How to Optimize High-Value GEP Areas to Identify Key Areas for Protection and Restoration: The Integration of Ecology and Complex Networks

Abstract

Identifying and protecting key sites of ecological assets and improving spatial connectivity and accessibility are important measures taken to protect ecological diversity. This study takes Guangxi as the research area. Based on the gross ecosystem product (GEP), the ecological source is identified, and the initial ecological network (EN) is constructed by identifying the ecological corridor with the minimum cumulative resistance model. The internal defects of the initial ecological network are extracted using the circuit theory, the priority areas for restoration and protection with clear spatial positions are determined according to the complex network analysis, and the network’s performance before and after optimization is comprehensively evaluated. The results show that 456 initial ecological sources and 1219 ecological corridors have been identified, forming the initial ecological network of Guangxi. Based on the circuit theory, 168 ecological barriers, 83 ecological pinch points, and 71 ecological stepping stones were extracted for network optimization. After optimizing the ecological network, there are 778 ecological sources with a total area of 73,950.56 km2 and 2078 ecological corridors with a total length of 23,922.07 km. The GEP of the optimized structure is 13.33% higher than that of the non-optimized structure. The priority areas for protection are distributed in a large area, and the attached GEP reaches USD 118 billion, accounting for 72% of the total GEP attached to the optimized ecological source area. The priority areas for restoration are scattered in small patches, with a GEP of USD 19.27 billion. The robustness and connectivity of the optimized ecological network have been improved obviously. This study attempts to identify key sites of ecological assets and the priority regions for restoration and conservation using genuine geographical location and reference materials for regional ecological network optimization and implementation.

Impacts and Predictions of Urban Expansion on Habitat Connectivity Networks: A Multi-Scenario Simulation Approach

“…The topological relationships of HCNs reflect the overall spatial structure of habitats [16]. In this study, the HCNs were described using six topological indicators, including average degree, diameter, modularity, clustering coefficient, eigenvector centrality, and average path length [19,27,28].…”

“…The number of edges in a network that are connected to a node determines its degree, which reflects how connected a habitat patch is to its nearby patches in the HCN. The average degree of the network is determined as the average of all nodes' degrees [19,28]. The specific formula is as follows: The conversion of unused land leads to changes in the original land cover, and thus data such as land cover and NDVI need to be adjusted.…”

“…The diameter is the maximum distance between nodes i and j in the network. The connectivity between habitats can be shown by the diameter [19,28]. The specific formula is as follows:…”

“…Usually, a modularity value greater than 0.44 indicates that the network has reached a certain degree of modularity. Modularity actually refers to the distinction between a network that is part of a certain community division and a random network, which means that a larger the difference means that the community division is better and the internal link density is higher [28,29]. The specific formulas are as follows:…”

“…The degree of connectedness between nearby nodes of a given node is represented by the clustering coefficient. The probability that two nodes connected to the same node will remain connected is known as the clustering coefficient of a node [19,28]. The specific formula is as follows:…”