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Changing Land Use Patterns and Their Impacts on Wild Ungulates in Kimana Wetland Ecosystem, Kenya


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In Kenya, wildlife numbers have drastically declined due to land use changes (LUCs) over the past three decades. This has affected wildlife habitats by converting them into farmlands and human settlements. This study used remote sensing data from landsat satellite to analyze the changing land use patterns between 1980 and 2013 and their impacts on wild ungulates in KWE. The objective of the study was to map out LUCs, determine the possible causes of LUCs, and examine the effects of LUCs on wild ungulates. The results showed a noticeable increase in the size of farmland, settlement, and other lands and a decline in forestland, grassland, wetland, and woodland. The main possible causes of LUC were found to be agricultural expansions, human population dynamics, economic factors, changing land tenure policy, politics, and sociocultural factors. The main effects of LUCs on wild ungulates in KWE include a decline in wild ungulate numbers, habitat destruction, increased human-wildlife conflicts, land degradation, and displacement of wild ungulates by livestock. The study recommends land use zoning of KWE and establishment of an effective and efficient wildlife benefit-sharing scheme(s).
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Research Article
Changing Land Use Patterns and Their Impacts on Wild
Ungulates in Kimana Wetland Ecosystem, Kenya
Stephen Kitina Nyamasyo1and Bonface Odiara Kihima2
1Wildlife Management, Kenya Wildlife S er vice Training Institute, P.O. Box 84 2, Naivasha 20117, Kenya
2Hospitality and Tourism, Kenyatta University, P.O. Box 16778, Mombasa 80100, Kenya
Correspondence should be addressed to Bonface Odiara Kihima;
Received  May ; Revised  October ; Accepted  October ; Published  December 
Academic Editor: Arianna Azzellino
Copyright ©  S. Kitina Nyamasyo and B. Odiara Kihima. is is an open access article distributed under the Creative
Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the
original work is properly cited.
In Kenya, wildlife numbers have drastically declined due to land use changes (LUCs) over the past three decades. is has aected
wildlife habitats by converting them into farmlands and human settlements. is study used remote sensing data from landsat
satellite to analyze the changing land use patterns between  and  and their impacts on wild ungulates in KWE. e objective
of the study was to map out LUCs, determine the possible causes of LUCs, and examine the eects of LUCs on wild ungulates. e
results showed a noticeable increase in the size of farmland, settlement, and other lands and a decline in forestland, grassland,
wetland, and woodland. e main possible causes of LUC were found to be agricultural expansions, human population dynamics,
economic factors, changing land tenure policy, politics, and sociocultural factors. e main eects of LUCs on wild ungulates in
KWE include a decline in wild ungulate numbers, habitat destruction, increased human-wildlife conicts, land degradation, and
displacement of wild ungulates by livestock. e study recommends land use zoning of KWE and establishment of an eective and
ecient wildlife benet-sharing scheme(s).
1. Introduction
Wild ungulates are hoofed wild mammals comprising large
grazers and browsers. ey account for the vast majority of
large herbivores currently on earth and are found in nearly
every biome especially in arid and semiarid grasslands [].
eir abundance and spatial distribution is highly related to
availability of quality forage and water resources [].
Wild ungulates have high economic values as a source
of revenue through consumptive and nonconsumptive uti-
lization [,]. eir resources provide environmental goods
aesthetic, and ecological values. In Kenya, wildlife resources
accounted for % of the gross tourism earnings, % of the
formal sector employment in  [].
Although Africa has been exceptional in retaining a con-
siderable diversity and concentration of its wildlife compared
to America and Australia, the populations of many of its
wild ungulate species have declined substantially inside and
outside protected areas over the past three decades [,].
East Africa was incomparable in sustaining relatively intact
population has declined sharply [].
Kenya is ranked second highest among African countries,
in bird and mammal species richness with an estimate of 
mammals,  birds,  reptiles ( lizards,  snakes,
and  crocodile),  amphibians, and  ( freshwater
her wildlife numbers have shrunk by between % and %
[] and, by , the number of threatened species in Kenya
included  species of mammals []. is decline in wildlife
numbers globally, regionally, and locally has more been
attributed to land use changes, human encroachment into
wildlife habitats, recurrent droughts, poaching, and other
Land use changes aect key aspects of the earth’s func-
tioning, including a direct impact on global biodiversity [,
Hindawi Publishing Corporation
International Journal of Biodiversity
Volume 2014, Article ID 486727, 10 pages
International Journal of Biodiversity
]. In East Africa, land use changes have transformed land
cover to farmlands, livestock grazing lands, mining grounds,
human settlements, and urban centers at the expense of
wildlife habitat []. ese changes are associated with
wildlife losses, habitat destruction, land degradation, and
blockage of wildlife corridors [,].
In Kenya, increase in human population is rapidly leading
to encroachment into wildlife habitats []leadingtothe
reduction of wildlife space and blockage of wildlife corridors.
If protected areas have no wildlife corridors, genetic dri and
inbreeding may occur, thus leading to population instability,
loss of ecological integrity, and possibly local extinction and
increase in human-wildlife conict [,]. Such conicts
create frustration and animosity towards wildlife and may
result in retaliatory killings [,]. e threat to wild
ungulate populations is, therefore, an eminent one for Kenya,
particularly when one considers the fact that many of her
protected areas are increasingly becoming insularized as a
result of the expanding human dominated areas [,].
Despite the massive conservation eorts backed by signif-
icant international support, changes in land use patterns and
associated impacts of human activities continue to accelerate
land use changes on wild ungulates in KWE have not yet been
fully appreciated. Information on their manifestation and
intensity is inadequate despite its importance in formulating
mitigation measures. is has made it dicult to mitigate
their conservation in KWE. e study sought to determine
and thereaer determine the possible causes of land use
changes and eects of land use on wild ungulate population.
e study hypothesized that changes in land use pattern
have led to a signicant decline on the population of wild
ungulates in KWE and that the increase in human population
and associated activities are the possible causes of land use
changes in KWE formulated as follows.
(a) HO: there has been no signicant increase on land use
changes in KWE from  to .
(b) HO:changesinlandusepatternsinKWEhavenot
had signicant eects on the wild ungulate popula-
(c) HO:increaseinlivestockpopulationandtourism
activities in KWE have not resulted into a noticeable
2. Study Area
Kimana wetland ecosystem (KWE) measures , Km2and
is located in Kajiado County. It lies between longitudes
󸀠and 󸀠east and latitudes 󸀠and 󸀠south.
KWE comprises Kimana division (. km2), Entonet divi-
sion (,. km2), Central division (,. km2), Imbirikani
Location (. km2), and Amboseli National Park (ANP)
( km2). It is a wildlife rich area consisting of ANP and
nine group ranches each with a conservancy or a wildlife
sanctuary. It borders Chyulu and Tsavo West National Parks
Tsavo-Amboseli ecosystem.
e area has a number of permanent swamps such
as Amboseli, Kimana, and NOLTRUSH swamps which
provide suitable ground for agriculture while the entire
range is suitable for wildlife and pastoralism []. Domi-
nant vegetation includes grasses, shrubs, and Acacia plant
ungulates found in the ecosystem include African elephant
(Loxodonta africana), African bualo (Syncerus caer caf-
fer), common eland (Tra gela phus o r y x), and Maasai girae
(Giraa camelopardalis tippelskirchi). e mini ungulates
found in the area include plains zebra (Equus burchelli),
common duiker (Sylvicapra grimmia), impala (Aepyceros
melampus), omsons gazelle (Gazella thomsonii), Grant’s
gazelle (Gazella granti), common waterbuck (Kobus ellip-
siprymnus), and Kirk’s dik-dik (Madoqua kirkii). e big cats
include lion (Panthera leo), spotted hyena (Crocuta crocuta),
leopard (Panthera pardus), and cheetah (Acinonyx jubatus).
e ecosystem also supports a large population of small
mammals, reptiles, and amphibians. e wetland has a rich
birdlife, with over  species recorded, of which  are birds
of prey []. It has globally threatened bird species such as
Lesser kestrel, restricted-range birds found only in a small area
such as the Taveta golden weaver, bird species that live only
in a particular vegetation type such as the grosbeak weaver,
and regionally threatened bird species such as martial eagles.
KWE is primarily habited by Maasai who are semino-
madic pastoralists with land being communally owned. eir
lifestyle has, however, undergone changes due to ongoing
land adjudication and subdivision of group ranches leading to
sales and created openings to emigrations especially in the
relatively high agricultural potential areas such as Kimana,
Entonet, and Kuku divisions. Emigrating communities are
farmers from other parts of the country.
3. Methodology
Primary data collected included TM Landsat satellite
(acquired from path/row / and /) images of
, , , , and . Current land use types,
notable land use changes over time, land tenure system, and
socioeconomic data from  households and  institutions
were collected using semistructured questionnaires and
interview schedules, respectively.
To arrive at the sample, the population was divided into
clusters including Amboseli, Olchoro, Imbirikani, Kimana,
Inkariak Ronkena, Kuku, Loolopon, and Entonet aer which
individuals were randomly selected depending on the size of
the area and human population density as follows: Kimana
and Kuku division,  households each; Olchoro, Loolopon,
Entonet, and Inkariak Ronkena,  households each; and
Amboseli and Imbirikani,  households each. Interviews
were conducted randomly using semistructured questions.
Only an adult family head (man or woman) was interviewed
per household but he or she was free to seek help from any
other family member in case of diculties in responding to
International Journal of Biodiversity
questions. For institutions, only the longest serving ocer
area was interviewed and he/she was free to seek help from
Secondary data collected included KWE topographical
map, land use types, causes of land use changes and its eects
on wild ungulates, human population census gures, wild
ungulate population census data, and livestock population
trend. Land use change data was gathered through acqui-
sition, interpretation, and analyses of satellite images using
ARCGIS and EDRAS soware. Human, livestock, and wild
ungulates numbers were obtained from Loitokitok district
development plan, KNBS, ILRI, DRSRS, and KWS.
Direct observation was also employed on identication
of wild ungulate species, current land use types, land degra-
dation, and wetland encroachment. is encompassed a line
transect walk running diagonally from Kimana sublocation
to Isenet through Kimana community wildlife sanctuary
covering a distance of  km. It was carried out with the
help of community game scouts patrolling at the edges of the
wildlife corridors where irrigated farms are concentrated. All
the wild ungulates species found at a distance of  Km on both
sides of the line transect walk were identied and individual
species counted. is took place during the month of April
(onset of the long rains)  and lasted three days. During
the month of April wild ungulates are mostly concentrated in
swampy places because of availability of water and pasture.
as an evidence of actual practices taking place.
To enhance the change detection, eld visits were con-
ducted to make ground truthing. is involved selecting of
of each site to check and verify whether the land use type on
of images representing a temporal period of  years from
 to  were acquired.
e TM Landsat satellite image of  was georeferenced
against a topographic map at a scale of /, using
a number of ground control points, which were obtained
aer visiting the study area. e projection was Universal
Transverse Mercator (UTM) with Arc  as datum. Image
to image registration was then performed by georeferencing
sat against the georeferenced TM Landsat satellite images of
and  images conform to georeferenced image of , to
enable pixel-by-pixel comparison of the images. e images
were then analyzed to determine various land use types.
accuracy assessment was done for all.
4. Data Analysis
e satellite images were analyzed and interpreted using GIS
Arc Map and ERDAS Images soware. Data on socioeco-
nomic, wild ungulate, and livestock statistics was analyzed
using SPSS soware version  and Microso excel. Land use
changes data was analyzed using descriptive statistics to show
the variation from  to . is involved calculation of
size of areas under each land use for the years , ,
, , and  and their sizes computed. is capability
is provided in area command under the database query
module in Arc Map. e areas were then entered in Microso
excel for the computation of land use changes over time. To
determine the land use change for a particular land use, the
area of land for year  was subtracted from the area of land
for the year . Pearson correlation coecient statistics
analysis was performed to determine association between
Frequencies of interviewed household heads and institutions
giving a particular response were summarized. e equalities
of frequencies were tested using chi-square goodness of t. To
establish possible causes of land use changes the frequencies
of the responses of those interviewed were summarized and
a chi-square cross tabulations analysis was performed to
determine the relationship with specic attributes. Statistical
tests were considered signicant if 𝑃values were equal to
or less than . and insignicant if 𝑃values were greater
than . []. For chi-square cross tabulations, if 𝑃value was
equal to or less than ., then a response was dependent on
an attribute and independent of the attribute if 𝑃value was
greater than ..
5. Findings
5.1. Major Land Use Changes in Kimana Wetland Ecosystem
(KWE). is research produced seven important land use
types including farm/cropland, settlement and urban areas,
forestland, grassland, woodland, wetland/swamps, and other
lands (bare and rocky grounds, built areas, quarries, roads,
dried up wetland/swamps, and abandoned human settle-
ment) (Figure ). In , Kimana wetland ecosystem had a
total area of ,. km2(Table )outofwhichfarm/crop-
land had occupied .km2; settlement and urban areas,
. km2;forestland,.km
2; grassland, ,. km2;
woodland, . km2; wetland/swamps, . km2;and
other lands, . km2. By  farm/cropland occupied an
area of . km2; settlement and urban areas, . km2;
other lands, . km2;forestland,.km
,. km2;wetland,.km
2; and woodland, . km2
(Figure ).
A signicant positive correlation occurred between crop-
land and settlement areas (𝑟 = 0.989,DF =2,𝑃 = 0.001)
and farm/cropland and other lands (𝑟 = 0.952,DF =2,𝑃=
0.013). On the other hand, a signicant negative correlation
occurred between cropland and forestland (𝑟 = −0.989,DF=
2,𝑃 = 0.001), cropland and grassland (𝑟 = −0.989,DF =2,
𝑃 = 0.001), cropland and wetland (𝑟 = −0.997,DF=,
𝑃 = 0.004), and cropland and woodland (𝑟 = −0.922,DF =
2,𝑃 = 0.026). Another negative correlation also occurred
between settlement and forestland (𝑟 = −0.986,DF =2,
𝑃 = 0.002), settlement and grassland (𝑟 = −0.980,DF =2,
𝑃 = 0.004), and other lands and wetland (𝑟 = −0.985,DF =2,
𝑃 = 0.015)(Table ).
International Journal of Biodiversity
T  :  e a r e a ( k m) and the percent land cover for land use changes in KWE.
Land use
(+) or
decrease ()
Settlement . . . .% . .% . .% . . .%
Woodland . .% . .% . .% . .% . . .%
Cropland . .% . .% . .% . .% . . .%
Forestland . .% . .% . .% . .% . . .%
Other lands . .% . .% . .% . .% . . .%
Wetland . .% . .% . .% . .% . . .%
Grassland . .% . .% . .% . .% . . .%
Total . .% . .% . .% . .% . .
Inkariak Rongena
Other lands
Wetl a n d s
Land use 1980
Admin boundary
Land use 1980
020 40 80
Coordinate system: arc 1960 UTM zone 37S
Projection: transverse mercator
Datum: arc19 60
False easting: 500,000.0000
False northing: 10,000,000.0000
Central meridian: 39.0000
Scale factor: 0.9996
Latitude of origin: 0.0000
Units: meter
F : Land use map of  for Kimana wetland ecosystem.
Source: author, .
Farm/cropland, settlement and urban areas, and other
lands in KWE are becoming important with time as they
replace the areas previously occupied by wetland/swamps,
grassland (wildlife dispersal, corridors, and breeding areas),
forestland, and woodlands. It was also established from the
and then converted to cropland ever reverted to wetland.
irrigated farming whereby all the water used for irrigation is
pumped directly from the wetlands.
5.2. Causes of Land Use Change in Kimana Wetland Ecosystem
(KWE). e main causes of land use change in KWE as
indicated by the respondents include agricultural expansions
Inkariak Rongena
Land use 2013
Other lands
Wetl a n d s
Admin boundary
Land use 2013
020 40 80
Coordinate system: arc 1960 UTM zone 37S
Projection: transverse mercator
Datum: arc19 60
False easting: 500,000.0000
False northing: 10,000,000.0000
Central meridian: 39.0000
Scale factor: 0.9996
Latitude of origin: 0.0000
Units: meter
F : Land use map of  for KWE. Source: author, .
(.%), human population dynamics (.%), economic
factors (high nancial gains from cultivation compared to
pastoralism and wildlife-based tourism) (%), changing
land tenure policy (.%), politics (.%), drought, and
sociocultural factors such as poverty, level of education,
urbanization, and culture change (Ta b l e  ). Only opinions
from those respondents who had lived in the area for more
than ve years were considered. Such was dependent on the
current land uses and length of stay (𝜒2= 47.64,DF =28,
𝑃 = 0.012;𝜒2= 46.52,DF=28,𝑃 = 0.024) but independent
on age, level of education, land tenure system, and sources of
livelihood (Table ). Farmers and business people attributed
expansions and economic gains while pastoralist attributed
it to drought and sociocultural factors.
International Journal of Biodiversity
T : Correlations between land use changes in KWE.
Land use type Correlation Cropland Wetland Settlement Other lands Forestland Grassland Woodland
Crop Pearson correlation .∗∗ .∗∗ ..∗∗ .∗∗ .
sig. (-tailed) . . . . . .
Wetland Pearson correlation .∗∗ ..∗∗ .∗∗ ..
sig. (-tailed) . . . . . .
Settlement Pearson correlation .∗∗ . ..∗∗ .∗∗ .
sig. (-tailed) . . . . . .
Other lands Pearson correlation ..∗∗ ....
sig. (-tailed) . . . . . .
Forestland Pearson correlation .∗∗ .∗∗ .∗∗ . .∗∗ .∗∗
sig. (-tailed) . . . . . .
Grassland Pearson correlation .∗∗ ..∗∗ ..∗∗  .
sig. (-tailed) . . . . . .
Woodland Pearson correlation .....∗∗ . 
sig. (-tailed) . . . . . .
∗∗Correlation is signicant at the . level (-tailed).
Correlation is signicant at the . level (-tailed).
T : Respondent’s opinions on the causes for land use change in
Possible causes of land use change Number of
respondents Percentage
Agricultural expansion  .
Human population increase  .
Economic forces  .
Politics  .
Changing land tenure policy  .
Drought  .
Poverty  .
Education level .
Total  .
Source: author, .
5.3. Agricultural Expansion. Irrigated agricultural eld in
KWE increased from . km2in  to over . km2in
 (Tab l e  ) suggesting that irrigated agricultural expansion
is one of the major causes of land use change (Tab l e  ). is
expansion has seen irrigated agriculture become the main
source of livelihood for the households in KWE as indicated
by % of the respondents. e main crops grown currently in
the area are horticultura l crops such as tomatoes, chilli, onion,
and kales among others while maize and beans were the main
food crops.
5.4. Demographic Dynamics. Over the past three decades,
KWE has experienced an estimated annual human pop-
ulation growth rate of . as per the  census. is
translated into , persons and a population density of
. persons/km2in  with Kimana division having the
2. It is projected that by the year
, the total human population in the area will be ,
1999 2009 2012 2020 2030
Number of people
Ye a r
F : Projected human population for KWE up to the year  .
Source: GoK [].
(Figure ). e rapid population growth in the area has
resulted in increased demand for more land for farming,
settlement, and infrastructure development which has led to
clearing of large areas of the wetland, forestland, woodland,
and grassland that serves as habitats for wild ungulates.
5.5. Economic Factors. Of the total number of respondents
interviewed, % pointed out that they receive virtually no
direct cash benets from the wildlife-based tourism industry.
ey only receive indirect benets in the form of school
bursaries, piped water, construction of classrooms and dis-
pensaries, livestock sales yards, and other related community
goods and services, which oen fail to benet those in most
need. In contrast, they indicated that they receive direct
benets from irrigated farming and selling or renting out
their land.
Increased demand for agricultural produce to satisfy the
local markets and border towns such as Moshi, Arusha, and
Tarakea in Tanzania has accelerated the rate of irrigated
International Journal of Biodiversity
T : Size of land owned by individuals in KWE.
Size of land Frequency Percent
<. Acres  
.– Acres  .
.–. Acres  
.– Acres 
Above . Acres  .
Total  
Source: author, .
farming in the area as denoted by % of the respondents.
e study found that approximately , tons of farm
produce is harvested and transported to Tanzania, Nairobi,
and Mombasa markets weekly.
Moreover, the introduction of irrigated small-scale farm-
ing in the area in the early s aer the collapse of group
ranch model gave rise to increased demand for more land
for irrigated farming as revealed by % of the respondents.
e government welcomed subdivision and in  enacted a
policy to promote it. is increased demand for more land
for irrigated farming resulted in further land subdivision.
e study found that % of those interviewed and owned
Moreover, .% of those interviewed own less than  acres
and only .% own more than . acres (Ta b l e  ). e
study established that when Kimana group ranch members
subdivided their land aer the failure of the group ranch
model, the mean size of the individual plots was . km2(.–
. km2).
of those interviewed were non-Maasai who had emigrated
from other areas. Moreover, % of the total respondents
who owned land in the areas indicated that they acquired it
through buying and .% through inheritance and .% (all
migrants) had rented the land they were using.
5.6. Eects of Land Use Change on Wild Ungulates in Kimana
Wetlan d E c o s y s t e m . In the views of % of those interviewed,
eectsonthewildungulatepopulations(𝜒2= 121.68,DF =
1,𝑃 = 0.000). e main eects include noticeable decline
in wild ungulate numbers, encroachment into wetlands and
other wildlife habitats, signicant increase in incidences of
human-wildlife conicts (𝜒2= 162.00,DF =1,𝑃=
0.000), habitat destruction, displacement of wild ungulates by
livestock, land degradation, and emergence of invasive plant
5.7. Trends in Abundance of Wild Ungulates in Kimana
Wetlan d E c o s y s t e m . Wild animal counts conducted in the
study area from  to  by various institutions on four
species of wild ungulates have showed a decline in their
numbers. As per the  animal count, wildebeest which had
a population estimate of approximately , individuals was
the most numerous wild ungulate, followed by common zebra
(,), Grant’s gazelle (,), and impala (). In ,
Number of individual species
Ye a r
Common zebra
Impala Wildebee st
Grant’s gazelle
F : Population estimates for zebra, impala, G. gazelle, and
wildebeest in KWE ( to ). Source: DRSRS and KWS.
2007 2010
Number of individual species
Common zebra
Wildebe est
Maasai girae
Cape eland
Harteb eest
African bualo
omson’s gazelle
Grant’s gazelle
F : Wild ungulate population estimates for  and  in
KWE. Source: DRSRS and KWS.
common zebra was the most numerous with a population
estimate of , followed by wildebeest (,), Grant’s
gazelle (), and impala () (Figure ).
in the same area indicated a decline in numbers for common
zebra, Grant’s gazelle, and wildebeest species and an increase
for impala. For example, the population of common zebra
from  to  dropped by .%, Grant’s gazelle by .%,
and wildebeest by .%, while impala increased by %
(Figure ).
e results from aerial counts carried out in Kimana
wetland ecosystem from  to  indicated a decline in
the number of African bualoes from  in  to  in
International Journal of Biodiversity
T : Characteristics of interviewee opinions in regard to land use changes and their eects on wild ungulates.
Information sought Responses from respondents Number of respondents
Chi square goodness of t;
df; 𝑃value
Gender Male  𝑟= ., df = , 𝑃= .
Female 
–  𝑟=.,df=,𝑃= .
– 
Above  
Level of education
None 
𝑟= ., df = , 𝑃= .
Primary 
Secondary 
Te rt i a r y  
Source of livelihood
Crop farming 
𝑟= ., df = , 𝑃= .
Livestock 
Rural self-employed
Urban self-employed
Wage emp l o y m e nt
Crop farming and livestock 
Livestock and wildlife 
Length of stay
<yrs 
𝑟= ., df = , 𝑃= .
.– yrs 
.– yrs 
.– yrs 
Above . yrs 
Individual size of land
<. Acres 
𝑟= ., df = , 𝑃= .
.– Acres 
.–. Acres 
.– Acres
Above . Acres 
Current land use
Crop farming 
𝑟=.,df=,𝑃= .
Livestock 
Wildlife 
Commercial 
Crop farming and livestock 
Has the land use been changing over
the years?
Ye s    𝑟= ., df = , 𝑃= .
No 
HasLUCaectedwildungulates? Ye s    𝑟= ., df = , 𝑃= .
No 
Distance of the individual homestead
from protected areas
- km 
𝑟= ., df = , 𝑃= .
.– km 
.– km 
.– km 
Over . 
Causes of land use change
Agricultural expansion 
𝑟=.,df=,𝑃= .
Economic forces 
Politics 
Changing land tenure policy 
Human population increase 
Drought 
Poverty 
Education level
International Journal of Biodiversity
T  : C ontinue d .
Information sought Responses from respondents Number of respondents
Chi square goodness of t;
df; 𝑃value
Is there HWC? Ye s    𝑟= ., df = , 𝑃= .
No 
Forms of HWC
Crop destruction 
𝑟= ., df = , 𝑃= .
Livestock predation 
Human injuries 
Death of human being 
Road kills 
Competition for food
Period of the year when the conicts
are severe
Januar y–March 
𝑟=.,df=,𝑃= .
April–June 
July–September 
October–December 
2000 2002 2007 2010
Number of individual species
Ye a r
Maasai girae
Cape eland
African bualo
F : Population estimates for girae, eland, and bualo in
KWE ( to ). Source: DRSRS and KWS.
 (.% decrease) and that of the Maasai girae remained
relatively stable with a slight increase, while that of the Cape
than  individuals in  (Figure ). In , most of
the bualoes were in ANP (.%) and Kimana Sanctuary
(.%) and the rest (.%) were in Kuku group ranch. All
the bualoes counted in  were within the protected area.
5.8. Livestock Population Trend in Kimana Wetland Ecosystem.
main livestock types kept in the study area. In the recent past,
farmers have started keeping dairy cattle (mainly Friesian)
under intensive zero grazing system. Livestock population
gures over the past three decades show that the numbers
Aerial counts for livestock in KWE from  to  indicate
an upward trend. Goats and sheep appeared to have increased
in the last ten years with a total population of , in 
compared to , in .
5.9. Tourism Activities. KWE is among the most popular
tourist destinations with Amboseli National Park being one
facilities within KWE has been rapid in response to the
increasing number of tourists. In , for example, the study
found that there were only ve hotels and lodges but at
present, there are over  hotels, lodges, and campsites with a
capacity of over , beds. In an attempt to have tourists view
wildlife at a close range, roads and nature trails have been
constructed in ecologically fragile areas that serve as breeding
and calving grounds for most wild ungulate species. ese
infrastructures have also disrupted wild ungulate migration
patterns and increased habitat fragmentation.
5.10. Human-Wildlife Conict (HWC). HWC incidences,
which were rare in the s, have been frequent as testied
by % of the respondents. Of these, .% have been victims
of the conicts. e most severe forms of HWC, in their
views, were crop and property damage by wild ungulates
(%), livestock predation by carnivores (%), death and
injury of human being (% and %, resp.), road kills (%),
and competition for resources between human, livestock, and
wild ungulates (Figure ).
6. Discussion and Conclusions
KWE is critical wildlife dispersal, breeding, and dry season
grazing area for Amboseli and Tsavo ecosystems []. Poor
land use zoning and land use controls have led to emergence
of uncoordinated and conicting land use types in KWE
[]. e decline in land under wetland, forestland, grassland,
and woodland that are wildlife habitats signies that wildlife
conservation is not an important source of livelihood for
the locals of KWE. It is, therefore, not possible to convince
the local people to conserve the wildlife habitat because of
its advantage in the long-term. Of notable concern is lack
of direct benets from the wildlife based-tourism industry.
In contrast, the local people do receive direct benets from
horticultural farming and selling or renting out their land.
International Journal of Biodiversity
Death of
human being
Road kills
Problem caused by wild animals
F : Problems caused by wild animals in KWE. Source: author,
erefore, it is no surprise that they have increasingly cleared
their land for irrigated farming, human settlement, and
infrastructure development in recent past at the expense of
wildlife conservation.
Traditionally, land in KWE was communally owned.
is system made it possible to practice nomadic pastoral-
ism, which ensured sustainable livestock production and
conservation of wildlife. However, because of changes in
government policy such as repealing of the “Land Group
Representatives and Land Adjudication Act” of , commu-
nally owned land has been subdivided into individual plots.
is has resulted into expansion of farmland by .%,
settlement and urban areas by .%, and other lands
by .% between  and . In addition, the area
under wetland has decreased by .%, forestland by .%,
grassland by .%, and woodland by .% during the same
e shi from wildlife conservation and pastoralism to
irrigated agriculture is not entirely new among pastoral com-
munity. However, in KWE, it has taken a more commercial
nature rather than subsistence farming leading to expansion
of farmlands through clearing of wetlands, forests, grassland,
and woodland. is has led to loss of key wetlands and other
wildlife habitats, which used to serve as grazing area for
livestock and wild ungulates. Land that is adjacent to wetland
that used to serve as a wildlife dispersal area has now become
individual property. Wild ungulates are no longer able to
access the areas that have been converted into farmland and
human settlements. Hence, large number of wild ungulates
concentrate in areas without (or with little) farms and human
settlement. Wild ungulates have completely avoided urban
areas and build areas such as schools, dispensaries, and
market places.
e subdivision of group ranches into individual plots has
also resulted in establishment of community sanctuaries. e
community sanctuaries are usually too small in area to be
viable conser vation units for they are not linked to each other.
ey are fragmented wildlife habitats. A preposition is also
supportedbyYoungandMcClanahan[], who argued that
community wildlife sanctuaries represent a fragment of the
entire ecosystem and violate good design approaches based
on principles of island biogeography. When these community
wildlife sanctuaries are established in isolation from other
protected areas, without linking them by wildlife corridors,
they result in isolation of protected areas. e isolation
of protected areas is likely to result in inbreeding of wild
animals, thus lowering their survival ability.
Overstocking of livestock by pastoralist has led to over-
grazing in the study area particularly in areas outside pro-
tected areas. e impacts of overgrazing as observed during
emergence of invasive plant species such as Lantana camara
and Solanum incanum. e latter is a drought resistant
nonpalatable herbaceous plant that does not have any natural
enemy and thrives well in disturbed ecosystem. e study
found that it was thriving well in areas that had been
overgrazed, on edges of human settlement and along built
areas. It dominates the herbaceous layer thus hindering
the growth of grass species and hinders free movement of
livestock and wild ungulates.
e fate of Kenya’s wildlife, therefore, depends on the
future of the protected areas and the surrounding wildlife dis-
persal areas. Changing land use patterns in KWE have led to
loss of key dry season grazing ground for wild ungulates and
decline in wild ungulate numbers in the last three decades.
their impacts on wild ungulates, the study recommends the
following: zoning of KWE into three categories (wildlife
areas, settlement area, and farming area); establishment of
an eective, transparent, fair, and equitable wildlife benet-
sharing scheme; establishment of an eective and transparent
management system for managing community conservan-
cies; rehabilitation of Kimana community wildlife sanctuary;
and farmers to embrace technology in their irrigated farming.
e future of wild ungulates in KWE is doomed unless
appropriate measures are put in place to reverse the observed
detrimental trends. It is extremely urgent, therefore, that
all the proposed measures by this study be taken into
consideration in order to strike a balance between wildlife
conservation and economic development if the observed
detrimental trends are to be reversed.
Areas for Further Research. (i) Kimana Community Wildlife
Sanctuary was the rst community sanctuary to be estab-
it has collapsed. erefore, more studies need to be carried
out to determine the eectiveness of community involvement
in the management of wildlife. (ii) ere have been many
wildlife conservation and management policies which have
been put in place to conserve and manage wildlife. It is,
therefore, necessary to analyze wildlife conservation and
management policies in Kenya with a view of determining
their eectiveness.
Conflict of Interests
e authors declare that there is no conict of interests
regarding the publication of this paper.
 International Journal of Biodiversity
e authors acknowledge the Kenya Wildlife Service which
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Dennis Macharia from RCMRD, for availing the ARCGIS
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... The resulting impact of such threats on wildlife and their habitats includes habitat loss, land degradation, over-utilization of natural resources, poaching and illegal wildlife trade, pollution and invasive species, siltation and over-abstraction of water bodies, and humanwildlife conflict. Over the last four decades, significant negative impacts on wildlife species have resulted in a decline in wildlife populations, as well as a severe degradation of native habitat Grunblatt et al. 1996;Ottichilo et al. 2000;Ottichilo et al. 2001;Reid et al. 2008;Nyamasyo 2016;Ogutu et al 2016). ...
... The rapid rise in human population has increased demand and competition for resources, resulting in an augmented exploitation of resources at the highest level, beyond the capacity of available resources (Scholte 2011;Kideghesho et al. 2013;Nyamasyo 2016). The demands are associated with wildlife and habitat destruction, including land for settlements, cultivation and livestock grazing, wood products, and water points for livestock and domestic use (Kideghesho et al. 2013). ...
Wildlife in Kenya is both a national resource and a key source of revenue for the government. Wildlife and tourism are interdependent and essential sectors in Kenya’s socio-economic development agenda. This chapter reviews the contribution of wildlife to tourism, wildlife management approaches, policy and legal framework, stakeholder involvement, as well as the challenges facing wildlife conservation and management. The insights and approaches illustrated may be used to formulate and implement solutions to enhance wildlife conservation and management for the benefit of all stakeholders. Kenya is at a crossroads with wildlife management. It is recommended that Kenya embrace a more holistic management approach that integrates effective political and related governance frameworks. This chapter proposes a novel vision of conservation in Kenya that includes additional space for wildlife, the adoption of a zero-tolerance policy on corruption and wildlife crime, substantial stakeholder participation, and a community-based approach to conservation.
... The role of arti cial water sources for wildlife has been of interest [6,12], and information on the subject has increased substantially during recent years. There are reports of water sources being unsustainable when deforestation and conversion to agricultural areas occurs [15, 16]. The agricultural areas may need more ground water to support the crop plants, then the arti cial ponds may construct in the areas. ...
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Background Access to suitable water sources is important for mammals. This study compared species diversity and the water use by mammals among water springs, standard artificial ponds, and water pans within the Khao Phaeng Ma Non-Hunting Area in 2020 and 2021. Methods Two camera traps were installed at each water sources for 749 nights with a total of 12 water sources. A total of 19,467 photographs were recorded comprising 13,777 photographs of gaur (Bos gaurus, the vulnerable and most important in the area), and 5,690 photographs of other mammals. Results In the wet seasons of 2020 and 2021, the relative use was highest at standard artificial pond number 2 and water pan number 1. In the dry seasons of 2020 and 2021, the relative use was highest at water pan number 1 and 2. During the study period, the number of species was highest in water pan number 1 (10 species, diversity index (H´) = 1.38), and water pan number 2 (11 species, H´ = 1.75). Gaur, sambar deer (Rusa unicolor), red barking deer (Muntiacus vaginalis), wild boar (Sus scrofa), and Asian black bear (Ursus thibetanus) used water pan and standard artificial pond rather than water spring. Conclusions The use of water spring was associated with water period (months), while standard artificial pond and water pans were associated with water surface area, water depth, altitude, species diversity, species richness, and number of mammals photographed. Water pans were more suitable for utilization by mammals than other water sources.
... Similarly, there has also been an inevitable growth of human and livestock populations. The commodification of wildlife in conservancies has not exempted rural communities from prevalent threats, including the loss of habitat to other land uses and intensification of human-wildlife interactions including human-wildlife conflict (HWC) (Conover & Messmer 2001;Lamarque et al. 2009;Nyamasyo & Kihima 2014). Such conflict involves people, their livelihoods, the natural environments, and their habitats. ...
... Within the cultivated land in Sanga, crop cultivation is intensifying, leading to land subdivisions. Furthermore, this rangeland experiences competition for forage and pasture by livestock and wildlife (Nyamasyo and Kihima, 2014) from the park. Moreover, the traditions and taboos that excluded farming communities from cultivating the landscape near Lake Mburo have changed, leading to the constant loss of the exclusive pastoralist landscapes (Mallarach, 2008). ...
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Socioeconomic, political, and policy decisions by the government may influence the socioecological changes of the land use and land cover pattern for rangeland people over time. This paper examines the historical political, policy, and socioeconomic decisions that influenced land use and land cover changes in the former pastoral community in Sanga-Lake Mburo Rangeland Ecosystem in South Western Uganda. Data on historical events were documented from literature and supplemented by an opportunistic discussion with Sanga residents. Data on land use and cover change detection between 1987 and 2020 were provided by Landsat 5 TM and Landsat 8 OLI/TIRS images and from field observations. Images were processed using QGIS version 3.20.1 employing a semi-automatic classification plugin. Political decisions and government policies related to land tenure and reforms, socioeconomics, and demographic changes were noted as underlying drivers of land use and cover changes. The overall accuracies for classified maps of 1987 and 2020 were 80.36% and 89.81%, respectively. Notably, woodland cover in the protected area increased by 170.53% between 1987 and 2020, while built-up areas and farmland increased 1348.15% and 405.03%, respectively. In the same period, wetland cover in protected and unprotected decreased immensely by 46.06%. Bareland in the park decreased by 23%, while outside the park, it increased by 25.07%. This study concludes that land use and land cover change resulted from sociocultural changes, political and policy decisions on ranches, park management, and land tenure restructuring. Keywords: Park, Political, Policy, Pastoralists, Vegetation, Sedentarization
... Promoting conservation of wildlife habitat in absence of direct benefits of the wildlife basedtourism industry for near communities is difficult. This results in clearance of natural forest for irrigated farming, human settlement, and infrastructure development at the expense of wildlife and biodiversity conservation [52]. The underperformance of many PAs network in the ecological, economic and social aspect reduces the beneficiary of surrounding communities [42]. ...
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Conversion of natural habitat to other forms of land use is the main threat to protected areas and biodiversity globally. The continued trend of land use land cover change in protected areas resulted in loss of a large portion of biodiversity, overexploitation by humans, transformation of natural land to human settlement, etc. In Ethiopia, the causes for land use land cover change in many protected areas are farmland expansion, deforestation, unsustainable grazing and settlement expansion, and are leading to loss of biodiversity and negative impacts of ecosystem services. In addition, Ethiopia’s protected areas entertain escalating threats and land cover changes due to human population growth, competing claims from the surrounding communities, incompatible investment, lack of environmental law enforcement, absence of complete plan and timely update for protected areas, etc. These have affected protected areas in the country namely the Bale Mountains National Park, Chocke Mountains, Babile Elephant sanctuary, Abijata Shalla Lakes National Park, Awash National Park and others. The continued land use land cover changes are aggravating ecosystem, soil and water resources degradation in mountainous protected areas while they are leading to biodiversity destruction and loss of forest cover in lowland protected areas. In order to halt and reduce the impact of land cover change on biodiversity conservation, undertaking complete land use planning and continuous monitoring of protected areas was found to be important. Similarly, integrating protected areas into the surrounding landscapes and a broader framework of national plans, promoting income generation means for communities surrounding protected areas, promoting biodiversity conservation directly linked to poverty alleviation, involving local communities and stakeholders in land use planning and sustainable management of protected areas, enhancing sound management in vulnerable mountain protected areas and restoring abandoned lands located in and around protected areas are crucial in the proper land use planning and management of protected areas. In addition, enhancing awareness creation and promoting natural resource information of protected areas and enhancing scientific study on land use land cover change pattern of protected areas are vital to undertake effective land use planning and management of protected areas in Ethiopia.
... This combination demands strict humans-elephant separation, while obscuring extra local processes like land reform, infrastructure and agribusiness investment which compromise elephant survival (Zimmerer, 1999:158;Forsyth and Walker, 2008:15;Maitima et al., 2009;Nyamasyo and Kihima, 2014:2). ...
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Existing literatures across the world highlighted the causes and rate of wetland loss; however, so far, no researches tried to analyze how these are guided by the socioeconomic and ecological conditions. The current review work wished to explore how economic and socioecological perspectives could control the rate and drivers of urban wetland loss. Through meta-analysis, this study also intended to explore the changing polarity in research publication and collaborative research. Total 287 original research articles indicating the rates and drivers of wetland loss from 1990 to June 2022 for the first objective and 1500 articles focusing wetland researches from Dimensions AI database for the last objective were taken. Results clearly revealed that the rate of urban wetland loss varies from 0.03 to 3.13% annually, and three main drivers like built-up, agricultural expansions, pollution were identified all across the world. Loss rate was found maximum in the developing and least developed countries. Pollution, built-up expansion, and agriculture expansion, respectively, in developed, developing, and least developed nations were identified as the most dominant drivers of urban wetland loss. Linking loss rate and drivers with socioecological and economic perspectives revealed that human development index (HDI), ecological performance index (EPI), sustainable development goal index (SDGI), and social progress index (SPI) is negatively associated with the rate of urban wetland loss. Contrarily, a poverty rate encouraged higher rate of loss. This study articulated that improving these socioecological and economic conditions could help wetland conservation and restoration to achieve SDGs.
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Wildlife Sanctuaries are vital because they are established by law to protect endangered and threatened species of plants and animals. They play a precious role in balancing the ecology of a region. Routine assessment of Land Use Land Cover is imperative in Wildlife Management because landuse and land cover change studies indicate variations and help to detect dominant changes over the region. This study aims to understand land use land cover changes in Cotigao Wildlife Sanctuary with the use of remotely sensed data from IRS, LISS III satellite images for 2008 and 2018. the area under Dense Mixed Jungle has decreased by 11.74 per cent. The area under settlement and agriculture has shown an increase by 3 and 2.08 per cent respectively. During the study period, the area covers water bodies, grasslands and plantations depicts a negative change with-0.33,-2.5 and-0.93 respectively. Two land classes namely Fairly Dense Mixed Jungle and Open Scrub have increased by 5.16 and 5.26 per cent respectively for the same period.
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A survey of the extent and impact of game ranching on the natural resources of the Northern Province was conducted during 1998. Approximations of the annual turnover, game numbers and socio-economic impact of game ranching were obtained. Questionnaires were distributed to game ranch owners and managers and exemption permits issued by the Provincial conservation authority were analyzed for trends. An estimated 2 300 game ranches existed in the Northern Province by August 1998. These ranches covered approximately 3.6 million hectares, which represents 26% of the total area of the Province. The main concentrations of game ranches in the Northern Province are in the Northern, Western and Bushveld sub-regions. Game ranching contributes significantly to the economy of the Northern Province, especially through hunting and live game trade. It attracts investors from other provinces and countries, and earns foreign currency through ecotourism and trophy hunting. Hunting makes the largest contribution to the annual turnover of the game-ranching industry, followed by live game trade and ecotourism.
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Kimana Group Ranch (KGR) is a critical wildlife dispersal area for Amboseli National Park in Kenya. But irrigated agriculture in the group ranch is leading to increased conflicts and competition for land and other critical resources. This study used semi - structured interviews with group ranch members on their interactions with wildlife, resource use and access, land use changes and livelihoods. Most group ranch members practiced agriculture as opposed to pastoralism. The community noted that critical resources such as water, pasture, plant resources and space were declining, and mostly available further from their homes. Members identified agriculture expansion and human development as the main land use changes. Most members also supported agriculture expansion as well as group ranch subdivision. Even most members supported wildlife use of their land, they were unhappy about the lack of compensation for losses. Most members wanted communal wildlife sanctuaries managed by the local community rather than a foreign investor. The competition for land and its resources due to increasing human population and land use changes is limiting wildlife use of the group ranch, and hence insularizing Amboseli Park. Potential solution is to have a negotiated land use plan that harmonizing environmental conservation and local livelihoods, while diversifying people's socio-economic opportunities to reduce poverty and dependence on natural resources.
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Since the dawn of agriculture, people and wild animals have been in conflict because agricultural crops generally offer a rich food source for wild animals as well as for people. Large, wild herbivores compete for pasture resources with livestock and can act as reservoirs of livestock diseases. Furthermore, livestock form a concentrated and vulnerable food source for predators. As a result, humans have extirpated many native animal species from agricultural areas, either directly, or indirectly through modifications in habitat availability or structure resulting from land use changes. As human populations have expanded in developing countries they have caused loss in biodiversity and species extinctions, and will continue to do so. I review attempts to change the interaction between people and large herbivores from one that is primarily negative to one that is positive by increasing the benefits which individuals, communities and society derive from wild, large herbivores. My proposition is that, in developing countries, it is only by using this approach that wild, large herbivores have a chance of surviving outside areas specifically set aside for their protection. In the developed world the opposite trend will occur as people move into the cities causing human populations to decline in rural regions. As a consequence, wildlife habitat will increase and wild, large herbivores will come into conflict with humans, particularly in peri-urban areas rather than in rural areas as happens at present. This will lead to a change in public attitude from one that supports wildlife conservation to one that sees wild, large herbivores as a threat; again, with potential negative consequences for wildlife conservation.
The northern part of the Serengeti/Mara ecosystem falls within the two Kenyan districts of Narok and Trans Mara. Within these two districts, major land use changes have been clear for quite some years. Previous work indicates that losing what are called the group ranches that surround the Maasai Mara National Reserve and the Mara Triangle could cause a permanent loss of perhaps 20% of the migratory wildebeest, which in turn could trigger major changes in the Serengeti ecosystem itself. These changes in land use are perplexing in view of the large revenues generated from wildlife tourism which should encourage investment in conservation on the part of landowners rather than land development. This chapter examines the apparent contradiction between the revenues generated by wildlife, on the one hand, and the land use changes initiated by the Maasai on the other. The objective is to describe the economic conditions of the Mara portion of the Serengeti ecosystem within which individual economic agents—Maasai households—are making decisions about land use investment and development.
Common understanding of the causes of land-use and land-cover change is dominated by simplifications which, in turn, underlie many environment-development policies. This article tracks some of the major myths on driving forces of land-cover change and proposes alternative pathways of change that are better supported by case study evidence. Cases reviewed support the conclusion that neither population nor poverty alone constitute the sole and major underlying causes of land-cover change worldwide. Rather, peoples’ responses to economic opportunities, as mediated by institutional factors, drive land-cover changes. Opportunities and
Kimana Group Ranch area is a critical area for Amboseli, Chyulu, Tsavo West national parks, and community sanctuaries in the Tsavo - Amboseli Ecosystem. However, growing human populations and associated activities are causing range contraction and wildlife displacement in this dispersal area. This study investigated the contraction of wildlife dispersal area through field mapping and spatial analysis. Human activities displaced wildlife from 140.01 km� (55.74%) of KGR, leaving only about 44% of the land for wildlife. The actual area occupied by these activities was 57.83 km 2 (23% of KGR). Wildlife kept 0.23 ± 0.04 km from Maasai homes, 0.18 ± 0.02 km from roads, 0.07 ± 0.04 km from electric fences, and 0.21 ± 0.02 km from livestock. No wildlife was seen close to agricultural areas, which covered 0.89 km 2 , 0.27%. Kimana and Namelok electric fences covered 52.98 km� (21.10%), but displaced wildlife from 69.29 km� (27.61%). Although Maasai homes covered only 0.24 km� (1.09%), they displaced wildlife from 28.11 km� (11.19%). Spatially, clusters of human activities were cutting off Amboseli and KCWS, forcing the wildlife to find alternative routes with Tsavo / Chyulu. Therefore, Kimana is diminishing as wildlife dispersal area and this will affect the viability of protected areas.
A significant proportion of Kenya's tourism is wildlife- based and 44,000 km2, representing about eight percent of the country's territory, has been set aside for wildlife protection. This has denied local communities access to invaluable herding and agricultural resources thereby creating conflicts between tourism and the well- being of local people who also suffer the destruction of life and property from wildlife. This paper probes government policies on the sharing of benefits from tourism with local communities in wildlife- protected areas. The analysis could provide lessons for other African countries where such conflicts are occurring. The findings show that although revenue-sharing has been initiated in some places, questions have been raised whether it is the local governments, communities or individual land-owners who should be compensated. So far, direct benefits to the landowners have been minimal. This has partly motivated certain communities to form wildlife associations with the aim of participating directly in tourism. This process is yielding some dividends but requires to be guided carefully in order to involve the majority of the local people in sharing in the benefits of wildlife management. Ultimately, this should motivate them to conserve wildlife even in the face of expanding human and animal populations in delicate ecologies.