Regional response of grassland productivity to changing environment conditions influenced by limiting factors.

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.

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 n class="Species">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 estimatinpan>g 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 in class="Chemical">mportant theoretical basis for solvinpan>g such problems. In natural ecosystems, based on Liebig’s law of “minpan>imal factors” [9], there is always one factor that reaches a state of n class="Disease">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 ten class="Chemical">mperature, 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 unpan>derstood 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 unpan>der different regional limiting factors.

Temperature, precipitation and nutrients are in class="Chemical">mportant 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 con class="Chemical">mparative transect, the present study focuses on the grasslands of the n class="Chemical">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 ten class="Chemical">mperature, precipitation, and nutrients, and the measured data for mean annual ten class="Chemical">mperature (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/).

Fig 1

Grassland distribution in the Northern Hemisphere and contrastive grassland transect between the Mongolian Plateau, loess Plateau, and Tibetan Plateau.

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.

After con class="Chemical">mparing 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 n class="Chemical">TP is >4000m, and n class="Chemical">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 n class="Chemical">TP, n class="Chemical">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 n class="Chemical">TP spanned ~1600 km at an altitude of 4104–4938 m. The transect on the n class="Chemical">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 estin class="Chemical">mated the total coverage and average height and measured the plant height, sub-coverage, and abundance of species. Finpan>ally, 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

n class="Chemical">AGB was obtained through plot survey in the late growing season of the grassland. For herb plants, n class="Chemical">AGB was considered as ANPP, and for shrub plants, we use the linear equation from Chen et al. [34]: 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 clin class="Chemical">mate data was extracted from onlinpan>e datasets based on the longitude and latitude. The n class="Chemical">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 san class="Chemical">mples. 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, n class="Chemical">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 n class="Chemical">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 n class="Chemical">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 n class="Chemical">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).

Fig 2

The distribution pattern of grassland aboveground net primary productivity (ANPP) between Mongolian Plateau, loess Plateau, and Tibetan Plateau.

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 n class="Chemical">ANPP to changes inpan> clin class="Chemical">mate (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.

Fig 3

The relationship of aboveground net primary productivity (ANPP) to climate factors and soil nutrient among different regions.

Solid line shows that ANPP was significantly correlated with environmental factors (p < 0.05). MAT, mean annual temperature; MAP, mean annual precipitation.

Fig 4

Changes in the response intensity of aboveground net primary productivity (ANPP) to climate factors and soil nutrients among the different regions.

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.

Solid line shows that n class="Chemical">ANPP was significantly correlated with environmental factors (p < 0.05). n class="Chemical">MAT, mean annual temperature; MAP, mean annual precipitation.

The response intensity is the slope of the linear fitting equation between n class="Chemical">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. n class="Chemical">MAT, mean annual temperature; MAP, mean annual precipitation.

The response intensity of n class="Chemical">ANPP with MAP on the n class="Chemical">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 n class="Chemical">ANPP (Table 1). When the total data from the three regions were considered, n class="Chemical">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.

Table 1

The master model of aboveground net primary productivity (ANPP) controlled by environmental factors in different regions.

Region	Model	Adj.R2	p
Mongolian Plateau	ANPP = -88.54910* + 8.22961 MAT + 0.40518** MAP	0.7105	0.0054
Loess Plateau	ANPP = -92.659* + 14.114* MAT + 6.060 N + 927.095 P	0.8915	<0.001
Tibetan Plateau	ANPP = 23.1177* + 3.1529** N	0.7184	0.0012
Total	ANPP = -17.71743 +5.51664***MAT + 0.17237** MAP + 1.47000 N	0.7046	<0.001

Initial factors include: MAT, mean annual temperature; MAP, mean annual precipitation; Aridity; N; P; and K

* represents the significance of the regression coefficient, *p < 0.05;

**: p<0.01;

***: p < 0.001

Initial factors include: n class="Chemical">MAT, mean annual ten class="Chemical">mperature; MAP, mean annual precipitation; Aridity; N; P; and K

Difference between regional specificity and simple regional integration

In each region, the contribution of climate factors and soil nutrients to grassland n class="Chemical">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.

Fig 5

Environmental contribution of grassland aboveground net primary productivity (ANPP) among different regions by canonical correspondence analysis.

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.

Clin class="Chemical">mate factors were mean annual ten class="Chemical">mperature (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.

Con class="Chemical">mparing 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 n class="Chemical">LP. Among them, the relative deviation of the MP, LP and TP decreased by 31.76%, 17.22%, and 2.23%, respectively.

Fig 6

Fitting comparison between aboveground net primary productivity (ANPP) of the regional model and integrated model.

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.

Dotted line ± 20%, indicating the range of n class="Chemical">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. n class="Chemical">MP, Mongolian Plateau; LP, Loess Plateau; TP, Tibetan Plateau.

Discussion

Regional variation in grassland ANPP

Within each plateau, n class="Chemical">ANPP showed a gradual upward trend as the longitude inpan>creased (Fig 2). Con class="Chemical">mparatively, 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 n class="Chemical">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 in class="Chemical">mportant to explore the response mechanism of n class="Chemical">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 n class="Chemical">MP, the response inpan>tensity of n class="Chemical">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 n class="Chemical">LP, grassland n class="Chemical">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 n class="Chemical">TP, the response inpan>tensity of n class="Chemical">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 inpan>tegration of different regional data, the effect of environmental factors on n class="Chemical">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 ten class="Chemical">mporal 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 n class="Chemical">ANPP to changinpan>g environments on the n class="Chemical">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.

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).

(TIF)

Click here for additional data file.

The basic information of the field-investigated.

(DOCX)

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16 Jul 2020

n class="Chemical">PONE-D-20-18058

Regional response of grassland productivity to changing environment influenced by limiting ecological factors

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Additional Editor Comments (if provided):

I now have two reports from expert reviewers. While reviewers consider the study in class="Chemical">mportant, they raised some serious concerns related to the technical part of the study. Reviewer #1 was more critical and had two major concerns, and recommended Reject. Both reviewers thinpan>k Enpan>glish needs to be signpan>ificantly in class="Chemical">mproved. I concur with the reviewers and agree that the topic of the study is interesting, and would like to give the authors an opportunity to address the reviewers's concerns if they can.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and san class="Chemical">mple sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: No

Reviewer #2: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting inforn class="Chemical">mation, or deposited to a public repository. For exan class="Chemical">mple, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or gramn class="Chemical">matical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: No

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The paper con class="Chemical">mpared the grassland productivity on the three plateaus (Tibetan Plateau, Mongolian Plateau, and Loess Plateau) and such differences related with environmental factors inpan> those regions. The authors postulate that the mainpan> limitinpan>g factor inpan> the three regions is low ten class="Chemical">mperature for Tibetan Plateau, low precipitation (water) for Mongolian Plateau, and low soil nutrient for Loess Plateau. Although each place has its specific and primary environmental limiting factor, I do not think such classification is accurate. There are two main concerns: 1) the three plateaus are large, covering different climatic conditions, thus such single factor analysis cannot fully explain their specific site differences; 2) the environmental factors are interacted, and single factor cannot clearly indicate the mechanisms. The authors did lots of field survey and data analysis, while such study did not meet the judge standards in rationality and research integrity. Thus, I do not suggest it to be accepted for publication in Plos One.

Some specific comments are as following:

1. English writing needs in class="Chemical">mprovement, and some sentences are vague.

2. Abstract: lack descriptions about experimental design and methods; the last sentence is redundant and can be deleted.

3. Introduction: some inforn class="Chemical">mation about the three plateaus is not exact, such as limiting factors and grassland distribution (line 92-93).

4. Methods: more explanations about such descriptions in environmental limitations (line 116). It was normally considered that there are two main factors, i.e. n class="Chemical">water and nutrient, especially n class="Chemical">water on the Loess Plateau. Line: 125: This in not correct, some parts on loess plateau belong to arid region. Line:135-137: such classifications clearly show the rainfall or water gradient. Line156: such survey cannot represent the three plateaus as typically. Data analysis descriptor is not enough, and more detailed explanations such as those in Table 1 needed.

5. Results: line 207, such great differences among three plateaus in class="Chemical">mplies that such con class="Chemical">mparisons cannot solely focus on environment factors. Line 220: how define the response density here? Line 233-235: such conclusion is incorrect. Line 239: suggest delete the sentence. Line 253: the title is unclear.

6. Discussion: generally, the discussion has poor relation with results obtained. The paper put environmental factors as fixed variable, while neglect their variations. Some conclusions lack of direct data results, while may be speculated (such as line 323).

7. Figure 1: there lack of statistical analysis for the three environmental factor limitations in columns. Figure 2: The n class="Chemical">ANPP inpan>crease exponentially with longitude on n class="Chemical">TP and LP? linealy on MP? correct or not? Figure 5: more information needed about the analysis?

8. Table 1: the results in the table conflict with the hypothesis, or expectations. The data was significant with whom as marked with *?

Reviewer #2: This study investigated the main regional limiting factors in the changes of grassland aboveground net primary productivity in Tibetan Plateau (n class="Chemical">TP), Mongolian Plateau (n class="Chemical">MP) and Loess Plateau (LP) by using a comparative transect survey dataset. This research topic is important as regional differences and regulation mechanism of vegetation productivity response to changing environmental is one of the core issues in macroecology research. To verify the main regional limiting factors, the authors used multiple analysis methods. However, analysis method usually has its own defects. For the analysis method of gradual regression, the significance depends not only on their intrinsic ecological implications but also on the variations of their selected samples. Why nutrient-N is the highest in TP, while the N relationship with TP productivity showed the best among the three regions. I think, at least partly, it is due to that the selected samples in TP have greater variations of N than those in the other two regions. Similarly, the variations of N in MP are much lower, thus it is difficult to develop a significant relationship between N and productivity. I suggest that the author fully acknowledged the limitations of this experimental design and clarified the potential methodological defects in the discussion. On the contrary, I like the results based on the canonical response analysis (CCA). When the data of the three regions are analyzed together, they found that the explanation degree of climate and soil nutrient factors reduced largely, indicating that the three plateaus may have different or even divergent productivity response mechanisms. I suggest that the author focus on this result for discussion. In addition, English writing could be improved. While the general writing was good, there were many Chinglish and long sentence that greatly affect the readability of this paper. For example, on Line 27, " MP is precipitation specific, LP is temperature and nutrient specific …"; Line 31" MP is mainly temperature and precipitation regulation, LP is temperature and nutrients, and TP is nutrients…". Lines 32-33, “The interpretation degree of environment to ANPP is 72.56% in the region, while only 27.18% after simple integration” Lines 341-345, “In the TP limited by low temperature, the response intensity…, which…,which…environmental factors in the TP.”

Based on the above reasons, I'd like to recommend a major revision before it can be accepted for publication.

**********

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Reviewer #1: No

Reviewer #2: No

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16 Sep 2020

Response to editor:

(Original comment and query in Bold, Response in upright Roman)

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming.

Response：Thanks for your comments. We have studied the forn class="Chemical">mat san class="Chemical">mples of PLOS ONE's literatures and adjusted the format of the title page and main body of this paper.

2. In your Methods section, please provide additional location inforn class="Chemical">mation of the study sites, including geographic coordinates for the data set if available.

Response：Thanks for your suggestion. After this revision, the geographic coordinates of the study area will be added to the Method section: [The typical grassland ecosystems widely distributed in the northern hemisphere were selected as the research object (31–45°N, 80–123°W; Tab.S1).]. The specific latitude and longitude and basic clin class="Chemical">mate inpan>forn class="Chemical">mation of the 30 sites involved can be viewed in Appendix 1 (Tab.S1).

3. In your Methods section, please provide additional inforn class="Chemical">mation regarding the permits you obtained for the work. Please ensure you have included the full name of the authority that approved the study sites access and, if no permits were required, a brief statement explaining why.

Response：Thanks for your proposal. This research was carried out with the support of the Institute of Geographical Sciences and Natural Resources Research of the Chinese Academy of Sciences, relying on the field stations of the China Ecosystem Research Network. The full names of such institutions will be written in the acknowledgment, and will not be covered in the Method section.

4. We note that you have stated that you will provide repository inforn class="Chemical">mation for your data at acceptance. Should your manuscript be accepted for publication, we will hold it unpan>til you provide the relevant accession numbers or DOIs necessary to access your data. If you wish to make changes to your Data Availability statement, please describe these changes in your cover letter and we will update your Data Availability statement to reflect the inforn class="Chemical">mation you provide.

Response：Thanks for your reminder. We do not need to change the Data Availability statement at this time

5.Thank you for stating the following in the Acknowledgments Section of your manuscript:

[This work was supported by National Natural Science Foundation of China, No. 31870437, No. 31988102, the Chinese Academy of Sciences Strategic Priority Research Program (XDA19020302), National Key R&D Program of China (2017YFA0604803)]

We note that you have provided funding information that is not currently declared inpan> your Fundinpan>g Statement. However, fundinpan>g inpan>formation should not appear in the Acknowledgments section or other areas of your manuscript. We will only publish funding information present in the Funding Statement section of the online submission form. Please remove any funding-related text from the manuscript and let us know how you would like to update your Funding Statement. Currently, your Funding Statement reads as follows: [The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.]

Response：Thanks for your proposal. After this revision, we have deleted the funding-related text from the manuscript and rewritten the Acknowledgment section to meet the requirements of your journal.

The Acknowledgments section is revised as follows: [We would like to thank the Institute of Geographical Sciences and Natural Resources Research of the Chinese Academy of Sciences for substantial support throughout this research. Within the China Ecosystem Research Network, the Lhasa Plateau Ecological Research Station, Ansai Research Station of Soil and n class="Chemical">Water Conservation, and Inner Mongolia Grassland Ecosystem Research Station provide in class="Chemical">mportant logistics support and botanical expertise.]

The Funding Statement is revised as follows: [we received support for this research from National Natural Science Foundation of China, No. 31870437, No. 31988102, the Chinese Academy of Sciences Strategic Priority Research Program (XDA19020302), 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.]

6. PLOS requires an ORCID iD for the corresponding author in Editorial Manager on papers submitted after December 6th, 2016. Please ensure that you have an ORCID iD and that it is validated in Editorial Manager. To do this, go to ‘Update my Inforn class="Chemical">mation’ (inpan> the upper left-hand corner of the mainpan> menu), and click on the Fetch/Validate linpan>k next to the ORCID field. This will take you to the ORCID site and allow you to create a new iD or authenticate a pre-existinpan>g iD inpan> Editorial Manager. Please see the followinpan>g video for inpan>structions on linpan>kinpan>g an ORCID iD to your Editorial Manager account: htn class="Chemical">tps://www.youtube.com/watch?v=_xcclfuvtxQ

Response：Thanks for your reminder. I am sure that the corresponding author in the Editor Manager has an ORCID iD and has been verified by the Editor Manager.

7.We note that [Figure(s) 1] in your submission contain [map/satellite] images which may be copyrighted. All PLOS content is published under the Creative Commons Attribution License (CC BY 4.0), which means that the manuscript, images, and Supporting Information files will be freely available onlinpan>e, and any third party is permitted to access, download, copy, distribute, and use these n class="Chemical">materials in any way, even commercially, with proper attribution. For these reasons, we cannot publish previously copyrighted maps or satellite images created using proprietary data, such as Google software (Google Maps, Street View, and Earth). For more information, see our copyright guidelines: http://journals.plos.org/plosone/s/licenses-and-copyright.

Response：Thanks for your reminder. The map data used in [Figure(s) 1] is freely available online to support any third-party scientific and educational purposes; and it con class="Chemical">mplies with the relevant regulations of the CC BY 4.0 license. After this revision, the followinpan>g description will be added to the method section: [The map data of Figure 1 comes from Land Cover Clin class="Chemical">mate Modeling Grid (CMG) (MCD12C1) (https://lpdaac.usgs.gov/products/mcd12c1v006/)]

Response to reviews:

(Original comment and query in Bold, Response in upright Roman)

To reviewer 1:

The paper con class="Chemical">mpared the grassland productivity on the three plateaus (Tibetan Plateau, Mongolian Plateau, and Loess Plateau) and such differences related with environmental factors inpan> those regions. The authors postulate that the mainpan> limitinpan>g factor inpan> the three regions is low ten class="Chemical">mperature for Tibetan Plateau, low precipitation (water) for Mongolian Plateau, and low soil nutrient for Loess Plateau. Although each place has its specific and primary environmental limiting factor, I do not think such classification is accurate. There are two main concerns: 1) the three plateaus are large, covering different climatic conditions, thus such single factor analysis cannot fully explain their specific site differences; 2) the environmental factors are interacted, and single factor cannot clearly indicate the mechanisms. The authors did lots of field survey and data analysis, while such study did not meet the judge standards in rationality and research integrity. Thus, I do not suggest it to be accepted for publication in Plos One.

Response：Thanks very much for your critical comments above. We fully understand the serious concerns of reviewer about the technical part of the article, and have made corresponding rigorous revisions to the entire article. It is true that the types of microclin class="Chemical">mates inpan> the plateau region are complex and changeable; and the interactions between environmental factors are difficult to decipher with simple models. We fully acknowledge the limitations of this experimental design and will clarify potential method flaws in the discussion. But the purpose of this research is to verify the possibility of deducing the main ecological limiting factors to a regional scale; and how well the limiting factors control the productivity response mechanism of the three plateaus. However, due to the time and manpower constraints of our research group, it is difficult to conduct grid-based, comprehensive field surveys on the three plateau regions. Although the setting of the terrestrial transect has its own shortcomings, it is also a better way to achieve the research purpose.

1. English writing needs in class="Chemical">mprovement, and some sentences are vague.

Response：Thanks for the reviewer’s reminder. In this revision, we have revised the entire text one by one.

2. Abstract: lack descriptions about experimental design and methods; the last sentence is redundant and can be deleted.

Response：Thanks very much for the comments of the reviewers. This time, the Abstract has been con class="Chemical">mpletely revised; the description of the experimental design and methods has been renewed (Line:23-28), and the last sentence has been deleted.

3. Introduction: some inforn class="Chemical">mation about the three plateaus is not exact, such as limiting factors and grassland distribution (line 92-93).

Response：Thanks for the reviewer's criticism. After this modification, related texts with inaccurate inforn class="Chemical">mation have been corrected. Among them, clearly poinpan>tinpan>g out that the determinpan>ation of regional limitinpan>g factors is only a reasonable inpan>ference based on Liebig's law inpan> this article, not an established fact (Linpan>e:75-81). Anpan>d whether the three plateaus are the mainpan> limitinpan>g factors for ten class="Chemical">mperature, precipitation, and nutrients are also worthy of verification and discussion in this study.

4. Methods: more explanations about such descriptions in environmental limitations (line 116). It was normally considered that there are two main factors, i.e. n class="Chemical">water and nutrient, especially n class="Chemical">water on the Loess Plateau. Line: 125: This in not correct, some parts on loess plateau belong to arid region. Line:135-137: such classifications clearly show the rainfall or water gradient. Line156: such survey cannot represent the three plateaus as typically. Data analysis descriptor is not enough, and more detailed explanations such as those in Table 1 needed.

Response：Thanks for the reviewer's comment. According to your question, we have revised and rewritten the text of the method section: 1) We fully agree with the reviewer's statement that the vegetation on the Loess Plateau is restricted by moisture and nutrients. There are many studies on the n class="Chemical">water threshold of the Loess Plateau, but forests and shrubs are mostly selected as the research objects; this research mainpan>ly focuses on grassland vegetation, and the effect of n class="Chemical">water restriction may be slightly weakened. Relevant instructions have been added to the Discussion (line: 307-309, 343-345). 2) For the determination of the climate zone of the Loess Plateau, we have changed the "semi-arid and semi-humid area" to the "semi-arid continental monsoon climate zone" to ensure the rigor of the text (line: 145). 3) The layout of the plateau transects in this study is exactly along the east-west water-heat gradient especially the precipitation gradient; this description will be added after this revision (line:153-154). The same is true for the regional characteristics of the transects (Fig 2a). 4) We acknowledge that the experimental design of this study will have inherent errors in sites selection, and we will also add corresponding explanations in the Discussion section (line:383-386). Although the transect survey can only simply characterize the general level of each region, the selection of sample points has been done through many literature references and the team’s early field investigations, which has tried to satisfy the objectivity and scientificity of the experiment. In the future, if we can get more time and financial support, we will be able to set up sites in a grid to reflect regional characteristics more truly. 5) The text of data analysis is a description of the process and method of drawing all charts; corresponding expanded instructions have been carried out.

5. Results: line 207, such great differences among three plateaus in class="Chemical">mplies that such con class="Chemical">mparisons cannot solely focus on environment factors. Line 220: how define the response density here? Line 233-235: such conclusion is incorrect. Line 239: suggest delete the sentence. Line 253: the title is unclear.

Response：Thanks for the reviewer's criticism. 1) The reasons for the differences in vegetation productivity are indeed not only environmental factors, and the in class="Chemical">mpact of n class="Species">human activities cannot be ignored. However, when we set up the transects, we have eliminated areas with obvious human activities through the field investigation in the early stage of the survey; the sites are mostly natural grasslands, and the productivity status reflected by them is mainly affected by factors such as climate and soil. After this revision, a description of "natural grassland" will also be added to the Method section (line: 156-157). 2) The response intensity is the slope of the linear regression. The greater the slope, the faster the rate of change of the region’s productivity with the environment, that is, the greater the response intensity. 3) We have modified [Fig 4] and completed the relevant conclusions and corrections of the discussion (line:258-260). 4) We have deleted the sentence to ensure the conciseness of the conclusion. 5) We have rewritten the title to summarize the selected information clearly and accurately (line: 274).

6. Discussion: generally, the discussion has poor relation with results obtained. The paper put environmental factors as fixed variable, while neglect their variations. Some conclusions lack of direct data results, while may be speculated (such as line 323).

Response：Thank you very much for your reminder. After this revision, we have added references to some of the arguments to in class="Chemical">mprove the scientific rigor of the text. In addition, as a natural phenomenon (law), the change of environmental factors has been visualized inpan> the results section, and its relationship with grassland productivity has also been demonstrated. Here we are more about discussinpan>g the possible reasons for this phenomenon and the law to appear inpan> this way. We deleted some redundant text and rewritten subsections. Anpan>d clarify the limitations of the con class="Chemical">mparative transect survey, that is, the potential method defects. However, we hope more to trigger discussion on ecological limiting factors at the regional scale, and in the regional integration analysis, we should pay attention to the problem that they may have different or even opposite response mechanisms.

7. Figure 1: there lack of statistical analysis for the three environmental factor limitations in columns. Figure 2: The n class="Chemical">ANPP inpan>crease exponentially with longitude on n class="Chemical">TP and LP? linearly on MP? correct or not? Figure 5: more information needed about the analysis?

Response：Thank you very much for your advice. [Figure 1]: We have con class="Chemical">mpleted the relevant signpan>ificance analysis, based on Duncan's multiple calculations. [Figure 2]: To be precise, the curve fittinpan>g is drawn uniformly accordinpan>g to the polynpan>omial (binpan>omial); however, due to typesettinpan>g restrictions, the fittinpan>g equation is not inpan>dicated on the figure. At this time, the linpan>ear fittinpan>g of n class="Chemical">LP and TP reaches the maximum R^2; after this modification, MP will draw the exponential equation to have the maximum R^2. [Figure 5]: Using canonical correspondence analysis (CCA), taking the species biomass matrix of the site as the response variable and the environmental factor matrix as the explanatory variable, the interpretation (R^2) is obtained.

8. Table 1: the results in the table conflict with the hypothesis, or expectations. The data was significant with whom as marked with *?

Response：Thank the reviewers for their comments. The main master model in [Table 1] reflects the environmental regulation mechanism of vegetation productivity under the influence of major regional limiting factors. It uses backward filtering in stepwise regression, and considers the model with the smallest AIC value as the optimal model. The results are shown in Table 1; we believe that there is no conflict with the previous statement. Under the limitation of low ten class="Chemical">mperature, the Tibetan Plateau has a relatively slow response rate to all environmental factors, and finpan>ally the master model of N element was screened out. Similar studies by others (linpan>e:356) have also corroborated this poinpan>t, which will only reminpan>d us to pay more attention to the changes of N element inpan> the Tibetan Plateau.

To reviewer 2:

This study investigated the main regional limiting factors in the changes of grassland aboveground net primary productivity in Tibetan Plateau (n class="Chemical">TP), Mongolian Plateau (n class="Chemical">MP) and Loess Plateau (LP) by using a comparative transect survey dataset. This research topic is important as regional differences and regulation mechanism of vegetation productivity response to changing environmental is one of the core issues in macroecology research. To verify the main regional limiting factors, the authors used multiple analysis methods. However, analysis method usually has its own defects. For the analysis method of gradual regression, the significance depends not only on their intrinsic ecological implications but also on the variations of their selected samples. Why nutrient-N is the highest in TP, while the N relationship with TP productivity showed the best among the three regions. I think, at least partly, it is due to that the selected samples in TP have greater variations of N than those in the other two regions. Similarly, the variations of N in MP are much lower, thus it is difficult to develop a significant relationship between N and productivity. I suggest that the author fully acknowledged the limitations of this experimental design and clarified the potential methodological defects in the discussion. On the contrary, I like the results based on the canonical response analysis (CCA). When the data of the three regions are analyzed together, they found that the explanation degree of climate and soil nutrient factors reduced largely, indicating that the three plateaus may have different or even divergent productivity response mechanisms. I suggest that the author focus on this result for discussion. In addition, English writing could be improved. While the general writing was good, there were many Chinglish and long sentence that greatly affect the readability of this paper. For example, on Line 27, " MP is precipitation specific, LP is temperature and nutrient specific …"; Line 31" MP is mainly temperature and precipitation regulation, LP is temperature and nutrients, and TP is nutrients…". Lines 32-33, “The interpretation degree of environment to ANPP is 72.56% in the region, while only 27.18% after simple integration” Lines 341-345, “In the TP limited by low temperature, the response intensity…, which…,which…environmental factors in the TP.”

Based on the above reasons, I'd like to recommend a major revision before it can be accepted for publication.

Response：Thank you very much for the encouragement and comments of the reviewers. We fully agree with your opinion and made corresponding changes. In the Discussion, the deficiencies of the experimental method design in this article are explained (line:378-391). Pay attention to the different or even opposite productivity regulation mechanisms in different n class="Species">biota, perhaps the mainpan> limitinpan>g factor inpan> the region can become a breakthrough poinpan>t. In addition, we have revised the entire text one by one to avoid lengthy sentences.

Thanks again!

Submitted filename: 1.Response to Reviewers.docx

Click here for additional data file.

23 Sep 2020

Regional response of grassland productivity to changing environment conditions influenced by limiting factors

n class="Chemical">PONE-D-20-18058R1

Dear Dr. He,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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n class="Chemical">8 Oct 2020

n class="Chemical">PONE-D-20-18058R1

Regional response of grassland productivity to changing environment conditions influenced by limiting factors

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