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Journal of Arid Land
Journal of Arid Land  2026, Vol. 18 Issue (4): 696-714    DOI: 10.1016/j.jaridl.2026.04.008     CSTR: 32276.14.JAL.20250279
Research article     
Effects of wetland connectivity on plant communities and vegetation patterns in the Qaidam Basin
YUE Yifan1, MA Dengke2,3, MA Yuanyuan2,3, KANG Wenrong2,3, ZHOU Guoying4, ZHAO Wenzhi2,*()
1 School of Forestry and Grassland Science, Ningxia University, Yinchuan 750021, China
2 Linze Inland River Basin Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
3 University of Chinese Academy of Sciences, Beijing 100049, China
4 Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Qinghai 810008, China
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Abstract  

The Qaidam Basin, a typical alpine arid inland basin on the northern Qinghai-Xizang Plateau, China, hosts wetland ecosystems that are strongly constrained by topography and extreme climate. These ecosystems exhibit pronounced spatiotemporal heterogeneity and fragmented distribution patterns, rendering them highly sensitive to environmental change. This study integrated Sentinel-2 remote sensing imagery with the SedInConnect model to delineate wetland patch distributions and calculate the Index of Connectivity (IC) values across the basin. Based on IC values, we stratified field sampling sites into high-, moderate-, and low- connectivity gradient groups to analyze the relationships among plant community characteristics, vegetation spatial patterns, and wetland connectivity in the Qaidam Basin. Partial Least Squares Path Modeling (PLS-PM) was further employed to quantify the driving mechanisms underlying wetland vegetation characteristics. The results revealed that wetland connectivity across the basin was generally low, with IC values up to 1.32 and displaying a west-to-east decreasing gradient. The west and northwest were characterized by relatively continuous high-connectivity wetland networks, while fragmented and low-connectivity wetlands predominated in the east and southeast. Connectivity regulated wetland vegetation patterns primarily by affecting patch size, fragmentation, and internal adjacency. High-connectivity areas had higher class area (CA), largest patch index (LPI), and area-weighted mean patch size (AREA_AM) than low-connectivity areas. Connectivity had the strongest effect on vegetation coverage, which declined sharply from 87.577% in high-connectivity areas to 12.152% in low-connectivity areas. Meanwhile, species diversity showed a moderately negative response to connectivity changes, whereas species evenness remained relatively unaffected. PLS-PM explained 78.300% and 67.500% of the variance in vegetation community and vegetation pattern, respectively. Climate played a dominant role in shaping vegetation characteristics, with significant negative effects on both vegetation community and pattern. Topography influenced vegetation indirectly through climate, and connectivity was influenced by both drivers and exerted positive effects on vegetation community and pattern. This study reveals the multi-pathway driving mechanisms underlying vegetation pattern formation in alpine wetlands, providing a theoretical foundation and decision-support framework for the scientific conservation and adaptive management of wetlands in the Qaidam Basin.

Key words： connectivity gradient      wetland vegetation      vegetation pattern      community characteristics      driving mechanisms     
Received: 21 December 2025      Published: 30 April 2026
Corresponding Authors: *ZHAO Wenzhi (E-mail: zhaowzh@lzb.ac.cn)
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YUE Yifan
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YUE Yifan, MA Dengke, MA Yuanyuan, KANG Wenrong, ZHOU Guoying, ZHAO Wenzhi. Effects of wetland connectivity on plant communities and vegetation patterns in the Qaidam Basin. Journal of Arid Land, 2026, 18(4): 696-714.

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http://jal.xjegi.com/10.1016/j.jaridl.2026.04.008     OR     http://jal.xjegi.com/Y2026/V18/I4/696

Fig. 1 Sampling site locations of the wetlands in the Qaidam Basin (a) and representative landscape photographs of high- (b), moderate- (c), and low-connectivity (d) sites.
Connectivity gradients Threshold range Sampling site IC value Geographic information
High-connectivity 0.28-1.32 S2 0.38 36°34′25.33′′N, 98°31′21.66′′E
S3 0.31 37°09′10.21′′N, 97°33′14.92′′E
S4 0.66 37°24′30.63′′N, 96°43′32.58′′E
S5 0.29 37°44′39.71′′N, 95°17′28.83′′E
S11 0.47 36°58′21.19′′N, 93°02′08.41′′E
Moderate-connectivity -1.79-0.28 S1 0.05 36°47′40.06′′N, 99°00′29.35′′E
S8 0.03 36°36′53.92′′N, 93°53′19.79′′E
S9 -1.75 36°52′39.82′′N, 93°23′53.26′′E
S10 0.05 36°36′16.16′′N, 93°54′13.67′′E
Low-connectivity < -1.79 S6 -2.16 36°36′16.80′′N, 94°58′28.85′′E
S7 -1.86 36°28′13.95′′N, 94°19′27.01′′E
S12 -2.33 37°22′07.14′′N, 92°30′37.78′′E
S13 -2.06 37°33′14.12′′N, 92°07′54.68′′E
Table 1 Sampling sites division
Fig. 2 Correlation analysis between the Index of Connectivity (IC) and wetland area
Fig. 3 Spatial distribution of different landscape
Fig. 4 Determination of the optimal analysis unit. (a), variation characteristics of wetland landscape total area (TA); (b), stability analysis of first-order differences (ΔTA) across different analysis unit. CV, coefficient of variation.
Fig. 5 Spatial pattern of different wetland connectivity gradients
Fig. 6 Comparative analysis of wetland vegetation community characteristics under different connectivity gradients. (a), fractional vegetation coverage (FVC); (b), Simpson's Dominance Index; (c), Pielou's Evenness Index; (d) Shannon-Wiener Diversity Index. The upper and lower boundaries of the box indicate the 25th and 75th percentiles, respectively; the bar is the standard deviation (SD); the horizontal line within each box indicates the median value; and the black line connects the mean values across analysis units; the symbol "×" indicates that only one species was recorded at that sampling site; and the point indicates the data of each sampling site. ns, non-significance; ***, significance at P<0.01 level.
Fig. 7 Characteristics of wetland vegetation pattern indices under different connectivity gradients. (a), class area (CA); (b), largest patch index (LPI); (c), area-weighted mean patch size (AREA_AM); (d), mean shape index (SHAPE_MN); (e), patch density (PD); (f), perimeter-area fractal dimension (PAFRAC); (g), mean contiguity index (CONTIG_MN); (h), mean Euclidean nearest-neighbor distance (ENN_MN); (i), aggregation index (AI).
Fig. 8 Analysis of drivers for wetland vegetation characteristics in the Qaidam Basin using PLS-PM. (a), the loading value of each explanatory variable; (b), the direct and indirect effects of environmental variables on wetland ecosystems from the PLS-PM. AVE, average variance extracted; MAP, mean annual precipitation; MAT, mean annual temperature; PET, potential evapotranspiration. GOF, Goodness-of-Fit. **, significance at P<0.05 level; ***, significance at P<0.01 level. Solid lines indicate significant relationships; and blue and red dashed lines indicate negative and positive insignificant relationships, respectively. The thickness of the line represents the strength of the causal relationship, supplemented by a standardized path coefficient.
Landscape Sampling sites Source Cartographic Accuracy
Wetland 83 (66) Field survey & Google Earth point 88.451%
Water body 92 Google Earth point 92.607%
Bare land 83 Google Earth point 87.923%
Other vegetation 76 Google Earth point 83.411%
Artificial surface 84 Google Earth point 85.655%
Snow 57 Google Earth point 80.127%
Table S1 Number of validation sampling sites and cartographic accuracy for each land cover type
Fig. S1 Spatial distribution of validation sampling sites used for accuracy assessment. The survey route denotes the actual vehicle survey route conducted during field investigations in the Qaidam Basin from June to July in 2025.
Fig. S2 Frequency distribution histogram of IC values with connectivity classification based on the Jenks Natural Breaks method
Sample Dominant species Life form Density Height (cm) Crown width (cm) Importance
value
S1 Agropyron cristatum (L.) Gaertn. Perennial herb 160 18.09±4.53 17.5±3.54 0.24
Phragmites australis (Cav.) Trin. ex Steud. Perennial herb 242 4.88±1.40 5.31±2.16 0.23
S2 Cyperus rotundus L. Annual herb 430 6.67±3.06 3.00±0.58 0.38
A. cristatum Perennial herb 76 102.5±37.89 4.50±0.71 0.36
S3 Nitraria tangutorum Bobrov Shrub 1 120.00±55.13 150.00±39.24 0.32
C. rotundus Annual herb 200 10.00±1.12 10.00±0.74 0.21
S4 Tamarix chinensis Lour. Shrub 1 200.00±42.14 150.00±23.22 0.37
P. australis Perennial herb 150 160.00±10.13 30.00±5.24 0.21
S5 P. australis Perennial herb 120 60.00±5.79 40.00±22.56 0.26
Calamagrostis epigeios (L.) Roth Perennial herb 100 150.00±26.28 30.00±9.86 0.31
S6 Apocynum venetum L. Semi-shrub 49 73.13±15.26 42.67±7.53 0.66
P. australis Perennial herb 21 25.93±10.94 16.93±12.30 0.26
S7 T. chinensis Shrub 3 200.00±50.00 260.00±69.28 0.34
A. venetum Semi-shrub 63 39.06±9.35 31.67±5.88 0.33
S8 P. australis Perennial herb 370 113.33±29.55 45.33±13.60 1.00
S9 P. australis Perennial herb 1760 47.65±22.05 25.56±9.38 0.63
T. chinensis Shrub 1 150.00±44.65 176.00±34.52 0.29
S10 T. chinensis Shrub 3 200.00±64.38 350.00±77.84 0.40
P. australis Perennial herb 35 35.00±14.23 30.00±10.24 0.35
S11 P. australis Perennial herb 740 65.67±23.42 17.94±7.04 1.00
S12 P. australis Perennial herb 70 50.78±31.33 23.94±14.87 1.00
S13 P. australis Perennial herb 97 22.36±7.68 7.90±5.24 0.30
T. chinensis Shrub 1 45.00±12.14 200.00±70.91 0.29
Table S2 Characteristics of wetland vegetation communities in the Qaidam Basin
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