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\begin{document}

\title{Spotted hyena navigation of social-ecological landscapes on a
coexistence frontier}



\author[1]{Christine E. Wilkinson}%
\author[1]{Wenjing Xu}%
\author[2]{Amalie Luneng Solli}%
\author[1]{Justin S. Brashares}%
\author[3]{Christine Chepkisich}%
\author[4]{Gerald Osuka}%
\author[1]{Maggi Kelly}%
\affil[1]{University of California Berkeley}%
\affil[2]{University of California Davis}%
\affil[3]{Wildlife Research and Training Institute}%
\affil[4]{Egerton University}%


\vspace{-1em}



  
  \date{May 25, 2024}


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\selectlanguage{english}
\begin{abstract}
``Coexistence frontiers'', or regions where human infrastructure and
activity are increasing rapidly or newly appearing, constitute novel
environments where wildlife must learn to navigate and coexist with
people. It is widely recognized that behaviorally flexible species are
more likely to persist in these human-dominated landscapes.
Nevertheless, we do not fully understand how these animals navigate
landscapes shaped by infrastructure, human activity, and human
tolerance. As a widely reviled and behaviorally plastic apex predator,
the spotted hyena (Crocuta crocuta) is a model species for understanding
how wide-ranging large carnivores navigate social-ecological landscapes
in an urbanizing world. Using high-resolution (minimum 5-min fix rates)
GPS collar data and supplemental camera trap imagery, we applied
resource selection and step selection functions to assess spotted hyena
landscape navigation and fine-scale movement decisions in relation to
social-ecological features in Lake Nakuru National Park and Soysambu
Conservancy, Kenya. Second, we used camera traps and barrier behavior
analysis (BaBA) to further examine hyena interactions with barriers. Our
results show that environmental covariates---including NDVI, terrain,
and proximity to water---were the best predictors of landscape-scale
resource selection by hyenas, while human infrastructure and the
likelihood of conflict with humans or livestock predicted fine-scale
hyena movement decisions. We also found that hyena selection for these
characteristics changed seasonally and across land management types.
Camera traps documented an exceptionally high number of individual
spotted hyenas (234) approaching the national park fence at 16 sites
during the study period, and BaBA results suggested that hyenas perceive
protected area boundaries' electric fences as risky but may cross them
out of necessity. Our results highlight that wildlife adaptability in
coexistence frontiers may be expressed differently depending on context
and scale. These results also point to the need to incorporate societal
factors into multiscale analyses of carnivore movement to effectively
plan for human-carnivore coexistence.%
\end{abstract}%



\sloppy


\textbf{Introduction}

Human land uses and development are altering landscapes worldwide and
restricting the movements of wide-ranging species such as large
carnivores (Crooks \emph{et al.} 2011; Ripple \emph{et al.} 2014). One
outcome of the expanding human footprint is that carnivores and people
are increasingly overlapping with one another. Several tools have been
employed in human-dominated landscapes to mitigate carnivore
interactions with people, including fencing protected areas or other
barriers and policies to separate humans from wildlife (e.g.,
McInturff~\emph{et al.} 2020). Depending on the relative scale and
permeability of these physical structures, such efforts often result in
new patterns of wildlife movement and navigation (e.g., Wilkinson
\emph{et al.} 2021b). On \emph{coexistence frontiers} , where human
infrastructure and activity have either recently appeared or are rapidly
increasing, carnivores must learn to traverse novel landscapes with
novel risks. In such regions, it is critical to identify predictors of
landscape navigation for carnivores and determine whether and how these
species can adaptively traverse these increasingly human-dominated
landscapes (Sanjayan \& Crooks 2005).

Understanding carnivore landscape navigation within coexistence
frontiers requires social-ecological approaches rather than only
considering ecological factors (Lute \emph{et al.} 2020; O'Neal
Campbell, 2014). In general, wildlife sharing space with people must
navigate three main elements present on the landscape, namely ecological
factors, human infrastructure, and human acceptance, that
determine~\emph{social-ecological landscape permeability} (e.g.,
Ghoddousi~\emph{et al.} 2021). This guiding framework is critical
because while certain carnivore species may be able to persist in
human-altered habitats (see Athreya \emph{et al.} 2013; Breck \emph{et
al.} 2019; Chapron \emph{et al.} 2014; Devens \emph{et al.} 2019), human
intolerance may be a strong enough limiting factor that it can override
adaptability for carnivore populations or survival of individuals
navigating developing landscapes (Moss, Alldredge \& Pauli 2016). Thus,
human perceptions are likely to be an important factor in determining
how carnivores navigate landscapes (Behr \emph{et al.} 2017). On
coexistence frontiers, people may not have developed tolerance or
acceptance for species which they have not encountered before or which
they are now encountering more frequently (e.g., Lute \& Carter 2020).
In this case, intolerance may serve as a proxy for people employing
nonlethal hazing or deterrents or as an indicator for tendencies to
conduct illicit persecutory actions toward wildlife (Benson \emph{et
al.} 2023; Ditmer \emph{et al.} 2022; Manfredo \emph{et al.} 2021), such
as poisoning (see Ogada 2014) or habitat destruction (see
Ripple~\emph{et al.} 2014). In these social-ecological spaces, people
also interact with, respond to, and alter ecological features and
processes that influence carnivore movement at different scales, such as
vegetation availability (e.g., Bateman \& Fleming 2012; Suraci \emph{et
al.} 2020) and climatic season (e.g., through changing livestock
movements; Schuette \emph{et al.} 2013).

Spatial and ecological scales are important for contextualizing human
coexistence with carnivores (Carter \& Linnell 2016). For instance,
anthropogenic development may have community-level effects by pushing
some species into limited remaining natural habitats (Parsons \emph{et
al.} 2018), yet at a fine scale, carnivores can exhibit adaptations to
anthropogenic development -- or even be synanthropic---on an individual
level (i.e., Moss \emph{et al.} 2016; Nisi \emph{et al.} 2021;
Wang~\emph{et al.} 2015; Suraci, Nickel \& Wilmers 2020). Through these
scale-dependent processes, human-dominated landscapes near protected
areas can result in a source-sink dynamic, whereby carnivore populations
from protected areas that venture into more densely human-populated
regions are more likely to die through anthropogenic causes
(Lamb~\emph{et al.} 2020). However, individuals may succeed in these
spaces by taking advantage of anthropogenic resources at finer scales,
depending on human tolerance (i.e., Moss \emph{et al.} 2016). Thus, for
large carnivores, which are often highly mobile, social-ecological
landscape permeability across scales is essential to maintain
populations. Carnivore species living in both human-dominated
environments and protected environments may avoid anthropogenic features
such as roads and fences (e.g., Baker \& Leberg 2018; McInturff \emph{et
al.} 2020; Young \emph{et al.} 2019) or change their activity patterns
to adjust for human presence (e.g., Gaynor \emph{et al.} 2018). Yet
carnivores may also either avoid or be attracted to human infrastructure
at different scales (Poessel \emph{et al.} 2014), underpinned by the
density of the infrastructure (Morales-Gonzalez \emph{et al.} 2020; Xu
\emph{et al} . 2023), the infrastructure's impact on resource
availability (i.e., Belton \emph{et al.} 2016), the species involved
(Wilkinson \emph{et al.} 2021b), or individual animal characteristics,
such as life stage (Thorsen \emph{et al.} 2022).

As a widely reviled (Glickman 1995; Macdonald \emph{et al.} 2022) and
behaviorally plastic apex predator (Holekamp \& Dloniak 2010), spotted
hyenas (\emph{Crocuta crocuta} , hereafter ``hyenas'') are a model
species for understanding the nature of carnivore adaptiveness to
human-caused landscape change and negative human perceptions. Hyenas are
generally considered one of the most behaviorally flexible carnivore
species, yet there have been few empirical conclusions regarding the
extent and mechanisms of their adaptiveness to human activities,
infrastructure, and tolerance. Green et al. (2018) found that hyena
populations in Maasai Mara, Kenya increased in an area of anthropogenic
disturbance, possibly linked to increased livestock consumption. In
densely populated areas in Ethiopia where native prey is depleted,
hyenas have become almost entirely dependent on anthropogenic food
(e.g., Yirga \emph{et al.} 2012). However, other studies have found
negative, neutral, or nuanced responses to people. In one study in
Kenya, hyena activity shifted in response to livestock grazing and other
anthropogenic activities (Kolowski \emph{et al.} 2007), while a study in
South Africa found that hyena propensity to visit anthropogenic sites
varied depending on season, age, or individual (Belton \emph{et
al.~}2018). Determining whether a species is surviving or thriving in
the presence of humans is complex, as even highly synanthropic species
may be exposed to greater levels of stress, toxicants, and disease while
living in anthropogenic landscapes (Murray \emph{et al.} 2016;
Murray~\emph{et al.} 2019). While movement is thus solely the broadest
scale at which to assess carnivore survival and wellbeing within
coexistence frontiers, understanding how hyenas and other behaviorally
plastic mammalian carnivores navigate anthropogenic landscape change is
fundamental to forecasting the resilience of movements, food webs, and
ecosystems in rapidly developing landscapes.

We sought to provide insight into spotted hyena abilities to navigate
coexistence frontiers by examining the following questions in and around
Lake Nakuru National Park and Soysambu Conservancy, Kenya: 1) How do
spotted hyenas navigate anthropogenic infrastructure, human activity,
tolerance, and ecological factors across scales, seasons, and management
types (i.e., fully protected vs. multi-use), and 2) How many spotted
hyenas cross through the region's conservation fence, and how does
spotted hyena fence navigation manifest at a fine scale? We predicted
that (1) hyenas would respond differently to ecological factors than
they do to anthropogenic infrastructure and activity, (2) hyena
selection for and against landscape characteristics would differ at
different scales (i.e., fine-scale and landscape scale), (3) hyenas
would exhibit different responses to covariates in the rainy vs. the dry
season, (4) hyenas with dens in the fully protected national park would
be more avoidant of barriers, less avoidant of roads, and more attracted
to verified and perceived livestock predation hotspots outside of the
park than would hyenas with dens in the multi-use conservancy, and (5)
fence crossing would be limited to a few select individuals and hyenas
likely perceive fence navigation to be risky. We employed resource
selection functions (RSFs) and step-selection functions (SSFs) to
determine hyena space use and landscape navigation at the home range and
step scales, respectively (Reinking \emph{et al.} 2019). We then used
this information to infer whether and how to consider a suite of
social-ecological factors when designing for hyena landscape
permeability, and present the implications of these inferences for
global human-carnivore coexistence.

\textbf{Materials and Methods}

Study site

Our study was conducted in Nakuru County, in the Rift Valley of
southwest Kenya, from June 2018- October 2020. The study area (0\selectlanguage{ngerman}°26' S,
36°1' E) includes two major wildlife protected areas: Lake Nakuru
National Park (LNNP, 188 km\textsuperscript{2}), which is one of two
fully fenced national parks in Kenya, and Soysambu Conservancy (190
km\textsuperscript{2}), which is mostly fenced and functions as both a
wildlife conservancy and a livestock ranch with over 10,000 cattle,
sheep, and goats. Fences used in both protected areas are typically
\textasciitilde{}2m tall and consist of parallel electrified wires,
though some stretches of fence are composed of other materials, are in
various states of maintenance, or have an additional component of woven
wire mesh to reduce wildlife digging. The two large alkaline lakes in
the region, Lake Nakuru and Lake Elementeita, are designated UNESCO
World Heritage sites. The region supports many species of large mammals,
including large carnivore species such as African lion (\emph{Panthera
leo}), spotted hyena, and leopard (\emph{Panthera pardus}), and several
mesocarnivore species, such as serval (\emph{Leptailurus serval}) and
black-backed jackal (\emph{Lupulella mesomelas}). Many carnivore
populations in the region remain stable despite heavy historical
persecution (Ogutu \emph{et al.} 2017). The region is characterized by
woodland, savanna, and dense brush habitats, as well as two rainy and
two dry seasons each year.

Field methods and data

\emph{Collar deployment and programming}

From February-March 2019, 3 female and 4 male spotted hyenas were
collared (Savanna Tracking GPS-GSM FlexTrack), representing 5 clans in
LNNP and Soysambu Conservancy. Six of these collars (still representing
5 clans) remained in function for most of the study period. Fixes were
taken between 6pm-7am, which are the primary active hours of hyenas in
this study area. Hourly fix rates were taken from February-April 2019,
after which 5-minute fixes were used until May 2020. After this point,
the collars were reprogrammed for 1-hour fixes, 24 hours per day.

\emph{Covariates}

The following covariates (Table 1) were used in analyses of hyena
landscape use and navigation: normalized difference vegetation index
(NDVI; 30m, Landsat 8 Surface Reflectance Tier 1, rainy and dry seasonal
averages for 2019), slope (30m, Shuttle Radar Topography Mission
{[}STRM{]}), elevation (30m, STRM), distance to rivers, distance to
lakes, distance to boundaries, distance to verified livestock predation
locations, distance to regions people perceive as being risky due to
hyenas, and distance to participatory mapped livestock predation
locations during the study period. The latter two variables were derived
using participatory mapping data from communities living within 2 km of
the protected area boundaries, while the verified predation dataset was
from the local wildlife authority, Kenya Wildlife Service
(Wilkinson~\emph{et al.} 2021a). Because the killing of or retaliation
against wildlife is illegal in Kenya, participatory mapped livestock
predation and participatory mapped risks from spotted hyenas can serve
as proxies for spatially explicit intolerance or acceptance of spotted
hyenas by local communities and are associated with the potential for
deterrents and aversive behaviors toward hyenas. Euclidean distance was
used for all distance layers, and road layers were derived through Open
Street Maps and by hand tracing. Fences assessed in barrier analyses
were mapped in person by driving and walking the boundaries of the
protected areas. All distance covariates were created as rasters with
30m spatial resolution to match the NDVI, slope, and elevation rasters.

\textbf{Analyses}

Assessing space use and landscape navigation

To determine the individual home ranges of the seven collared hyenas, we
used the `adehabitatHR' package in R (R Core Team, 2018) to assess 50\%
and 95\% kernel utilization distributions (KUD) and calculated overlap
among home ranges.

To determine landscape feature selection by spotted hyenas, we derived
resource selection functions (RSFs) using the `lme4' package in R, using
only the six collared hyenas (3 male and 3 female) whose collars
remained active throughout the study. Random points generated were equal
to the number of GPS points used, and we found no evidence of
collinearity among our covariates (Dellinger \emph{et al.} 2013). We
assessed resource selection using generalized linear mixed-effects
models with a logit link. Using the raster package and base R, we scaled
the following covariates for use in the RSFs: normalized difference
vegetation index (NDVI; 30m, Landsat 8), elevation, slope, distance to
rivers, distance to lakes, distance to roads, and distance to protected
area boundaries. NDVI in this study site can serve as a proxy for land
cover, because areas of higher NDVI are generally brush or forest,
whereas lower NDVI areas are typically grasslands. After initial data
exploration, we included an interaction term---individual and distance
to boundary---as a random effect in our candidate model to control for
variation in individual boldness. We assessed a global model (for all
hyenas) and compared global seasonal (rainy and dry) models, as well as
models for hyenas whose dens were in LNNP (n=3), and for hyenas whose
dens were in Soysambu Conservancy (n=3). We also assessed and compared
models with the following combinations of variables: ecological
variables only, ecological and infrastructure, infrastructure only,
infrastructure and human perception/experience, and human
perception/experience only. Models were ranked based on their Akaike
Information Criterion (AIC; Burnham \& Anderson 2002), and models within
\selectlanguage{greek}Δ \selectlanguage{english}AIC [?]2 were retained to use when assessing coefficient values.

Assessing fine-scale movement decisions

To understand how hyenas select landscape features at the fine/step
scale, we derived step selection functions (SSFs) using the amt package
in R (R Core Team, 2018). We prepared the hyena data by creating tracks
from the data using the mk\_track() package, subset the data to only the
5-minute fix rates, and filtered to assure bursts would have a minimum
of 2 points. Five random steps were generated for each step used. Scaled
covariates and model comparisons reflected those conducted for the RSF
analyses. We fit conditional logistic regressions on the covariates,
while also considering hyena ID as a cluster term and log of step length
(i.e., speed of movement) as an interaction term with boundaries and
roads. We used quasi-likelihood independence model criterion (QIC) to
rank models and determine top models. We then used acf.test() on the
model that best predicted the data to determine the lag at which
autocorrelation is no longer observed, and employed destructive sampling
to address autocorrelation, removing 2 points between each individual's
clusters. Models were then fit on the destructively sampled data. Last,
we created a function in R for individual SSF models and the parameters
from the global model to visualize the data.

Barrier interactions

To understand hyena behavior around fences and determine the locations
of weak or robust fences on the protected area boundaries, we used the
Barrier Behavior Analysis (BaBA) methodology developed by Xu et al.
(2020). BaBA examines whether, where, and how often animal movements
were altered by linear barriers such as fences by classifying movement
trajectories within set buffers around target barriers into normal
movement (\emph{quick cross} , average movement), altered movement
(\emph{bounce} , \emph{trace} , or \emph{back and forth} ), and trapped
movement. To assess the appropriate sensitivity for the BaBA results, we
used BaBA with 50m, 100m, and 150m fence buffer distances within which
GPS locations were classified as fence encounters (Xu \emph{et
al.~}2020).

Finally, to supplement our understanding of hyena interactions with and
crossing of the boundary fences, we used images of spotted hyenas from
camera traps placed on sixteen key crossing points at the LNNP fence
from June 2018 - November 2019 (see Wilkinson \emph{et al.} 2021b for
detailed methodology). Spotted hyenas in camera trap images were
individually identified using spot patterns and compared to individuals
previously listed in both the LNNP and Soysambu Conservancy hyena ID
books (see Supporting Information). Hyenas appearing at the fence were
first compared to the clan with a range closest to the camera trap site
but then expanded to all others in the book if not identified. The hyena
was labeled as unknown if we could not definitively identify the
individual. These unknown individuals were later added to the Lake
Nakuru or Soysambu Conservancy ID Books under a new ID code and used for
further identification of images. We assessed the frequency of fence
approaches at each site and by specific individuals, as well as the
number of different fence crossing sites visited by each individual.

\textbf{Results}

Landscape-scale space use

Spotted hyena 50\% and 95\% home ranges (Figure 1) comprised between
6.06-27.29 km\textsuperscript{2} ((x \selectlanguage{english}= 11.6) and 31.38-132.91
km\textsuperscript{2} (x \selectlanguage{english}= 61), respectively. Dry season 50\% and 95\%
home ranges comprised 5.88-23.73 km\textsuperscript{2} (x \selectlanguage{english}= 10.99) and
30.71-143.59 km\textsuperscript{2} (x \selectlanguage{english}= 87.89), respectively. Rainy
season 50\% and 95\% home ranges comprised 6.06-23.85
km\textsuperscript{2} (x \selectlanguage{english}= 11.46) and 28.53-111.92
km\textsuperscript{2}(x \selectlanguage{english}= 58.08), respectively. The proportion of 95\%
home range overlap among different clans spanned between 0.108 and
0.317.

For all spatial analyses, all variables were retained for global models
after testing for pairwise correlation (maximum correlation was 0.52,
while most pairwise correlations were below 0.2). The global model,
including all covariates for the RSFs, revealed selection for higher
NDVI, roads, lakes, verified livestock predation, and areas of
participatory mapped risk from hyenas, and against elevation, steep
slopes, rivers, boundaries, and areas of participatory mapped livestock
predation (Table 2). Of these, selection for distance to participatory
mapped livestock predation (i.e., greater distances away from these
regions; \selectlanguage{greek}β \selectlanguage{english}= 0.271, p \textless{} 0.001) and selection against distance
to verified livestock predation (i.e., closer distances to these
regions; \selectlanguage{greek}β \selectlanguage{english}= -0.255, p \textless{} 0.001) showed the strongest effects.
When comparing models containing combinations of ecological,
infrastructure, and human experience/perception covariates, the model
that best predicted the data was the model with all covariates, followed
by the model containing only ecological and infrastructure covariates.

Seasonal RSFs comparing all covariates across the rainy and dry seasons
showed that in the dry season hyenas exhibit an increase in
landscape-scale selection for NDVI, boundaries, and areas of
participatory mapped livestock predation, and a decrease in selection
for elevation, rivers, areas of verified livestock predation, and areas
of participatory mapped risk from hyenas (Figure 2a).

When comparing global models across land management types, hyenas with
dens in Soysambu showed stronger selection for or against human
experience/acceptance covariates than hyenas with dens in LNNP (Figure
3a). Soysambu hyenas also showed statistically significant selection
against boundaries at the landscape scale (\selectlanguage{greek}β \selectlanguage{english}= 0.236, p \textless{}
0.001), which was not exhibited as strongly by LNNP hyenas (\selectlanguage{greek}β \selectlanguage{english}= 0.122, p
= 0.46).

Fine-scale movement decisions

The global model, including all covariates for the SSFs, revealed step
selection for higher NDVI, lakes, roads, areas of verified livestock
predation, and areas of participatory mapped livestock predation, and
selection against rivers, steep slopes, elevation, boundaries, and areas
of participatory mapped risk from hyenas (Table 3). Of these, selection
against boundaries showed the strongest effect (\selectlanguage{greek}β \selectlanguage{english}= 0.273, p \textless{}
0.01), yet there was marked individual variation in selection for all
covariates (Figure 4). When comparing models containing combinations of
ecological, infrastructure, and human experience/perception factors, the
two models within \selectlanguage{greek}Δ \selectlanguage{english}AIC [?]2 were the infrastructure-only model and the
model containing infrastructure and human experience/perception
covariates.

Seasonal SSFs comparing all covariates across the rainy and dry seasons
showed that in the dry season hyenas exhibit an increase in fine-scale
selection for elevation, lakes, and roads, and selection against
boundaries (Figure 2b).

When comparing global models across land management types, Soysambu
hyenas differed from LNNP hyenas in that they showed selection for roads
(\selectlanguage{greek}β \selectlanguage{english}= -0.19, p \textless{} 0.001). In contrast, LNNP hyenas differed from
Soysambu in that they showed selection for lower NDVI (\selectlanguage{greek}β \selectlanguage{english}= -0.077, p
\textless{} 0.001) and areas of verified livestock predation (\selectlanguage{greek}β \selectlanguage{english}=
-0.552, p \textless{} 0.001) and against boundaries (\selectlanguage{greek}β \selectlanguage{english}= 0.62, p
\textless{} 0.001, Figure 3b). A case study on a single hyena that was
known to frequently cross between the two protected areas (Supporting
Information) showed selection against boundaries during the dry season
(\selectlanguage{greek}β \selectlanguage{english}= 0.186, p \textless{} 0.001) and for boundaries in the rainy season
(\selectlanguage{greek}β \selectlanguage{english}= -0.068, p \textless{} 0.01), as well as an increase in speed of
movement (i.e., log of step length) in relation to boundary selection
during the rainy season (\selectlanguage{greek}β \selectlanguage{english}= 0.062, p \textless{} 0.001) as compared to
the dry season (\selectlanguage{greek}β \selectlanguage{english}= 0.035, p \textless{} 0.001).

Barrier interactions

A 50-meter fence buffer best captured \emph{quick cross} events, or
events where hyenas quickly crossed a fence after approaching it.
Spotted hyena individuals encountered fences on average 193 (\selectlanguage{greek}σ\selectlanguage{english}=168.5)
times during the study period. Overwhelmingly, hyenas encountering
fences either exhibited \emph{quick cross} (n=583, or 49\% of all fence
encounters) or \emph{bounce} (n=507, 42.7\%), with \emph{average
movement} (n=45, 3.8\%), \emph{trace (} 3 times, 0.25\%), and \emph{back
and forth} (n=7, 0.59\%) exhibited occasionally (Figure
5a).\emph{Bounce} is a behavior in which hyenas that encounter the fence
immediately walk away from the fence. There was marked individual
variation in overall fence encounters, as illustrated by high standard
deviations in average fence encounter frequency. \emph{Bounce} behaviors
were slightly more prevalent during the dry season (prop. = 0.44 in the
dry season, .41 in the wet season), though this result was insignificant
(Mann-Whitney p = 0.48 and p = 0.699; Figure 5b). \emph{Quick
cross}behaviors remained the same across seasons (prop. = 0.49). Judging
by differences in hyena behaviors around different fence segments, some
segments may be more permeable than others. The highest concentration of
quick cross behaviors appeared to be on the fence lines between the two
protected areas, indicating high permeability for those fence segments.
Meanwhile, the bounce behaviors had a considerably wider spread along
the boundaries (see Supporting Information), indicating regions where
hyenas may have attempted to cross but could not due to fence
impermeability. Notably, fence behaviors revealed several crossing
points connecting LNNP to Soysambu Conservancy.

Camera trap data revealed 234 individual hyenas spanning at least 4
clans and various social ranks approaching the fence across the 16
studied sites, with one site having a minimum of 67 different
individuals appearing at the fence (Table 4; Supporting Information).
Across all sites, 63 individuals appeared at the fence in more than 10
images during the study period.

\textbf{Discussion}

Spotted hyenas in this rapidly developing landscape appear to be
selecting for and against environmental, infrastructural, and human
tolerance characteristics at different scales, demonstrating that it is
crucial to consider social-ecological landscape permeability on
coexistence frontiers and in human-dominated landscapes generally. We
found that parallel wire electric fences may be perceived as a risk by
hyenas -- as indicated by their interactions with the fence largely
consisting of either quickly crossing or immediately leaving the
area---while simultaneously being highly permeable to this species,
which has implications for coexistence and movement for this apex
predator. Additionally, the hyenas in this region exhibited several
landscape use and navigation propensities that differ from previous
studies on this species conducted in landscapes that are on the high or
low extremes of anthropogenic influences.

Space use

Hyena ranges were considerably larger than expected given the small size
of the two protected areas (Honer \emph{et al.} 2002; Watts \& Holekamp
2008), potentially further enabled by the high degree of overlap among
home ranges. Other studies have shown that adapting to human-dominated
environments may change the fundamental social behaviors of certain
carnivore species (e.g., Widdows \& Downs 2015). In our study area,
hyenas from different clans exhibited consistent range overlap and are
known to frequently enter one another's ranges for anthropogenic
resources, such as discarded livestock carcasses (C.E. Wilkinson, pers.
obs.). This stands in contrast to some other studies (i.e.,
Barker\emph{et al.} 2022) and runs counter to the known territorial
behaviors of spotted hyenas (Boydston \emph{et al.} 2001; Watts \&
Holekamp 2007), indicating potential resource- or space-driven
intraspecific social behavior changes which warrant further research.

Hyena home ranges expanded during the dry season, in contrast to studies
in ecologically similar regions that show wildlife tends to disperse
more widely in the wet season (Koziarski~\emph{et al. 2016} ). Previous
research has also shown that spotted hyenas have wider ranges in the wet
season due to the seasonal movement and presence of their wild ungulate
prey (Trinkel~\emph{et al.} 2004). Similarly, a study on leopards
(\emph{Panthera pardus}) in a mixed-use landscape revealed that they
avoided protected areas during the dry season and instead favored tea
plantations and forest patches (Naha \emph{et al.} 2021). Our observed
counterintuitive increase in hyena range sizes during the dry season
rather than the rainy season could thus be due to two factors inherent
to this fenced ecosystem. First, due to the electric boundary fences,
many ungulate species cannot disperse during the rainy season
(Wilkinson~\emph{et al.} 2021b), meaning hyenas have little opportunity
or need to expand ranges to seasonally track wild prey. Second, the
small sizes of the protected areas, coupled with an ongoing rise in lake
waters in the national park (James 2022), may be driving seasonal
resource limitations for spotted hyenas and causing them to expand their
ranges during the dry season.

Social-ecological landscape navigation and fine-scale movement decisions

Hyenas in this region are selecting for different factors at the
landscape scale than at a fine scale. Differences in resource selection
at different scales were particularly apparent for infrastructure and
human experience characteristics, and less apparent for environmental
characteristics, selection for which largely remained the same across
RSF and SSF results. On a fine scale, hyena navigation appears to be
more heavily influenced by roads, fences, and human experiences and
acceptance than by environmental characteristics such as NDVI and
proximity to water. These scale-dependent affects likely stem from a
combination of factors, with fine-scale movements dictated by hyena
behavioral flexibility, and landscape scale selection dictated by
broader resource availability. While there are changes in the magnitude
of effect, at both broad and fine scales hyenas in this region generally
select for vegetation greenness, lakes, and roads, and against rivers,
boundaries, slopes, and high elevation. In arid environments, spotted
hyena hydration is mostly derived from prey rather than water sources
(Green \emph{et al.} 1984), and hyenas can also survive for a week or
more without water (Holekamp \& Dloniak 2010); thus, selection against
rivers could reflect the aridity of the region. Additionally, though the
two major lakes in the region are highly alkaline, hyenas have been
observed hunting flamingos, wallowing, and using the lakesides' heavy
vegetation as refuge during the heat of the day (authors' observations).
Thus, even undrinkable water sources can serve as critical habitat for
hyenas (e.g., Matsumoto-Oda 2021).

At the landscape scale, hyenas are selecting against participatory
mapped livestock predation areas and for areas of participatory mapped
risk, while the opposite holds true for fine scale selection. The
landscape-scale selection for and against these human acceptance
covariates may indicate that hyenas are broadly selecting for areas in
which they may face hazing or be poisoned (i.e., perceived risks from
and low tolerance for hyenas), and against areas that people use for
livestock grazing (i.e., participatory mapped livestock predation).
People are also likely perceiving the highest risks from hyenas in
places that constitute suitable hyena habitat, which supports hyena
landscape-scale selection for these areas. In densely populated areas
where tolerance is high or there are policies against wildlife killing,
carnivore populations may thrive (Athreya \emph{et al.} 2013;
Gebresenbet \emph{et al.} 2018), yet in areas where tolerance is low
(such as in our study site), carnivore populations can be negatively
affected by retaliation and other practices (Ripple \emph{et al.} 2014).
Hyenas in this study site may also be avoiding participatory mapped
livestock predation areas---which are likely most often used for
grazing---as locations with consistent, predictable risks (i.e., through
deterrents and higher levels of human activities). This reflects
findings from previous studies in regions with less development, in
which spotted hyenas reduced their activities in response to livestock
grazing (i.e., in the Maasai Mara, Kolowski \emph{et al.} 2007).

Our results also showed seasonal differences in landscape-scale and fine
scale hyena selection for environmental and anthropogenic
characteristics. At the landscape scale, in the dry season, hyenas
exhibited an increase in selection for vegetation greenness, boundaries,
and participatory mapped livestock predation. The latter two support our
findings that hyenas may be expanding their ranges and traveling outside
of the protected areas during the dry season. When resources are scarce,
animals living alongside people may be more likely to choose
anthropogenic food sources (Johnson \emph{et al.} 2015), and hyena
predation on livestock or scavenging of livestock and other
anthropogenic food sources may be increasing during dry seasons. The
boundary navigation result at the landscape level also supports these
hyenas' tendency toward dry season range expansion. Meanwhile, at a fine
scale, the dry season showed a minor increase in hyena navigation toward
roads, with minimal seasonal differences in selection for environmental
and human experience characteristics. While species in other studies
have been known to use roads for easier traversal of the landscape
(Abrahms \emph{et al.} 2015; Hill \emph{et al.} 2020), hyenas in this
study area may also use roads in the dry season for dust bathing and
access to artificial water points, particularly in the conservancy.

When looking at variation in movement for hyenas living in different
management types across both seasons, at the landscape scale roads were
more likely to be avoided by Soysambu hyenas than LNNP hyenas, despite
human use of roads being extremely low in the conservancy at night. One
reason for this could be that despite being active at night, hyenas in
Soysambu associate roads with the abundance of human activities that
occur during the day on the roads within the conservancy, while in LNNP,
there is only one activity happening on roads: tourism. Vehicle speeds
in the national park are also heavily regulated. Hyena avoidance of
roads in Soysambu stands in contrast to research that has found that
animals may select for human infrastructure at night for resources or
ease of movement while avoiding it during the day when humans are more
active (e.g., Toverud 2019). However, hyenas are generally more skittish
in the conservancy than in the national park, possibly due to historical
hyena bounties (K. Combes, pers. comm.) before the conservancy's
designation as a wildlife habitat. The anthropogenic activity signature
on Soysambu's roads may thus have a strong enough effect on the spotted
hyena's human-caused ``landscape of fear'' (Smith \emph{et al.} 2017;
Suraci \emph{et al.} 2019) as to contribute to nighttime avoidance of
roads that are devoid of human activity.

SSFs revealed that hyenas with dens in LNNP are also selecting against
boundaries to a much stronger degree than hyenas with dens in Soysambu.
However, fine scale selection against vegetation greenness and toward
verified livestock predation locations outside of the park, as well as
known fence-crossing behaviors by LNNP hyenas (Wilkinson \emph{et
al.~}2021b), point to a lack of sufficient resources or space in the
national park.

Fence behaviors

The abundance of \emph{quick cross} and \emph{bounce} behaviors captured
by the barrier behavior analysis, as opposed to walking along the fence
or exhibiting average movements near the fence, implies that hyenas may
perceive boundaries as risky in this rapidly developing area, and may
approach the fence only out of need. When they reach the fence, if they
cannot cross, they appear to immediately move away
(i.e.,~\emph{bounce}), and if the fence is permeable, they cross
quickly. While McInturff et al. (2020) concluded that fences can create
interspecific ``ecological winners and losers'', the hyena populations
in this region may be a combination of both, depending on the
individual, season, land management type, or other factors.

Though our study was able to assess movements of hyenas representing 5
clans, the sample size for assessing fence navigation was limited since
not every collared hyena approached the fence lines. Our supplementary
camera trap analyses of individual hyenas at the fence line revealed
hyenas are approaching the fence and possibly crossing in and out of the
national park in extraordinary numbers. Previous studies have suggested
that social rank, age, and sex influence spotted hyena risk-taking
behavior (Belton \emph{et al.} 2018; Green \emph{et al.} 2018) and space
use (Boydston \emph{et al.} 2003), yet our analysis shows that
individuals spanning different demographics and social ranks may be
crossing in and out of the national park. While evidence suggests these
behaviors may be caused by resource limitations within this relatively
small protected area, further research is needed to assess the
ecological factors driving these behaviors.

Implications for landscape permeability

At broad scales, hyenas in this developing region appear to be selecting
for ecological characteristics that reflect their resource selection in
other, less developed systems. In contrast, movement choices at a fine
scale are more nuanced and influenced by anthropogenic factors. Hyena
clan sizes in this region are relatively large (with more than 50
animals per group for clans assessed thus far) despite the small size of
the protected areas, which could be influencing the apparent movement of
hyenas toward people and likely toward anthropogenic resource subsidies.
Other studies in similar environments have estimated hyena carrying
capacity to be orders of magnitudes lower than the populations seen in
our study site (e.g., Holekamp \& Smale 1995; Yirga \emph{et al.} 2014).

Despite this suspected significant reliance on anthropogenic resources,
hyenas showed a general aversion to roads, with different selection
strengths depending on scale and land management type, which is contrary
to what we expected. Fences also present a semi-permeable barrier for
spotted hyenas, which appear to cross them as quickly as possible rather
than lingering. Other studies have found that keeping development and
subdivision below a certain threshold may allow for sustained carnivore
navigation of the landscape between core habitat areas (Smith \emph{et
al.} 2019; Xu \emph{et al.} 2023). This may also prove true for the
spotted hyenas, which appear to have complex relationships with
infrastructure within and surrounding the two protected areas. Yet,
hyena relationships with fences can also provide information that is
helpful for management efforts. We can use fence behavior analyses to
determine existing permeable fence segments (Xu \emph{et al} . 2020) and
make ecologically-informed decisions about where carnivore (and other
wildlife) corridors in and out of fenced regions will be the most
useful, practical, and cost-effective.

Overall, our results imply that anthropogenic factors may influence fine
scale decision making differently than landscape-scale selection. Hyenas
may be adaptable enough to switch to anthropogenic food sources in
regions of depleted natural prey or limited resources, yet their ability
to rely on anthropogenic food may be linked to regional tolerance of
hyenas (e.g., Yirga \emph{et al.} 2012). Spatially explicit human
acceptance and experience have the potential to predict where wildlife
corridors are likely to succeed for certain species or taxa, while also
providing insight into how wildlife may be using anthropogenic resources
(Behr \emph{et al.} 2017; Ghoddousi \emph{et al.} 2021). Coupled with
hyena context-specific selection for and against infrastructure
characteristics, these results demonstrate that a multiscale and
multidisciplinary understanding of social-ecological landscape use and
navigation can help to determine where and when this species may thrive
in human-dominated landscapes. This approach is essential for a species
that is key for removing carcasses and diseases from the environment
(Sonawane \emph{et al.} 2021), and in a location that is becoming
increasingly fenced, but the social-ecological approaches used here can
also be applied to movements and reintroductions of other controversial
wildlife species in other settings (see Ditmer \emph{et al.} 2022;
Manfredo \emph{et al} . 2021; Vasudev \emph{et al.} 2023;
Williamson~\emph{et al.} 2023; Williamson \& Sage 2020). Future research
on social-ecological landscape permeability for wildlife should include
the incorporation of detailed land cover covariates, in-depth
quantification of tolerance and experience as spatial proxies for
behaviors, and testing of GPS collar data across RSF- and SSF-informed
social-ecological least cost corridor models.

\textbf{Conclusion}

Spotted hyenas are one of the most behaviorally flexible large
carnivores. Yet, their reputation for adaptiveness has previously
discouraged studies on whether and to what extent people impact their
movements and behaviors. As a widespread species across Africa, spotted
hyenas provide us with a litmus test for understanding carnivore
abilities to live alongside people and navigate landscapes on
coexistence frontiers. Yet, we also know that coexistence requires
adaptation by both people and carnivores to succeed (Chapron \emph{et
al.} 2014). This study has demonstrated that integrating spatial and
contextual information on ecology, infrastructure, and human tolerance
can help us to better examine how carnivores may adapt to proliferating
human disturbances and learn to navigate human-dominated landscapes at
different scales. By gaining these holistic understandings of the
effects of ongoing global urbanization on behaviorally flexible,
ecologically critical species, we may be able to design and redesign
anthropogenic landscapes that prioritize ecological resilience and
environmental justice.

\textbf{Data availability statement} : Data will be made public upon
acceptance of this manuscript (https://doi.org/10.5061/dryad.0vtb8h52).

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\textbf{Figures and Tables}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Figure 1\textbf{.} 50\% (left panel) and 95\% (right panel) kernel
utilization distribution home ranges based on GPS collar data for 7
spotted hyenas representing 5 clans in Lake Nakuru National Park
(western side of maps) and Soysambu Conservancy (eastern side of maps),
Kenya. Hyenas 1 and 3 are in the same clan and hyenas 5 and 9 are in the
same clan.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Figure 2.} Seasonal results using a) resource selection
functions and b) step selection functions for spotted hyena. Bounds =
protected area boundaries, VC= verified livestock predation, PC =
participatory mapped livestock predation, and risk = participatory
mapped risk from spotted hyenas.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

\textbf{Figure 3.} A) Resource selection and b) step selection function
model outputs across land management types (LNNP: fully protected, or
Soysambu: multi-use) for spotted hyena. Bounds = protected area
boundaries, VC= verified livestock predation, PC = participatory mapped
livestock predation, and risk = participatory mapped risk from spotted
hyenas. ``N.S.'' indicates not significant.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

\textbf{Figure 4.} Relative covariate selection strength by individual
collared spotted hyenas living in and near Lake Nakuru National Park and
Soysambu Conservancy, Kenya, revealed through step selection functions
using conditional logistic regression. Only the six hyenas whose data
were retained for the movement analyses are included.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image8/image8}

\end{center}
\end{figure}

\textbf{Figure 5.} A) Total frequency of fence behaviors revealed
through Barrier Behavior Analysis (BaBA) using a buffer distance of 50,
and b) seasonal frequency of fence behaviors for spotted hyenas living
in and near Lake Nakuru National Park and Soysambu Conservancy, Kenya.

Table 1. Covariates analyzed for resource selection and step selection
functions. All rasters were resampled to 30m\textsuperscript{2} spatial
resolution.\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
\textbf{Covariate Type} & \textbf{Covariate} &
\textbf{Source}\tabularnewline
Ecological & Normalized difference vegetation index (NDVI); rainy season
average for 2019 & Landsat 8 Surface Reflectance Tier 1\tabularnewline
& NDVI; dry season average for 2019 & Landsat 8 Surface Reflectance Tier
1\tabularnewline
& Slope & Shuttle Radar Topography Mission\tabularnewline
& Elevation & Shuttle Radar Topography Mission\tabularnewline
& Distance to rivers & Derived from authors' georeferenced,
ground-verified data\tabularnewline
& Distance to lakes & Derived from authors' georeferenced,
ground-verified data\tabularnewline
Anthropogenic infrastructure & Distance to roads & Open Street Maps and
authors' ground-verified data\tabularnewline
& Distance to boundaries & Derived from Kenya Wildlife Service and
authors' ground-verified data\tabularnewline
Human tolerance & Distance to verified livestock predation locations &
Derived from Kenya Wildlife Service (see Wilkinson et al.
2021a)\tabularnewline
& Distance to participatory mapped livestock predation locations &
Derived from participatory mapped data (see Wilkinson et al.
2021a)\tabularnewline
& Distance to regions perceived as risky due to hyenas & Derived from
participatory mapped data (see Wilkinson et al. 2021a)\tabularnewline
\bottomrule
\end{longtable}

Table 2. Results from global resource selection function model
(generalized linear model with a logit link) for spotted hyenas.\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\emph{Variable} & \emph{coeff} & \emph{se} & \emph{z-value} &
\emph{p-value}\tabularnewline
NDVI & 0.142 & 0.003 & 42.738 & \textless{}0.001\tabularnewline
Elevation & -0.151 & 0.004 & -36.702 & \textless{}0.001\tabularnewline
Slope & -0.031 & 0.003 & -11.448 & \textless{}0.001\tabularnewline
Distance to rivers & 0.111 & 0.007 & 16.684 &
\textless{}0.001\tabularnewline
Distance to roads & -0.172 & 0.003 & -59.327 &
\textless{}0.001\tabularnewline
Distance to lakes & -0.207 & 0.003 & -60.249 &
\textless{}0.001\tabularnewline
Distance to boundaries & 0.148 & 0.0923 & 1.609 & 0.108\tabularnewline
Distance to perceived livestock predation locations & 0.271 & 0.006 &
47.135 & \textless{}0.001\tabularnewline
Distance to verified livestock predation locations & -0.255 & 0.004 &
-63.387 & \textless{}0.001\tabularnewline
Distance to locations perceived as risky due to hyenas & -0.16 & 0.005 &
-35.14 & \textless{}0.001\tabularnewline
\bottomrule
\end{longtable}

Table 3. Results from global step selection function model for spotted
hyenas.\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\textbf{Variable} & \textbf{coeff} & \textbf{se} & \textbf{z-value} &
\textbf{p-value}\tabularnewline
Distance to rivers & 0.179 & 0.047 & 0.862 & 0.389\tabularnewline
NDVI & 0.026 & 0.004 & 0.458 & 0.647\tabularnewline
lakes\_s\_end & -0.193 & 0.028 & -2.303 & \textless{}0.05\tabularnewline
slope\_s\_end & -0.027 & 0.003 & -0.835 & 0.404\tabularnewline
roads\_s\_end & -0.121 & 0.007 & -2.160 & \textless{}0.05\tabularnewline
elev\_s\_end & -0.08 & 0.012 & -3.346 & \textless{}0.001\tabularnewline
bounds\_s\_end & 0.273 & 0.017 & 2.791 & \textless{}0.01\tabularnewline
vc\_s\_end & -0.163 & 0.029 & -1.193 & 0.233\tabularnewline
pc\_s\_end & -0.002 & 0.028 & -0.015 & 0.988\tabularnewline
risk\_s\_end & 0.013 & 0.017 & 0.163 & 0.87\tabularnewline
\bottomrule
\end{longtable}

Table 4. Number of spotted hyena individuals appearing on camera at the
Lake Nakuru National Park fence line across 8 sites.\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\textbf{Site} & \textbf{\# Individuals} & \textbf{Total frequency of
hyena photographs} & \textbf{Total frequency of hyena
photographs}\tabularnewline
C1 & 24 & 505 & 505\tabularnewline
C2 & 56 & 301 & 301\tabularnewline
C3 & 37 & 505 & 505\tabularnewline
C4 & 22 & 273 & 273\tabularnewline
C6 & 3 & 10 & 10\tabularnewline
C7 & 1 & 2 & 2\tabularnewline
C8 & 47 & 241 & 241\tabularnewline
C17 & 67 & 3848 & 3848\tabularnewline
\textbf{Total \# Individuals on Camera*: 234} \emph{*some individuals
appeared at more than one site} & \textbf{Total \# Individuals on
Camera*: 234} \emph{*some individuals appeared at more than one site} &
\textbf{Total \# Individuals on Camera*: 234} \emph{*some individuals
appeared at more than one site} &\tabularnewline
\bottomrule
\end{longtable}

\selectlanguage{english}
\FloatBarrier
\end{document}

