Biodiversity and Conservation (2020) 29:3591–3608
https://doi.org/10.1007/s10531-020-02039-w(0123456789().,-volV)(0123456789().,-volV)

ORIGINAL PAPER

Counting cats for conservation: seasonal estimates of leopard
density and drivers of distribution in the Serengeti
Maximilian L. Allen1
Miha Krofel4• Shaodong Wang2 • Lucas O. Olson3 • Qing Li2 •Received: 6 October 2019 / Revised: 24 May 2020 / Accepted: 14 August 2020 /Published online: 19 August 2020
Ó Springer Nature B.V. 2020

Abstract
Large carnivore conservation is important for ecosystem integrity and understanding driversof their abundance is essential to guide conservation efforts. Leopard (Panthera pardus)
populations are in a general state of decline, although local studies demonstrated large
variation in their population trends and density estimates vary widely across their range. We
used spatially-explicit capture-recapture models for unmarked populations with camera trap
data from a citizen science project to estimate previously-unknown leopard population
densities in Serengeti National Park, Tanzania, and determine potential biological drivers of
their abundance and distribution. We estimated leopard densities, at 5.41 (95% CrI =2.23–9.26) and 5.72 (95% CrI = 2.44–9.55) individuals/100 km2, in the dry and wet season,
respectively, which confirmed Serengeti National Park as one of the strongholds of thisspecies in Africa. In contrast to abundance estimates, we found that drivers of leopard
abundance and distribution varied among the dry and wet seasons, and were primarily
affected by interactions with other larger carnivores and cover. The underlying driver of
leopard distribution may be the dynamic prey availability which shifts between seasons,leading to an avoidance of dominant carnivores when prey availability is low in the dry season
but an association with dominant carnivores when prey availability is high in the wet season.
As efforts to conserve large carnivore populations increase worldwide, our results highlightthe benefits of using data from citizen science projects, including large camera-trapping
surveys, to estimate local carnivore abundances. Using a Bayesian framework allows of
estimation of population density, but it is also important to understand the factors that dictate
their distribution across the year to inform conservation efforts.
Keywords Competition  Leopard  Panthera pardus  Population density  Predator–preydynamics  Seasonal variation  Spatially explicit capture recapture

Communicated by Karen E. Hodges.
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10531-02002039-w) contains supplementary material, which is available to authorized users.
& Maximilian L. Allenmaxallen@illinois.edu
Extended author information available on the last page of the article

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Introduction
Effective conservation for species at risk is dependent on understanding their abundanceand drivers affecting it, but these can be difficult to determine for cryptic species such aslarge carnivores (Gese 2001; Rich et al. 2013; Allen et al. 2018a). Large carnivores occupy
the highest trophic levels, and play important roles in ecosystem regulation (Estes andPalmisano 1974; McLaren and Peterson 1994) and disease and population control (Packer
et al. 2003; Ripple et al. 2014). Many populations worldwide, however, are declining due
to increasing human populations and development, habitat degradation, and unsustainablehunting (Ripple et al. 2014). These anthropogenic effects are accelerating for large carnivores, whose naturally low population densities and birth rates make their populations
sensitive to changes in the environment (Ripple et al. 2014). The density and conservation
status of large carnivore populations often varies across geographical and jurisdictional
ranges (Chapron et al. 2014; Jacobson et al. 2016; Durant et al. 2017), making it important
to identify and conserve strongholds of high density populations.
Leopards (Panthera pardus) are cryptic solitary carnivores, whose IUCN Red List
status has recently been up-listed to Vulnerable (Stein et al. 2020). Leopards suffered a
substantial reduction in their range and currently occupy 25–37% of their historic range(Jacobson et al. 2016). Leopard populations across their range are also in a general state of
decline with many of the nine leopard subspecies being critically endangered (Jacobson
et al. 2016). The density estimates for leopards vary widely, with estimates ranging from
0.1 to 30.3 leopards/100 km2 (Grey et al. 2013; Jacobson et al. 2016), and rigorous
estimates from sub Saharan Africa ranging from 0.25 to 12.67 leopards/100 km2 (Supplementary Material 1). Such high variation in population densities and limited number ofregions sampled precludes extrapolation to other parts of their range, particularly because
leopards have high variability in home-range size (Jacobson et al. 2016), prey preferences(Hayward et al. 2006), and interactions with humans and larger carnivores (Stein et al.
2020). There is a concurrent lack of research on density and abundance in many parts ofleopards range and they are difficult to predict based on estimates from other parts of therange (Jacobson et al. 2016). This for now limits our ability to create range-wide population estimates, and as a result there is currently no robust estimate for the number of
leopards across their range (Stein et al. 2020). Thus additional population surveys acrossthe range of leopards, while also examining the effects of potential drivers, is important for
both their local and range-wide conservation.
Densities of leopards vary across their range based on anthropogenic (i.e., development,
hunting, and conservation activities) and ecological (i.e., competitors, prey, seasons, andhabitat) factors (Bailey 2005; Balme et al. 2009; Harihar et al. 2011; Grey et al. 2013; Qi
et al. 2015; Rosenblatt et al. 2016). Interactions with humans, including habitat loss anddegradation (Jacobson et al. 2016; Stein et al. 2020), prey base depletion (Datta et al. 2008;
Gray and Prum 2012), conflicts over livestock (Kissui 2008; Athreya et al. 2011) andunsustainable hunting practices (Packer et al. 2009; Gray and Prum 2012) are a leading
cause of mortality and population declines for leopards. Leopard populations can also be
suppressed by dominant carnivores, as documented in Asia for tigers (Panthera tigris)(Harihar et al. 2011), and suggested in Africa for African lions (Panthera leo), and spottedhyenas (Crocuta crocuta) (Packer et al. 2011). Besides interference competition, one of thepossible mechanism of top-down suppression could be connected with kleptoparasitism,
which was shown to reduce reproductive success in African leopards (Balme et al. 2017a).

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However, recent research gives equivocal results on the importance of top-down effects
and avoidance of dominant carnivores on leopard densities and activity (Steinmetz et al.
2013; Vanak et al. 2013; du Preez et al. 2015; Balme et al. 2017b). This suggests regional
differences in the factors driving leopard abundance and further studies are therefore
needed also to better understand the importance of interspecific interactions for leopards
across their range.
Serengeti National Park (hereafter Serengeti) is the site of many long-term ecology
studies (Mduma et al. 1999; Sinclair et al. 2007) and is a conservation stronghold for many
species, but is not immune from the impacts of human encroachment and environmental
change (Veldhuis et al. 2019). The leopard population in the Serengeti is anecdotally
thought to be robust (Kruuk and Turner 1967; Schaller 1976), but abundance estimates forthe leopard population are very rough (e.g., 800–1000) and lacking explanation of methods(e.g., Borner et al. 1987). With a full suite of carnivores and a large diversity of prey, theSerengeti is an ideal area to understand leopard population densities in a large natural
system and how interspecific interactions, seasons, and habitat may shape leopard densities. Such knowledge can then be used as a reference to guide conservation efforts aimed torestore carnivore guilds in previously degraded ecosystems (Hayward et al. 2007a;
Dalerum et al. 2008).
Although rarely used, the application of modern analytical tools in combination withdata generated through citizen-science projects has the potential to improve our understanding of carnivore populations and the factors that affect densities across their range.
Our objectives were to estimate leopard population densities in the Serengeti, and determine potential biological drivers of their abundance and distribution. Leopard densities in
the Serengeti are unknown, and we used camera trap data from the largest citizen-science
project to date (Swanson et al. 2015) to obtain our estimates. We first used spatiallyexplicit capture-recapture (SECR) models for unmarked populations to estimate a leopard
population from the Serengeti in 2012 during both the wet and dry season. In this way weestablished the baseline information on leopard abundance, to which future surveys could
be compared in order to understand population trends and detect potential conservation
problems. We then tested 18 zero-inflated Poisson (ZIP) models in an a-priori modelingframework using Akaike Information Criterion (AIC) to compare models based on 10
variables (Table 1) to determine the impact of habitat, dominant carnivores and preyavailability on leopard abundance documented during camera trapping in each season.

Materials and methods
Study area
The National Park in the Serengeti, established in 1951, is a vast area covering 14,750 km2
in northwestern Tanzania. The Serengeti is characterized by a mix of open and dense
woodlands in the north and treeless grass plains in the south, interspersed with rivers(Norton-Griffiths et al. 1975; Grant et al. 2005). Grasslands vary in height, depending on
species, soil type, and rainfall (Norton-Griffiths et al. 1975). Rocky outcrops or ‘kopjes’
support trees and bushes and are found throughout the Serengeti, often providing the only
cover on the plains (Durant 1998). Mean annual rainfall ranges from 350 mm in thesoutheast to 1200 mm in the northwest, and is concentrated in the wet season (November–May) with very little rain in the dry season (June–October) (Norton-Griffiths et al. 1975;
Durant 1998). Ungulates perform long-distance seasonal migrations in the Serengeti

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Table 1 Individual variables considered in a-priori models of leopard abundance in Serengeti National
Park, Tanzania
Name

Abbreviation

Description

Hypothesized effectBushbuckabundance

BSBK

The relative abundance of bushbucksat the camera site

Positive (Hayward et al. 2006)

Cover

COVR

The percent of tree and shrub coveravailable within 1 km2

Positive (Ray-Brambach et al.
2018; Balme et al. 2019)

Trap nights

DAYS

The number of trap nights

Positive

Dikdikabundance

DKDK

The relative abundance of dikdiks at
the camera site

Positive (Hayward et al. 2006)

Spotted hyenaabundance

HYAB

The relative abundance of spottedhyenas at the camera site

Negative (Vanak et al. 2013, duPerez et al. 2015)

Impalaabundance

IMPL

The relative abundance of impalas at
the camera site

Positive (Hayward et al. 2006)

Lion abundance

LNAB

The relative abundance of African
lions at the camera site

Negative (Vanak et al. 2013, duPerez et al. 2015)

Distance to river

RIVR

Distance to the nearest river

Positive (Mkonyi et al. 2018;
Strampelli et al. 2018)

Shade

SHAD

The amount of shade available

Positive (Bailey 2005)

Thomson’s
gazelleabundance

THGZ

The relative abundance ofThomson’s gazelles at the camera
site

Positive (Hayward et al. 2006)

Included in the table is the variable name abbreviation, description, and hypothesized effect; listed alphabetically by abbreviation

leading to strong seasonal patterns in their density and distribution (Sinclair and Arcese
1995; Mduma et al. 1999).

Data collection and preparation
We used data collected by the Snapshot Serengeti Project (Swanson et al. 2015). The
project systematically deployed camera traps in a randomly distributed grid using 5 km2intervals, with camera traps placed in strategic locations within 250 m of the center of eachgrid to maximize wildlife detections. We used data collected during the dry season (June–
October; n = 115 functioning camera traps) and wet season (November–May; n = 118
functioning camera traps) in 2012. The date, time, and camera trap site were recorded foreach photograph, and the project used community scientists (historically called ‘‘citizen
scientists’’) to determine the species in the photographs (Kosmala et al. 2016).
To reduce false replicates, we considered photos of the same species at the same camera
trap to be the same event if they occurred within 30 min of a previous photo (Rich et al.
2017; Allen et al. 2019). For each event we used the largest number of animals present in
the 30 min time period as the number of individuals for that time period. We then calculated the relative abundance (events per 100 trap nights) as:ðevents=trap nightsÞ  100
for each dominant competitor (African lions and spotted hyenas) and potential preferred123

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prey (bushbucks, Tragelaphus sylvaticus; dik-diks, Madoqua sp.; impalas, Aepyceros
melampus; and Thomson’s Gazelles, Eudorcas thomsonii).
We determined habitat variables (Table 1) for each camera trap. Shade was estimated onan ordinal scale at each camera trap. We calculated other habitat variables using layers in
Arc GIS (Environmental Systems Research Institute, Redlands, CA). For percent of forest
cover, we created a 1 km2 buffer around the camera trap and determined the percent offorest cover within the buffer using land cover information (available from https://
geoportal.rcmrd.org/layers/servir%3Atanzania_sentinel2_lulc2016). For rivers we calculated the Euclidian planar distance from the camera trap to the nearest river. River features
were available from a hydrology layer produced from LANDSAT TM images (available
from https://www.fao.org/geonetwork/srv/en/main.home).

Statistical analyses
We used unmarked SECR analyses based on (Chandler and Royle 2013; Royle et al. 2013).
SECR methods are generally non-invasive, using information from camera trapping andgenetic sampling to unstructured methods or other data (Royle et al. 2013; Broekhuis andGopalaswamy 2016). SECR methods allow estimation of heterogeneously distributed
populations by creating site-specific density estimates (Chandler and Royle 2013). A baseassumption of marked SECR analyses is that all marked individuals are identified correctly
at every incident, while partially marked SECR analyses assume that all marked individuals are identified correctly, and only unmarked individuals remain unknown. Individual leopards are identifiable based on their unique spot patterns (Gray and Prum 2012;
Grey et al. 2013), but for all individuals to be identified it is necessary to have two camera
traps placed parallel in order to record both sides of the individual’s body (Rovero andZimmermann 2016). This was not possible in our study, but other studies have used datafrom the side of the body captured more often (Strampelli et al. 2020). While this approachworks to determine an estimate, it also removes over half of the data (one side of the bodyand blurry or otherwise unidentifiable photographs) and in doing so likely leads to higher
rates of error estimation than using all of the data collected in a Bayesian framework(Chandler and Royle 2013; Royle et al. 2013).
In our model we assumed that each individual (i) had an activity center (si) that was
distributed within the study area (i.e. our state space, S). To define S, we used a portion of
our study area representing a square grid (13 camera traps 9 13 camera traps), and used abuffer equaling two scale parameters (r; see below) around the outermost coordinates ofour camera traps (e.g., Royle et al. 2013) for an S (including our buffer area) of 2420 km2.
We used three monitoring periods of 15 days each in both seasons, using the 15th day andthe 7 days on either side of it for January, February, March, and July, August, September,respectively. We used the 15-day monitoring periods instead of daily periods because
leopards are spatially dispersed and relatively infrequently caught on camera traps, anddetection rates close to zero can lead to estimation errors in SECR models (Rich et al.
2014). We also limited the data to a period of 3 months to meet the assumption of
population closure. We documented the number of detections for leopards for each camera
trap location during each trapping period. In our model we assumed that the number ofdetections of a given leopard (i) at a given camera trap (j) during a given encounteroccasion (k), yijk, was a Poisson random variable with mean encounter rate kij . We thencalculated the mean encounter rate for individuals as a function of the distance (dij) fromthe given trap (xj ) to the individual’s activity center (si), using a half-normal encounter rate

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model. The half-normal encounter rate model was based on the detection rate (W) andencounter rate for a hypothetical camera trap (k0), and r which we assumed was constantacross all encounter occasions. In general, the whole model isX yijk  Poisson Kj
Yjk ¼
i

Kj ¼

X

kij

i

( 
 )
xj  si 2
zij
kij ¼ k0 exp 
2r2

zij  bernoulliðwÞ
where zij is the indicator of detection, zij ¼ 1 if individual i is able to be detected by camera
trap j; otherwise zij ¼ 0.
We did not have data from our study area for our prior model parameters (W, k0, r) so
we performed literature searches for studies from similar nearby systems to estimate theparameters, and used the same parameters for each season. For W we used estimates from(O’Brien and Kinnaird 2011); daily W of 0.0595), (Swanepoel et al. 2015); six daily W
values ranging from 0.003 to 0.039), and (Chapman and Balme 2010); W across 4 days of
0.112). We translated this to a detection rate for our 15-day monitoring period using 1 –(1-W)15, to arrive at a mean of 0.322 and a range of 0.044–0.602, which we used to create
the beta distribution of our prior distribution. We based k0 on our detection rates andnumber of detections, to create a gamma distribution for our prior distribution. The halfnormal detection function we assumed in our model implies a bivariate normal model ofspace use, which allowed us to translate r into a 95% home-range radius, r, using theformulapﬃﬃﬃﬃﬃﬃﬃﬃﬃ
r ¼ r  5:99
(Royle et al. 2013). To estimate home range sizes we used the two closest studies to ourstudy area (Hamilton 1981; Mizutani and Jewell 1988), and used the values from all adultleopard home ranges to calculate a mean and 95% CI (mean = 27.07, 95%
CI 20.52–33.63). To ensure inclusion of the home range centers from all possible sampledleopards, we used the value for the radius from the home range at the upper limit of ourcalculated 95% CI multiplied by 1.5 to account for home range centers not being uniformly
distributed in a home range as our upper limit, and converted to a normal distribution forour prior distribution.
We estimated the population density of leopards using data augmentation in a Bayesianframework and used Markov chain Monte Carlo (MCMC) method to sample from theposterior distributions using their full conditional distributions. We implemented MCMC
code (Supplementary Material 2) in the program R version 3.5.1 (R Core Team 2018),
adapted from the code developed by (Royle et al. 2013). In our MCMC model we used
three chains, with 100,000 total iterations and 20,000 iterations in each chain used as burnin. We considered models to have converged if trace plots exhibited adequate mixing and if123

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point estimates of the Gelman–Rubin statistic were \ 1.01 (Gelman and Rubin 1992). We
then used our density results to model the site level population density across the study areaby plotting the activity centers (si) of each individual (i) across the state space (S) for eachof the model iterations, and then scaling these numbers to derive a mean of the density foreach camera trap. We report the estimates, naı̈ve SE, and 95% CrI for each variable in eachseason.
To determine the drivers of the spatial distribution and abundance of leopards we tested
ZIP models using the pscl package (Zeileis et al. 2008). We used ZIP models because thehistogram of our leopard detection data revealed more zeros than expected in a Poisson
distribution, and ZIP models effectively account for detection rates using the presence of
non-informative, structured zeros in a binomial model followed by a Poisson model with
non-informative zeros accounted for (Zeileis et al. 2008). We used the same months as in
Table 2 A-priori candidate models for leopard abundance in Serengeti National Park, Tanzania
Variables

ReasonCOVR

Leopards will select for forested habitat with cover

RIVR

Leopards will use areas based on availability of water

SHAD

Leopards will use areas based on availability of shade

COVR ? RIVR

Leopards will use areas based on availability of cover near water

SHAD ? RIVR

Leopards will seek out cool areas near water for thermoregulation

COVR*SHAD

Leopards will select for forested habitat with shade forthermoregulation and camouflage

COVR*SHAD*RIVR

Leopards will select for forested habitat with cover and shade near
water

LION

The larger sympatric African lion will exclude the smaller andsubordinate leopard

HYNA

The larger sympatric spotted hyena will exclude the smaller andsubordinate leopard

LION*HYNA

Larger social carnivores will exclude the smaller and solitaryleopard

COVR*LION*HYNA

Leopards will select for areas with forest and shrub cover to mitigate
the negative effects of larger pack carnivores

THGZ

Leopards will seek out areas where their preferred prey, Thomson’s
gazelle, are most abundant

THGZ ? IMPL

Leopards will seek out areas where their top two preferred prey,Thomson’s gazelle and impala, are most abundant

THGZ ? IMPL ? BSBK ? DKDK

Leopards will seek out areas where their four common prey are mostabundant

COVR ? RIVR ? THGZ ? IMPL

Leopards will seek out areas with cover near water and highavailability of their preferred prey for hunting

COVR*SHAD ? THGZ ? IMPL

Leopards will seek out areas with cover and shade for camouflage
with a high availability of their preferred prey for hunting

Null

Selection of areas used by leopards is independent of variables weconsidered

GlobalThe selection of areas used by leopards is complex and can only be
explained by a combination of all of the variables

Included in the table are the combination(s) of variables and reasoning for each model

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our population estimate for our ZIP models, but used the data from the entire month andaccounted for variation in the number of trap nights among cameras. For each season we
considered trap nights as the predictor of non-informative zeros in the binomial process,and tested along with the variables for each of 18 a-priori models in the Poisson process(Table 2). We compared the models in an AIC framework using AIC weight (w) after
removing models that did not converge from our comparisons, and considered any modelwith \ 0.90 cumulative w to be a top model (Burnham and Anderson 2003).

Results
Population density
In the dry season, our posterior mean estimate of the leopard density in the study area was
5.41 (± 0.004 SE, 95% CrI = 2.23–9.26) individuals/100 km2. Leopards were heterogeneously distributed during the dry season with the posterior mean number of leopards/km2
per camera trap being 0.276 (range = 0.201–0.934; Fig. 1a). We found that r was not
updated by the model with a posterior mean of 1.22 km (± 0.0004 SE, 95% CrI =0.80–1.65), similar to the prior mean and distribution. In contrast, both k0 and W were
updated by the model. The mean k0 was 0.069 (± 0.0001 SE, 95% CrI = 0.018–0.194),
lower than the prior mean and distribution. The mean W was 0.37 (± 0.0003 SE, 95%
CrI = 0.15–0.63), slightly higher than the prior mean and distribution. The mixing of themodel was even (Fig. 2) and the Gelman–Rubin convergence statistic was \ 1.01 for eachparameter.
In the wet season, our posterior mean estimate of the leopard density in the study area
was 5.71 (± 0.004 SE, 95% CrI = 2.44–9.55) individuals/100 km2. Leopards were
heterogeneously distributed during the wet season with the posterior mean number of
leopards/km2 per camera trap being 0.298 (range = 0.186–1.036; Fig. 1b). We found thatr was updated by the model with a posterior mean of r was 1.34 km (± 0.0004 SE, 95%
CrI = 0.95–1.75), slightly larger than the prior mean and distribution. We found that k0
was updated by the model, with a posterior mean of 0.078 (± 0.0001 SE, 95% CrI =0.025–0.197); lower than the prior mean and distribution. We found that W was also
updated by the model with a posterior mean of 0.39 (± 0.0003 SE, 95% CrI = 0.17–0.65),
slightly higher than the prior mean and distribution. The mixing of the model was even(Fig. 3) and the Gelman–Rubin convergence statistic was \ 1.01 for each parameter.

Drivers of density
During the dry season, the global model was our highest ranking model (w = 0.70), 15
times higher than the next closest model (Table 3, Supplementary Information 3). In thismodel, BSBK had significant positive effect (ß = 0.92 ± 0.28, p = 0.011); and DKDK had
a significant negative effect (ß = - 0.82 ± 0.36, p = 0.02) on leopard density. SHAD had
a marginally significant positive effect (ß = 0.51 ± 0.28, p = 0.07), while LION had amarginally significant negative effect (ß = - 0.17 ± 0.11, p = 0.12).
In the wet season our top models were COVR*LION*HYNA (w = 0.37) and
LION*HYNA (w = 0.36; Table 3, Supplementary Information 3). In the first model, theeffects of each variable were positive (COVR ß = 2.95 ± 1.88, p = 0.11; LION
ß = 1.28 ± 0.44, p = 0.004; HYNA ß = 0.80 ± 0.30, p = 0.008), all of the single interactions were negative (ßCOVR:LION = - 2.07 ± 0.97, p = 0.03; ßCOVR:HYNA = -123

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b Fig. 1 Leopard density estimates during the dry (a) and wet (b) seasons within our camera grid in Serengeti
National Park, Tanzania

Fig. 2 Trace plots (left) and posterior density plots (right) of the posterior estimates for each model
parameter, including r, k0, W and total abundance (N), of leopards during the dry season in Serengeti
National Park, Tanzania

0.82 ± 0.39, p = 0.04; ßLION:HYNA = - 0.19 ± 0.12, p = 0.12), and the COVR:LION:HYNA interaction was positive but not statistically significant (ß = 0.23 ± 0.28,
p = 0.42). In the second best model, the effects of each variable were positive (LIONß = 0.33 ± 0.09, p = 0.0002; HYNA ß = 0.20 ± 0.06, p = 0.002), but the LION:HYNA
interaction was negative (ß = - 0.04 ± 0.01, p = 0.005).

Discussion
We determined the seasonal population density of leopards in the Serengeti and examinedthe ecological factors that may be affecting site-level abundance. Densities showed minimal variation among seasons, with 5.41 individuals/100 km2 in the dry season and 5.72
individuals/100 km2 in the wet season, but the drivers of abundance changed amongseasons. These densities are slightly higher than many other areas (Fig. 4), as would be
expected in a large protected area. But importantly, the seasonal variation in drivers
indicates that leopard density is affected by ecological factors including dominant123

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Fig. 3 Trace plots (left) and posterior density plots (right) of the posterior estimates for each model
parameter, including r, k0, W and total abundance (N), of leopards during the wet season in Serengeti
National Park, Tanzania
Table 3 Model selection results of top a-priori models ranked by their AIC weight (w) for drivers of leopard
density in the dry season and wet season in Serengeti National Park, Tanzania
Poisson variables

AIC

DAIC

wAIC

Cumulative wAIC

1

Global119.26

0.00

0.64

0.64

2

RIVR

124.70

5.43

0.05

0.75

3

SHAD

124.90

5.64

0.04

0.79

4

LION

125.32

6.05

0.03

0.82

5

SHAD ? RIVR

125.65

6.38

0.03

0.85

6

HYNA

125.79

6.53

0.03

0.88

Rank
Dry season

Wet season
1

COVR*LION*HYNA

107.91

0.00

0.37

0.37

2

LION*HYNA

107.99

0.09

0.36

0.73

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Fig. 4 A comparison of leopard densities from rigorous peer-reviewed studies from sub Saharan Africa. The
densities of studies are shown in ascending order in gray, and our density estimates from the wet and dry
season in the Serengeti are shown in black

carnivores and access to water and other resources. Density estimates for leopards vary
widely across their range (Grey et al. 2013; Jacobson et al. 2016), making local studies ofdensity and their drivers important for understanding the species’ conservation status. The
relatively high leopard densities in the Serengeti suggest that leopards are a successful
species in natural conditions when not faced with human-related mortality (e.g., Balmeet al. 2019), even if faced with high densities of dominant carnivores. The Serengeti is aconservation stronghold for many species, and our study confirms anecdotal reports that it
is a stronghold for leopards as well (Kruuk and Turner 1967; Schaller 1976). Considering
the substantial range-wide contractions for leopards (Jacobson et al. 2016), the Serengeti
leopard population may be particularly important for their future conservation.
When compared to other leopard studies from sub Saharan Africa, the densities from the
Serengeti are fairly robust (Fig. 4), but we must consider how methodology and size ofstudy areas affect estimates. Smaller study areas (i.e., \ 1000 km2) have been shown tohave higher densities in Africa (e.g., 10.2 leopards/100 km2; (Grey et al. 2013), but thesesmall areas can be biased towards prime habitat, exclude surrounding areas with noleopards present or be inflated due to artificial factors, such as fences, habitat modifications, artificially increased ungulate density or extermination of larger competitors (Power
2002; Hayward et al. 2007b; Mysterud 2010). Non-spatial population density models havealso been found to overestimate leopard density compared to spatial population models(Devens et al. 2018), indicating our density estimates may be even larger relative to someother areas. However, leopards vary at least 300-fold in their densities across their range
and it is important to understand leopard density across different landscapes for theireffective conservation (Jacobson et al. 2016). Much of the variation is based on habitat
productivity and prey populations (Jacobson et al. 2016), but the size of study areas should
also be considered when comparing density estimates.
The prior mean and distribution that should be considered most strongly in futurestudies is the k0, which may vary among study areas or based on habitat, season, or othervariables. The posterior means and distributions for k0 was lower than the prior means anddistributions in both seasons, likely due to the cryptic nature of leopards and the difficultyof documenting them. These could possibly be limited because the camera traps were not
set specifically for leopards, but instead were placed to document all wildlife species in the123Biodiversity and Conservation (2020) 29:3591–3608

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Serengeti which notably enhances the data gained for a similar amount of effort (Rich et al.
2019). However, there are often sex-biased patterns in detection of leopards and other large
felids with the greater mobility of males and avoidance of trails by females leading tomale-biased detection (Gray and Prum 2012; Allen et al. 2016; Harmsen et al. 2017; RayBrambach et al. 2018). This is likely to affect detection in any SECR or camera trap study,
and may lead to underestimates in k0 and abundance. In contrast, the posterior means anddistributions of W were slightly higher than the prior means and distributions in bothseasons, with the wet season being slightly higher than the dry season. This contrasts withprevious studies that found W was higher in the dry season (Mkonyi et al. 2018). The
posterior means and distributions of r were updated from the prior means and distributions
to be slightly higher in the wet season, but were still relatively small. This is because theprior r we used was specific to the Serengeti, where the home ranges of leopards areamong the smallest documented (leopard home ranges vary from 8 to over 2000 km2;
(Marker and Dickman 2005). Our small r may be specific to the study area and larger
values should be expected in other areas with larger home ranges, and study area specificprior values should always be considered in SECR models.
We used a SECR model for an unmarked population (Chandler and Royle 2013; Royleet al. 2013), which tend to have larger variation than partially marked SECR or marked
SECR analyses (Chandler and Royle 2013; Royle et al. 2013), but are more accurate thanmany estimates made using frequentist frameworks (e.g., Strampelli et al. 2020). The low
sample sizes and detection rates for large carnivores tend to lead to larger variation andconfidence intervals in estimates of their density (Gray and Prum 2012), and our study was
no exception. However, our posterior abundance estimates appear robust (Figs. 2 and 3)
and provide the first rigorous, peer-reviewed population density estimates for leopards in
the Serengeti. Our low k0 rates indicate the difficulty and infrequency of capturing leopardson camera, which is one of the limitations of studying many large carnivores. Estimatesprovided in this study highlight the benefit of analyses using a Bayesian framework anddata collected with citizen-science projects for understanding populations, but a worthwhile follow up study would be a comparison of all population methods using the samedataset.
The distribution of animals often matches their resources, but also reflects a tradeoff ofrisk vs. reward. Leopards had different relationships with the abundance of dominantcarnivores during the dry and wet seasons, which may indicate changes in risk for leopardsin the different seasons or be an indication of prey abundance. During the dry season, ourtop model indicated leopard density was negatively affected by the abundance of lions, butduring the wet season both of our top models indicated a positive effect of lion and hyenaabundance on leopard density. Other studies have found unexpected positive relationships
among carnivores (Rich et al. 2017; Allen et al. 2018b; Davis et al. 2018; Lamichhane
et al. 2019), and specifically leopards exhibiting no avoidance of lions (Vanak et al. 2013;
Balme et al. 2017b, 2019; Strampelli et al. 2018). However, the underlying driver of
leopard distribution may be the dynamic prey availability which shifts on the landscape
between seasons. When prey availability is low, as in the dry season, it appears thatleopards avoid areas with more lions, an effect which has been found among other subordinate carnivores on the Serengeti (Swanson et al. 2014, 2016). During the wet season
when prey is abundant, there is less competition for prey and thus leopard abundance ispositively associated with lions which may be a proxy for prey availability in this system(Steinmetz et al. 2013; Strampelli et al. 2018), as positive associations among competing
carnivores can also be result of their shared habitat affinities (Davis et al. 2018). We did nottest for the causation of the effects of dominant carnivores, but (Balme et al. 2019) found

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that leopard detections correlated with areas with high hyena detections was actuallycaused by hyenas seeking out and following leopards in order to kleptoparasitize theirkilled prey. Lions and hyenas kleptoparasitize leopards, which can have negative effects on
leopard reproductive success (Balme et al. 2017a) and cause leopards in areas with higher
abundance of dominant carnivores to feed on smaller prey (Hayward et al. 2006) and cachemore of their prey in trees to avoid loss of food (Stein et al. 2015; Balme et al. 2017a).
Our study demonstrates the benefits of large, generalized camera-trapping surveys toprovide abundance estimates for a variety of species. Projects involving citizen scientists
can be used to provide density estimates for elusive carnivores from areas lacking speciesfocused studies. Equally important, such databases can provide important information on
drivers of local abundances. In our case, our seasonal analyses of the distribution of
leopards revealed season-specific relationships among large predators and various habitat
characteristics. The conservation efforts for one species can negatively affect others(Krofel and Jerina 2016), and the Serengeti is known for robust populations of many large
carnivores. Our study shows that the Serengeti is also a stronghold for leopard conservation, but that leopards are also affected by dominant carnivores. In areas where it isimportant to conserve or restore entire guild of large carnivores, a key step may beensuring that there is a sufficiently large prey base throughout the year and we suggest that
Serengeti could serve as a reference for such efforts.
Acknowledgements We thank the Snapshot Serengeti team (www.snapshotserengeti.org) for generously
providing the data for this study, and Ali Swanson specifically for comments on earlier drafts that greatly
improved the manuscript. We thank the Illinois Natural History Survey, University of Illinois and Slovenian
Research Agency (Grant P4-0059) for funding.

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Afﬁliations
Maximilian L. Allen1
Miha Krofel4
1• Shaodong Wang2 • Lucas O. Olson3 • Qing Li2 •Illinois Natural History Survey, University of Illinois, 1816 S. Oak Street, Champaign, IL 61820,
USA

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2

Department of Industrial & Manufacturing Systems Engineering, Iowa State University, 3004
Black Engineering Building, 2529 Union Drive, Ames, IA 50011, USA

3

University of Wisconsin, 1630 Linden Drive, Madison, WI 53706, USA

4

Department of Forestry, Biotechnical Faculty, University of Ljubljana, Večna pot 83,
1000 Ljubljana, Slovenia

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