Grassland-type ecosystem stability in China differs under the influence of drought and wet events
CAO Wenyu1, BAI Jianjun1,*(), YU Leshan2
1School of Geography and Tourism, Shaanxi Normal University, Xi'an 710119, China 2International Business School, Shaanxi Normal University, Xi'an 710119, China
Ecological stability is a core issue in ecological research and holds significant implications for humanity. The increased frequency and intensity of drought and wet climate events resulting from climate change pose a major threat to global ecological stability. Variations in stability among different ecosystems have been confirmed, but it remains unclear whether there are differences in stability within the same terrestrial vegetation ecosystem under the influence of climate events in different directions and intensities. China's grassland ecosystem includes most grassland types and is a good choice for studying this issue. This study used the Standardized Precipitation Evapotranspiration Index-12 (SPEI-12) to identify the directions and intensities of different types of climate events, and based on Normalized Difference Vegetation Index (NDVI), calculated the resistance and resilience of different grassland types for 30 consecutive years from 1990 to 2019 (resistance and resilience are important indicators to measure stability). Based on a traditional regression model, standardized methods were integrated to analyze the impacts of the intensity and duration of drought and wet events on vegetation stability. The results showed that meadow steppe exhibited the highest stability, while alpine steppe and desert steppe had the lowest overall stability. The stability of typical steppe, alpine meadow, temperate meadow was at an intermediate level. Regarding the impact of the duration and intensity of climate events on vegetation ecosystem stability for the same grassland type, the resilience of desert steppe during drought was mainly affected by the duration. In contrast, the impact of intensity was not significant. However, alpine steppe was mainly affected by intensity in wet environments, and duration had no significant impact. Our conclusions can provide decision support for the future grassland ecosystem governance.
CAO Wenyu, BAI Jianjun, YU Leshan. Grassland-type ecosystem stability in China differs under the influence of drought and wet events. Journal of Arid Land, 2024, 16(5): 615-631.
Fig. 1Distribution of six grassland types in China. Note that this map is based on the standard map (No. GS (2020)4619) of the Map Service System (http://bzdt.ch.mnr.gov.cn/) marked by the Ministry of Natural Resources of the People's Republic of China, and the base map has not been modified.
Climate event type
SPEI-12
Climate event type
SPEI-12
Severe drought
< -1.28
Mild to moderate wet
0.67-1.28
Mild to moderate drought
-1.28- -0.67
Severe wet
≥1.28
Normal
-0.67-0.67
Table 1 Category of climate event types of grassland ecosystem based on Standardized Precipitation Evapotranspiration Index-12 (SPEI-12) value
Fig. 2Percentage of area influenced by different climate event types in six grassland types in China from 1990 to 2019. (a), meadow steppe; (b), typical steppe; (c), desert steppe; (d), alpine steppe; (e), temperate meadow; (f), alpine meadow.
Fig. 3Annual duration of drought events in six grassland types in China from 1990 to 2019. (a), meadow steppe; (b), typical steppe; (c), desert steppe; (d), alpine steppe; (e), temperate meadow; (f), alpine meadow. The dotted line indicates the trend of annual duration as the number of years increase. The missing data points in the graph indicate that no climate events were detected in that particular year.
Fig. 4Annual duration of wet events in six grassland types in China from 1990 to 2019. (a), meadow steppe; (b), typical steppe; (c), desert steppe; (d), alpine steppe; (e), temperate meadow; (f), alpine meadow. The dotted line indicates the trend of annual duration as the number of years increase. The missing data points in the graph indicate that no climate events were detected in that particular year.
Fig. 5Distribution of resistance value (a) and resilience value (b) of grassland in China from 1990 to 2019. Note that these maps are based on the standard map (No. GS(2020)4619) of the Map Service System (http://bzdt.ch.mnr.gov.cn/) marked by the Ministry of Natural Resources of the People's Republic of China, and the base map has not been modified.
Fig. 6Resistance value (a) and resilience value (b) of six different grassland types in China from 1990 to 2019. The lines across the boxes indicate the median values, and the points represent the mean values. The lower and upper boxes show the interquartile range (the 25th and 75th percentiles, respectively). The whiskers (the lines on the ends of the boxes) in a box plot correspond to the range within 1.5 times the interquartile range.
Fig. 7Rank of resistance (a) and resilience (b) levels of six grassland types under different climate event types in China. SD, severe drought; MD, mild to moderate drought; MW, mild to moderate wet; SW, severe wet.
Fig. 8Effect of the duration and intensity of drought and wet climate events on desert steppe (a) and alpine steppe (b)
Grassland type
Capability
Aspect
Estimate
SE
t-value
P (>|t|) value
Desert steppe
Resistance to drought event
Intensity
0.061
0.005
12.578
0.000***
Duration
-0.020
0.005
-4.204
0.000***
Resistance to wet event
Intensity
-0.114
0.005
-21.690
0.000***
Duration
0.164
0.005
31.240
0.000***
Resilience to drought event
Intensity
-0.001
0.014
-0.104
0.917
Duration
0.093
0.014
6.559
0.000***
Resilience to wet event
Intensity
0.167
0.012
13.372
0.000***
Duration
-0.085
0.012
-6.788
0.000***
Alpine steppe
Resistance to drought event
Intensity
0.101
0.003
29.050
0.000***
Duration
-0.107
0.003
-30.610
0.000***
Resistance to wet event
Intensity
-0.059
0.003
17.211
0.000***
Duration
-0.001
0.003
-0.385
0.700
Resilience to drought event
Intensity
0.147
0.018
8.146
0.000***
Duration
-0.183
0.018
-10.164
0.000***
Resilience to wet event
Intensity
0.182
0.013
13.370
0.000***
Duration
-0.157
0.013
-11.530
0.000***
Table S1 Results of the response model of the resistance and resilience of desert steppe and alpine steppe to the intensity and duration of climate event under the influence of drought and wet climate event
Fig. S1 Box plot of mean Normalized Different Vegetation Index (NDVI) for different grassland types in China from 1990 to 2019. The lines across the boxes indicate the median values, and the points represent the mean values. The lower and upper boxes show the interquartile range (the 25th and 75th percentiles, respectively). The whiskers (the lines on the ends of the boxes) in a box plot correspond to the range within 1.5 times the interquartile range.
[1]
Beguería S, Vicente-Serrano S M, Reig F, et al. 2014. Standardized precipitation evapotranspiration index (SPEI) revisited: parameter fitting, evapotranspiration models, tools, datasets and drought monitoring. International Journal of Climatology, 34(10): 3001-3023.
[2]
Chen J J, Chi Y G, Zhou W, et al. 2021. Quantifying the dimensionalities and drivers of ecosystem stability at global scale. Journal of Geophysical Research-Biogeosciences, 126(4): e2020JG006041, doi: 10.1029/2020JG006041.
[3]
Creedy T J, Asare R A, Morel A C, et al. 2022. Climate change alters impacts of extreme climate events on a tropical perennial tree crop. Scientific Reports, 12(1): 19653, doi: 10.1038/s41598-022-22967-7.
pmid: 36385148
[4]
de Keersmaecker W, Lhermitte S, Tits L, et al. 2015. A model quantifying global vegetation resistance and resilience to short-term climate anomalies and their relationship with vegetation cover. Global Ecology and Biogeography, 24(5): 539-548.
[5]
Easterling D R, Meehl G A, Parmesan C, et al. 2000. Climate extremes: Observations, modeling, and impacts. Science, 289(5487): 2068-2074.
doi: 10.1126/science.289.5487.2068
pmid: 11000103
[6]
Editorial Board of Vegetation Map of China. 2007. Vegetation Atlas of China (1: 1000000). Beijing: Geological Publishing House.
[7]
Fan X, Hao X M, Hao H C, et al. 2021. Comprehensive assessment indicator of ecosystem resilience in Central Asia. Water, 13(2): 124, doi: 10.3390/w13020124.
[8]
Fu G, Shen Z X, Zhang X Z. 2018. Increased precipitation has stronger effects on plant production of an alpine meadow than does experimental warming in the Northern Tibetan Plateau. Agricultural and Forest Meteorology, 249: 11-21.
[9]
García-Palacios P, Gross N, Gaitan J, et al. 2018. Climate mediates the biodiversity-ecosystem stability relationship globally. Proceedings of the National Academy of Sciences of the United States of America, 115(33): 8400-8405.
[10]
Grilli J, Barabas G, Michalska-Smith M J, et al. 2017. Higher-order interactions stabilize dynamics in competitive network models. Nature, 548(7666): 210-213.
[11]
Gross N, Bagousse-Pinguet Y L, Liancourt P, et al. 2017. Functional trait diversity maximizes ecosystem multifunctionality. Nature Ecology & Evolution, 1(5): 0132, doi: 10.1038/s41559-017-0132.
[12]
Han F S, Yu C Q, Fu G. 2023. Non-growing/growing season non-uniform-warming increases precipitation use efficiency but reduces its temporal stability in an alpine meadow. Frontiers in Plant Science, 14: 1090204, doi: 10.3389/fpls.2023.1090204.
[13]
Hansen B B, Gamelon M, Albon S D, et al. 2019. More frequent extreme climate events stabilize reindeer population dynamics. Nature Communications, 10: 1616, doi: 10.1038/s41467-019-09332-5.
pmid: 30962419
[14]
Hossain M L, Li J F, Hoffmann S, et al. 2022. Biodiversity showed positive effects on resistance but mixed effects on resilience to climatic extremes in a long-term grassland experiment. Science of the Total Environment, 827: 154322, doi: 10.1016/j.scitotenv.2022.154322.
[15]
Hu Y Z, Ding R S, Kang S Z, et al. 2022. The trade-offs between resistance and resilience of forage stay robust with varied growth potentials under different soil water and salt stress. Science of the Total Environment, 846: 157421, doi: 10.1016/j.scitotenv.2022.157421.
[16]
Huang K, Xia J Y. 2019. High ecosystem stability of evergreen broadleaf forests under severe droughts. Global Change Biology, 25(10): 3494-3503.
doi: 10.1111/gcb.14748
pmid: 31276270
[17]
Huang W J, Wang W, Cao M, et al. 2021. Local climate and biodiversity affect the stability of China's grasslands in response to drought. Science of the Total Environment, 768: 145482, doi: 10.1016/j.scitotenv.2021.145482.
[18]
Isbell F, Craven D, Connolly J, et al. 2015. Biodiversity increases the resistance of ecosystem productivity to climate extremes. Nature, 526(7574): 574-577.
[19]
Ives A R, Carpenter S R. 2007. Stability and diversity of ecosystems. Science, 317(5834): 58-62.
doi: 10.1126/science.1133258
pmid: 17615333
[20]
Jiao W Z, Wang L X, Wang H L, et al. 2022. Comprehensive quantification of the responses of ecosystem production and respiration to drought time scale, intensity and timing in humid environments: A FLUXNET synthesis. Journal of Geophysical Research-Biogeosciences, 127(5): e2021JG006431, doi: 10.1029/2021JG006431.
[21]
King A D, Donat M G, Fischer E M, et al. 2015. The timing of anthropogenic emergence in simulated climate extremes. Environmental Research Letters, 10(9): 094015, doi: 10.1088/1748-9326/10/9/094015.
[22]
Lavery M R, Acharya P, Sivo S A, et al. 2019. Number of predictors and multicollinearity: What are their effects on error and bias in regression? Communications in Statistics-Simulation and Computation, 48(1): 27-38.
[23]
Li M, Wu J S, He Y T, et al. 2020a. Dimensionality of grassland stability shifts along with altitudes on the Tibetan Plateau. Agricultural and Forest Meteorology, 291: 108080, doi: 10.1016/j.agrformet.2020.108080.
[24]
Li X Y, Piao S L, Wang K, et al. 2020b. Temporal trade-off between gymnosperm resistance and resilience increases forest sensitivity to extreme drought. Nature Ecology & Evolution, 4(8): 1075-1083.
[25]
Liu Y, Ren H, Hu T, et al. 2022. Spatiotemporal dynamics of NDVI of grassland and its response to multi-scale drought in China. Research of Soil and Water Conservation, 29(1): 153-161. (in Chinese)
[26]
Liu Y J, You C H, Zhang Y G, et al. 2021. Resistance and resilience of grasslands to drought detected by SIF in Inner Mongolia, China. Agricultural and Forest Meteorology, 308-309: 108567, doi: 10.1016/j.agrformet.2021.108567.
[27]
Schwalm C R, Anderegg W R L, Michalak A M, et al. 2017. Global patterns of drought recovery. Nature, 548(7666): 202-205.
[28]
Scornet E. 2016. Random forests and kernel methods. IEEE Transactions on Information Theory, 62(3): 1485-1500.
[29]
Si Y F, Li H, Li Z H, et al. 2023. Response of functional traits of key species in meadow steppe to long-term grazing and grazing exclusion. Agricultural Sciences in China, 56(18): 3693-3708. (in Chinese)
[30]
Sirami C, Gross N, Baillod A B, et al. 2019. Increasing crop heterogeneity enhances multitrophic diversity across agricultural regions. Proceedings of the National Academy of Sciences of the United States of America, 116(33): 16442-16447.
[31]
Stott P. 2016. How climate change affects extreme weather events: Research can increasingly determine the contribution of climate change to extreme events such as droughts. Science, 352(6293): 1517-1518.
[32]
van Ruijven J, Berendse F. 2010. Diversity enhances community recovery, but not resistance, after drought. Journal of Ecology, 98(1): 81-86.
[33]
Vicente-Serrano S M, Beguería S, Lopez-Moreno J I. 2010. A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index. Journal of Climate, 23(7): 1696-1718.
[34]
Wang Z Z, Fu B J, Wu X T, et al. 2023. Vegetation resilience does not increase consistently with greening in China's Loess Plateau. Communications Earth & Environment, 4(1): 336, doi: 10.1038/s43247-023-01000-3.
[35]
Xiao J Y, Yu C Q, Fu G. 2023. Response of aboveground net primary production, species and phylogenetic diversity to warming and increased precipitation in an alpine meadow. Plants, 12(17): 3017, doi: 10.3390/plants12173017.
[36]
Xu Z X, di Vittorio A. 2021. Hydrological analysis in watersheds with a variable-resolution global climate model (VR-CESM). Journal of Hydrology, 601: 126646, doi: 10.1016/j.jhydrol.2021.126646.
[37]
Yang J W, Chen H, Hou Y K, et al. 2019. A method to identify the drought-flood transition based on the meteorological drought index. Acta Geographica Sinica, 74(11): 2358-2370. (in Chinese)
doi: 10.11821/dlxb201911012
[38]
Yoo S H, Park C H. 2013. MCP, kernel density estimation and LoCoH analysis for the core area zoning of the red-crowned Crane's feeding habitat in Cheorwon, Korea. Korean Journal of Environment and Ecology, 27(1): 11-21. (in Korean)
[39]
Yuan Q, Xu Z, Shi W, et al. 2004. Establishment of the sharing information system of grassland resources in China. Grassland of China, 26(4): 16-20. (in Chinese)
[40]
Zhang D, Guo Y F, Qi W, et al. 2023. Study on the composition and diversity of plant communities in different degradation succession sequences of the Ordos desert grassland. Inner Mongolia Water Resources, 10: 3-5. (in Chinese)
[41]
Zhang F Y, Quan Q, Ma F F, et al. 2019. Differential responses of ecosystem carbon flux components to experimental precipitation gradient in an alpine meadow. Functional Ecology, 33(5): 889-900.
[42]
Zhou X, Wang Y, Li J. 2023. Response of plant community composition to precipitation changes in typical grasslands in the Loess Plateau. Biodiversity Science, 31(3): 42-51.