1. Introduction
Human-induced habitat loss and fragmentation are the largest global
threats to biodiversity (Kaszta, Cushman, & Macdonald, 2020; Mohammadi
et al., 2018). Habitat loss and fragmentation can impact ecosystems and
species by reducing habitat carrying capacity and increasing mortality
risk preventing dispersal of individuals, and thus their genes, across
landscapes (Kaszta et al., 2020) This synergistically increases the risk
of local extinction (Kaszta et al., 2020; Khosravi, Hemami, & Cushman,
2018). Large carnivores are particularly vulnerable to habitat
fragmentation and habitat loss (Broekhuis, Cushman, & Elliot, 2017).
They live in low densities and typically have large home ranges (Carroll
& Miquelle, 2006; Hilty, Brooks, Heaton, & Merenlender, 2006).
Carnivores’ large area requirements demand vast and connected habitat
areas where they are protected from human persecution. Increasing land
use change and habitat fragmentation have threatened carnivore
populations by reducing habitat areas and increasing their isolation,
leading to a synergy of increased direct human-caused mortality, reduced
local carrying capacity, and reduced ability for populations to be
integrated by dispersal (Cushman, Elliot, Macdonald, & Loveridge,
2016).
Large carnivores are also particularly vulnerable to vehicle collisions
and barrier effects because of their life-history characteristics
(Mohammadi & Kaboli, 2016). (They have low population densities, low
fecundity, and relatively large home ranges (Grilo, Bissonette, &
Santos-Reis, 2009; Mohammadi et al., 2018; Mohammadi & Kaboli, 2016;
Parchizadeh et al., 2018). Vehicle collision poses a substantial threat
to wildlife species in central Iran (Shahnaseri et al., 2019). Roads,
especially those with high traffic, disrupt both structural and
functional connectivity for large carnivores, including grey wolf
(Canis lupus ), striped hyena (Hyaena hyaena ) and golden
jackal (Canis aureus ), and can lead to reduced gene flow among
meta-populations (Shahnaseri et al., 2019). Thus, transportation
managers need reliable data to identify when and where particular
species are susceptible to high road-kill rates to implement mitigation
measures during the road design planning and/or exploration stage (Grilo
et al., 2009; Santos et al., 2015).
Large carnivore conservation requires both protection of extensive core
areas and the establishment of movement corridors among them (Cushman et
al., 2018), particularly when core habitat patches are isolated by road
networks (McClure, Ware, Carlisle, & Barber, 2017). Connectivity is
critical for long-term species conservation and plays a crucial role in
maintaining the genetic and demographic processes that ensure long-term
viability (Bennett & Saunders, 2010). Connectivity of populations is of
paramount importance to both conserve species locally and to secure
their range shifts in response to future hazards such as land use change
(Cushman, McRae, et al., 2013), and climate change (Karami, Rezaei,
Shadloo, & Naderi, 2020; T. Wasserman, Cushman, Shirk, Landguth, &
Littell, 2012; T. N. Wasserman, Cushman, Littell, Shirk, & Landguth,
2013). Enhancing connectivity in conservation networks may reduce the
negative impacts of habitat loss and fragmentation (Betts et al., 2014).
Connectivity models provide practical tools for assessing potential
fragmentation effects of roads on wildlife and help inform management
and conservation planning (Almasieh, Rouhi, & Kaboodvandpour, 2019). A
wide variety of methods have been proposed for connectivity analysis,
including least-cost path modelling (Adriaensen et al., 2003), current
flow (McRae, Dickson, Keitt, & Shah, 2008), factorial least-cost path
density (Cushman, McKelvey, & Schwartz, 2009), resistant kernels
(Compton, McGarigal, Cushman, & Gamble, 2007) and randomized shortest
path algorithm (Panzacchi et al., 2016). The factorial least-cost path
and cumulative resistant kernel approaches are strong methods to be used
in combination to accurately identify core habitats, fracture zones and
corridors across a broad landscape (Cushman et al., 2018; Cushman,
Lewis, & Landguth, 2014; Moqanaki & Cushman, 2017).
Understanding the different factors that affect species distribution and
habitat selection is important for carnivore conservation (Khosravi et
al., 2018; Shahnaseri et al., 2019; Mohammadi et al., 2021). Many other
habitat suitability models are available. Among which machine-learning
models such as random forests (RF) may perform better than the
regression-based algorithms (Cushman, Macdonald, Landguth, Malhi, &
Macdonald, 2017; Rodriguez-Galiano, Ghimire, Rogan, Chica-Olmo, &
Rigol-Sanchez, 2012). Furthermore, ensemble modeling, in which several
species distribution models (SDMs) are combined to quantify a range of
predictions across more than one set of uncertainty sources, has been
found to increase often the accuracy of model predictions (Araújo &
New, 2007; Shahnaseri et al., 2019) and decrease the uncertainty
associated with using a single SDM (Shirk et al., 2018).
The central region of Iran accommodates a variety of carnivore species.
Grey wolf, golden jackal and striped hyena are the most widely
distributed carnivores in central Iran. Most conservation efforts for
conserving wildlife diversity in Iran have relied on establishing
Protected Areas (hereafter, PAs). However, the existing PA network is
not efficient for the long-term conservation of most carnivores
(Shahnaseri et al., 2019). Due to the reduction in wild prey species
density in Iranian PAs (Behdarvand et al., 2014; Mohammadi, Kaboli, &
López-Bao, 2017; Mohammadi, Kaboli, Sazatornil, & López-Bao, 2019),
occurrence of carnivore species across inhabited rural areas has
increased (Mohammadi et al., 2019). Anthropogenic food resources,
notably livestock and garbage, also contribute to these carnivores’ diet
and incentivize carnivores moving to high-risk locations in the
landscape near human habitations (Babrgir, Farhadinia, & Moqanaki,
2017; Behmanesh, Malekian, Hemami, & Fakheran, 2019; Mohammadi et al.,
2019).
In this study, we addressed three main objectives regarding grey wolf
and golden jackal status and vulnerability in central Iran. First, we
determined the most significant environmental and anthropogenic factors
influencing habitat suitability for both species. Second, we defined
core areas for each species using resistant kernel modeling, and
identified corridor routes among these core areas using factorial
least-cost path modeling. Third, we used spatial randomization of
vehicle collision locations to test the predictive ability of resistant
kernel and factorial least-cost path predictions of movement (Cushman et
al., 2014). The results provide clarity on the drivers of habitat
quality for multiple carnivore species, and the patterns of habitat
extent and connectivity for these species across Central Iran which is
critical for conservation management planning of carnivores in Iran.