MARA Migration

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O R IGINA L A R T IC L E

Douglas E. Musiega Sanga-Ngoie KazadiKaoru Fukuyama

A framework for predicting and visualizing the East African wildebeestmigration-route patterns in variable climatic conditions using geographicinformation system and remote sensing

Received: 24 March 2004 / Accepted: 14 December 2005 / Published online: 7 June 2006 The Ecological Society of Japan 2006

Abstract In the SerengetiMara ecosystem of EastAfrica, the migrating wildebeests (Connochaetes tauri-nus) response to food resource distribution and terrain

complexity impacts their movement characteristics. Thismanifests in varying ways such zas movement speed,direction, turning frequency, and moving distance. Toinvestigate these characteristics, indices derived fromvegetation quantity (normalized difference vegetationindex, NDVI) and relief (slope) were used in our previ-ous work to quantify the relationships between migra-tion route location versus vegetation, relief complexity,and their combination. Least cost pathways determinedusing these indices were representative of approximatemigration routes. The simulated routes were shown to bestrongly influenced by vegetation during the dry season.However the impact of climatic change (rainfall) on

route location was not investigated though known toinfluence vegetation recovery patterns. This paper spe-cifically addresses the impact of climatic change on routelocation. The mean monthly rainfall data were used toclassify the rainy and dry seasons in the Serengeti, theWestern, and the Mara areas as normal, drier, or wetterthan normal, over the 19861997 period. The classifi-cation is based on the mean monthly rainfall variabilityabout the 11-year seasonal mean. Regression analysisshowed strong linear relationships between rainfall andmean NDVI for each one of the three areas. The sub-sequent seasonal classification based on the corre-sponding habitat vegetation characteristics (NDVI)

revealed the relative variation of vegetation with rainfall.Using the derived general categories, migration routesare then predicted for the various categories using aroute attractivity index. The seasonal migrationroutes were shown to change depending on the relative

abundance of the rainfall during the dry season. Dryseason migration routes tended toward areas with bettervegetation activity, i.e., those characterized by higher

NDVI gradients. Our results showed that during thewestern trek, wetter dry seasons have the effect ofdelaying the herds movement northwestward. Duringthe northern trek, wetter dry seasons have the effect ofdelaying the tendency to move westward. However thevariation in rainfall conditions during the rainy and dryseason had no significant influence on the southern trekroute location. We assume that predicted routes basedon average general category conditions for differentyears are representative of main migration route pat-terns for similar seasons, therefore they are well suitedfor approximate route prediction, if the climatic char-acteristics of the year are known.

Keywords SerengetiMara Wildebeest Migration Remote sensing GIS

Introduction

The predominant megafauna species in the SerengetiMara ecosystem is the wildebeest, whose populationvaries between 1 and 1.5 million heads every yeardepending on climatic conditions. It is a key or flagshipspecies in the ecosystem because of its occurrence in

large numbers, its massive grazing activities, and trans-border migration. This also creates favorable conditionsfor the survival of other large herbivores and carnivoresin the habitat: this facilitating effect attracts hundredsof thousands of other herbivores (antelopes, zebras, etc.)and carnivores (lions, cheetahs) (Haltenorth and Diller1980). These large numbers of herbivores affect theecosystem, altering distribution of plants and nutrients.The Maasai and their herds, who share the same livingspace, are also affected, often resulting in fierce humanwildlife conflicts.

D. E. Musiega Sanga-Ngoie K. (&) K. FukuyamaGraduate School of Bioresources, Mie University,1577 Karima-Machiya, Tsu 514-8507, JapanE-mail: [email protected].: +81-59-2319539Fax: +81-59-2319539

Ecol Res (2006) 21:530543DOI 10.1007/s11284-006-0175-9


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The wildebeest herds begin their migration from theSerengeti plains (Tanzania), in the southeast of the studyarea, where they reside for the JanuaryApril period.Based on radio-tracking data, we have shown that at theonset of the dry season (MayJune), the herds movewest. In the JulyAugust period, they turn east into thewestern corridor and, come AugustNovember, theymove northward into the Maasai Mara area, Kenya.During the rainy season (DecemberJanuary), the herdsembark on the southern trek back to the Serengeti plains(Fig.1; Musiega and Sanga-Ngoie 2004). The routestaken vary every year depending on, among others, thefood supply, which is also dependent on the rainfalldistribution (Mduma et al. 1999; Ottichilo et al. 2001).

These route changes, while necessary for the survivalof the wildebeest, are often an unwelcome phenomenonamong the resident pastoral communities in the habitat(Boone et al. 2002). Images of harmonious co-existencebetween Maasai and savanna wildlife may have beentenable in the period preceding the creation of state-protected parks, and the establishment of tourismfacilities and infrastructure on land previously owned by

the Maasai people in Kenya and Tanzania. At presentMaasai people are often in severe, persistent, and

accelerating conflicts with the over-grazing wildlife(Campbell et al. 2000; Boone et al. 2002; Johannesenand Skonhoft2004). This is partly because the designedparks include important dry season grazing areas for thenomad pastoralists (Akama 1999). These communitiesincur immediate environmental costs from tourism andwildlife conservation. They suffer damage by wildlifeand forego opportunities for using their land for agri-culture. The people and their cattle endeavor to har-moniously co-exist with wildlife, nonetheless. Wildlifemanagers in the region are seeking ways and means ofpredicting these route patterns to facilitate planning ormitigating these conflicts.

The Maasai, like other pastoralists, move in search ofpasture, planning and timing their movements carefullyto maximize the use of available water and grass re-sources. Vegetation comes in patches due to differencesin soil types, nutrients, and terrain (Kumar et al.2002).Wildebeests, unlike cattle herds, make movement deci-sions on their own. Etzenhouser et al. (1998) have shownthat there is a relationship between environmental het-erogeneity and herbivore foraging movements. Like for

other herbivores, wildebeest movements depend on thespatial distribution of food and non-food items in the

Fig. 1 The map of the studyarea, the SerengetiMaraecosystem, and the differentphases of the migration. Themean route location determinedby radio-tracking techniquesfor the period 19961997 isindicated. The ecosystemconsists of the SerengetiNational Park (N.P.),surrounded by the Mara GameReserve (G.R.), the LoliondoGame Reserve and theNgorongoro ConservationArea (C.A.). The Maswa, theGrumeti, and the IkorongoGame Reserves are on thewestern side

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environment. While it is acknowledged that differentanimals respond differently to landscape heterogeneity,experiments show that complex environment hindersanimal movements (Gross et al. 1995).

Pennycuick (1975), Mduma et al. (1999), Wolanskiet al. (1999), Ottichilo et al. (2001), and Wolanski andGereta (2001) cite the main causes of migration as: (1)the search for food supply, which is dependent on un-even distribution of rainfall, (2) search for specificnutrients in forage, (3) interspecies competition amongthe herds, (4) the herds response to complex environ-ment, (5) search for surface water during the westerntrek, and (6) disasters and population explosions. Thelong-term population trend of the herds for the 19771997 period indicates that the population increased withincreasing annual mean rainfall, and vice versa (Otti-chilo et al. 2001). Also, population explosion anddrought necessitate migration to the western andnorthern parts of the habitat due to increased competi-tion for food resources in the southeast grasslands(Pennycuick 1975). Furthermore, high salinity in therivers along the migration route influences the timing of

the migration trigger from the southeast grasslands tothe west (Wolanski et al. 1999; Wolanski and Gereta2001). It has been shown that the herds are in constantmovement toward areas of abundant food resources(Wilmhurst et al. 1999) and that agro-pastoral activitieshave strongly contributed to habitat degradation(Serneels and Lambin 2001; Homewood et al. 2001) inthe ecosystem.

Field observations and behavioral studies of thewildebeest revealed vital characteristics of the foragingbehavior that formed the basis of modeling rules forsimulating movement using geographic informationsystem (GIS). First, the animal looks ahead at potential

feeding sites and makes route choices based on what itperceives to be the shortest distance to the site ofinterest. In moving to the new site, the nearest availableis preferred among available alternatives (Stuart andStuart1997). Secondly, the reward of eating ought to bedeemed greater than the cost of traveling to a site towarrant a visit. Third, herbivores consume what can beseen at a distance of up to approximately 67 km (Stuartand Stuart 1997), intervisibility permitting, and rulesthat help reduce the length of travel between stations areemployed. Fourth, when plants are visible, animals canclearly contrast and differentiate between wanted andunwanted plants, and foraging is governed by small-

scale patterns (Haltenorth and Diller 1980). Fifth, asabundance of forage declines, ability to locate and movebetween forage locations efficiently can strongly affectthe patterns of resource depletion and ultimately sur-vival of the animals. The time and cost of traveling be-tween feeding stations is substantial, and animalsrespond in economically viable ways (Gross et al.1995).

Geographic information system is a powerful tool forgeo-spatial data analysis and management, whosecapabilities can be harnessed for wildlife habitat con-servation and monitoring works. Its capacity to facilitate

manipulation and analysis of geo-referenced data fromdifferent sources has found ready application in thiswork. Distribution of wildebeests is characterized bygraphic locations and seasonal variations in movementpatterns. The habitats environmental conditions andland cover influence this distribution. GIS enabled us toresolve the challenges faced in this study, which included(1) manipulation and analysis of a large volume of ani-mal and satellite vegetation data across varying spatialscales, and (2) implementation of analysis at the regionalscale while verifying a relationship between habitatcondition and wildlife at the local scale (Wei and Joske2000). A framework for the representation of wildlifemovement in GIS proposed in Bian (2000) was used.The landscape and animal characteristics that interplay,influencing specific animal movements, are identified.Forage quality, quantity, geometry, distribution, andintervisibility between patches influence decisions ani-mals make concerning route location. Interspeciescompetition (i.e., wildebeest versus other animals) andintraspecies interactions (internal) impact on animalmotion differently. Competition within the herds

(intraspecies competition) for available forage resourcesis mainly responsible for decisions to relocate or spreadout during local feeding patterns. The combined effectresults in animal properties that define movement(moving direction, moving distance, moving sinuosity,and moving velocity) (Bian2000).

The normalized difference vegetation index (NDVI)relates to the proportion of photosynthetically absorbedradiation (Nonomura et al. 2003). Healthy vegetationabsorbs most of the visible light (VIS) that hits it, andreflects a large portion of the near-infrared light (NIR),while unhealthy or sparse vegetation reflects more visiblelight and less near-infrared light. It is defined as the ratio

of the difference to the sum of the two reflectances,

NVDI NIR VIS=NIRVIS 1

Wilmhurst et al. (1999) reported that wildebeest choiceof feeding stations is governed by what is perceived to begreener vegetation. The same had been shown to dependon biomass availability in the SerengetiMara ecosys-tem. Vegetation greenness (NDVI) was used to monitormigration, having been found to have a strong correla-tion to wildebeest movement (Ottichilo et al. 2001), andto be appropriate for quantifying relative vegetationactivity in ecosystems.

In a previous work, Musiega and Sanga-Ngoie (2004)

developed indices derived from vegetation (NDVI) andrelief (slope) to quantify the relationships betweenmigration route location versus vegetation, relief com-plexity criteria, and their combination. Then, migrationroutes were simulated for different phases of themigration cycle based on the different criteria and theircombination using GIS. The simulated route locationresults were compared to the actual ground measure-ments to ascertain relative influence of different criteria(landscape and vegetation) at different phases of themigration. Furthermore, the wildebeest migration had

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been shown to be influenced by differences in biomassgradient caused by varying rainfall intensity in the wetand dry seasons of each year (Pennycuick 1975; Wo-lanski et al. 1999; Wilmhurst et al. 1999).

However the influence of climatic change (rainfall) onroute location was not investigated. To address thisshortcoming in our previous work, the followingobjectives were undertaken in the present research.

1. To classify the seasonal characteristics in the eco-system especially for each of its three main grazingareas (Serengeti, Western area, and Mara area), overthe period of study, based on rainfall observations.

2. To establish the relationships between rainfall andvegetation activity (NDVI) in the ecosystem, withspecial attention to each one of the three grazingareas.

3. To identify seasons having similar characteristics andgeneralize the vegetation conditions of the SerengetiMara that characterize them using rainfall and NDVIdata.

4. Based on the emerging patterns, to predict possible

migration routes using generalized characteristics fordifferent rainfall patterns and NDVI scenarios in theecosystem.

5. To visualize the resultant route patterns and discussthe possibility of developing a framework for pre-dicting migration routes in different climatic condi-tions.

This paper is structured into four sections. Theintroduction begins with a background of the migration,its impact on the habitat, and the problem statement. Itexplains the foraging behavior of the wildebeest, brieflyreviews our prior work (Musiega and Sanga-Ngoie2004) among others, and ends with the objectives of this

research. The Materials and methods section describesthe study area, the data used, and the analysis method.TheResults, Discussion, and Conclusion follow there-after.

Materials and methods

The SerengetiMara ecosystem (Fig.1) lies betweenlatitudes 1N and 4S, and longitudes 34E and 36E.The vegetation cover is mainly wooded savanna to thenortheast and open grassland to the southeast. Theelevation ranges between 1,300 and approximately1,700 m above sea level (ASL). The mean daily tem-perature is relatively constant with a yearly mean of20C. The variations in the monthly mean temperaturerecord very small fluctuations. The average fluctuationin temperature is about 34C, while closer to lakeVictoria, the fluctuations are even less (12C) (Griffiths1972). Rainfall pattern is bimodal, with short rainsfalling in NovemberDecember and the long rains inMarchMay, commonly with a dry period betweenJanuary and February (Charlier 1998; Serneels and

Lambin 2001). The ecosystem consists mainly of theSerengeti National Park (SNP), surrounded by theMaasai Mara Game Reserve to the north, the LoliondoGame Reserve, and the Ngorongoro Conservation Areato the eastern and southeastern sides. The Maswa GameReserve is on the southwest while the Grumeti andIkorongo are on the western side (Fig.1).

The Global Precipitation Climatology Center(GPCC) precipitation data for the period 19861997were used. The data had already been validated byprecipitation data observed in situ by gauges operated inmeteorological or hydrological observation networks(Rudolf et al. 1994). The GCCP data covering the Ser-engetiMara ecosystem consisting of monthly precipi-tations at 11 grid resolution were downloaded fromthe GPCC Homepage (http://www.gpcc.dwd.de), andextracted for the study area as bounded by 3436Eand 1N4S. GCCP data encompassing Ngorongoroand Narok areas were compared with available observedgauge data and found to be in good agreement.

For vegetation, the monthly mean National Oceanicand Atmospheric Administration/Advanced Very High

Resolution Radiometer (NOAA/AVHRR) NDVI(88 km spatial resolution) data covering the SerengetiMara ecosystem for the period 19861997 were collected(Musiega and Sanga-Ngoie2004). Monthly mean NDVIvalues for the period of study were computed and usedfor interseasonal analysis of the vegetation. The UnitedStates Geological Surveys GTOPO30 digital elevationmodel (DEM) data at 11 km resolution provided thetopographic base from which information about terraincharacteristics were determined.

Mean migration route is based on data collectedduring the 19951997 period by radio-tracking tech-niques according to Wilmhurst et al. (1999). A receiver

aboard an aircraft tracked signals from transmitters at-tached to a few wildebeests at monthly intervals. Thecoordinates of the animal were recorded when contactwas established. In cases where the positional locationsgreatly differed perpendicularly to the general movingdirection for any particular time epoch of measurement,the mean of the locations was assumed (Fig. 1).

Foraging observations had shown that areas charac-terized with better vegetation activity (higher NDVIvalues) attract route location while lower values repel it.Steep slopes too repel the route location as explained inadditional energy cost the herds incur in ascending reliefgradient. A combination of the forage quality, slope,

and altitude influences on migration route location isbest treated in Musiega and Sanga-Ngoie (2004) using aforaging path attraction index (F), which was defined asfollows:

F NDVIi k= jHi Hoj S1i

2

whereSis the slope at any pixel, H0 the average heightin MaraSerengeti grassland area,Hiis the altitude of alocation (Himax=2,778 m ASL, Himin=538 m ASL,and H0 mean=1,655.77 m ASL), and k is a constant,

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used to regulate the relative influence of relief and slopeto ensure equal weighting. Cost distance routes betweenthe origin and the destination foraging stations weredetermined using GIS (Musiega and Sanga-Ngoie2004).The Clark Laboratory GIS software IDRISI32 andCARTALINX, the database builder software (Eastman2001), were used for database creation and analysis. Theorigin and destination of movement were determinedusing GIS with positional bias toward areas of highestseasonal NDVI/slope ratio. Areas of highest NDVI andleast slope were chosen as targeted visiting sites, andconsequently are the most likely to be depleted first. Inareas of equal magnitude over large expanse, the centerof gravity of shape of area was chosen (Musiega andSanga-Ngoie 2004; Sanga-Ngoie and Musiega 2002,2003,2004). A summary of the analysis steps is given inthe analysis diagram (Fig.2).

Climatic features in the ecosystem

Considering that the calendar year separates the same

rainfall season between two consecutive years, we op-ted to define a year as the period from November toOctober of the following year. The overall annualrainfall patterns for Serengeti and Mara are shown inFig. 3. The year was further divided into the rainyseason (NovemberMay) and the dry season (JuneOctober). Using the monthly rainfall observations overthe period 19861997 for the Serengeti, the Western,and the Mara areas (Rudolf et al. 1994), rainfall var-iability analysis was done to determine the seasonalcharacteristics. For each one of these areas, the rainfall

totals for every rainy (NovemberMay) and dry (JuneOctober) season over the 19861997 period werecomputed and the respective rainy or dry season meanvalue and standard deviation determined. A rainyseason (NovemberMay) wetter than normal was de-fined as a rainy season in which the seasonal rainfalltotal was more than the rainy seasons mean valueplus one standard deviation for Serengeti, the Western,and the Mara areas, respectively. A drier than normalrainy season was defined as one whose seasonal rain-fall was less than the rainy season mean minus onestandard deviation. The normal rainy season was onethat fell between the wetter and drier rainy seasonranges. A similar treatment was adopted to classify thedry seasons (JuneOctober), dividing them into thewetter, normal, and drier categories, according to theclimatic conditions over each one of the three areas(Table 1).

In general, the Mara area is shown to be the wettestof the three grazing areas, in both the rainy and the dryseasons. Moreover, rainy seasons tend to be wetter overthe Western area (mean 720.45 mm) than over the

Serengeti (mean 637.66 mm), while the opposite is ob-served for the dry seasons. No one year showed similarclimatic conditions (normal, wetter, or drier) in allthree areas for both the dry and rainy seasons. How-ever, normal rainy seasons (years 19861987 and 19961997) or normal dry seasons (years 19911992 and19921993) were observed over the three areas simul-taneously, with the marked tendency of the Westernarea to show more severe dry season climatic condi-tions, milder ones over the Serengeti, and much wetterones on the Mara area (Table 1).

Seasonal characteristics

Quantify vegetation

Least cost pathways

in GIS

General tendencies in route

location

Landscape complexity

Route pattern in different

categories

Model linking route location, vegetation

and landscape complexity

GTOPO30 DEMCategories

Average NDVI for

migration epochsCorrelation between

Rainfall and NDVI

Classify wet & dry seasonNDVI characteristics

NOAA / AVHHR NDVI

Classify rainy & dry

season characteristics

Mean Monthly rainfall

Classify year categories

into dry, wet and normal

Fig. 2 The analysis flowchart

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Climatic categories of years

Mduma et al. (1999) and Ottichilo et al. (2001) notedthat rainfall in the dry season was the most determinantfactor for the food supply to the wildebeest. Thereforeclassification of the years was done based on the rainfallconditions during the dry season for the Serengeti,the Western, and the Mara areas separately. Based onthe findings in Table1, the classification resulted in thefollowing eight climatic categories.

1. N-N-N: years with a normal dry season (June

October) in Serengeti, a normal dry season in theWestern area, and a normal dry season in the Maraarea: 19911992 and 19921993.

2. N-D-N: years with a normal dry season (JuneOctober) in Serengeti, a drier dry season in theWestern area, and a normal dry season in the Maraarea: 19871988 and 19891990.

3. N-N-W: years with a normal dry season (JuneOctober) in Serengeti, a normal dry season in theWestern area, and a wetter dry season in the Maraarea: 19961997.

4. N-W-W: years with a normal dry season (JuneOctober) in Serengeti, a wetter dry season in the

Western area, and a wetter dry season in the Maraarea: 19951996.

5. D-D-N: years with a drier dry season (JuneOctober)in Serengeti, a drier dry season in the Western area,and a normal dry season in the Mara area: 19881989and 19931994.

6. D-D-D: years with drier dry seasons (JuneOctober)in the Serengeti, the Western area, and in the Maraarea: 19901991.

7. W-N-D: years with a wetter dry season (JuneOcto-ber) in the Serengeti, a normal dry season in the

Western area, and a drier dry season in the Maraarea: 19861987.

8. W-N-N: years with a wetter dry season (JuneOcto-ber) in the Serengeti, a normal dry season in theWestern area, and a normal dry season in the Maraarea: 19941995.

Results

Relationship between rainfall and NDVI

To establish the relationship between rainfall and NDVIat the three major migration epochs, regression analysiswas done using the monthly rainfall and monthly NDVIdata for the 19861997 period, as independent anddependent variables, respectively. The results show astrong linear relationship between the annual rainfalland NDVI for the Serengeti, Western, and Mara areas(Fig. 4). Similar results with strong linear relationshipsare obtained for the rainy and dry seasons over each oneof these areas. Calculated based on the similar time andspatial scales, the correlation is shown to be relativelyweaker (q=0.69) during the dry season for the Western

area, as compared to the stronger values (q 0.73)obtained for the other areas. These relationships shedsome new light on the link between rainfall and vege-tation conditions in the ecosystem on various temporal(annual or seasonal) and spatial (the SerengetiMaasaiMara ecosystem or each one of the three main areas)scales. They thus emphasize the strong dependency ofvegetation greenness (NDVI), and therefore the migra-tion route, on the seasonal abundance of rainfall duringeach migration epoch, especially for those treks takenduring dry seasons.

0.00

50.00

100.00

150.00

200.00

J J A S O N D J F M A M

Months

Mara

Serengeti

Dry season Wet season

Western

corr

Northern

trek MaraSouth

trek

Western

trekSerengeti

Rainfall(mm)

50 mm rainfall mark

Fig. 3 Rainfall patterns and seasons in the SerengetiMara ecosystem versus the herds location. The mean monthly rainfall is plottedagainst the respective month for both the Mara and Serengeti areas. Vertical lines show the standard deviation values

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NDVI characteristics for various year categories

Modeling the interrelationships between climatologicalrainfall and NDVI over the continent, Nonomura et al.(2003) had shown that a minimum period of 1 month isrequired between rainfall occurrence and real effect ongrass recovery in African ecosystems. For this reason, alag period of 1 month was allowed for in the presentanalysis. Comparisons of correlation determined withand without the lag period revealed a minimal yet sig-nificant improvement in the correlation between rainfalland NDVI for dry season as follows. Serengeti areavalue ofq improved to 0.83 from 0.80, while the Westernarea correlation improved to 0.69 from 0.68. The Maraarea did not indicate any improvement in correlationwith the lag period. This leads us to perform non-laganalyses only in this work.

The average NDVI values were extracted for theSerengeti, the Western, and the Mara areas, andgrouped into the corresponding annual classes (Table 2)for the 19861997 period. The NDVI values for differentcategories in the rainy and dry seasons show an

increasing gradient westward and northward, with Ser-engeti values being comparatively the smallest (Fig. 5).The general conditions over each of these ecosystemscould then be classified as poor, normal, or good basedon the status of the vegetation activity (i.e., the NDVI)for every years dry season (Table 2).

Linear regression between rainfall and NDVI asindependent and dependent variables, respectively, wasperformed once more to determine the correlation be-tween the annual rainfall characteristics of the yearsgrouped into each one of the climatic categories. Basedon the correlation coefficients, those that were leastcorrelated (q


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origin/destination points were chosen in the Serengetigrasslands, the Western area, the end of western corri-dor, and the Mara area, following the method devised inour previous work (Musiega and Sanga-Ngoie 2004).For each climatic category, least-cost pathways weredetermined between the respective origin and destinationpoints, for every phase of the migration cycle (westerntrek, western corridor, northern trek, and southern trek)using GIS. For all categories the results for the westerntrek, the western corridor/northern trek and southerntrek, are shown in Figs. 6,7, and8, respectively.

Migration routes were predicted, grouping the eightclimatic categories in three groups, based on the climaticconditions of the Serengeti plains: (1) categorieswhich consisted of a normal dry season in the Serengeti(N-N-N, N-D-N, N-W-W, and N-N-W), (2) categoriesthat consisted of drier dry seasons in the Serengeti(D-D-N and D-D-D), and (3) categories with wetter dryseasons in the Serengeti (W-N-D and W-N-N). We haveto note here, however, that although the observed time-lag between rainfall and NDVI was shown to slightlyimprove the rainfallvegetation correlation over the

Serengeti area

0

0.5

0.4

0.3

0.2

0.1

0 20 40 60 80 100 120 140 160 180 0 20 40 60 80 100 120 140 160 180 0 20 40 60 80 100 120 140 160 180

NDVI

NDVI=0.000379*rain+0.31658,

(n=12, =0.74)

Western area

0

0.1

0.2

0.3

0.4

0.5

NDVI

NDVI=0.000547*rain+0.29625,

(n=12, =0.65)

Mara area

0

0.1

0.2

0.3

0.4

0.5

Mean monthly rainfall (mm)Mean monthly rainfall (mm)Mean monthly rainfall (mm)

Mean monthly rainfall (mm) Mean monthly rainfall (mm) Mean monthly rainfall (mm)

NDVI

NDVI=0.000520*rain+0.0.3032202,

(n=12, =0.73)

Mara area (dry season)

0

0.1

0.2

0.3

0.4

0 20 40 60 80 100 1200 20 40 60 80 100 120

NDVI

NDVI=0.000721*rain+0.0.29086,

(n=5, =0.88)

Western area (dry season)

0

0.1

0.2

0.3

0.4

NDVI

NDVI=0.000784*rain+0.27684,

(n=5, =0.69)

Serengeti area (dry season)

0

0.1

0.2

0.3

0.4

0 20 40 60 80 100 120

NDVI

NDVI=0.000675*rain+0.30395,

(n=5, =0.83)

Fig. 4 Relationships between normalized difference vegetationindex (NDVI) and rainfall in the Serengeti, Western area, andMara area obtained by linear regression with rainfall as theindependent variable (x-axis) and NDVI as the dependent variable

(y-axis), using the annual mean data (upper diagrams) or the dryseason mean data (lower diagrams) over each one of the threegrazing areas

Table 2 Seasonal vegetation conditions for the Serengeti, theWestern area, and the Mara areas as determined from normalizeddifference vegetation index (NDVI). Poor condition is defined asone whose average NDVI is less than the 11-year mean NDVI

minus one standard deviation. Good condition is an NDVI greaterthan mean annual NDVI plus one standard deviation. The normalcondition falls between the poor and good conditions

Year Climaticcategory

Mean NDVI Serengeti Western area Mara area Vegetation activity (dry)

Serengeti West Mara Rainy Dry Rainy Dry Rainy Dry Serengeti Western Mara

19861987 W-N-D 0.33 0.338 0.361 0.350 0.303 0.357 0.328 0.372 0.345 Normal Poor Poor

19871988 N-D-N 0.338 0.351 0.379 0.360 0.307 0.373 0.337 0.394 0.359 Normal Normal Normal19881989 D-D-N 0.337 0.349 0.378 0.355 0.312 0.364 0.347 0.38 0.375 Normal Good Good19891990 N-D-N 0.345 0.353 0.377 0.364 0.319 0.370 0.347 0.385 0.366 Good Good Normal19901991 D-D-D 0.346 0.351 0.373 0.369 0.313 0.372 0.341 0.382 0.360 Good Normal Normal19911992 N-N-N 0.33 0.337 0.36 0.348 0.306 0.356 0.330 0.371 0.345 Normal Poor Poor19921993 N-N-N 0.329 0.342 0.37 0.352 0.298 0.358 0.336 0.372 0.366 Poor Normal Normal19931994 D-D-N 0.33 0.339 0.365 0.351 0.300 0.358 0.331 0.372 0.355 Normal Poor Poor19941995 W-N-N 0.335 0.346 0.373 0.356 0.305 0.365 0.337 0.381 0.362 Normal Normal Normal19951996 N-W-W 0.325 0.34 0.371 0.341 0.302 0.356 0.335 0.38 0.360 Normal Normal Normal19961997 N-N-W 0.331 0.345 0.375 0.351 0.304 0.362 0.339 0.381 0.366 Normal Normal NormalMean 0.334 0.345 0.371 0.354 0.306 0.363 0.337 0.379 0.360Std dev. 0.007 0.006 0.007 0.008 0.006 0.006 0.006 0.007 0.009

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Serengeti and the Western areas, it did not show anyeffect on migration route locations. We therefore did notinclude it further in our analyses.

The routes predicted from the Serengeti plains for thewestern trek show a very striking difference in choicepreferences according to climatic conditions over thesouthern part of the ecosystem. When the Serengeti iswetter than normal and the Western area dry season is

normal (W-N-D and W-N-N categories), the abundanceof food on the plains and relatively greener dry seasonconditions extending northwestward imply less pressureupon the herds to move more westward to the LakeVictoria river system before bending northwestward:they steadily move north first before heading to westerndestination point. The N-W-W category (year 19951996) is characterized by a consistent build up in bio-mass as result of a normal rainy season in the Serengetiduring the previous year (Table 1), followed by a normaldry season over the Serengeti, and a wetter than normal

dry season in both the Western and the Mara areas. Forthis category, the herds move midway westward beforemoving northwestward, keeping a midway line betweenthe W-N-D/W-N-N routes in the east and the far-westroutes along the rivers system that are preferred duringrelatively drier years. We have to note here that pre-dicted routes for these latter categories are closer to theradio-tracking route observed for the 19961997, itself a

N-N-W year.The predicted routes for the western corridor and the

northern trek (Fig.7) show no difference in generalorientation pattern, keeping almost along the radio-tracking route up to the Mara area, except for the lati-tudes at which the herd shifts to the west during thenorthern trek. The western corridor keeps a fairlystraight course eastward in close distance to the riversystem, until the herds start their northern movement.During the northern trek, a shift westward at a givenlatitude is likely done in order to take advantage of the

Dry season NDVI for different categories

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

N-N-N N-D-N N-N- W N-W-W D-D-N D-D-D W-N- D W-N-N

Category

NDVI

Rainy season NDVI for different categories

0.39

0.38

0.37

0.36

0.35

0.34

0.33

0.32N-N-N N-D-N N-N-W N-W-W D-D-N D-D-D W-N-D W-N-N

Category

NDVI

(b)(a)

Mara areaWestern areaSerengeti area

Fig. 5 Variation in vegetation conditions (mean normalized difference vegetation index, NDVI) in the Serengeti, Western, and Maraareas, for different categories, during the dry ( a) and rainy (b) seasons

Table 3 The climatic categories versus the average vegetation characteristics in the ecosystem. The extracted mean normalized differencevegetation index values for the three major epochs of the migration cycle, i.e., the Serengeti plains, the Western area, and the Maasai Maraarea, are shown for the rainy and dry seasons, respectively

Year(s) Climaticcategory

Serengeti Western area Mara area

Rainy Dry Rainy Dry Rainy Dry

19911992 and 19921993 N-N-N 0.350 0.302 0.357 0.333 0.372 0.35619871988 and 19891990 N-D-N 0.350 0.302 0.357 0.333 0.372 0.35619961997 N-N-W 0.351 0.304 0.362 0.339 0.381 0.36619951996 N-W-W 0.341 0.302 0.356 0.335 0.380 0.36019881989 and 19931994 D-D-N 0.346 0.303 0.359 0.337 0.381 0.36319901991 D-D-D 0.343 0.313 0.372 0.341 0.382 0.36019861987 W-N-D 0.350 0.303 0.357 0.328 0.372 0.34519941995 W-N-N 0.356 0.305 0.365 0.337 0.381 0.362

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greener vegetation facilitated by the river system and thegrasslands in the proximity of Lake Victoria. Although

this westward movement seems to be controlled by thepresence of the Ikorongo Game Reserve, which acts as abuffer zone preventing herds from encroaching intohuman settlements in the western area (Fig.1), the cli-mate conditions over the area seem to determine thetiming of this shift. During wetter years (W-N-N, W-N-D,and N-W-W categories) routes deviate westward laterand at the highest latitudes compared to the other driercategories (N-N-W/N-N-N and D-D-D/D-D-N/N-D-N), because of the availability of plentiful green grassesalong the trek. However, during the southern trek,which is taken during rainy seasons, all predicted routesfairly overlap, independently of the climatic conditions

(i.e., categories), and keep very close to the 19961997radio-tracking path (Fig.8).

Discussion

In our quest for a computer-based simulation and pre-diction of migration routes for the wildebeest in EasternAfrica, first, we visually noticed a consistent relationshipbetween the herds movement and the grass recoverypatterns through computer-simulated animation of the

mean monthly NDVI images over the SerengetiMaraecosystem. The general tendency was that the herds

headed toward greener areas depicted by higher NDVIvalues, therefore higher food availability. Furthermore,considering the importance of landscape on the routechoice by the herds, we developed a computer-basedroute prediction model using a route attractivity indexdefined according to NDVI and DEM data using theleast-cost pathways algorithm on a GIS platform(Musiega and Sanga-Ngoie2004).

Our results (Musiega and Sanga-Ngoie2004) showedthe predicted routes to deviate from the 19961997radio-tracking route (taken here as the mean route)during the different phases of the migration (the westerntrek, the western corridor, the northern trek, and the

southern trek), but with all the predicted migrationroutes well within a reasonable range of accuracy ascompared to the radio-tracking route: 2.13 km(1.6 km) for the western trek, 1.3 km (1.28 km) forthe western corridor, and 2.0 km (1.3 km) for thenorthern trek. Our estimates of the relative error inpredicted routes at different phases of the migrationwere based on these findings. In general it was shownthat, during the migration from the Serengeti up to theMara area, which happens in dry seasons (Figs. 1,3),the herds motion was dominated by the search for food

Fig. 6 Predicted western trekroutes for different yearcategories. The differentcategories show varyinginfluence on route location. Thebackground is the mean dryseason normalized differencevegetation index data for theN-N-N category. The meanmigration route shown wasdetermined by radio-tracking

techniques for the period19961997 (N-N-W)

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and water, while avoiding energy-consuming ruggedlandscapes. However, during the southern trek (rainyseason), animals moved through abundant forages,giving priority to the quality, and not the quantity, of

the available food. The method and approach we pro-posed in the paper explored the possibility of predictingthe approximate route location for the migrating wil-debeest, given an a priori estimate of the expected finaldestination. The assumption that each pixel locationconsists of uniform vegetation and/or relief character-istics is, however, far from reality. In spite of this, theoverall general movement pattern was shown to bedeterminable to acceptable limits of accuracy in view ofthe large numbers involved and the vast spatial scale ofthe migration.

The quantity of biomass in the grasslands variesduring the course of the year in response to seasonal

changes in the monthly rainfall, and from year to year inresponse to the interannual climatic variability. Thesefluctuations in primary biomass determine food habits,movement patterns, and habitat utilization as noted byMduma et al. (1999) and Sinclair et al. (2000). Ottichiloet al. (2001) found a strong correlation between theannual and the wet season rainfall and NDVI for bothSerengeti and Maasai Mara. Giving support to thesefindings, we detect in this work the existence of a stronglinear rainfallNDVI relationship manifested by strongpositive correlation coefficients between them, not only

in terms of annual mean rainfall over the ecosystem, butalso over each one of the three main grazing areas(Serengeti plains, Western area, and Mara area), forboth the dry and the rainy seasons (Fig.4). This leads us

to postulate that any increase in rains during the rainy ordry seasons over either one of the three areas will have apositive influence on biomass accumulation (higherNDVI) in the ecosystem and will therefore influence thechoice of the migration route accordingly. And viceversa for decreased rainfalls.

Pennycuick (1975) and Ottichilo (2000) propose thatmigration is caused by the differences in biomass gra-dient in the ecosystem. Confirming this finding, we firstof all show that the Mara area always has higher NDVIthan the Western area and Serengeti in any season, inany year (Table3; Fig.5a, b). This seems to suggest theexistence of two important poles in the primary pro-

duction systems and the differences between these twopoles surely offer a good clue as to why wildebeestmigrate. Therefore, we can state that the wildebeestmigrate from the Serengeti plains to Maasai Mara insearch of the relatively better vegetation conditionsalong the NDVI gradient, with the Western area as anecessary transitional grazing area on their trip towardthe north (Fig. 5).

The crucial importance of dry season rainfalls onmigration route choices is suggested by Mduma et al.(1999) and Ottichilo et al. (2001). Giving new support

Fig. 7 Predicted westerncorridor and northern trek fordifferent year categories. Thedifferent categories showvarying influence on routelocation. The background is themean dry season normalizeddifference vegetation index datafor the N-N-W category. Theroutes for all categoriescoincide during the western

corridor

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to this finding, we showed that the longest portion ofthe migration effectively occurs during the dry season,

the period during which the herds move in search ofgreener vegetation from the drier Serengeti to thewetter Mara area (Figs.1, 3). We therefore classifiedthe climatic conditions (rainfalls) over each one of thethree main grazing areas in dry seasons during the19871998 period, and obtained eight main climaticcategories (Table3), according to which further analy-ses on vegetation activity and route prediction wereperformed.

Our results show that year-to-year variations inrainfall during the dry seasons in the Serengeti, theWestern area, and the Mara areas deeply influence thelocation of the migration path, with distinguishable

patterns, which can be illustrated as follows. Take theyear 19961997 (climatic category N-N-W), which cor-responds to the time the radio-tracking migration routewas observed. The herds move from the Serengeti plains(NDVI=0.304) toward the Western area(NDVI=0.339) and later toward Mara (NDVI=0.366)(Table 3). These data embody a common feature, sharedby all the migrations routes in any climatic categories:increase in NDVI gradient along the path of movement,albeit in small quantities. This causes the herds to movewestward first, before moving into the western corridor

and undertaking the northern trek toward the Maraarea, always in search of greener grazing lands.

In fact, the movement toward Maasai Mara occursduring the MayOctober period (dry season), and theoccurrence or non-occurrence of rainfall during thisperiod seems to be critical in determining the overallvegetation conditions, and therefore, the migration pat-terns. Trigger motion always begins in the southerngrasslands. Our results show that relatively wetter con-ditions in dry seasons (W-N-N, W-N-D, and N-W-Wcategories) cause a reluctance of the herds to move fur-ther west as sufficient vegetation is still available in theSerengeti plains, while the contrary conditions (drier dryseasonsN-N-W/N-N-N and D-D-D/D-D-N/N-D-Ncategories) cause the herds to move further west to take

advantage of the greener vegetation facilitated by theriver system and the proximity of Lake Victoria in thewest. During the drier years, the herds are shown to movewestward first, and to change direction to northwestlater, heading toward the Lake Victoria area where theyare attracted by greener vegetation in contrast to the dryconditions in the grasslands. The rivers system offersgood watering points on both sides of the migrationroute as the herds keep a strategic in-between position.However, Wolanski and Gereta (2001) report that therivers soon become muddy pools of water that are hardly

Fig. 8 Predicted routes for theMara and southern trek. Thebackground is of the meanrainy season normalizeddifference vegetation indexcomponent for the N-N-Wcategory

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of much value to the herds due to high salinity, and thistherefore triggers the motion along the western corridor,and then northward toward the much greener Mara areawhich is reached at the beginning of the rainy season.

Contrary to expectations, the herds embark on thesouthern trek during the rainy season. There is anapparent preference for the southern grasslands, whichat this point in the year, are nonetheless of lower averageNDVI (Table2; Fig.5). The predicted southern trek(DecemberJanuary) for all the categories agrees withthe mean observed route. The herds constantly keepclear of higher altitudes on the western side of the Ser-engeti National Park, leading us to suggest that vege-tation quality may be playing a relatively moreimportant role in choice of routes during the rainy sea-sons, in the same way as the vegetation quantity does inthe dry seasons. However, during the southern trek, thewildebeest seem to prefer short and more digestiblegrass: the abundant tall grasslands to the north areabandoned in preference for shorter ones in the Seren-geti. On arrival in Serengeti grasslands, the herds spreadout in reaching the Ngorongoro Conservation Area.

This behavior could be attributed to the need to maxi-mize daily energy intake as discussed by Wilmhurst et al.(1999): by this time the herds are more discriminative indiet selection (Sinclair et al. 2000) because the majorityof females are pregnant and shortly thereafter a multi-tude of new young are born and have to grow strongerduring the Serengeti plains rainy season, beforeembarking for a new annual trip along the trek at thebeginning of the following dry season.

The modeling of movement with respect to vegetationquality discrimination and wildebeest preferences is be-yond the scope of this paper. However, having noticedthe crucial importance of the river system, and therefore

any water points in general, on the route choices duringdry seasons, we foresee that including the waterparameter (rivers, ponds, etc.) in our analysis couldimprove the location of the predicted migration routes,especially during drier years.

Conclusion

Based on data for the period between 1986 and 1997, themost prevalent climatic conditions (eight main catego-ries) possible over the three main grazing areas of Ser-engetiMara ecosystem have been identified for normal,

wet, and dry years. These categories form a representa-tion of the typical seasonal climatic patterns. To makeuseful gains by findings in this research, the meteoro-logical departments climatic forecast of rainfall patternswill enable prediction of the seasonal category to whicha year may belong. Together with the inclusion of theecosystems water points and river system in the analy-sis, a more comprehensive basis can be obtained forpredicting the most probable location of the herdscenter of mass for the movement, and give more insightsinto the migration patterns. In this way disaster

mitigation or early warning mechanisms could bedeveloped and advances made in monitoring the wilde-beest in the ecosystem. Better planning for touristicpurposes would thus maximize the national profits fromthis natural asset for both Kenya and Tanzania.

Although GIS enables the choice of the most likelysubsequent feeding station based on highest NDVI/sloperatio, this method does not take into consideration allother parameters that may affect animal judgment dur-ing route choice. This exercise ought not be based purelyon human interpretation of the most viable sites, but onanimal perception and interpretation of their environ-ment. The herds health conditions, previous experiencewith predators, intraspecies competition, vegetationquality discrimination and preferences, etc., may influ-ence decisions. Such eventualities have not been factoredinto our model. Experienced game wardens knowledgemay come in handy in verifying if the chosen stations arerepresentative of reality. Records of past routes, too,may help make more intelligent estimates of the originand destinations for timely route prediction. The au-thors propose a continuous monitoring and updating of

the typical category route patterns annually to facilitatecontinual improvement of the model.

Nonetheless, the typical migration route patternsdetermined for every climatic category offer a goodindication of the mean location of the likely routelocation for the respective seasons. The method is apromising endeavor to determine migration route pat-terns in variable climatic conditions.

Acknowledgements This research is funded by the Japanese Gov-ernment through its Mombukasho (Ministry of Education, Science,Sports and Culture) Scholarship program. The authors would liketo thank the Kenya Wildlife Services, the East African Tours andSafaris, and the Department of Resource Surveys and Remote

Sensing, Nairobi. Last but not least, the support given to us by themembers Laboratory of Climate and Ecosystems Dynamics, MieUniversity, is highly appreciated.

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