Spatiotemporal variations and driving factors of habitat quality in the loess hilly area of the Yellow River Basin: A case study of Lanzhou City, China

doi:10.1007/s40333-022-0097-6

Journal of Arid Land

2022, Vol. 14

Issue (6): 637-652 DOI: 10.1007/s40333-022-0097-6

Research article

Spatiotemporal variations and driving factors of habitat quality in the loess hilly area of the Yellow River Basin: A case study of Lanzhou City, China

DONG Jianhong¹, ZHANG Zhibin², LIU Benteng¹^,^*(

), ZHANG Xinhong¹^,^*(

), ZHANG Wenbin², CHEN Long²

¹School of Design and Art, Lanzhou University of Technology, Lanzhou 730050, China
²School of Geography and Environment Science, Northwest Normal University, Lanzhou 730070, China

Download:

HTML

PDF(3229KB)
Export: BibTeX | EndNote (RIS)

Abstract

Rapid industrialization and urbanization have led to the most serious habitat degradation in China, especially in the loess hilly area of the Yellow River Basin, where the ecological environment is relatively fragile. The contradiction between economic development and ecological environment protection has aroused widespread concern. In this study, we used the habitat quality of Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST-HQ) model at different scales to evaluate the dynamic evolution characteristics of habitat quality in Lanzhou City, Gansu Province of China. The spatiotemporal variations of habitat quality were analyzed by spatial autocorrelation. A Geographical Detector (Geodetector) model was used to explore the driving factors that influencing the spatial differentiation of habitat quality, including natural factors, socio-economic factors, and ecological protection factors. The results showed that the habitat quality index of Lanzhou City decreased from 0.4638 to 0.4548 during 2000-2018. The areas with reduced the habitat quality index were mainly located in the Yellow River Basin and Qinwangchuan Basin, where are the main urban areas and the new economic development areas, respectively. The spatial distribution of habitat quality presented a trend of high in the surrounding areas and low in the middle, and showed a significant positive spatial autocorrelation. With the increase of study scale, the spatial distribution of habitat quality changed from concentrated to dispersed. The spatial differentiation of habitat quality in the study area was the result of multiple factors. Among them, topographic relief and slope were the key factors. The synergistic enhancement among these driving factors intensified the spatial differentiation of habitat quality. The findings of this study can provide a scientific basis for land resources utilization and ecosystem restoration in the arid and semi-arid land.

Key words： Habitat quality spatiotemporal variations driving factors InVEST-HQ model Geodetector model Lanzhou City Yellow River Basin

Received: 30 March 2022 Published: 30 June 2022

Corresponding Authors: * LIU Benteng (E-mail: liubt@lut.edu.cn);ZHANG Xinhong (E-mail: zhangxh1981@lut.edu.cn)

	Service
	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	DONG Jianhong
	ZHANG Zhibin
	LIU Benteng
	ZHANG Xinhong
	ZHANG Wenbin
	CHEN Long

Cite this article:

DONG Jianhong, ZHANG Zhibin, LIU Benteng, ZHANG Xinhong, ZHANG Wenbin, CHEN Long. Spatiotemporal variations and driving factors of habitat quality in the loess hilly area of the Yellow River Basin: A case study of Lanzhou City, China. Journal of Arid Land, 2022, 14(6): 637-652.

URL:

http://jal.xjegi.com/10.1007/s40333-022-0097-6 OR http://jal.xjegi.com/Y2022/V14/I6/637

Fig. 1 Overview of Lanzhou City

Table 1 Driving factors of habitat quality used in the Geographical Detector (Geodetector) model

Fig. 2 Classification of driving factors for the spatial differentiation of habitat quality in Lanzhou City. (a), elevation; (b), slop; (c), topographic relief; (d), ≥10°C accumulated temperature; (e), precipitation; (f), soil erosion; (g), population; (h), gross domestic product (GDP); (i), road density; (j), distance from county (district); (k), nighttime light index; (l), increased area of woodland.

Table 2 Threat factors of habitat quality and calculated results from the habitat quality of Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST-HQ) model

Table 3 Relative sensitivity of habitat types to threat factors

Table 4 Changes of the habitat quality index in Lanzhou City in 2000, 2010, and 2018

Fig. 3 Spatial distribution (a-c) and changes of the habitat quality index (d-f) in Lanzhou City from 2000 to 2018 at grid scale.

Fig. 4 Spatial distribution (a-c) and changes of the habitat quality index (d-f) in Lanzhou City from 2000 to 2018 at town (street) scale. The classification criteria of the habitat quality index at town (street) scale is based on the natural breakpoint method.

Fig. 5 Variations of the habitat quality index at county (district) scale in Lanzhou City from 2000 to 2018. Bars mean standard errors.

Table 5 Moran's I of habitat quality in Lanzhou City from 2000 to 2018

Table 6 Contribution rate of driving factors for the spatial differentiation of habitat quality in Lanzhou City

Table 7 Interactive detection of driving factors for the spatial differentiation of habitat quality in Lanzhou City


[1]	Aguilar R, Cristóbal-Pérez E J, Balvino-Olvera F J, et al. 2019. Habitat fragmentation reduces plant progeny quality: a global synthesis. Ecology Letters, 22(7): 1163-1173. doi: 10.1111/ele.13272 pmid: 31087604

[2]	Shirmohammadi B, Malekian A, Salajegheh A, et al. 2020a. Impacts of future climate and land use change on water yield in a semiarid basin in Iran. Land Degradation & Development, 31(10): 1252-1264. doi: 10.1002/ldr.3554

[3]	Shirmohammadi B, Malekian A, Salajegheh A, et al. 2020b. Scenario analysis for integrated water resources management under future land use change in the Urmia Lake region, Iran. Land Use Policy, 90: 104299, doi: 10.1016/j.landusepol.2019.104299. doi: 10.1016/j.landusepol.2019.104299

[4]	Balkanlou K R, Müller B, Cord A F, et al. 2020. Spatiotemporal dynamics of ecosystem services provision in a degraded ecosystem: A systematic assessment in the Lake Urmia basin, Iran. Science of the Total Environment, 716: 137100, doi: 10.1016/j.scitotenv.2020.137100. doi: 10.1016/j.scitotenv.2020.137100

[5]	Baral H, Keenan R J, Sharma S K, et al. 2014. Spatial assessment and mapping of biodiversity and conservation priorities in a heavily modified and fragmented production landscape in north-central Victoria, Australia. Ecological Indicators, 36: 552-562. doi: 10.1016/j.ecolind.2013.09.022

[6]	Cardoso de Mendonça M J, Sachsida A, Loureiro P R A. 2003. A study on the valuing of biodiversity: the case of three endangered species in Brazil. Ecological Economics, 46(1): 9-18. doi: 10.1016/S0921-8009(03)00080-6

[7]	Coates P S, Casazza M L, Ricca M A, et al. 2016. Integrating spatially explicit indices of abundance and habitat quality: an applied example for greater sage-grouse management. Journal of Applied Ecology, 53: 83-95. pmid: 26877545

[8]	Costa G C, Nogueira C, Machado R B, et al. 2009. Sampling bias and the use of ecological niche modeling in conservation planning: a field evaluation in a biodiversity hotspot. Biodiversity and Conservation, 19: 883-899. doi: 10.1007/s10531-009-9746-8

[9]	Hao R F, Yu D Y, Liu Y P, et al. 2017. Impacts of changes in climate and landscape pattern on ecosystem services. Science of the Total Environment, 579: 718-728. doi: 10.1016/j.scitotenv.2016.11.036

[10]	He B, Chang J X, Guo A J, et al. 2022. Assessment of river basin habitat quality and its relationship with disturbance factors: A case study of the Tarim River Basin in Northwest China. Journal of Arid Land, 14(2): 167-185. doi: 10.1007/s40333-022-0058-0

[11]	He J H, Huang J L, Li C. 2017. The evaluation for the impact of land use change on habitat quality: A joint contribution of cellular automata scenario simulation and habitat quality assessment model. Ecological Modelling, 366: 58-67. doi: 10.1016/j.ecolmodel.2017.10.001

[12]	Hu X Y, Pan J H. 2016. The Stability of landscape pattern in Lanzhou, Gansu, China. Journal of Desert Research, 36(2): 556-563. (in Chinese)

[13]	Kefalas G, Kalogirou S, Poirazidis K, et al. 2019. Landscape transition in Mediterranean islands: The case of Ionian islands, Greece 1985-2015. Landscape and Urban Planning, 191: 103641, doi: 10.1016/j.landurbplan.2019.103641. doi: 10.1016/j.landurbplan.2019.103641

[14]	Krauss J, Bommarco R, Guardiola M, et al. 2010. Habitat fragmentation causes immediate and time-delayed biodiversity loss at different trophic levels. Ecology Letters, 13: 597-605. doi: 10.1111/j.1461-0248.2010.01457.x

[15]	Lanzhou Municipal Bureau of Statistics. 2019. Lanzhou Statistical Yearbook. [2021-12-29]. http://tjj.lanzhou.gov.cn/art/2019/12/11/art_4866_834601.html. (in Chinese)

[16]	Lanzhou Municipal Bureau of Statistics. 2021. Lanzhou Statistical Yearbook. [2021-12-29]. http://tjj.lanzhou.gov.cn/art/2021/12/29/art_4866_1083444.html. (in Chinese)

[17]	Li F X, Wang L Y, Chen Z J, et al. 2018. Extending the SLEUTH model to integrate habitat quality into urban growth simulation. Journal of Environmental Management, 217: 486-498. doi: 10.1016/j.jenvman.2018.03.109

[18]	Li H L, Peng J, Liu Y X, et al. 2017. Urbanization impact on landscape patterns in Beijing City, China: A spatial heterogeneity perspective. Ecological Indicators, 82: 50-60. doi: 10.1016/j.ecolind.2017.06.032

[19]	Liu C F, Wang C, Liu L C. 2018. Spatio-temporal variation on habitat quality and its mechanism within the transitional area of the Three Natural Zones: A case study in Yuzhong county. Geographical Research, 37(2): 419-432. (in Chinese)

[20]	Liu M Y, Jiang F, Liu Yong, 2016. Land use pattern changes over the last two decades in Qinwangchuan area, Lanzhou, China. Journal of Arid Land Resources and Environment, 30(11): 111-116. (in Chinese)

[21]	Lin D, Ma H L, Ren Z C, et al. 2015. Dynamic analysis of ecosystem service value based on land use/cover change in Lanzhou City. Ecological Science, 35(2): 134-142. (in Chinese)

[22]	Maes J, Paracchini M L, Zulian G, et al. 2012. Synergies and trade-offs between ecosystem service supply, biodiversity, and habitat conservation status in Europe. Biological Conservation, 155: 1-12. doi: 10.1016/j.biocon.2012.06.016

[23]	McDonald R I, Forman R T T, Kareiva P, et al. 2009. Urban effects, distance, and protected areas in an urbanizing world. Landscape and Urban Planning, 93: 63-75. doi: 10.1016/j.landurbplan.2009.06.002

[24]	McDonald R I, Güneralp B, Huang C W, et al. 2018. Conservation priorities to protect vertebrate endemics from global urban expansion. Biological Conservation, 224: 290-299. doi: 10.1016/j.biocon.2018.06.010

[25]	Miller J R, Groom M, Hess G R, et al. 2009. Biodiversity conservation in local planning. Conservation Biology, 23(1): 53-63. doi: 10.1111/j.1523-1739.2008.01110.x pmid: 19016822

[26]	Moreira M, Fonseca C, Vergílio M, et al. 2018. Spatial assessment of habitat conservation status in a Macaronesian island based on the InVEST model: a case study of Pico Island (Azores, Portugal). Land Use Policy, 78: 637-649. doi: 10.1016/j.landusepol.2018.07.015

[27]	Newbold T, Hudson L N, Hill S L, et al. 2015. Global effects of land use on local terrestrial biodiversity. Nature, 520: 45-50. doi: 10.1038/nature14324

[28]	Poniatowski D, Stuhldreher G, Löffler F, et al. 2018. Patch occupancy of grassland specialists: Habitat quality matters more than habitat connectivity. Biological Conservation, 225: 237-244. doi: 10.1016/j.biocon.2018.07.018

[29]	Riedler B, Lang S. 2018. A spatially explicit patch model of habitat quality, integrating spatio-structural indicators. Ecological Indicators, 94: 128-141. doi: 10.1016/j.ecolind.2017.04.027

[30]	Sala O E, Chapin F S, Armesto J J, et al. 2000. Global biodiversity scenarios for the year 2100. Science, 287: 1770-1774. pmid: 10710299

[31]	Sallustio L, De Toni A D, Strollo A, et al. 2017. Assessing habitat quality in relation to the spatial distribution of protected areas in Italy. Journal of Environmental Management, 201: 129-137. doi: S0301-4797(17)30613-8 pmid: 28651222

[32]	Sharp R, Douglass J, Wolny S, et al. 2020. InVEST User Guide. [2021-12-11]. https://invest-userguide.readthedocs.io/en/latest/index.html.

[33]	Song S X, Liu Z F, He C Y, et al. 2020. Evaluating the effects of urban expansion on natural habitat quality by coupling localized shared socioeconomic pathways and the land use scenario dynamics-urban model. Ecological Indicators, 112: 106071, doi: 10.1016/j.ecolind.2020.106071. doi: 10.1016/j.ecolind.2020.106071

[34]	Sun X, Lu Z M, Li F, et al. 2018. Analyzing spatio-temporal changes and trade-offs to support the supply of multiple ecosystem services in Beijing, China. Ecological Indicators, 94: 117-129. doi: 10.1016/j.ecolind.2018.06.049

[35]	Sun X Y, Jiang Z, Liu F, et al. 2019. Monitoring spatio-temporal dynamics of habitat quality in Nansihu Lake basin, eastern China, from 1980 to 2015. Ecological Indicators, 102: 716-723. doi: 10.1016/j.ecolind.2019.03.041

[36]	Tang F, Fu M C, Wang L, et al. 2020. Land-use change in Changli County, China: Predicting its spatio-temporal evolution in habitat quality. Ecological Indicators, 117: 106719, doi: 10.1016/j.ecolind.2020.106719. doi: 10.1016/j.ecolind.2020.106719

[37]	Tang X G, Li H P, Xu X B, et al. 2016. Changing land use and its impact on the habitat suitability for wintering Anseriformes in China's Poyang Lake region. Science of the Total Environment, 557-558: 296-306. doi: 10.1016/j.scitotenv.2016.03.108

[38]	Terrado M, Sabater S, Chaplin-Kramer B, et al. 2016. Model development for the assessment of terrestrial and aquatic habitat quality in conservation planning. Science of the Total Environment, 540: 63-70. doi: 10.1016/j.scitotenv.2015.03.064

[39]	Vellend M, Lilley P L, Starzomski B M. 2008. Using subsets of species in biodiversity surveys. Journal of Applied Ecology, 45: 161-169. doi: 10.1111/j.1365-2664.2007.01413.x

[40]	Wilson M C, Chen X Y, Corlett R T, et al. 2015. Habitat fragmentation and biodiversity conservation: key findings and future challenges. Landscape Ecology, 31: 219-227. doi: 10.1007/s10980-015-0312-3

[41]	Xie Y J, Ng C N. 2013. Exploring spatio-temporal variations of habitat loss and its causal factors in the Shenzhen River cross-border watershed. Applied Geography, 39: 140-150. doi: 10.1016/j.apgeog.2013.01.001

[42]	Xu B R, Liu Y C, Dong Y, et al. 2021. Evaluation of habitat quality in Lanzhou Region based on InVEST model. Journal of Desert Research, 41(5): 120-129. (in Chinese)

[43]	Yang J, Xie B P, Zhang D G. 2021. Spatial-temporal heterogeneity of ecosystem services trade-off synergy in the Yellow River Basin. Journal of Desert Research, 41(6): 78-87. (in Chinese)

[44]	Zhou K H, Liu Y L, Tan R H, et al. 2014. Urban dynamics, landscape ecological security, and policy implications: A case study from the Wuhan area of central China. Cities, 41: 141-153. doi: 10.1016/j.cities.2014.06.010

[45]	Zhou W X, Shi P J, Wang Y N, et al. 2017. Effect of ecosystem service values of river valley city: A case study of Lanzhou. Arid Zone Research, 34(1): 232-241. (in Chinese)

[46]	Zhu C M, Zhang X L, Zhou M M, et al. 2020. Impacts of urbanization and landscape pattern on habitat quality using OLS and GWR models in Hangzhou, China. Ecological Indicators, 117: 106654, doi: 10.1016/j.ecolind.2020.106654. doi: 10.1016/j.ecolind.2020.106654

[1]	WANG Jing, WEI Yulu, PENG Biao, LIU Siqi, LI Jianfeng. Spatiotemporal variations in ecosystem services and their trade-offs and synergies against the background of the gully control and land consolidation project on the Loess Plateau, China[J]. Journal of Arid Land, 2024, 16(1): 131-145.

[2]	LIN Yanmin, HU Zhirui, LI Wenhui, CHEN Haonan, WANG Fang, NAN Xiongxiong, YANG Xuelong, ZHANG Wenjun. Response of ecosystem carbon storage to land use change from 1985 to 2050 in the Ningxia Section of Yellow River Basin, China[J]. Journal of Arid Land, 2024, 16(1): 110-130.

[3]	ZHANG Shubao, LEI Jun, TONG Yanjun, ZHANG Xiaolei, LU Danni, FAN Liqin, DUAN Zuliang. Temporal and spatial responses of ecological resilience to climate change and human activities in the economic belt on the northern slope of the Tianshan Mountains, China[J]. Journal of Arid Land, 2023, 15(10): 1245-1268.

[4]	LIU Yifeng, GUO Bing, LU Miao, ZANG Wenqian, YU Tao, CHEN Donghua. Quantitative distinction of the relative actions of climate change and human activities on vegetation evolution in the Yellow River Basin of China during 1981-2019[J]. Journal of Arid Land, 2023, 15(1): 91-108.

[5]	LUAN Yongfei, HUANG Guohe, ZHENG Guanghui. Spatiotemporal evolution and prediction of habitat quality in Hohhot City of China based on the InVEST and CA-Markov models[J]. Journal of Arid Land, 2023, 15(1): 20-33.

[6]	LUO Jing, XIN Liangjie, LIU Fenggui, CHEN Qiong, ZHOU Qiang, ZHANG Yili. Study of the intensity and driving factors of land use/cover change in the Yarlung Zangbo River, Nyang Qu River, and Lhasa River region, Qinghai-Tibet Plateau of China[J]. Journal of Arid Land, 2022, 14(4): 411-425.

[7]	GE Qianqian, XU Wenjie, FU Meichen, HAN Yingxin, AN Guoqiang, XU Yuetong. Ecosystem service values of gardens in the Yellow River Basin, China[J]. Journal of Arid Land, 2022, 14(3): 284-296.

[8]	HE Bing, CHANG Jianxia, GUO Aijun, WANG Yimin, WANG Yan, LI Zhehao. Assessment of river basin habitat quality and its relationship with disturbance factors: A case study of the Tarim River Basin in Northwest China[J]. Journal of Arid Land, 2022, 14(2): 167-185.

[9]	WANG Shiqing, TAO Zefu, SUN Piling, CHEN Sijia, SUN Huiying, LI Nan. Spatiotemporal variation of forest land and its driving factors in the agropastoral ecotone of northern China[J]. Journal of Arid Land, 2022, 14(1): 1-13.

[10]	WANG Junjie, SHI Bing, ZHAO Enjin, CHEN Xuguang, YANG Shaopeng. Synergistic effects of multiple driving factors on the runoff variations in the Yellow River Basin, China[J]. Journal of Arid Land, 2021, 13(8): 835-857.

[11]	WANG Wanrui, CHEN Yaning, WANG Weihua, XIA Zhenhua, LI Xiaoyang, Patient M KAYUMBA. Hydrochemical characteristics and evolution of groundwater in the dried-up river oasis of the Tarim Basin, Central Asia[J]. Journal of Arid Land, 2021, 13(10): 977-994.

[12]	SI Jianhua, FENG Qi, YU Tengfei, ZHAO Chunyan. Nighttime sap flow and its driving forces for Populus euphratica in a desert riparian forest, Northwest China[J]. Journal of Arid Land, 2015, 7(5): 665-674.

Viewed

Full text

Abstract

Cited

Shared

Discussed