1 Introduction
Land use changes caused by human activities have led to the habitat loss
and fragmentation at local, regional, landscape and global scales, which
can hinder the migration and dispersal of species at gene, individual,
population levels, further alter the structure and configuration of the
landscape, and have become an important factor threatening biodiversity
(Kruess and Tscharntke, 1994 ;Ewers and Didham, 2006 ;Fischer and Lindenmayer, 2007 ).
Compounding the effects of habitat loss and fragmentation are key
drivers of global biodiversity loss
(Fahrig, 2003 ). Loss of natural
habitats reduce the proportion of patches large enough to sustain a
population over time, and make species highly dependent on connections
between habitat patches (Baguette et
al., 2013 ; Sahraoui et al.,
2017 ).
Landscape connectivity is the degree to which the landscape facilitates
or impedes movement among resource patches
(Taylor et al., 1993 ;Tischendorf and Fahring, 2000 ).
Connectivity determines the effective and availability habitat area
within a territory for a given species
(Saura and Pascual-Hortal, 2007 ).
High Connectivity can facilitate the capacity movement to satisfy
ecological requirements and reduce population isolation
(Minor and Urban, 2008 ). An
animal’s ability to utilize a resource patch is determined not only by
the distance between patches, but also by the biophysical nature of the
routes between two patches and the biology and behavior of the organism
(Henein and Merriam, 1990 ). Some
routes facilitate or allow unimpeded movement among patches; others
impede to varying degrees the amount, or success, of movement. Improving
landscape connectivity can promote species migration, dispersal,
foraging, reproduction and survival, and reduce the risk of extinction.
Hence landscape connectivity will obviously vary according to both
organism properties and landscape features. Maintaining and improving
landscape connectivity is today considered a key part of efforts to
protect biodiversity (Taylor
et al., 1993 ).
Preserving and restoring connectivity has become a major conservation
priority on biodiversity conservation. Conservation practitioners have
long recognized ecological connectivity as a global priority for
preserving biodiversity and ecosystem function. Understanding
broad-scale ecological processes that depend on connectivity, and making
effective conservation planning decisions, requires quantifying how
connectivity is affected by landscape features. Connectivity among
populations and habitats is important for a wide range of ecological
processes. The assessment of landscape functional connectivity needs to
comprehensively consider the spatial distribution of habitat patches,
species movement characteristics, and landscape context. But field
observations are extremely labor intensive and fail to provide a
complete understanding of functional connections at a large scale, and
it is often difficult to assess landscape functional connectivity
because of data limitations
(Sahraoui et al., 2017 ;Petsas et al., 2020 ).
A graph-theoretic approach employs fast algorithms and compact data
structures that are easily adapted to landscape-level focal species
analysis. Graphs is built using GIS coverages to define habitat patches
and determine the functional distance between the patches with
least-cost path models. In landscape graphs, a graph is a set of nodes
and links such that each link connects two nodes. Nodes represent sites
of suitable habitat patches of a given species while links symbolize the
potential ability of a species to directly disperse between two nodes.
This method has been shown to be a powerful way of performing complex
analysis of potential functional connectivity at relatively large scales
(Bunn et al., 2000 ;Urban and Keitt, 2001 ;Jordán et al., 2003 ;Pascual-Hortal and Saura, 2006 ,2008 ). The advantage of a
graph-theoretic approach is that it has relatively modest data
requirements and improved from the initial results. Graph-theoretic
approaches provide a new research idea for powerful leverage on
ecological processes concerned with connectivity as defined by
dispersal. Graph theory-based models and least-cost path models have
gained increasing attention and are widely applied in connectivity
modeling, conservation planning, and monitoring of land use changes and
their environmental impacts (Minor
and Lookingbill, 2010 ; Theobald et
al., 2011 ; Piquer-Rodriguez et
al., 2012 ; Rubio et al., 2012 ;Devi et al., 2013 ;Wang et al., 2016 ;Sahraoui et al., 2017 ;Liu et al., 2020 ;Machado et al., 2020 ).
The circuit theory modeling is an alternative approach to model gene
flow and the dispersal or movement routes of organisms,and widely
applied for assessing ecological functional connectivity across
heterogeneous landscapes. This theory does not require new ways of
representing landscape data; rather, it takes advantage of
graph-theoretic data structures, and can be applied in graph-theoretic
or raster GIS frameworks (McRae et
al., 2008 ). Circuits are defined as networks of nodes connected by
resistors (electrical components that conduct current) and are used to
represent and analyze graphs. In circuit theory-based models, landscape
is transformed into a cost map (called resistance surface), effective
resistance, indicating landscape’s resistance to current flow between
two nodes separated by a network of resistors, and is calculated through
multiple pathways. Comparing to least-cost path models that measured
along a single optimal pathway or corridor with the lowest cost, circuit
theory integrates multiple possible pathways in a landscape into
distance calculations and offers a measure of isolation assuming a
random walk. The multipath results obtained from the circuit
theory-based connectivity models make it more realistic and objective in
practical application. By predicting net movement probabilities through
nodes, a new landscape raster is created (called current map), where
every cell is assigned a value that reflects the probability of an
animal traveling from one node to another. Current density can be used
to identify landscape corridors or “pinch points,” high current
through a node indicates that removing it will have a high impact on
connectivity (McRae and Beier,
2007 ; McRae et al.,
2008 ). Circuit theory has already been shown to be useful for
predicting movement patterns and probabilities of successful dispersal
moving across complex landscapes, generating measures of connectivity or
isolation of habitat patches, or protected areas, and to identifying
important connective elements for conservation planning and designing
robust reserve network (Epps et al.,
2011 ; Howey, 2011 ;Walpole et al., 2012 ;Koen et al., 2014 ;Pelletier et al., 2014 ;McClure et al., 2016 ;Dickson et al., 2019 ;An et al., 2021 ;Hyseni et al., 2021 ;Leskova et al., 2022 ).
The North China leopard (Panthera pardus japonensis , commonly
referred to as Panthera pardus fontanierii in the Chinese
literature), is the most widespread subspecies of leopard and one of the
rare and endangered species in China. Recent camera-trap surveys and
other evidence revealed the presence of the North China leopard in
Shanxi, Shaanxi, northern Hebei, Ningxia, and northern Henan. Most
populations in these provinces are small, and occur in several isolated
protected areas (Consolee et al.,
2020 ). More and more attention has been paid to the protection of
North China leopard. In Tieqiaoshan Provincial Nature Reserve, Shanxi
Province, studies indicated that the North China leopard is in serious
conflict with the locals, causing them personal economic losses due to
lack of prey (Consolee et al.,
2020 ; Vitekere et al., 2020 ).
The North China leopard density was 4.23 individuals/100
km2, the population density increased with the
distribution of wild boars (Vulpes vulpe s), and decreased with
the distribution of roe deer (Capreolus capreolus )
(Zhu et al., 2021 ). Habitat
environmental factors and anthropogenic interference also significantly
affected the population density and spatial distribution of the North
China leopard. Previous studies were mostly based on literature, expert
and villager interviews, and camera-trap surveys to determine the
current status and distribution of North China leopard, and most studies
were conducted within protected areas. But there are few studies on the
impact of land use changes on the ecological network connectivity of
North China leopard at a large scale.
The Great Taihang Region is the dominant distribution area of North
China leopard and contains the most densely distributed population of
North China leopard in China
(Laguardia et al., 2017 ;Cao et al., 2020 ). Due to the
Human-Leopard conflict, North China leopard is mainly distributed in
several isolated protected areas. In the past 30 years, with the
implementation of a series of forestry projects, such as National
Natural Forest Protection Program, Three-North Forest Shelterbelt
Program and Grain for Green Project, great changes have taken place in
the study area. Yet there is little insight concerning on the impacts of
land use changes on the habitat ecological network connectivity of North
China leopard. The present study, therefore, was an attempt to clarify
the influences of land use changes on landscape connectivity for North
China leopard. The main objectives of this study were: (1) to detect the
land use changes in the Great Taihang Region from 1990 to 2020, (2) to
clarify the influences of land use changes on landscape connectivity for
North China leopard, and to answer the following research question, (3)
how are the changes of the key barrier areas that affect the habitat
ecological network connectivity of North China leopard? The purpose of
this study was to explore the key barrier areas affecting the migration
of North China leopard, and to provide the basis for this unique and
rare wildlife species conservation planning in China.