predicting patterns of crop damage by wildlife around kibale national park, uganda

156

Conservation Biology, Pages 156–168Volume 12, No. 1, February 1998

Predicting Patterns of Crop Damage by Wildlife around Kibale National Park, Uganda

LISA NAUGHTON-TREVES

Department of Geography, 550 N. Park Street, University of Wisconsin, Madison, WI 53706, U.S.A., email [email protected]

Abstract:

Crop loss to wildlife impedes local support for conservation efforts at Kibale National Park,Uganda. Systematic monitoring of crop loss to wildlife (mammals larger than 3 kg) and livestock was con-ducted in six villages around Kibale over a 2-year period. Five wildlife species accounted for 85% of crop dam-age events: baboons, bushpigs, redtail monkeys, chimpanzees, and elephants. Marked variation in frequencyand extent of damage is reported within villages, between villages, and between wildlife species. Fields lyingwithin 500 m of the forest boundary lost 4–7% of crops per season on average, but the distribution of damagewas highly skewed such that maize and cassava fields were on occasion completely destroyed. Multivariateanalysis was used to test predictors of damage, including human population density, guarding, hunting, sightdistance, and distance from the forest. Tests were performed at two levels of analysis, field and village. Dis-tance from the forest edge explained the greatest amount of variation in crop damage, although hunting alsoinfluenced the extent of crop damage. Elephants inflicted catastrophic damage to farms but their forays wererare and highly localized. Livestock caused considerable damage to crops but farmers seldom complained be-cause they had institutionalized modes of restitution. Although most of the crop damage by wildlife is re-stricted to a narrow band of farmers living near the forest edge, risk perception among these farmers hasbeen amplified by legal prohibitions on killing wild animals. Elevating local tolerance for wildlife will requirediverse approaches, including channeling economic benefits to Kibale’s neighbors and providing compensa-tion in limited cases.

Predicción de Patrones de Daño a Cosechas por Vida Silvestre en los Alrededores del Parque Nacional Kibale enUganda

Resumen:

Pérdidas de cosechas debido a vida silvestre impide el soporte local para los esfuerzos de conser-vación del parque nacional Kibale en Uganda. Monitoreos sistemáticos de pérdidas de cosechas debido avida silvestre (mamíferos

.

3 kg) y ganado fueron conducidos en seis villas de los alrededores de Kibale porun período de 2 años. Cinco especies de vida silvestre fueron responsables del 85% de los eventos perjudicialesen las cosechas: baboons, bushpigs, monos cola roja, chimpancés y elefantes. Se reporta una marcada vari-ación en la frecuencia y extensión del daño reportado dentro de las villas, entre villas y entre especies. Cam-pos ubicados dentro de 500 m de los limites del bosque perdiéron 4–7% de las cosechas por temporada en pro-medio, sin embargo, la distribución del daño estuvo altamente sesgada de tal manera que los campos demaíz y cassava fueron en ocasiones completamente destruídos. Se utilizó un análisis multivariado paraprobar predicciones de daño, incluyendo densidad humana poblacional, cuidado, caza, visibilidad y distan-cia del bosque. Las pruebas fueron realizadas a dos niveles de análisis: campo y villa. La distancia al límitedel bosque explicó la mayor cantidad de varaición en daño a las cosechas aunque la caza también influyóen la extensión del daño. Elefantes provocaron daños catastróficos en granjas, pero sus incursiones fueronraras y altamente localizadas. El ganado causó considerable daño a cosecha, pero los granjeros rara vez sequejaron, puesto que existen formas institucionales de restitución. Aunque la mayoría del daño a cosechascausado por vida silvestre es restringido a una banda angosta de granjas localizadas cerca de los límites delbosque, la perceptión del riesgo entre estos granjeros ha sido ampliada por prohibiciones legales en el sacrifi-

Paper submitted October 7, 1996; revised manuscript accepted April 3, 1997.

Conservation BiologyVolume 12, No. 1, February 1998

Naughton-Treves Predicting Crop Loss to Wildlife

157

cio de animales silvestres. Incrementar la tolerancia local hacia la vida silvestre requerirá de diversas aprox-imaciones incluyendo la canalización de beneficios económicos para los vecinos del parque Kibale y en al-

gunos casos, proveer compensaciones.

Introduction

The integration of conservation with other land uses isespecially difficult where densely settled agriculturalland abuts a protected area containing large or poten-tially dangerous animals, as is the case for several parksin Africa and Asia (Dudley et al. 1992; Sukumar 1995).Although surrounding crops may provision wildlife con-fined to meager patches of protected natural habitat(Milton & Binney 1980; Ganzhorn & Abraham 1991), lo-cal farmers are unlikely to tolerate crop loss withoutcomplaint. At sites where the risk of property damageand loss of life by wildlife is perceived to be significant,local communities may be hostile to wildlife and opposeconservation programs.

The complaints of neighboring communities have ledpark managers to invest millions of dollars in preventinganimal depredation by constructing trenches and elec-tric fences (Seidensticker 1984) or planting nonpalatablecrops, such as tea, around parks (Reuling et al. 1992

a

).In addition to the cost of constructing and maintainingsuch barriers, these efforts exacerbate the insular condi-tions of parks (Western 1994).

Whereas the bitter complaints of farmers capture theattention of protected-area managers, only rarely is theactual impact of different wildlife species measured, orfactors tested to predict damage (but see Newmark et al.1994). The absence of this information hinders effectivemanagement, accurate comparisons between sites, andappropriate policy formulation. Given the politically vol-atile nature of conflict between humans and wildlife andthe high cost of current management options, there isan urgent need for careful analysis of field data. More-over, if local residents are to take an active role in man-aging wildlife, it is important to understand the extentand distribution of crop loss within the community.

I quantify the pattern of crop loss to wildlife in six vil-lages around Kibale National Park, Uganda. Here a di-verse assemblage of animals forage on subsistence andcash crops, and crop raiding is a dominant concernamong local residents (Aluma et al. 1989). I first presentdata regarding the frequency, extent, and predictabilityof crop loss, and the crop preferences of different spe-cies. This information reveals which animals are bestable to exploit the forest-agriculture edge habitat. Usingmultivariate methods, I then test several social and phys-ical variables as predictors of damage. Human popula-tion density, guarding, hunting, sight distance and dis-

tance from the forest are incorporated at two levels ofanalysis, the field and the village. These analyses revealhow landscape features, shaped by individual and collec-tive action, interact to affect the extent of damage. The re-sults inform management efforts to reduce crop loss towildlife and build local support for wildlife conservation.

Human-wildlife conflict is a relatively new researchsubject for conservation biologists (Dudley et al. 1992;Dublin 1995; Tchamba 1996). Most published researchon crop damage by wildlife around protected areas isbased on interviews with farmers (Hawkes 1991; Hill1993; Newmark et al. 1994). Such studies offer valuableinsight, particularly into the human perception of risk ofcrop loss. But relying entirely on interviews introducesinaccuracies. At several sites, investigations have re-vealed a disparity between reported and observed dam-age, with farmers most often overestimating the amountof crops lost to wildlife (Wakeley & Mitchell 1981;Mwathe 1992; Reuling et al. 1992

b

; Languy 1996). Otherstudies introduce error when they extrapolate observa-tions from a single site to an entire park or reserve. Be-cause research frequently focuses on sites where cropraiding is most intense, damage may again be overesti-mated (Bell 1984). I offer a detailed description and dis-cussion of methods of monitoring and analysis. Consid-ering the potential cost of management options and theimportance of the subject to local communities, croploss to wildlife merits careful field measurement and anunderstanding of its variability.

Study Area

Kibale National Park (Fig. 1) is a 766-km

2

remnant ofmid-altitude forest that once extended across the Rwen-zori Mountains to Zaire (Osmaston 1959). Regionalrecords from the nineteenth century describe a vast for-est inhabited by agriculturalists residing in scattered set-tlements (

,

40 households each; Taylor 1962). Tradi-tionally, farmers hunted wildlife in their fields, therebyhypothetically mitigating the costs of crop loss (Vansina1990). Nonetheless, crop damage by wildlife, particu-larly by elephants (

Loxodonta africana

), made some ar-able land uninhabitable (Osmaston 1959; Vansina 1990).During much of this century, the Ugandan Game Depart-ment aimed to confine wildlife to parks, and elsewhereto eradicate problem animals such as elephants, hippo-potami (

Hippopotamus amphibus

), and leopards (

Pan-

158

Predicting Crop Loss to Wildlife Naughton-Treves


thera pardus

), allowing the agricultural frontier to ex-pand (Uganda Game Department archives 1949–1972;Graham 1973).

The conversion of forest to agriculture in westernUganda was closely correlated with human populationgrowth. Human population density around Kibale forestmore than tripled between 1959 and 1990 (World Bank1993) because of the growth of the indigenous Toropopulation and the arrival of Kiga immigrants (Turya-hikayo-Rugyema 1974). Currently, 58% of land within1.5 km of the park boundary is used in smallholder agri-culture (Mugisha 1994), where family farms average 1.4ha (

6

1.4 ha,

n

5

97). Both Toro and Kiga farmers culti-vate a mix of subsistence and cash crops immediately onthe forest edge. The rapid population growth in the re-gion has resulted in land scarcity and has left Kibale as alast refuge for wildlife.

Today conservationists celebrate Kibale for its excep-tional diversity and density of primates (Struhsaker1981

a

) and regard it as a possible Pleistocene forestrefugium because it contains Congo Basin species atthe eastern extreme of their range (Struhsaker 1981

b

).Edge habitat is abundant at Kibale due to previous log-

ging and agricultural activities within the park. Todayabout 20% of Kibale is under fire-maintained grassland(Howard 1991). The mosaic of tall canopy forest, dis-turbed forest, agricultural fallows, and grassland pro-vides optimal habitat for wildlife species able to exploitthe forest-savannah edge (T. Butynski, personal commu-nication).

Among the diverse wildlife present at Kibale are spe-cies notorious for their crop damage elsewhere in Af-rica. The community of primates includes characteristicinhabitants of forest edge environments: olive baboons(

Papio anubis

) as well as redtail (

Cercopithecus asca-nius

), vervet (

C. aethiops

), blue (

C. mitis

), and the en-dangered L’hoest’s monkeys (

C. lhoesti

) (Kavanagh1980; Else 1991; Thomas 1991). Elephants have a longhistory of damaging timber stock, nurseries, and cropsin the area (Osmaston 1959). Also present are bushpigs(

Potamochoerus porcus

or

P. larvatus

) and crested por-cupines (

Hystrix africae-australis

), two little-studied an-imals notorious for crop raiding (Goldman 1986; Ver-cammen et al. 1993).

Information regarding the distribution and density ofthese species within Kibale is incomplete. The spatialheterogeneity of the forest derives from climatic,edaphic, and human disturbance factors, and the distri-bution of large vertebrates appears to be similarly patchy(Wing & Buss 1970; Chapman et al., in press). Previous re-search at Kibale has compared wildlife species densitieswith forest disturbance levels; for example, elephantsprefer selectively felled areas (Nummelin 1990; Struh-saker 1996). Census data indicate that densities of pri-mates vary significantly between sites within Kibale(Struhsaker 1981

c

; Skorupa 1988; Butynski 1990).During pilot interviews, farmers neighboring Kibale

reported crop loss to several wildlife species, amongthese, baboons and bushpigs were most frequently men-tioned (Naughton-Treves 1996). Farmers residing 3 kmor more from the forest described birds and rodents as“worst pests,” whereas those on the forest edge alsocomplained about elephants, chimpanzees, and redtailmonkeys. Despite ubiquitous complaints, visible signs ofdamage by large animals (

.

3 kg) were encountered onlywithin 300 m of the forest edge. Farmers responded tocrop loss by guarding (60%), leaving land fallow at theforest edge (50%), placing snares, traps, or poison bait intheir fields (15%), and/or abandoning cultivation of theirfarm altogether (4%).

Methods

Monitoring Damage

Six villages were selected to monitor crop damage fromJune 1992 through May 1994 (Fig. 1). Villages were se-lected according to three criteria: (1) crops were

Figure 1. Six study sites at Kibale National Park, Uganda. Sites 1–6: Sebitoli, Nyabubale, Kanyasohera, Kabucikire, Rurama, and Nkingo, respectively.



159

present within 0.5 km of the forest boundary, (2) the vil-lage was accessible by bicycle from the field station,and, most important, (3) the citizens approved of the re-search. The northernmost and southernmost villages inthe study were 25 km apart.

At each of the six villages, a grid was superimposed onfarms. This grid ran 1.0 km along the forest boundaryand extended 0.5 km away from the forest edge. Withineach grid, crops, guarding huts, domestic compounds,fallows, and land holdings were mapped at a resolutionof 250 m

2

(10

3

25 m). Intercropping occurred regu-larly, so a grid cell of 250 m

2

contained from zero to fivecrops. Maps were updated each planting season (twiceper year).

For 2 years, each village was visited weekly and thegrid was completely canvassed for crop damage. Ani-mals were viewed foraging in farms only occasionally;therefore, we relied on tracks, dung, dental impressionsin plants, diggings, wadges, and other physical remainsto identify the animal causing the damage.

Each season the accuracy and reliability of speciesidentifications were assessed on events of known originin blind interobserver tests. Observers were unanimouswith regard to the animal responsible for the damage,except for damage caused by the closely relatedL’hoest’s and redtail monkeys. In all analyses I combinethese congeners as redtails because this species was re-sponsible for 20 of 21 confirmed events and has a muchhigher population density (Struhsaker 1981

c

). It was notpossible to test interobserver reliability for crested por-cupine or civet (

Civettictis civetta

) damage due to theirrarity. Amounts of damage and distance measures werecalibrated by having observers sequentially canvass thesame area. These tests confirmed the skill of the fivefield assistants, whose life-long experience as farmers inthe region enormously facilitated data collection.

Damage by rodents (other than the crested porcu-pine) was not measured because the focus of this studywas on large (

.

3 kg) vertebrates, and farmers com-plained of rodent damage primarily to harvested andstored food rather than to standing crops. Rodent dam-age should not be assumed to be insignificant, however(Kamarudin & Lee 1989; S. Lahm, unpublished data).

Amount of damage was recorded by counting dam-aged stems of individually planted crops (e.g., bananas,maize, cassava, yams) and converting this to squaremeters using average planting densities for each crop.Damage on sown crops (e.g., sorghum, millet) was mea-sured directly in square meters (Sukumar 1989). Cropmaturity and parts consumed were noted. Distance tothe forest edge (from closest point of damage) was re-corded by a physical rather than a legal definition of for-est edge. The exact context of each damage event wasthus recorded in relation to the mapped grids. Becausemonitoring data were collected on a weekly basis, theage of damage events in days since the last time the grid

was canvassed was also recorded. Three categories ofage of events were established: 0–1, 2–3, and 4–6 dayselapsed.

Crop preference for each animal species was deter-mined by comparing observed and expected frequen-cies of raiding. The observed frequency was calculatedas the number of raids on a specific crop divided by thetotal number of raids on all crops. The expected fre-quency of raids on each crop was predicted simply byits availability (area and time planted) as a proportion ofall crop’s availability. The most preferred crops had thehighest positive deviations of observed from expectedfrequencies. Significance was assessed by a chi-squareanalysis.

Determining Independence in Damage Events

To compare crop loss between sites requires transpar-ent definitions of units of analysis and independent sam-ples. If an elephant damaged two farms in a single foray,for example, it could be tallied as one or two events de-pending on whether one takes the perspective of the ele-phant or the farmers. Because this study focused on wild-life behavior, the appropriate unit of analysis was theforay, defined as the exit from and return to the forest.

Forays by wildlife into farmlands often damaged multi-ple crops in separate fields. If damage to every grid cell(250 m

2

) were recorded as a separate event, the sampleof events would be inflated by the nonindependence ofnearby cells. On the other hand, frequent raiders maymake multiple entries and retreats each day (personal ob-servation), leaving a scatter of damage due to separate for-ays. A statistically independent damage event should en-compass all cells damaged in one foray. Because I did notobserve animals foraying but only the aftermath, all cellsdamaged on the same day by the same type of animal inthe same study site were potentially nonindependent.

I approached this problem by assuming that thegreater the distance between two cells damaged on thesame day by the same species the less likely they weredamaged in a single foray. Following this assumption,identifying independent forays for each species requirestwo steps: (1) calculating the probability of damage atever-increasing distances from a damage “epicenter” and(2) calculating the background probability that a ran-domly chosen cell will be damaged by the given species.

In the first step, 30 damaged cells (hereafter “epicen-ters”) were chosen randomly from the complete data setof damaged cells for each of the principal species (ele-phants, bushpigs, baboons, chimpanzees, and redtailmonkeys). At incremental distances from the epicenter(

X

5

10, 20, . . . 700 m), the proportion of cells dam-aged that day by the same species was calculated. Theexamination of 30 epicenters thus produced a distribu-tion of probabilities of damage as a function of distancefor each species.

160

Predicting Crop Loss to Wildlife Naughton-Treves


For each species at a certain distance (

X

), the proba-bility of damage drops to the random, background prob-ability for that species. That distance,

X

v

, is designatedas the vicinity of dependence. According to this defini-tion, damaged cells separated by a distance larger than

X

v

reflect damage caused by different forays. What re-mains is to calculate the random, background probabil-ity of simultaneous yet independent events (

B

).The random, background probability

B

is expected tovary by type of animal. Without direct observation of in-dividuals, I was constrained to an indirect method of cal-culating

B

. The proportion of weeks in which a set unitarea was damaged by a species over the course of 2years is the best estimate available for the frequencywith which independent events occur.

Analysis of Variation in Crop Damage between Villages

The first step in explaining variation in crop damage be-tween villages was to rank the villages in terms of avail-ability of preferred crops. This was calculated for eachvillage by the following equation:

where

S

is the area (m

2

) of

n

fields of the top five ani-mal’s two most preferred crops, and

D

is the shortestperpendicular distance (m) between each of

n

preferredcrop fields and the forest edge. This index (

V

) thusweighs preferred crops more heavily if they lie close tothe forest.

Once

V

was calculated for each of the six villages, therelative rank of each village was its

V

divided by thesummed

V

for the six villages. This relative ranking re-flects the expected frequency of wildlife raiding for a cer-tain village. The observed frequency was derived from thesum of independent raiding events by the top five animalsat a village, divided by the number of events at all villages.Thus, observed and expected frequencies are in the samedimensionless unit, ranging from 0 to 1. Deviations werecalculated as the observed minus the expected and weretested against four predictor variables: (1) percent areaunder fallow, (2) guarding intensity, (3) hunting intensity,and (4) human population density.

Analysis of Variation in Crop Damage between Fields

Fields were chosen for the second level of analysis be-cause they represent discrete units of relevance to bothanimals and farmers. Five variables were measured tocharacterize fields: (1) field size (m

2

), (2) distance fromforest (m), (3) guarding intensity, (4) hunting intensity,and (5) sight distance.

Using a raster-based geographic information system, Idefined a single field as an accumulation of all pixels (25m

2

) containing a certain crop lying within 20 m of each

Ω villageΣ S1( . . . Sn )

Σ D1( . . .Dn )/n------------------------------------,=

other (including intercropped fields). This objective def-inition allowed me to determine field size even with thecomplex geometry of intercropping. For example,maize fields, thus defined, ranged in size from 2 to 347pixels (50–8675 m

2

). Multivariate correlation was performed separately for

each of three locally abundant crops: bananas (

Musaspp.

:

n

5

68 fields), maize (

Zea mays

:

n

5

180 fields),and cassava (

Manihot esculenta

:

n

5

87 fields). Analy-ses were run separately for each wildlife species due todiffering crop preferences.

Defining Predictor Variables

Contextual variables used to predict damage were iden-tified from pilot research at Kibale, local farmer’s re-ported defensive strategies, and studies elsewhere. Hu-man population density was estimated by counting thenumber of domestic compounds per hectare of farm-land for each village. The average number of individualsper compound around Kibale was approximately five(

n

5

219).An index of guarding intensity was calculated for each

farm based on the following factors: (1) the number ofweekly observations of individuals guarding over 2 yearswas converted to a rank order ranging from 0 to 4 and(2) over a 2-year period the presence or absence ofguarding huts, guarding fires, noisemakers, scarecrows,guard dogs, and so forth was tallied (1

5

present, 0

5

absent), yielding values ranging from 0 to 4. Factors 1and 2 were added to compute the index of guarding,which resulted in values ranging from 0 to 7.2. Thisrough index presumably fails to distinguish betweenfarms with minor variation in guarding investments, butit certainly distinguishes farms lying near the extremes.Measuring the relative effectiveness of each type ofguarding was beyond the scope of this study.

To estimate the distance at which a guard could hypo-thetically spot an animal entering a field, an index ofsight distance was calculated based on a variation of amethod discussed in Schemnitz (1980). For each of the13 major field types planted (e.g., maize and cassava,maize alone, bananas, etc.) as well as for fallows of dif-ferent ages, three fields were randomly selected. In eachfield, an object 0.75 m high was hidden from three ob-servers. Beginning at a distance of 50 m, the three ob-servers searched for the object, and upon spotting it, re-corded their distance to it. The exercise was repeatedthree times for each field type. The distances were aver-aged between observers and within the field types. Theobject roughly corresponded to the height of a bushpig,the largest crop-raider, not including elephants. Sightdistance was not estimated for elephants which raidnocturnally at Kibale without exception. Sight distancewas later entered into a GIS and averaged within disks ofthree different sizes (radius

5

5, 15, and 90 m).



161

An index of hunting intensity was calculated based ona tally over 2 years of traps, snares, poison bait, huntingdogs, and remains of butchered animals found on farms(1 point for each observation). Summing this tallyyielded values from 0 to 5 (mean

5

0.35

6

0.95,

n

5

99). Hunting intensity within the forest was not esti-mated because individuals typically hunt 2–8 km insidethe forest (J. Okwilo, park guard, personal communica-tion), making it inappropriate to estimate and differenti-ate the consequences at a field or village level.

Statistical Analysis

Analyses at the village level used the nonparametricKruskal-Wallis test. Analyses at the field level used multi-variate regression techniques. The multivariate analysisof damage to fields required that predictor variableswere not collinear. This was assessed with a correlationmatrix. Only the three measures of sight distance werehighly collinear (

r

.

0.5). I used sight distance averagedin a radius of 15 m and discarded the measures at 5 or 90m because a 15-m radius approximates most field sizesbut does not obscure variation in vegetation structure, aweakness of the broader measure.

For the field level of analysis, I employed a stepwise re-gression incorporating all five predictor variables (fieldsize, distance from the forest, guarding index, hunting in-dex, and sight distance). This method allowed me to de-termine which variables were influential without reduc-ing degrees of freedom, but it did not permit calculationof one variable’s influence independent of that of othervariables. I then calculated a multiple regression usingonly those variables identified in the stepwise analysis todetermine the independent influence of each variable.

Results

Comparison of Damage among Animals

Over a 2–year period, 17 species of vertebrates were re-corded damaging crops at the six villages (Table 1). De-termining the vicinity of dependence (

X

v

, the maximumdistance separating damaged cells due to the same foray)for each animal was required for all subsequent analyses.The

X

v

for the top five wildlife species was as follows:redtail monkeys 25 m, bushpigs 175 m, chimpanzees290 m, baboons 520 m, and elephants 710 m. Imposingthis independence criterion on the data reduced thesample from 2635 to 1873 independent events. The re-duction was most pronounced for elephants: 71 recordsdropped to 34 independent events.

Cercopithecines (primarily redtails) were the most fre-quently observed forager on crops (Fig. 2), causing 51.8%of total independent events. Next came livestock (17.7%,goat and cow damage combined), followed by baboons(9.6%). The area damaged by each species reveals a differ-ent order: baboons (24% of total area damaged), elephants(21%), and livestock (16%). Elephants caused greater dam-age in a single foray than did any other animal (Table 1).

To determine the predictability of amount of damage,I calculated a regression based on the frequency ofevents suffered by a field and the percentage of that fielddamaged over the course of the study. The percentageof variance explained (

r

2

) is thus a relative indicator ofthe predictability of amount of damage due to one foray(redtails:

r

2

5

0.9,

p

, 0.0001, n 5 152 fields; chimpan-zees: r2 5 0.90, p , 0.0001, n 5 118; bushpigs: r 2 5 0.6,p , 0.0001, n 5 156; baboons: r2 5 0.5, p , 0.0001, n 5145; elephants: r 2 5 0.3, p , 0.0001, n 5 89).

Table 1. Amount and distribution of crop damage by animals around Kibale National park.

Animal Scientific name

Damage amountDamage

distribution

No. of eventsEvent (m2)

Mean 6 SD (range)Percent relativeto total areaa

(% of farms)b

n 5 97

Redtail monkeyc Cercopithecus ascanius 1252 16 6 23 (1–625) 15 88Livestock Capra sp., Bos sp. 414 52 6 76 (1–2000) 8 79Olive baboon Papio cynocephalus 228 136 6 350 (1–2774) 24 72Bushpig Potamochoerus procus 208 94 6 180 (1–2080) 15 72Palm civet Nandinia binotata 38 29 6 16 (1–102) 1 26Chimpanzee Pan troglodytes 146 61 6 219 (3–2500) 7 15Elephant Loxodonta africana 34 874 6 1530 (9–6510) 21 8Black & white colobus Colobus guereza 11 11 6 7 (1–21) ,1 8African civet Civettictis civetta 12 not available ,1 4Crested porcupine Hystrix africae-australis 6 7 6 7 (1–19) ,1 3Vervet monkey Cercopithecus aethiops 103 28 6 62 (1–625) 2 2Red duiker Cephalophus spp. 6 not available ,1 2Bushback Tragelaphus scriptus 3 not available ,1 1aTotal area damaged at six sites over 23 months is 13 ha.bn 5 97cIncludes damage by the closely related L’hoesti monkey (Cercopithecus lhoesti).

162 Predicting Crop Loss to Wildlife Naughton-Treves


Over 90% of all independent damage events occurredwithin 160 m of the forest boundary (Fig. 3), but this var-ied by species. For example, 90% of observed chimpan-zee forays occurred within 140 m of the forest, whereas90% of bushpig forays were observed within 300 m.

Thirty different crops were damaged by wildlife orlivestock. Each species showed different preferences(Table 2). Crop preference was not calculated for live-stock because farmers generally determine where goatsand cattle are maintained on a farm and thus influencethe crops selected. Similarly, data on livestock damageto crops were not included in tests to predict forayingbehavior at the village or field level.

Crop Damage and Analysis of Predictor Variables at the Village Level

Crop loss varied considerably both in the amount of dam-age suffered and the species causing damage. For exam-ple, baboons, bushpigs, and redtail and L’hoest’s monkeysdamaged crops at five villages, elephants at three, porcu-pines at two, and vervet monkeys at only one village. Vil-lages differed significantly in the frequency of crop raid-

ing by chimpanzees (Kruskal-Wallis H 5 16.5, p 50.0056), bushpigs (H 5 18.6, p 5 0.0023), and elephants(H 5 22.9, p 5 0.0004). Pooling damage for all animals,Nyabubale suffered 5.4 times more damage per unit areaof crops planted than did Nkingo (Fig. 1).

Significant deviations from the expected frequency ofdamage based on the index of crop availability (V) wereobserved (Naughton-Treves 1996). In other words, theobserved frequency of damage for each village did notconform to the expected frequency based on the avail-ability of preferred crops. I then tested for correlationbetween the four predictor variables and the deviations.Only the index of hunting intensity explained differ-ences among villages (H 5 10.6, p 5 0.034). A greaterinvestment in hunting was therefore associated with lessfrequent crop damage at the village level. This relation-ship was stronger when elephant raids were excluded( p 5 0.029).

Crop Damage and Analysis of Predictor Variables at the Field Level

The distribution of damage to fields was positivelyskewed: on average, banana fields suffered 3.5% damage

Figure 2. Relative contributions by principal species to total number of damage events (n 5 1868) and to-tal area damaged (n 5 13 ha) (June 1992–May 1994).

Figure 3. Frequency distribution of crop damage events by five wildlife species (baboon, bushpig, chim-panzee, elephant, redtail monkey) as a function of dis-tance from Kibale Forest edge.

Table 2. Crop preferences of five wildlife species at Kibale National park.

Animal

Rank of crop preferencesa

first second third Chi-squared

Baboons maize sweet potato ground nuts 31b

Bushpigs cassava yam sweet potato 165b

Chimpanzees brewing banana sweet banana sugar cane 30b

Elephants sweet banana cooking banana sweet potato 4Redtail monkeys sweet banana maize brewing banana 295b

aPreference was determined by significant positive departures in observed versus expected damage frequencies. Expected damage to each cropwas calculated on the basis of its availability. Rank order of crop preference was based on magnitude of observed minus expected values.bSignificance was assessed with the chi-squared test (p , 0.05).


Naughton-Treves Predicting Crop Loss to Wildlife 163

by area (n 5 68 fields, SD 5 7.2%), cassava fields 6.8%(n 5 88 fields, SD 5 19.5%), and maize fields 5.5% (n 5180 fields, SD 5 14.5%) (Fig. 4). Both maize and cassavafields were completely destroyed by wildlife on occa-sion, whereas banana fields never were (maximumloss 5 44%). Both frequency and amount of crop losswere regressed on the predictor variables for each typeof animal. The results of the multiple regression formaize, brewing bananas, and cassava follow.

The frequency of baboon damage to maize was nega-tively correlated with distance from the forest (n 5 145,F 5 10.1, p 5 0.0018); the amount of damage done bybaboons was negatively correlated with distance fromthe forest edge and positively correlated with sight dis-tance (n 5 144, F 5 7.2, p 5 0.0011). Frequency andamount of redtail damage were both negatively corre-lated with sight distance and both positively correlatedwith the index of guarding (n 5 151, F 5 9.1, p 50.0002; F 5 5.6, p 5 0.0044, respectively).

The frequency and amount of damage by elephants tobrewing banana fields were both positively correlatedwith the index of guarding (frequency: n 5 35, F 510.5, p 5 0.0027), whereas amount was also positivelycorrelated with field size (n 5 35, F 5 6.1, p 5 0.0006).The frequency of chimpanzee events was negatively cor-related with distance from the forest (n 5 42, F 5 4.2,p 5 0.046).

The frequency and amount of damage by baboons oncassava fields was positively correlated with sight dis-tance (n 5 65 fields, F 5 4.2, p 5 0.046; F 5 4.1, p 50.047, respectively). Although no single predictor vari-able explained a significant proportion of variance in fre-quency of bushpig events on cassava fields, the multipleregression model incorporating all five variables was sig-nificant (n 5 44, F 5 2.6, p 5 0.038).

Discussion

“Problem” Animal Species and Patterns of Crop Damage

The diverse assemblage of animals present at Kibale isreflected in the diversity of animals damaging crops. Pri-mates accounted for 71% of recorded events and 48% oftotal damage to crops. Primates also dominated the as-semblage of crop raiders around nearby forests of Bud-ongo, Bwindi, and Ituri (Hill 1993; Cooperative for Assis-tance and Relief Everywhere [CARE] 1994; Mubalama1996). At these sites farmers rank baboons and certain cer-copithecine monkeys as the worst pests; among primates,these appear to be best able to exploit the edge betweenforest and agriculture (Masau & Strum 1984; Else 1991;Thomas 1991). Given that forests are being fragmented andconverted to agriculture throughout East Africa, these om-nivorous, adaptable primates (e.g., redtail monkeys) mayappear better off than those requiring interior, closed-can-opy forest (Kavanagh 1980). Their long-term survival is atrisk, however, from the low human tolerance for “pests”and the potential impact of eradication schemes (Urquhart1961; Altman & Muruthi 1988). Moreover, this study indi-cates that most large wildlife species, even baboons andvervets, rely on a refuge of natural habitat and do not travelfar into densely settled agricultural land (Else 1991).

Around other African parks and reserves, a great diver-sity of wildlife has been recorded damaging crops (Bell1984; Hawkes 1991; Balakrishnan & Ndhlovu 1992;CARE 1994; Newmark et al. 1994; Lahm 1995; Mubal-ama 1996; Plumptre & Bizumuremyi 1996; Hill, in press).Dominating the lists are some of the same animals identi-fied in this study, namely baboons, cercopithecine mon-keys, bushpigs, and elephants. Such medium- to large-size mammals (.3 kg) receive higher ranking than dorodents and birds. This accords with the general obser-vation that larger animals receive greater attention, bothin management response and in farmers’ complaints(Bell 1984). But other researchers have proposed thatsmall animals, rodents in particular, cause greater dam-age (Dudley et al. 1992; Lahm 1994). In the absence ofsystematically collected field data, the relationship be-tween damage by large versus small animals—includinginsects—remains unclear (Caldecott 1988).

Confounding our understanding of crop loss to wildlifeis the variable and often vague description of study area.Surveys of crop raiding carried out on a national basis(Lahm 1994) or in villages several kilometers from a pro-tected area (Hawkes 1991) are more likely to identify birdsand rodents as problem animals than are similar studies onfarms adjacent to a park (Balakrishnan & Ndhlovu 1992;Plumptre & Bizumuremyi 1996). Productive discussion re-garding the relative agricultural damage by different ani-mals requires not only systematic data but a careful delin-eation of the extent of the study area and its proximity tonatural habitat or reservoir of raiding animals.

Figure 4. Extent of damage per season to fields of three crops (as percent area). Brewing bananas (n 5 68 fields), maize (n 5 180 fields), cassava (n 5 88 fields). Box plots depict spread of data (50% of points lie within the shaded box).



Interpreting the pattern of damage by animals of dis-parate size and behavior requires careful attention toscale, both spatial and temporal. Therefore, it is appro-priate to make such comparisons at different levels—thefield, the village, the park—and over different time peri-ods. Every week, redtail monkeys cause damage to manyfarms around Kibale National Park. Although each eventis predictably small, the sum of their damage over 2years at six sites matches or exceeds that for two largeranimals (chimpanzees and bushpigs). The pattern of ele-phant damage is quite different. Most farms aroundKibale suffer no damage by elephants. However, whenelephants visit a single farm, the amount of damage isunpredictable and may be overwhelming, destroying anentire farm’s production. Elephant damage summed forsix sites over 2 years was exceeded only by baboons.Moreover, elephant events are highly localized (Hawkes1991; Dudley et al. 1992). Thus redtail monkey damageappears to be relatively evenly spread among farms onthe edge of the forest, whereas elephant damage ap-pears clumped. Smaller animals also exhibit clumpeddamage patterns (Kamarudin & Lee 1989), but theclumps are typically observed at the level of individualplants, a fine scale of resolution beyond the scope of thisstudy.

Ultimately, the explanation for the localized pattern ofelephant damage remains a mystery, considering thatthey move throughout Kibale (P. Baguma, personal com-munication). The fact that elephants damaged only threeof the six study sites is especially interesting given thatGame Department archives describe persistent elephantdamage at these same sites (e.g., Sebitoli) dating to1951. Elephant foraging patterns are likely to be shapedby a complex set of poorly understood factors operatingat a large scale, such forest disturbance by logging in thenorth of Kibale and heavy poaching in the south. The lo-calized and persistent distribution of crop damage by el-ephants at Kibale parallels observations from other sites(Bell 1984; Hawkes 1991; Reuling et al. 1992b; Damiba& Ables 1993; Lahm 1994; Languy 1996; Tchamba 1996;Winer 1996).

The pattern of damage by different wildlife specieshas implications for farmers’ capacity to absorb damage.An individual farmer suffering elephant damage is likelyto view it as a catastrophic loss that overwhelms damagecaused by other animals. The size of the maximum ele-phant damage event recorded in this study (0.7 ha dam-age in less than 2 hours) exceeds the size of 35% of fam-ily farms at Kibale’s edge. In my study only elephantraiding caused entire farms to be abandoned.

In addition to variation in the distribution or magni-tude of damage by the wildlife species, each animal ex-hibited a different crop preference. Bushpigs targetedtuber crops such as cassava and yams, baboons tooksweet potatoes and maize, redtails ate sweet bananasand maize, chimpanzees ate the pith and fruit of brew-

ing and sweet bananas, and elephants destroyed the en-tire plant of bananas. Maize was consumed by all of themost frequent crop foragers. Compared to the other pre-ferred crops, maize stands out by having a short growingseason and ripening uniformly. Because maize fields arerelatively small, they represent a discrete, super-richfood patch when ripe. These characteristics make maizea risky crop for farmers to grow near the forest edge.

The discovery in this study that livestock damage tocrops is comparable or exceeds that caused by manywildlife species is significant because rarely do peoplecomplain to the government or outsiders about live-stock damage. The lack of complaint is explained by lo-cal perception that livestock are providing people bene-fits and that communities have established rules ofrestitution for livestock damage. Under customary law,the owner of a goat or cow must compensate the victimif his or her animal causes crop damage. Meanwhile,farmers view wildlife as the government’s property anddraw the analogy of the government being a bad neigh-bor, allowing its animals to damage crops but not offer-ing compensation. As one respondent put it, “Whyshould I feed these animals? These are not my animals.”

Predicting Damage at the Level of the Village and the Field

Predicting wildlife foraging behavior in a biologicallycomplex system subject to daily management decisionsby farmers is fraught with difficulties. Crop loss variedconsiderably between study sites in the amount of dam-age suffered and the animals causing damage. This varia-tion was unexpected, considering the proximity ofsome of the villages (e.g., Kabucikire and Kanyasoheraare less than 1 km apart). Crop availability did not fullyexplain intervillage variation in damage frequency.

Hunting partially explained deviations from the ex-pected damage based on crop availability for a village.Crop damage was less frequent at villages where farmersset more traps, snares, and poison in their fields. Yetonly 15% of farmers were observed using these tradi-tional hunting methods. Farmers at Kibale were not ob-served attempting to kill elephants (Milton & Binney1980; Sukumar 1989). Apparently, legal prohibition dis-couraged farmers from hunting. Although hunting inten-sity in a particular field was not observed to influencethe amount of crop loss for that same field, a nonhuntermight expect to benefit from the hunting activities ofneighbors.

Many more farmers attempted to defend their cropsby legal guarding than by illegal hunting. But multivari-ate tests did not reveal a significant reduction of croploss with increased investment in guarding at either thevillage or field level. Given the scope of the study, theactual efficacy of guarding by hour of investment couldnot be assessed. Farmers guard more frequently at theedge of the forest, which obscures the causal relation-



ship between animal forays and guarding. The positiveassociation of guarding and crop loss to redtail monkeysdoes not indicate that guarding attracts these animalsbut rather that farmers are responding to obvious risk,especially in relation to ripening maize. Similarly, farm-ers responded to recent elephant damage on brewingbananas with an increased investment in guarding—thusthe positive association between guarding and elephantdamage.

The fact that the majority of farmers do some guardingindicates that they perceive an associated benefit. Ani-mals have been observed to await the departure of farm-ers to move into fields (Kavanagh 1980; Horrocks &Baulu 1988; R. Wrangham, personal communication).Similarly, on several occasions animals fled from fieldswhen pursued by guards. In my study, although guard-ing did not predict patterns of crop loss, farmers’ invest-ment in guarding revealed indirect costs of crop raiding:labor investment and lost opportunities (e.g., childrenstay home from school to guard fields). Guarding andhunting are complex behaviors that are difficult to mon-itor accurately. Despite methodological challenges,farmers’ risk assessment and decisions regarding invest-ment in guarding and hunting merit further study.

Sight distance was included as a predictor variable ofcrop damage due to farmers’ reports of increased dam-age in “bushy” conditions. The fact that greater sight dis-tance (less vegetative cover) correlated positively withbaboon damage may be best explained by the prefer-ence of baboons for low-growing crops (e.g., maize,sweet potatoes, and groundnuts). Meanwhile, damageby the largely arboreal redtail monkey showed a nega-tive correlation with sight distance when it was feedingon maize. Because sight distance encodes informationabout arboreal habitat, this result could indicate that ar-boreal travel routes potentially limit redtail movements.Similar results were recorded elsewhere for crop raidingby the vervet monkey (Horrocks & Baulu 1988).

Human population density varied four-fold betweenvillages (Sebitoli: 119 individuals/km2, Kabucikire: 512 /km2), but it did not explain the amount of damage orthe animals recorded as raiders. Elsewhere, human pop-ulation density has been linked to variation in patternsof wildlife crop damage; for example, densely populatedareas suffer damage from small rather than large animals(Newmark et al. 1994). All six villages at Kibale had rela-tively high human population densities, yet medium andlarge animals caused damage, not only small animals(contra Newmark et al. 1994). Again, discrepancy in thesize of the study area may account for contrasting obser-vations between sites. If this study had monitored dam-age up to 12 km from the forest edge, rodents and birdswould likely figure more prominently as problem ani-mals (Newmark et al. 1994). Data indicating that over90% of crop damage by animals larger than 3 kg oc-curred within 200 m of Kibale’s boundary supports this

claim (Fig. 3). Additional surveys at four sites on theeastern face of Kibale revealed variation in animals re-sponsible (baboons generally predominated) but con-firmed the close relationship between forest distanceand damage.

Ultimately, proximity to the forest was the strongestpredictor of damage by baboons, bushpigs, redtail mon-keys, and chimpanzees. This represents a departurefrom farmers’ reports of severe losses up to 3 km fromthe forest boundary. Analysis of this disparity and detailon farmer perceptions and coping strategies are pre-sented elsewhere (Naughton-Treves 1997). The skeweddistribution of damage toward the forest edge does notmean that wildlife raid only at the forest edge. I ob-served elephant damage 2 km from Kibale’s edge on afarm where elephants had last visited over 50 years ago.But crop damage detectable at a significant level in this2-year study was concentrated in farms lying within 200m of the forest boundary.

Conclusions

Recent articles describe human-wildlife conflict as oneof the major conservation problems in Africa today(Dublin 1995; Tchamba 1996). Many conservationistsdescribe human-wildlife conflict as a new problem cre-ated by burgeoning rural populations settling within oradjacent to wildlife habitat (Baldus 1988; Balakrishnan &Ndhlovu 1992; CARE 1994; Pitkin 1995). But human-wildlife conflict at agricultural frontiers in Africa has ex-isted for decades, if not centuries (Osmaston 1959; Gra-ham 1973; Vansina 1990; Booth et al. 1992; Ville 1995).Human-wildlife conflict today occurs in a dramaticallydifferent setting. The survival of rare and endangeredspecies is at stake, and local farmers are constrained intheir coping strategies by land scarcity and hunting pro-hibitions.

In Uganda the combination of official vermin eradica-tion programs and the conversion of forest to agricultureduring this century has drastically reduced wildlife-hu-man conflict at the national level. In 1929, for example,elephants ranged over 70% of Uganda (Brooks & Buss1962). Today they persist within only part of the 7.9% ofnational territory with protected-area status (World Re-sources Institute 1994). Similarly, gorillas today occupyonly about 10% of their original range in Uganda (Butyn-ski 1984). Even species that thrive on forest edges, suchas baboons and redtail monkeys, have been eliminated asforest remnants are destroyed (Baranga 1993). MostUgandan farmers today need not fear crop loss caused bymedium-to-large wildlife species.

From a national or international perspective, a loss of4–7% of planted fields within 500 m of Kibale’s bound-ary appears a trivial price for maintaining threatenedhabitat and biological diversity. Some would argue that



the zone of heaviest crop loss (,200 m from the forestboundary) provides about 3000 ha of “extra” habitat forwildlife at Kibale (area calculated by Mugisha 1994). Butliving within this “extra” habitat are approximately 4000farmers who are frustrated and occasionally overwhelmedby crop loss to wildlife and who cannot legally utilizetheir full range of traditional defensive strategies, such ashunting. Moreover, estimates of average losses mask thegreat variation in amounts lost by different farmers andvillages. To the farmer who has lost an entire year’s pro-duction in a single night to elephants, average losses aremeaningless.

The great variation in crop damage between farms andvillages at Kibale accords with studies around other Afri-can parks, making it difficult to compare the severity ofthe problem between sites (Bell 1984; Hawkes 1991;Damiba & Ables 1993; Languy 1996). In Cameroon, ele-phants damaged from less than 10% to 30% of cropsaround a national park (Tchamba 1996), depending onthe type of crop and the village sampled. In Niger, an av-erage of 0.2% damage was recorded for farmers neigh-boring a park, yet certain individuals lost 100% (R. D.Taylor in Tchamba 1996). Average crop loss to wildlifeat a site in Malawi was estimated at 17%, but, again, indi-vidual farmers suffered from 0 to 100% (Bell 1984). Suchvariation calls for greater transparency in future studieswith regard to how the study area is defined and cropdamage sampled.

Farmers neighboring Kibale attempt to reduce croploss to wildlife by various methods, both legal (guard-ing) and illegal (hunting). Multivariate tests suggest thatonly hunting and modifying planting distance from theforest significantly reduce losses. Farmers owning rela-tively large farms (.5 ha) have the greatest flexibility incoping with crop raiding. They can maximize the dis-tance between their food crops and the forest by plant-ing less palatable crops near the boundary or by rentingout the most vulnerable parts of their farms (land valuesare roughly 20% lower at the forest boundary). Most vul-nerable are those individuals growing food crops onsmall farms (,1 ha) adjacent to the forest and at siteswhere elephant damage is common. Indeed, no strata-gem is identified here to reduce the risk posed by ele-phants. Maintaining elephants in a crowded landscape istherefore a profound challenge requiring interventionfrom institutions beyond the level of the individual orvillage.

No single management strategy will prevent all croploss to wildlife. The goal of management should not beto tightly control the problem but rather to raise generaltolerance of wildlife among the farmers, enhance theirmethods of defense, and lessen the impact of severelosses by elephants. My study indicates that a narrowband of farms absorbs most of the damage, evidencethat an individual’s best defense against crop loss towildlife is to have a neighboring farm between his or her

crops and the forest. This confirms other studies indicat-ing that a densely settled band of farms forms the bestbarrier to wildlife incursions deep into agricultural land(Bell 1984; Sukumar 1989; Hawkes 1991; Plumptre & Bi-zumuremyi 1996). Yet this result also proves that mostlarge wildlife species rarely travel outside of protectedareas amidst dense agriculture.

Elevating the tolerance of wildlife among Kibale’s im-mediate neighbors will require a variety of approaches(Naughton-Treves, in press). Efforts are underway toidentify unpalatable, low-growing, profitable buffer cul-tivars (G. I. Basuta, personal communication). Consider-ing the abysmal record of compensation schemes else-where in Africa (Booth et al. 1992), direct monetarycompensation should be considered cautiously and onlyin the case of losses to elephants. Similarly, electric fenc-ing should be considered only as a last resort whenother strategies have failed due to high construction andmaintenance costs, particularly in a moist forest environ-ment (Blair & Noor 1979).

Building local tolerance for wildlife should draw on thelesson provided by livestock. If farmers perceive directbenefits from wildlife conservation, they are more likelyto accept crop damage. Elsewhere, access to game meathas increased public support for conservation (Bell 1984;Damiba & Ables 1993). Bushpigs thrive at boundaries be-tween forest and agriculture (Vercammen et al. 1993),and their meat is highly valued. One option is to legalizehunting of bushpigs by farmers in their fields. Consider-ing the heavy toll of snares on endangered species suchas chimpanzees at Kibale (Ghiglieri 1984), bushpig hunt-ing would have to be controlled so as not to includeother species and not to occur within the park.

Farmers neighboring Kibale currently obtain severalbenefits from the forest (Naughton-Treves 1996), butmany of them are illicit, such as firewood collection orgrazing livestock in the park. Concerted effort should bemade to channel economic benefits from less destruc-tive sources, such as tourism revenue, employment op-portunities, and conservation trust funds, to the neigh-bors of Kibale, particularly to those who are absorbingthe most crop loss. The Uganda Park Service has re-cently begun promoting village conservation councilsalong the forest edge (Mugisha 1996). The aim of thesecouncils is to receive and allocate tourism revenue, man-age buffer zones, and (perhaps) administer compensa-tion programs (A. Rwetsiba, personal communication).By supporting these councils, the Ugandan Park Servicehas taken a first step toward building long-term localsupport and responsibility for Kibale.

Acknowledgments

This research was approved by the Ugandan NationalResearch Council and received generous support from



the Fulbright-Hays Dissertation Fellowship Program, theMakerere University Biological Field Station, the WildlifeConservation Society, and the American Association ofUniversity Women. A MacArthur Foundation postdoc-toral fellowship allowed me to prepare this paper duringmy stay at the Center of International Studies, PrincetonUniversity. I am grateful to P. Baguma, F. Mugurusi andP. Kagoro for their excellent field assistance. C. Chap-man and K. Redford offered invaluable advice and criti-cal analysis. Three anonymous reviewers provided use-ful comments. I especially thank A. Treves for hisboundless enthusiasm and rigorous thought.

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