Precipitation dominates the mortality of <i>Caragana korshinskii</i> on the Loess Plateau with a threshold response

doi:10.1016/j.jaridl.2026.10.008

Journal of Arid Land

2026, Vol. 18

Issue (10): 1813-1830 DOI: 10.1016/j.jaridl.2026.10.008 CSTR: 32276.14.JAL.20260195

Research article

Precipitation dominates the mortality of Caragana korshinskii on the Loess Plateau with a threshold response

MU Mengyu¹^,², GUAN Chao¹^,², ZHAO Chenguang¹^,²^,³, SONG Xinyang¹^,², WANG Jiazheng¹^,², ZHAO Changming¹^,²^,^*(

)

¹ State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, College of Ecology, Lanzhou University, Lanzhou 730000, China
² Yuzhong Mountain Ecosystem Observation and Research Station, Lanzhou University, Lanzhou 730000, China
³ Alxa League Forestry and Grassland Protection Station, Alxa League 750306, China

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Abstract

Shrub mortality is accelerating globally, yet the mechanisms and thresholds driving vegetation mortality in dryland shrubs remain insufficiently understood. Caragana korshinskii Kom., a key species for dryland restoration in China, has experienced widespread die-off in recent decades. This study aimed to identify the important drivers of C. korshinskii mortality on the Loess Plateau, China. We surveyed 35 C. korshinskii plantation plots (three 30 m×30 m subplots per sampling plot) across the western Loess Plateau. In each subplot, we measured the plant height, canopy diameter, and basal diameter of 30 randomly selected individuals and recorded the total number of live and dead shrubs to calculate the annual mortality probability (AMP). Environmental factors encompassing climate, soil, topography, and disturbance were compiled for each sampling plot. Spearman's correlation and ridge regression analyses were applied to determine the most driving factors. Piecewise linear models were then used to determine their breakpoints, and partial least squares path model (PLS-PM) was employed to quantify their effects on AMP. The results revealed that AMP exhibited a clear spatial gradient, decreasing from the west to the east across the study area. AMP decreased with increasing mean annual precipitation (MAP) but increased sharply when the MAP fell below 311 mm, suggesting a discontinuous response around this statistical breakpoint. Similarly, AMP increased markedly when the mean annual temperature (MAT) exceeded its statistical breakpoint of 9.13°C. Higher total nitrogen (TN) was associated with a gradual decline in AMP, whereas wind speed and soil thickness were not clearly related. PLS-PM confirmed that MAP is the principal direct driving factor, with TN and MAT making secondary contributions. Our findings reveal that MAP is the key environmental driver of C. korshinskii mortality, with the statistical breakpoint (311 mm) providing critical insights into vegetation stability and implications for the sustainable management and restoration of dryland ecosystems under climate change.

Key words： shrub mortality Caragana korshinskii Kom. annual mortality probability (AMP) mean annual precipitation statistical breakpoints dryland ecosystems Loess Plateau

Received: 29 April 2026 Published: 31 October 2026

Corresponding Authors: *ZHAO Changming (E-mail: zhaochm@lzu.edu.cn)

About author: First author contact:Author contributions

Conceptualization: GUAN Chao; Data curation: MU Mengyu, ZHAO Chenguang, SONG Xinyang, WANG Jiazheng; Formal analysis: MU Mengyu; Investigation: MU Mengyu, ZHAO Chenguang, SONG Xinyang; Methodology: GUAN Chao, ZHAO Chenguang; Resources: ZHAO Changming; Visualization: MU Mengyu; Writing - original draft: MU Mengyu, ZHAO Chenguang, SONG Xinyang, WANG Jiazheng; Writing - review and editing: GUAN Chao, ZHAO Changming; Funding acquisition: ZHAO Changming; Supervision: GUAN Chao, ZHAO Changming. All authors approved the manuscript.

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Cite this article:

MU Mengyu, GUAN Chao, ZHAO Chenguang, SONG Xinyang, WANG Jiazheng, ZHAO Changming. Precipitation dominates the mortality of Caragana korshinskii on the Loess Plateau with a threshold response. Journal of Arid Land, 2026, 18(10): 1813-1830.

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http://jal.xjegi.com/10.1016/j.jaridl.2026.10.008 OR http://jal.xjegi.com/Y2026/V18/I10/1813

Fig. 1 Spatial distribution of sampling plots and land use types in the study area (Loess Plateau)

Table 1 Basic environmental characteristics of the sampling plots

Table S1 Information on the remote sensing data

Fig. 2 Spatial distribution of annual mortality probability (AMP; a), shrub height (b), canopy diameter (c), and basal diameter (d) in the study area. The point color and size are proportional to the values; darker colors and larger points indicate higher values, whereas lighter colors and smaller points indicate lower values. The basemap data were obtained from the GEOVIS Earth Platform (https://geovisearth.com/) in 2024.

Fig. 3 Correlations between AMP and environmental factors, as well as the correlations among these environmental factors. SLO, slope; ASP, aspect; TN, total nitrogen; SWS, soil water storage; ST, soil thickness; CLY, clay content; BD, bulk density; PD, planting density; RD, rodent density; GI, grazing intensity; WS, wind speed; SPEI, standardized precipitation evapotranspiration index; MAT, mean annual temperature; MAP, mean annual precipitation. The network graph shows correlations between environmental factors and AMP, with line thickness indicating correlation strength, and solid and dashed lines representing positive and negative correlations, respectively. The line color indicates the significance level, with the dark blue, light blue, and gray lines representing P<0.001, P<0.050, and P≥0.050 levels, respectively. Each square in the matrix represents the Spearman's correlation coefficient (Spearman's r) between two environmental factors, with the color indicating the strength and direction of the correlation. Significance levels in the correlation matrix are indicated as follows: *** for P<0.001 level, ** for P<0.010 level, and * for P<0.050 level.

Fig. 4 Standardized ridge regression coefficients for environmental factors of AMP. CVR², cross-validated coefficient of determination.

Fig. 5 Relationships of AMP with MAP (a), MAT (b), TN (c), WS (d), and ST (e). The blue and green points represent observations before and after the statistical breakpoint, respectively. The blue and green text shows the fitted equations, R² values, and P values for the relationships between AMP and environmental factors on either side of the statistical breakpoint. The solid line represents the fitted relationship between AMP and environmental factor. The shaded area indicates the 95.0% confidence interval (CI) of the fitted relationship, and the hatched region indicates the 95.0% CI of the statistical breakpoint.

Fig. 6 Pathways of factors affecting AMP (a) and standardized effects of factors from the partial least squares path model (b). The blue and red arrows indicate negative and positive relationships, respectively; the dashed arrows indicate nonsignificant relationships; the standardized coefficients of the paths indicate the strength of the path relationship. The values above the lines indicate path coefficients, with positive values representing positive effects and negative values indicating negative effects. R² indicates the proportion of variation in the variable explained by the model. ^**, P<0.010 level; ^*, P<0.050 level.

Fig. S1 Alternative partial least squares path model including vapor pressure deficit (VPD) as a potential mediator of the effect of mean annual temperature (MAT) on annual mortality probability (AMP; a) and standardized effects of environmental factors from the partial least squares path model (b). MAP, mean annual precipitation; TN, total nitrogen; SWS, soil water storage; WS, wind speed; ST, soil thickness. The blue and red arrows indicate negative and positive relationships, respectively; the dashed arrows indicate nonsignificant relationships; the standardized coefficients of the paths indicate the strength of the path relationship. The values above the lines indicate path coefficients, with positive values representing positive effects and negative values indicating negative effects. R² indicates the proportion of variation in the variable explained by the model. ^***, P<0.001 level; ^**, P<0.010 level; ^*, P<0.050 level.


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