| Literature DB >> 35954940 |
Qian Zuo1,2, Yong Zhou1,2, Jingyi Liu1,2.
Abstract
High-intensity urban development and economic exploitation have led to the fragmentation and isolation of regional habitat patches, and biodiversity is under serious threat. Scientific identification and effective optimization of ecological networks are essential for maintaining and restoring regional ecosystem connectivity and guiding sustainable socio-economic development. Taking the mountainous areas of southwest Hubei Province (MASHP) in central China as an example, this study first developed a new integrated approach to identify ecological sources based on a quantitative assessment of ecosystem services and the morphological spatial pattern analysis (MSPA) method; it then used the Linkage Mapper tool to extract ecological corridors, applied the principle of hydrological analysis to identify ecological nodes, evaluated each ecological element to quantify its importance, and finally constructed the ecological network and further proposed some optimization countermeasures. The results show that the ecological network in the MASHP is dominated by ecological resources composed of forestland. Connectivity in the central region is significantly better than in other regions, including 49 ecological sources with an area of 3837.92 km2, 125 ecological corridors with a total length of 2014.61 km, and 46 ecological nodes. According to the spatial distribution of crucial ecological landscape elements, a complete and systematic ecological framework of "two verticals, three belts, three groups, and multiple nodes" was proposed. The internal optimization of the ecological network in mountainous areas should focus on improving ecological flow, and strategies such as enhancing the internal connectivity of ecosystems, unblocking ecological corridors, and dividing ecological functional zones can be adopted. Based on the above analyses, this study also made recommendations for ecological protection and development and construction planning in mountainous areas. This study can provide realistic paths and scientific guidelines for ecological security and high-quality development in the MASHP, and it can also have implications for the construction of ecological networks and comprehensive ecological management in other mountainous areas.Entities:
Keywords: ecological function zones; ecological network; ecosystem services; landscape connectivity; morphological spatial pattern analysis
Mesh:
Year: 2022 PMID: 35954940 PMCID: PMC9368242 DOI: 10.3390/ijerph19159582
Source DB: PubMed Journal: Int J Environ Res Public Health ISSN: 1660-4601 Impact factor: 4.614
Figure 1Location of the study area: (a) location in Hubei Province, China; (b) digital elevation map (DEM); (c) land cover.
Data information table.
| Data Types | Format | Data Sources |
|---|---|---|
| Land use data | Grids at 30 m resolution in 2020 | Resource and Environment Science and Data Center ( |
| Digital elevation model (DEM) | Grids at 30 m resolution | Geospatial Data Cloud site ( |
| Traffic road and river data | Lines in 2020 | National Catalogue Service for Geographic Information ( |
| Meteorological data | Grids at 1 km resolution in 2020 | National Meteorological Information Center ( |
| Soil attributes | Grids at 1 km resolution | Harmonized World Soil Database v 1.2 from Food and Agriculture Organization of the United Nations ( |
| Normalized difference vegetation index (NDVI) | Grids at 1 km resolution in 2019 | Resource and Environment Science and Data Center ( |
Figure 2Framework of this study.
Weights and coefficients of resistance factors.
| Resistance Factor | Resistance Coefficient | Weight | ||||
|---|---|---|---|---|---|---|
| 10 | 30 | 50 | 70 | 90 | ||
| Land use type | Forestland/Grassland | Water bodies | Cultivated land | Unused land | Construction land | 0.5299 |
| Slope (°) | <8 | [8–20) | [20–30) | [30–40) | ≥40 | 0.0636 |
| Elevation (m) | <374 | [374–755) | [755–1085) | [1085–1441) | ≥1441 | 0.0636 |
| Distance from river (km) | <2 | [2–5) | [5–8) | [8–10) | ≥10 | 0.1034 |
| Distance from settlements (km) | ≥25 | [15–25) | [9–15) | [4–9) | <4 | 0.1273 |
| Distance from main roads (km) | ≥75 | [55–75) | [35–55) | [15–35) | <15 | 0.1122 |
Evaluation index system of ecological corridor importance.
| Target Layer | Criteria Layer | Solution Layer | Grading Evaluation | Description |
|---|---|---|---|---|
| Ecological corridor importance | Corridor function importance (0.62) | Total area of patches connected by corridors (0.22) | 5 | The sum of the area of connected ecological sources by the corridor, the larger the area, the more important the corridor. |
| 3 | ||||
| 1 | ||||
| Landscape connectivity of main connected | 5 | The higher the | ||
| 3 | ||||
| 1 | ||||
| Corridor condition (0.38) | Corridor length (0.10) | 5 | The longer the corridor, the greater the risk of breakage. | |
| 3 | ||||
| 1 | ||||
| Corridor quality I (0.10) | 5 | The ratio of CWD to the Euclidean distance represents the ease of animal migration between sources. When the value is larger, the corridor quality is poorer | ||
| 3 | ||||
| 1 | ||||
| Corridor quality II (0.18) | 5 | The ratio of CWD to the least-cost path length (LCPL) can study the quality of corridors. When the CWD/LCPL value is larger, species suffer greater resistance to migration or dispersal through this corridor, and the corridor quality is poorer. | ||
| 3 | ||||
| 1 |
Classification criteria of ecological nodes.
| Levels of Ecological Nodes | Classification Criteria |
|---|---|
| 1 | Located at the intersection of key ecological corridors and the “ridge lines”. |
| 2 | Located at the intersection of important ecological corridors and the “ridge lines”. |
| 3 | Located at the intersection of ordinary ecological corridors and the “ridge lines”. |
Figure 3Spatial pattern of ecosystem services and identification of cold and hot areas of ecosystem.
Figure 4Effect of the minimum area threshold on the number of patches and area of patches from 1 to 15 km2.
Figure 5MSPA-based landscape feature type map.
Figure 6Ecological sources in the study area.
Figure 7Spatial distribution of comprehensive resistance surface and minimum cumulative resistance.
Figure 8Ecological network in the study area.
Figure 9Ecological framework in the study area.
Area of different land use types in corridors with different widths (km2).
| Land Use Type | Corridor Width (m) | ||||||
|---|---|---|---|---|---|---|---|
| 30 | 60 | 100 | 400 | 600 | 800 | 1000 | |
| Cultivated land | 2.49 | 6.15 | 15.55 | 126.66 | 213.37 | 295.95 | 383.74 |
| Forestland | 96.85 | 175.34 | 311.20 | 1161.49 | 1734.31 | 2305.15 | 2900.60 |
| Grassland | 7.89 | 14.44 | 25.42 | 97.13 | 145.43 | 194.68 | 247.85 |
| Water bodies | 1.38 | 2.76 | 5.37 | 25.46 | 38.69 | 51.49 | 64.21 |
| Construction land | 0.17 | 0.39 | 0.96 | 6.44 | 10.97 | 15.75 | 21.04 |
Figure 10Area proportion of different land use types in corridors with different widths.
Figure 11Relationship of grid number and minimal accumulated resistance.
Figure 12Ecological function zoning based on ecological network.