1 INTRODUCTION
Global and regional ecological security patterns are facing grand
challenges (Xie & Wu, 2020). The rapid urbanization and
industrialization in most countries had led to various ecological
problems (Bai et al., 2018; Ma, Bo, Li, Fang, & Cheng, 2019; Xie, He,
Choi, Chen, & Cheng, 2020), such as ecological sources encroachment and
destruction (Han, Liu, & Wang, 2015), habitat fragmentation (Ng, Xie,
& Yu, 2011), biodiversity loss (Kong, Yin, Nakagoshi, & Zong, 2010),
ecosystem deterioration (Ernstson,
2013), and ecological function and service value deterioration (Costanza
et al., 1997). A well-performing ecological security network (ESN) is
necessary to enhance landscape connectivity, protect biodiversity,
mitigate ecosystem degradation, improve
ecological security patterns, and
promote sustainable development (Cui, Wang, Sun, & Lv, 2020; Dai, Liu,
& Luo, 2020; Dame & Christian, 2008; Zhao, Ma, Wang, & You, 2019). As
one of the most important concepts and methods of landscape ecology
(Forman, 1995), the ESN is a spatial organization system that identifies
the characteristics of linear ecological corridors
(i.e.,
network edges), connects various ecological sources (i.e., network
nodes), and reflects the combinations of spatial elements with
structural and functional characteristics in a specific space (i.e., the
overall network). The ESN couples network structure,
function, and ecological processes.
Since the 1970s, scholars have been
studying different aspects of ESNs, such as network construction (Fath,
Scharler, Robert, & Hannon, 2017), network planning and assessment
(Cook, 2002), nature conservation (Hepcan, Hepcan, Bouwma, Jongman, &
Ozkan, 2009), collaborative environmental governance (Bodin et al.,
2016), species interactions (Delmas, 2018), and biodiversity
conservation (Modica et al., 2021). However, most studies in the
literature have focused on the
development of the methods for ESN
identification, reconstruction, and optimization (Hu et al., 2018; Kong
et al., 2010; Shen, Wang, & Fu, 2014; Shi et al., 2020; Xie, Zhou, &
Guan, 2014; Yin et al., 2011; Zhang, Yang, & Fath, 2010). A few studies
have dealt with spatial and temporal evolution and systematic assessment
of the constructed ESNs (Fan & Yang, 2019; Wang et al., 2020; Yu et
al., 2018; Zhou, Lin, Ma, Qi, & Yan, 2020). In China, assessing the
constructed ESNs is still at the
exploratory stage by using landscape pattern index or traditional
network structure index of graph theory (Liu et al., 2019;
Pascual-Hortal & Saura, 2006), apparently lacking a systematic
evaluation index system based on the complex network theory. In
addition, a few studies had explored the scale effect (Dong et al.,
2021), especially on network stability and connectivity under different
disturbance mechanisms
(Fu,
Mo, Peng, Xie, & Gao, 2019; Pocock, Evans, & Memmott, 2012). It is
necessary to develop a set of scientifically defensible index systems
for ESN assessment that will help to discover the similarities and
differences between different ecosystems and to provide decision-makers
with science-based network optimization strategies.
In the recent years, complex network
analysis derived from graph theory has been widely used to assess the
pattern and process of various networks (Yong, Donner, Marwan, Donges,
& Kurths, 2018), such as urban networks (Fang, Yu, Zhang, Fang, & Liu,
2020), social networks (Vahidzadeh, Bertanza, Sbaffoni, & Vaccari,
2021), water networks (Sitzenfrei, 2021),
transportation networks (Soh et al.,
2016), ecological networks (Pocock, Evans, & Memmott, 2012), energy
metabolism (Zhai, Huang, Liu, & Zhang, 2019), economic performance
(Gao, Tian, Zhang, Shi, & Shi, 2021), land use decision-making (Xia,
Li, Zhou, Zhang, & Xu, 2020), land use effects (Xia & Chen, 2020), and
CO2 transfer (Wang et al., 2021). A complex network is
often understood as a distributed system that consists of multiple
interconnected components with the network functionality being largely
influenced by its structure, which, in turn, depends on the complexity
of the network and the level of the interactions among components
(Sitzenfrei, 2021). The network’s ubiquity, importance, and complexity
intrigued the scientific community to study the formation and the growth
dynamics of the network, as well as to explore the security of the
network, the vulnerability of the network’s structure, and its
robustness to random disaster and targeted disturbances (Callaway,
Newman, Strogatz, & Watts, 2000; He, Liu, & Zhan, 2013; Yazdani &
Jeffrey, 2011). Many statistical parameters have been proposed to
describe the topology of complex networks (Jing & Wang, 2020), among
which, degree, aggregation degree, betweenness centrality, closeness
centrality, and network diameter are the most important
parameters (Chen, Lu, Shang,
Zhang, & Zhou, 2012; Liu, Slotine, & Barabási, 2011; Xia et al.,
2018). These parameters cover many aspects of the network, such as
static statistical characteristics, relatedness, connectivity,
robustness, and resilience. In addition, some scholars have also
evaluated the cluster
characteristics of the network (Liu, Cao, Liu, Shi, & Liu, 2020).
The purpose of this study is to develop a framework and index system to
assess the spatial topological structure of the ecological security
network by integrating landscape
ecology theory, graph theory, and complex network analysis for a rapid
urbanization region in China, the urban agglomeration around Hangzhou
Bay (UAHB) (see Supplemental Information (SI) Study area & Figure S1).
The specific tasks include: (i) to construct a four-dimension assessment
index system in terms of the network’s nodes, edges, connectivity, and
robustness; (ii) to explore the stability and anti-disturbance ability
of the network through scenario simulation; (iii) to propose the
feasible measures for the optimization of the UHAB’s ecological security
network. The framework and the technical roadmap of this study are shown
in Figure 1.
The potential innovation of our
study include: (i) evaluating the ESNs from the perspective of the
network’s topological structure by integrating landscape ecology theory,
graph theory, and complex network analysis; (ii) comparing the
characteristics of the ESNs under
two different disturbance scenarios to reveal the dynamic evolution
process of the connectivity and stability of the ESNs; and (iii)
proposing measures for the optimization of the ESNs.