. Moreover, the change trend of ANPP along the transect was consistent with the regional vegetation zonality, which demonstrated that the design of the transect can successfully obtain the characteristics of regional vegetation.
Compared with that on the TP and MP, the grasslands of ANPP on the LP was highest. These results verified that, compared with nutrient limitation, temperature and precipitation limitations have a greater effect on vegetation productivity on the TP and MP. The growth inhibition caused by nutrient deficiency is more common in trees or shrubs [20] compared with herb, so grassland shows higher ANPP. The grasslands on the MP were mostly affected by the extreme arid climate [30], but precipitation extremes have declined in recent years [40]. The water stress due to extreme drought can easily result in water imbalance in grasslands [40]. Most surviving plants present resource-conservative functional traits [41], such as higher growth of underground roots [42] or an earlier-ending growing season [43], resulting in lower ANPP. The extreme low temperature on the TP is a long-term stress factor, and the low productivity is understandable. Low temperature can inhibit the activity of plant cell enzymes, resulting in slower plant growth and limited organic matter accumulation during the short period in which the soil thaws [44]. In addition, the alpine vegetation on the TP is more dwarf [45], and grows in a unique high-density "straw felt" pattern, allowing plants to gather together for warmth.
Specific performance and response mechanisms of ANPP to environmental changes modified by the main limiting factors
It is important to explore the response mechanism of ANPP to global change, and regional limiting factors may be the key to understanding the productivity response mechanism [46, 47]. Regional characteristics can be showed by comparing large-scale transects, and the regional vegetation productivity regulatory mechanisms maybe not easily change. For example, along these transects, ANPP had significant linear relationships with environmental factors, irrespective of the plateaus (Fig 3), although the response intensity of grassland ANPP in different regions was also significant different (Fig 4). Among them, the response intensity of ANPP with changes in K was not significant, although there were differences among region [48].
On the MP, the response intensity of ANPP with changes in MAP was much higher than that on the LP and TP. Previous studies have demonstrated that precipitation is the main factor affecting ANPP in arid and semi-arid regions [49], and semi-arid grasslands are highly sensitive to fluctuation in precipitation [50]. After filtering the autocorrelated factors, ANPP was comprehensively regulated by the MAT and MAP (Table 1), being specified by aridity (Fig 4). Temperature is important for enzyme activity to promote plant photosynthesis [51] and more efficiently utilize precipitation and soil nutrients.
On the LP, grassland ANPP had a specific response to temperature and nutrients (Fig 4), and was comprehensively regulated by MAT, N, and P (Table 1). Owing to severe soil erosion and nutrient loss on this plateau [52], plants must rapidly respond to changing nutrients. The vegetation of LP was considered to be close to the threshold of regional water resources carrying capacity [53]. Compared with grassland, forest was the main body that affects the balance of water use [54]. Therefore, the precipitation limitation of grassland vegetation may not be strong. Temperature may directly affect plant metabolism and transpiration [55], further influencing the rate of photosynthesis and the absorption of water and nutrients by roots. Furthermore, the sensitivity to temperature can alleviate the growth limitation due to nutrient deficiency [28].
On the TP, the response intensity of ANPP to environmental changes was overall low (Fig 4), reflecting the overall suppression of plant growth under extremely low temperatures [56]. As the important water source of China and even Asia, the TP is not water limited [57, 58], and soil nutrients are mostly stored in an organic state [59]. However, too low temperature may depress soil N mineralization, resulting in an apparent limitation of available N [60]. When autocorrelated factors were filtered, the strong regulation of soil N content was shown (Table 1). Under long-term low temperature stress, alpine plants have evolved a variety of adaptive strategies, such as dense villi and stolon or mat-like growth [61]. This shows that, under long-term adaptation, cold-tolerant herbs grow well on TP and have formed stable plant physiological characteristics. Although the grasslands of the TP are more sensitive to warming, at the regional scale, the vegetation rejuvenation period has not significantly advanced [62] and the optimal length of the growing season has shortened [23]. Moreover, the warming and drying trend in the western region [63] have no significant effect on grassland productivity.
Regional limiting factors should be emphasized during regional integration
In a simple integration of different regional data, the effect of environmental factors on ANPP greatly decreased (Fig 5), and the fitting bias of the simple model increased (Fig 6). This evidence reveals that differences in environmental regulatory mechanisms are common among different macrosystems. Owing to the differences in the environmental conditions of different macrosystems, a simple model is not sufficient for reflecting the whole system, and regional limited factors should be emphasized. Therefore, to efficiently estimate productivity, we not only need to judge regional characteristics [6, 64–66] to establish a model but also to consider more information regarding the key limiting factors on, e.g., regression trees and neural networks.
We are far from identifying the main limiting factors at the regional level because few studies have been reported on this subject. A more complete theoretical foundation is needed to further discuss the temporal and spatial scale of limiting factors. Furthermore, how to determine the main limiting factors at regional scale is dependent upon the environmental parameters collected in a specific study. In the present study, data on regional vegetation, being simply reflected in the transect survey, may have inherent errors due to the selection of sites and the influence of investigation time. In practice, the setup of the plateau transect basically follows the existing transect proposed and established by previous researchers [67, 68]. The present study is the first, to our knowledge, to attempt to systematically compare these transects. In the future, more systematic transect surveys can be carried out using a consistent protocol, even covering different fields (e.g., plants, animals, microorganisms, and soil) and different data collection scales (e.g., ground, remote sensing, lidar, models, and flux).
Conclusion
There are significant regional differences in the response of grassland productivity to changing environment conditions, and the main limitation factors in different regions can modify the regulatory mechanisms. The response of ANPP to changing environments on the LP, MP and TP was mostly related to the specific limiting factors in each region but has different mechanisms driving the response rate and direction. When using the model to simulate grassland ANPP at a large scale, the regional limiting factor, as a breakthrough point, should be emphasized to improve its simulation accuracy. In future, the comparative transects are not only ideal for exploring the response mechanism of productivity but also represent a research platform for multidisciplinary integration (e.g., plants, animals, and microorganisms). This is also expected to be important for the verification of regional limiting factors.
Supporting information
S1 Fig. Difference of drought degree of grassland types in Mongolia Plateau, Loess Plateau and Tibetan Plateau.
Error line represents 1 * standard error; different letters (a, b) indicate significant difference (P < 0.05).
https://doi.org/10.1371/journal.pone.0240238.s001
(TIF)
S1 Table. The basic information of the field-investigated.
https://doi.org/10.1371/journal.pone.0240238.s002
(DOCX)
Acknowledgments
We would like to thank these stations from China Ecosystem Research Network (CERN). In field investigation, Lhasa Plateau Ecological Research Station, Ansai Research Station of Soil and Water Conservation, and Inner Mongolia Grassland Ecosystem Research Station provide important logistics support and botanical expertise.
References
- 1. Liu HX, Zhang AB, Jiang T, Zhao AZ, Zhao YL, Wang DL. Response of vegetation productivity to climate change and human activities in the shaanxi-gansu-ningxia region, China. Journal of the Indian Society of Remote Sensing. 2018; 46(7): 1081–92.
- 2. Vitousek PM, Mooney HA, Lubchenco J, Melillo JM. Human domination of earth’s ecosystems. Science. 1997; 277: 494–9.
- 3. Nyström M, Jouffray JB, Norström A, Crona B, Jørgensen P, Carpenter S, et al. Anatomy and resilience of the global production ecosystem. Nature. 2019; 575:98–108. pmid:31695208
- 4. Feng X, Sun Q, Lin B. NPP process models applied in regional and global scales and responses of NPP to the global change. Ecology and Environmental Sciences. 2014; 23(3): 496–503.
- 5. Karlsen SR, Anderson HB, van der Wal R, Hansen BB. A new NDVI measure that overcomes data sparsity in cloud-covered regions predicts annual variation in ground-based estimates of high arctic plant productivity. Environ Research Letters. 2018; 13(2): ARTN 025011.
- 6. Zhang Y, Zhang XL. Estimation of net primary productivity of different forest types based on improved CASA model in Jing-Jin-Ji region, China. Journal of Sustainable Forestry. 2017; 36(6):568–82.
- 7. Zhang YH, Loreau M, He NP, Wang JB, Pan QM, Bai YF, et al. Climate variability decreases species richness and community stability in a temperate grassland. Oecologia. 2018; 188(1): 183–92. pmid:29943096
- 8. Gao T, Xu B, Yang XC, Deng SQ, Liu YC, Jin YX, et al. Aboveground net primary productivity of vegetation along a climate-related gradient in a Eurasian temperate grassland: spatiotemporal patterns and their relationships with climate factors. Environmental Earth Sciences. 2017;76(1):
- 9. Du E, Terrer C, Pellegrini A, Ahlström A, Van Lissa C, Zhao X, et al. Global patterns of terrestrial nitrogen and phosphorus limitation. Nature Geoscience. 2020; 7: 221–226.
- 10. Ahuja I, de Vos RCH, Bones AM, Hall RD. Plant molecular stress responses face climate change. Trends in Plant Science. 2010;15(12):664–74. pmid:20846898
- 11. Tian DS, Reich PB, Chen HYH, Xiang YZ, Luo YQ, Shen Y, et al. Global changes alter plant multi-element stoichiometric coupling. New Phytologists. 2019;221(2):807–17.
- 12. Bussotti F, Ferrini F, Pollastrini M, Fini A. The challenge of Mediterranean sclerophyllous vegetation under climate change: From acclimation to adaptation. Environmental and Experimental Botany. 2014; 103: 80–98.
- 13. Koch G, Vitousek P M., Steffen W L., Walker B. Terrestrial transects for global change research. 1995;121(1–2):53–65.
- 14. Zhou G, He Q. Terrestrial transect study on the responses of ecosystems to global change. Advances in Earth Science. 2012; 27(5): 563–72.
- 15. Wu JS, Li M, Fiedler S, Ma WL, Wang XT, Zhang XZ, et al. Impacts of grazing exclusion on productivity partitioning along regional plant diversity and climatic gradients in Tibetan alpine grasslands. Journal of Environmental Management. 2019; 231:635–45. pmid:30390448
- 16. Zhang J, Miao Y, Zhang T, Wei Y, Qiao X, Miao R, et al. Drought timing and primary productivity in a semiarid grassland. Land Degradation and Development. 2020. 31:
- 17. Schleuss P, Widdig M, Heintz-Buschart A, Kirkman K, Spohn M. Interactions of nitrogen and phosphorus cycling promote P acquisition and explain synergistic plant growth responses. Ecology. 2020; 101: pmid:32020599
- 18. Chai X, Shi PL, Song MH, Zong N, He YT, Zhao GS, et al. Carbon flux phenology and net ecosystem productivity simulated by a bioclimatic index in an alpine steppe-meadow on the Tibetan Plateau. Ecological Modelling. 2019;394: 66–75.
- 19. Zhang T, Zhang YJ, Xu MJ, Zhu JT, Chen N, Jiang YB, et al. Water availability is more important than temperature in driving the carbon fluxes of an alpine meadow on the Tibetan Plateau. Agricultual and Forest Meteorology. 2018; 256: 22–31.
- 20. Shi WH, Huang MB, Wu LH. Prediction of storm-based nutrient loss incorporating the estimated runoff and soil loss at a slope scale on the Loess Plateau. Land Degradation and Development. 2018;29(9):2899–910.
- 21. Hu B, Zhou MH, Dannenmann M, Saiz G, Simon J, Bilela S, et al. Comparison of nitrogen nutrition and soil carbon status of afforested stands established in degraded soil of the Loess Plateau, China. Forest Ecology and Management. 2017; 389: 46–58.
- 22. Qin F, Jia G, Yang J, Na Y, Hou M, Narenmandula. Spatiotemporal variability of precipitation during 1961–2014 across the Mongolian Plateau. Journal of Mountain Science. 2018; 15: 992–1005.
- 23. Wang H, Liu H, Cao G, Ma Z, Li Y, Zhang F, et al. Alpine grassland plants grow earlier and faster but biomass remains unchanged over 35 years of climate change. Ecology Letters. 2020; 23(4): 701–10.
- 24. Zhang ZY, Cheng DM, Li CS, Hu W, Zhan XH, Ji HL. The complexity of climate reconstructions using the coexistence approach on Qinghai-Tibetan Plateau. Journal of Palaeogeogaphy. 2019; 8(1): 5.
- 25. Hu YF, Dao RN, Hu Y. Vegetation Change and Driving Factors: Contribution Analysis in the Loess Plateau of China during 2000–2015. Sustainability-Basel. 2019;11(5):1320.
- 26. Li G, Sun SB, Han JC, Yan JW, Liu WB, Wei Y, et al. Impacts of Chinese Grain for Green program and climate change on vegetation in the Loess Plateau during 1982–2015. Science of Total Environment. 2019; 660: 177–87.
- 27. Chen D, Chen HW. Using the Köppen classification to quantify climate variation and change: An example for 1901–2010. Environmental Development. 2013; 6: 69–79.
- 28. Gao H, Pang G, Li Z, Cheng S. Evaluating the potential of vegetation restoration in the Loess Plateau. Acta Geographica Sinica. 2017; 72(5): 863–74.
- 29. Beck HE, Zimmermann NE, McVicar TR, Vergopolan N, Berg A, Wood EF. Present and future Köppen-Geiger climate classification maps at 1-km resolution. Scientific Data. 2018; 5(1): 180214.
- 30. Tong S, Zhang J, Bao Y, Lai Q, Lian X, Li N, et al. Analyzing vegetation dynamic trend on the Mongolian Plateau based on the Hurst exponent and influencing factors from 1982–2013. Journal of Geographical Sciences. 2018;28(5):595–610.
- 31. Liu CC, He NP, Zhang JH, Li Y, Wang QF, Sack L, et al. Variation of stomatal traits from cold temperate to tropical forests and association with water use efficiency. Functional Ecology. 2018; 32(1): 20–8.
- 32. Shen HH, Zhu YK, Zhao X, Geng XQ, Fang JY. Analysis of current grassland resources in China. Chinese Science Bulletin. 2016; 61:139.
- 33. Wei P, Xu L, Pan X, Hu Q, Li Q, Zhang X, et al. Spatio-temporal variations in vegetation types based on a climatic grassland classification system during the past 30 years in Inner Mongolia, China. Catena. 2019;185: 104298.
- 34. Chen SP, Wang WT, Xu WT, Wang Y, Wan HW, Chen DM, et al. Plant diversity enhances productivity and soil carbon storage. Proceedings of the National Academy of Sciences of the United States of America. 2018; 115(16): 4027–32. pmid:29666315
- 35.
Trabucco A, Zomer R. Global Aridity Index and Potential Evapotranspiration (ET0). Climate Database v22019.
- 36.
Oksanen, FJ. Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. vegan: Community Ecology Package. 2018. https://CRAN.R-project.org/package=vegan.
- 37.
Wei TY, Simko V. R package "corrplot": Visualization of a Correlation Matrix 2017. https://github.com/taiyun/corrplot.
- 38.
Venables WN, Ripley BD. Modern Applied Statistics with S New York: Springer; 2002. http://www.stats.ox.ac.uk/pub/MASS4.
- 39. Jiao C, Yu G, He N, Ma A, Ge J, Hu Z. The spatial pattern of grassland aboveground biomass and its environmental controls in the Eurasian steppe. Acta Geographica Sinica. 2016; 71(5): 781–96.
- 40. Li CL, Filho WL, Wang J, Fudjumdjum H, Fedoruk M, Hu RC, et al. An analysis of precipitation extremes in the inner mongolian plateau: Spatial-temporal patterns, causes, and implications. Atmosphere-Basel. 2018; 9(8): 322.
- 41. Li Y, Liu CC, Zhang JH, Yang H, Xu L, Wang QF, et al. Variation in leaf chlorophyll concentration from tropical to cold-temperate forests: Association with gross primary productivity. Ecological Indicators. 2018; 85: 383–9.
- 42. Eziz A, Yan Z, Tian D, Han W, Tang Z, Fang J. Drought effect on plant biomass allocation: A meta-analysis. Ecology and Evolution. 2017; 7: 201719.
- 43. Miao LJ, Muller D, Cui XF, Ma MH. Changes in vegetation phenology on the Mongolian Plateau and their climatic determinants. Plos One. 2017;12(12):e0190313. pmid:29267403
- 44. Sun Q, Li B, Zhou G, Jiang Y, Yuan Y. Delayed autumn leaf senescence date prolongs the growing season length of herbaceous plants on the Qinghai–Tibetan Plateau. Agricultural and Forest Meteorology. 2020; 284: 107896.
- 45. Liu A, Yang T, Xu W, ShangGuan Z, Wang J, Liu H, et al. Status, issues and prospects of belowground biodiversity on the Tibetan alpine grassland. Biodiversity Science. 2018; 26(9): 972–87.
- 46. Smith MD, La Pierre KJ, Collins SL, Knapp AK, Gross KL, Barrett JE, et al. Global environmental change and the nature of aboveground net primary productivity responses: insights from long-term experiments. Oecologia. 2015; 177(4): 935–47. pmid:25663370
- 47. Ren HY, Xu ZW, Isbell F, Huang JH, Han XG, Wan SQ, et al. Exacerbated nitrogen limitation ends transient stimulation of grassland productivity by increased precipitation. Ecol Monogr. 2017; 87(3): 457–69.
- 48. Guo X, Meng J, Tian X, Zhou T, Liang J, Chen G, et al. Effects of potassium application on the distribution, utilization efficiency of potassium in rice and soil potassium balance. Soil and Fertilizer Sciences in China. 2019; 21(6):154–60.
- 49. Fan Y, Li XY, Wu XC, Li L, Li W, Huang YM. Divergent responses of vegetation aboveground net primary productivity to rainfall pulses in the Inner Mongolian Plateau, China. Journal of Arid Environment. 2016;129:1–8.
- 50. Zhang B, Zhu J, Liu H, Pan Q. Effects of extreme rainfall and drought events on grassland ecosystems. Chinese Journal of Plant Ecology. 2014; 38(9):1008–1018.
- 51. Huai J, Zhang X, Li J, Ma T, Zha P, Jing Y, et al. SEUSS and PIF4 Coordinately Regulate Light and Temperature Signaling Pathways to Control Plant Growth. Molecular Plant. 2018; 11(7):928–42. pmid:29729397
- 52. Shi W, Huang M, Wu L. Prediction of storm-based nutrient loss incorporating the estimated runoff and soil loss at a slope scale on the Loess Plateau. Land Degradation and Development. 2018; 29(9): 2899–2910.
- 53. Feng XM, Fu BJ, Piao S, Wang SH, Ciais P, Zeng ZZ, et al. Revegetation in China’s Loess Plateau is approaching sustainable water resource limits. Nature Climate Change. 2016; 6(11):1019–24.
- 54. Jin Z, Guo L, Yu Y, Luo D, Fan B, Chu G. Storm runoff generation in headwater catchments on the Chinese Loess Plateau after long-term vegetation rehabilitation. Science of Total Environment. 2020; 748:141375.
- 55. Ilnitsky O, Pashtetsky A, Plugatar Y, Korsakova S. Dependency of a photosynthesis rate in nerium oleander L. on environmental factors, leaf temperature, transpiration, and their change during vegetation in subtropics. Russian Agricultural Sciences. 2018; 44: 224–228.
- 56. Wang XY, Wang T, Guo H, Liu D, Zhao YT, Zhang TT, et al. Disentangling the mechanisms behind winter snow impact on vegetation activity in northern ecosystems. Global Change Biology. 2018; 24(4):1651–62. pmid:28994227
- 57. Li J, Liu D, Wang T, Li YN, Wang SP, Yang YT, et al. Grassland restoration reduces water yield in the headstream region of Yangtze River. Scientfic Reports-Uk. 2017;7(1): 2162.
- 58. Deng HJ, Pepin NC, Liu Q, Chen YN. Understanding the spatial differences in terrestrial water storage variations in the Tibetan Plateau from 2002 to 2016. Climatic Change. 2018; 151(3–4):379–93.
- 59. Dai L, Ke X, Cao Y, Zhang FW, Du YG, Li YK, et al. Allocation patterns of above- and belowground biomass and its response to meteorological factors on an alpine meadow in Qinghai-Tibet Plateau. Acta Ecologica Sinica. 2019; 39(2):486–93.
- 60. DeMarco J, Mack MC, Bret-Harte MS, Burton M, Shaver GR. Long-term experimental warming and nutrient additions increase productivity in tall deciduous shrub tundra. Ecosphere. 2014; 5(6): 72.
- 61.
Wang H. Effects of nitrogen, phosphoru and potassium addition on the leaf founction traits and plant community of the alpine meadow in Qinghai. Thesis: Peking University; 2012. http://www.wanfangdata.com.cn/details/detail.do?_type=degree&id=Y2216942.
- 62. Shen MG, Sun ZZ, Wang SP, Zhang GX, Kong WD, Chen AP, et al. No evidence of continuously advanced green-up dates in the Tibetan Plateau over the last decade. Proceedings of the National Academy of Sciences of the United States of America. 2013; 110(26): pmid:23661054
- 63. Liu H, Mi Z, Lin L, Wang Y, Zhang Z, Zhang F, et al. Shifting plant species composition in response to climate change stabilizes grassland primary production. Proceedings of the National Academy of Sciences. 2018;115(16):4051.
- 64. Zhao F, Xu B, Yang XC, Xia L, Jin YX, Li JY, et al. Modelling and analysis of net primary productivity and its response mechanism to climate factors in temperate grassland, northern China. International Journal of Remote Sensing. 2019; 40(5–6):2259–2277.
- 65. Wu DH, Ciais P, Viovy N, Knapp AK, Wilcox K, Bahn M, et al. Asymmetric responses of primary productivity to altered precipitation simulated by ecosystem models across three long-term grassland sites. Biogeosciences. 2018; 15(11): 3421–37.
- 66. Liu LB, Peng SS, AghaKouchak A, Huang YY, Li Y, Qin DH, et al. Broad consistency between satellite and vegetation model estimates of net primary productivity across global and regional scales. Journal of Geophysical Research-Biogeosciences. 2018;123(12):3603–3616.
- 67. Zhang X, Gao Q, Yang D, Zhou G, Ni J, Wang Q. A gradient analysis and prediction on the Northeast China Transect (NECT) for global change study. Acta Botanica Sinica. 1997; 39(9):785–99.
- 68. Zhang X, Yang Y, Piao S, Bao W, Wang S. Ecological change on the Tibetan Plateau. Chinese Science Bulletin. 2015; 60(32): 3048–56.