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Environment and Natural Resources Research; Vol. 8, No. 3; 2018
ISSN 1927-0488 E-ISSN 1927-0496
Published by Canadian Center of Science and Education
148
Characteristics of Human-Wildlife Conflicts in Kenya: Examples of
Tsavo and Maasai Mara Regions
Joseph M. Mukeka1,2, Joseph O. Ogutu3, Erustus Kanga4 & Eivin Røskaft2
1 Kenya Wildlife Service, PO Box 40241-00100 Nairobi, KENYA
2 Department of Biology, NTNU Gløshaugen, 7491 Trondheim, NORWAY
3 University of Hohenheim, GERMANY
4 Ministry of Tourism and Wildlife, KENYA
Correspondence: Joseph M. Mukeka, Kenya Wildlife Service, PO Box 40241-00100 Nairobi, KENYA. Tel:
254-713-124-271. E-mail Jmukeka@kws.go.ke; mukekajoe@yahoo.com
Received: August 10, 2018 Accepted: August 22, 2018 Online Published: September 26, 2018
doi:10.5539/enrr.v8n3p148 URL: https://doi.org/10.5539/enrr.v8n3p148
Abstract
Human-wildlife conflict (HWC) is a widespread and persistent challenge to conservation. However, relatively few
studies have thus far examined long-term monitoring data to quantify how the type, and severity of HWC varies
across species, seasons, years and ecosystems. Here, we examine human-wildlife conflicts in Tsavo and Maasai
Mara, two premier wildlife conservation areas in Kenya. Using Kenya Wildlife Service (KWS) data (2001-2016),
we show that both the type and severity of conflicts vary among species such that the African elephant (Loxodonta
africana), is the leading conflict species in both the Tsavo (64.3%, n= 30664) and Mara (47.0%, n=12487)
ecosystems. The next four most notorious conflict animals, in decreasing order, are nonhuman primates (Tsavo
11.4%, n=3502; Mara 11.8%, n=1473), African buffalo (Syncerus caffer, Tsavo 5.5%, n=1676; Mara 11.3%,
n=1410), lion (Panthera leo,Tsavo 3.6%, n=1107; Mara 3.3%, n=416) and spotted hyena (Crocuta crocuta, Tsavo
2.4%, n=744; Mara 5.8%, n=729). We group the observed conflict incidences (n= 43,151) into four major conflict
types, including crop raiding, the most common conflict type, followed by human and livestock attacks and
property damage. The severity of conflicts also varies markedly seasonally and inter-annually. Crop raiding peaks
in May-July, during and at the end of the wet season when crops are maturing but is lowest in November during the
late dry season and beginning of the early rains. Attacks on humans and livestock increased more than other
conflict types in both Tsavo (from 2001) and Mara (from 2013). Relatively fewer people in Mara (7.2%, n=901)
than in Tsavo (38.2%, n = 11714) felt threatened by wildlife, suggesting that the Maasai people are more tolerant
of wildlife. Minimizing HWC is tightly linked to successfully resolving the broader conservation challenges,
including enhancing ecosystem connectivity, community engagement and conservation benefits to communities.
Keywords: Human-wildlife conflicts; crop raiding; human and livestock attacks; African elephant; Tsavo and
Mara ecosystems
1. Introduction
Wildlife often interacts with humans in different ways, however, when such interactions adversely affect or are
perceived to affect the lives and livelihoods of people, then conflicts occur (Woodroffe, Thirgood, &
Rabinowitz, 2005). These negative interactions result in human-wildlife conflicts (HWC), the most common of
which include: crop raiding, livestock depredation, and attacks on humans (Thouless, 1994; Woodroffe et al.,
2005). Conflicts are caused by different wildlife species and occur at different intensities in different countries or
parts of the same country. The African (Loxodonta africana) and Asian (Elephas maximus) elephants are key
conflict animals and are involved in crop raiding and attacks on humans in these two continents (Gadd, 2005;
Sitati, Walpole, Smith, & Leader-Williams, 2003; Sarker & Røskaft, 2014). Carnivores such as lions (Panthera
leo), tigers (Panthera tigris), brown bears (Ursus arctos) and wolves (Canis lupus) often attack, and injure or kill
people and livestock in many countries (Kolowski & Holekamp, 2006, Woodroffe et al., 2005; Patterson, Kasiki,
Selempo, & Kays, 2004; Löe & Røskaft, 2004).
The main factors driving human-wildlife conflicts include human population increase, changing land use, habitat
loss, degradation and fragmentation, high livestock population density, low abundance and restricted distribution
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149
of wild prey, high wildlife population density, and climatic factors. Further, stochastic events such as fires and
increasing interest in ecotourism and access to nature reserves also contribute to increased HWC (Distefano,
2005). These factors contribute to human-wildlife conflicts differentially in different regions of the world. For
instance, in Kenya, human-elephant conflicts (HEC) are attributed to increasing human population and changes
in land use (Hoare, 1999; Thouless, 1994), that has increased the interphase between people and wildlife.
Human-dominated areas are more likely to be settled by people who practice agriculture, a major pull factor for
elephants as a source of alternative succulent and nutritious forage (Røskaft et al., 2014).
1.1 Human-Wildlife Conflicts in Kenya
Kenya, like many other countries, is experiencing fast human population growth and the associated demand for
more space for agriculture, human settlements, and other developments. Human population increase is
accompanied with progressive habitat fragmentation and demand for space as people seek alternative
livelihoods. Nevertheless, tourism is an important foreign exchange earner in Kenya (Kenya Government, 2005)
and is based mainly on wildlife watching. As a result, wildlife conservation is given a high priority by the
Kenyan Government. The Kenya Wildlife Service (KWS), created in 1989, has the aim of overseeing wildlife
conservation in all protected and non-protected areas in Kenya, including wildlife parks, reserves, sanctuaries,
and community conservancies.
Wildlife in Kenya faces many threats including poaching, habitat loss, competition for water and food with
livestock and human-wildlife conflicts (HWC). KWS has been collecting data on HWC since the early 1990s for
some of the areas under its jurisdiction, such as the Tsavo and Maasai Mara (Mara) regions. These two regions
support most of the wildlife in Kenya (as described in details below), including the largest terrestrial mammal in
the world (Ogutu et al., 2016), the African elephant, as well as some of the largest felid species, such as the lion
(Panthera leo) and leopard (Panthera pardus).
Here, we use HWC monitoring data collected by KWS during 2001-2016 for both Tsavo and Mara to analyze
variation in human-wildlife conflicts across species, seasons, years and regions. Specifically, we examine and
compare HWC patterns for 19 wildlife species in these two important transboundary conservation ecosystems in
Kenya. Our analysis differs from previous studies in these regions that have mostly concentrated on single
species (e.g., Smith & Kasiki, 2000; Sitati et al., 2003; Kaelo, 2007, Kanga et al., 2012; Mijele et al., 2013) by
seeking to understand HWC patterns over the two regions during 2001-2016. HWC analyses involving multiple
species monitored over long time frames are scarce because of the dearth of reliable long-term monitoring data.
Kanga et al., (2012) used 12 years’ (1997-2008) data from KWS to study hippopotamus (Hippopotamus
amphibius) conflicts in Kenya and found a peak in June-August during crop harvest. Patterson et al., (2004) used
data for four years to study livestock depredation in Tsavo and found that lions and spotted hyenas (Crocuta
crocuta) killed most cattle, while cheetah (Acinonyx jubatus) killed only sheep and goats. In the Mara, over 50%
of livestock attacks during one year of study were attributed to the spotted hyena (Kolowski & Holekamp, 2006).
Sitati et al., (2003) examined human-elephant conflicts (HEC) in the Mara and noted that crop raiding by
elephants could be predicted from the area of cultivated land. Habitat fragmentation due to cultivation and
increasing human settlements have also been identified as major drivers of HECs in the Maasai Mara Wildlife
Conservancies (Kaelo, 2007; Røskaft et al., 2014). Besides, these studies are based on a single region or
ecosystem, and none of them have attempted to understand the patterns of HWC between two important wildlife
areas in Kenya. In this study, we examined interspecific and temporal variation in HWC in the two premier
conservation regions of Kenya.
Our main objective was to analyze reported incidences of human-wildlife conflicts in the Tsavo and Mara
regions, identify and characterize the conflicts caused by wildlife species and the variation across seasons, years
and between the two study regions. We test the following six hypotheses:
H1: Human-elephant conflicts are more likely to occur where there are high elephant densities, close to
protected areas and in areas with high human population densities. Furthermore, there is likely to be more HEC
in landscapes in which agriculture is the dominant land use than in landscapes where traditional pastoralism is
the predominant land use. Because of the high elephant population and the fact that the major land use outside
the Tsavo National Parks is agriculture, we therefore, expect relatively more conflicts with elephants in Tsavo
than in Mara. Human-elephant conflicts can be expected to be more intense in landscapes whenever these land
uses are practiced.
H2: Elephant is the leading animal species in terms of crop raiding, as well as attacks on humans. Among the
other large herbivores, we expect buffalo (Syncerus caffer) and hippopotamus to have frequent conflicts with
humans, because they occur in relatively large numbers in the Tsavo and Mara regions and have been shown to
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150
frequently cause conflicts with humans (Woodroffe et al., 2005); Dunham, Ghiurghi, Cumbi, & Urbano, 2010;
Kanga et al., 2012).
H3: Among large carnivores, we hypothesize that lions will have the highest levels of reported cases of attacks
on both humans and livestock. Other large carnivores such as spotted hyenas and leopards will also occasionally
have conflicts with humans while cheetah and African wild dogs (Lyacaon pictus) only rarely attack humans or
their livestock.
H4: We also postulate that many nonhuman primates will be involved in conflicts related to crop raiding.
Baboons (Papio spp) and vervet monkeys (Cercopithecus spp.) are adapted to living at the edges of protected
areas and raid farms when wild fruits are scarce.
H5: We expect more HWC to occur during the dry than the wet season when food and water are plenty. During
the dry season, surface drinking water sources are fewer and wildlife move and congregate around a few
permanent water sources outside of protected areas where people and their livestock are found.
H6: We expect to find fewer people in the Mara feeling threatened than in the Tsavo during HWC encounters.
This is because the Mara is predominantly inhabited by the Maasai people who have a long history of
co-existence with, and tolerance of, wildlife because of their traditional nomadic and pastoral lifestyles
(Guggisberg, 1975; Okello, 2005; Conroy, 2013). Further, we expect to find more human fatalities during
conflicts involving attacks on humans, particularly by mega-herbivores and the big cats.
2. Study area
2.1 Tsavo Region
The Tsavo ecosystem covers a total area of about 66,500 km2 and lies between longitudes 37°7'E - 39°59'E and
latitudes 0°58'S - 4°22'S to the south of Kenya. Rainfall is bimodal but erratic, with the short rains occurring in
November - December and the long rains in March- May (Van Wijngaarden, 1985). Two major rivers, the
Galana and the Tsavo, pass through this extensive area. It harbors the highest number of elephants (about 13000,
Ngene et al., 2017) in a contiguous land mass in Kenya. The Tsavo Ecosystem consists of two of the largest
National Parks (Tsavo East: 11,747 km2 and Tsavo West: 9065 km2) in Kenya plus Chyullu National Park (736
km2), an important water catchment. South Kitui National Reserve (1133 km2) is situated to the north of Tsavo
East NP. The Taita Ranches, sandwiched between Tsavo West and East National Parks, is an important wildlife
dispersal area and is home to the Taita people. The Taita Hills found here are densely populated owing to high
rainfall and intensive agriculture (Van Wijngaarden, 1985; Figure 1). The regions adjacent to the protected areas
serve as important seasonal wildlife dispersal areas.
The fauna in Tsavo consist of large herbivores, including the African elephant, African buffalo, hippopotamus,
giraffe (Giraffa camelopardalis), Burchell's zebra (Equus quagga), eland (Taurotragus oryx), waterbuck (Kobus
ellipsiprymnus), Coke's hartebeest (Alcelaphus buselaphus cokii), Grant's gazelle (Gazella granti), impala
(Aepyceros melampus), lesser kudu (Tragelaphus imberbis), gerenuk (Litocranius walleri), warthog
(Phacochoerus africanus), fringe-eared oryx (Oryx gazella callotis) and black rhino (Diceros bicornis). A wide
array of large carnivores are found in the region, including the lion, cheetah, leopard, spotted hyena and the
African wild dog. The population numbers of these species are monitored by KWS through tri-annual aerial
surveys (once every three years) and annual ground animal censuses. For instance, between 2014 and 2017,
elephant abundance increased by 4.9% while about 8600 buffaloes were counted in 2017 (Ngene et al., 2017).
Long-term monitoring of elephants in the Tsavo region indicates continuous elephant population growth from
9447 (1999) through 9284 (2002), 11742 (2005), 11733 (2008), 12573 (2011), 11217 (2014) to 12866 (2017)
individuals (Ngene et al., 2017). The major land use types include agriculture, which is both practiced in small-
and large-scale farms that rely on either rainfall or irrigation. Closely related to this is livestock keeping either in
small or large-scale farms, wildlife conservation in ranches adjacent to the two Tsavos, intensive infrastructure
development, and settlements in towns such as Voi (Ngene et al., 2017). The human population has also been
increasing at a similar rate as the rest of Kenya in the areas around Tsavo, especially in the Taita Taveta County
(1999, n = 469,244; 2009, n = 720,352) (https://www.knbs.or.ke). In 1999, about 2.7 million people lived in and
adjacent to the Tsavo region (https://www.knbs.or.ke). This population increased to about 4.5 million people in
2009 (https://www.knbs.or.ke), translating to a population growth rate of about 4.0% per annum.
2.2 Maasai Mara region
The Mara region (18,500 km2) is found within Narok County between longitudes 34°34'E - 36°26'E and latitudes
0°24'S - 2°6'S to the southwestern part of Kenya, bordering Tanzania. The famous Maasai Mara National
Reserve (MMNR) (1510 km2) to the south-west of Narok County adjoins the Serengeti National Park (SNP) in
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eport). Huma
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), forest con
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in 1999 and
n
p
opulation g
r
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pulation size
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ner) with sur
r
i
on of Taveta i
n
d
area
Vol. 8, No. 3;
o
riginates fro
m
a
vo Ecosyste
m
M
NR used to
a
nged drastica
l
l
dings and fe
n
s
within the
N
g
outwar
d
s to
c
a
urinus), Burc
h
to Tsavo. Li
k
in 2017, elep
h
a
ditionally co
u
d
a variable (
d
d
ividuals) to
0
elephants in
a
nt form of lan
S
estimated 35
0
n
immigratio
n
d
life conserv
a
s
ervation is a
m
increased to
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r
owth rate of
a
of about 1,00
0
r
ounding disp
e
n the Tsavo re
2018
m
the
m
, the
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oam
l
ly as
n
ced,
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arok
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nted
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e to
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use
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into
n
cies
m
ajor
a
bout
a
bout
0
,000
e
rsal
g
ion
enrr.ccsenet.org Environment and Natural Resources Research Vol. 8, No. 3; 2018
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3. Methods
3.1 Human-Wildlife Conflict Database
KWS established an elaborate radio network covering the whole of Kenya and its headquarters in Langata,
Nairobi. Every event or incident observed by KWS field personnel, or reported to KWS by communities,
conservation Non-Governmental Organizations (NGOs) and governmental agencies are relayed as radio
messages through the network. As a result, KWS has been able to record numerous HWC incidences since 1990.
These datasets assist KWS to appropriately respond to reports concerning HWC and act as evidence for
compensation claims relating to HWC fatalities and injuries. The HWC variables reported and recorded include
the date (day, month and year) of occurrence; conflict types (human death, i.e., at least one person is killed
during the animal attack; human injury, i.e., at least one person is physically injured during the animal attack;
human threat, i.e., at least one person felt threatened by the encounter, however, no person was injured or killed).
Other variables included obstruction (wildlife obstructing school going children, vehicles, or herders); crop
damage (wildlife invade farms and damage crops), and property damage (water pipes, grain stores or other
property damage); livestock killed or injured; species involved (i.e., species responsible for the conflict). Mostly,
one species is involved in the conflict, however, at times multiple species are involved, and sometimes the
conflict species is not identified.
Based on these variables, we identified four main human-wildlife conflict types; 1) attack on humans, 2)
livestock attack, 3) crop raiding and 4) property damage. Attacks on humans refer to those conflicts where a wild
animal is involved in an encounter with humans, and the incident is captured in the database as human death,
human injury, a threat to humans, or obstruction to school-going children or general public insecurity. Livestock
attacks include incidences where livestock are killed or injured and are captured in the database as livestock
depredation. Crop raiding refers to incidents where crops are either destroyed or eaten by wildlife when farms
are invaded or raided. Property damage denotes incidents including damage to property such as water pipes,
grain stores, and houses. The last form of conflicts is referred to as "others" and includes any other reported
human-wildlife incident involving one or more than one wildlife species and incidents such as automobile
accidents involving wildlife.
In some cases, we pooled together several species commonly involved in conflicts in one group. Thus, the term
antelope is used to group together Kirk's dik-dik (Madoqua kirkii, n = 4), common duikers (Sylvicapra grimmia,
n = 1), hartebeest (Alcelaphus buselaphus, n = 1), impala (n = 77) bushbuck (n = 21), lesser kudu (n = 2),
reedbuck (Redunca fulvorufula, n = 3), Thomson's gazelle (n = 10), Grant's gazelle (Gazella granti, n = 39),
wildebeest (n = 95) or when term "antelope" was used as an umbrella conflict "species" (n = 135) in the data.
Small carnivores such as the serval cat (Leptailurus serval, n = 17), caracal (Caracal caracal, n = 4), jackal
(Canis Spp., n = 4), mongoose (family herpestidae, n = 7), honey badger (mellivora capensis, n=19) and civets
(family Viverridae, n =1) are also pooled into one group. Furthermore, primates mean baboons (Papio spp., n =
4328) and monkeys (Cercopithecus spp., n = 647) (all nonhuman primates), while bush pigs (Potamochoerus
larvatus, n = 50), warthogs (Phacochoerus africanus, n = 52) and wild pigs (Sus scrofa, n = 74) are grouped as
pigs. The last species type, “others” pools together records for which no species was indicated (n = 152), plus
conflict incidents involving birds (eagles, vultures (accipitrids) and guinea fowls (numidids), n = 30), bees (Apis
mellifera scutellata, n = 4), porcupine (Hystrix cristata, n = 9), squirrel (sciurids, n = 3), scorpions (bothrirurids, n
= 1), as well as mixed conflict instances (several species, n = 16) and vehicles ( n = 7). Thus, the final lis t compri sed
19 wildlife species and one group labeled "others" (Table 1).
We also added another variable to the database called conflict outcome to denote the severity of conflicts involving
humans based on a scale of 0 to 3; 0 = nothing happened to humans, 1 = humans felt threatened, 2 = humans were
injured, and 3 = humans were killed. We examine whether the conflicts resulted in any one of these four outcomes.
We use the term livestock to refer to all types of domesticated animals such as cattle, sheep, goats, donkeys,
camels, dogs and poultry. Livestock attacks are grouped into three outcome categories, 0 = nothing happened to
livestock, 1 = livestock were injured, and 2 = livestock were killed.
We associate each conflict with the month in which it occurred to enable seasonal analysis. It is important to note
that, although the total area of the Tsavo region is comparatively larger than that of the Mara, we compare
relative frequencies of conflict cases which are independent of area and not the absolute human conflict numbers
between the two regions.
Statistical analyses were done using SPSS, version 24.0 (IBM Corp. Release 2016. NY, USA). Since our data
were count data, we used descriptive statistics as well as cross tabulations to compare relative frequencies
between the two conservation regions. Most statistical tests were non-parametric Chi-square goodness of fit tests
while few were cumulative frequency bar charts. Statistical significance is assessed at alpha = 0.05.
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4. Results
4.1 The Relative Contribution of Species to Conflict by Region
A total of 45,151 cases of human-wildlife conflicts were reported between 2001 and 2016 for Tsavo and Mara
combined. Tsavo had a total of 30,664 conflict cases compared to 12,487 cases for the Mara. This translates to
an average of 1,900 human-wildlife conflict incidences per year for the Tsavo and 780 incidents for the Mara.
Most of the reported cases of conflict involved the African elephant for both the Tsavo (64.3%) and Mara
(47.0%). However, the percentage of conflict incidences involving elephants was significantly higher for Tsavo
than the Mara (Table 1, P = 0.001). Primates were the second most common cause of conflicts and their relative
contributions to the total conflict incidents in Tsavo (11.4%), and Mara (11.8%) did not differ between the two
regions (Table 1). Buffalo was the third most frequent conflict animal in both regions but was almost twice as
likely to cause conflicts in the Mara (11.3%) as in Tsavo (5.5%, Table 1). The hippopotamus (2.6%) and zebra
(2.5%) were the seventh and eighth most common causes of conflicts, respectively. However, while hippo
conflict incidences were more common in Tsavo than in the Mara, the converse was the case for zebra (Table 1).
Among the carnivore species, lions had the highest but non-significant number of reported cases (3.5%) followed
by the spotted hyena (3.4%) and the leopard (2.8%, Table 1). The cheetah and the wild dog ranked 16th and
scored low overall (0.3%) and together with small carnivores (0.1%) made negligible contributions to the
conflict incidences (Table 1). Carnivores made a minor contribution to the conflicts relative to the large
herbivores. Conflicts involving reptiles were due to pythons as well as unidentified snakes and ranked ninth
(1.6%) and tenth (1.5%), respectively. Moreover, the crocodile often considered likely to cause conflicts, was
ranked behind snakes at the 13th position (0.5%; Table 1). However, the relative frequencies for all these animals
differed significantly between the two regions (Table 1).
The antelopes (0.9%) had somewhat many reported cases in Mara (2.50%). Waterbuck (0.10%, n = 64) and
giraffe (0.10%, n = 26) were only very rarely reported as conflict species (Table 1). The “others” group was also
an insignificant source of conflicts and was ranked 13th (0.5%, n = 222; Table 1). Overall, our results indicate
significant differences between the two regions (χ2 = 5451.2, df = 19, P < 0.001).
Table 1. The common English and scientific names of the human-wildlife conflicts species, ordered by the number
of cases of conflicts involving each species in Tsavo and Mara, and chi-squared goodness of fit tests for the null
hypothesis that the percentage contribution of each species to the total conflicts differs between the two regions (n =
number of reported cases, % is n expressed as a percentage of the total number of cases for the region)
No Common English name of species Scientific name of species Tsavo Mara Pearson Chi-square test
𝑛 % 𝑛 % X
2 df P < 0.05
1 Elephant Loxodonta africana 19719 64.3 5875 47.0 1094.8 1 0.001
2 Primates Cercopithecidae family 3502 11.4 1473 11.8 1.2 1 0.267
3 Buffalo Syncerus caffer 1676 5.5 1410 11.3 453.6 1 0.001
4 Lion Panthera leo 1107 3.6 416 3.3 2.0 1 0.155
5 Spotted hyena Crocuta crocuta 744 2.4 729 5.8 313.3 1 0.001
6 Leopard Panthera pardus 526 1.7 698 5.6 483.3 1 0.001
7 Hippopotamus Hippopotamus amphibius 1032 4.4 71 0.6 278.7 1 0.001
8 Zebra Equus quagga 64 0.2 1013 8.1 2277.9 1 0.001
9 Python Python sebae 695 2.3 0 0.0 287.6 1 0.001
10 Snake Serpentes suborder 596 1.9 70 0.6 111.7 1 0.001
11 Antelope (assorted) Bovidae family 73 0.2 315 2.5 519.8 1 0.001
12 Eland Taurotragus oryx 275 0.9 37 0.3 44.6 1 0.001
13 Others*1 100 0.3 122 1.0 73.5 1 0.001
13 Crocodile*1 Crocodylus niloticus 199 0.6 14 0.1 52.1 1 0.001
15 Pigs (assorted)*3 Suidae family 86 0.3 90 0.7 42.4 1 0.001
16 Cheetah*2 Acinonyx jubatus 130 0.4 13 0.1 27.5 1 0.001
16 Wild dog*2 Lycaon pictus 35 0.1 104 0.8 142.8 1 0.001
18 Waterbuck*3 Kobus ellipsiprymnus 38 0.1 26 0.2 4.3 1 0.039
18 Small carnivores*3 *4 52 0.2 0 0.0 21.2 1 0.001
18 Giraffe*3 Giraffa camelopardalis 15 0.0 11 0.1 2.3 1 0.133
Total 30664 100 12487 100 5451.2 19 0.001
*1-3 These species had an equal overall frequency contribution to HWCs (0.5%, 0.3%, and 0.1%) respectively.
*4 Families - Viverridae, Canidae, Herpestidae, Felidae, and Mustelidae
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154
4.2 Frequency of Conflict Types by Species Between Tsavo and Mara
The elephant emerged as the leading conflict animal in three out of the five conflict types, namely attacks on
humans (Tsavo = 74.6%, Mara = 64.8%), crop raiding (Tsavo = 65.8%, Mara = 47.3%) and property damage
(Tsavo = 90.5%, Mara = 89.4%), with Tsavo reporting a relatively higher number of cases. However, the leading
species causing livestock attacks was the lion (Tsavo = 31.8%, Mara = 16.7%) and the spotted hyena (Tsavo =
23.2%, Mara = 35%). The contributions of primates to crop raiding incidences were similar for the Tsavo
(20.8%) and Mara (19.8%). There were higher attacks on humans incidents ascribed to buffalo in the Mara
(20.9%) than in the Tsavo (8.0%). The spotted hyena (Mara = 35%, Tsavo = 23.2%) and leopard (Mara = 30.3%,
Tsavo = 14.9%) accounted for higher livestock attacks in the Mara than Tsavo. Pythons attacked humans (4.8%)
and livestock (1.4%) only in Tsavo. Snake bite incidences were relatively higher for Tsavo (4.3%, n = 578) than
Mara (1.6%, n = 62) (Table 2).
Table 2. Conflict types by species
The contribution of each of the 10 leading conflict species to the most prevalent conflict types in both Tsavo and
Mara. The Chi-squared goodness of tests for the null hypothesis that each species makes a uniform contribution
to all the conflict types in each region
Attacks on
humans
Crop raiding Livestock
attack
Property
damage
Other Pearson Chi-square test
NO Species n % n % n % n % n % X
2 df P <0.05
Tsavo
1 Elephant 10093 74.6 9094 65.8 84 3.1 446 90.5 2 2.9 5391.8 4 0.001
2 Primate 182 1.3 2869 20.8 410 14.9 20 4.1 21 30.9 2634.4 4 0.001
3 Buffalo 1083 8.0 565 4.1 13 0.5 5 1.0 10 14.7 382.3 4 0.001
4 Lion 219 1.6 9 0.1 876 31.8 1 0.2 2 2.9 6971.6 4 0.001
5 Hyena 97 0.7 7 0.1 637 23.2 1 0.2 2 2.9 5499.9 4 0.001
6 Leopard 110 0.8 5 0.0 410 14.9 1 0.2 0 0.0 3142.5 4 0.001
7 Hippo 262 1.9 751 5.4 6 0.2 5 1.0 8 11.8 373.8 4 0.001
8 Zebra 10 0.1 48 0.3 0 0.0 2 0.4 4 5.9 136.3 4 0.001
9 Python 654 4.8 2 0.0 38 1.4 1 0.2 0 0.0 739.6 4 0.001
10 Snake 578 4.3 3 0.0 15 0.5 0 0.0 0 0.0 691.9 4 0.001
Mara
1 Elephant 2193 64.8 3051 47.3 90 4.6 463 89.4 78 47.6 2227.8 4 0.001
2 Primate 59 1.7 1279 19.8 112 5.7 16 3.1 7 4.3 844.4 4 0.001
3 Buffalo 708 20.9 650 10.1 14 0.7 12 2.3 26 15.9 588.6 4 0.001
4 Lion 78 2.3 1 0.0 329 16.7 0 0.0 8 4.9 1345.5 4 0.001
5 Hyena 37 1.1 0 0.0 688 35.0 3 0.6 1 0.6 3608.9 4 0.001
6 Leopard 98 2.9 3 0.0 597 30.3 0 0.0 0 0.0 2746.2 4 0.001
7 Hippo 58 1.7 9 0.1 0 0.0 3 0.6 1 0.6 110.9 4 0.001
8 Zebra 5 0.1 998 15.5 0 0.0 7 1.4 3 1.8 969.6 4 0.001
9 Python* - -
10 Snake 62 1.8 0 0.0 6 0.3 0 0.0 2 1.2 141.2 4 0.001
*Python conflict incidents were not reported in the Mara region.
4.3 Frequency of Conflict Types in Tsavo and Mara
Crop raiding was the leading type of conflict in both the Mara (51.7%, n = 6455) and Tsavo (45.1%, n = 13820)
and accounted for 47% of all the reported cases in both regions. The number of attacks on humans were higher
for Tsavo (44.1%, n = 13532) than the Mara (27.1%, n = 3382) and accounted for 39.2% of all the conflict cases
for both regions combined. Livestock attack was the third most common conflict type (10.9%), but was
relatively higher for the Mara (15.8%, n = 1968) than the Tsavo (9.0%, n = 2751). Other conflict types were far
fewer even though property damage accounted for 2.3% of the cases in both regions (Table 3). The overall
relative frequency differed significantly between the two regions for all the conflict types (P < 0.001, Table 3)
when the two regions are combined.
enrr.ccsene
t
Table 3.
T
chi-squar
e
the total i
n
Conflict typ
e
Crop raidi
n
Attack on
h
Livestock
a
Property d
a
Other
Total
4.4 Seaso
n
The most
across bo
t
p
ronounc
e
year (Fig.
livestock
a
not only r
e
Figur
e
Crop raid
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thereafter
p
eak in Ja
n
4.5 Inter-
A
There we
r
In Tsavo,
in 2010 b
e
still lower
t
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T
he contributi
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e
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e
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g
h
umans
a
ttack
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mage
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al Variation
i
frequent type
t
h regions (Fi
g
e
d in March-S
3). Attacks
o
a
ttacks, prope
r
e
latively rare
b
e
2. The distri
b
frequen
c
i
ng incidence
s
(Figure 3). U
n
n
uary-Februa
r
A
nnual Variat
i
r
e evident tem
p
crop raiding
w
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fore increasi
n
than the leve
l
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n
o
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pendence for
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e
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M
o
n humans w
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b
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like for the
M
r
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re
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n
w
as the most c
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2
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vironment and
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erent conflict
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8 0.
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g
e
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n
M
ara and Jan
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ere the next
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n
d others, in
d
y
ed no eviden
t
c
umulative fre
q
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b
a
increased fr
M
ara, Tsavo e
x
r
y peak in Ma
y
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C
n
the major co
n
o
mmon confl
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a
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2
001-2007. I
n
N
atural Resour
c
155
types in Tsa
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M
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6455
1
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0
1968
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12487
m
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T
g
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ecreasing or
d
t
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n
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een pooled o
v
om March to
x
perienced a
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-Jul (Figure
3
C
ommon Con
fl
n
flict types, b
u
i
ct type durin
g
1-2016. How
e
n
sharp contra
s
c
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v
o and Mara
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c
h conflict typ
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M
ara
%
51.7
27.1
15.8
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100.0
T
ypes
i
n May-July
w
e
ach region s
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and May-Jul
y
conflict typ
e
d
er. These hu
m
n
both regions
c
ommon conf
l
v
er the Tsavo
a
a unimodal
b
imodal peak
3
).
fl
ict Types
u
t the trends
d
g
2001-2007 b
u
e
ver, the frequ
e
s
t to crop raid
i
fo
r the period
2
e
makes a si
m
P
e
X
2
156.3
1081.8
419.9
250.3
197.7
1555.7
w
hen the frequ
e
e
parately, cro
p
y
in Tsavo tha
n
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after crop r
a
m
an-wildlife
c
(Figs 2 and 3
)
l
ict types acro
s
a
nd Mara regi
o
peak in July
in crop raidin
g
d
iffered betwe
e
u
t dropped pr
e
e
ncy of crop r
i
ng, attacks o
n
Vol. 8, No. 3;
2
001-2016 an
m
ilar contributi
e
arson Chi-Squa
r
df P<
0
1
0
1
0
1
0
1
0
1
0
4
0
e
ncies were p
o
p
raiding was
n
in the rest
o
a
iding follow
e
c
onflict types
)
.
s
s months. Th
e
o
ns
and then de
c
g
with a seco
n
e
n the two re
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cipitously to
a
aiding in 201
6
n
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e
2018
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r
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0
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ased
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strikingly
(Figure 4)
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i
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2001-201
3
temporal
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Figure 3.
T
Figure
4
t
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from 2001 to
. Though rela
t
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3
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n
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T
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4
. The inter-a
n
E
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2016 and su
r
t
ively less fre
q
h
e Mara, crop
e
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h
n
creasing in 2
0
regions (Figu
r
o
n of the cum
u
n
nual variatio
n
n
vironment and
N
r
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a
q
uent, livestoc
k
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t
h
ereafter. Ho
w
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r
e 4).
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lative freque
n
Mara (
b
n
in the cumul
a
(top) and
M
N
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c
156
a
iding as the
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k
attacks also
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tacks on hu
m
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c
). Conflicts r
e
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n
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tive frequenc
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M
ara (bottom)
r
c
es Research
m
ost commo
n
increased fro
m
m
ans increase
d
c
k attacks re
m
e
lated to prop
e
types across
m
n
s
y
of the com
m
r
egions
n
conflict type
m
2001 to 201
d
markedly fr
o
m
ained relati
v
e
rty damage s
h
m
onths in Tsav
m
on conflict ty
p
Vol. 8, No. 3;
during 2009
-
6, similarly to
o
m 2001 to pe
v
ely stable d
u
h
owed no ap
p
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a
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es for the Tsa
v
2018
2016
crop
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k in
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ring
arent
a
asai
v
o
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4.6 Conflict Type Outcome Differences between Tsavo and Mara
Here, we examine the outcomes of conflicts involving human and livestock attacks. Humans either felt
threatened, were injured or killed during many conflict incidents in both the study regions (Table 4).Twice as
many people were killed in Tsavo (0.9%, n = 278) as in the Mara (1.0%, n = 126) during 2001-2016 though the
difference was not statistically significant (P = 0.316). In contrast, more conflict incidents reported for the Mara
(89%, n = 11113) than for Tsavo (56%, n=17174) did not involve human injury, death or threats to people (Table
4).
Table 4. Outcomes of conflicts involving humans in the Tsavo and Mara regions
Conflict outcome Tsavo Mara Pearson Chi-square test
n % n % n df P<0.05
Nothing happened to humans 17174 56.0 11113 89.0 4276.7 1 0.001
Humans felt threatened 11714 38.2 901 7.2 4118.1 1 0.001
Humans were injured 1498 4.9 347 2.8 96.2 1 0.001
Humans were killed 278 0.9 126 1.0 1.0 1 0.316
Total 30664 100 12487 100 4480.4 3 0.001
4.7 Human Wildlife Conflict Outcomes in Tsavo and Mara
Conflicts involving attacks on humans resulted in the highest cases of humans getting killed (2.4%, n = 398),
human injuries (10.7%), and humans feeling threatened (74.6%). Tsavo had relatively higher frequency of
incidents in which humans either felt threatened (86.6%, n = 11712; 26.6%, n = 901) or were injured (10.8%, n =
1466; 10.2%, n = 344) but fewer incidents involving human fatalities (2.0%, n = 272; 3.7%, n = 126) than the
Mara.
During conflicts involving livestock attacks, very few cases also resulted in humans being either killed or
injured. Tsavo had more cases of human injuries (0.9%, n = 25) during livestock attacks than the Mara (0.1%, n
= 2). The Mara reported no cases of humans being killed during livestock attacks between 2001 and 2016 as
opposed to Tsavo (0.2%, n = 5).
Though they were the most frequently recorded conflict types, conflicts involving crop or property damage were
rarely associated with human injuries. Thus, for the Tsavo region, crop damage (0.0%, n = 6) was hardly
associated with human injuries (See Table S5 in the appendix)
4.8 Livestock Attack Conflict Outcomes
Livestock attacks resulted in livestock either being killed (59.3% of the cases) or not (35.2%). In addition, a
small proportion of livestock attacks (5.5%) resulted in livestock being injured (Table 6). Livestock was also
either injured (0.1%) or killed (3.4%) during conflicts classified as property damage. Some attacks on humans
(0.3%) and crop raiding (0.1%) incidents also resulted in livestock being killed.
Livestock attacks resulted in relatively more incidents of livestock being killed in Tsavo (81.3%, n = 2237) than
the Mara (28.5%, n = 561), while Mara (13.1%, n = 257) had relatively higher incidences of livestock injuries
than Tsavo (0.1%, n = 4). Tsavo region also had livestock killed during property (6.9%, n = 34) and human
(0.4%, n = 50) attacks, while similar outcomes were relatively rare for the Mara (Table S6 in the supplementary
materials).
5. Discussion
5.1 Prevalence of Human Elephant Conflicts in Tsavo and Mara Regions
The results reveal that the African elephant is the leading wildlife conflict species in both Tsavo and Mara regions,
but there are higher relative conflict incidences for the Tsavo than for the Mara. This supports hypotheses H1 and
H2. In H1, we hypothesized that human elephant conflicts (HEC) are more likely to occur where elephant density is
high, close to protected areas and where human population density is high. According to the KWS’ aerial survey of
2017, there are 12866 elephants in the Tsavo region and the southern part of the Tsavo East National Park has 7.01
elephants/km2, Taveta has 1.86 elephants/km2, and Tsavo West National Park has 2.99 elephants/km2 (Ngene et al.,
2017). However, in Mara, a KWS’ aerial survey of 2017 found a total of 2493 elephants with a density ranging from
1.73 elephants/km2 in the protected area to 0.01 elephants/km2 in the adjacent dispersal area (Mwiu et al., 2017,
unpublished report). The Tsavo region is surrounded to the southern, south western and northern sides by the Taita
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158
and Kamba communities who practice agriculture in small farms adjacent to the protected areas. Further, the Kasigau
corridor (important for wildlife) joins Tsavo East and Tsavo West NP through Taita area (Wildlife Works, 2013).
This makes them more vulnerable to HEC conflicts due to the increasing elephant and human populations. This is
unlike in the Mara where the indigenous Maasai people predominantly practice pastoralism, which is more
compatible with wildlife conservation (Conroy, 2013, Okello, 2005). The trend of increasing HEC observed in the
Mara region is due to the gradual sedentarization of these once nomadic pastoralists and the increasing conversion of
land to large-scale wheat farming and human settlements. Nomadic pastoralism in Kenya is decreasing principally
due to the scarcity of land and water (Okello, 2005) and is giving way to agro-pastoralism as a form of livelihoods
for the Maasai. The elephant is the largest living terrestrial mammal and requires large amounts of food and water per
day, and hence have large home ranges to obtain these resources. These movements lead to frequent conflicts
between elephants and humans. This supports H2 which predicts that elephants should lead in crop raiding and
attacking humans. Further, buffalo accounted for 7.2% and the hippopotamus for 2.6% of the reported cases of
conflict. Buffalo was second to elephants in terms of attacks on humans, while hippopotamus was linked to crop
raiding as earlier reported by Kanga et al. (2013). Indeed, based on these results, the elephant may be labeled as the
'most notorious' conflict species in Kenya, accounting for 80% of all conflict types and being the leading conflict
species in four out of five conflict types. Persistent human-elephant conflicts pose great challenges to conservation
managers as a form of land use, and the Kenyan state will likely have to shoulder greater compensation burdens for
human fatalities in future. Land use entails a tradeoff between what local communities perceive to be more lucrative,
which is often agriculture (Okello, 2005), and this threatens wildlife conservation by reducing conservation space.
5.2 Occurrence and Differences in Human Carnivore Conflicts for Tsavo and Mara
Overall, our results indicate that the lion is the leading cause of large carnivore-related conflicts. A closer
examination of conflict types revealed differences between the Tsavo and the Mara regions. In the Mara,
livestock attack conflicts were most likely to be caused by the spotted hyena, the leopard, and the lion. These
results are consistent with those of Kolowski and Holekamp (2006) who also found livestock attacks in the Mara
to be caused mainly by the spotted hyena (53%), the leopard (32%) and the lion (15%). In the Tsavo region, by
contrast, livestock attacks were most often caused by the lion, the spotted hyena, and the leopard. However, the
lion accounted for more incidences of attacks on humans in both the Tsavo and Mara, followed by the leopard
and the hyena. This accords with H3 which predicts that lions should have the highest reported cases of attacks
on both humans and livestock while other large carnivores, such as spotted hyenas and leopards (Tweheyo et al.,
2012) should less frequently cause conflict with humans. Carnivores are known to kill livestock (Patterson et al.,
2004) as they experience reduced range and where their wild prey base has been reduced, and other forms of
land use are being practiced. There were relatively more incidences of livestock attacks by the spotted hyena and
leopard in the Mara than the Tsavo. The Mara is experiencing drastic land use changes and population increase
(from births and immigration) which jeopardize the harmony that once existed between traditional pastoralism
and wildlife (Lamprey & Reid, 2004; Kolowski & Holekamp, 2006; Schuette et al., 2013) resulting in increased
HWC. It is likely that conflict incidences associated with the spotted hyena and leopard in the Mara involve
primarily sheep and goats that are kept in large numbers, as they are more tolerant to droughts and can be kept in
smaller land parcels than cattle. The once pastoral Maasai community has changed progressively to
agro-pastoralism, thus fragmenting wildlife habitats (Okello, 2005; Conroy, 2013). Sheep and goat numbers
increased in Narok County during 1977-2016 (Ogutu et al., 2016). However, this regional difference can also be
attributed to differences in carnivore densities and husbandry practices (Kolowski & Holekamp, 2006). There
were rare but noteworthy wild dog conflict incidences reported for the Mara, which abuts the Serengeti National
Park in Tanzania. Lyamuya et al., (2014) and Holmern et al., (2007) have recently reported livestock predation
by wild dogs in Serengeti, Tanzania. It is likely that these conflicts will persist in the future in both regions.
Increased livestock depredation is likely to lead to decreased tolerance of carnivores (Kolowski & Holekamp,
2006) by local communities and therefore compromise their conservation.
5.3 Occurrence of other HWC Including Primates, Pythons, and Snakes in Tsavo and Mara
Our results also indicate that primates are a major cause of HWC in Kenya, second only to the elephant. Primates
were mainly responsible for crop raiding and livestock attacks but rarely attacked humans. This supports
hypothesis H4 predicting frequent conflicts related to crop raiding by baboons and monkeys. Like other wildlife,
the primates, too, are faced with shrinking habitats and are often forced to co-exist with humans. This often
results in baboons and monkeys becoming 'primate pests' due to their role in crop raiding (Strum, 2010; Hill,
1997) and being negatively perceived by local communities and thus becoming of conservation concern
(Dickman, 2012). Python and snakes made greater contributions to HWC occurrences in Tsavo than in the Mara.
This makes python and snake bites an important conflict type for humans. In Uganda, pythons have been
enrr.ccsenet.org Environment and Natural Resources Research Vol. 8, No. 3; 2018
159
reported as responsible for livestock attacks (Tweheyo et al., 2012). In Kenya, the Wildlife Conservation and
Management Act 2013 recognizes that snakes can often lead to fatal or serious human injuries and therefore
provides that victims be compensated, unlike the previous Act of 1989, which lacked this provision.
Other wildlife species also played key roles in the HWC conflicts in the two regions. However, giraffe caused
the least number of conflicts and was ranked the last species in order of relative frequencies of conflict
incidences. This shows that all other wildlife species, such as giraffe, though they only rarely come into conflicts
with humans can cause conflicts. For instance, the crocodile often considered a very dangerous animal and
feared by many people, accounted for few of the conflict incidences in Tsavo (0.6%, n = 199) and Mara (0.1%, n
= 14). The few cases of crocodile conflicts in these two regions do not necessarily reflect the national threat
posed to humans and livestock by crocodiles and may reflect the fact that crocodiles inhabit sections of large
rivers within protected areas with low human and livestock populations. Small carnivores also had few reported
incidences, although most of their conflicts, e.g., targeting poultry, may often go unreported.
5.4 Seasonality and Inter-Annual Variation in Human-Wildlife Conflicts
Severe droughts can lead to serious water and food shortages for wildlife and therefore increase competition for
resources between humans and livestock (Kanga et al. 2013). Crop raiding, the most frequent type of conflict
peaked in May - July for the two regions. May is the time of year when most crops reach maturity while June
and July are the typical harvesting times for most crops grown in these regions. This implies succulent and
nutritious food that is attractive to wild herbivores is abundant during these three months. This is consistent with
H5 that seasonality is an important predictor of HWC and that there are fewer HWC during the wet than the dry
season (Tweheyo et al., 2012) when food and water resources are scarce. Patterson et al., (2004) found that there
was a higher incidence of lion depredation on livestock in Tsavo in the wet season. The extended continuous
period for incidences of crop raiding in Mara (March-September) compared to Tsavo (January-February and
May-July) reflect the fact that the two regions receive rains at different times but may also indicate other
underlying factors not considered in this study. For instance, the Tsavo region receives bimodal rainfall with
very distinctive short and long dry seasons (Van Wijngaarden, 1985). All these periods correspond to harvesting
times for most crops (e.g., wheat and maize). Other conflict types were not seasonal, likely reflecting their nature
and causes, e.g., attacks on humans can occur during any time of the year irrespective of season.
Human disturbance of wildlife habitats (Lamprey & Reid, 2004; Ogutu et al., 2014) increase HWC but its effects
differ from one region to another. The temporal trends in conflict incidences show that incidences of attacks on
humans have been increasing in the Tsavo regions since 2001 and surpassed crop raiding, which was at its
lowest in 2010. Livestock attacks in the Mara, by contrast, have been increasing since 2013. We suggest that
more attacks on humans are occurring in Tsavo due to increasing human and wildlife populations in the
protected and adjacent areas while increasing livestock numbers (Ogutu et al. 2011; 2016) in the Mara are
responsible for increasing livestock attacks there. Furthermore, KWS has stepped up a fencing programme
around the Tsavo protected areas hence keeping crop raiding elephants away from farms, but this fails to prevent
the smaller-bodied carnivores from moving out.
5.5 Conflict Type Outcomes for Tsavo and Mara
Conflicts involving attacks on humans resulted in many incidences of people feeling threatened, injured or
killed. Occasionally, people are injured or killed during livestock attacks and property damage as they try to
protect their livestock from depredation or property from being damaged. In Tsavo (38.2%), more people
reported feeling threatened than in the Mara (7.2%). That relatively fewer people felt threatened by wildlife in
the Mara reflects the historically relatively more harmonious co-existence of the Maasai community with
wildlife (H6). This is unlike in the Tsavo where the people living in the dispersal areas are agriculturists and are
more likely to report any wildlife they encounter to the government. The Maasai people are known to tolerate
wildlife unless their livestock or lives are in danger and it is not rare to find cattle grazing together with wildlife
(Conroy, 2013).
We also examined the outcomes of conflicts involving livestock based on three outcome categories (nothing
happened to livestock, livestock were injured or killed) for the five conflict types. The most frequent incidents of
livestock being killed occurred during livestock attacks. That livestock was rarely killed or injured during other
conflict types, such as property damage, shows that human-wildlife conflicts can occur in multiple dimensions.
Relatively more livestock attack incidences in Tsavo (81.3%) resulted in livestock being killed than in the Mara
(28.5%). This could be because the Maasai have morans (warriors) who aggressively protect their livestock from
predators when attacked (Lyamuya et al., 2016). This underlies the problem of depredation of livestock in Tsavo
and Mara regions.
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6. Conclusions and Recommendations for Wildlife Conservation and Management
Human-wildlife conflicts differ starkly in their types and frequencies between the Mara and Tsavo regions of
Kenya. However, crop raiding is the most common type of HWC in both regions. Although the wildlife species
involved in HWC also differ between the two regions, the elephant is the leading HWC species regardless of
region. Non-human primates are the second most important group of wildlife species causing HWC in both
regions. Elephant and primates are also the two leading groups of crop raiding wildlife species, not only in the
two study regions but also elsewhere in Kenya (Strum, 1994, Graham, Notter, Adams, Lee, & Ochieng, 2010,
Conroy, 2013). Thus, a recent trend of decreasing crop raiding and increasing attacks on humans in Tsavo
implicates either shifting land use or effective HEC mitigation measures. For example, KWS has recently
intensified fencing efforts around Tsavo PAs to limit elephant movements (Wambua et al., Unpub Report).
Rainfall seasonality is a key driver of HWC in both the Mara and Tsavo regions. However, climate change, by
reducing or making rainfall more erratic, can amplify the effect of rainfall seasonality on HWC. A marked
change in rainfall seasonality can heighten HWC by aggravating competition for food and water between
livestock, wildlife, and people (Reed, 2012). Unsurprisingly, crop raiding conflicts peak immediately after the
wet season, when food and water become limiting.
Thus, the following alternatives for addressing human-wildlife conflicts in Kenya are envisioned:
1) HWC mitigation measures should aim to reduce the influence of rainfall seasonality on wildlife and local
communities through the provision of water (to homesteads and wildlife) and other interventions that
minimize resource competition. Effective strategies and methods are needed to counteract the harmful
impacts of HWC on wildlife and human communities. Ideally, such methods should take account of
distinctions in HWC incidence types and frequencies across regions, seasons, predominant land use types
and wildlife species. Methods developed thus far to combat crop raiding by elephants and other large
herbivores in Kenya include erecting fences, barriers (vegetative, moats and ditches, stone walls), and
active management (scaring, translocations, problem animal control (PAC)). In contrast, approaches used
for primates mostly involve active management, such as translocation, guarding farms and PAC. For
carnivores, the most widely used methods in Kenya are predator-proof livestock holdings (Hill, 1997,
Omondi, Bitok, & Kagiri, 2004). The strategy adopted in particular localities vary depending on the type of
HWC and the target wildlife species. However, HWC mitigation strategies are not always effective. For
example, translocating a problem animal can work well in some situations, but can amount to transferring
the problem in others (Dickman, 2010, Massei, Quy, Gurney, & Cowan, 2010,White & Ward, 2011).
Technology is being increasingly used to aid HWC prevention and mitigation measures. For example,
HWC prevention is being enhanced by geo-fencing in both study regions and elsewhere in Kenya by fitting
elephants and lions with GPS-enabled collars to allow timely responses to problem animals. This is already
producing useful data for understanding species movements in space and time, enabling timely responses to
HWC incidences.
2) Fencing is one of the widely used interventions to contain HWC. Even so, the effects of fences are contested
in conservation circles (Packer et al., 2013, Woodroffe, Hedges, & Durant, 2014). Fences (electric or
non-electric) need to be built and maintained (both expensive) along PA boundaries to prevent large
herbivores from raiding farms, as predator-proof livestock enclosures, and to protect agricultural farms and
schools. However, fences are not an effective solution to HWC for all species and are often ineffective for
primates, birds, burrowing animals and other species. The future of wildlife conservation in the two study
areas, as in most others, will thus most strongly depend upon the good will and support of the local
communities. These can be enhanced by conservation education targeting communities living adjacent to PAs
or within human-dominated pastoral systems (Gadd, 2005, Gambay, 2014, Mmassy & Røskaft, 2014).
3) A growing threat to biodiversity conservation that is increasing HWC is spiraling human population
density. If well- educated people prefer smaller households and care more for the environment (Lutz,
Cuaresma, & Sanderson, 2008), then investing in better education may help to reduce human population
and hence HWC. Better conservation benefits to communities and more equitable benefit sharing schemes
can encourage positive community attitudes toward and support for wildlife conservation (Kala &
Maikhuri, 2011). As tourism is a leading foreign exchange earner for Kenya escalating HWC poses serious
challenges not only to wildlife conservation but also to national development. Economic benefits to local
communities from ecotourism enterprises in the two study regions, and the rest of Kenya, have encouraged
communities to set community-based wildlife conservancies that have greatly expanded the space available
for wildlife conservation in recent years. This has also reduced HWC by reducing contacts between people,
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livestock and wildlife as landowners voluntarily vacate their land parcels for wildlife conservancies in
return for land rents and resettle elsewhere (Bedelian & Ogutu 2017; Ogutu et al. 2017).
4) The prevention and mitigation of HWC in Kenya and hence the success of conservation is complicated by
the interplay of several other factors, including land use change, privatization of land ownership, land
subdivision and declining traditional pastoralism, which was more compatible with wildlife conservation
(Lamprey & Reid, 2004). The Kenyan state needs to seriously consider reviewing the national policy on
land use and spatial planning, and giving greater priority to protecting wildlife habitats, including dispersal
and migratory corridors to reduce HWC and promote wildlife conservation. This will reduce habitat
fragmentation and maintain habitat connectivity for migratory and wide-ranging wildlife species. Close
monitoring and effective law enforcement are needed to ensure that the intended goals are achieved.
5) Combating HWC places a huge burden on wildlife managers, conservationists, and communities and
requires substantial human and financial resources. The Kenyan state and the international community
would do well to work together to address this growing challenge as well as establishing a functional
mechanism for funding compensation schemes for HWC-related losses. A similar approach is also needed
in the provision of anti-venom drugs for snake bites, which are common in the Tsavo region. This will
encourage and improve local communities’ good will and support for conservation.
6) Because the Tsavo and Mara are cross-border ecosystems shared by Kenya and Tanzania, HWC prevention
and mitigation strategies should ideally involve transboundary collaboration between the two states. HWC
represents a serious and mounting challenge to contemporary conservation. Securing the future of wildlife
and their ecosystems in the context of the expanding human population, changing land use developments,
climate change and other factors calls for enhancing investments in conservation to improve HWC
prevention and mitigation strategies.
Acknowledgements
We thank KWS for allowing us to use the conflict data. KWS Tsavo and Mara Research Centers have been
instrumental in keeping some of the longest HWC datasets in Kenya. The field researchers at the two research
stations assisted with compiling the conflict data. JO was supported by a grant from the German Research
Foundation (DFG, Grant No. OG 83/1-1). This project has received funding from the European Union’s Horizon
2020 research and innovation programme under Grant Agreement No. 641918 (AfricanBioServices).
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Appendixes
Table S5. The contributions of the common conflict types to outcomes involving threats to humans, human
injuries or fatalities in the Tsavo and Mara regions
Conflict type Attacks on humans Crop raiding Livestock attack Property damage Other P earson Chi-square test
Conflict outcome n % n % n % n % n % X2 df p
Tsavo Nothing happened to humans 82 0.6 13814 100.0 2719 98.8 491 99.6 68 100.0 30172.4 4 0.001
Humans felt threatened 11712 86.6 0 0.0 2 0.1 0 0.0 0 0.0 23983.1 4 0.001
Humans were injured 1466 10.8 6 0.0 25 0.9 1 0.2 0 0.0 1848.1 4 0.001
Humans were killed 272 2.0 0 0.0 5 0.2 1 0.2 0 0.0 329.3 4 0.001
Mara Nothing happened to humans 2011 59.5 6455 100.0 1966 99.9 517 99.8 164 100.0 4131.6 4 0.001
Humans felt threatened 901 26.6 0 0.0 0 0.0 0 0.0 0 0.0 2614.3 4 0.001
Humans were injured 344 10.2 0 0.0 2 0.1 1 0.2 0 0.0 938.4 4 0.001
Humans were killed 126 3.7 0 0.0 0 0.0 0 0.0 0 0.0 342.7 4 0.001
Tsavo and Mara Nothing happened to humans 2093 12.4 20269 100.0 4685 99.3 1008 99.7 232 100.0 34839.8 4 0.001
Humans felt threatened 12613 74.6 0 0.0 2 0.0 0 0.0 0 0.0 27638.0 4 0.001
Humans were injured 1810 10.7 6 0.0 27 0.6 2 0.2 0 0.0 2808.9 4 0.001
Humans were killed 398 2.4 0 0.0 5 0.1 1 0.1 0 0.0 602.6 4 0.001
Table S6. Outcomes of conflicts involving livestock in the Tsavo and Mara regions (separately and pooled) and
the contributions of the common conflict types to the outcomes
Conflict type
Pearson Chi-square test
Attack on humans Crop raiding Livestock attack Property damage Other
n % n % n % n % n % X2 df p
Tsavo Nothing happened to livestock 13482 99.6 13796 99.8 510 18.5 459 93.1 68 100.0 23300.3 4 0.001
Livestock were injured 0 0.0 5 0.0 4 0.1 0 0.0 0 0.0 17.0 4 0.002
Livestock were killed 50 0.4 19 0.1 2237 81.3 34 6.9 0 0.0 23309.4 4 0.001
Mara Nothing happened to livestock 3373 99.7 6453 100.0 1150 58.4 517 99.8 164 100.0 4590.7 4 0.001
Livestock were injured 1 0.0 1 0.0 257 13.1 1 0.2 0 0.0 1380.7 4 0.001
Livestock were killed 8 0.2 1 0.0 561 28.5 0 0.0 0 0.0 3074.1 4 0.001
Mara and Tsavo Nothing happened to livestock 16855 99.7 20249 99.9 1660 35.2 976 96.5 232 100.0 25646.0 4 0.001
Livestock were injured 1 0.0 6 0.0 261 5.5 1 0.1 0 0.0 2059.9 4 0.001
Livestock were killed 58 0.3 20 0.1 2798 59.3 34 3.4 0 0.0 23280.7 4 0.001
enrr.ccsenet.org Environment and Natural Resources Research Vol. 8, No. 3; 2018
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Table S7. Seasonal variation in each conflict types for the Tsavo and Mara regions
Month Pearson Chi-Square test
Jan Fe b Mar Apr Ma y Jun Jul Au g Sep Oct Nov Dec X2 df P<0.05
Tsavo Attack on humans n 1010 929 1080 1018 1180 1246 1437 1336 1117 1119 1041 1019
% 7.5 6.9 8.0 7.5 8.7 9.2 10.6 9.9 8.3 8.3 7.7 7.5 195.8 11 0.001
Crop raiding n 1657 1095 996 992 1422 1442 1466 1067 948 928 856 951
% 12.0 7.9 7.2 7.2 10.3 10.4 10.6 7.7 6.9 6.7 6.2 6.9 350.6 11 0.001
Livestock attac k n 225 182 221 239 214 229 242 272 249 242 242 194
% 8.2 6.6 8.0 8.7 7.8 8.3 8.8 9.9 9.1 8.8 8.8 7.1 62.8 11 0.001
Property damage n 32 26 26 29 20 46 66 67 76 63 26 16
% 6.5 5.3 5.3 5.9 4.1 9.3 13.4 13.6 15.4 12.8 5.3 3.2 110.6 11 0.001
Other n 12 9 7 4 2 5 6 5 3 8 2 5
% 17.6 13.2 10.3 5.9 2.9 7.4 8.8 7.4 4.4 11.8 2.9 7.4 16.6 11 0.096
Mara Attack on human n 348 352 311 192 273 209 304 282 258 318 250 284
% 10.3 10.4 9.2 5.7 8.1 6.2 9.0 8.3 7.6 9.4 7.4 8.4 474.5 11 0.001
Crop raiding n 274 279 517 682 850 921 969 750 440 279 246 243
% 4.2 4.3 8.0 10.6 13.2 14.3 15.0 11.6 6.8 4.3 3.8 3.8 1070.9 11 0.001
Livestock attack n 184 173 165 161 206 186 135 162 140 148 143 163
% 9.4 8.8 8.4 8.2 10.5 9.5 6.9 8.2 7.1 7.5 7.3 8.3 113.9 11 0.001
Property damage n 40 49 30 17 28 18 28 36 54 72 92 54
% 7.7 9.5 5.8 3.3 5.4 3.5 5.4 6.9 10.4 13.9 17.8 10.4 281.2 11 0.001
Other n 17 4 8 2 4 16 17 9 13 20 15 39
% 10.4 2.4 4.9 1.2 2.4 9.8 10.4 5.5 7.9 12.2 9.1 23.8 126.3 11 0.001
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