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Impact of climate change on primary production of Inner Mongolian grasslands
Global Ecology and Conservation 22 (2020) e00928
Contents lists available at ScienceDirect
Global Ecology and Conservation journal homepage: http://www.elsevier.com/locate/gecco
Original Research Article
Impact of climate change on primary production of Inner Mongolian grasslands Rina Su a, *, 1, Tao Yu b, 1, Buddhi Dayananda c, Rentuya Bu a, Jinhua Su a, Qingyun Fan a a
Inner Mongolia Environmental Monitoring Center Station, Hohhot, 010011, China College of Forestry, Beijing Forestry University, No. 35 Tsinghua East Road, Haidian District, Beijing, 100083, China c School of Life Sciences, University of Technology Sydney, Broadway, NSW, 2007, Australia b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 14 August 2019 Received in revised form 29 December 2019 Accepted 16 January 2020
Climate change has dramatic impacts on the aboveground net primary productivity (ANPP) of grassland ecosystems. Understanding the impacts of interannual climate change on grassland ecosystems allows strategies to be developed for their sustainable management and protection. However, climate change coupled with overgrazing contribute to the degradation of grassland ecosystems worldwide, affecting the ANPP. In this study, we quantified the effects of temperature, precipitation, and richness of the ANPP across the meadow, typical, and desert steppes on the Inner Mongolian Plateau between 2011 and 2013. Our results indicated that the ANPP of the three grassland types increased significantly as the national ecological program of “Returning Grazing Land to Grassland” implemented by the Chinese government” progressed. There was a significant positive correlation between precipitation and the ANPP of the three types of grasslands. While, temperature had a significant impact on the desert steppes of ANPP. It also implied interaction models between climate factors and richness of three different grassland types. These results have important implications for predicting grassland productivity and developing a grassland restoration plan under climate change in the Inner Mongolian Plateau and beyond. © 2020 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: Climate change Aboveground net (ANPP) Grassland type Mongolian plateau
primary
productivity
1. Introduction Grassland ecosystems are essential components of many terrestrial ecosystems, and cover nearly 25% of the land surface of the earth (Dafeng and Jackson, 2006; Parton et al., 2010). Grassland not only provides the livestock products and plant resources, but also has various ecological service functions such as pollution prevention, soil erosion control, soil erosion reduction, and environmental protection. The grasslands ecosystems in inner Mongolia are located in arid and semi-arid regions, that is an important part of the world-famous grassland of the Eurasian grassland and an imperative national green barrier.
* Corresponding author. E-mail address: [email protected] (R. Su). 1 These authors contributed equally to this work and share the first authorship. https://doi.org/10.1016/j.gecco.2020.e00928 2351-9894/© 2020 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/ 4.0/).
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Grassland aboveground net primary productivity (ANPP) is an important indicator of grassland conditions, fluctuation based on climate change, grazing, and other human interference (Dangal et al., 2016; Fernandez-Gimenez and Allen-Diaz, 1999). Thus, quantifying the grassland ANPP in response to climate change could provide significant information for sustainable grassland management strategies, as well as forage availability and carbon balance (Dangal et al., 2016; Schimel et al., 2001). Nevertheless, the influence of climate change and grazing on ANPP in the Inner Mongolia grassland is still controversial (Ma et al., 2010; Ying et al., 2011). Temperate grasslands in arid and semi-arid regions are an important aspect of the Eurasian steppe and are highly sensitive to environmental change (Cherwin, 2012; M Rebecca et al., 2002; Nippert et al., 2006), and mineral exploration, grazing, industrial pollution, and farmland reclamation has further contributed to grassland degradation in China (Su et al., 2017). This makes it extremely difficult to study the pure climate change effects on grassland productivity. Previous discussions of climate change did not eliminate the effects of grazing interference make conclusion may be disturbed. Research in the Inner Mongolia grassland showed that climate change has no significant effect on grassland productivity and it was mainly affected by precipitation in the first half of the year between JanuaryeJuly. During vegetation growing seasons, precipitation may have a greater influence on the above-ground biomass of grasslands compared to temperature (Yang et al., 2009). For example, in North America the interannual change of precipitation is the key factor of grassland biomass (McCulley et al., 2005). Furthermore, different grassland types have various responses to precipitation. Short steppe interannual ANPP was affected by current-year or previous-year precipitation in North America, while tall grass prairies were more sensitive to the seasonal variation in precipitation across North America (Knapp and Smith, 2001). These studies have produced estimates of variation ANPP in the Inner Mongolia grassland, but there is still insufficient data for understand restored grasslands ANPP fluctuations due to climate change. To date, there has been little agreement on how to restored grasslands respond to climatic factors and what the best strategies for the management of grassland. In 2003, the national ecological program “Returning Grazing Land to Grassland” was implemented by the China National Development and Reform Commission, and intended to restore damaged ecosystems to a healthy state and promote an equilibrium between ecological protection and socioeconomic development (Lu et al., 2014). As the project progressed, provided large area distribution and different varieties make it an ideal habitat to investigate the climate-driven, year-to-year dynamics of grassland ecosystems eliminated the interference of humans and grazing. This helped to understand grassland ANPP fluctuations due to climate change and provided crucial information for the evaluation and management of grazing disturbance in the Inner Mongolia grasslands. There, we investigated the interannual variability of the Inner Mongolia grasslands over three years (2011e2013). The objectives of this study were to (1) explore the interannual variability of ANPP and compare its sensitivity to climatic fluctuation within three kinds of grassland types; meadow steppe, typical steppe, and desert steppe, and (2) quantify the effects of climatic fluctuations and plant richness on the interannual variation of ANPP in the Inner Mongolia grasslands.
2. Materials and methods 2.1. Description of the studied area Inner Mongolia grasslands are an important part of the Eurasian steppe region (37.4 e53.38 N, 97.2 e126.07 E) covering an area of 0.88 million km2, and belong to the continental monsoon climate with uneven distribution of precipitation and short and hot frost-free periods (Fig. 1). From west to east, the mean annual precipitation (MAP) of these grasslands gradually increased from 50 mm to 450 mm, and higher precipitation was recorded from May to September (Ma et al., 2010). The grassland vegetation is represented in three types: meadow steppe, typical steppe, and desert steppe and typical species were showed in Table S1 (Su et al., 2017). 2.2 Experiment designs and data collection Three quadrats (1 m 1 m) in each treatment pasture transect were randomly placed and we collected plants from 250 study sites starting from August 15, 2011 for three consecutive years to clearly interannual climate variability effect on primary production of Inner Mongolian grasslands. We measured aboveground plant community biomass and species richness with no less than 250 m between treatment pastures. On the basis of the spatial distribution of vegetation types and community associations, the “Returning Grazing Land to Grassland” project established a total of 250 fenced sites in the Inner Mongolian grassland, including 63 fenced sites for meadow steppe, 148 fenced sites for typical steppe, and 39 fenced sites for desert steppe (Fig. 1). Since 2003, all the quadrats have been enclosed by wire netting to prevent livestock grazing. We counted the number of species in the quadrats, placed the plant material at ground level in each quadrat in a drying oven (model) at 65 C for 48 h, and weighed the dried material (g) in the laboratory. We recorded the site’s longitude, latitude, elevation, and community types. We collected the climate data from 824 meteorological stations (Su et al., 2017). On thethis basis of these collected data, we used geographic information system-based multiple regression methods (using latitude, longitude, and elevation) as predictors to interpret monthly precipitation and temperature data for the areas we surveyed. We then interpreted monthly precipitation data for our investigated site via a Geographic Information System-based multiple regression method, which used latitude, longitude, and elevation as predicators (Ninyerola et al., 2000). Finally, according to longitude and latitude of each sites extracted precipitation and temperature, and the growing season precipitation (GSP) and growing season temperature (GST) at each site was calculated by summing the interpreted monthly precipitation data from May to September, the mean annual precipitation (MAP) and mean annual temperature (MAT) at each site was calculated by
R. Su et al. / Global Ecology and Conservation 22 (2020) e00928
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Fig. 1. Locations of the study sites in Inner Mongolia.
the interpreted monthly precipitation temperature data from January to December. Since 2003, all the quadrats have been enclosed by wire netting to prevent livestock grazing.
2.2. Statistical analyses We used the Generalized linear model (GLM) and Generalized Additive Models(GAM) to examine the relationship between ANPP with climate factors (temperature and precipitation) and species richness of meadow steppe, typical steppe, and desert steppe, and used climate factors data to calculate annual average and growing season. To test temperature effects, precipitation effects, richness effects and the combined effects (temperature þ precipitation, temperature þ richness, precipitation þ richness, and temperature þ precipitation þ richness) effect on ANPP, We used GLM and GAM model. In addition their interactions (temperature precipitation, temperature richness, precipitation richness, and temperature precipitation richness) also set in the GLM model. GLM was conducted with a Gaussian family. A nested model test was performed to find the best fit model (Table S1). The akaike information criterion (AIC) was used to select GAM models: lower AIC value model was chosen as the best model. All statistical analyses were performed using R software 3.6.0 (R Core Team, 2013).
3. Results The average ANPP fluctuated from 603.09 to 2301.70 kg ha1 between three kinds of grassland. The lowest ANPP (603.09 kg ha1) occurred in the desert steppe in 2011, and the highest ANPP (2301.70 kg ha1) occurred in meadow steppe in 2013. The ANPP of each grassland type in decreasing order were, meadow steppe(mean 2106.08 kg ha1, min 140 kg ha1, max 5400 kg ha1), typical steppe (mean 1636.44 kg ha1, min 60 kg ha1, max 5040 kg ha1), and desert steppe (mean 672.32 kg ha1, min 50 kg ha1, max 3151 kg ha1) (Fig. 2). There was a significantly correlation between mean annual and growing season precipitations with the total ANPP in the three types of grassland indicating that precipitation is the most important factor affecting ANPP (Fig. 3). There was no significant difference between mean annual and growing season precipitations. The explanatory power was highest in meadow steppe, and lowest in typical steppe. Most significantly, it was found that both mean annual and growing season temperature was negatively correlated with total ANPP in the three types of grassland (Fig. 3). The only significant effects in the desert steppe were mean annual and growing season temperature, which explained 29.6% and 37.3% of the ANPP, respectively. Meadow steppe and typical steppe ANPP didn’t show sensitivity to temperature. Richness explanatory power in decreasing order were, meadow steppe, typical steppe, and desert steppe. In meadow steppe, richness had a high degree of interpretation (14.3%) (see Fig. 4; Fig. 5).
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Fig. 2. Box plot of ANPP statistics for different grasslands. The line in the center of the box represent the median values, the edges of the box represent the first and third quartiles, and the whiskers above and below the box show the range of values. The X-axis is in units of kg.ha1.
Fig. 3. Relationship between mean annual climate factors and ANPP in the different grasslands.
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Fig. 4. Relationship between mean grow season climate factors and ANPP in the different grasslands.
Furthermore, mean annual and growing season climate factors were introduced in the GLMs, respectively. All models showed that climate fluctuations had relatively high explanatory power for ANPP (Table 1). The best GLM model for desert steppe ANPP was the precipitation and temperature interactions model (temperature precipitation) (p < 0.01). There were no significant difference between mean annual and growing season precipitation and temperature. For typical steppe, the best GLM model was growth temperature richness growth precipitation (p < 0.01) and mean annual temperature mean annual precipitation (p < 0.01), using annual and growing season climate data. No factors were significant in the growth temperature richness growth precipitation model (p < 0.01). In the mean annual temperature mean annual precipitation model, mean annual precipitation and mean annual temperature þmean annual precipitation was significant (p < 0.01). For meadow steppe, the best GLM model was growth temperature þ richness þ growth precipitation (p < 0.01), using growing season data, and mean annual precipitation þ richness (p < 0.01) using mean annual data (Table 2). In GAM model, different grassland types ANPP best models all showed that precipitation þ richness þ temperature had relatively high explanatory power for ANPP (Table 1). In terms of the significance degree of each factor in the GAM model, the results are similar to GLM. Precipitation factor has significant in three grassland types, no matter mean growing season and mean annual average. Temperature show different interpretations in different grassland types, in meadow steppe mean growing season temperature had no significant effect on ANPP. In desert steppe richness no significant effect on ANPP.
4. Discussion The aim of this study was to assess how climate variability (temperature and precipitation) affects ANPP in three types of grasslands in Inner Mongolia. Based on previous analyses of observational data, Inner Mongolia grasslands have been well restored in the past ten years (Guo et al., 2012). The three types of grassland ANPP had increased, especially in the meadow steppe and typical steppe grasslands. This provides a basis for assessing the changes of grassland productivity in Inner Mongolia under non-grazing and degradation conditions.
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Fig. 5. Relationship between species richness and ANPP in the different grasslands.
The precipitation is the most important factor affecting ANPP and this result is consistent with Ma’s research (Ma et al., 2010). A linear MAPeANPP relationship was found, this was consistent with a previous study in Inner Mongolia (Guo et al., 2012). The slope of the MAPeANPP relationship increased as the climate shifted from arid to humid, supporting previous research in the Inner Mongolia grasslands and other arid regions (Guo et al., 2012; Hsu et al., 2012). This result explained that the ANPP of each vegetation type was, meadow steppe, typical steppe, and desert steppe in decreasing order. However, the findings of the current study do not support the previous research by Guo et al. (2012) as there was no significant differences between the seasonal and mean annual precipitation effects on ANPP among three grassland types. These findings further support the idea that richness is an important factor in humid environments. Through our GLM model results, the humid environment has relatively high plant biodiversity, which is superior in ANPP response to increasing precipitation due to the compensatory effects among species. A possible explanation for this might be that richness improves the stability of grassland productivity and water use efficiency. It has conclusively been shown that in high diversity areas, different plants dominate in humid and drought years. Due to their compensatory effect, the community aboveground biomass was not as variable as a single species or functional group (O’connor et al., 2001). In the meadow steppe, richness and precipitation interactions effected ANPP, but in drought areas, richness didn’t show significant effects. It can thus be suggested that interaction between precipitation and richness improves the productivity of grasslands. Several studies have revealed that an increase in temperature has a negative relationship with the grasslands ANPP, increasing evaporation and intensifying droughts (Dangal et al., 2016; Ni, 2004). Controlled temperature increase experiments suggest that increased evaporation and associated water stress may offset any positive effects of higher temperatures on plant growth (Dulamsuren et al., 2013). These studies may explain the ANPP difference of three types of grasslands.
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Table 1 Summary of the significant estimates of the grow season climate factors and richness GLM and GAM results for predicting ANPP. Only predictors with significant estimates (a ¼ 0.01) are shown. Total
Desert steppe
Typical steppe
Meadow steppe
GLM model mean grow temperature mean grow precipitation mean grow temperature: mean grow precipitation GAM model mean grow temperature richness mean grow precipitation mean grow temperature þ richness þmean grow precipitation GLM model mean grow temperature richness mean grow precipitation mean grow temperature: richness mean grow temperature: mean grow precipitation richness: mean grow precipitation mean grow temperature: richness: mean grow precipitation GAM model mean grow temperature richness mean grow precipitation mean grow temperature þ richness þmean grow precipitation GLM model mean grow temperature richness mean grow precipitation mean grow temperature þ richness þmean grow precipitation GAM model mean grow temperature richness mean grow precipitation mean grow temperature þ richness þ mean grow precipitation
P
R2
0.31614 0.00149** 0.00249**
0.356
7.07e-14*** 0.16 4.04e-07 *** 0.625 0.215 0.977 0.380 0.931 0.531 0.580 0.536
0.141
6.11e-09*** 0.00651** 5.01e-11*** 0.291 0.97673 0.00828** 1.1e-10** 0.395 0.002156** 0.000563*** 1.98e-06*** 0.439
Table 2 Summary of the significant estimates of the mean annual climate factors and richness GLM and GAM results for predicting ANPP. Only predictors with significant estimates (a ¼ 0.01) are shown. Total
Desert steppe
Typical steppe
Meadow steppe
GLM model mean annual mean annual mean annual GAM model mean annual richness mean annual mean annual GLM model mean annual mean annual mean annual GAM model mean annual richness mean annual mean annual GLM model richness mean annual mean annual GAM model mean annual richness mean annual mean annual
P
R2
temperature precipitation temperature: mean annual precipitation
0.019769* 3.32e-06*** 0.000173***
0.356
temperature
3.47e-10*** 0.118 3.46e-08***
precipitation temperature þ richness þ mean annual precipitation
0.648
temperature precipitation temperature:mean annual precipitation
0.09415 6.25e-09*** 0.00921**
temperature
0.000408** 0.019627* 4.52e-10**
precipitation temperature þ richness þ mean annual precipitation
precipitation precipitation þ richness temperature precipitation temperature þ richness þ mean annual precipitation
0.125
0.213 0.00477** 1.11e-10*** 0.396 0.1422 0.0347* 6.97e-08*** 0.39
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Meadow steppe had the optimum precipitation to temperature ratio, while excessive evapotranspiration and scarce rainfall reduced the ANPP in desert steppe. In our study, the mean annual temperature and mean growing season temperature resulted in a significant decline in the grassland ANPP in desert steppe, while the effect on the other two grassland types were not significant. A possible explanation for this might be connected to the special environmental conditions. In arid environments, water is the dominant factor controlling ecosystem processes. Increased temperature may enhance soil evaporation, increase aridity, and consequently reduce ANPP. That may be because increased precipitation weakens the influence of rising temperature on evapotranspiration. This study produced results which corroborate the findings of a great deal of the previous “Returning Grazing Land to Grassland” work in Inner Mongolia grassland. It shows the important role of enclosure in grassland protection. In the future, with an increase in the number of people husbanding animals, Enclosure protection should continue to be implemented and specific climate factors should be observed under different grassland types. 5. Conclusions We found that the average ANPP of the three grassland types increased significantly as the “Returning Grazing Land to Grassland” program progressed. Precipitation was the factor that effected all three types of grasslands and temperature only had a significant impact on desert steppe ANPP. Richness was also an important factor that affected ANPP, in particular, when combined with precipitation. These results indicate the recovery degree of Inner Mongolia grasslands and provide basic data information for subsequent research of Inner Mongolia grassland ANPP. It will enhance our understanding to evaluate the effects of grassland changes on grassland productivity, and clarify the effects of grazing and climate fluctuation. Based on this research, the setting of long-term grassland monitoring and grazing interference comparison experiments will be the next step to reveal the direction of ANPP fluctuation in grassland. Declaration of competing interest The authors declares no conflict of interest. Acknowledgments This work was supported by Study on Evaluation Index System of Inner Mongolia Ecology Based on Ecosystem and Vegetation Type. We thank Dr Kenji Iwasaki for reading of the manuscript and helpful comments. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.gecco.2020.e00928. References Cherwin, K., 2012. Unexpected patterns of sensitivity to drought in three semi-arid grasslands. Oecologia 169, 845e852. Dafeng, H., Jackson, R.B., 2006. Geographical and interannual variability in biomass partitioning in grassland ecosystems: a synthesis of field data. New Phytol. 169, 85e93. Dangal, S.R.S., Tian, H., Lu, C., Pan, S., Pederson, N., Hessl, A., 2016. Synergistic effects of climate change and grazing on net primary production of Mongolian grasslands. Ecosphere 7 e01274, 1e20. Dulamsuren, C., Wommelsdorf, T., Zhao, F., Xue, Y., Zhumadilov, B.Z., Leuschner, C., Hauck, M., 2013. Increased summer temperatures reduce the growth and regeneration of Larix sibirica in southern boreal forests of eastern Kazakhstan. Ecosystems 16, 1536e1549. Fernandez-Gimenez, M.E., Allen-Diaz, B., 1999. Testing a non-equilibrium model of rangeland vegetation dynamics in Mongolia. J. Appl. Ecol. 36, 871e885. Guo, Q., Hu, Z., Li, S., Li, X., Sun, X., Yu, G., 2012. Spatial variations in aboveground net primary productivity along a climate gradient in Eurasian temperate grassland: effects of mean annual precipitation and its seasonal distribution. Global Change Biol. 18, 3624e3631. Hsu, J.S., Powell, J., Adler, P.B., 2012. Sensitivity of mean annual primary production to precipitation. Global Change Biol. 18, 2246e2255. Knapp, A.K., Smith, M.D., 2001. Variation among biomes in temporal dynamics of aboveground primary production. Science 291 (5503), 481e484. Lu, H., Ge, S., Liu, Y., Gao, Z., He, J., Shi, T., Wu, B., 2014. Effects of precipitation on grassland ecosystem restoration under grazing exclusion in Inner Mongolia, China. Landsc. Ecol. 29, 1657e1673. M Rebecca, S., Zavaleta, E.S., Chiariello, N.R., Cleland, E.E., Mooney, H.A., Field, C.B., 2002. Grassland responses to global environmental changes suppressed by elevated CO2. Science 298 (5600), 1987e1990. Ma, W., Liu, Z., Wang, Z., Wang, W., Liang, C., Tang, Y., He, J.-S., Fang, J., 2010. Climate change alters interannual variation of grassland aboveground productivity: evidence from a 22-year measurement series in the Inner Mongolian grassland. J. Plant Res. 123, 509e517. McCulley, R.L., Burke, I.C., Nelson, J.A., Lauenroth, W.K., Knapp, A.K., Kelly, E.F., 2005. Regional patterns in carbon cycling across the great plains of North America. Ecosystems 8, 106e121. Ni, J., 2004. Estimating net primary productivity of grasslands from field biomass measurements in temperate northern China. Plant Ecol. 174, 217e234. Ninyerola, M., Pons, X., Roure, J.M., 2000. A methodological approach of climatological modelling of air temperature and precipitation through GIS techniques. Int. J. Climatol. 20, 1823e1841. Nippert, J.B., Knapp, A.K., Briggs, J.M., 2006. Intra-annual rainfall variability and grassland productivity: can the past predict the future? Plant Ecol. 184, 65e74. O’connor, T.G., Haines, L.M., Snyman, H.A., 2001. Influence of precipitation and species composition on phytomass of a semi-arid African grassland. J. Ecol. 89, 850e860. Parton, W.J., Scurlock, J.M.O., Ojima, D.S., Schimel, D.S., Veen, J.A. van, 2010. Impact of climate change on grassland production and soil carbon worldwide. Global Change Biol. 1, 13e22.
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Schimel, D.S., House, J.I., Hibbard, K.A., Bousquet, P., Ciais, P., Peylin, P., Braswell, B.H., Apps, M.J., Baker, D., Bondeau, A., 2001. Recent patterns and mechanisms of carbon exchange by terrestrial ecosystems. Nature 414, 169e172. Su, R., Cheng, J., Chen, D., Bai, Y., Jin, H., Chao, L., Wang, Z., Li, J., 2017. Effects of grazing on spatiotemporal variations in community structure and ecosystem function on the grasslands of Inner Mongolia. China Sci. Rep. 7 (40), 1e20. Team, R.C., 2013. R: A Language and Environment for Statistical Computing. Yang, Y.H., Fang, J.Y., Pan, Y.D., Ji, C.J., 2009. Aboveground biomass in Tibetan grasslands. J. Arid Environ. 73, 91e95. Ying, Z.G., Chen, Q., Shan, L., Giese, M., Brueck, H., 2011. Resource manipulation effects on net primary production, biomass allocation and rain-use efficiency of two semiarid grassland sites in Inner Mongolia, China. Oecologia 165, 855e864.
Contents lists available at ScienceDirect
Global Ecology and Conservation journal homepage: http://www.elsevier.com/locate/gecco
Original Research Article
Impact of climate change on primary production of Inner Mongolian grasslands Rina Su a, *, 1, Tao Yu b, 1, Buddhi Dayananda c, Rentuya Bu a, Jinhua Su a, Qingyun Fan a a
Inner Mongolia Environmental Monitoring Center Station, Hohhot, 010011, China College of Forestry, Beijing Forestry University, No. 35 Tsinghua East Road, Haidian District, Beijing, 100083, China c School of Life Sciences, University of Technology Sydney, Broadway, NSW, 2007, Australia b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 14 August 2019 Received in revised form 29 December 2019 Accepted 16 January 2020
Climate change has dramatic impacts on the aboveground net primary productivity (ANPP) of grassland ecosystems. Understanding the impacts of interannual climate change on grassland ecosystems allows strategies to be developed for their sustainable management and protection. However, climate change coupled with overgrazing contribute to the degradation of grassland ecosystems worldwide, affecting the ANPP. In this study, we quantified the effects of temperature, precipitation, and richness of the ANPP across the meadow, typical, and desert steppes on the Inner Mongolian Plateau between 2011 and 2013. Our results indicated that the ANPP of the three grassland types increased significantly as the national ecological program of “Returning Grazing Land to Grassland” implemented by the Chinese government” progressed. There was a significant positive correlation between precipitation and the ANPP of the three types of grasslands. While, temperature had a significant impact on the desert steppes of ANPP. It also implied interaction models between climate factors and richness of three different grassland types. These results have important implications for predicting grassland productivity and developing a grassland restoration plan under climate change in the Inner Mongolian Plateau and beyond. © 2020 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: Climate change Aboveground net (ANPP) Grassland type Mongolian plateau
primary
productivity
1. Introduction Grassland ecosystems are essential components of many terrestrial ecosystems, and cover nearly 25% of the land surface of the earth (Dafeng and Jackson, 2006; Parton et al., 2010). Grassland not only provides the livestock products and plant resources, but also has various ecological service functions such as pollution prevention, soil erosion control, soil erosion reduction, and environmental protection. The grasslands ecosystems in inner Mongolia are located in arid and semi-arid regions, that is an important part of the world-famous grassland of the Eurasian grassland and an imperative national green barrier.
* Corresponding author. E-mail address: [email protected] (R. Su). 1 These authors contributed equally to this work and share the first authorship. https://doi.org/10.1016/j.gecco.2020.e00928 2351-9894/© 2020 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/ 4.0/).
2
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Grassland aboveground net primary productivity (ANPP) is an important indicator of grassland conditions, fluctuation based on climate change, grazing, and other human interference (Dangal et al., 2016; Fernandez-Gimenez and Allen-Diaz, 1999). Thus, quantifying the grassland ANPP in response to climate change could provide significant information for sustainable grassland management strategies, as well as forage availability and carbon balance (Dangal et al., 2016; Schimel et al., 2001). Nevertheless, the influence of climate change and grazing on ANPP in the Inner Mongolia grassland is still controversial (Ma et al., 2010; Ying et al., 2011). Temperate grasslands in arid and semi-arid regions are an important aspect of the Eurasian steppe and are highly sensitive to environmental change (Cherwin, 2012; M Rebecca et al., 2002; Nippert et al., 2006), and mineral exploration, grazing, industrial pollution, and farmland reclamation has further contributed to grassland degradation in China (Su et al., 2017). This makes it extremely difficult to study the pure climate change effects on grassland productivity. Previous discussions of climate change did not eliminate the effects of grazing interference make conclusion may be disturbed. Research in the Inner Mongolia grassland showed that climate change has no significant effect on grassland productivity and it was mainly affected by precipitation in the first half of the year between JanuaryeJuly. During vegetation growing seasons, precipitation may have a greater influence on the above-ground biomass of grasslands compared to temperature (Yang et al., 2009). For example, in North America the interannual change of precipitation is the key factor of grassland biomass (McCulley et al., 2005). Furthermore, different grassland types have various responses to precipitation. Short steppe interannual ANPP was affected by current-year or previous-year precipitation in North America, while tall grass prairies were more sensitive to the seasonal variation in precipitation across North America (Knapp and Smith, 2001). These studies have produced estimates of variation ANPP in the Inner Mongolia grassland, but there is still insufficient data for understand restored grasslands ANPP fluctuations due to climate change. To date, there has been little agreement on how to restored grasslands respond to climatic factors and what the best strategies for the management of grassland. In 2003, the national ecological program “Returning Grazing Land to Grassland” was implemented by the China National Development and Reform Commission, and intended to restore damaged ecosystems to a healthy state and promote an equilibrium between ecological protection and socioeconomic development (Lu et al., 2014). As the project progressed, provided large area distribution and different varieties make it an ideal habitat to investigate the climate-driven, year-to-year dynamics of grassland ecosystems eliminated the interference of humans and grazing. This helped to understand grassland ANPP fluctuations due to climate change and provided crucial information for the evaluation and management of grazing disturbance in the Inner Mongolia grasslands. There, we investigated the interannual variability of the Inner Mongolia grasslands over three years (2011e2013). The objectives of this study were to (1) explore the interannual variability of ANPP and compare its sensitivity to climatic fluctuation within three kinds of grassland types; meadow steppe, typical steppe, and desert steppe, and (2) quantify the effects of climatic fluctuations and plant richness on the interannual variation of ANPP in the Inner Mongolia grasslands.
2. Materials and methods 2.1. Description of the studied area Inner Mongolia grasslands are an important part of the Eurasian steppe region (37.4 e53.38 N, 97.2 e126.07 E) covering an area of 0.88 million km2, and belong to the continental monsoon climate with uneven distribution of precipitation and short and hot frost-free periods (Fig. 1). From west to east, the mean annual precipitation (MAP) of these grasslands gradually increased from 50 mm to 450 mm, and higher precipitation was recorded from May to September (Ma et al., 2010). The grassland vegetation is represented in three types: meadow steppe, typical steppe, and desert steppe and typical species were showed in Table S1 (Su et al., 2017). 2.2 Experiment designs and data collection Three quadrats (1 m 1 m) in each treatment pasture transect were randomly placed and we collected plants from 250 study sites starting from August 15, 2011 for three consecutive years to clearly interannual climate variability effect on primary production of Inner Mongolian grasslands. We measured aboveground plant community biomass and species richness with no less than 250 m between treatment pastures. On the basis of the spatial distribution of vegetation types and community associations, the “Returning Grazing Land to Grassland” project established a total of 250 fenced sites in the Inner Mongolian grassland, including 63 fenced sites for meadow steppe, 148 fenced sites for typical steppe, and 39 fenced sites for desert steppe (Fig. 1). Since 2003, all the quadrats have been enclosed by wire netting to prevent livestock grazing. We counted the number of species in the quadrats, placed the plant material at ground level in each quadrat in a drying oven (model) at 65 C for 48 h, and weighed the dried material (g) in the laboratory. We recorded the site’s longitude, latitude, elevation, and community types. We collected the climate data from 824 meteorological stations (Su et al., 2017). On thethis basis of these collected data, we used geographic information system-based multiple regression methods (using latitude, longitude, and elevation) as predictors to interpret monthly precipitation and temperature data for the areas we surveyed. We then interpreted monthly precipitation data for our investigated site via a Geographic Information System-based multiple regression method, which used latitude, longitude, and elevation as predicators (Ninyerola et al., 2000). Finally, according to longitude and latitude of each sites extracted precipitation and temperature, and the growing season precipitation (GSP) and growing season temperature (GST) at each site was calculated by summing the interpreted monthly precipitation data from May to September, the mean annual precipitation (MAP) and mean annual temperature (MAT) at each site was calculated by
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Fig. 1. Locations of the study sites in Inner Mongolia.
the interpreted monthly precipitation temperature data from January to December. Since 2003, all the quadrats have been enclosed by wire netting to prevent livestock grazing.
2.2. Statistical analyses We used the Generalized linear model (GLM) and Generalized Additive Models(GAM) to examine the relationship between ANPP with climate factors (temperature and precipitation) and species richness of meadow steppe, typical steppe, and desert steppe, and used climate factors data to calculate annual average and growing season. To test temperature effects, precipitation effects, richness effects and the combined effects (temperature þ precipitation, temperature þ richness, precipitation þ richness, and temperature þ precipitation þ richness) effect on ANPP, We used GLM and GAM model. In addition their interactions (temperature precipitation, temperature richness, precipitation richness, and temperature precipitation richness) also set in the GLM model. GLM was conducted with a Gaussian family. A nested model test was performed to find the best fit model (Table S1). The akaike information criterion (AIC) was used to select GAM models: lower AIC value model was chosen as the best model. All statistical analyses were performed using R software 3.6.0 (R Core Team, 2013).
3. Results The average ANPP fluctuated from 603.09 to 2301.70 kg ha1 between three kinds of grassland. The lowest ANPP (603.09 kg ha1) occurred in the desert steppe in 2011, and the highest ANPP (2301.70 kg ha1) occurred in meadow steppe in 2013. The ANPP of each grassland type in decreasing order were, meadow steppe(mean 2106.08 kg ha1, min 140 kg ha1, max 5400 kg ha1), typical steppe (mean 1636.44 kg ha1, min 60 kg ha1, max 5040 kg ha1), and desert steppe (mean 672.32 kg ha1, min 50 kg ha1, max 3151 kg ha1) (Fig. 2). There was a significantly correlation between mean annual and growing season precipitations with the total ANPP in the three types of grassland indicating that precipitation is the most important factor affecting ANPP (Fig. 3). There was no significant difference between mean annual and growing season precipitations. The explanatory power was highest in meadow steppe, and lowest in typical steppe. Most significantly, it was found that both mean annual and growing season temperature was negatively correlated with total ANPP in the three types of grassland (Fig. 3). The only significant effects in the desert steppe were mean annual and growing season temperature, which explained 29.6% and 37.3% of the ANPP, respectively. Meadow steppe and typical steppe ANPP didn’t show sensitivity to temperature. Richness explanatory power in decreasing order were, meadow steppe, typical steppe, and desert steppe. In meadow steppe, richness had a high degree of interpretation (14.3%) (see Fig. 4; Fig. 5).
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Fig. 2. Box plot of ANPP statistics for different grasslands. The line in the center of the box represent the median values, the edges of the box represent the first and third quartiles, and the whiskers above and below the box show the range of values. The X-axis is in units of kg.ha1.
Fig. 3. Relationship between mean annual climate factors and ANPP in the different grasslands.
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Fig. 4. Relationship between mean grow season climate factors and ANPP in the different grasslands.
Furthermore, mean annual and growing season climate factors were introduced in the GLMs, respectively. All models showed that climate fluctuations had relatively high explanatory power for ANPP (Table 1). The best GLM model for desert steppe ANPP was the precipitation and temperature interactions model (temperature precipitation) (p < 0.01). There were no significant difference between mean annual and growing season precipitation and temperature. For typical steppe, the best GLM model was growth temperature richness growth precipitation (p < 0.01) and mean annual temperature mean annual precipitation (p < 0.01), using annual and growing season climate data. No factors were significant in the growth temperature richness growth precipitation model (p < 0.01). In the mean annual temperature mean annual precipitation model, mean annual precipitation and mean annual temperature þmean annual precipitation was significant (p < 0.01). For meadow steppe, the best GLM model was growth temperature þ richness þ growth precipitation (p < 0.01), using growing season data, and mean annual precipitation þ richness (p < 0.01) using mean annual data (Table 2). In GAM model, different grassland types ANPP best models all showed that precipitation þ richness þ temperature had relatively high explanatory power for ANPP (Table 1). In terms of the significance degree of each factor in the GAM model, the results are similar to GLM. Precipitation factor has significant in three grassland types, no matter mean growing season and mean annual average. Temperature show different interpretations in different grassland types, in meadow steppe mean growing season temperature had no significant effect on ANPP. In desert steppe richness no significant effect on ANPP.
4. Discussion The aim of this study was to assess how climate variability (temperature and precipitation) affects ANPP in three types of grasslands in Inner Mongolia. Based on previous analyses of observational data, Inner Mongolia grasslands have been well restored in the past ten years (Guo et al., 2012). The three types of grassland ANPP had increased, especially in the meadow steppe and typical steppe grasslands. This provides a basis for assessing the changes of grassland productivity in Inner Mongolia under non-grazing and degradation conditions.
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Fig. 5. Relationship between species richness and ANPP in the different grasslands.
The precipitation is the most important factor affecting ANPP and this result is consistent with Ma’s research (Ma et al., 2010). A linear MAPeANPP relationship was found, this was consistent with a previous study in Inner Mongolia (Guo et al., 2012). The slope of the MAPeANPP relationship increased as the climate shifted from arid to humid, supporting previous research in the Inner Mongolia grasslands and other arid regions (Guo et al., 2012; Hsu et al., 2012). This result explained that the ANPP of each vegetation type was, meadow steppe, typical steppe, and desert steppe in decreasing order. However, the findings of the current study do not support the previous research by Guo et al. (2012) as there was no significant differences between the seasonal and mean annual precipitation effects on ANPP among three grassland types. These findings further support the idea that richness is an important factor in humid environments. Through our GLM model results, the humid environment has relatively high plant biodiversity, which is superior in ANPP response to increasing precipitation due to the compensatory effects among species. A possible explanation for this might be that richness improves the stability of grassland productivity and water use efficiency. It has conclusively been shown that in high diversity areas, different plants dominate in humid and drought years. Due to their compensatory effect, the community aboveground biomass was not as variable as a single species or functional group (O’connor et al., 2001). In the meadow steppe, richness and precipitation interactions effected ANPP, but in drought areas, richness didn’t show significant effects. It can thus be suggested that interaction between precipitation and richness improves the productivity of grasslands. Several studies have revealed that an increase in temperature has a negative relationship with the grasslands ANPP, increasing evaporation and intensifying droughts (Dangal et al., 2016; Ni, 2004). Controlled temperature increase experiments suggest that increased evaporation and associated water stress may offset any positive effects of higher temperatures on plant growth (Dulamsuren et al., 2013). These studies may explain the ANPP difference of three types of grasslands.
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Table 1 Summary of the significant estimates of the grow season climate factors and richness GLM and GAM results for predicting ANPP. Only predictors with significant estimates (a ¼ 0.01) are shown. Total
Desert steppe
Typical steppe
Meadow steppe
GLM model mean grow temperature mean grow precipitation mean grow temperature: mean grow precipitation GAM model mean grow temperature richness mean grow precipitation mean grow temperature þ richness þmean grow precipitation GLM model mean grow temperature richness mean grow precipitation mean grow temperature: richness mean grow temperature: mean grow precipitation richness: mean grow precipitation mean grow temperature: richness: mean grow precipitation GAM model mean grow temperature richness mean grow precipitation mean grow temperature þ richness þmean grow precipitation GLM model mean grow temperature richness mean grow precipitation mean grow temperature þ richness þmean grow precipitation GAM model mean grow temperature richness mean grow precipitation mean grow temperature þ richness þ mean grow precipitation
P
R2
0.31614 0.00149** 0.00249**
0.356
7.07e-14*** 0.16 4.04e-07 *** 0.625 0.215 0.977 0.380 0.931 0.531 0.580 0.536
0.141
6.11e-09*** 0.00651** 5.01e-11*** 0.291 0.97673 0.00828** 1.1e-10** 0.395 0.002156** 0.000563*** 1.98e-06*** 0.439
Table 2 Summary of the significant estimates of the mean annual climate factors and richness GLM and GAM results for predicting ANPP. Only predictors with significant estimates (a ¼ 0.01) are shown. Total
Desert steppe
Typical steppe
Meadow steppe
GLM model mean annual mean annual mean annual GAM model mean annual richness mean annual mean annual GLM model mean annual mean annual mean annual GAM model mean annual richness mean annual mean annual GLM model richness mean annual mean annual GAM model mean annual richness mean annual mean annual
P
R2
temperature precipitation temperature: mean annual precipitation
0.019769* 3.32e-06*** 0.000173***
0.356
temperature
3.47e-10*** 0.118 3.46e-08***
precipitation temperature þ richness þ mean annual precipitation
0.648
temperature precipitation temperature:mean annual precipitation
0.09415 6.25e-09*** 0.00921**
temperature
0.000408** 0.019627* 4.52e-10**
precipitation temperature þ richness þ mean annual precipitation
precipitation precipitation þ richness temperature precipitation temperature þ richness þ mean annual precipitation
0.125
0.213 0.00477** 1.11e-10*** 0.396 0.1422 0.0347* 6.97e-08*** 0.39
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Meadow steppe had the optimum precipitation to temperature ratio, while excessive evapotranspiration and scarce rainfall reduced the ANPP in desert steppe. In our study, the mean annual temperature and mean growing season temperature resulted in a significant decline in the grassland ANPP in desert steppe, while the effect on the other two grassland types were not significant. A possible explanation for this might be connected to the special environmental conditions. In arid environments, water is the dominant factor controlling ecosystem processes. Increased temperature may enhance soil evaporation, increase aridity, and consequently reduce ANPP. That may be because increased precipitation weakens the influence of rising temperature on evapotranspiration. This study produced results which corroborate the findings of a great deal of the previous “Returning Grazing Land to Grassland” work in Inner Mongolia grassland. It shows the important role of enclosure in grassland protection. In the future, with an increase in the number of people husbanding animals, Enclosure protection should continue to be implemented and specific climate factors should be observed under different grassland types. 5. Conclusions We found that the average ANPP of the three grassland types increased significantly as the “Returning Grazing Land to Grassland” program progressed. Precipitation was the factor that effected all three types of grasslands and temperature only had a significant impact on desert steppe ANPP. Richness was also an important factor that affected ANPP, in particular, when combined with precipitation. These results indicate the recovery degree of Inner Mongolia grasslands and provide basic data information for subsequent research of Inner Mongolia grassland ANPP. It will enhance our understanding to evaluate the effects of grassland changes on grassland productivity, and clarify the effects of grazing and climate fluctuation. Based on this research, the setting of long-term grassland monitoring and grazing interference comparison experiments will be the next step to reveal the direction of ANPP fluctuation in grassland. Declaration of competing interest The authors declares no conflict of interest. Acknowledgments This work was supported by Study on Evaluation Index System of Inner Mongolia Ecology Based on Ecosystem and Vegetation Type. We thank Dr Kenji Iwasaki for reading of the manuscript and helpful comments. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.gecco.2020.e00928. References Cherwin, K., 2012. Unexpected patterns of sensitivity to drought in three semi-arid grasslands. Oecologia 169, 845e852. Dafeng, H., Jackson, R.B., 2006. Geographical and interannual variability in biomass partitioning in grassland ecosystems: a synthesis of field data. New Phytol. 169, 85e93. 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