Patterns and drivers of vegetation resilience across China and the negative impact of warm droughts
Xueming Zhao , Zhaoju Zheng , Bernhard Schmid , Yujin Zhao , Dan Zhao , Bingfang Wu , Yuan Zeng
Geography and Sustainability ›› 2026, Vol. 7 ›› Issue (3) : 100448
Ecological resilience -defined as the capacity of ecosystems to withstand perturbations -is an important dimension of ecosystem stability. However, the spatial patterns and underlying drivers of resilience in forests relative to grasslands remain poorly understood. Using a temporal autocorrelation index (AC1) derived from satellite-based vegetation indices, we assessed high-resolution (250 m) vegetation resilience from 2000 to 2020 across China’s natural terrestrial vegetation. We found that vegetation in forest-dominated regions was more resilient than in grassland-dominated regions, with divergent associations to climate, vegetation, soil, topography, and human drivers. Specifically, forest resilience was primarily associated with climate variables, with path analysis indicating a potential indirect link through vegetation characteristics. By contrast, grassland resilience was strongly related to soil and topographic properties. Particularly, among the climate variables examined, long-term warm drought condition, represented by the negative of Standardized Precipitation Evapotranspiration Index (SPEI), generally showed spatial negative associations with resilience, especially in temperate steppe and subtropical forest regions. Notably, the resilience of cold forests and high-altitude grasslands showed weaker or even positive relationship with warm droughts. These findings highlight the threats posed by ongoing climate change to vegetation resilience, and emphasize the need for ecosystem-specific strategies to enhance resilience in the future.
Ecological resilience / Temporal autocorrelation / Warm droughts / Remote sensing / Vegetation index
| [1] |
Allen, C.D., Macalady, A.K., Chenchouni, H., Bachelet, D., McDowell, N., Vennetier, M., Kitzberger, T., Rigling, A., Breshears, D.D., Hogg, E.H., Gonzalez, P., Fensham, R., Zhang, Z., Castro, J., Demidova, N., Lim, J.H., Allard, G., Running, S.W., Semerci, A., Cobb, N., 2010. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For. Ecol. Manage. 259 (4), 660-684. doi: 10.1016/j.foreco.2009.09.001. |
| [2] |
Bao, G., Bao, Y.H., Qin, Z.H., Xin, X.P., Bao, Y.L., Bayarsaikan, S., Zhou, Y., Chuntai, B., 2016. Modeling net primary productivity of terrestrial ecosystems in the semi-arid climate of the Mongolian Plateau using LSWI-based CASA ecosystem model. Int. J. Appl. Earth Obs. Geoinf. 46, 84-93. doi: 10.1016/j.jag.2015.12.001. |
| [3] |
Bernath-Plaisted, J.S., Ribic, C.A., Hills, W.B., Townsend, P.A., Zuckerberg, B., 2023. Microclimate complexity in temperate grasslands: implications for conservation and management under climate change. Environ. Res. Lett. 18 (6), 064023. doi: 10.1088/1748-9326/acd4d3. |
| [4] |
Bernath-Plaisted, J.S., Handler, S.D., Ahlering, M., Brandt, L.A., Maresh Nelson, S.B., Niemuth, N.D., Ontl, T., Peterson, C.L., Ribic, C.A., Strohmeyer, D., Zuckerberg, B., 2025. A climate adaptation menu for North American grasslands. Conserv. Sci. Pract. 7 (4), e70017. doi: 10.1111/csp2.70017. |
| [5] |
Beven, K.J., Kirkby, M.J., 1979. A physically based, variable contributing area model of basin hydrology. Hydrol. Sci. Bull. 24 (1), 43-69. doi: 10.1080/02626667909491834. |
| [6] |
Bivand, R., Piras, G., 2015. Comparing implementations of estimation methods for spatial econometrics. J. Stat. Softw. 63 (18), 1-36. doi: 10.18637/jss.v063.i18. |
| [7] |
Breiman, L., 2001. Random forests. Mach. Learn. 45, 5-32. doi: 10.1023/A:1010933404324. |
| [8] |
Buermann, W., Forkel, M., O’Sullivan, M., Sitch, S., Friedlingstein, P., Haverd, V., Jain, A.K., Kato, E., Kautz, M., Lienert, S., Lombardozzi, D., Nabel, J.E.M.S., Tian, H.Q., Wiltshire, A.J., Zhu, D., Smith, W.K., Richardson, A.D., 2018. Widespread seasonal compensation effects of spring warming on northern plant productivity. Nature 562 (7725), 110-114. doi: 10.1038/s41586-018-0555-7. |
| [9] |
Cabacinha, C.D., de Castro, S.S., 2009. Relationships between floristic diversity and vegetation indices, forest structure and landscape metrics of fragments in Brazilian Cerrado. For. Ecol. Manage. 257 (10), 2157-2165. doi: 10.1016/j.foreco.2009.02.030. |
| [10] |
Cabello, J., Fernández, N., Alcaraz-Segura, D., Oyonarte, C., Piñeiro, G., Altesor, A., Delibes, M., Paruelo, J.M., 2012. The ecosystem functioning dimension in conservation: insights from remote sensing. Biodivers. Conserv. 21 (13), 3287-3305. doi: 10.1007/s10531-012-0370-7. |
| [11] |
Camps-Valls, G., Campos-Taberner, M., Moreno-Martínez, Á., Walther, S., Duveiller, G., Cescatti, A., Mahecha, M.D., Muñoz-Marí, J., García-Haro, F.J., Guanter, L., Jung, M., Gamon, J.A., Reichstein, M., Running, S.W., 2021. A unified vegetation index for quantifying the terrestrial biosphere. Sci. Adv. 7 (9), eabc7447. doi: 10.1126/sciadv.abc7447. |
| [12] |
Carnicer, J., Vives-Ingla, M., Blanquer, L., Méndez-Camps, X., Rosell, C., Sabaté, S., Gutiérrez, E., Sauras, T., Peñuelas, J., Barbeta, A., 2021. Forest resilience to global warming is strongly modulated by local-scale topographic, microclimatic and biotic conditions. J. Ecol. 109 (9), 3322-3339. doi: 10.1111/1365-2745.13752. |
| [13] |
Chen, J.H., Wang, S.Q., Shi, H., Chen, B., Wang, J.B., Zheng, C., Zhu, K., 2023. Radiation and temperature dominate the spatiotemporal variability in resilience of subtropical evergreen forests in China. Front. For. Glob. Change 6, 1166481. doi: 10.3389/ffgc.2023.1166481. |
| [14] |
Chen, J.M., Ju, W.M., Ciais, P., Viovy, N., Liu, R.G., Liu, Y., Lu, X.H., 2019a. Vegetation structural change since 1981 significantly enhanced the terrestrial carbon sink. Nat. Commun. 10 (1), 4259. doi: 10.1038/s41467-019-12257-8. |
| [15] |
Chen, L.T., Chen, X.H., Cheng, L.Y., Zhou, P., Liu, Z.Y., 2019b. Compound hot droughts over China: identification, risk patterns and variations. Atmos. Res. 227, 210-219. doi: 10.1016/j.atmosres.2019.05.009. |
| [16] |
Chen, Z.C., Li, S., Wan, X.C., Liu, S.R., 2022. Strategies of tree species to adapt to drought from leaf stomatal regulation and stem embolism resistance to root properties. Front. Plant Sci. 13, 926535. doi: 10.3389/fpls.2022.926535. |
| [17] |
Chen, Z.Q., Yu, B.L., Yang, C.S., Zhou, Y.Y., Yao, S.J., Qian, X.J., Wang, C.X., Wu, B., Wu, J.P., 2021. An extended time series (2000-2018) of global NPP-VIIRS-like nighttime light data from a cross-sensor calibration. Earth Syst. Sci. Data 13 (3), 889-906. doi: 10.5194/essd-13-889-2021. |
| [18] |
Cleveland, R.B., Cleveland, W.S., Terpenning, I., 1990. STL: a seasonal-trend decomposition procedure based on loess. J. Off. Stat. 6 (1), 3. |
| [19] |
Cowles, J.M., Wragg, P.D., Wright, A.J., Powers, J.S., Tilman, D., 2016. Shifting grassland plant community structure drives positive interactive effects of warming and diversity on aboveground net primary productivity. Glob. Change Biol. 22 (2), 741-749. doi: 10.1111/gcb.13111. |
| [20] |
D’Orangeville, L., Houle, D., Duchesne, L., Phillips, R.P., Bergeron, Y., Kneeshaw, D., 2018. Beneficial effects of climate warming on boreal tree growth may be transitory. Nat. Commun. 9 (1), 3213. doi: 10.1038/s41467-018-05705-4. |
| [21] |
Dakos, V., Kéfi, S., 2022. Ecological resilience: what to measure and how. Environ. Res. Lett. 17 (4), 043003. doi: 10.1088/1748-9326/ac5767. |
| [22] |
Dakos, V., Scheffer, M., van Nes, E.H., Brovkin, V., Petoukhov, V., Held, H., 2008. Slowing down as an early warning signal for abrupt climate change. Proc. Natl. Acad. Sci. U.S.A. 105 (38), 14308-14312. doi: 10.1073/pnas.0802430105. |
| [23] |
de Vries, F.T., Brown, C., Stevens, C.J., 2016. Grassland species root response to drought: consequences for soil carbon and nitrogen availability. Plant Soil 409 (1), 297-312. doi: 10.1007/s11104-016-2964-4. |
| [24] |
Didan, K., 2021. MODIS/Terra vegetation indices 16-day L3 global 250 m SIN grid V061. NASA EOSDIS Land Processes Distributed. Active Archive Center. doi: 10.5067/MODIS/MOD13Q1.061. |
| [25] |
Doležal, J., Altman, J., Jandová, V., Chytrý, M., Conti, L., Méndez-Castro, F.E., Klimešová, J., Zelený, D., Ottaviani, G., 2022. Climate warming and extended droughts drive establishment and growth dynamics in temperate grassland plants. Agric. For. Meteorol. 313, 108762. doi: 10.1016/j.agrformet.2021.108762. |
| [26] |
Folke, C., Carpenter, S., Walker, B., Scheffer, M., Elmqvist, T., Gunderson, L., Holling, C.S., 2004. Regime shifts, resilience, and biodiversity in ecosystem management. Annu. Rev. Ecol. Evol. Syst. 35 (1), 557-581. doi: 10.1146/annurev.ecolsys.35.021103.105711. |
| [27] |
Forzieri, G., Dakos, V., McDowell, N.G., Ramdane, A., Cescatti, A., 2022. Emerging signals of declining forest resilience under climate change. Nature 608 (7923), 534-539. doi: 10.1038/s41586-022-04959-9. |
| [28] |
Forzieri, G., Miralles, D.G., Ciais, P., Alkama, R., Ryu, Y., Duveiller, G., Zhang, K., Robertson, E., Kautz, M., Martens, B., Jiang, C.Y., Arneth, A., Georgievski, G., Li, W., Ceccherini, G., Anthoni, P., Lawrence, P., Wiltshire, A., Pongratz, J., Piao, S.L., Sitch, S., Goll, D.S., Arora, V.K., Lienert, S., Lombardozzi, D., Kato, E., Nabel, J.E.M.S., Tian, H.Q., Friedlingstein, P., Cescatti, A., 2020. Increased control of vegetation on global terrestrial energy fluxes. Nat. Clim. Chang. 10 (4), 356-362. doi: 10.1038/s41558-020-0717-0. |
| [29] |
Fu, B.J., Liu, Y.X., Meadows, M.E., 2023. Ecological restoration for sustainable development in China. Natl. Sci. Rev. 10 (7), nwad033. doi: 10.1093/nsr/nwad033. |
| [30] |
Gampe, D., Zscheischler, J., Reichstein, M., O’Sullivan, M., Smith, W.K., Sitch, S., Buermann, W., 2021. Increasing impact of warm droughts on northern ecosystem productivity over recent decades. Nat. Clim. Chang. 11 (9), 772-779. doi: 10.1038/s41558-021-01112-8. |
| [31] |
García-Palacios, P., Gross, N., Gaitán, J., Maestre, F.T., 2018. Climate mediates the biodiversity-ecosystem stability relationship globally. Proc. Natl. Acad. Sci. U.S.A. 115 (33), 8400-8405. doi: 10.1073/pnas.1800425115. |
| [32] |
Simpson, G.L., 2024. gratia: an R package for exploring generalized additive models. J. Open Source Softw. 9 (104), 6962. doi: 10.21105/joss.06962. |
| [33] |
Gillson, L., Ekblom, A., 2009. Resilience and thresholds in savannas: nitrogen and fire as drivers and responders of vegetation transition. Ecosystems 12 (7), 1189-1203. doi: 10.1007/s10021-009-9284-y. |
| [34] |
Hammond, W.M., Williams, A.P., Abatzoglou, J.T., Adams, H.D., Klein, T., López, R., Sáenz-Romero, C., Hartmann, H., Breshears, D.D., Allen, C.D., 2022. Global field observations of tree die-off reveal hotter-drought fingerprint for Earth’s forests. Nat. Commun. 13 (1), 1761. doi: 10.1038/s41467-022-29289-2. |
| [35] |
He, Q., Wang, M., Liu, K., 2023. GPRChinaSPEI1km: high spatial resolution and century-long SPEI datasets for China from 1901 to 2020 generated by machine learning doi: 10.5281/zenodo.8312201. |
| [36] |
Hirota, M., Holmgren, M., Van Nes, E.H., Scheffer, M., 2011. Global resilience of tropical forest and savanna to critical transitions. Science 334 (6053), 232-235. doi: 10.1126/science.1210657. |
| [37] |
Holling, C.S., 1973. Resilience and stability of ecological systems. Annu. Rev. Ecol. Evol. Syst. 4, 1-23. doi: 10.1146/annurev.es.04.110173.000245. |
| [38] |
Hoover, D.L., Knapp, A.K., Smith, M.D., 2014. Resistance and resilience of a grassland ecosystem to climate extremes. Ecology 95 (9), 2646-2656. doi: 10.1890/13-2186.1. |
| [39] |
Hou, J.Y., Yin, R.S., Wu, W.G., 2019. Intensifying forest management in China: what does it mean, why, and how? For. Policy Econ. 98, 82-89. doi: 10.1016/j.forpol.2017.10.014. |
| [40] |
Hu, Y., Fu, B.J., Michaelides, K., De Kauwe, M.G., Wang, J., Shen, M.G., Zhang, W.M., Wang, Y., Xiao, X.M., Qin, Y.W., Feng, X.M., Wu, C.Y., Chen, Y.Z., Wang, Z.Z., Wan, L.F., 2025. Contrasting trends in onset of spring green-up between grasslands and forests in China. Earths Future 13 (3), e2024EF005379. doi: 10.1029/2024ef005379. |
| [41] |
Huang, L., Xiao, T., Zhao, Z.P., Sun, C.Y., Liu, J.Y., Shao, Q.Q., Fan, J.W., Wang, J.B., 2013. Effects of grassland restoration programs on ecosystems in arid and semiarid China. J. Environ. Manage. 117, 268-275. doi: 10.1016/j.jenvman.2012.12.040. |
| [42] |
Keenan, T.F., Riley, W.J., 2018. Greening of the land surface in the world’s cold regions consistent with recent warming. Nat. Clim. Chang. 8 (9), 825-828. doi: 10.1038/s41558-018-0258-y. |
| [43] |
Kuhn, M., 2008. Building predictive models in R using the caret package. J. Stat. Softw. 28, 1-26. doi: 10.18637/jss.v028.i05. |
| [44] |
Lefcheck, J.S., 2016. piecewiseSEM: piecewise structural equation modeling in R for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573-579. doi: 10.1111/2041-210X.12512. |
| [45] |
Li, H.Q., Li, F., Xiao, J.F., Chen, J.Q., Lin, K.J., Bao, G., Liu, A.J., Wei, G., 2024. A machine learning scheme for estimating fine-resolution grassland aboveground biomass over China with Sentinel-1/2 satellite images. Remote Sens. Environ. 311, 114317. doi: 10.1016/j.rse.2024.114317. |
| [46] |
Li, H., Si, B.C., Zhang, Z.Q., Miao, C.H., 2022a. Deep soil water storage and drainage following conversion of deep rooted to shallow rooted vegetation. Agric. Water Manage. 261, 107359. doi: 10.1016/j.agwat.2021.107359. |
| [47] |
Li, Q.L., Shi, G.S., Shangguan, W., Nourani, V., Li, J.D., Li, L., Huang, F.N., Zhang, Y., Wang, C.Y., Wang, D.G., Qiu, J.X., Lu, X.J., Dai, Y.J., 2022b. A 1 km daily soil moisture dataset over China using in situ measurement and machine learning. Earth Syst. Sci. Data 14 (12), 5267-5286. doi: 10.5194/essd-14-5267-2022. |
| [48] |
Lin, X.D., Shang, R., Chen, J.M., Zhao, G.S., Zhang, X.P., Huang, Y.P., Yu, G.R., He, N.P., Xu, L., Jiao, W.Z., 2023. High-resolution forest age mapping based on forest height maps derived from GEDI and ICESat-2 space-borne lidar data. Agric. For. Meteorol. 339, 109592. doi: 10.1016/j.agrformet.2023.109592. |
| [49] |
Liu, C., Siri, M., Li, H., Ren, C., Huang, J., Feng, C.L., Liu, K.S., 2023. Drought is threatening plant growth and soil nutrients of grassland ecosystems: a meta-analysis. Ecol. Evol. 13 (5), e10092. doi: 10.1002/ece3.10092. |
| [50] |
Liu, F., Wu, H.Y., Zhao, Y.G., Li, D.C., Yang, J.L., Song, X.D., Shi, Z., Zhu, A.X., Zhang, G.L., 2022b. Mapping high resolution national soil information grids of China. Sci. Bull. 67 (3), 328-340. doi: 10.1016/j.scib.2021.10.013. |
| [51] |
Liu, F., Zhang, G.L., Song, X.D., Li, D.C., Zhao, Y.G., Yang, J.L., Wu, H.Y., Yang, F., 2020. High-resolution and three-dimensional mapping of soil texture of China. Geoderma 361, 114061. doi: 10.1016/j.geoderma.2019.114061. |
| [52] |
Liu, H.Y., Mi, Z.R., Lin, L., Wang, Y.H., Zhang, Z.H., Zhang, F.W., Wang, H., Liu, L.L., Zhu, B., Cao, G.M., Zhao, X.Q., Sanders, N.J., Classen, A.T., Reich, P.B., He, J.S., 2018. Shifting plant species composition in response to climate change stabilizes grassland primary production. Proc. Natl. Acad. Sci. U.S.A. 115 (16), 4051-4056. doi: 10.1073/pnas.1700299114. |
| [53] |
Liu, M., Liu, S.B., Xu, X.L., Soromotin, A.V., Kuzyakov, Y., 2025. Does restoration of degraded grasslands follow the theory of multiple stable states? Agric. Ecosyst. Environ. 383, 109508. doi: 10.1016/j.agee.2025.109508. |
| [54] |
Liu, P., Chi, Y.G., Huang, Z., Zhong, D.W., Zhou, L., 2024b. Multidimensional response of China’s grassland stability to drought. Glob. Ecol. Conserv. 52, e02961. doi: 10.1016/j.gecco.2024.e02961. |
| [55] |
Liu, X.Q., Feng, Y.H., Hu, T.Y., Luo, Y., Zhao, X.X., Wu, J., Maeda, E.E., Ju, W.M., Liu, L.L., Guo, Q.H., Su, Y.J., 2024a. Enhancing ecosystem productivity and stability with increasing canopy structural complexity in global forests. Sci. Adv. 10 (20), eadl1947. doi: 10.1126/sciadv.adl1947. |
| [56] |
Liu, X.Q., Su, Y.J., Hu, T.Y., Yang, Q.L., Liu, B.B., Deng, Y.F., Tang, H., Tang, Z.Y., Fang, J.Y., Guo, Q.H., 2022a. Neural network guided interpolation for mapping canopy height of China’s forests by integrating GEDI and ICESat-2 data. Remote Sens. Environ. 269, 112844. doi: 10.1016/j.rse.2021.112844. |
| [57] |
Liu, Z.C., Fu, Y.H., Shi, X.R., Lock, T.R., Kallenbach, R.L., Yuan, Z.Y., 2022c. Soil moisture determines the effects of climate warming on spring phenology in grasslands. Agric. For. Meteorol. 323, 109039. doi: 10.1016/j.agrformet.2022.109039. |
| [58] |
Loarie, S.R., Duffy, P.B., Hamilton, H., Asner, G.P., Field, C.B., Ackerly, D.D., 2009. The velocity of climate change. Nature 462 (7276), 1052-1055. doi: 10.1038/nature08649. |
| [59] |
Lü, Y.H., Fu, B.J., Wei, W., Yu, X.B., Sun, R.H., 2011. Major ecosystems in China: dynamics and challenges for sustainable management. Environ. Manage. 48 (1), 13-27. doi: 10.1007/s00267-011-9684-6. |
| [60] |
Ma, F.F., Wang, J.S., He, Y.L., Luo, Y.Q., Zhang, R.Y., Tian, D.S., Zhou, Q.P., Niu, S.L., 2023. Nitrogen enrichment differentially regulates the response of ecosystem stability to extreme dry versus wet events. Sci. Total Environ. 887, 164152. doi: 10.1016/j.scitotenv.2023.164152. |
| [61] |
Mattivi, P., Franci, F., Lambertini, A., Bitelli, G., 2019. TWI computation: a comparison of different open source GISs. Open Geospat. Data Softw. Stand. 4 (1), 6. doi: 10.1186/s40965-019-0066-y. |
| [62] |
May, R.M., 1977. Thresholds and breakpoints in ecosystems with a multiplicity of stable states. Nature 269 (5628), 471-477. doi: 10.1038/269471a0. |
| [63] |
McDowell, N., Pockman, W.T., Allen, C.D., Breshears, D.D., Cobb, N., Kolb, T., Plaut, J., Sperry, J., West, A., Williams, D.G., Yepez, E.A., 2008. Mechanisms of plant survival and mortality during drought: why do some plants survive while others succumb to drought? New. Phytol. 178 (4), 719-739. doi: 10.1111/j.1469-8137.2008.02436.x. |
| [64] |
McGaughran, A., Laver, R., Fraser, C., 2021. Evolutionary responses to warming. Trends Ecol. Evol. 36 (7), 591-600. doi: 10.1016/j.tree.2021.02.014. |
| [65] |
Mushagalusa, C.A., Fandohan, A.B., Glèlè Kakaï, R., 2024. Random forest and spatial cross-validation performance in predicting species abundance distributions. Environ. Syst. Res. 13 (1), 23. doi: 10.1186/s40068-024-00352-9. |
| [66] |
Mushtaq, R., 2011. Augmented dickey fuller test. SSRN Electron. J. 13. doi: 10.2139/ssrn.1911068. |
| [67] |
Ni, J., Chen, Z.X., Dong, M., Chen, X.D., Zhang, X.S., 1998. An ecogeographical regionalization for biodiversity in China. Acta Bot. Sin. 40 (4), 370-382 (in Chinese). |
| [68] |
Niu, S.L., Luo, Y.Q., Li, D.J., Cao, S.H., Xia, J.Y., Li, J.W., Smith, M.D., 2014. Plant growth and mortality under climatic extremes: an overview. Environ. Exp. Bot. 98, 13-19. doi: 10.1016/j.envexpbot.2013.10.004. |
| [69] |
Park, T., Ganguly, S., Tømmervik, H., Euskirchen, E.S., Høgda, K.A., Karlsen, S.R., Brovkin, V., Nemani, R.R., Myneni, R.B., 2016. Changes in growing season duration and productivity of northern vegetation inferred from long-term remote sensing data. Environ. Res. Lett. 11 (8), 084001. doi: 10.1088/1748-9326/11/8/084001. |
| [70] |
Pedersen, E.J., Miller, D.L., Simpson, G.L., Ross, N., 2019. Hierarchical generalized additive models in ecology: an introduction with mgcv. PeerJ 7, e6876. doi: 10.7717/peerj.6876. |
| [71] |
R Core Team, 2024. R: a language and environment for statistical computing. R Foundation for Statistical Computing. |
| [72] |
Randazzo, N.A., Michalak, A.M., Miller, C.E., Miller, S.M., Shiga, Y.P., Fang, Y.Y., 2021. Higher autumn temperatures lead to contrasting CO 2 flux responses in boreal forests versus tundra and shrubland . Geophys. Res. Lett. 48 (18), e2021GL093843. doi: 10.1029/2021GL093843. |
| [73] |
Reyer, C.P.O., Brouwers, N., Rammig, A., Brook, B.W., Epila, J., Grant, R.F., Holmgren, M., Langerwisch, F., Leuzinger, S., Lucht, W., Medlyn, B., Pfeifer, M., Steinkamp, J., Vanderwel, M.C., Verbeeck, H., Villela, D.M., 2015. Forest resilience and tipping points at different spatio-temporal scales: approaches and challenges. J. Ecol. 103 (1), 5-15. doi: 10.1111/1365-2745.12337. |
| [74] |
Runge, K., Tucker, M., Crowther, T.W., Fournier de Laurière, C., Guirado, E., Bialic-Murphy, L., Berdugo, M., 2025. Monitoring terrestrial ecosystem resilience using earth observation data: identifying consensus and limitations across metrics. Glob. Change Biol. 31 (3), e70115. doi: 10.1111/gcb.70115. |
| [75] |
Sabatini, F.M., Jiménez-Alfaro, B., Jandt, U., Chytrý, M., Field, R., Kessler, M., Lenoir, J., Schrodt, F., Wiser, S.K., Arfin Khan, M.A.S., Attorre, F., Cayuela, L., De Sanctis, M., Dengler, J., Haider, S., Hatim, M.Z., Indreica, A., Jansen, F., Pauchard, A., Peet, R.K., Petřík, P., Pillar, V.D., Sandel, B., Schmidt, M., Tang, Z.Y., van Bodegom, P., Vassilev, K., Violle, C., Alvarez-Davila, E., Davidar, P., Dolezal, J., Hérault, B., Galán-de-Mera, A., Jiménez, J., Kambach, S., Kepfer-Rojas, S., Kreft, H., Lezama, F., Linares-Palomino, R., Monteagudo Mendoza, A., N’Dja, J.K., Phillips, O.L., Rivas-Torres, G., Sklenář, P., Speziale, K., Strohbach, B.J., Vásquez Martínez, R., Wang, H.F., Wesche, K., Bruelheide, H., 2022. Global patterns of vascular plant alpha diversity. Nat. Commun. 13 (1), 4683. doi: 10.1038/s41467-022-32063-z. |
| [76] |
Saxe, H., Cannell, M.G.R., Johnsen, Ø., Ryan, M.G., Vourlitis, G., 2001. Tree and forest functioning in response to global warming. New. Phytol. 149 (3), 369-399. doi: 10.1046/j.1469-8137.2001.00057.x. |
| [77] |
Scheffer, M., Carpenter, S., Foley, J.A., Folke, C., Walker, B., 2001. Catastrophic shifts in ecosystems. Nature 413 (6856), 591-596. doi: 10.1038/35098000. |
| [78] |
Scheffer, M., Carpenter, S.R., 2003. Catastrophic regime shifts in ecosystems: linking theory to observation. Trends Ecol. Evol. 18 (12), 648-656. doi: 10.1016/j.tree.2003.09.002. |
| [79] |
Scheffer, M., Carpenter, S.R., Dakos, V., van Nes, E.H., 2015. Generic indicators of ecological resilience: inferring the chance of a critical transition. Annu. Rev. Ecol. Evol. Syst. 46 (1), 145-167. doi: 10.1146/annurev-ecolsys-112414-054242. |
| [80] |
Schwalm, C.R., Anderegg, W.R.L., Michalak, A.M., Fisher, J.B., Biondi, F., Koch, G., Litvak, M., Ogle, K., Shaw, J.D., Wolf, A., Huntzinger, D.N., Schaefer, K., Cook, R., Wei, Y.X., Fang, Y.Y., Hayes, D., Huang, M.Y., Jain, A., Tian, H.Q., 2017. Global patterns of drought recovery. Nature 548 (7666), 202-205. doi: 10.1038/nature23021. |
| [81] |
Shipley, B., 2009. Confirmatory path analysis in a generalized multilevel context. Ecology 90 (2), 363-368. doi: 10.1890/08-1034.1. |
| [82] |
Smith, T., Boers, N., 2023a. Global vegetation resilience linked to water availability and variability. Nat. Commun. 14 (1). doi: 10.1038/s41467-023-36207-7. |
| [83] |
Smith, T., Boers, N., 2023b. Reliability of vegetation resilience estimates depends on biomass density. Nat. Ecol. Evol. 7 (11), 1799-1808. doi: 10.1038/s41559-023-02194-7. |
| [84] |
Smith, T., Traxl, D., Boers, N., 2022. Empirical evidence for recent global shifts in vegetation resilience. Nat. Clim. Chang. 12 (5), 477-484. doi: 10.1038/s41558-022-01352-2. |
| [85] |
Steel, Z.L., Foster, D., Coppoletta, M., Lydersen, J.M., Stephens, S.L., Paudel, A., Markwith, S.H., Merriam, K., Collins, B.M., 2021. Ecological resilience and vegetation transition in the face of two successive large wildfires. J. Ecol. 109 (9), 3340-3355. doi: 10.1111/1365-2745.13764. |
| [86] |
Sturm, J., Santos, M.J., Schmid, B., Damm, A., 2022. Satellite data reveal differential responses of Swiss forests to unprecedented 2018 drought. Glob. Change Biol. 28 (9), 2956-2978. doi: 10.1111/gcb.16136. |
| [87] |
Su, Y.J., Guo, Q.H., Guan, H.C., Hu, T.Y., Jin, S.C., Wang, Z.H., Liu, L.L., Jiang, L., Guo, K., Xie, Z.Q., An, S.Z., Chen, X.L., Hao, Z.Q., Hu, Y.M., Huang, Y.M., Jiang, M.X., Li, J.X., Li, Z.J., Li, X.K., Li, X.W., Liang, C.Z., Liu, R.L., Liu, Q., Ni, H.W., Peng, S.L., Shen, Z.H., Tang, Z.Y., Tian, X.J., Wang, X.H., Wang, R.Q., Xie, Y.Z., Xu, X.N., Yang, X.B., Yang, Y.C., Yu, L.F., Yue, M., Zhang, F., Chen, J., Ma, K.P., 2022. Human-climate coupled changes in vegetation community complexity of China since 1980s. Earths Future 10 (7), e2021EF002553. doi: 10.1029/2021EF002553. |
| [88] |
Su, Y.X., Zhang, C.Q., Cescatti, A., Yu, K.L., Ciais, P., Smith, T., Shang, J.L., Carnicer, J., Liu, J., Chen, J.M., Green, J.K., Wu, J.P., Ponce-Campos, G.E., Zhang, Y.G., Zuo, Z.Y., Liao, J.B., Wu, J.P., Lafortezza, R., Yan, K., Yang, X.Q., Liu, L.Y., Ren, J.S., Yuan, W.P., Chen, X.Z., Wu, C.Y., Zhou, W.Q., 2025. Pervasive but biome-dependent relationship between fragmentation and resilience in forests. Nat. Ecol. Evol. 9 (9), 1670-1684. doi: 10.1038/s41559-025-02776-7. |
| [89] |
Suggitt, A.J., Gillingham, P.K., Hill, J.K., Huntley, B., Kunin, W.E., Roy, D.B., Thomas, C.D., 2011. Habitat microclimates drive fine-scale variation in extreme temperatures. Oikos 120 (1), 1-8. doi: 10.1111/j.1600-0706.2010.18270.x. |
| [90] |
van de Leemput, I.A., Dakos, V., Scheffer, M., van Nes, E.H., 2018. Slow recovery from local disturbances as an indicator for loss of ecosystem resilience. Ecosystems 21 (1), 141-152. doi: 10.1007/s10021-017-0154-8. |
| [91] |
Vavrus, S., Ruddiman, W.F., Kutzbach, J.E., 2008. Climate model tests of the anthropogenic influence on greenhouse-induced climate change: the role of early human agriculture, industrialization, and vegetation feedbacks. Quat. Sci. Rev. 27 (13), 1410-1425. doi: 10.1016/j.quascirev.2008.04.011. |
| [92] |
Verbesselt, J., Umlauf, N., Hirota, M., Holmgren, M., Van Nes, E.H., Herold, M., Zeileis, A., Scheffer, M., 2016. Remotely sensed resilience of tropical forests. Nat. Clim. Chang. 6 (11), 1028-1031. doi: 10.1038/nclimate3108. |
| [93] |
Wagg, C., Roscher, C., Weigelt, A., Vogel, A., Ebeling, A., de Luca, E., Roeder, A., Kleinspehn, C., Temperton, V.M., Meyer, S.T., Scherer-Lorenzen, M., Buchmann, N., Fischer, M., Weisser, W.W., Eisenhauer, N., Schmid, B., 2022. Biodiversity-stability relationships strengthen over time in a long-term grassland experiment. Nat. Commun. 13 (1), 7752. doi: 10.1038/s41467-022-35189-2. |
| [94] |
Wan, X., Wang, W.Q., Liu, J.M., Tong, T.J., 2014. Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC Med. Res. Methodol. 14 (1), 135. doi: 10.1186/1471-2288-14-135. |
| [95] |
Wang, D.J., Peng, Q.Y., Li, X.Q., Zhang, W., Xia, X.S., Qin, Z.C., Ren, P.Y., Liang, S.L., Yuan, W.P., 2024b. A long-term high-resolution dataset of grasslands grazing intensity in China. Sci. Data 11 (1), 1194. doi: 10.1038/s41597-024-04045-x. |
| [96] |
Wang, H., Ciais, P., Sitch, S., Green, J.K., Tao, S.L., Fu, Z., Albergel, C., Bastos, A., Wang, M.J., Fawcett, D., Frappart, F., Li, X.J., Liu, X.Z., Li, S.C., Wigneron, J.P., 2024a. Anthropogenic disturbance exacerbates resilience loss in the Amazon rainforests. Glob. Change Biol. 30 (1), e17006. doi: 10.1111/gcb.17006. |
| [97] |
Wang, L.Y., Yuan, X., Xie, Z.H., Wu, P.L., Li, Y.H., 2016. Increasing flash droughts over China during the recent global warming hiatus. Sci. Rep. 6 (1), 30571. doi: 10.1038/srep30571. |
| [98] |
Wang, Z.Z., Fu, B.J., Wu, X.T., Li, Y.J., Feng, Y.H., Wang, S., Wei, F.L., Zhang, L.W., 2023. Vegetation resilience does not increase consistently with greening in China’s Loess Plateau. Commun. Earth Environ. 4 (1), 336. doi: 10.1038/s43247-023-01000-3. |
| [99] |
Wright, M.N., Ziegler, A., 2017. ranger: a fast implementation of random forests for high dimensional data in C++ and R. J. Stat. Softw 77, 1-17. doi: 10.18637/jss.v077.i01. |
| [100] |
Wu, B., Qian, J., Zeng, Y., 2017. Land Cover Atlas of the People’s Republic of China (1:1000,000). |
| [101] |
Wu, G.L., Cheng, Z., Alatalo, J.M., Zhao, J.X., Liu, Y., 2021. Climate warming consistently reduces grassland ecosystem productivity. Earths Future 9 (6), e2020EF001837. doi: 10.1029/2020EF001837. |
| [102] |
Wu, J.C., Sun, Z.Y., Yao, Y., Liu, Y.X., 2023. Trends of grassland resilience under climate change and human activities on the Mongolian Plateau. Remote Sens. 15 (12), 2984. doi: 10.3390/rs15122984. |
| [103] |
Xu, X., 2017. China population spatial distribution kilometer grid dataset. Data Registration and Publishing System of Resource and Environmental Science Data Center. Chinese Academy of Sciences. doi: 10.12078/2017121101. |
| [104] |
Yi, K.P., Zhao, X.M., Zheng, Z.J., Zhao, D., Zeng, Y., 2023. Trends of greening and browning in terrestrial vegetation in China from 2000 to 2020. Ecol. Indic. 154, 110587. doi: 10.1016/j.ecolind.2023.110587. |
| [105] |
Zeng, Y.L., Hao, D.L., Huete, A., Dechant, B., Berry, J., Chen, J.M., Joiner, J., Frankenberg, C., Bond-Lamberty, B., Ryu, Y., Xiao, J.F., Asrar, G.R., Chen, M., 2022. Optical vegetation indices for monitoring terrestrial ecosystems globally. Nat. Rev. Earth Environ. 3 (7), 477-493. doi: 10.1038/s43017-022-00298-5. |
| [106] |
Zhang, L.X., Zhou, T.J., 2015. Drought over East Asia: a review. J. Clim. 28 (8), 3375-3399. doi: 10.1175/JCLI-d-14-00259.1. |
| [107] |
Zhang, P., Jeong, J.H., Yoon, J.H., Kim, H., Wang, S.Y.S., Linderholm, H.W., Fang, K.Y., Wu, X.C., Chen, D.L., 2020. Abrupt shift to hotter and drier climate over inner East Asia beyond the tipping point. Science 370 (6520), 1095-1099. doi: 10.1126/science.abb3368. |
| [108] |
Zhang, Y., Wang, J.A., Berner, L.T., Goetz, S.J., Zhao, K.G., Liu, Y.L., 2024. Warming and disturbances affect Arctic-boreal vegetation resilience across northwestern North America. Nat. Ecol. Evol. 8 (12), 2265-2276. doi: 10.1038/s41559-024-02551-0. |
| [109] |
Zhao, D., Qiu, J., Fan, Z., Ti, C.P., Huang, Z.L., Yan, X.Y., Xia, Y.Q., 2025. Global warming has imbalance impact on soil nitrogen transformation rates. Earths Future 13 (3), e2024EF004756. doi: 10.1029/2024ef004756. |
| [110] |
Zhao, Y.J., Schmid, B., Zheng, Z.J., Wang, Y., Wu, J., Wang, Y., Chen, Z.Y., Zhao, X., Zhao, D., Zeng, Y., Bai, Y.F., 2024. Remotely sensed variables predict grassland diversity better at scales below 1000 km as opposed to abiotic variables that predict it better at larger scales. Earths Future 12 (11), e2024EF004648. doi: 10.1029/2024EF004648. |
| [111] |
Zhu, Q.A., Chen, H., Peng, C.H., Liu, J.X., Piao, S.L., He, J.S., Wang, S.P., Zhao, X.Q., Zhang, J., Fang, X.Q., Jin, J.X., Yang, Q.E., Ren, L.L., Wang, Y.F., 2023. An early warning signal for grassland degradation on the Qinghai-Tibetan Plateau. Nat. Commun. 14 (1), 6406. doi: 10.1038/s41467-023-42099-4. |
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