水热条件共同驱动新疆湿地植物丰富度空间分布格局

doi:10.12118/j.issn.1000-6060.2022.265

干旱区地理 ›› 2023, Vol. 46 ›› Issue (1): 86-93.doi: 10.12118/j.issn.1000-6060.2022.265

水热条件共同驱动新疆湿地植物丰富度空间分布格局

韩大勇¹(),牛忠泽²(),伍永明³,高健²

1.伊犁师范大学生物与地理科学学院，新疆伊宁 835000
2.中国科学院新疆生态与地理研究所沙漠工程勘察设计所，新疆乌鲁木齐 830011
3.新疆维吾尔自治区林业和草原局，新疆乌鲁木齐 830099

收稿日期:2022-06-03 修回日期:2022-08-20 出版日期:2023-01-25 发布日期:2023-02-21
通讯作者: 牛忠泽（1969-），男，高级经济师，主要从事湿地及自然保护地研究. E-mail: niuzz@vip.sina.com
作者简介:韩大勇（1978-），男，博士，教授，主要从事干旱区植物多样性和湿地植被恢复等方面研究. E-mail: 17986754@ylnu.edu.cn
基金资助:
新疆维吾尔自治区自然科学基金项目(2019D01C332);伊犁师范大学引进博士科研启动基金(2018005)

Spatial distribution pattern of wetland plant species richness driven by water and heat conditions collectively in Xinjiang

HAN Dayong¹(),NIU Zhongze²(),WU Yongming³,GAO Jian²

1. College of Biological and Geographical Sciences, Yili Normal University, Yining 835000, Xinjiang, China
2. Desert Engineering Survey and Design Institute, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, Xinjiang, China
3. Forestry and Grassland Bureau of Xinjiang Uygur Autonomous Region, Urumqi 830099, Xinjiang, China

Received:2022-06-03 Revised:2022-08-20 Online:2023-01-25 Published:2023-02-21

摘要/Abstract

摘要：

在地理空间尺度上，气候因素（如热量、降水量等）一直被认为是物种多样性的主要驱动因素。然而，气候因素能否解释湿地植物多样性格局仍不清楚。研究探讨了环境因素尤其水分和热量条件对湿地物种分布的影响，具体包括经度、纬度、海拔、年平均降水量、年平均气温、年平均蒸发量和年平均日照时数总计7个指标，研究对象涉及新疆3个二级流域的26处湿地公园，应用结构方程模型分析了各指标对湿地植物丰富度影响的相对大小及其相互作用关系。另外，还利用莫兰指数（Moran’s I）对各变量残差进行了空间相关性分析，以评估空间相关性的影响。结果表明：（1）结构方程模型总计解释了41.8%的物种丰富度变异，以年平均降水量对物种丰富度总效应最高，为0.47，其次是年平均日照时数，为-0.42，其中年平均降水量为正效应，年平均日照时数为负效应。其他各指标对物种丰富度的效应均不显著。（2）年平均降水量对植物丰富度的影响主要表现为直接效应，占总效应的92.86%，年平均日照时数对植物丰富度的影响主要是间接效应，占总效应的54.76%。（3）空间相关性分析表明年平均降水量和年平均日照时数的残差均不存在空间相关性，莫兰指数在-0.15~0.10范围内波动，可以认为是可靠的预测指标。综上，新疆湿地植物丰富度主要受水热条件的共同驱动，且热量的作用依赖于水分条件，在未来湿地植物多样性保护工作中，应加强气候变化对植物多样性影响的评估和应对措施。

关键词: 水热动态假说, 结构方程模型, 空间相关性, 干旱区, 湿地保护

Abstract:

On the geographic spatial scale, climate factors (such as environmental energy and precipitation) are the main driving factors of plant species diversity. However, it remains unclear whether climatic factors can explain the plant diversity in wetlands. This study discusses the influence of environmental factors, especially the effect of water and heat conditions on the distribution of wetland species. Specifically, it includes four categories and seven indicators, including spatial factors (longitude and latitude), terrain factors (altitude), water factors (annual average precipitation and evaporation), and heat factors (annual average air temperature and sunshine hours). The research objects involve 26 wetland parks in three secondary river basins in Xinjiang, China. The structural equation model is used to explore the relative importance of each indicator on wetland plant richness and their interaction. In addition, Moran’s I index is used to analyze the spatial correlation of the residuals of each variable to evaluate the impact of spatial correlation. The results show that (1) the structural equation model explains 41.8% of the variation in plant species richness. The total effect of annual average precipitation on species richness is the highest, which is 0.47, followed by the annual average sunshine hours, which is -0.42. Among them, the annual average precipitation has a positive effect, whereas the annual average sunshine hours have a negative effect. The effects of other indices on species richness are insignificant. (2) The influence of annual average precipitation on plant richness is primarily a direct effect, which is 0.39, accounting for 92.86% of the total effect. The influence of annual average sunshine hours on plant richness is primarily an indirect effect, which is -0.23, accounting for 54.76% of the total effect. (3) Spatial correlation analysis shows that there is no spatial correlation between the residuals of annual average precipitation and sunshine hours on different spatial scales, and the Moran’s I index fluctuates within the range of -0.15 to 0.10, which could be considered reliable prediction indices. (4) The direct effects of spatial factors such as longitude and latitude on plant richness are insignificant, whereas the indirect effects are significant. Longitude significantly affects the annual average precipitation, and latitude significantly affects the annual average sunshine hours and temperature, indicating that spatial factors indirectly affect the species richness by affecting the annual average precipitation and sunshine hours. In conclusion, the plant richness of the wetlands in Xinjiang is primarily driven by water and heat conditions. The role of heat depends on the water conditions. In future wetland plant diversity protection studies, the assessment and response measures of the impact of climate change on plant diversity should be strengthened.

Key words: water-energy dynamics hypothesis, structural equation model, spatial correlation, arid area, wetland protection

韩大勇, 牛忠泽, 伍永明, 高健. 水热条件共同驱动新疆湿地植物丰富度空间分布格局[J]. 干旱区地理, 2023, 46(1): 86-93.

HAN Dayong, NIU Zhongze, WU Yongming, GAO Jian. Spatial distribution pattern of wetland plant species richness driven by water and heat conditions collectively in Xinjiang[J]. Arid Land Geography, 2023, 46(1): 86-93.

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二级流域	三级流域	湿地公园或保护区	代码
I阿尔泰山南麓诸河	I1乌伦古河	1福海乌伦古湖国家湿地公园^*	WLGH
	I2哈巴河	2哈巴河阿克齐国家湿地公园^*	AKQ
	I3额尔齐斯河	3富蕴可可托海国家湿地公园	KKTH
II天山北麓诸河	II1艾比湖水系	4艾比湖湿地国家级自然保护区^*	ABH
	II2东段诸河	5呼图壁大海子国家湿地公园	DHZ
		6阜康特纳格尔国家湿地公园	TNGE
		7吉木萨尔北庭国家湿地公园^*	BT
		8玛纳斯国家湿地公园	MNS
	II3中段诸河	9博乐博尔塔拉河湿地公园^*	BL
		10赛里木湖国家湿地公园	SLM
		11温泉博尔塔拉河国家湿地公园^*	WQ
III中亚西亚内陆河区	III1伊犁河	12伊宁伊犁河国家湿地公园^*	YN
		13霍城伊犁河国家湿地公园^*	HC
		14特克斯国家湿地公园^*	TKS
		15察布查尔县伊犁河国家湿地公园^*	CBCE
		16昭苏特克斯河国家湿地公园^*	ZS
		17尼勒克喀什河国家湿地公园^*	NLK
		18新疆天山阿合牙孜国家湿地公园^*	AHYZ
	III2额敏河	19塔城五弦河国家湿地公园	WXH
IV塔里木河源流	IV1阿克苏河	20阿合奇托什干河国家湿地公园^*	TSGH
	IV2开孔河	21博斯腾湖国家湿地公园^*	BSTH
		22尉犁罗布淖尔国家湿地公园	LBNE
		23和硕塔什汗国家湿地公园	TSH
		24焉耆相思湖国家湿地公园	XSH
V塔里木盆地荒漠区	V1塔克拉玛干沙漠	25民丰尼雅国家湿地公园	NY
VI吐哈盆地小河	VI1吐鲁番盆地	26吐鲁番艾丁湖国家湿地公园	ADH

水热条件共同驱动新疆湿地植物丰富度空间分布格局

Spatial distribution pattern of wetland plant species richness driven by water and heat conditions collectively in Xinjiang

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