Impact of nitrogen addition and precipitation on net ecosystem exchange in the Urat desert steppe, China

doi:10.1007/s40333-025-0050-6

Journal of Arid Land

2025, Vol. 17

Issue (3): 337-349 DOI: 10.1007/s40333-025-0050-6 CSTR: 32276.14.JAL.02500506

Research article

Impact of nitrogen addition and precipitation on net ecosystem exchange in the Urat desert steppe, China

ZHANG Xiaoxue¹^,²^,³^,⁴, YUE Ping¹^,²^,³, SONG Zhaobin¹^,²^,³^,⁴, ZUO Xiaoan¹^,²^,³^,^*(

), ZHANG Rui¹^,³^,⁵, WANG Zhengjiaoyi¹^,²^,³^,⁴, QIAO Jingjuan¹^,²^,³^,⁴

¹National Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
²Urat Desert-Grassland Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
³Key Laboratory of Stress Physiology and Ecology in Cold and Arid Region of Gansu Province, Lanzhou 730000, China
⁴University of Chinese Academy of Sciences, Beijing 100049, China
⁵Naiman Desertification Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China

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Abstract

Amid global climate change, rising levels of nitrogen (N) deposition have attracted considerable attention for their potential effects on the carbon cycle of terrestrial ecosystems. The desert steppes are a crucial yet vulnerable ecosystem in arid areas, but their response to the combination of N addition and precipitation (a crucial factor in arid areas) remains underexplored. This study systematically explored the impact of N addition and precipitation on net ecosystem exchange (NEE) in a desert steppe in northern China. Specifically, we conducted a 2-a experiment from 2022 to 2023 with eight N addition treatments in the Urat desert steppe of Inner Mongolia Autonomous Region, China, to examine changes in NEE and explore its driving factors. The structural equation model (SEM) and multiple regression model were applied to determine the relationship of NEE with plant community characteristics and soil physical-chemical properties. Statistical results showed that N addition has no significant effect on NEE. However, it has a significant impact on the functional traits of desert steppe plant communities. SEM results further revealed that N addition has no significant effect on NEE in the desert steppe, whereas annual precipitation can influence NEE variations. The multiple regression model analysis indicated that plant functional traits play an important role in explaining the changes in NEE, accounting for 62.15% of the variation in NEE. In addition, plant height, as an important plant functional trait indicator, shows stronger reliability in predicting the changes in NEE and becomes a more promising predictor. These findings provide valuable insights into the complex ecological mechanisms governing plant community responses to precipitation and nutrient availability in the arid desert steppes, contributing to the improved monitoring and prediction of desert steppe ecosystem responses to global climate change.

Key words： nitrogen addition net ecosystem exchange plant functional traits structural equation model (SEM) multiple regression model Urat desert steppe

Received: 15 October 2024 Published: 31 March 2025

Corresponding Authors: ^*ZUO Xiaoan (E-mail: xazuo@126.com)

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	ZHANG Xiaoxue
	YUE Ping
	SONG Zhaobin
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	ZHANG Rui
	WANG Zhengjiaoyi
	QIAO Jingjuan

Cite this article:

ZHANG Xiaoxue, YUE Ping, SONG Zhaobin, ZUO Xiaoan, ZHANG Rui, WANG Zhengjiaoyi, QIAO Jingjuan. Impact of nitrogen addition and precipitation on net ecosystem exchange in the Urat desert steppe, China. Journal of Arid Land, 2025, 17(3): 337-349.

URL:

http://jal.xjegi.com/10.1007/s40333-025-0050-6 OR http://jal.xjegi.com/Y2025/V17/I3/337

Fig. 1 Location of the study site (Urat Desert-Grassland Research Station) in Inner Mongolia Autonomous Region (a) and experimental design using a randomized block design (b). N0, N0.5, N1, N3, N6, N12, N24, and N48 represent the N addition levels of 0.0, 0.5, 1.0, 3.0, 6.0, 12.0, 24.0, and 48.0 g/(m²•a), respectively. Note that the figure is based on the standard map (GS(2020)4619) of the Ministry of Natural Resources of the People's Republic of China (https://www.mnr.gov.cn/sj/sjfw/), and the boundary of the standard map has not been modified.

Fig. 2 Monthly average temperature (a) and monthly precipitation (b) at the Urat Desert-Grassland Research Station in 2022 and 2023

Table 1 Changes in soil physical-chemical properties under different nitrogen (N) addition treatments

Fig. 3 Community-weighted mean (CWM) values of six functional traits under eight N addition treatments. (a), CWM of SLA (specific leaf area); (b), CWM of LDMC (leaf dry matter content); (c), CWM of LNC (leaf nitrogen content); (d), CWM of LCC (leaf carbon content); (e), CWM of plant height; (f), CWM of LT (leaf thickness). Different lowercase letters indicate significant differences in CWM values among different N addition treatments at P<0.050 level. Error bars indicate standard errors.

Fig. 4 Effects of N addition on net ecosystem exchange (NEE) under eight N addition treatments in 2022 and 2023. (a), the first assessment of NEE in 2022; (b), the second assessment of NEE in 2022; (c), the third assessment of NEE in 2022; (d), the first assessment of NEE in 2023; (e), the second assessment of NEE in 2023; (f), a comparison of the average NEE between 2022 and 2023. Different lowercase letters indicate significant differences in NEE values among different N addition treatments at P<0.050 level. Error bars indicate standard errors. Note that the NEE measurements of the N0.5 treatment for the first assessment in 2023 could not be conducted due to equipment failure.

Fig. 5 Characteristics of plant communities in response to N addition treatments in 2022 and 2023. (a), species richness; (b), plant density; (c) AGB (aboveground biomass). Different lowercase letters within the same year indicate significant differences in plant community characteristics between N addition treatments at P<0.050 level. Error bars indicate the standard errors.

Fig. 6 Structural equation model (SEM) illustrating the relationship of NEE with annual precipitation, species richness, and plant height (a) and the relationship of NEE with N addition treatment, annual precipitation, species richness, and plant height (b). Single arrows indicate directional pathways in the model. The black solid line indicates significant relationship, while the gray dashed line indicates nonsignificant relationship. Standardized regression weights are displayed along the paths. The total variance is shown in the top-right corner of each rectangle, representing the influence of all predictors on that variable. Statistically significant pathways are denoted by the asterisks, with ^* representing P<0.100 level, ^** representing P<0.010 level, and ^*** representing P<0.001 level. Chi-square, standardized Chi-square; df, degrees of freedom; AIC, Akaike Information Criterion; NFI, Normed Fit Index.

Fig. 7 Impact of multiple predictive factors on NEE. Each model includes the standardized regression coefficient (average parameter estimate) and its corresponding 95% confidence interval. Species diversity factors (in blue color) include species richness and plant density; plant functional trait factors (in green color) include plant height, LT, SLA, LDMC, LCC, and LNC; soil physical-chemical factors (in orange color) include TNC (total nitrogen content), TCC (total carbon content), pH, EC (electrical conductivity), SWC (soil water content), and BD (bulk density). The figure also shows the accounted percentage of variance for all factors, illustrating their relative importance in the model. ^*, P<0.100 level; ^**, P<0.010 level.


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