Optimization of Landscape Structure Based on Ecological Network Analysis and Graph theory

Landscape fragmentation reduces the patch area of internal habitat, hinders the operating and regulating ability of normal landscape ecological processes, and damages ecological corridors. Therefore, connecting isolated broken ecological patches and stepping stones through potential corridors within the borders can improve the impact of fragmented landscapes on biodiversity and the connectivity of landscape and promote the exchanges of genetic material and species between patches, which would effectively improve the service functions of natural ecosystems and have an important ecological significance. Basically correct landscape pattern requires ecological network and ecological system. Ecological network helps planners to increase the landscape connectivity between habitat patches. Network optimization is mainly based on the improvement in network connectivity, including the optimization of corridors and nodes. The optimization of corridors mainly refers to the increase in the number of corridors and the repair of ecological breakpoints in the corridors based on the degree of connectivity. Corridor connectivity should be increased in areas with low landscape connectivity. In recent years, the morphological spatial pattern analysis (MSPA) approach, which mainly focuses on structural connectivity, has been increasingly applied in ecological network analysis. This model is mainly used for the analysis of structural connectivity and can be used to accurately distinguish between landscape types and structures. The MSPA method applies four parameters, namely “connectivity”, “edge width”, “transition” and “intext” to classify landscape. Landscape connectivity can be used as a quantitative indicator of how facilitating a source landscape patch is for species migration, as a high degree of connectivity facilitates biodiversity protection and the maintenance of landscape ecological functions. The connectivity of the landscape and the importance of the various landscape patches to landscape connectivity can be reflected under graph.
In northern Iranian provinces like Gilan province, cities have experienced irregular and horizontal urban sprawls during recent decades due to the existence of Hyrcanian Forests, special climatic setting, presence of green areas and adjacency to Caspian Sea, high population density, and the development of economic activities across the region. As a result of land-use change, urban growth and land degradation, the distributions of some terrestrial species have changed in recent years. Phasianus colchicus is one of the focal species in this region. Dispersal distance, which is species specific, is a critical process determining the distance threshold. The maximal dispersal distance of the Phasianus colchicus 3.2. The species prefers forests with canopy cover of 5–25% because these forests are largely covered by shrubs and bushes, which common pheasant use as a refuge. Pheasants live out their lives within a home range of about one square mile (640 acres), requiring all habitat components (nesting cover, brood habitat, winter cover and food plots) to be in close proximity. Ideally, a minimum of 30-60 acres (about 5-10 percent) of this range should be nesting cover. Larger blocks of cover are preferable to narrow linear strips.
In this study, seeking to make a more comprehensive assessment of landscape connectivity, the core habitats and corridors will be identified according to the habitat type and dispersal distance of the focal species.

The study area in this study is located in the two watersheds of Lahijan Chabaksar (49 12 to 5005 E, 37 07 to 37 25 N) and Astaneh-Kuchesfahan (5021 to 50 26 E, 37 02 to 37 06 N), in the east and center of Gilan province, respectively.
In the first step to classify the land cover in this study, the total Landsat 8 images in the period 01/01/2019 to 31/12/2019, which had a cloud cover below 10%, were used. Then, using Google Earth Engine and the products and instructions of vegetation index (NDVI) which related to the four seasons in 2019, urban lands, tree canopy cover to identify forest areas with trees height above 30 meters and finally the data removed from the ground and entered into the system by the user Land cover was classified into eight categories: forest land, rangeland, farmland, water, residential area, and tea farmland, garden and open space. According to the classified map of NDVI and land cover index and finally the identification of rangelands, gardens, forest lands with canopy cover less than 30%, agricultural lands and tea cultivation on the one hand and on the other hand considering the minimum area, elevation (Less than 1200 m above sea level) and slope (low to medium) required for the habitat of this species, the habitats of pheasant species in the region were identified. Then, MSPA analysis was used to form the ecological network and obtain core area. So forest land is extracted to be the foreground, and other land as the background, a series of image processing methods are used to divide the foreground into seven non-overlapping categories (namely, core, bridge, edge, branch, loop, islet and preformation), and then categories that are important for maintaining connectivity are identified, which increases the scientific nature of the ecological source and ecological corridor selection. The level of landscape connectivity in a region can quantitatively characterize whether a certain landscape type is suitable for species exchange and migration, which is of great significance for biodiversity protection and ecosystem balance. In this study, in the aspect of landscape connectivity evaluation, the integral index of connectivity (IIC), the probability of connectivity (PC), the delta of PC (dPC) and the delta of IIC (dIIC) are commonly used as the important indicators of landscape pattern and function, which can reflect well the degree of connection between core patches in the regional level and are calculated by Conefor 2.6 software. As the dispersal ability of different species varies, we assigned the dispersal distance 3.2 km and ring-necked pheasant, respectively. Finally, the top 8 patches with value of dPC above 4 were chosen as the most important habitats. The using least-cost path the corridors between them were determined. The least-cost path is often used to optimize a grid module. The resistance value of a grid describes its facilitating or impeding influences on dispersal processes of species. The resistance value is attached to each land cover unit to calculate the connectivity between two habitats (Table 1). The least-cost path model makes it possible to calculate the minimum cumulative link (corridors) between the target patch and the nearest source patch (habitat). We calculated the path of least resistance for the organism to migrate along and obtained the potential corridors between source patches using the “cost path” analysis in ArcGIS. The different resistance values of each land cover class were the key factors affecting the result.