research article changing land use patterns and their

Research ArticleChanging Land Use Patterns and Their Impacts on WildUngulates in Kimana Wetland Ecosystem Kenya

Stephen Kitina Nyamasyo1 and Bonface Odiara Kihima2

1Wildlife Management Kenya Wildlife Service Training Institute PO Box 842 Naivasha 20117 Kenya2Hospitality and Tourism Kenyatta University PO Box 16778 Mombasa 80100 Kenya

Correspondence should be addressed to Bonface Odiara Kihima odiarabyahoofr

Received 26 May 2014 Revised 15 October 2014 Accepted 29 October 2014 Published 3 December 2014

Academic Editor Arianna Azzellino

Copyright copy 2014 S Kitina Nyamasyo and B Odiara Kihima This is an open access article distributed under the CreativeCommons Attribution License which permits unrestricted use distribution and reproduction in any medium provided theoriginal work is properly cited

In Kenya wildlife numbers have drastically declined due to land use changes (LUCs) over the past three decades This has affectedwildlife habitats by converting them into farmlands and human settlements This study used remote sensing data from landsatsatellite to analyze the changing land use patterns between 1980 and 2013 and their impacts onwild ungulates in KWEThe objectiveof the study was to map out LUCs determine the possible causes of LUCs and examine the effects of LUCs on wild ungulates Theresults showed a noticeable increase in the size of farmland settlement and other lands and a decline in forestland grasslandwetland and woodland The main possible causes of LUC were found to be agricultural expansions human population dynamicseconomic factors changing land tenure policy politics and sociocultural factors The main effects of LUCs on wild ungulates inKWE include a decline in wild ungulate numbers habitat destruction increased human-wildlife conflicts land degradation anddisplacement of wild ungulates by livestockThe study recommends land use zoning of KWE and establishment of an effective andefficient wildlife benefit-sharing scheme(s)

1 Introduction

Wild ungulates are hoofed wild mammals comprising largegrazers and browsers They account for the vast majority oflarge herbivores currently on earth and are found in nearlyevery biome especially in arid and semiarid grasslands [1]Their abundance and spatial distribution is highly related toavailability of quality forage and water resources [2]

Wild ungulates have high economic values as a sourceof revenue through consumptive and nonconsumptive uti-lization [3 4] Their resources provide environmental goodsand services for the livelihood of the people socioculturalaesthetic and ecological values In Kenya wildlife resourcesaccounted for 70 of the gross tourism earnings 25 of thegross domestic product (GDP) and more than 10 of totalformal sector employment in 2011 [5]

Although Africa has been exceptional in retaining a con-siderable diversity and concentration of its wildlife comparedto America and Australia the populations of many of its

wild ungulate species have declined substantially inside andoutside protected areas over the past three decades [6 7]East Africa was incomparable in sustaining relatively intactwildlife but in the last three decades the wild ungulatepopulation has declined sharply [8]

Kenya is ranked second highest amongAfrican countriesin bird and mammal species richness with an estimate of 394mammals 1100 birds 201 reptiles (100 lizards 100 snakesand 1 crocodile) 100 amphibians and 950 (250 freshwaterand 700 marine) fish species However over the last 30 yearsher wildlife numbers have shrunk by between 35 and 50[9] and by 2006 the number of threatened species in Kenyaincluded 33 species of mammals [10] This decline in wildlifenumbers globally regionally and locally has more beenattributed to land use changes human encroachment intowildlife habitats recurrent droughts poaching and otheranthropogenic activities [11 12]

Land use changes affect key aspects of the earthrsquos func-tioning including a direct impact on global biodiversity [13

Hindawi Publishing CorporationInternational Journal of BiodiversityVolume 2014 Article ID 486727 10 pageshttpdxdoiorg1011552014486727

2 International Journal of Biodiversity

14] In East Africa land use changes have transformed landcover to farmlands livestock grazing lands mining groundshuman settlements and urban centers at the expense ofwildlife habitat [15] These changes are associated withwildlife losses habitat destruction land degradation andblockage of wildlife corridors [15 16]

In Kenya increase in human population is rapidly leadingto encroachment into wildlife habitats [17] leading to thereduction of wildlife space and blockage of wildlife corridorsIf protected areas have no wildlife corridors genetic drift andinbreeding may occur thus leading to population instabilityloss of ecological integrity and possibly local extinction andincrease in human-wildlife conflict [18 19] Such conflictscreate frustration and animosity towards wildlife and mayresult in retaliatory killings [20 21] The threat to wildungulate populations is therefore an eminent one for Kenyaparticularly when one considers the fact that many of herprotected areas are increasingly becoming insularized as aresult of the expanding human dominated areas [21 22]

Despite themassive conservation efforts backed by signif-icant international support changes in land use patterns andassociated impacts of human activities continue to acceleratethe rate of wildlife habitat conversion in KWEThe impacts ofland use changes onwild ungulates in KWEhave not yet beenfully appreciated Information on their manifestation andintensity is inadequate despite its importance in formulatingmitigation measures This has made it difficult to mitigatewetlands wildlife habitats and wild ungulate losses as well astheir conservation in KWE The study sought to determinethe changing land use patterns in KWE from 1980 to 2013and thereafter determine the possible causes of land usechanges and effects of land use on wild ungulate populationThe study hypothesized that changes in land use patternhave led to a significant decline on the population of wildungulates in KWE and that the increase in human populationand associated activities are the possible causes of land usechanges in KWE formulated as follows

(a) HO there has been no significant increase on land usechanges in KWE from 1980 to 2013

(b) HO changes in land use patterns in KWE have nothad significant effects on the wild ungulate popula-tions

(c) HO increase in livestock population and tourismactivities in KWE have not resulted into a noticeabledecline in wild ungulate numbers in KWE

2 Study Area

Kimana wetland ecosystem (KWE) measures 3348Km2 andis located in Kajiado County It lies between longitudes36∘51015840 and 37∘51015840 east and latitudes 1∘01015840 and 3∘01015840 southKWE comprises Kimana division (3588 km2) Entonet divi-sion (13493 km2) Central division (12803 km2) ImbirikaniLocation (3398 km2) and Amboseli National Park (ANP)(392 km2) It is a wildlife rich area consisting of ANP andnine group ranches each with a conservancy or a wildlifesanctuary It borders Chyulu and Tsavo West National Parks

to the east and is a critical wildlife dispersal area for the largerTsavo-Amboseli ecosystem

The area has a number of permanent swamps suchas Amboseli Kimana and NOLTRUSH swamps whichprovide suitable ground for agriculture while the entirerange is suitable for wildlife and pastoralism [23] Domi-nant vegetation includes grasses shrubs and Acacia plantspecies that are adapted to long drought periods The megaungulates found in the ecosystem include African elephant(Loxodonta africana) African buffalo (Syncerus caffer caf-fer) common eland (Tragelaphus oryx) and Maasai giraffe(Giraffa camelopardalis tippelskirchi) The mini ungulatesfound in the area include plains zebra (Equus burchelli)common duiker (Sylvicapra grimmia) impala (Aepycerosmelampus) Thomsonrsquos gazelle (Gazella thomsonii) Grantrsquosgazelle (Gazella granti) common waterbuck (Kobus ellip-siprymnus) and Kirkrsquos dik-dik (Madoqua kirkii) The big catsinclude lion (Panthera leo) spotted hyena (Crocuta crocuta)leopard (Panthera pardus) and cheetah (Acinonyx jubatus)The ecosystem also supports a large population of smallmammals reptiles and amphibians The wetland has a richbirdlife with over 400 species recorded of which 40 are birdsof prey [23] It has globally threatened bird species such asLesser kestrel restricted-range birds found only in a small areasuch as the Taveta golden weaver bird species that live onlyin a particular vegetation type such as the grosbeak weaverand regionally threatened bird species such as martial eagles

KWE is primarily habited by Maasai who are semino-madic pastoralists with land being communally ownedTheirlifestyle has however undergone changes due to ongoingland adjudication and subdivision of group ranches leading toprivate land tenure systemThis has increased the rate of landsales and created openings to emigrations especially in therelatively high agricultural potential areas such as KimanaEntonet and Kuku divisions Emigrating communities arefarmers from other parts of the country

3 Methodology

Primary data collected included TM Landsat satellite(acquired from pathrow 168062 and 167062) images of1980 1990 2000 2010 and 2013 Current land use typesnotable land use changes over time land tenure system andsocioeconomic data from 200 households and 20 institutionswere collected using semistructured questionnaires andinterview schedules respectively

To arrive at the sample the population was divided intoclusters including Amboseli Olchoro Imbirikani KimanaInkariak Ronkena Kuku Loolopon and Entonet after whichindividuals were randomly selected depending on the size ofthe area and human population density as follows Kimanaand Kuku division 30 households each Olchoro LooloponEntonet and Inkariak Ronkena 25 households each andAmboseli and Imbirikani 20 households each Interviewswere conducted randomly using semistructured questionsOnly an adult family head (man or woman) was interviewedper household but he or she was free to seek help from anyother family member in case of difficulties in responding to

International Journal of Biodiversity 3

questions For institutions only the longest serving officerwho was perceived to be more knowledgeable on the studyarea was interviewed and heshe was free to seek help fromany other officer in case of difficulties

Secondary data collected included KWE topographicalmap land use types causes of land use changes and its effectson wild ungulates human population census figures wildungulate population census data and livestock populationtrend Land use change data was gathered through acqui-sition interpretation and analyses of satellite images usingARCGIS and EDRAS software Human livestock and wildungulates numbers were obtained from Loitokitok districtdevelopment plan KNBS ILRI DRSRS and KWS

Direct observation was also employed on identificationof wild ungulate species current land use types land degra-dation and wetland encroachment This encompassed a linetransect walk running diagonally from Kimana sublocationto Isenet through Kimana community wildlife sanctuarycovering a distance of 60 km It was carried out with thehelp of community game scouts patrolling at the edges of thewildlife corridors where irrigated farms are concentrated Allthe wild ungulates species found at a distance of 1 Kmon bothsides of the line transect walk were identified and individualspecies counted This took place during the month of April(onset of the long rains) 2013 and lasted three days Duringthe month of April wild ungulates are mostly concentrated inswampy places because of availability of water and pasturePhotographs were employed to capture data using a digitalcamera It helped in the classification of land uses in the areasas an evidence of actual practices taking place

To enhance the change detection field visits were con-ducted to make ground truthing This involved selecting ofsites at random from the latest land use base map and visitingof each site to check and verify whether the land use type onthe actual ground was what was shown on the map Five setsof images representing a temporal period of 33 years from1980 to 2013 were acquired

TheTMLandsat satellite image of 1980 was georeferencedagainst a topographic map at a scale of 150000 usinga number of ground control points which were obtainedafter visiting the study area The projection was UniversalTransverse Mercator (UTM) with Arc 1960 as datum Imageto image registration was then performed by georeferencingsatellite images of 1990 2000 2010 and 2013 from TM Land-sat against the georeferenced TM Landsat satellite images of1980 This ensured that the pixel grids of 1990 2000 2010and 2013 images conform to georeferenced image of 1980 toenable pixel-by-pixel comparison of the images The imageswere then analyzed to determine various land use typesChanges in land use classes were mapped and quantified andaccuracy assessment was done for all

4 Data Analysis

The satellite images were analyzed and interpreted using GISArc Map and ERDAS Images software Data on socioeco-nomic wild ungulate and livestock statistics was analyzedusing SPSS software version 17 andMicrosoft excel Land use

changes data was analyzed using descriptive statistics to showthe variation from 1980 to 2013 This involved calculation ofsize of areas under each land use for the years 1980 19902000 2010 and 2013 and their sizes computedThis capabilityis provided in area command under the database querymodule in ArcMapThe areas were then entered inMicrosoftexcel for the computation of land use changes over time Todetermine the land use change for a particular land use thearea of land for year 1980 was subtracted from the area of landfor the year 2013 Pearson correlation coefficient statisticsanalysis was performed to determine association betweenchanges in different land uses in Kimana wetland ecosystemFrequencies of interviewed household heads and institutionsgiving a particular response were summarizedThe equalitiesof frequencies were tested using chi-square goodness of fit Toestablish possible causes of land use changes the frequenciesof the responses of those interviewed were summarized anda chi-square cross tabulations analysis was performed todetermine the relationship with specific attributes Statisticaltests were considered significant if 119875 values were equal toor less than 005 and insignificant if 119875 values were greaterthan 005 [24] For chi-square cross tabulations if119875 value wasequal to or less than 005 then a response was dependent onan attribute and independent of the attribute if 119875 value wasgreater than 005

5 Findings

51 Major Land Use Changes in Kimana Wetland Ecosystem(KWE) This research produced seven important land usetypes including farmcropland settlement and urban areasforestland grassland woodland wetlandswamps and otherlands (bare and rocky grounds built areas quarries roadsdried up wetlandswamps and abandoned human settle-ment) (Figure 1) In 1980 Kimana wetland ecosystem had atotal area of 334866 km2 (Table 1) out of which farmcrop-land had occupied 6997 km2 settlement and urban areas208 km2 forestland 6227 km2 grassland 255571 km2woodland 5893 km2 wetlandswamps 49266 km2 andother lands 1084 km2 By 2013 farmcropland occupied anarea of 43817 km2 settlement and urban areas 1626 km2other lands 30347 km2 forestland 3265 km2 grassland234913 km2 wetland 15305 km2 and woodland 5578 km2(Figure 2)

A significant positive correlation occurred between crop-land and settlement areas (119903 = 0989 DF = 2 119875 = 0001)and farmcropland and other lands (119903 = 0952 DF = 2 119875 =0013) On the other hand a significant negative correlationoccurred between cropland and forestland (119903 = minus0989 DF =2 119875 = 0001) cropland and grassland (119903 = minus0989 DF = 2119875 = 0001) cropland and wetland (119903 = minus0997 DF = 2119875 = 0004) and cropland and woodland (119903 = minus0922 DF =2 119875 = 0026) Another negative correlation also occurredbetween settlement and forestland (119903 = minus0986 DF = 2119875 = 0002) settlement and grassland (119903 = minus0980 DF = 2119875 = 0004) and other lands andwetland (119903 = minus0985 DF = 2119875 = 0015) (Table 2)


Table 1 The area (km2) and the percent land cover for land use changes in KWE

Land usetypes

1980(km2)

RLC()

1990(km2)

RLC()

2000(km2)

RLC()

2010(km2)

RLC()

2013(km2)

RLC()

increase(+) or

decrease (minus)Settlement 208 000 416 012 778 023 1510 045 1626 049 68157Woodland 5893 176 5893 176 5654 169 5624 168 5578 167 minus535Cropland 6997 209 17606 526 26363 787 42123 1258 43817 1309 52621Forestland 6227 186 576 172 4512 135 3453 103 3265 098 minus4756Other lands 10804 323 20261 605 2463 736 27178 812 30347 906 18089Wetland 49266 1471 36892 1102 26272 785 19763 590 15305 457 minus6893Grassland 255571 7632 248037 7407 246656 7366 235195 7024 2349132 7015 minus808Total 334866 10000 334865 10000 334865 10000 334846 10000 334851 10000

Amboseli

Olorika

Kuku

Imbirikani

Kimana

Loolopon

Inkariak Rongena

OlchoroEntonet

CroplandForestlandGrassland

Other landsSettlementsWetlandsWoodlands

Land use 1980

Admin boundaryLand use 1980

0 20 40 80

(km)

N

Coordinate system arc 1960 UTM zone 37SProjection transverse mercatorDatum arc 1960False easting 5000000000False northing 100000000000Central meridian 390000Scale factor 09996Latitude of origin 00000Units meter

Figure 1 Land use map of 1980 for Kimana wetland ecosystemSource author 2013

Farmcropland settlement and urban areas and otherlands in KWE are becoming important with time as theyreplace the areas previously occupied by wetlandswampsgrassland (wildlife dispersal corridors and breeding areas)forestland and woodlands It was also established from thestudy that none of the areas that had initially been wetlandsand then converted to cropland ever reverted to wetlandField observations revealed that wetlands play a key role inirrigated farming whereby all the water used for irrigation ispumped directly from the wetlands

52 Causes of Land Use Change in KimanaWetland Ecosystem(KWE) The main causes of land use change in KWE asindicated by the respondents include agricultural expansions

Amboseli

Olorika

Kuku

Imbirikani

Kimana

Loolopon

Inkariak Rongena

OlchoroEntonet

Land use 2013




0 20 40 80

(km)


N

Figure 2 Land use map of 2013 for KWE Source author 2013

(315) human population dynamics (255) economicfactors (high financial gains from cultivation compared topastoralism and wildlife-based tourism) (13) changingland tenure policy (105) politics (105) drought andsociocultural factors such as poverty level of educationurbanization and culture change (Table 3) Only opinionsfrom those respondents who had lived in the area for morethan five years were considered Such was dependent on thecurrent land uses and length of stay (1205942 = 4764 DF = 28119875 = 0012 1205942 = 4652 DF = 28 119875 = 0024) but independenton age level of education land tenure system and sources oflivelihood (Table 5) Farmers and business people attributedthe causes of land use change in the area to agriculturalexpansions and economic gains while pastoralist attributedit to drought and sociocultural factors


Table 2 Correlations between land use changes in KWE

Land use type Correlation Cropland Wetland Settlement Other lands Forestland Grassland Woodland

Crop Pearson correlation 1 minus0979lowastlowast 0989lowastlowast 0952lowast minus0989lowastlowast minus0989lowastlowast minus0922lowast

sig (2-tailed) 0004 0001 0013 0001 0001 0026

Wetland Pearson correlation minus0979lowastlowast 1 minus0946lowast minus0990lowastlowast 0974lowastlowast 0951lowast 0939lowast

sig (2-tailed) 0004 0015 0001 0005 0013 0018

Settlement Pearson correlation 0989lowastlowast minus0946lowast 1 0903lowast minus0986lowastlowast minus0980lowastlowast minus0918lowast

sig (2-tailed) 0001 0015 0036 0002 0004 0028

Other lands Pearson correlation 0952lowast minus0990lowastlowast 0903lowast 1 minus0934lowast minus0931lowast minus0890lowast

sig (2-tailed) 0013 0001 0036 0020 0022 0043

Forestland Pearson correlation minus0989lowastlowast 0974lowastlowast minus0986lowastlowast minus0934lowast 1 0959lowastlowast 0967lowastlowast

sig (2-tailed) 0001 0005 0002 0020 0010 0007

Grassland Pearson correlation minus0989lowastlowast 0951lowast minus0980lowastlowast minus0931lowast 0959lowastlowast 1 0856sig (2-tailed) 0001 0013 0004 0022 0010 0064

Woodland Pearson correlation minus0922lowast 0939lowast minus0918lowast minus0890lowast 0967lowastlowast 0856 1sig (2-tailed) 0026 0018 0028 0043 0007 0064

lowastlowastCorrelation is significant at the 001 level (2-tailed)lowastCorrelation is significant at the 005 level (2-tailed)

Table 3 Respondentrsquos opinions on the causes for land use change inKWE

Possible causes of land use change Number ofrespondents Percentage

Agricultural expansion 63 315Human population increase 51 255Economic forces 26 130Politics 21 105Changing land tenure policy 21 105Drought 8 40Poverty 6 30Education level 4 20Total 200 1000Source author 2013

53 Agricultural Expansion Irrigated agricultural field inKWE increased from 6997 km2 in 1980 to over 43817 km2 in2013 (Table 1) suggesting that irrigated agricultural expansionis one of the major causes of land use change (Table 3) Thisexpansion has seen irrigated agriculture become the mainsource of livelihood for the households in KWE as indicatedby 39of the respondentsThemain crops grown currently inthe area are horticultural crops such as tomatoes chilli onionand kales among others while maize and beans were themainfood crops

54 Demographic Dynamics Over the past three decadesKWE has experienced an estimated annual human pop-ulation growth rate of 467 as per the 2009 census Thistranslated into 84297 persons and a population density of252 personskm2 in 2009 with Kimana division having thehighest at 57 personskm2 It is projected that by the year2030 the total human population in the area will be 210789

0

50000

100000

150000

200000

250000

1999 2009 2012 2020 2030

Num

ber o

f peo

ple

Year

Number

Figure 3 Projected human population for KWEup to the year 2030Source GoK [25]

(Figure 3) The rapid population growth in the area hasresulted in increased demand for more land for farmingsettlement and infrastructure development which has led toclearing of large areas of the wetland forestland woodlandand grassland that serves as habitats for wild ungulates

55 Economic Factors Of the total number of respondentsinterviewed 82 pointed out that they receive virtually nodirect cash benefits from the wildlife-based tourism industryThey only receive indirect benefits in the form of schoolbursaries piped water construction of classrooms and dis-pensaries livestock sales yards and other related communitygoods and services which often fail to benefit those in mostneed In contrast they indicated that they receive directbenefits from irrigated farming and selling or renting outtheir land

Increased demand for agricultural produce to satisfy thelocal markets and border towns such as Moshi Arusha andTarakea in Tanzania has accelerated the rate of irrigated


Table 4 Size of land owned by individuals in KWE

Size of land Frequency Percentlt25 Acres 80 4025ndash5Acres 73 36551ndash75 Acres 20 1076ndash10Acres 16 8Above 101 Acres 11 55Total 200 100Source author 2013

farming in the area as denoted by 86 of the respondentsThe study found that approximately 20000 tons of farmproduce is harvested and transported to Tanzania Nairobiand Mombasa markets weekly

Moreover the introduction of irrigated small-scale farm-ing in the area in the early 1970s after the collapse of groupranch model gave rise to increased demand for more landfor irrigated farming as revealed by 82 of the respondentsThe government welcomed subdivision and in 1981 enacted apolicy to promote it This increased demand for more landfor irrigated farming resulted in further land subdivisionThe study found that 65 of those interviewed and ownedland had subdivided their land and sold or leased it outMoreover 765 of those interviewed own less than 5 acresand only 55 own more than 101 acres (Table 4) Thestudy established that when Kimana group ranch memberssubdivided their land after the failure of the group ranchmodel themean size of the individual plots was 12 km2 (06ndash46 km2)

The selling of land is verifiable by the fact that 80of those interviewed were non-Maasai who had emigratedfrom other areas Moreover 69 of the total respondentswho owned land in the areas indicated that they acquired itthrough buying and 245 through inheritance and 65 (allmigrants) had rented the land they were using

56 Effects of Land Use Change on Wild Ungulates in KimanaWetland Ecosystem In the views of 89of those interviewedchanges in land use patterns in the study had significanteffects on the wild ungulate populations (1205942 = 12168 DF =1 119875 = 0000) The main effects include noticeable declinein wild ungulate numbers encroachment into wetlands andother wildlife habitats significant increase in incidences ofhuman-wildlife conflicts (1205942 = 16200 DF = 1 119875 =0000) habitat destruction displacement of wild ungulates bylivestock land degradation and emergence of invasive plantspecies

57 Trends in Abundance of Wild Ungulates in KimanaWetland Ecosystem Wild animal counts conducted in thestudy area from 1979 to 2010 by various institutions on fourspecies of wild ungulates have showed a decline in theirnumbers As per the 1979 animal count wildebeest which hada population estimate of approximately 6700 individuals wasthemost numerouswild ungulate followed by common zebra(5600) Grantrsquos gazelle (5495) and impala (4820) In 2010

0

2000

4000

6000

8000

10000

12000

1979

1981

1982

1983

1985

1987

1989

1991

1993

1995

1997

1999

2000

2002

2003

2005

2007

2010

Num

ber o

f ind

ivid

ual s

peci

es

Year

Common zebraImpala Wildebeest

Grantrsquos gazelle

Figure 4 Population estimates for zebra impala G gazelle andwildebeest in KWE (1979 to 2010) Source DRSRS and KWS

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

2007 2010

Num

ber o

f ind

ivid

ual s

peci

es

Species

Common zebraWildebeest

Maasai giraffeCape elandImpala

Hartebeest

African buffaloGerenukHippopotamus

Thomsonrsquos gazelleGrantrsquos gazelle

Figure 5 Wild ungulate population estimates for 2007 and 2010 inKWE Source DRSRS and KWS

common zebra was the most numerous with a populationestimate of 4232 followed by wildebeest (3990) Grantrsquosgazelle (769) and impala (607) (Figure 4)

A comparison of 2007 and 2010 count for the same speciesin the same area indicated a decline in numbers for commonzebra Grantrsquos gazelle and wildebeest species and an increasefor impala For example the population of common zebrafrom2007 to 2010 dropped by 548Grantrsquos gazelle by 558and wildebeest by 486 while impala increased by 42(Figure 5)

The results from aerial counts carried out in Kimanawetland ecosystem from 2000 to 2010 indicated a decline inthe number of African buffaloes from 367 in 2000 to 200 in


Table 5 Characteristics of interviewee opinions in regard to land use changes and their effects on wild ungulates

Information sought Responses from respondents Number of respondents(119899 = 200)

Chi square goodness of fitdf 119875 value

Gender Male 147119903 = 44180 df = 1 119875 = 0000

Female 53

Age18ndash35 77

119903 = 7090 df = 2 119875 = 002936ndash50 74Above 51 49

Level of education

None 45

119903 = 26520 df = 3 119875 = 0000Primary 74Secondary 57Tertiary 24

Source of livelihood

Crop farming 78

119903 = 123400 df = 6 119875 = 0000

Livestock 35Rural self-employed 15Urban self-employed 15Wage employment 6

Crop farming and livestock 34Livestock and wildlife 17

Length of stay

lt5 yrs 33

119903 = 60700 df = 4 119875 = 000051ndash10 yrs 36101ndash15 yrs 83151ndash20 yrs 23

Above 201 yrs 25

Individual size of land

lt25 Acres 80

119903 = 112650 df = 4 119875 = 000025ndash5Acres 7351ndash75 Acres 2076ndash10Acres 16

Above 101 Acres 11

Current land use

Crop farming 88

119903 = 76450 df = 4 119875 = 0000Livestock 35Wildlife 18

Commercial 33Crop farming and livestock 26

Has the land use been changing overthe years

Yes 180119903 = 128000 df = 1 119875 = 0000

No 20

Has LUC affected wild ungulates Yes 178119903 = 115520 df = 1 119875 = 0000

No 22

Distance of the individual homesteadfrom protected areas

0-5 km 89

119903 = 85050 df = 4 119875 = 000051ndash10 km 19101ndash15 km 20151ndash20 km 44Over 201 28

Causes of land use change

Agricultural expansion 63

119903 = 129760 df = 7 119875 = 0000

Economic forces 26Politics 21

Changing land tenure policy 21Human population increase 51

Drought 8Poverty 6

Education level 4


Table 5 Continued



Is there HWC Yes 190119903 = 162000 df = 1 119875 = 0000

No 10

Forms of HWC

Crop destruction 88

119903 = 141820 df = 5 119875 = 0000

Livestock predation 50Human injuries 17

Death of human being 26Road kills 11

Competition for food 8

Period of the year when the conflictsare severe

JanuaryndashMarch 41

119903 = 59320 df = 3 119875 = 0000AprilndashJune 25JulyndashSeptember 96

OctoberndashDecember 38

0

200

400

600

800

1000

1200

2000 2002 2007 2010

Num

ber o

f ind

ivid

ual s

peci

es

YearMaasai giraffeCape elandAfrican buffalo

Figure 6 Population estimates for giraffe eland and buffalo inKWE (2000 to 2010) Source DRSRS and KWS

2010 (468decrease) and that of theMaasai giraffe remainedrelatively stable with a slight increase while that of the Capeeland rose from less than 200 individuals in 2007 to morethan 1000 individuals in 2010 (Figure 6) In 2000 most ofthe buffaloes were in ANP (809) and Kimana Sanctuary(109) and the rest (82) were in Kuku group ranch Allthe buffaloes counted in 2007 were within the protected area

58 Livestock Population Trend inKimanaWetland EcosystemIndigenous cattle (Zebu) goats sheep and donkeys are themain livestock types kept in the study area In the recent pastfarmers have started keeping dairy cattle (mainly Friesian)under intensive zero grazing system Livestock populationfigures over the past three decades show that the numbersof cattle goats and sheep are on the increase in KWE [25]Aerial counts for livestock inKWE from2000 to 2010 indicatean upward trend Goats and sheep appeared to have increasedin the last ten years with a total population of 104520 in 2010compared to 49230 in 2000

59 Tourism Activities KWE is among the most populartourist destinations with Amboseli National Park being oneof the premium parks in Kenya Development of tourismfacilities within KWE has been rapid in response to theincreasing number of tourists In 1980 for example the studyfound that there were only five hotels and lodges but atpresent there are over 25 hotels lodges and campsites with acapacity of over 1700 beds In an attempt to have tourists viewwildlife at a close range roads and nature trails have beenconstructed in ecologically fragile areas that serve as breedingand calving grounds for most wild ungulate species Theseinfrastructures have also disrupted wild ungulate migrationpatterns and increased habitat fragmentation

510 Human-Wildlife Conflict (HWC) HWC incidenceswhich were rare in the 1980s have been frequent as testifiedby 95 of the respondents Of these 885 have been victimsof the conflicts The most severe forms of HWC in theirviews were crop and property damage by wild ungulates(44) livestock predation by carnivores (25) death andinjury of human being (13 and 8 resp) road kills (6)and competition for resources between human livestock andwild ungulates (Figure 7)

6 Discussion and Conclusions

KWE is critical wildlife dispersal breeding and dry seasongrazing area for Amboseli and Tsavo ecosystems [17] Poorland use zoning and land use controls have led to emergenceof uncoordinated and conflicting land use types in KWE[26]Thedecline in land underwetland forestland grasslandand woodland that are wildlife habitats signifies that wildlifeconservation is not an important source of livelihood forthe locals of KWE It is therefore not possible to convincethe local people to conserve the wildlife habitat because ofits advantage in the long-term Of notable concern is lackof direct benefits from the wildlife based-tourism industryIn contrast the local people do receive direct benefits fromhorticultural farming and selling or renting out their land


05

101520253035404550

Crop

destr

uctio

n

Live

stock

pred

atio

n

Hum

anin

jurie

s

Dea

th o

fhu

man

bei

ng

Road

kill

s

Com

petit

ion

for

reso

urce

s

()

Problem caused by wild animals

Figure 7 Problems caused by wild animals in KWE Source author2013

Therefore it is no surprise that they have increasingly clearedtheir land for irrigated farming human settlement andinfrastructure development in recent past at the expense ofwildlife conservation

Traditionally land in KWE was communally ownedThis system made it possible to practice nomadic pastoral-ism which ensured sustainable livestock production andconservation of wildlife However because of changes ingovernment policy such as repealing of the ldquoLand GroupRepresentatives andLandAdjudicationActrdquo of 1968 commu-nally owned land has been subdivided into individual plotsThis has resulted into expansion of farmland by 52621settlement and urban areas by 68157 and other landsby 19543 between 1980 and 2013 In addition the areaunderwetland has decreased by 6893 forestland by 4756grassland by 81 and woodland by 535 during the sameperiod

The shift from wildlife conservation and pastoralism toirrigated agriculture is not entirely new among pastoral com-munity However in KWE it has taken a more commercialnature rather than subsistence farming leading to expansionof farmlands through clearing of wetlands forests grasslandand woodland This has led to loss of key wetlands and otherwildlife habitats which used to serve as grazing area forlivestock and wild ungulates Land that is adjacent to wetlandthat used to serve as a wildlife dispersal area has now becomeindividual property Wild ungulates are no longer able toaccess the areas that have been converted into farmland andhuman settlements Hence large number of wild ungulatesconcentrate in areas without (or with little) farms and humansettlement Wild ungulates have completely avoided urbanareas and build areas such as schools dispensaries andmarket places

The subdivision of group ranches into individual plots hasalso resulted in establishment of community sanctuariesThecommunity sanctuaries are usually too small in area to beviable conservation units for they are not linked to each otherThey are fragmented wildlife habitats A preposition is alsosupported by Young and McClanahan [27] who argued thatcommunity wildlife sanctuaries represent a fragment of theentire ecosystem and violate good design approaches based

on principles of island biogeographyWhen these communitywildlife sanctuaries are established in isolation from otherprotected areas without linking them by wildlife corridorsthey result in isolation of protected areas The isolationof protected areas is likely to result in inbreeding of wildanimals 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 The impacts of overgrazing as observed duringthe field visit include land degradation soil erosion andemergence of invasive plant species such as Lantana camaraand Solanum incanum The latter is a drought resistantnonpalatable herbaceous plant that does not have any naturalenemy and thrives well in disturbed ecosystem The studyfound that it was thriving well in areas that had beenovergrazed on edges of human settlement and along builtareas It dominates the herbaceous layer thus hinderingthe growth of grass species and hinders free movement oflivestock and wild ungulates

The fate of Kenyarsquos wildlife therefore depends on thefuture of the protected areas and the surroundingwildlife dis-persal areas Changing land use patterns in KWE have led toloss of key dry season grazing ground for wild ungulates anddecline in wild ungulate numbers in the last three decadesTo alleviate the changing land use patterns and countertheir impacts on wild ungulates the study recommends thefollowing zoning of KWE into three categories (wildlifeareas settlement area and farming area) establishment ofan effective transparent fair and equitable wildlife benefit-sharing scheme establishment of an effective and transparentmanagement system for managing community conservan-cies rehabilitation of Kimana community wildlife sanctuaryand farmers to embrace technology in their irrigated farmingThe future of wild ungulates in KWE is doomed unlessappropriate measures are put in place to reverse the observeddetrimental trends It is extremely urgent therefore thatall the proposed measures by this study be taken intoconsideration in order to strike a balance between wildlifeconservation and economic development if the observeddetrimental trends are to be reversed

Areas for Further Research (i) Kimana Community WildlifeSanctuary was the first community sanctuary to be estab-lished inKenya in 1996 but due to poormanagement systemsit has collapsed Therefore more studies need to be carriedout to determine the effectiveness of community involvementin the management of wildlife (ii) There have been manywildlife conservation and management policies which havebeen put in place to conserve and manage wildlife It istherefore necessary to analyze wildlife conservation andmanagement policies in Kenya with a view of determiningtheir effectiveness

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper


Acknowledgments

The authors acknowledge the Kenya Wildlife Service whichmade it possible for this study to be carried out and also MrDennis Macharia from RCMRD for availing the ARCGISand EDRAS software used to perform image and dataanalysis

References

[1] T FlanneryThe Eternal Frontier An Ecological History of NorthAmerica and Its Peoples Atlantic Monthly Press New York NYUSA 2001

[2] H H T Prins ldquoThe pastoral road to extinction competitionbetween wildlife and traditional pastoralism in East AfricardquoEnvironmental Conservation vol 19 no 2 pp 117ndash123 1992

[3] C Van Der Waal and B Dekker ldquoGame ranching in theNorthern Province of South Africardquo South African Journal ofWildlife Research vol 30 no 4 pp 151ndash156 2000

[4] Z A Ogutu ldquoThe impact of ecotourism on livelihood and nat-ural resource management in Eselenkei Amboseli EcosystemKenyardquo Land Degradation and Development vol 13 no 3 pp251ndash256 2002

[5] Government of KenyaThe National Wildlife Conservation andManagement Policy 2012 Ministry of Forestry and WildlifeGovernment Printer 2011

[6] D T Bolger W D Newmark T A Morrison and D FDoak ldquoThe need for integrative approaches to understand andconserve migratory ungulatesrdquo Ecology Letters vol 11 no 1 pp63ndash77 2008

[7] D Western S Russell and I Cuthil ldquoThe status of wildlifein protected areas compared to non-protected areas of KenyardquoPLoS ONE vol 4 no 7 Article ID e6140 2009

[8] D Western and D L Manzolillo-Nightingale ldquoEnvironmentalchange and the vulnerability of pastoralists to drought a casestudy of theMaasai in Amboseli Kenyardquo inAfrica EnvironmentOutlook Case Studies Human Vulnerability to EnvironmentalChange pp 35ndash50 UNEP Nairobi Kenya 2004

[9] J M Grunblatt M Said and P Wargute National RangelandsReport Summary of Population Estimates of Wildlife and Live-stock (1977ndash1994) Department of Resource Surveys andRemoteSensing Ministry of Planning and National DevelopmentNairobi Kenya 1996

[10] IUCN IUCN Red List of Threatened Species IUCN GlandSwitzerland 2008 httpwwwiucnredlistorg

[11] M Loibooki H Hofer K L I Campbell and M L EastldquoBushmeat hunting by communities adjacent to the SerengetiNational Park Tanzania the importance of livestock ownershipand alternative sources of protein and incomerdquo EnvironmentalConservation vol 29 no 3 pp 391ndash398 2002

[12] M Norton-Griffiths M Y Said S Serneels et al ldquoLand useeconomics in the mara area of the serengeti ecosystemrdquo inSerengeti III Human Wildlife Interactions C Packer and AR E Sinclair Eds pp 379ndash416 University of Chicago PressChicago Ill USA 2009

[13] O E Sala F S Chapin J J Armesto et al ldquoGlobal biodiversityscenarios for the year 2100rdquo Science vol 287 no 5459 pp 1770ndash1774 2000

[14] E F Lambin B L Turner H J Geist et al ldquoThe causes of land-use and land-cover change moving beyond the mythsrdquo GlobalEnvironmental Change vol 11 no 4 pp 261ndash269 2001

[15] J M Maitima M M Simon R S Robin et al ldquoThe linkagesbetween land use change land degradation and biodiversityacross East Africardquo African Journal of Environmental Scienceand Technology vol 3 no 10 pp 310ndash325 2009

[16] I J Gordon ldquoWhat is the future for wild large herbivores inhuman-modified agricultural landscapesrdquoWildlife Biology vol15 no 1 pp 1ndash9 2009

[17] M M Okello E Buthmann B Mapinu and H C KahildquoCommunity opinions on wildlife resource use and livelihoodcompetition in Kimana group ranch near Amboseli KenyardquoOpen Conservation Biology Journal vol 5 no 1 pp 1ndash12 2011

[18] W D Newmark ldquoThe role and design of wildlife corridors withexamples from Tanzaniardquo Ambio vol 22 no 8 pp 500ndash5041993

[19] D J Campbell H Gichohi AMwangi and L Chege ldquoLand useconflict in Kajiado District Kenyardquo Land Use Policy vol 17 no4 pp 337ndash348 2000

[20] I Sindiga ldquoWildlife based tourism in Kenya land use conflictsand government compensation polices over protected areasrdquoJournal Tourism Study vol 6 no 2 pp 45ndash55 1995

[21] M M Okello ldquoLand use changes and human-wildlife conflictin the Amboseli Areardquo Human Dimension Wildlife vol 10 no1 pp 19ndash28 2005

[22] M M Okello and J W Kiringe ldquoThreats to biodiversity andtheir implications in protected and adjacent dispersal areas ofKenyardquo Journal of Sustainable Tourism vol 12 no 1 pp 55ndash692004

[23] M M Okello ldquoContraction of wildlife dispersal area anddisplacement by human activities in Kimana Group Ranch nearAmboseli National Park Kenyardquo Conservation Biology Journalvol 3 pp 49ndash56 2009

[24] J H Zar Biostatistical Analysis Prentice-Hall Upper SaddleRiver NJ USA 4th edition 1999

[25] Government of Kenya Loitokitok District Development Plan2008ndash2012 The Government Printer Nairobi Kenya 2008

[26] KWS Amboseli Ecosystem Management Plan 2008ndash2018 KWSNairobi Kenya 2009

[27] T P Young and T R McClanahan ldquoIsland biogeographyand species extinctionrdquo in East African Ecosystems and TheirConservation T R McClanahan and T P Young Eds pp 292ndash293 Oxford University Press New York NY USA 1996

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Anatomy Research International

PeptidesInternational Journal of


Hindawi Publishing Corporation httpwwwhindawicom

International Journal of

Volume 2014

Zoology


Molecular Biology International

GenomicsInternational Journal of


The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014


BioinformaticsAdvances in

Marine BiologyJournal of



Signal TransductionJournal of


BioMed Research International

Evolutionary BiologyInternational Journal of



Biochemistry Research International

ArchaeaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Genetics Research International


Advances in

Virolog y

Hindawi Publishing Corporationhttpwwwhindawicom

Nucleic AcidsJournal of

Volume 2014

Stem CellsInternational



Enzyme Research



Microbiology


14] In East Africa land use changes have transformed landcover to farmlands livestock grazing lands mining groundshuman settlements and urban centers at the expense ofwildlife habitat [15] These changes are associated withwildlife losses habitat destruction land degradation andblockage of wildlife corridors [15 16]

In Kenya increase in human population is rapidly leadingto encroachment into wildlife habitats [17] leading to thereduction of wildlife space and blockage of wildlife corridorsIf protected areas have no wildlife corridors genetic drift andinbreeding may occur thus leading to population instabilityloss of ecological integrity and possibly local extinction andincrease in human-wildlife conflict [18 19] Such conflictscreate frustration and animosity towards wildlife and mayresult in retaliatory killings [20 21] The threat to wildungulate populations is therefore an eminent one for Kenyaparticularly when one considers the fact that many of herprotected areas are increasingly becoming insularized as aresult of the expanding human dominated areas [21 22]

Despite themassive conservation efforts backed by signif-icant international support changes in land use patterns andassociated impacts of human activities continue to acceleratethe rate of wildlife habitat conversion in KWEThe impacts ofland use changes onwild ungulates in KWEhave not yet beenfully appreciated Information on their manifestation andintensity is inadequate despite its importance in formulatingmitigation measures This has made it difficult to mitigatewetlands wildlife habitats and wild ungulate losses as well astheir conservation in KWE The study sought to determinethe changing land use patterns in KWE from 1980 to 2013and thereafter determine the possible causes of land usechanges and effects of land use on wild ungulate populationThe study hypothesized that changes in land use patternhave led to a significant decline on the population of wildungulates in KWE and that the increase in human populationand associated activities are the possible causes of land usechanges in KWE formulated as follows

(a) HO there has been no significant increase on land usechanges in KWE from 1980 to 2013

(b) HO changes in land use patterns in KWE have nothad significant effects on the wild ungulate popula-tions

(c) HO increase in livestock population and tourismactivities in KWE have not resulted into a noticeabledecline in wild ungulate numbers in KWE

2 Study Area

Kimana wetland ecosystem (KWE) measures 3348Km2 andis located in Kajiado County It lies between longitudes36∘51015840 and 37∘51015840 east and latitudes 1∘01015840 and 3∘01015840 southKWE comprises Kimana division (3588 km2) Entonet divi-sion (13493 km2) Central division (12803 km2) ImbirikaniLocation (3398 km2) and Amboseli National Park (ANP)(392 km2) It is a wildlife rich area consisting of ANP andnine group ranches each with a conservancy or a wildlifesanctuary It borders Chyulu and Tsavo West National Parks

to the east and is a critical wildlife dispersal area for the largerTsavo-Amboseli ecosystem

The area has a number of permanent swamps suchas Amboseli Kimana and NOLTRUSH swamps whichprovide suitable ground for agriculture while the entirerange is suitable for wildlife and pastoralism [23] Domi-nant vegetation includes grasses shrubs and Acacia plantspecies that are adapted to long drought periods The megaungulates found in the ecosystem include African elephant(Loxodonta africana) African buffalo (Syncerus caffer caf-fer) common eland (Tragelaphus oryx) and Maasai giraffe(Giraffa camelopardalis tippelskirchi) The mini ungulatesfound in the area include plains zebra (Equus burchelli)common duiker (Sylvicapra grimmia) impala (Aepycerosmelampus) Thomsonrsquos gazelle (Gazella thomsonii) Grantrsquosgazelle (Gazella granti) common waterbuck (Kobus ellip-siprymnus) and Kirkrsquos dik-dik (Madoqua kirkii) The big catsinclude lion (Panthera leo) spotted hyena (Crocuta crocuta)leopard (Panthera pardus) and cheetah (Acinonyx jubatus)The ecosystem also supports a large population of smallmammals reptiles and amphibians The wetland has a richbirdlife with over 400 species recorded of which 40 are birdsof prey [23] It has globally threatened bird species such asLesser kestrel restricted-range birds found only in a small areasuch as the Taveta golden weaver bird species that live onlyin a particular vegetation type such as the grosbeak weaverand regionally threatened bird species such as martial eagles

KWE is primarily habited by Maasai who are semino-madic pastoralists with land being communally ownedTheirlifestyle has however undergone changes due to ongoingland adjudication and subdivision of group ranches leading toprivate land tenure systemThis has increased the rate of landsales and created openings to emigrations especially in therelatively high agricultural potential areas such as KimanaEntonet and Kuku divisions Emigrating communities arefarmers from other parts of the country

3 Methodology

Primary data collected included TM Landsat satellite(acquired from pathrow 168062 and 167062) images of1980 1990 2000 2010 and 2013 Current land use typesnotable land use changes over time land tenure system andsocioeconomic data from 200 households and 20 institutionswere collected using semistructured questionnaires andinterview schedules respectively

To arrive at the sample the population was divided intoclusters including Amboseli Olchoro Imbirikani KimanaInkariak Ronkena Kuku Loolopon and Entonet after whichindividuals were randomly selected depending on the size ofthe area and human population density as follows Kimanaand Kuku division 30 households each Olchoro LooloponEntonet and Inkariak Ronkena 25 households each andAmboseli and Imbirikani 20 households each Interviewswere conducted randomly using semistructured questionsOnly an adult family head (man or woman) was interviewedper household but he or she was free to seek help from anyother family member in case of difficulties in responding to







4 Data Analysis



5 Findings





Land usetypes

1980(km2)

RLC()

1990(km2)

RLC()

2000(km2)

RLC()

2010(km2)

RLC()

2013(km2)

RLC()

increase(+) or


Amboseli

Olorika

Kuku

Imbirikani

Kimana

Loolopon

Inkariak Rongena

OlchoroEntonet



Land use 1980


0 20 40 80

(km)

N





Amboseli

Olorika

Kuku

Imbirikani

Kimana

Loolopon

Inkariak Rongena

OlchoroEntonet

Land use 2013




0 20 40 80

(km)


N







sig (2-tailed) 0004 0001 0013 0001 0001 0026


sig (2-tailed) 0004 0015 0001 0005 0013 0018


sig (2-tailed) 0001 0015 0036 0002 0004 0028


sig (2-tailed) 0013 0001 0036 0020 0022 0043


sig (2-tailed) 0001 0005 0002 0020 0010 0007









0

50000

100000

150000

200000

250000

1999 2009 2012 2020 2030

Num

ber o

f peo

ple

Year

Number













0

2000

4000

6000

8000

10000

12000

1979

1981

1982

1983

1985

1987

1989

1991

1993

1995

1997

1999

2000

2002

2003

2005

2007

2010

Num

ber o

f ind

ivid

ual s

peci

es

Year


Grantrsquos gazelle


0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

2007 2010

Num

ber o

f ind

ivid

ual s

peci

es

Species



Hartebeest











Gender Male 147119903 = 44180 df = 1 119875 = 0000

Female 53

Age18ndash35 77

119903 = 7090 df = 2 119875 = 002936ndash50 74Above 51 49

Level of education

None 45



Crop farming 78

119903 = 123400 df = 6 119875 = 0000



Length of stay

lt5 yrs 33


Above 201 yrs 25


lt25 Acres 80


Above 101 Acres 11

Current land use

Crop farming 88




Yes 180119903 = 128000 df = 1 119875 = 0000

No 20


No 22


0-5 km 89




119903 = 129760 df = 7 119875 = 0000



Drought 8Poverty 6

Education level 4


Table 5 Continued




No 10

Forms of HWC

Crop destruction 88

119903 = 141820 df = 5 119875 = 0000








0

200

400

600

800

1000

1200

2000 2002 2007 2010

Num

ber o

f ind

ivid

ual s

peci

es










05

101520253035404550

Crop

destr

uctio

n

Live

stock

pred

atio

n

Hum

anin

jurie

s

Dea

th o

fhu

man

bei

ng

Road

kill

s

Com

petit

ion

for

reso

urce

s

()














Acknowledgments


References



































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology







4 Data Analysis



5 Findings





Land usetypes

1980(km2)

RLC()

1990(km2)

RLC()

2000(km2)

RLC()

2010(km2)

RLC()

2013(km2)

RLC()

increase(+) or


Amboseli

Olorika

Kuku

Imbirikani

Kimana

Loolopon

Inkariak Rongena

OlchoroEntonet



Land use 1980


0 20 40 80

(km)

N





Amboseli

Olorika

Kuku

Imbirikani

Kimana

Loolopon

Inkariak Rongena

OlchoroEntonet

Land use 2013




0 20 40 80

(km)


N







sig (2-tailed) 0004 0001 0013 0001 0001 0026


sig (2-tailed) 0004 0015 0001 0005 0013 0018


sig (2-tailed) 0001 0015 0036 0002 0004 0028


sig (2-tailed) 0013 0001 0036 0020 0022 0043


sig (2-tailed) 0001 0005 0002 0020 0010 0007









0

50000

100000

150000

200000

250000

1999 2009 2012 2020 2030

Num

ber o

f peo

ple

Year

Number













0

2000

4000

6000

8000

10000

12000

1979

1981

1982

1983

1985

1987

1989

1991

1993

1995

1997

1999

2000

2002

2003

2005

2007

2010

Num

ber o

f ind

ivid

ual s

peci

es

Year


Grantrsquos gazelle


0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

2007 2010

Num

ber o

f ind

ivid

ual s

peci

es

Species



Hartebeest











Gender Male 147119903 = 44180 df = 1 119875 = 0000

Female 53

Age18ndash35 77

119903 = 7090 df = 2 119875 = 002936ndash50 74Above 51 49

Level of education

None 45



Crop farming 78

119903 = 123400 df = 6 119875 = 0000



Length of stay

lt5 yrs 33


Above 201 yrs 25


lt25 Acres 80


Above 101 Acres 11

Current land use

Crop farming 88




Yes 180119903 = 128000 df = 1 119875 = 0000

No 20


No 22


0-5 km 89




119903 = 129760 df = 7 119875 = 0000



Drought 8Poverty 6

Education level 4


Table 5 Continued




No 10

Forms of HWC

Crop destruction 88

119903 = 141820 df = 5 119875 = 0000








0

200

400

600

800

1000

1200

2000 2002 2007 2010

Num

ber o

f ind

ivid

ual s

peci

es










05

101520253035404550

Crop

destr

uctio

n

Live

stock

pred

atio

n

Hum

anin

jurie

s

Dea

th o

fhu

man

bei

ng

Road

kill

s

Com

petit

ion

for

reso

urce

s

()














Acknowledgments


References



































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology



Land usetypes

1980(km2)

RLC()

1990(km2)

RLC()

2000(km2)

RLC()

2010(km2)

RLC()

2013(km2)

RLC()

increase(+) or


Amboseli

Olorika

Kuku

Imbirikani

Kimana

Loolopon

Inkariak Rongena

OlchoroEntonet



Land use 1980


0 20 40 80

(km)

N





Amboseli

Olorika

Kuku

Imbirikani

Kimana

Loolopon

Inkariak Rongena

OlchoroEntonet

Land use 2013




0 20 40 80

(km)


N







sig (2-tailed) 0004 0001 0013 0001 0001 0026


sig (2-tailed) 0004 0015 0001 0005 0013 0018


sig (2-tailed) 0001 0015 0036 0002 0004 0028


sig (2-tailed) 0013 0001 0036 0020 0022 0043


sig (2-tailed) 0001 0005 0002 0020 0010 0007









0

50000

100000

150000

200000

250000

1999 2009 2012 2020 2030

Num

ber o

f peo

ple

Year

Number













0

2000

4000

6000

8000

10000

12000

1979

1981

1982

1983

1985

1987

1989

1991

1993

1995

1997

1999

2000

2002

2003

2005

2007

2010

Num

ber o

f ind

ivid

ual s

peci

es

Year


Grantrsquos gazelle


0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

2007 2010

Num

ber o

f ind

ivid

ual s

peci

es

Species



Hartebeest











Gender Male 147119903 = 44180 df = 1 119875 = 0000

Female 53

Age18ndash35 77

119903 = 7090 df = 2 119875 = 002936ndash50 74Above 51 49

Level of education

None 45



Crop farming 78

119903 = 123400 df = 6 119875 = 0000



Length of stay

lt5 yrs 33


Above 201 yrs 25


lt25 Acres 80


Above 101 Acres 11

Current land use

Crop farming 88




Yes 180119903 = 128000 df = 1 119875 = 0000

No 20


No 22


0-5 km 89




119903 = 129760 df = 7 119875 = 0000



Drought 8Poverty 6

Education level 4


Table 5 Continued




No 10

Forms of HWC

Crop destruction 88

119903 = 141820 df = 5 119875 = 0000








0

200

400

600

800

1000

1200

2000 2002 2007 2010

Num

ber o

f ind

ivid

ual s

peci

es










05

101520253035404550

Crop

destr

uctio

n

Live

stock

pred

atio

n

Hum

anin

jurie

s

Dea

th o

fhu

man

bei

ng

Road

kill

s

Com

petit

ion

for

reso

urce

s

()














Acknowledgments


References



































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology





sig (2-tailed) 0004 0001 0013 0001 0001 0026


sig (2-tailed) 0004 0015 0001 0005 0013 0018


sig (2-tailed) 0001 0015 0036 0002 0004 0028


sig (2-tailed) 0013 0001 0036 0020 0022 0043


sig (2-tailed) 0001 0005 0002 0020 0010 0007









0

50000

100000

150000

200000

250000

1999 2009 2012 2020 2030

Num

ber o

f peo

ple

Year

Number













0

2000

4000

6000

8000

10000

12000

1979

1981

1982

1983

1985

1987

1989

1991

1993

1995

1997

1999

2000

2002

2003

2005

2007

2010

Num

ber o

f ind

ivid

ual s

peci

es

Year


Grantrsquos gazelle


0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

2007 2010

Num

ber o

f ind

ivid

ual s

peci

es

Species



Hartebeest











Gender Male 147119903 = 44180 df = 1 119875 = 0000

Female 53

Age18ndash35 77

119903 = 7090 df = 2 119875 = 002936ndash50 74Above 51 49

Level of education

None 45



Crop farming 78

119903 = 123400 df = 6 119875 = 0000



Length of stay

lt5 yrs 33


Above 201 yrs 25


lt25 Acres 80


Above 101 Acres 11

Current land use

Crop farming 88




Yes 180119903 = 128000 df = 1 119875 = 0000

No 20


No 22


0-5 km 89




119903 = 129760 df = 7 119875 = 0000



Drought 8Poverty 6

Education level 4


Table 5 Continued




No 10

Forms of HWC

Crop destruction 88

119903 = 141820 df = 5 119875 = 0000








0

200

400

600

800

1000

1200

2000 2002 2007 2010

Num

ber o

f ind

ivid

ual s

peci

es










05

101520253035404550

Crop

destr

uctio

n

Live

stock

pred

atio

n

Hum

anin

jurie

s

Dea

th o

fhu

man

bei

ng

Road

kill

s

Com

petit

ion

for

reso

urce

s

()














Acknowledgments


References



































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology









0

2000

4000

6000

8000

10000

12000

1979

1981

1982

1983

1985

1987

1989

1991

1993

1995

1997

1999

2000

2002

2003

2005

2007

2010

Num

ber o

f ind

ivid

ual s

peci

es

Year


Grantrsquos gazelle


0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

2007 2010

Num

ber o

f ind

ivid

ual s

peci

es

Species



Hartebeest











Gender Male 147119903 = 44180 df = 1 119875 = 0000

Female 53

Age18ndash35 77

119903 = 7090 df = 2 119875 = 002936ndash50 74Above 51 49

Level of education

None 45



Crop farming 78

119903 = 123400 df = 6 119875 = 0000



Length of stay

lt5 yrs 33


Above 201 yrs 25


lt25 Acres 80


Above 101 Acres 11

Current land use

Crop farming 88




Yes 180119903 = 128000 df = 1 119875 = 0000

No 20


No 22


0-5 km 89




119903 = 129760 df = 7 119875 = 0000



Drought 8Poverty 6

Education level 4


Table 5 Continued




No 10

Forms of HWC

Crop destruction 88

119903 = 141820 df = 5 119875 = 0000








0

200

400

600

800

1000

1200

2000 2002 2007 2010

Num

ber o

f ind

ivid

ual s

peci

es










05

101520253035404550

Crop

destr

uctio

n

Live

stock

pred

atio

n

Hum

anin

jurie

s

Dea

th o

fhu

man

bei

ng

Road

kill

s

Com

petit

ion

for

reso

urce

s

()














Acknowledgments


References



































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology





Gender Male 147119903 = 44180 df = 1 119875 = 0000

Female 53

Age18ndash35 77

119903 = 7090 df = 2 119875 = 002936ndash50 74Above 51 49

Level of education

None 45



Crop farming 78

119903 = 123400 df = 6 119875 = 0000



Length of stay

lt5 yrs 33


Above 201 yrs 25


lt25 Acres 80


Above 101 Acres 11

Current land use

Crop farming 88




Yes 180119903 = 128000 df = 1 119875 = 0000

No 20


No 22


0-5 km 89




119903 = 129760 df = 7 119875 = 0000



Drought 8Poverty 6

Education level 4


Table 5 Continued




No 10

Forms of HWC

Crop destruction 88

119903 = 141820 df = 5 119875 = 0000








0

200

400

600

800

1000

1200

2000 2002 2007 2010

Num

ber o

f ind

ivid

ual s

peci

es










05

101520253035404550

Crop

destr

uctio

n

Live

stock

pred

atio

n

Hum

anin

jurie

s

Dea

th o

fhu

man

bei

ng

Road

kill

s

Com

petit

ion

for

reso

urce

s

()














Acknowledgments


References



































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


Table 5 Continued




No 10

Forms of HWC

Crop destruction 88

119903 = 141820 df = 5 119875 = 0000








0

200

400

600

800

1000

1200

2000 2002 2007 2010

Num

ber o

f ind

ivid

ual s

peci

es










05

101520253035404550

Crop

destr

uctio

n

Live

stock

pred

atio

n

Hum

anin

jurie

s

Dea

th o

fhu

man

bei

ng

Road

kill

s

Com

petit

ion

for

reso

urce

s

()














Acknowledgments


References



































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


05

101520253035404550

Crop

destr

uctio

n

Live

stock

pred

atio

n

Hum

anin

jurie

s

Dea

th o

fhu

man

bei

ng

Road

kill

s

Com

petit

ion

for

reso

urce

s

()














Acknowledgments


References



































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


Acknowledgments


References



































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology








Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology

research article changing land use patterns and their

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