Mammalian Biology 89 (2018) 90–94

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Mammalian Biology
journal homepage: www.elsevier.com/locate/mambio

Short communication

No respect for apex carnivores: Distribution and activity patterns ofhoney badgers in the Serengeti
Maximilian L. Allen a,∗ , Brittany Peterson a , Miha Krofel bab

Department of Forest and Wildlife Ecology, University of Wisconsin, 1630 Linden Drive, Madison, WI 53706, United States
Wildlife Ecology Research Group, Department of Forestry, Biotechnical Faculty, University of Ljubljana, Večna pot 83, SI-1000 Ljubljana, Slovenia

a r t i c l e

i n f oArticle history:
Received 18 August 2017
Accepted 10 January 2018
Handled by Sabine Begall
Keywords:
Abundance
Activity patterns
Distribution
Honey badger
Interspeciﬁc interactions
Mellivora capensis

a b s t r a c t
Honey badgers are cryptic carnivores that occur at low densities and range across large areas. The processes behind site-level honey badger abundance and detection rates are poorly understood, and there
are conﬂicting results about their avoidance of larger carnivores from different regions. We used datafrom 224 camera traps set up in the Serengeti National Park, Tanzania to evaluate patterns in detection
rates, spatial distribution, and activity patterns of honey badgers. Our top models showed that the relative
abundance of larger carnivores (e.g., African lions, Panthera leo, and spotted hyenas, Crocuta crocuta) was
important, but surprisingly was positively related to honey badger distribution. These results suggestthat honey badgers were not avoiding larger carnivores, but were instead potentially seeking out similar
habitats and niches. We also found no temporal avoidance of larger carnivores. Honey badgers exhibitedseasonal variation in activity patterns, being active at all times during the wet season with peaks during
crepuscular hours, but having a strong nocturnal peak during the dry season. Our detection rates of honeybadgers at individual camera traps were low (3402 trap nights/detection), but our study shows that withadequate effort camera traps can be used successfully as a research tool for this elusive mustelid.
© 2018 Deutsche Gesellschaft für Säugetierkunde. Published by Elsevier GmbH. All rights reserved.

The geographical patterns of a species’ abundance affect range
limits, gene ﬂow and population dynamics (Brown, 1984; Sagarin
et al., 2006). The geographical range and abundance of species,
however, can be highly variable: a species can be widespread but
have low abundance throughout the range or only be found in asmall geographic range but have high abundance (Rabinowitz et al.,1986). The former likely best describes honey badgers (Mellivora
capensis), which are large, solitary mustelids with extensive homeranges (Johnson et al., 2000; Begg et al., 2005). Honey badgers exist
at low densities across the majority of their range, which includes
most of sub-Saharan Africa and portions of the Arabian and Indian
peninsulas (Do Linh San et al., 2016). Despite their extensive distribution, honey badgers have not been well studied (Begg et al.,
2003; Proulx et al., 2016), and there is a need to better understandtheir distribution, abundance, habitats and ecology to help deﬁne
local and overall population status (Do Linh San et al., 2016) anddevelop proper conservation programs (Proulx et al., 2016).
Detection rates and abundance estimates for carnivores canbe affected by many factors. Detection rates are affected by survey intensity (Rovero and Marshall, 2009) and how well surveys∗ Corresponding author.
E-mail address: maximilian.allen@wisc.edu (M.L. Allen).

account for the habitat use of the species (Meek et al., 2014), andmay also be inﬂuenced by species behavior and activity patterns.
For example, seasonal patterns in distribution and activity are often
based on breeding behavior (Vogt et al., 2014; Allen et al., 2015)
or prey activity (Sinclair, 1979; Durant et al., 2010) and modulated by the light regime (Heurich et al., 2014). The distribution ofsmaller carnivores can also be affected by the presence, abundance
and activity of larger carnivores (Durant, 1998; Wang et al., 2015;
Newsome et al., 2017). Honey badgers are sometimes killed by large
carnivores (Begg, 2001), but previous studies have given conﬂicting
results regarding honey badger reactions to larger carnivores (e.g.,
Ramesh et al., 2017; Rich et al., 2017), making it unclear whether
potential intraguild predation results in spatial and/or temporal
avoidance.
The main difﬁculties in obtaining accurate estimates of honeybadger abundance are their large home ranges, cryptic behavior,and low densities. Mean sizes of home ranges in Kalahari semidesert were estimated as 541 km2 for adult males, 151 km2 foryoung males, and 126 km2 for adult females (Begg et al., 2005).
These relatively large home ranges compared to body size were
attributed to low prey availability and a long period of cub dependence (12–16 months). Honey badgers have been documented ina variety of habitats from rain forests (Bahaa-el-din et al., 2013,
Greengrass, 2013) to woodlands (Bird and Mateke, 2013) andhttps://doi.org/10.1016/j.mambio.2018.01.001
1616-5047/© 2018 Deutsche Gesellschaft für Säugetierkunde. Published by Elsevier GmbH. All rights reserved.

M.L. Allen et al. / Mammalian Biology 89 (2018) 90–94

deserts (Begg et al., 2003), but preferences and selection of speciﬁc habitats, as well as effects of habitat characteristics on local
abundance are largely unknown. Honey badgers are often nocturnal (Bird and Mateke, 2013), but activity in South Africa appears to
shift to diurnal during the cool season (Begg et al., 2016).
Camera traps are being increasingly used to understand the
populations (Chandler and Royle, 2013; Parsons et al., 2017) andbehaviors of cryptic wildlife (Vogt et al., 2014; Allen et al., 2016).
The use of systematic grid sampling with camera traps allows forcontinuous surveying across large spatial and temporal scales. We
used this approach in a National Park in Tanzania to understandpatterns in detection rates of honey badgers. Our objectives wereto 1) calculate detection rates and determine the viability of using
camera traps for studying honey badgers, 2) determine whether
honey badger activity varied between seasons, and 3) determine
the variables that are driving the distribution of detections forhoney badgers, for which we used an a-priori modeling framework to test a series of model hypotheses composed of 10 variables
explaining honey badger distribution.
Camera trap surveys were conducted in the Serengeti National
Park (hereafter referred to as Serengeti), Tanzania. The Serengeti
is characterized by open woodlands in the northern portions, withtreeless grass plains in the south (Grant et al., 2005). Rocky outcropsor ‘kopjes’ support trees and shrubs and are found throughout the
Serengeti, often providing the only cover on the plains (Durant,1998). Mean annual rainfall ranges from 350 mm in the southeast
to 1200 mm in the northwest (Norton-Grifﬁths et al., 1975) andis concentrated in the wet season (November to May) with very
little rain in the dry season (June to October) and temperatures
remaining consistent across seasons (Sinclair, 1979; Durant, 1998).
We used data collected as part of the Snapshot Serengeti Project(see Swanson et al., 2015, 2016) for monitoring the carnivore community. The project systematically deployed 224 camera traps in arandomly distributed grid using 5 km2 intervals, with camera traps
placed in strategic locations within 250 m of the center of each grid
to maximize wildlife detection. We used the data from July 2010
to May 2013 for this study. The date, time, and camera trap site
ID was recorded for each photograph, and the project used citizen
scientists to determine the species and behavior (standing, resting,moving or eating) in the photographs (Swanson et al., 2016). We
then proofed each of the photos that were tagged as honey badgers,
and subsequently removed 5 events that had been misidentiﬁed as
honey badgers. To reduce pseudoreplication, we considered photosof a species at a camera trap site within 30 min of a previous photo to
be the same event (Wang et al., 2015; Rich et al., 2017). Events withmultiple individuals were treated as a single event with the highest number of individuals documented. We then collected habitat
variables (Appendix A in Supplementary material) for each camera trap using available layers in Arc GIS (Environmental Systems
Research Institute, Redlands, CA).
We used program R version 3.3.1 (R Core Team, 2016) for all
statistical analyses. We ﬁrst calculated summary statistics for ourdata and then tested our series of hypotheses and models.
We calculated our detection rate as the ‘number of detections
per trap night’ for each camera trap, and reported overall detection
rates, and the range and mean for individual camera trap sites. We
then totaled the number of independent events (E) for each species,and determined their relative abundance (RA) at each camera trapas:
RA = (E/TN) × 100
where TN is the total number of trap nights that the camera trap was
operational. We used relative abundance as our measure because
they are often an accurate index of abundance (Parsons et al., 2017),
and our limited sample sizes precluded us from using occupancy.

91

To understand the activity patterns of honey badgers we ﬁrst
changed the time of each event to radians and then ﬁt the data toa circular kernel density and used its distribution to estimate theactivity level using the overlap package (Ridout and Linkie, 2009).
To determine whether honey badger activity varied by season weused the overlapEst function to test whether activity patterns varied
between the wet and dry season, where we considered 1 > 0.80
to be strong overlap and 0.50–0.79 to be medium overlap (Lynamet al., 2013). We then used the overlapEst function to test whether
honey badgers varied in activity patterns from larger carnivores(spotted hyena, Crocuta crocuta, and leopard, Panthera pardus). We
did not test for African lions because their activity data was biased
in favor of mid-day activity, because several camera traps werelocated at location regularly used as a daybed (A. Swanson, PersonalComm.), and therefore we believe that the records do not reﬂect
true circadian activity for this species.
To understand our detection patterns for honey badgers wetested among the 14 a-priori models (Table 1). We ﬁtted our models
using Generalized Linear Models (GLMs) with a binomial logit linkindicating whether a honey badger was detected or not at a camera
trap. We compared our models using AIC weight (AICw ) (Burnham
and Anderson, 2002), and considered each of the models until acumulative AICw = 0.90 to be our top models.
We had a total of 98,644 trap nights at 224 camera traps. We
detected 29 honey badger events at 23 individual camera traps,
among which 6 events included 2 honey badgers. In 86% of events
the honey badgers were moving, but we also documented standing(10%) and eating (4%).
Our overall detection rate among all camera traps was 3402 trap
nights/detection. Our detection rates for the 23 individual camera
traps with detections ranged from 94 to 720 trap nights/detection,with a mean rate for individual camera traps of 404.4 (± 40.6 SE)
trap nights/detection. October had the highest number of detections (n = 5), followed by 3 detections in each of February, March,and June. No detections were recorded in July or August.
We found that honey badger activity was generally similar
among seasons with a medium overlap (1 = 0.69). The main difference being that during the wet season (n = 15) honey badgers
were active during all times, with peaks during crepuscular hours,
but during the dry season (n = 14) they showed a strong nocturnal
peak (Fig. 1). Honey badgers had strong temporal overlap with bothspotted hyenas (1 = 0.85) and leopards (1 = 0.82) (Fig. 1).
Our best model ‘Lion Abundance’ had 2 times more explanatory value (AICw = 0.57) as the next best model ‘Apex Carnivore
Avoidance’ (AICw = 0.27). These two were our top models (Table 2),
however, the variables in the models did not always affect honeybadger abundance in the way we predicted. In ‘Lion Abundance’,
African lions had a positive relationship (ßlion = 0.009), and in ‘Apex
Carnivore Avoidance’, both African lions and spotted hyenas had
positive relationships (ßlion = 0.009, ßhyena = 0.001).
The small number of studies focused on honey badgers in the
published literature may be directly related to the difﬁculty ofﬁnding and detecting this elusive mustelid. In our study, we documented 29 honey badger events across 98,644 trap nights (anaverage of 3402 trap nights needed for each detection). Some of
the camera traps had notably higher success, but even our mostfrequently visited camera trap still needed on average 94 nights
per detection. These detection rates appear to be much lower thanthose found with nocturnal vehicle surveys (e.g., 0.1 honey badgers/km; Waser 1980), or those of other carnivores (e.g., Ramesh
et al., 2017; Rich et al., 2017). The low detection rate limited ourability to detect differences in behaviors and detections by month ortime of day, and also precluded us from using occupancy modelingfor our analyses.
Our top models suggest that honey badger distribution is notnegatively affected by interspeciﬁc interactions with larger car-92

M.L. Allen et al. / Mammalian Biology 89 (2018) 90–94

Table 1
Our a-priori models to explain honey badger distribution at camera sites in Serengeti National Park. We provide the name of the model, the variables included and the
hypothesis behind the model together with references used to develop it.
Name

Variables

Hypothesis

Habitat Preference

HABT*GRHT

Water Availability

RIVR

Habitat and Water Availability

HABT * GRHT + RIVR

Cover
Kopje

COVR
KOPJ

Cover and Kopje
Human Abundance

COVR + KOPJ
HUAB

Leopard Abundance

LPAB

Leopard Abundance and Cover

LPAB + COVR

Distribution of honey badgers will be dictated by their preferred habitat (Durant et al., 2010;
Vanderhaar and Hwang, 2003).
The distribution of honey badgers will be dictated by the proximity to water and riparian habitats(Durant et al., 2010).
The distribution of honey badgers will be dictated by a combination of available habitat and water(Durant et al., 2010; Vanderhaar and Hwang, 2003).
The availability of cover will dictate the distribution of honey badgers (Durant et al., 2010).
The availability of kopjes for cover, possible den sites, and habitat heterogeneity will dictate the
distribution of honey badgers (Durant et al., 2010).
The distribution of honey badgers will be dictated by areas that provide cover (Durant et al., 2010).
The distribution of honey badgers will be dictated by trying to avoid areas frequently used byhumans (Proulx et al., 2016).
The distribution of honey badgers will be dictated by trying to avoid areas frequently used byleopards (Begg, 2001; Do Linh San et al., 2016; Ramesh et al., 2017).
The distribution of honey badgers will be dictated by avoiding leopards and cover to escape(Ramesh et al., 2017).
The distribution of honey badgers will be dictated by trying to avoid areas used by African lions(Begg, 2001; Do Linh San et al., 2016).
Honey badgers will be more frequently found where lions are abundant due to their deterring theleopards (Ramesh et al., 2017)
The distribution of honey badgers will be dictated by trying to avoid areas frequently used bygroup-living apex carnivores (Begg, 2001; Do Linh San et al., 2016).
The number of camera trap nights will have greater inﬂuence on the distribution of honey badgers
than biological factors (Wegge et al., 2004).

Lion AbundanceCascading Carnivore Abundance

LPAB * LNAB

Apex Carnivore Abundance

LNAB + HYAB

Trapping Effort

TRAP

Table 2
The results of our a-priori model selection for honey badger distribution in Serengeti National Park, with individual models ranked based on their AICw .
Name

Variables

AIC

AIC

AICw

Cum AICw

Lion AbundanceApex Carnivore AvoidanceCascading Predation
Cover and Kopje
Cover
Leopard Abundance and Cover
Kopje
Human AvoidanceTrapping Effort
Water Availability
Leopard Abundance
Habitat and Water Availability
Habitat Preference

LNAB
LNAB + HYABLPAB * LNABCOVR + KOPJ
COVR
LPAB + COVR
KOPJ
HUAB
TRAP
RIVR
LPAB
HABT * COVR + RIVR
HABT * GRHT

140.09141.58143.64146.82146.94148.89149.89150.18151.88152.13152.53152.62154.94

0.00
1.49
3.55
6.73
6.85
8.80
9.80
10.09
11.79
12.04
12.44
12.53
14.85

0.57
0.27
0.10
0.02
0.02
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00

0.57
0.84
0.94
0.96
0.98
0.99
0.99
0.99
1.00
1.00
1.00
1.00
1.00

nivores, but instead honey badgers seek out similar habitats andniches as larger carnivores. In our top two models the interactions with African lions and spotted hyenas were important, while
our third best model also included leopards, and presence of all
three species coincided with higher detection of honey badgers at
camera trap sites. Honey badgers also exhibited strong temporal
overlap with spotted hyenas and leopards suggesting there wasalso no temporal avoidance of larger carnivores. It could be that our
low sample size limited our ability to determine what drives sitelevel detection of honey badgers, but the data suggest that honeybadgers are not avoiding larger carnivores in Serengeti, as was suggested by Ramesh et al. (2017) for badgers and leopards in South
Africa. Instead, our results support the study by Rich et al. (2017)
and Balme et al. (2017), who reported a lack of repulsion of carnivore guild species. Although it has been documented that large
carnivores occasionally kill honey badgers (Begg, 2001), honey badgers are notorious for their aggressive behavior and high resource
holding potential even when faced with much larger carnivores(Estes, 1992). Their aggressive threat display is often successful
in preventing predation even against largest of carnivores, such
as African lions, spotted hyenas, and leopards (Begg, 2001). Our
results suggest that honey badgers may exhibit less avoidance oflarge carnivores in space and time than other, less aggressive meso-carnivores, which are often constrained by apex carnivores (Durant,
1998; Hayward and Slotow, 2009; Newsome et al., 2017).
The beneﬁt of systematic grid sampling with camera traps is thatit allows for continuous surveying across large spatial and temporalscales (Swanson et al., 2015; Rich et al., 2017) while not affecting
the activity or behavior of wildlife (Vogt et al., 2014; Allen et al.,2016). We found that honey badgers exhibited seasonal variationin their activity, which is in accord with studies from other ecosystems (Begg et al., 2016; Bird and Mateke, 2013). During the wet
season honey badgers were active throughout the 24-h cycle, butduring the dry season honey badgers showed a strong nocturnal
peak. Honey badgers avoid the hottest times of the day by staying
in burrows in other systems (Begg et al., 2003; Bird and Mateke,2013), but temperatures in the Serengeti stay similar throughout
the year, suggesting that the circadian cycles are not dictated solely
by temperatures. This could, however, be an artifact of our low sample sizes, or the placement of cameras as honey badgers may beseeking out shaded areas during day similar to African lions.
Adjustments to the systematic camera trap design used may
allow for higher detection rates in the future. First, it is importantto understand that these camera traps were placed in areas thatwould allow for detections of all wildlife species (Swanson et al.,2015), and not just honey badgers. A camera trap study target-

M.L. Allen et al. / Mammalian Biology 89 (2018) 90–94

93

References

Fig. 1. The temporal activity and overlap (gray-shaded area), estimated with kernel
densities, of honey badgers and large carnivores in the Serengeti. (A) represents
honey badger activity during different seasons, with wet season represented as asolid line and dry season represented as a dotted line. (B&C) represent overlap ofhoney badger activity with spotted hyenas (B) and leopards (C), where honey badger
activity is represented as solid lines and the large carnivore activity as a dotted line.

ing honey badgers could increase success by choosing microhabitat
variables most frequently used by honey badgers, such as heterogeneous aspects of the landscape, or placing camera traps near
known tracks, marking sites, food sources or dens. Adjustments
like these may be especially important for species like honey badger that are characterized by very low detection rates. Nevertheless,due to the large home ranges of honey badgers and low population
densities, detection rates are always likely to be low. The camera
traps were placed in the center of 5 km2 grids, which should be sufﬁciently large enough for determining population sizes of honeybadgers in a spatially explicit capture recapture modeling framework (Chandler and Royle, 2013). Other important topics for future
research include increasing detection rates, understanding honeybadger selection of microhabitat features, and determining what
may affect the distribution of honey badgers. While research ofhoney badger is difﬁcult and presents many challenges, we encourage future research to understand their ecology and to develop
effective conservation and management efforts, where needed.
Acknowledgements
thank
the
Snapshot
Serengeti
team
(www.
We
snapshotserengeti.org) for generously providing the data forthis study, and Ali Swanson, Emmanuel Do Linh San, and an
anonymous reviewer for comments on earlier drafts that greatly
improved the manuscript.
Appendix A. Supplementary data
Supplementary data associated with this article can be found,in the online version, at https://doi.org/10.1016/j.mambio.2018.01.
001.

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