Spatial pattern of soil multifunctionality and its correlation with environmental and vegetation factors in the Junggar Desert, China

doi:10.17520/biods.2022097

Abstract

Abstract:

Aims: This study was conducted to analyze the soil multifunctionality (SMF) pattern and their driving factors of the Junggar Desert. We tested that whether climate factors (temperature, precipitation and Aridity), soil environment (soil water content, soil temperature and pH) and vegetation factors would be the main driving factors of the spatial variability of SMF in the Junggar Desert.

Methods: The data of the sampling were collected from 79 sample sites of the Junggar Desert. The SMF indices were calculated by mean method and factor analysis method. The spatial characteristics of SMF in the Junggar Desert were obtained by using Kriging interpolation method in ArcGIS. Correlation analysis between single soil function and SMF was performed in R language software, and the best-fit model was used to fit the environmental factors and SMF of the 79 sample sites. The best-fit model was selected applied on the R² and the AIC value of the model. Structural equation model (SEM) analysis was performed using the “Lavaan” package in R language. Direct and indirect effects of different variables on SMF were identified, and the driving factors of spatial variability of the SMF in the Junggar Desert were determined.

Results: Overall, the SMF in the Junggar Desert showed large heterogeneity in spatial distribution, with an increasing trend of SMF from west to east, and trend of increasing first and then decreasing from south to north of the desert. The best-fit model showed that SMF had a significant quadratic function with MAP (mean annual precipitation) and MAT (mean annual temperature), and showed a decreasing first and then increasing trend of with the increase in MAP and MAT. The SMF had a significant primary function with pH and EVI (enhanced vegetation index). Specially, SMF had a trend of significant decreasing along with the increase in pH, and a significant increasing trend along with the increase in EVI. The SMF and Aridity (drought) showed both quadratic and linear (R² was the same for both) relationship, with SMF decreasing with the increase in Aridity. The results of structural equation modeling (SEM) indicated that, SWC was the most important driver of SMF change, followed by EVI. Soil pH, SWC (soil water content), MAT, Aridity and EVI had significant direct effects on SMF in the desert area, with SWC and EVI having significant positive effects and the others having negative effects. MAP, Lon (longitude), Lat (latitude) and Alt (altitude) had indirect effects on SMF by affecting factors such as MAT.

Conclusion: The results of this study indicate that the changes in SMF are caused by the combined effect of multiple environmental conditions. The results are important for the in-depth understanding of the spatial pattern and driving factors of the SMF in the Junggar Desert, which will be beneficial for the assessment of the effects of environmental changes on the multifunctionality and for the ecosystem managements of the desert ecosystems.

Key words: Junggar Desert, soil multifunctionality, driving factors, spatial variation

Shihang Zhang, Ye Tao, Yusen Chen, Hao Guo, Yongxing Lu, Xing Guo, Chaohong Liu, Xiaobing Zhou, Yuanming Zhang. Spatial pattern of soil multifunctionality and its correlation with environmental and vegetation factors in the Junggar Desert, China[J]. Biodiv Sci, 2022, 30(8): 22097.

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URL: https://www.biodiversity-science.net/EN/10.17520/biods.2022097

https://www.biodiversity-science.net/EN/Y2022/V30/I8/22097

Figures/Tables 7

Fig. 1 Soil sampling sites in the Junggar Desert

Fig. 2 Linear relationship of the soil multifunctional index obtained determined by factor analysis (MF1) and mean method (MF2)

Table 1 Correlation between single soil function and soil multifunctionality

	有机碳 SOC	氮 N	磷 P	碳氮比 C∶N	碳磷比 C∶P	氮磷比 N∶P	速效氮 AN	速效磷 AP	MF1	MF2
MF1	0.940**	0.519**	0.637**	0.609**	0.924**	0.344**	0.644**	0.812**	1	0.963**
MF2	0.845**	0.723**	0.569**	0.396**	0.828**	0.572**	0.511**	0.786**	0.963**	1

Table 2 Descriptive statistics of desert soil, vegetation and climate parameters in the Junggar Desert. ST, soil temperature; SWC, Soil water content; EVI, Vegetation enhancement index. CV, Coefficient of variation.

参数 Parameter	有机碳 SOC (g/kg)	全氮 N (g/kg)	全磷 P (g/kg)	速效氮 AN (mg/kg)	速效磷 AP (mg/kg)		年降水 MAP (mm)		年均温 MAT (℃)	干旱程度 Aridity	土壤温度 ST (℃)	土壤含水率 SWC (%)	pH	植被增强指数 EVI
平均值 Mean	5.73	0.21	0.35	32.09	9.03		140.06		7.70	0.91	2.54	2.20	8.30	0.09
最大值 Max	24.10	0.54	0.42	155.59	35.00		192.64		22.17	0.95	14.46	2.60	8.80	0.22
最小值 Min	0.90	0.07	0.26	8.46	2.26		80.27		?4.76	0.84	?3.83	1.90	7.80	0.04
标准误差 SE	4.33	0.08	0.03	26.91	7.90		20.09		6.11	0.02	2.20	0.01	0.20	0.03
变异系数 CV	0.75	0.37	0.10	0.84	0.87	0.14		0.79		0.02	0.86	0.07	0.02	0.37

Fig. 3 Spatial variation characteristics of soil multifunctionality (SMF) in the Junggar Desert

Fig. 4 Linear or nonlinear relationship between environmental factors and soil multifunctionality

Fig. 5 SEM analysis results about direct and indirect effects of environmental factors on soil multifunctionality. Different thickness is directly proportional to the load coefficient beside the arrow. RMSEA, Root mean square of approximate error. The components of spatial location, climate and soil (environment) are included in the model as independent observable variables, but for the simplicity of illustration, we put them in the same box. See Figure 4 for abbreviations of environmental factors. χ2 = 0.127, P = 0.881, GFI = 0.999, AGFI = 0.977, RMSEA = 0.018. * P < 0.05, ** P < 0.01, *** P < 0.001.

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