Accelerated global climate change and intensified human activities profoundly alter landscape patterns and ecosystem services (ESs), making the quantitative evaluation of their dynamic interactions essential for advancing regional sustainable development. This study focused on Qilian Mountain National Park and employed FRAGSTATS 4.2 to analyze the landscape pattern evolution from 2000 to 2020. The Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model was used to assess five key ESs: water yield (WY), carbon storage (CS), water quality purification (ND), soil retention (SR), and habitat quality (HQ). Ecosystem service bundles (ESBs) were identified using a self-organizing map (SOM) approach, and nonlinear relationships between landscape pattern indices and ESs were examined using the Boosted Regression Tree (BRT) model combined with spearman correlation and clustered heatmap analyses. The results indicated that the landscape pattern of Qilian Mountain National Park exhibits a clear east to west gradient. The spatiotemporal dynamics of ESs showed divergent trends, with CS and ND consistently improving, whereas WY exhibited pronounced nonlinear fluctuations. ESBs were classified into four types: ESB I (ecosystem transition bundle), ESB II (ecosystem regulation and protection bundle), ESB III (ecosystem degradation and protection bundle), and ESB IV (ecosystem restoration bundle), reflecting a shift from single function dominance toward multifunctional synergies. A nonlinear coupling relationship existed between landscape pattern indices and total ecosystem services (TES), characterized by a notable decline in TES and continued degradation of ES performance and stability. Together, this study provides a robust scientific foundation for developing differentiated zoning management strategies. The findings deliver valuable scientific insights for the management of ESs in the Qilian Mountain National Park and similar mountain ecosystems, while offering a reference for promoting sustainable development in fragile ecological regions worldwide.
PEI Ruian, MA Binbin, SU Jingjing, HOU Xiaohua, ZHANG Yike. Nonlinear effects and threshold regulation of landscape pattern indices on ecosystem services: a case study of Qilian Mountain National Park. Journal of Arid Land, 2026, 18(6): 968-993.
Fig. 1Spatial distribution of the land use/land cover (LULC) types in the Qilian Mountain National Park. The LULC data were obtained from the Resource and Environmental Science Data Platform (https://www.resdc.cn).
Data type
Period
Spatial resolution
Data source
Land use/land cover (LULC)
2000-2020
30 m
Resource and Environmental Science Data Platform (http://www.resdc.cn)
Landsat 5 TM
2000-2020
30 m
United States Geological Survey (https://www.usgs.oov)
Landsat 8 OLI
2000-2020
30 m
United States Geological Survey (https://www.usgs.oov)
Digital elevation model (DEM)
/
30 m
Geospatial Data Cloud (http://www.gscloud.cn)
Precipitation
2000-2020
1 km
National Earth System Science Data Center (http://www.geodata.cn)
Soid data
2009
1 km
Soil dataset for China Based on the World Soil Database (http://eco.gssdc.cn)
Potential evapotranspiration (PET)
2000-2020
1 km
National Earth System Science Data Center (http://www.geodata.cn )
Administrative boundaries
/
/
Resource and Environmental Science Data Platform (http://www.resdc.cn)
Basin data
2000-2020
1 km
Resource and Environmental Science Data Platform (http://www.resdc.cn)
Table 1 Description of data sources
Index
Definition
Formula
Formula notation
Patch density (PD; number/km2)
Number of patches per unit area
$\text{PD}=\text{N}/\text{A}$
N is the number of landscape patches; and A is the total area of the landscape (km2).
Largest Patch Index (LPI; %)
Maximum patch size as a proportion of total landscape area
Pi is the area share of patch type i; and m is the total number of patch types.
Table 2 Detials of the five landscape pattern metrics
Module
Parameter name
Arable land
Forestland
Grassland
Water bodies
Constructon Land
Unutilized Land
Water yield
Root_depth (mm)
2100
6000
2600
1
1
1500
Kc
0.65
1.00
0.85
1.00
0.30
0.50
LULC_veg
1
1
1
0
0
1
Carbon storage
Caboveground (t/hm2)
4.56
51.08
1.02
0.00
0.00
0.12
Cunderground (t/hm2)
7.45
37.51
6.15
0.00
0.00
1.57
Csoil (t/hm2)
106.30
174.95
128.51
170.27
0.00
1.57
Cdead (t/hm2)
3.67
6.71
3.30
0.00
0.00
0.00
Soil retention
C
0.31
0.05
0.14
1.00
1.00
0.90
P
0.5
1.0
1.0
0.6
1.0
0.0
Water quality purification
TN (kg/(hm2•a))
37
40
81
25
30
107
TP (kg/(hm2•a))
4.0
3.5
7.0
0.4
3.0
6.0
TN/TP retention efficiency
0.45
0.40
0.95
0.05
0.10
0.70
Table 3 Details of parameter settings for different LULC types in different modules
Threat factor
Maximum influence distance (km)
Weight
Attenuation type
Arable land
4
0.6
Linear
Construction land
6
0.6
Exponential
Unutilized land
7
0.7
Exponential
Table 4 Threat factors of habitat quality in this study
LULC type
Habitat suitability
Habitat sensitivity
Arable land
Construction land
Unutilized land
Arable land
0.50
0.30
0.40
0.40
Forestland
1.00
0.75
0.80
0.80
Grassland
0.80
0.50
0.50
0.50
Water bodies
1.00
0.70
0.80
0.80
Construction land
0.00
0.00
0.00
0.00
Unutilized land
0.30
0.30
0.30
0.30
Table 5 Habitat suitability and sensitivity to threat factors for different LULC types
Fig. 2Spatial distributions of patch density (PD), Largest Patch Index (LPI), Landscape Shape Index (LSI), Contagion Index (CONTAG), and Shannon Evenness Index (SHEI) in the Qilian Mountain National Park in 2000 (a1-e1), 2010 (a2-e2), and 2020 (a3-e3)
Fig. 3Spatial distributions of water yield (WY), carbon storage (CS), water quality purification (ND), soil retention (SR), and habitat quality (HQ) within the Qilian Mountain National Park in 2000 (a1-e1), 2010 (a2-e2), and 2020 (a3-e3)
Fig. 4Quantitative relationships among ESs in the Qilian Mountain National Park in 2000 (a), 2010 (b), and 2020 (c), as well as the tendency from 2000 to 2020 (d). In the pie charts, larger sectors indicate stronger correlation strengths. Correlations with statistical significance are denoted by asterisks (*, P<0.050 level; **, P<0.010 level). Synergistic enhancements are depicted by blue vectors, while temporal dynamics are illustrated by red connecting elements.
Fig. 5Spatial distributions of ES trade-offs and synergies in the Qilian Mountain National Park in 2000 (a), 2010 (b), and 2020 (c)
Fig. 6Spatiotemporal distributions of ecosystem service bundles (ESBs; a1-a3), proportions of each ESB type (b), and composition of ESs within each ESB type (c) in 2000, 2010, and 2020. ESB Ⅰ indicates the ecosystem transition bundle; ESB Ⅱ indiates the ecosystem regulation and protection bundle, ESB Ⅲ indicates the ecosystem degradation and protection bundle; and ESB Ⅳ indicates the ecosystem restoration bundle. The angular width of each sector denotes the relative contribution of the corresponding ES within the bundle, while the radial length represents the normalized mean supply level of that ES within the bundle.
Fig. 7Nonlinear impacts of SHEI, LSI, LPI, CONTAG, and PD on total ecosystem services (TES) and identification of critical thresholds in the Qilian Mountain National Park in 2000 (a1-a5), 2010 (b1-b5), and 2020 (c1-c5). "Max" denotes the coordinates of the point where the maximum TES is located; "Min" denotes the coordinates of the point where the minimum TES is located; "Intersection" denotes the coordinates of the critical threshold point in the nonlinear response of TES to each landscape pattern index.
Fig. 8Clustered heatmap of the associations between ES trade-offs and synergies and landscape pattern indices in 2000 (a), 2010 (b), and 2020 (c). Hierarchical clustering tree represents similarity. The closer the branches are, the more similar the response patterns are.
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