替代稳态下阜康北部荒漠生态弹性的时空格局

doi:10.13866/j.azr.2023.05.13

干旱区研究 ›› 2023, Vol. 40 ›› Issue (5): 808-817.doi: 10.13866/j.azr.2023.05.13

替代稳态下阜康北部荒漠生态弹性的时空格局

许毓哲^1,²(),林涛³,李君^1,^2,⁴()

1.中国科学院新疆生态与地理研究所，荒漠与绿洲生态国家重点实验室，新疆乌鲁木齐 830011
2.中国科学院大学，北京 100049
3.自然资源部荒漠-绿洲生态监测与修复工程技术创新中心，新疆乌鲁木齐 830022
4.中国科学院新疆生态与地理研究所，阿克苏绿洲农田生态系统国家野外科学观测研究站，新疆乌鲁木齐 830011

收稿日期:2022-12-08 修回日期:2023-01-10 出版日期:2023-05-15 发布日期:2023-05-30
通讯作者: 李君. E-mail: lijun@ms.xjb.ac.cn
作者简介:许毓哲（1997-），男，硕士研究生，主要从事资源环境遥感研究. E-mail: xuyuzhe20@mails.ucas.ac.cn
基金资助:
第三次新疆综合科学考察项目(2021xjkk0900)

Spatial and temporal patterns of ecological resilience under alternative stable states in the desert of the north Fukang region

Xu Yuzhe^1,²(),Lin Tao³,Li Jun^1,^2,⁴()

1. State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Science, Urumqi 830011, Xinjiang, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. Technology Innovation Center for Desert -Oasis Ecological Monitoring and Restoration, Ministry of Natural Resources, Urumqi 830022, Xinjiang, China
4. Akesu National Station of Observation and Research for Oasis Agro-ecosystem, Xinjiang Institute of Ecology and Geography, Chinese Academy of Science, Urumqi 830011, Xinjiang, China

Received:2022-12-08 Revised:2023-01-10 Online:2023-05-15 Published:2023-05-30

摘要/Abstract

摘要：

替代稳态下的荒漠生态系统生态弹性反映了系统承受环境干扰后的恢复能力，这对认识荒漠生态系统过程具有重要的理论意义。当前研究主要集中在湖泊和森林生态系统，而有关荒漠生态系统弹性的研究普遍未考虑替代稳态，也鲜有考虑弹性的时间变化。本研究以阜康北部的古尔班通古特沙漠南缘至沙漠腹地样带为例，运用2001年1月至2020年12月的MODIS全球植被指数遥感数据，采用BFAST（Breaks for Additive Season and Trend）和状态空间建模对数据进行处理和提取，通过不同状态的退出时间量化得出不同时段替代稳态条件下的生态弹性，同时根据计算结果分析了该地区生态弹性时空演变特征及其影响机制。研究结果表明：（1）研究区弹性⁺和弹性^-整体呈现先下降后上升的趋势，但沙漠边缘至腹地空间差异显著。（2）生态弹性对降水变化存在滞后响应。（3）降水季节变化的差异会降低降水量与生态弹性之间的相关性。综上所述，生态弹性的空间分布总体受降水格局控制，但立地条件导致的植被空间异质性增加了生态弹性空间分布的复杂性，而生态弹性与降水变化的关系取决于植被群落构成、植物对降水变化响应、降水量变化趋势和季节分布。本研究在认识荒漠生态系统功能稳定性维持机制以及荒漠生态保护和修复方面具有重要的理论与实践意义。

关键词: 替代稳态, 生态弹性, 退出时间, 古尔班通古特沙漠

Abstract:

The ecological resilience of desert ecosystems under alternative stable states reflects the ecosystem’s ability to recover from environmental disturbances, which has important theoretical implications for understanding the desert ecosystem processes. Recent research has focused on some intensively studied ecosystems such as lakes and forests, whereas studies related to the ecological resilience of desert ecosystems have hardly considered alternative stable states as well as the temporal changes in resilience. In this study, based on the MODIS global vegetation index from January 2001 to December 2020, we used Breaks for Additive Season and Trend and state-space modeling to determine the temporal-spatial pattern of ecological resilience under alternative stable states quantified by the exit times for a transect extending from the southern edge to the hinterland of Gurbantunggut Desert in north Fukang. Moreover, mechanisms underlying the temporal-spatial pattern of ecological resilience were explored. The results of this study indicated the following: (1) Resilience⁺ and Resilience^- showed a decreasing trend within the study period but with significant differences between the southern edge and the hinterland of the desert. (2) Ecological resilience also showed a lagged response to precipitation changes. (3) Differences in the seasonal patterns of precipitation could weaken the correlation between precipitation and ecological resilience. In summary, the spatial distribution of ecological resilience is generally controlled by the precipitation patterns, but the spatial heterogeneity of vegetation due to site conditions increases the complexity of the spatial distribution of ecological resilience, with the relationship between ecological resilience and precipitation resulting from the combined effects of the composition of vegetation, adaptation of plants to changes in precipitation, and the seasonal pattern of precipitation. Our study is important for understanding the mechanisms that desert ecosystems utilize to maintain functional stability as well as in desert conservation and ecological restoration.

Key words: alternative stable states, ecological resilience, exit time, Gurbantunggut Desert

许毓哲, 林涛, 李君. 替代稳态下阜康北部荒漠生态弹性的时空格局[J]. 干旱区研究, 2023, 40(5): 808-817.

Xu Yuzhe, Lin Tao, Li Jun. Spatial and temporal patterns of ecological resilience under alternative stable states in the desert of the north Fukang region[J]. Arid Zone Research, 2023, 40(5): 808-817.

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参考文献 41

[1]	Holling C S. Resilience and stability of ecological systems[J]. Annual Review of Ecology and Systematics, 1973, 4(1): 1-23. doi: 10.1146/ecolsys.1973.4.issue-1
[2]	Holling C S. Engineering resilience versus ecological resilience[J]. Engineering within Ecological Constraints, 1996, 31(1996): 32.
[3]	Scheffer M. Critical Transitions in Nature and Society[M]. Princeton, USA: Princeton University Press, 2009.
[4]	Charney J G. Dynamics of deserts and drought in Sahel[J]. Quarterly Journal of the Royal Meteorological Society, 1975, 101(428): 193-202. doi: 10.1002/(ISSN)1477-870X
[5]	Scheffer M, Carpenter S, Foley J A, et al. Catastrophic shifts in ecosystems[J]. Nature, 2001, 413(6856): 591-596. doi: 10.1038/35098000
[6]	Xu Y, Yang J, Chen Y. NDVI-based vegetation responses to climate change in an arid area of China[J]. Theoretical and Applied Climatology, 2016, 126(1): 213-222. doi: 10.1007/s00704-015-1572-1
[7]	王新军, 赵成义, 杨瑞红, 等. 基于像元二分法的沙地植被景观格局特征变化分析[J]. 农业工程学报, 2016, 32(3): 285-294.
	[Wang Xinjun, Zhao Chengyi, Yang Ruihong, et al. Dynamic characteristics of sandy vegetation landscape pattern based on dimidiate pixel model[J]. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(3): 285-294. ]
[8]	Verbesselt J, Hyndman R, Newnham G, et al. Detecting trend and seasonal changes in satellite image time series[J]. Remote Sensing of Environment, 2010, 114(1): 106-115. doi: 10.1016/j.rse.2009.08.014
[9]	Verbesselt J, Hyndman R, Zeileis A, et al. Phenological change detection while accounting for abrupt and gradual trends in satellite image time series[J]. Remote Sensing of Environment, 2010, 114(12): 2970-2980. doi: 10.1016/j.rse.2010.08.003
[10]	Verbesselt J, Zeileis A, Herold M. Near real-time disturbance detection using satellite image time series[J]. Remote Sensing of Environment, 2012, 123: 98-108. doi: 10.1016/j.rse.2012.02.022
[11]	Von Keyserlingk J, De Hoop M, Mayor A G, et al. Resilience of vegetation to drought: Studying the effect of grazing in a Mediterranean rangeland using satellite time series[J]. Remote Sensing of Environment, 2021, 255: 112270. doi: 10.1016/j.rse.2020.112270
[12]	Verbesselt J, Umlauf N, Hirota M, et al. Remotely sensed resilience of tropical forests[J]. Nature Climate Change, 2016, 6(11): 1028-1031. doi: 10.1038/NCLIMATE3108
[13]	Dakos V, Carpenter S R, Brock W A, et al. Methods for detecting early warnings of critical transitions in time series illustrated using simulated ecological data[J]. Plos One, 2012, 7(7): e41010. doi: 10.1371/journal.pone.0041010
[14]	Scheffer M, Carpenter S R, Lenton T M, et al. Anticipating critical transitions[J]. Science, 2012, 338(6105): 344-348. doi: 10.1126/science.1225244 pmid: 23087241
[15]	Hishe H, Oosterlynck L, Giday K, et al. A combination of climate, tree diversity and local human disturbance determine the stability of dry Afromontane forests[J]. Forest Ecosystems, 2021, 8(1): 1-16. doi: 10.1186/s40663-020-00279-4
[16]	De Keersmaecker W, Lhermitte S, Tits L, et al. A model quantifying global vegetation resistance and resilience to short-term climate anomalies and their relationship with vegetation cover[J]. Global Ecology and Biogeography, 2015, 24(5): 539-548. doi: 10.1111/geb.2015.24.issue-5
[17]	Arani B M S, Carpenter S R, Lahti L, et al. Exit time as a measure of ecological resilience[J]. Science, 2021, 372(6547): eaay4895. doi: 10.1126/science.aay4895
[18]	Dakos V, Kefi S. Ecological resilience: What to measure and how[J]. Environmental Research Letters, 2022, 17(4): 043003. doi: 10.1088/1748-9326/ac5767
[19]	Nolting B C, Abbott K C. Balls, cups, and quasi-potentials: Quantifying stability in stochastic systems[J]. Ecology, 2016, 97(4): 850-864. pmid: 27220202
[20]	Meng Y Y, Liu X N, Ding C, et al. Analysis of ecological resilience to evaluate the inherent maintenance capacity of a forest ecosystem using a dense Landsat time series[J]. Ecological Informatics, 2020, 57: 101064. doi: 10.1016/j.ecoinf.2020.101064
[21]	曾勇. 古尔班通古特沙漠植物多样性对降水变化的敏感性研究[D]. 石河子: 石河子大学, 2015.
	[Zeng Yong. Study of Sensitivity of Plant Diversity to Precipitation Change in the Gurbantünggüt Desert[D]. Shihezi: Shihezi University, 2015. ]
[22]	李培基. 中国西部积雪变化特征[J]. 地理学报, 1993, 60(6): 505-515.
	[Li Peiji. Dynamic characteristic of snow cover in western China[J]. Acta Geographica Sinica, 1993, 60(6): 505-515. ]
[23]	张立运, 陈昌笃. 论古尔班通古特沙漠植物多样性的一般特点[J]. 生态学报, 2002, 22(11): 1923-1932.
	[Zhang Liyun, Chen Changdu. On the general characteristics of plant diversity of Gurbantunggut sandy desert[J]. Acta Ecologica Sinica, 2002, 22(11): 1923-1932. ]
[24]	王雪芹, 蒋进, 雷加强, 等. 古尔班通古特沙漠短命植物分布及其沙面稳定意义[J]. 地理学报, 2003, 70(4): 598-605.
	[Wang Xueqin, Jiang Jin, Lei Jiaqiang, et al. The distribution of ephemeral vegetation on the longitudinal dune surface and its stabilization significance in the Gurbantunggut Desert[J]. Acta Geographica Sinica, 2003, 70(4): 598-605. ]
[25]	陈曦, 姜逢清, 胡汝骥, 等. 中国干旱区自然地理[M]. 北京: 科学出版社, 2015.
	[Chen Xi, Jiang Fengqing, Hu Ruji, et al. A brief Introduction to Physical Geography of Arid Land in China[M]. Beijing: Science Press, 2015. ]
[26]	罗宁, 刘尊驰, 于航, 等. 古尔班通古特沙漠南部植物多样性的区域差异[J]. 生态学报, 2016, 36(12): 3572-3581.
	[Luo Ning, Liu Zunchi, Yu Hang, et al. Regional differences in plant diversity in the southern Gurbantonggut desert[J]. Acta Ecologica Sinica, 2016, 36(12): 3572-3581. ]
[27]	Holben B N. Characteristics of maximum-value composite images from temporal AVHRR data[J]. International Journal of Remote Sensing, 1986, 7(11): 1417-1434. doi: 10.1080/01431168608948945
[28]	杨怡, 吴世新, 庄庆威, 等. 2000—2018年古尔班通古特沙漠EVI时空变化特征[J]. 干旱区研究, 2019, 36(6): 1512-1520.
	[Yang Yi, Wu Shixin, Zhuang Qingwei, et al. Spatiotemporal change of EVI in the Gurbantunggut Desert from 2000 to 2018[J]. Arid Zone Research, 2019, 36(6): 1512-1520. ]
[29]	岳天祥. 地球表层系统模拟分析原理与方法[M]. 北京: 科学出版社, 2017.
	[Yue Tianxiang. Principles and Methods for Simulating Earth’s Surface Systems[M]. Beijing: Science Press, 2017. ]
[30]	Auger-Methe M, Newman K, Cole D, et al. A guide to state-space modeling of ecological time series[J]. Ecological Monographs, 2021, 91(4): e01470.
[31]	Holmes E E, Ward E J, Wills K. MARSS: Multivariate autoregressive state-space models for analyzing time-series data[J]. The R Journal, 2012, 4(1): 11-19. doi: 10.32614/RJ-2012-002
[32]	Myers J L, Well A D, Lorch R F. Research design and statistical analysis[M]. New York, USA: Routledge, 2013.
[33]	Bai J, Shi H, Yu Q, et al. Satellite-observed vegetation stability in response to changes in climate and total water storage in Central Asia[J]. Science of The Total Environment, 2019, 659: 862-871. doi: 10.1016/j.scitotenv.2018.12.418
[34]	高洁, 赵勇, 姚俊强, 等. 气候变化背景下中亚干旱区大气水分循环要素时空演变[J]. 干旱区研究, 2022, 39(5): 1371-1384.
	[Gao Jie, Zhao Yong, Yao Junqiang, et al. Spatiotemporal evolution of atmospheric water cycle factors in arid regions of Central Asia under climate change[J]. Arid Zone Research, 2022, 39(5): 1371-1384. ]
[35]	Fu A, Wang W, Li W, et al. Resistance and resilience of desert riparian communities to extreme droughts[J]. Forests, 2022, 13(7): 1032. doi: 10.3390/f13071032
[36]	于丹丹, 唐立松, 李彦, 等. 古尔班通古特沙漠白梭梭群落林下层物种多样性的空间分异[J]. 干旱区研究, 2010, 27(4): 559-566.
	[Yu Dandan, Tang Lisong, Li Yan, et al. Spatial variation of the diversity characteristics of understory plant species of Haloxylon persicum in the Gurbantunggut Desert[J]. Arid Zone Research, 2010, 27(4): 559-566. ]
[37]	张荣, 刘彤. 古尔班通古特沙漠南部植物多样性及群落分类[J]. 生态学报, 2012, 32(19): 6056-6066. doi: 10.5846/stxb
	[Zhang Rong, Liu Tong. Plant species diversity and community classification in the southern Gurbantunggut Desert[J]. Acta Ecologica Sinica, 2012, 32(19): 6056-6066. ] doi: 10.5846/stxb
[38]	李功麟, 张定海, 张志山, 等. 古尔班通古特沙漠沙丘主要灌木的种群数量动态[J]. 中国沙漠, 2021, 41(2): 129-137. doi: 10.7522/j.issn.1000-694X.2020.00103
	[Li Gonglin, Zhang Dinghai, Zhang Zhishan, et al. Population dynamics of main sand-fixing shrubs in the Gurbantunggut Desert[J]. Journal of Desert Research, 2021, 41(2): 129-137. ] doi: 10.7522/j.issn.1000-694X.2020.00103
[39]	蒋超亮, 吴玲, 安静, 等. 古尔班通古特沙漠旱生植物时空分布特征[J]. 生态学报, 2019, 39(3): 936-944.
	[Jiang Chaoliang, Wu Ling, An Jing, et al. Spatio-temporal distribution of xerophytes in the Gurbantunggut Desert[J]. Acta Ecologica Sinica, 2019, 39(3): 936-944. ]
[40]	丁俊祥, 范连连, 李彦, 等. 古尔班通古特沙漠6种荒漠草本植物的生物量分配与相关生长关系[J]. 中国沙漠, 2016, 36(5): 1323-1330. doi: 10.7522/j.issn.1000-694X.2015.00107
	[Ding Junxiang, Fan Lianlian, Li Yan, et al. Biomass allocation and allometric relationships of six desert herbaceous plants in the Gurbantunggut Desert[J]. Journal of Desert Research, 2016, 36(5): 1323-1330. ] doi: 10.7522/j.issn.1000-694X.2015.00107
[41]	李志忠, 靳建辉, 刘瑞, 等. 古尔班通古特沙漠风沙地貌研究进展评述[J]. 中国沙漠, 2022, 42(1): 41-47. doi: 10.7522/j.issn.1000-694X.2021.00186
	[Li Zhizhong, Jin Jianhui, Liu Rui, et al. Review and prospect of aeolian geomorphology research in Gurbantunggut Desert, China[J]. Journal of Desert Research, 2022, 42(1): 41-47. ] doi: 10.7522/j.issn.1000-694X.2021.00186

Spearman相关系数		整体		南部		中部		北部
Spearman相关系数		同期降水	前期降水	同期降水	前期降水	同期降水	前期降水	同期降水	前期降水
t₁	弹性⁺	-0.279 ^***	-	0.107	-	-0.049	-	-0.464^***	-
t₁	弹性^-	-0.071	-	0.596^***	-	-0.511^***	-	-0.409^***	-
t₂	弹性⁺	0.604 ^***	0.592 ^***	0.741^***	0.701^***	0.061	-0.086	-0.288^**	-0.084
t₂	弹性^-	0.075	0.084^*	0.336^***	0.318^**	-0.010	-0.143	-0.511^***	-0.392^***
t₃	弹性⁺	0.018^**	-0.182	0.689^***	0.682^***	-0.373^**	-0.238^*	0.016	0.145
t₃	弹性^-	0.033^*	0.190^***	0.197	0.183	-0.215^*	-0.252^*	-0.162	0.054
t₄	弹性⁺	0.281^***	0.387^***	0.192	0.244^*	0.232	0.017	0.178	0.254^**
t₄	弹性^-	0.105	0.059	0.336^**	0.322^**	0.327^**	0.320^**	0.015	-0.119

Spearman相关系数		整体		南部		中部		北部
Spearman相关系数		同期降水	前期降水	同期降水	前期降水	同期降水	前期降水	同期降水	前期降水
t₁-t₂	弹性⁺	0.070	-	-0.024	-	0.302^*	-	-0.384^**	-
t₁-t₂	弹性^-	-0.122	-	0.137	-	-0.081	-	-0.155	-
t₂-t₃	弹性⁺	0.418^***	-0.027	0.171	-0.158	0.010	0.074	-0.288^*	0.499^***
t₂-t₃	弹性^-	-0.071	0.285^***	0.156	0.133	0.213^*	0.274^**	-0.326^**	0.208
t₃-t₄	弹性⁺	-0.260^***	-0.387^***	-0.277^*	-0.231^*	0.422^***	0.316^**	-0.211^*	-0.027
t₃-t₄	弹性^-	-0.111	0.350^***	-0.166	-0.089	-0.146	0.291^***	0.139	0.246^*

替代稳态下阜康北部荒漠生态弹性的时空格局

Spatial and temporal patterns of ecological resilience under alternative stable states in the desert of the north Fukang region

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