ORIGINAL ARTICLE

Andrew J. Plumptre Æ Debby Cox

Counting primates for conservation: primate surveys in Uganda

Received: 6 September 2004 / Accepted: 26 February 2005 / Published online: 13 August 2005� Japan Monkey Centre and Springer-Verlag 2005

Abstract Primate census techniques have been devel-oped over the past 35–40 years yet there is still someconfusion and great variation in the methods used. Thisprecludes comparisons between sites where differenttechniques have been used. This paper discusses thevariations between the methods that seem to be prac-ticed currently and then describes a census of primates inthe forests of western Uganda. Primate density andbiomass varied greatly between forests as well as withinforests and this is probably related to food availability.Chimpanzee (Pan troglodytes) density was stronglycorrelated with nest encounter rates from reconnais-sance walks in the forest. This result can be used toestimate chimpanzee density in forests where it is diffi-cult to survey this species (e.g., due to security reasons).A total of 4,980 chimpanzee was estimated for Ugandawhich is higher than previously guessed, but still ofconservation concern. Only four forests had more than500 individuals which gives concern for long-term pop-ulation viability.

Keywords Biomass Æ Chimpanzee Æ Primate census ÆTropical forest Æ Uganda

Introduction

Primate censusing techniques have been developed sincethe late 1960s. Currently, there is general agreement thatline transect surveys are the most efficient method forsurveying most forest species, but there are still severaldifferent propositions as to what data should be col-lected and how they should be analyzed (Ghiglieri 1984;

Tutin and Fernandez 1984; Whitesides et al. 1988;Hashimoto 1995; Plumptre and Reynolds 1996;Struhsaker 1997). Different methods will give differentresults and there is a need to develop uniform methodsthat are repeated over time if we are to successfullymonitor primate populations for conservation purposes.Monitoring using line transect techniques does lead torelatively high standard errors and coefficients of vari-ation (Plumptre 2000) which makes it difficult to detectpopulation changes unless these are of the order of25–30%. Using different censusing techniques intro-duces additional sources of unknown error and as suchmakes it practically impossible to monitor populations.

Counting primates is useful for conservation inseveral ways. It provides:

1. A baseline density or total number that can bemonitored in the future

2. An assessment of the importance of different habitatsfor primate conservation

3. An assessment of changes in numbers since a previ-ous census period

4. An assessment of population trends if censuses arecarried out frequently enough.

Each of these can help conservation managers decidewhere to intervene to best conserve a species and alsoassess whether their conservation actions are trulyworking to conserve a species.

Methods

Primate census techniques

Direct counts versus line transects

Two types of counts that involve direct sightings exist.The first is a ‘‘complete’’ count of all animals. Examplesof this include the gorilla (Gorilla beringei) counts in theVirunga Volcanoes and Bwindi Impenetrable national

A.J. Plumptre (&)Wildlife Conservation Society, PO Box 7487,Kampala, UgandaE-mail: aplumptre@wcs.org

D. CoxJane Goodall Institute, Entebbe, Uganda

Primates (2006) 47: 65–73DOI 10.1007/s10329-005-0146-8

park (Harcourt and Fossey 1981; Mcneilage et al. 1998),where teams of people sweep an area in a regular fashionand aim to count all individuals that occur in the area byidentifying individuals from group size and characteris-tics of sign (size of dung and silver hairs). This method isusually only possible over small areas and, while notcounting all individuals, it is estimated to count at least95% (Mcneilage et al. 1998). The more usual method isto use line transect sampling to obtain an estimate ofprimate density which can then be converted to a pop-ulation density (provided the correct sampling proce-dure has been used) by multiplying the density from thesample area by the total area being surveyed. In general,line transects are located in a random or stratified ran-dom manner and observers walk the lines recording thenumber of primate groups and/or individuals seen andsome measure of distance from the transect/observer tothe primates. These distances are then used to estimatethe decline in visibility of primates with distancefrom the transect and counts corrected by fitting math-ematical functions to the drop-off in visibility (White-sides et al. 1988; Buckland et al. 1993; Laake et al. 1994;Struhsaker 1997).

Several variations exist in what information is re-corded and measured when undertaking line transectsurveys and it is useful to review them here and assessthe merits of each.

Perpendicular distance versus observer distance Struh-saker (1975, 1981) pioneered primate census techniquesin Kibale Forest in Uganda. He tested various meth-ods in areas where the primate density was fairly wellknown because the number of groups and the numberof individuals within groups were known. He foundthat using the perpendicular distance between thetransect and the primate group tended to overestimateprimate density, and he also found that using thedistance between the observer and animals when theyfirst sighted a primate group (observer distance) gavebetter estimates of the ‘‘true’’ density. However,‘‘observer distance’’ has no theoretical basis unlikeperpendicular distance sampling which has a rigorousmathematical framework (Buckland et al. 1993). Dis-cussions with the statisticians who analyze these typesof data have confirmed that using observer distance isan invalid technique because what matters is what anobserver fails to see perpendicular to the transect, nothow far in advance they see it (S. Buckland, personalcommunication). Observer distances simply provide ameans to reduce the density to a lower number. In-stead, what should be asked is why perpendiculardistances overestimated densities in the Kibale studies.Fashing and Cords (2000) found that observer distancecould both underestimate and overestimate groupdensity of colobus (Colobus guereza) and blue (Cer-copithecus mitis) monkeys and also advised againstusing this method. Perpendicular distances should bethe measurement taken.

Counting animals seen versus multiplying by an averagegroup size Most studies propose that groups are coun-ted from the line transect and that an average group sizeis calculated to multiply by the group density to obtainthe density of individual animals. Average group size isusually measured from known groups of habituatedanimals or from specific visits to non-habituated animalswhere a total count is attempted to determine a moreaccurate estimate of group size. This is because, whencounting from a transect, groups flee when they sight theobserver and it can be impossible to estimate the numberin the group. However, groups can split during a dayand, even in areas with small ranges, can be 200 m ormore apart (Plumptre 2000). As such, they would becounted as separate groups if encountered. This wouldincrease group density obtained from transect counts,and multiplying by the average group size wouldincrease estimates of individual density. If split groupswere counted as separate groups in the Kibale studies byStruhsaker, then this could be one reason why the per-pendicular distance technique overestimated primatedensity when compared with ‘‘true’’ densities. Similarly,estimating average group size from known groups maybias results because habituation of primates is usuallyundertaken for another objective (such as behaviouralstudies or tourism purposes). As a result, the largergroups tend to be habituated and small groups or loneanimals ignored. Also, when multiplying by an averagegroup size, there is the need to make the assumption thatgroup size is consistent across a forest and often betweenforests where there are no habituated monkey groupsfrom which to calculate average size. We would advo-cate that only the animals seen and counted whilewalking the transect are used as the ‘‘subgroup’’ size.This number can be used to estimate total primatedensity and corrections can be made for changes ingroup size observed with distance from the transect(Laake et al. 1994). Perpendicular distance countingmethods only assume that all individuals on the transectline are counted. Therefore, it is only when the centre ofthe subgroup falls on the transect line that it is assumedthat all individuals in the subgroup are counted; if offsetfrom the line it does not matter if individuals in thesubgroup are missed. Sighting all individuals on the linecan be difficult, particularly where primates are hunted,but if an estimate of the percentage of the subgroupmissed can be calculated then it is possible to correct theestimates using DISTANCE.

Perpendicular distance measured to centre versus nearestanimal seen Several studies suggest that the distancefrom the transect to the primate group should be madeto the nearest primate seen while others suggest esti-mating the centre of the group. Those studies that sug-gest the nearest primate then calculate the additionaldistance to the centre of the group by calculating a meangroup spread by recording group spread from habitu-ated groups and assuming a circular distribution

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(Whitesides et al. 1988). However, it is likely that groupspread varies with group size, it is known to vary duringthe day (Plumptre 2000), and it is very unlikely to becircular (Struhsaker 1997; Plumptre 2000). While groupsthat move in an elongated fashion might in the long runapproximate to a circular group with a diameter of theaverage length of the spread of the group, the variationswith time of day and group size will still confound theproblem. Consequently, it makes more sense to measurethe perpendicular distance to the estimated centre of thesubgroup/group of individuals actually observed, as thisis a value that is seen in the field while censusing ratherthan based on an average measured elsewhere. It is alsoimportant that this measurement is accurate (anassumption of all perpendicular distance estimators isthat measurements are exact) and they should be mea-sured with a tape measure or rangefinder rather thanestimated by eye. Estimating the centre of a group/subgroup is subjective and depends on a quick decisionwhen the monkeys are first sighted. Whether this sub-jective assessment is any less accurate than estimates ofdiameters of circular groups from the nearest individualneeds testing. However, it is possible to group perpen-dicular data into distance categories for analysis inDISTANCE, which allows this estimation error to bemanaged.

In the census results presented below, perpendiculardistance was measured to the centre of groups/sub-groups seen and analysed using DISTANCE (Laakeet al. 1994) with corrections for changes in group sizewith distance from the transect. These correctionslooked at where the drop off in visibility occurred forlone animals (around 10 m) and then calculated a meangroup/subgroup size for groups detected within thisdistance of the transect and multiplied group/subgroupdensity by this mean group/subgroup size.

Indirect counts

For certain species that occur at low density it is oftennecessary to employ counts of signs rather than animalssighted in order to achieve a sufficient sample size toreduce the standard errors to a level that allow mea-surement of future population changes (Plumptre 2000).Apes are therefore usually counted by counting their dayand night nests rather than sightings of animals(Plumptre 2000; Plumptre and Reynolds 1996, 1997).When counting signs of animals the same commentsabove apply, although because nests are static countingand measuring perpendicular distance is much easier.Nest counts require correcting to animal numbers bycalculating how many are constructed on average perday and their average rate of decay (Ghiglieri 1984).Counts can be designed to avoid the need to calculatenest decay rates by repeating counts along transectssufficiently regularly to record all new nests constructedand analysing density based upon counts of newlyconstructed nests (marked nest count; Plumptre and

Reynolds 1996). Nest decay is highly variable and canvary between seasons and sites. As such counts can bemore accurate by avoiding the calculation of decay rates.However, it does take more time to undertake a repeatedcensus using a marked count technique.

There is still a need to test the suggestions we havemade above in areas where we know the ‘‘true’’ primatedensity to assess whether these variations of the censustechniques can calculate true densities. In order tomeasure true density all animals in groups and all loneindividuals must be counted in a study area. We believethat keeping ‘‘correction factors’’ to a minimum isessential. Each correction factor has an associated errorand these must be factored into the overall error of thepopulation estimate (Plumptre 2000). Minimizing thecorrection factors will therefore minimize the associatederrors.

Censuses of primates in Uganda

As part of a programme of surveys of biodiversity in theAlbertine Rift forests of Uganda we censused primatesin eight forests with line transects and made reconnais-sance walks (Walsh and White 1999) in these eight andseven others (Fig. 1). Reconnaissance walks involvemoving through the forest in as direct a line as possible,

Fig. 1 The forests of western Uganda (dark stippling). The savannaparks and reserves are in medium stipple and the lakes in the lighteststipple. Forests surveyed with transects are in italics

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but moving around impassable areas that you would cutthrough when establishing a transect. It provides aquicker means of travelling through the forest but isbiased. Reconnaissance walks were made in areas wheretransects were established to allow correlations to bemade between encounter rates from reconnaissancewalks and densities obtained from transects for chim-panzee marked nest counts. The distance walked wasmeasured with a topofil thread.

In forests where transects were established, a strati-fied random sampling method was used where the forestwas divided into sectors of approximately equal size on amap and, within each sector, the forest was divided intofour strips of equal width and then a transect establishedat random within each strip. A baseline transect wouldbe cut in each sector to be censused and this baselinedivided into four equal lengths. A random numberwould be selected between 1and 100 from a randomnumbers table and used to determine the distance alongthe baseline that should be measured to the startingpoint of the transect in each strip along the baseline.Usually transects were cut in an east-west or north-southdirection (occasionally these directions were not appro-priate if the transects were to stay in the sector, and anorth east-south west or south east-north west directionwas selected). Transects were generally cut to be 4 kmlong. Transects were walked every 2 weeks for a periodof 3–4 months. Repeat counts along transects wereanalyzed as dependent sightings.

Primates were counted from early morning (0700–0800 hours) up to about 1100 hours along the transectsand data obtained were analyzed in DISTANCE. Pri-mate species encountered included chimpanzee, bluemonkey (Cercopithecus mitis), redtail monkey (C. asca-nius), L’hoest’s monkey (C. lhoesti), black and whitecolobus, red colobus (Pilocolobus badius), olive baboon(Papio anubis) and grey-cheeked mangabey (Lophocebusalbigena). Although gorillas (Gorilla beringei) occur inBwindi Impenetrable National Park they were censusedwith a total count in 1997 (Mcneilage et al. 1998).Nocturnal primates were not assessed.

Of the eight forests censused with transects, Budon-go, Bugoma and Kagombe Forest Reserves are at sim-ilar altitude, around 1,000–1,200 m; Kibale NationalPark and Itwara, Kasyoha–Kitomi and Kalinzu Forest

Reserves lies between 1,200 and 1,600 m and BwindiImpenetrable National Park lies from 1,600 to 2,700 m.Total distance walked in each forest on transects was asfollows: Budongo (513.7 km); Bugoma (511.2 km);Bwindi (285.6 km); Itwara (126.6 km); Kagombe(165.8 km); Kalinzu (311.0 km); Kasyoha-Kitomi(477.7 km); Kibale (564.1 km).

Two teams of six observers each were employed inmost of the forests. Rangefinders were used to measureperpendicular distance. Observers were tested withrangefinders and were accurate to 2 m over 99% of thetime and 1 m over 80% of the time (Plumptre andReynolds 1997). There is likely to be variation betweenobservers in their ability to detect primate groups.Observers therefore always travelled in pairs to increasethe chance of sightings, varied which transect theywalked when repeating transect counts to ensure eachobserver walked each transect at least one time, and thetwo teams surveyed every sector in the forest alternatelyto ensure each pair of observers walked each transect ineach sector. This will have evened out any variationbetween observers and sites.

Results

Primate density

Mean primate density per forest varied greatly betweensites, even for forests at similar altitude (Table 1). This islikely to be due to forest composition and food avail-ability. The blue monkey has an unusual density distri-bution where it is very common in Budongo forest, butvery rare in Bugoma, Kagombe and Kibale. In Bugomaand Kagombe forests this species was only observedonce in 3 months field work there. Where this specieswas studied in detail in Kibale (Butynski 1990), it wassuggested that low density in one sector of the forestmay have been due to a disease outbreak in the early1980s, because food availability appeared to be high andthere did not appear to be anything constraining thepopulation. However, the continued low density prob-ably means that other factors are contributing to limitthe population in this forest. This may also be true forBugoma and Kagombe. It may be that competition with

Table 1 Average density of primate species (no. km�2) for eachforest in which line transect surveys were made, calculated usingDISTANCE. Baboons were not readily censused using line transect

surveys because they are very terrestrial and were rarely encoun-tered. Where a species does not occur in a forest the cell is blank, ifat very low density it occurs as ‘‘low’’

Forest P.troglodytes C.mitis C.ascanius C.lhoesti C.guereza P.badius P.anubis L.albigena

Budongo 1.32 40.48 28.89 31.09 LowBugoma 1.99 Low 20.77 1.57 Low 7.26Bwindi 0.43 10.10 13.20 12.20 1.90 LowItwara 1.35 4.13 59.66 22.35 LowKagombe 0.97 Low Low 4.28 LowKalinzu 1.67 17.09 40.98 6.02 18.89 4.01Kasyoha-Kitomi 0.94 15.86 84.89 5.40 46.13 5.52 LowKibale 2.43 1.29 61.99 2.28 12.61 63.27 Low 33.88

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other species keeps their populations low, but this needstesting. In two of these forests where blue monkeysoccur at low density, chimpanzee density is high (Kibaleand Bugoma) and chimpanzees are known to prey onthis species. There is a significant negative correlationbetween blue monkey density and chimpanzee density(Spearman rank: r=�0.341, p<0.05). However, inKibale, the main prey species is known to be the redcolobus and in Budongo where there are no red colobusthe main monkey prey has been black and white colobusrather than the abundant population of blue monkeys.Redtail monkeys occur at relatively high densities inKibale and Bugoma and yet have been shown to havevery similar diets where they occur together with bluemonkeys (Cords 1986; Plumptre, in preparation: Bud-ongo forest). As such, we would expect a correlationbetween redtail and blue densities but there was none.However, in Kahuzi Biega National Park, blue monkeyswere a significant prey item for chimpanzees althoughhunting rates were low (Basabose and Yamagiwa 1997).It is unlikely though that in Kibale chimpanzee preda-tion accounts for the low density of blue monkeys be-cause of the preference for red colobus monkeys. Whyblue monkeys have remained at a low density since the1970s remains a mystery.

Chimpanzees are known to hunt red colobus wher-ever they occur together and to preferentially select thisspecies (Mitani and Watts 1999). In some sites, chim-panzees can have a major impact on red colobus pop-ulations harvesting up to 20–25% of the population in ayear (Stanford 1998). There was a significant negativecorrelation between chimpanzee nest encounter rate andred colobus encounter rate in the different sectors of theforest surveyed in Kibale National park (Spearmanrank: r=�0.767, p<0.05) implying that chimpanzeesmay be having an impact on the colobus populations inthis forest, where it is known that up to 90% of chim-panzee kills are red colobus (Mitani and Watts 1999).

Primate biomass

Primate biomass in the different forests ranged from 98to 1,411 kg km�2. However, within forests there couldalso be significant variation (Table 2). Kibale NationalPark on average ranked highest in terms of total primatebiomass with Kasyoha–Kitomi Forest Reserve closelyfollowing it. There was one site in Kasyoha–Kitomi

though that had a higher biomass than any site inKibale. Kibale has often been cited as having the highestprimate biomass recorded (Oates et al. 1990), but com-parisons have often been affected by the use of differentcensus techniques to calculate the primate densities. Ourresults have used the same method and so are morecomparable but they estimate a lower biomass densitythan has been previously estimated for Kibale (1,411 vs2,100–2400 kg km�2). This may be partly due to censustechniques (particularly multiplying group/subgroupdensity by an average group size), but also could be dueto the fact that our surveys were made throughout theforest rather than at a few selected sites, which wereselected for primate research or tourism and hence arelikely to have higher densities (Chapman and Lambert2000).

Chimpanzees formed about 4–8% of the primatebiomass in most of the forests surveyed, except BugomaForest Reserve and the adjacent Kagombe ForestReserve (Table 2). Both of these forests have low totalprimate biomasses and it would appear that chimpan-zees are faring better in these forests compared to otherprimates. Why this is so is unclear, as in general chim-panzees are more frugivorous than monkeys and wouldnot be expected to be as adaptable as species that canalso survive on leaves as well. There is little hunting ofprimates in Uganda by people so it is unlikely thatmonkey populations have been reduced by hunters.

Primate biomass increased significantly (r=0.76,p=0.03) with the number of diurnal primate species thatare found in the forest (Fig. 2). This may indicate thatspecies are avoiding competition so that, where morespecies occur, niche separation allows them to coexistand increase the total primate biomass. On the otherhand, it may be that only forests that can sustain ahigher biomass can also sustain more species and‘‘poorer quality’’ habitats may lose species throughcompetition. Which of these hypotheses is correct couldonly be determined through manipulation of habitatquality.

Chimpanzee population size in Uganda

One of the goals of the surveys of these forests was todetermine the number of chimpanzees in Uganda. Thesurveys reported above were made in most of the largeforests where chimpanzees occur in this country.

Table 2 The mean biomass ofprimates (kg km�2) for theforests surveyed with range overall sectors surveyed and thepercentage of the biomassformed by chimpanzees

Forest Meanbiomass

Biomassrange

Percentage formedby P. troglodytes

Budongo 845.07 377–1,157 6.1Bugoma 244.74 205–300 28.1Bwindi 214.45 152–246 6.4Itwara 671.48 548–795 7.5Kagombe 97.90 71–125 33.1Kalinzu 641.66 472–808 8.2Kasyoha-Kitomi 1,213.13 586–1,911 4.1Kibale 1,411.47 1,027–1,898 6.5

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However, for logistical and security reasons, it was notpossible to establish transects and walk them regularlyfor 3 months in all forest blocks where this species oc-curs. In the eight forest blocks where transects werewalked, a marked nest count method was used to obtainnest density estimates (Plumptre and Reynolds 1996).Nest density was converted to chimpanzee density bydividing by the number of nests built per day (1.09;Plumptre and Reynolds 1997) and multiplying by afactor of 1.20 to correct for the number of chimpanzeesthat do not build nests (less than 4 years old). The 20%correction factor was calculated from an average of thenumber of individuals less than four years in fourhabituated communities in Budongo and Kalinzu Forestreserves (S. Ohara, C. Hashimoto, personal communi-cation), and Kibale and Bwindi Impenetrable NationalParks (R.Wrangham, C. Stanford, personal communi-cation). There is a need to calculate the number of nestsconstructed daily at other sites to confirm whether thevalue of 1.09 obtained from Budongo Forest Reserve istypical of other communities. In Budongo, male chim-panzees built about 40% of day nests which was sig-nificantly lower than expected (v2 =4.52; df=1,p=0.033). There was no significant difference between

sexes in the reuse of nests (v2 =6.44; df=3, p=0.092). Ifsex ratios vary between sites there is therefore a likeli-hood that the number of nests constructed per day atother sites will vary as a result.

It was possible, however, to obtain encounter rates ofnests from reconnaissance walks in all forests wherechimpanzees are reasonably abundant and these walkswere made in areas where transects were censused sothat the two methods could be compared. The encounterrate of nests was strongly correlated with nest density(Fig. 3) and the regression line gradients did not differsignificantly (t=0.442, p=n.s.) between lower altitudeforests (650–1,250 m) and higher altitude forests (1,250–3,000 m). Regressing encounter rates of nests fromreconnaissance walks in all forests with chimpanzeedensity and forcing it through the origin gave a highlysignificant result (F=198.2; df=1,33; p=0.000, R2

adj=0.853). The regression equation is simply:

Density of chimpanzees ¼ 0:471ðEncounter RateÞ

Using this equation we predicted chimpanzee densityfrom forests where we were only able to obtainencounter rate data from reconnaissance walks. Five

0.00

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Fig. 2 Total primate biomassdensity plotted against thenumber of diurnal primatespecies found in the forest

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Fig. 3 Relationship betweennest encounter rates (no. km�1)from reconnaissance walks andchimpanzee density calculatedfrom transect censuses. Crossesare high altitude forests anddiamonds low altitude

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forest areas where chimpanzees were known to occur atvery low density were not surveyed with reconnaissancewalks, but estimates for chimpanzee density from forestsin the vicinity were used to extrapolate to these areas orestimates made from people who had worked there.These five sites only increased the estimate by 5.5%.

A total of 4,980 chimpanzees (95% confidence limits:3,500–6,270) was estimated for the whole of Uganda.This is higher than had been guessed at in the past(Teleki 1989; Edroma et al. 1997) but still low comparedwith human population densities in Uganda. The recenthuman population census resulted in 24 million peoplefor the country, of which none of the 164 counties andonly 1.6% of the subcounties had fewer people than thenumber of chimpanzees in the whole country (UgandaBureau of Statistics, personal communication). KibaleNational Park contained 25% of Uganda’s chimpanzeesand only four forests had populations with more than500 individuals (Plumptre et al. 2003).

Chimpanzee nest encounter rate varied with altitudewith higher encounter rates observed between 1,000 and1,500 m above sea level (Fig. 4), although low encounterrates could also be observed between these limits. Bal-comb et al. (2000) found that chimpanzee density wasstrongly correlated with the density of trees producinglarge fleshy fruits in Kibale National Park and the samewas true for Budongo Forest Reserve (Plumptre et al.1997). It may be the case that medium altitude forestsfavor a higher density of large fleshy-fruit producingtrees.

Discussion

Census methods

Methods used to count primates are still very variableand hence provide variable results. There is a need todevelop some consistency if good comparisons are to bemade between sites for both primate density and primatebiomass. There is still a need to test some of themethodological variations that occur in areas of known

primate density (preferably semi-captive situations,(such as monkeys on islands, as ‘‘known’’ density sites inthe wild may not have included all lone animals). Inparticular, there is a need to test whether countingsubgroups and estimating average subgroup size basedon observations within a minimum strip width (10 m inUganda) is a better method than multiplying by meangroup sizes obtained from habituated groups. This is themethod we used, but it needs testing in areas of‘‘known’’ primate density.

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0 500 1000 1500 2000 2500 3000

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Fig. 4 The encounter rate ofchimpanzee nests (no. km�1)plotted against altitude

Fig. 5 The variation in chimpanzee density mapped for KibaleNational Park where 25% of Uganda’s chimpanzees are found.The larger the circle the higher the density. The map shows theoutline of the forest (lighter stipple is woodland)

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In line transect sampling, perpendicular distancemethods should always be used and observer-animaldistance methods dropped because of the lack of anyunderlying theoretical framework for the latter method.There is also a need to investigate the measurement ofperpendicular distance and to determine whether dis-tance to the centre of subgroups as judged by the ob-server is better than distance to the nearest animal with afactor added for mean subgroup spread. The latter willallow a more accurate measure of perpendicular distanceas the observer knows exactly where the nearest animalis rather than estimating a group centre, but this isconfounded by the variability in group/subgroup spreadthat is added to the more accurate measure to obtain the‘‘true’’ perpendicular distance. As a result, we believedthat estimating the centre of what you can see is likely tobe as accurate as correcting for average group spread.

Consistent application of the same method allowsvalid comparisons between sites. Sometimes this is all astudy aims to do, but often the numbers produced arethen compared with separate studies which have usedvery different methods. Sometimes density estimates forthe same forest vary because of the sites where censusestook place in the forest were different. Chimpanzee den-sity varied greatly between sites in Kibale National Park(Fig. 5) and locations of censuses can therefore changeresults dramatically. The difference in the average pri-mate biomass in Kibale National Park estimated by thisstudy compared with that reported in previous studies ismarked because both methods and sites in the park havevaried. Comparisons of similar sites (Kanyawara andNgogo) gave more similar biomass estimates (1,898 and1,123 kg km�2), but still lower than previous estimates(2,100–2,300 kg km�2) indicating that different methodscan provide very different results. Observer-animal dis-tance has usually been used in line transect surveys in thepast in Kibale which makes comparisons difficult.

Primate densities and biomass in Uganda

The results from the eight forests surveyed exhibited awide variation in densities for the same species and intotal primate biomass. There was also significant varia-tion between sites within individual forests (Fig. 5) forindividual species and total biomass. This variation maybe a result of food availability and certainly some re-search suggests that food availability is correlated withchimpanzee density in Kibale and Budongo (Plumptreet al. 1997; Balcomb et al. 2000), red colobus in Kibale(Chapman Chapman 1999) and black and white colobusin Budongo (Plumptre 1997; and in preparation). Thedensity of species such as the blue monkey seem to beharder to predict through food availability alone, al-though in Budongo blue monkey density was correlatedwith the abundance of ripe fruit in their diets (Plumptreet al. 1997).

Predation may also be an important factor influenc-ing density in certain cases, and indeed in Kibale

National Park there was a negative correlation betweenred colobus density and chimpanzee nest abundance.Red colobus are known to be the favored prey ofchimpanzees. For this species, it is likely therefore thatboth predation and food may be important. Whetherpredation by other predators is important for otherprimates is uncertain. The other significant primatepredator, the crowned eagle (Stephanoaetus coronatus),was estimated to remove between 3–7% of the primatebiomass per year, from 18 months of all day follows of17 primate groups in Budongo Forest (Plumptre et al.1997). In Kibale, Struhsaker and Leakey (1990) esti-mated only 2.4% of the primate biomass was removedeach year by this eagle. Compared with the estimates ofchimpanzee predation on red colobus in Gombe (18%;Stanford 1998) these values are low, but more similar toestimates for chimpanzee predation in Tai Forest (3–7%; Boesch and Boesch-Achermann 2000).

How disease and accidents such as falls from treesaffect primate populations is poorly known. In the siteswhere long term studies have been made in Uganda(Kibale, Budongo, Kalinzu and Bwindi), there has notbeen an obvious decline in numbers of a species or ofsick animals being found. However, it is possible thatsick animals are soon predated and hence are not easilyfound and disease may be more important than is cur-rently acknowledged.

Primate conservation

Counts of primates provide useful baseline numbers thatallow subsequent monitoring of populations. Whereprimates have been recorded in the past using the samemethods (such as Budongo Forest), the numbers ofchimpanzees appear to be relatively stable since 1993(Plumptre and Reynolds 1996; Plumptre et al. 1997; thisstudy), while the numbers of monkeys increased in themid 1990s and have returned to the levels of the early1990s. Monitoring primate populations, particularlythose that are threatened with extinction, should be aregular activity wherever there are long term field sites.Yet it is only the mountain gorilla populations that havebeen consistently monitored over the years (Mcneilageet al. 1998; Plumptre and Williamson 2001). It is plannedto target the key forests in Uganda for chimpanzees andmonitor their populations every 5 years. The regressionresult obtained between nest encounter rate and chim-panzee density may be a useful tool to allow inexpensivemonitoring of this species, but we believe that furtherassessment of its accuracy is needed before we commit tomeasuring encounter rates only.

The results of the chimpanzee census were used todevelop a national strategic action plan for chimpanzeesin Uganda for the next 5 years. The fact that only fourforests in Uganda have more than 500 chimpanzees is ofconservation concern as the smaller populations maynot be genetically viable in the long-term. Severalforests with smaller populations are currently linked by

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corridors of natural/semi-natural vegetation whichcould be conserved and managed to allow chimpanzeemovements, improving gene flow. One of the key rec-ommendations of the action plan is to conserve theseremaining corridors. The census provided a usefulstarting point and focus to bring together decisionmakers and managers in Uganda to think about theconservation of this species. It also enabled the WildlifeConservation Society and Jane Goodall Institute topublicise the plight of chimps through the media. Cen-suses can therefore be a useful tool to use to attractattention and generate debate about what should bedone to conserve primate species, provided somethought goes into how the results will be used whenplanning the surveys.

Acknowledgements Much of the work described here was fundedby the Wildlife Conservation Society and the Jane Goodall Insti-tute. Particular donors included the Daniel K. Thorne Foundation,The John D. and Catherine T. MacArthur Foundation, US Fishand Wildlife Great Ape Conservation Fund, Crane Bank, Total,Uganda Breweries, Cleveland Metroparks Zoological Society,Berggorilla and Regenwald Direkthilfe, and UNDP/GEF throughWWF. We are grateful to the Uganda Wildlife Authority andUganda Forest Department and their many staff who supportedthese surveys. We would also like to thank Professor John Oatesand an anonymous reviewer for comments made on drafts of thispaper.

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