2 MATERIALS AND METHODS
2.1 Complex network
analysis
Network structure analysis is the
basis of network function optimization
(Wang et al., 2019). As shown in
Figure 2(a), a landscape is composed of ecological sources and
ecological corridors (Turner, Gardner, & O’Neill, 2001). The
constructed ESN of a landscape may be simplified into a
topological network of nodes and
edges (Figure 2(b)). As more and more topological networks join
together, they form a complex network system as shown in Figure 2(c).
Therefore, an ESN in this study is a complex spatial network with
hierarchy and ecological attributes (Dehmer, Emmert-Streib, & Shi,
2017; Dai, Liu, & Luo, 2020). This study used complex network analysis
to explore the spatial topological structure of the ESN. The topological
structure of the ESN mainly includes nodes, edges, and the overall
network. Nodes represent the basic units of the network and edges
indicate the functional relations among the basic units. More details of
assessment methods including ecological corridors, ecological nodes,
network connectivity, and network robustness can be found in SI Complex
network analysis methods and Eq. S1 to S11.
2.2 Disturbance scenarios
In this study, we designed two
disturbance scenarios including non-human disturbance (NHD) and human
disturbance (HD) based on the robustness assessment. NHD can be regarded
as random natural disasters (e.g., forest fire, debris flow), whereas HD
can be understood as purposeful human activities (e.g., land use change,
urban sprawl).
In terms of NHD, we randomly deleted the nodes in the network by the
“online random number generator” tool (https://rand.91maths.com/) to
ensure the randomness of disturbance. The corresponding nodes were
deleted in random numerical order to calculate the connectivity
robustness and vulnerability robustness of
the ESN. HD refers to the purposeful
deletion of nodes in the ESN. We introduce the
“disturbance radius” to control
the initial direction of the disturbance. The specific operation in
ArcGIS took the boundary of the developed land as the starting point and
the value of 500 m as the radius to conduct buffer and spatial overlay
analysis and to obtain the corresponding nodes distribution. The nodes
within the radius were removed according to the comprehensive importance
of the nodes, and then the nodes outside the radius were deleted to
calculate the network robustness.
To simplify the problem, the following three assumptions were made when
simulating network robustness:
- When the nodes or edges of the
network are disturbed, the destructive power of each disturbance and
the time needed for the disturbance to result in consequences are not
considered; that is, once the disturbance occurs, the nodes or edges
will be deleted and cannot be recovered.
- Once the nodes in the network are removed all the edges related to the
removed nodes are also deleted; accordingly, that is, information
transmission and energy flow through the removed nodes are blocked.
- A node in the disturbed network will become an isolated node when its
neighboring nodes are deleted, but it can still perform some
functions. Only when the node is destroyed by the direct disturbance,
the node will be completely deleted.
2.3 Data sources and statistical
analysis
The UAHB’s ESN in 1995 and 2015
was constructed by Zhou, Lin, Ma, Qi, & Yan (2020), where the details
of original data and analysis can also be found. In 1995, 43 ecological
sources, 85 ecological nodes, and 134 key ecological corridors were
identified or extracted (Figure 3); in 2015, these numbers were 41, 96,
and 161, respectively.
In this study, network analyses were mainly performed
in ArcGIS® (version 10.6), Excel®
(version 2019), Pajek® (version
2.00), SPSS® (version 25.0), and R® (version 36.3). The indexes
including degree, clustering coefficient, core of node, and node
centrality indexes were calculated using Pajek software. The robustness
assessment was achieved using R® and Pajek®. It should be noted that the
isolated nodes in the ESNs were not included in calculating clustering
coefficient, core of node, node
comprehensive importance, and network robustness because their presence
does not affect the functions of the network.