Does the tropical agricultural matrix bear potential for primate conservation? A baseline study from Western Uganda

Journal for Nature Conservation 21 (2013) 383–393

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Does the tropical agricultural matrix bear potential for primate conservation? A baseline study from Western Uganda Víctor Blanco a,b,∗ , Matthias Waltert a a b

Georg-August-Universität Göttingen, Department of Conservation Biology, Bürgerstrasse 50, 37073 Göttingen, Germany University of Edinburgh, Institute of Geography & the Lived Environment, School of GeoSciences, Drummond Street, Edinburgh EH8 9XP, UK

a r t i c l e

i n f o

Article history: Received 8 September 2012 Received in revised form 27 April 2013 Accepted 29 April 2013 Keywords: Agrobiodiversity Budongo Land-use Land sparing Mixed landscape Primates

a b s t r a c t As for most large mammals, conservation research on primates usually focuses on protected areas, and yet not much is known about primate communities in land-use systems in the absence of hunting. Using line transects, we estimated population densities for four primate species in a mixed agroforest landscape up to a distance of 4000 m from a forest reserve, in a region where no primate hunting takes place. We then modelled encounter rates and cluster size in relation to landscape parameters by means of bivariate analysis and Generalised Linear Models (GLMs). Black-and-white colobus (Colobus guereza) were most common, with confidence intervals for density estimates of 31.1–62.8 individuals/km2 . Red-tailed monkeys (Cercopithecus ascanius) and blue monkeys (Cercopithecus mitis) occurred at 15.2–37.9 ind/km2 and 13.9–36.7 ind/km2 respectively, and chimpanzees (Pan troglodytes) at 1.0–2.8 ind/km2 . Chimpanzee nest numbers and distribution appeared to be significantly constrained by transect forest coverage, forest coverage within 500 m and distance from the main forest, whereas monkey sightings were generally less restricted by landscape variables. The considerable population density of monkeys suggests that, in the absence of hunting, mixed agroforest systems may play a relevant role in primate conservation and highlight that it is useful to consider primate ecology in land sharing approaches to conservation. © 2013 Elsevier GmbH. All rights reserved.

Introduction Alongside rapid human population growth, overhunting and habitat conversion are thought to be the main drivers of the halving in the abundance of large mammals occurring in African protected areas since the 1970s (Baillie et al. 2004; Craigie et al. 2010). In West Africa for example, the demand for rich soils for agriculture, including those within parks and reserves (Bongaarts 2009; UN 1998), has resulted in the region being the most fragmented area of rainforest in the world (Minnemeyer et al. 2002; Rudel & Roper 1997). Due to the rapid loss of habitat resulting from logging and agriculture (FAO 2006; Gyasi et al. 1995; Mayaux et al. 2004) and uncontrolled bushmeat exploitation (Brashares et al. 2004), protected areas in this region show the strongest large mammal population declines (Craigie et al. 2010). For many wide-ranging species strict protection via protected areas is often not possible over large spatial scales (Stokes et al. 2010; Waltert et al. 2008). The concept of conservation landscape (Sanderson et al. 2002) assumes the existence of effective protected areas but also entails “biodiversity friendly” land-use

∗ Corresponding author. E-mail addresses: [email protected], [email protected] (V. Blanco), [email protected] (M. Waltert). 1617-1381/$ – see front matter © 2013 Elsevier GmbH. All rights reserved. http://dx.doi.org/10.1016/j.jnc.2013.04.001

practices in actively managed buffer zones to protect critical habitat for different species (Gardner et al. 2007), contributing to the long-term conservation value of the core protected areas (De Fries et al. 2007; Margules & Pressey 2000). Land sharing strategies (Phalan et al. 2011) may support conservation landscapes by spatially integrating biodiversity conservation and agricultural production, through wildlife-friendly farming (Fischer et al. 2008; Green et al. 2005). As a counterpoint, land sparing approaches segregate land for conservation from land for crops, attempting to prevent further encroachment by increasing yield production in already converted land (Balmford et al. 2005; Green et al. 2005; Perfecto & Vandermeer 2008). Even though both approaches aim to maximise the benefits for biodiversity and agriculture, neither seems to invariably guarantee effective wildlife preservation (Bhagwat et al. 2008; Kleijn et al. 2006; Matson & Vitousek 2006; Rudel et al. 2009). The vision of an agricultural landscape matrix which supports biodiversity conservation outside protected areas should also require an understanding of the effects of land-use systems on larger wildlife. So far, large wildlife has not been used as an indicator group as frequently as e.g. invertebrates and birds (e.g. Abrahamczyk et al. 2008; Lawton et al. 1998; Lung et al. 2010; Waltert et al. 2011), and even small mammals have only been treated occasionally (Medellin & Equihua 1998; Horvath et al. 2001). Thus, large mammals are almost totally excluded from

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discussions on agrobiodiversity and protected areas have generally been proposed as the only option for the conservation of large species with extensive area requirements (Terborgh 1999; Terborgh et al. 2002). There is also very little knowledge on the value of wildlifefriendly farming systems for forest primates (e.g. Anderson et al. 2007; Asensio et al. 2009; Estrada & Coastes-Estrada 1996). This is mainly due to the fact that primate hunting for bushmeat is widespread in many parts of the tropics, and especially throughout West and Central Africa (Ape Alliance 1998; Barnes 2002; BowenJones & Pendry 1999; Brashares et al. 2004; Fa et al. 2005), and empirical information on primate populations in land-use systems is therefore often masked by the effects of hunting. Thus, over much of the distributional range of African forest primates, scientists cannot easily study the effects of modified landscapes on primates in the field. In a first approach to study the issue in Africa, Chapman and Lambert (2000) investigated the impact of past anthropogenic landuse (abandoned farms and degraded forest) on present primate populations inside Kibale National Park, showing that relatively large populations of nearly all species were present across most of the formerly encroached land. Their study compared different sites with different histories of land-use practices and intensities, rather than assessing a large mixed landscape as a whole. Later on, Anderson et al. (2007) investigated the use of the matrix between forest fragments by Colobus angolensis palliatus using semi-structured interviews with local informants instead of standard survey techniques, and concluded that certain matrix habitats can be important for this colobus if located close enough to the forest edge, and/or the coverage of tall (>6 m) vegetation and/or food trees is sufficient. Some primates are known to inhabit forest fragments spread over a landscape mosaic (e.g. Onderdonk and Chapman 2000; Tutin et al. 1997), travel through croplands with considerable frequency (Anderson et al. 2007; Mandujano et al. 2004; Pozo-Montuy & Serio-Silva 2003) or use arboreal plantations for travelling and foraging (Anderson et al. 2007; Estrada & Coastes-Estrada 1996). Some studies even showed primates being more abundant along forest boundaries (Butynski 1985) or in forest fragments (Tutin et al. 1997) than in continuous forest. Even though some tree-dwelling primates can travel through farmland, long-distance terrestrial movement of arboreal primates seems to be relatively uncommon, perhaps due to higher energy costs, increased exposure to predation (Olupot & Waser 2001; Waser et al. 1994), and scarcity of food, shelter and refuge from predators (Baum et al. 2004; Bennett 1998). The use of each vegetation type in a matrix habitat that includes plantations appears to depend on structural similarity to forest, availability of food trees and distance to nearest forest patch. Furthermore, perennial (e.g. coconut, cashew nut, mango) and timber (e.g. Eucalyptus spp., Pinus, spp., Cupressus spp.) plantations provide dense coverage of tall vegetation and good structural connectivity between habitat patches (Anderson et al. 2007; Laidlaw 2000; Tischendorf & Fahrig 2000; Umapathy & Kumar 2000). Generally the ability of a primate species to survive in forest patches will be limited by its home range size (Estrada & Coastes-Estrada 1996; Lovejoy et al. 1986), its skill for dispersal among patches and to and from the main habitat (Anderson et al. 2007; Boyle and Smith 2010), and its diet requirements, especially for highly frugivorous species (Boyle & Smith 2010; Estrada & Coastes-Estrada 1996; Lovejoy et al. 1986). Different primate species in a given community often respond differently to fragmentation (Estrada & Coastes-Estrada 1996; Lovejoy et al. 1986; Onderdonk & Chapman 2000; Tutin et al. 1997) subject to their ability to use both the patches and the landscape matrix. Thus, when studying primates or designing conservation plans for them in fragmented landscapes, it is important to consider forest fragments as

functional components of the landscape, instead of as isolated biotic entities (Estrada & Coastes-Estrada 1996). For this study we wanted to assess (1) the composition of the primate community in a mixed landscape dominated by forest galleries/patches and agricultural land and (2) variance in primate density as a function of the proportion of forest/agricultural land and the distance to continuous forest habitat. We expected primate densities to be lower in the studied landscape mosaic compared to the continuous forest for which data are available from Babweteera et al. (Unpublished results). Given that different species may not be equally elastic in their response to environmental change (Estrada & Coastes-Estrada 1996) we also expected to find species-specific responses. Against this information we discuss in what way agroforest landscape mosaics could contribute to the conservation of primates in areas where forest conversion to farmland is prevalent. The data acquired would also be a basis to potentially include primate population data into the design of protection landscapes (Anderson et al. 2007).

Materials and methods Study area In the study presented here, we describe the forest primate community in a mixed agroforest landscape on unprotected land that ranges as far as ca. 4000 m from the edge of a protected area. We decided to choose the surroundings of Budongo Forest Reserve in Uganda as our study area owing to the fact that primates are generally not hunted there due to local taboos (Babweteera pers comm. 2010; Chapman pers comm. 2010). All five species of diurnal primates occurring in the forest reserve, chimpanzee (Pan troglodytes schweinfurthii), black-and-white colobus monkey (or guereza; Colobus guereza occidentalis), olive baboon (Papio hamadryas anubis), blue monkey (Cercopithecus mitis stuhlmanni) and red-tailed monkey (Cercopithecus ascanius schmidti), are also present throughout different ranges in West and/or Central Africa or have ecologically similar congeners (Groves 2007; Hoffmann 2008; Kingdon 2008; Kingdon et al. 2008; Wilson et al. 2008). Moreover, Budongo is bordered by a landscape mosaic dominated by farmland. The Budongo Forest Reserve, next to which the study area was located, lies between 1◦ 35 and 1◦ 55 N and 31◦ 8 and 31◦ 42 E, in the Masindi District, western Uganda, at an average altitude of 1100 m a.s.l. The reserve was gazetted as a central forest reserve in 1932. It includes a mixture of tropical high forest with a large population of mahoganies, woodlands and savanna grasslands believed to be capable of supporting forest. It covers about 825 km2 , 53% of which is a continuous tropical forest; the remainder comprises grassland communities (Hamilton 1984; Howard 1991). The Budongo Forest Reserve has been subjected to timber harvesting in most of its compartments during the 20th century until present (Eggeling 1947; Plumptre 1996; Tweheyo et al. 2004). Rainfall in this area presents a bimodal pattern (Tweheyo et al. 2005), with the first rainy season taking place between March and May and the second one between September and November. Mean monthly rainfall is 139 mm ± 67 SD, and monthly mean temperature 20.9 ◦ C ± 1 SD (Tweheyo et al. 2005). Most farmers around Budongo cultivate two crops, and sometimes three during years when precipitation is abundant (Tweheyo et al. 2005). Mainly subsistence crops are cultivated, such as cassava (Manihot esculenta), mango fruits (Mangifera indica) or pawpaw fruits (Carica papaya) (Tweheyo et al. 2005). Also sugarcane is extensively farmed to the south of the forest reserve. Primates are known to raid crops in this area, especially sugarcane.

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approximately 850 m/hour. Four out of five species present in the Budongo Forest Reserve were targeted by this study: C. ascanius; C. mitis; C. guereza; and, P. troglodytes. Because our intention was to focus on forest primates, P. hamadryas, generally described as savannah-dwelling (Rowell 1966), was left out of the survey. While direct sightings of groups of C. ascanius, C. mitis and C. guereza were recorded, for P. troglodytes only indirect data (nests) were collected. Sampling of chimpanzee nests was done using the ‘standing crop nest count’ method (Kühl et al. 2008; Tutin & Fernandez 1984), which requires the recording of all nests encountered, and the later use of auxiliary variables to convert nest counts to chimpanzee abundance. Nests were counted only once for each transect during the first run. The density estimate obtained with this method refers only to weaned chimpanzees (83% of the population, Plumptre & Cox 2006). Distances from the observer to the centre of a primate group or group of chimpanzee nests were measured with the help of a laser range-finder (Leica LRF 800 Rangemaster), and the angle between the direction of the transect and the observer-to-group centre segment was measured with a Silva sighting compass. Group centre was defined by the presence of a visible object (e.g. tree trunk, branch) that could be targeted in the centre of the observed group. Habitat type where a primate or nest group was observed was also recorded for each sighting. Habitat categories were forest, forest fragment, forest edge, forest corridor, woodland, bushland, grassland and farmland. Fig. 1. Study area within the frame of the Budongo Forest context, Uganda. Source of land cover information: BIOTA-E02 (Lung and Schaab 2009)

Nevertheless, it is P. hamadryas (which is not included in our study) that has been reported to be by far the most destructive of the crop raiding species in the area (Tweheyo et al. 2005). Our study site covered a mixed agroforest landscape of approximately 128.7 km2 located to the south of Budongo Forest Reserve, next to the forest edge (between 1◦ 38 27 -1◦ 43 50 N and 31◦ 25 42 -31◦ 32 40 W). Here the matrix is essentially composed of gallery forest and forest patches interspersed among croplands, sugar-cane plantations and some small patches of grassland. Data collection Line transect sampling (Buckland et al. 2010) was conducted between March and April 2011. We used a systematic sample of 32 line transects (Fig. 1). The sample as a whole was established with one random starting point. Transects were 1 km long and were separated 1.5 km from each other. They were located with the aid of a GPS (Garmin GPSMAP 76CSx) and were cut to their whole length except if they crossed plantations or cropped farmlands, through which we walked without cutting. When a transect crossed a plantation of mature sugar cane the segment falling within the cane was not surveyed and encounter rates along the segment were assumed to be zero; because of the impenetrability of these cane plantations for both humans and primates, no forest primates were expected to be present or seen from inside the plantations. Transect segments crossing sugar cane plantations amounted to 5.7% of the overall transect length surveyed distributed among six different transects. Transect surveying was carried out by V. Blanco and a single field assistant. Transects were walked from north to south and each one was surveyed four times, accumulating a total survey effort of 128 km. Data collection generally started at 7:00 and would go on until 13:30 hours at the latest. To minimise the possibility of obtaining biased estimates because of sampling being carried out along a 6.5 hour time span each transect was surveyed at different times of the day. Progression speed along transects was

Landscape composition To evaluate how the landscape composition could affect primate densities we measured six variables: transect forest coverage; landscape context within 500, 1000 and 1500 m from the transect; distance from the main body of the forest; and, distance from populated areas (Table 1). Data concerning these variables were obtained by analysing land-use spatial data with the GIS software ArcGIS 9.2. Geodata were originally generated by the BIOTA East Africa subproject E02 via remote sensing from satellite images from 2003 (Lung & Schaab 2008). For the landscape analyses, we firstly simplified the original geodatabase consisting of 14 land-cover types into two basic landscape units: forest, comprised by primary/“near natural” and secondary forest, considered to be the natural habitat for the four forest-dwelling primates under survey; and the agricultural matrix, composed of agricultural land (which accounts for ca. 47% of the land-cover for this landscape unit), grassland (ca. 25%), bushland (ca. 12%), woodland (ca. 8%), burnt areas, settlements and roads, pine plantations and wetland. The transect forest coverage was defined by the percentage of primary and secondary tropical forest traversed by each transect. The landscape context was defined here by the proportion of primary and secondary tropical forest within a given distance from the transect. We set three different distances: 500, 1000 and 1500 m, to account for the influence that different scales of approach to landscape context might have on primate densities (Fig. A1). Distance from the main forest was defined as the distance from the middle point of each transect (i.e. 500 m from either end) to the nearest point of the edge of what we characterised as the main body of the forest. To delineate such forest we selected the largest continuous fragment of the Budongo forest and defined a new boundary located 210 m inwards from the ecotone (Fig. A2). With this new boundary we left out a 210 m buffer zone of forest, so that the remaining block used to define our variable constituted suitable habitat for the primates away from the potentially more degraded forest edge. Finally, the distance from populated areas was defined as the shortest distance from the middle point of each transect to the nearest focal point of a village’s trade centre.

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Table 1 Definition of the landscape variables considered in the evaluation of the effects of landscape composition on primate densities. Landscape variable

Variable definition

Transect forest coverage Forest coverage within 500 m – landscape context

Proportion of primary and secondary tropical forest traversed by each transect Proportion of primary and secondary tropical forest within an area of 500 m of radius from any point of the transect Proportion of primary and secondary tropical forest within an area of 1000 m of radius from any point of the transect Proportion of primary and secondary tropical forest within an area of 1500 m of radius from any point of the transect Distance from the middle point of each transect to the nearest point of the edge of what we characterised as the main body of the forest. The main body of the forest was defined as the area contained within the largest continuous fragment of the Budongo forest excluding a peripheral zone of 210 m from the forest ecotone inwards Distance from populated areas was defined as the shortest distance from the middle point of each transect to the nearest focal point of a village’s trade centre

Forest coverage within 1000 m – landscape context Forest coverage within 1500 m – landscape context Distance from the main body of the forest

Distance from populated areas

Data analysis Data gathered during the survey were analysed with the software Distance 6.0 (Thomas et al. 2010), using Conventional Distance Sampling as the analysis engine. Density estimates for the four primate species were calculated. For P. troglodytes density estimation, a nest production rate of 1.09 nest/day (Plumptre & Reynolds 1997) and a nest decay rate of 54.6 days (Plumptre & Reynolds 1996) were included in the model. To estimate the detection function uniform, half-normal, hazard rate and negative exponential key functions were considered, together with the most adequate series adjustment for each of them (Buckland et al. 2001). We found half-normal functions to provide the best fit to data for all species except to that for C. ascanius, which was modelled using a uniform function (Table A). For a better fit of the model data for C. ascanius, C. mitis, C. guereza and chimpanzee nests were truncated at 60, 55, 100 and 23 m distance respectively. Cluster size in wildlife assessment terminology is the number of individual objects within a detected unit of objects of interest (e.g. in primates: a relatively tight aggregation of individuals of the same species, mainly sub-groups of larger sociobiological units) and is calculated from independent detection events (see Buckland et al. 2010). A chimpanzee nest cluster was defined as being composed of all nests of the same age class within 15 m of the other nearest nest (Baldwin et al. 1981). We used the size-biased regression method (regressing log cluster size to detection probability) to estimate expected cluster size, and relied on data truncated to 30 m from the transect for the guenons and to 50 m for C. guereza, to rely only on groups which were sighted from a relatively near distance. Encounter rate variance was calculated empirically using the estimator S2 (for systematic designs with non-overlapping strata). The model yielding the lowest Akaike’s Information Criterion value (AIC) and highest chi-squared goodness of fit (GOF) p-value for a given set of data was selected as the best fit for those data. Detection functions offered a good fit to data for the three monkey species and chimpanzee nests, yielding GOF p-values between 0.848 and 0.944. To analyse the influence of landscape features on primate numbers, we calculated encounter rates (no. observations/km), maximum cluster size and mean cluster size for monkeys and nests for each transect. These where then cross-correlated against each of the landscape variables using bivariate analysis in R (R Development Core Team 2011). Since some variables were not normally distributed (i.e. C. ascanius encounter rate, chimpanzee nest mean cluster size and distance to main forest), we used non-parametric Spearman rank correlations. To control for the expected proportion of false positives among all significant correlation coefficients obtained from Kolmogorov–Smirnov tests (type I error), we used the False Discovery Rate (FDR) method (Benjamini

& Hochberg 1995). This control method produces a q-value for each individual correlation, which is the minimum FDR at which a correlation coefficient may be called significant. To try to understand how primate numbers vary with changes in the landscape as a whole, Generalised Linear Models (GLMs) were constructed. With these we attempted to explain encounter rate variation and mean cluster size by the combined effects of four landscape variables: transect forest coverage; distance from the main body of the forest; distance from populated areas; and, either forest coverage within 500 or 1000 or 1500 m. Since the latter three landscape context variables, were nested variables and therefore interdependent, only the one most highly correlated with each primate’s encounter rate and mean cluster size would be included in the set of four variables of the corresponding GLM. Not normally distributed explanatory variables (i.e. distance from main forest) were log-transformed for model construction. Each model was subsequently run a number of times, and each time the least significant variable would be removed from the model until a minimum value for the corrected Akaike’s Information Criterion (AICc) (Burnham & Anderson 2004) was reached. At this point further removal of variables would result in AICc value increase. Then the resulting model was compared to the null model with a chi-square test. Goodness of fit of the model was tested using the generalised coefficient of determination (R2 ).

Results We sighted 218 C. ascanius, 260 C. mitis, 439 C. guereza and 231 chimpanzee nests, distributed within 85, 112, 139 and 112 clusters respectively along 128 km of surveyed transects. Encounter rates per transect varied between 0 and 3.75 clusters/km for the two guenon species, 0–3 clusters/km for C. guereza and 0–4.25 clusters/km for chimpanzee nests (Fig. 2). Cluster size varied between 1 and 9 individuals for the guenons, 1–8 for C. guereza and 1–11 for chimpanzee nests (Fig. 3). Among the whole study area, monkeys were sighted most often in the forest (73% of observations), along the forest edge (13%), in forest fragments or in forest corridors (11%). Few observations were obtained from groups sighted in farmland (0.9%), grassland (0.9%), woodland (0.6%) or bushland (0.6%). Chimpanzee nests, however, were only found inside the forest, on forest edges, in forest corridors and in one large fragment (2.43 km2 ). No nests were seen within the agricultural matrix. Resulting values of distance from the main body of the forest and distance from populated areas showed large differences among transects. Distances from transects to the main body of the forest ranged 0–4.30 km, while those from transects to populated areas ranged from 0.31 to 4.23 km.

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Fig. 2. Spatial distribution of mean encounter rates (ER) (clusters/km) per transect along the study area for (a) red-tailed monkey, (b) blue monkey, (c) black-and-white colobus monkey and (d) chimpanzee nests. Increasing diameter corresponds to larger encounter rates as shown in the legend. Source of land cover information: BIOTA-E02 (Lung and Schaab 2009)

Primate densities across the landscape

moderately correlated with a third variable (Table 3). However, similar tests for the three monkey species showed, in general, only moderate or weak correlations (Table 3). Encounter rates for C. ascanius were moderately correlated with transect forest coverage (Spearman rank correlation coefficient R = 0.61), forest coverage within 500 m (R = 0.41) and the distance to populated areas (R = 0.41). Maximum cluster size for this species was only moderately correlated with transect forest coverage (R = 0.41), and only weak or no correlations were found between mean cluster size and the landscape variables (Table 3). Clusters of four and five individuals were seen when surveying transects located within a largely agricultural landscape context (towards the south-west of the study area), generally resting in small forest fragments. Even a cluster consisting of seven individuals was sighted during survey resting on a tree isolated in the middle of the agricultural matrix. C. mitis encounter rates were only moderately correlated with transect forest coverage (R = 0.40) and the forest coverage within

Among the four species surveyed, C. guereza was the most common, with density estimates of 44.1 ± 7.8 SE ind/km2 (Table 2). Both guenon species showed fairly similar density values, with 24.0 ± 5.4 ind/km2 for C. ascanius and 22.6 ± 5.4 ind/km2 for C. mitis. P. troglodytes densities were estimated at 1.7 ± 0.4 ind/km2 . Landscape features’ relevance Among the three variables defining the landscape context (within 500, 1000 and 1500 m), the one which showed the strongest correlation coefficients against encounter rate, maximum and mean cluster size for all four primate species was forest coverage within 500 m. Spearman’s rank correlation tests showed chimpanzee nest encounter rates, maximum cluster size and mean cluster size to be highly correlated with two out of four landscape variables and

Table 2 Estimates of cluster size (corrected for size bias) (CS) and of density (D: individuals/km2 ), abundance (N), abundance 95% Confidence Interval (N 95% CI) and respective coefficients of variation (CS CV, D CV) for four primate species surveyed across a 129 km2 section of a mixed forest-agricultural landscape in West Uganda. Indirect data (nests) was used to assess P. troglodytes density, whereas direct data was used for all other species. Species

D

D CV

N

C. ascanius C. mitis C. guereza P. troglodytes

24.0 22.6 44.1 1.7

0.23 0.24 0.18 0.25

3086 2911 5682 220

N 95% CI

CS

CS CV

1950–4884 1794–4721 3997–8078 134–362

2.67 2.41 3.80 2.51

0.10 0.08 0.07 0.06

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Fig. 3. Spatial distribution of mean cluster size (CS) per transect along the study area for (a) red-tailed monkey, (b) blue monkey, (c) black-and-white colobus monkey and (d) chimpanzee nests. Increasing diameter corresponds to larger encounter rates as shown in the legend. Source of land cover information: BIOTA-E02 (Lung and Schaab 2009)

500 m (R = 0.37). Maximum cluster size showed larger moderate correlations against transect forest coverage (R = 0.52) and forest coverage within 500 m (R = 0.49), plus a moderate negative correlation against the distance from the main forest (R = −0.38). Mean cluster size was also moderately correlated with transect forest coverage (R = 0.43) and forest coverage within 500 m R = (0.40), and again showed a low to moderate correlation with the distance from the main forest (R = −0.33).

There was a low to moderate correlation between encounter rates of C. guereza and transect forest coverage (R = 0.34), and with forest coverage within 500 m (R = 0.35). Similarly for C. mitis, there was a reasonably larger moderate correlation between maximum cluster size and transect forest coverage (R = 0.50), and with forest coverage within 500 m (R = 0.50); there was also a low to moderate correlation between maximum cluster size and the distance from populated areas (R = 0.35). Mean

Table 3 Spearman correlation coefficients for four landscape variables: transect forest coverage; forest coverage within 500 m from the forest line; distance from the main body of the forest; and, distance from the nearest populated area vs. encounter rate (ER), maximum cluster size observed (Max CS) and mean cluster size (␮ CS) for each of the three monkey species and chimpanzee nests. Significant correlations, after applying the false discovery rate method, with a q-value <˛ = 0.05 appear in bold and those with q-value <˛ = 0.01 appear in bold and with a larger font size. Correlation coefficient values between ≤0.1 and ≥−0.1 are represented with a trace (–).

C. ascanius

Transect forest coverage Forest coverage within 500m Distance from main forest Distance from populated areas

C. mitis

C. guereza

Chimpanzee nests

ER

Max CS

μ CS

ER

Max CS

μ CS

ER

Max CS

μ CS

ER

Max CS

μ CS

0.61

0.41

0.29

0.40

0.52

0.43

0.34

0.50

0.43

0.72

0.80

0.77

0.41

0.29

0.20

0.37

0.49

0.40

0.35

0.50

0.42

0.65

0.74

0.73

-0.30

-0.18

-

-0.28

-0.38

-0.33

-0.14

-0.32

-0.38

-0.52

-0.60

-0.59

0.41

0.32

0.28

-

0.13

0.23

0.27

0.35

0.38

0.30

0.38

0.35

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Table 4 Landscape associations for encounter rates (ER) and cluster size (CS) for three monkey species and chimpanzee nests in a mixed forest-agricultural landscape in West Uganda. Figures for the landscape variables are ˇ coefficients from GLMs obtained from minimum adequate models based on AICc minimisation. “Model 2 − p” indicates p-value significance for chi-squared tests used to compare the obtained model against the null model. Predictive value of the models was estimated with the coefficient of determination (R2 ). Population parameter

Species

Intercept

% forest transect

% forest within 500 m

ER ER ER ER CS CS CS CS

C. ascanius C. mitis C. guereza Chimpanzee nests C. ascanius C. mitis C. guereza Chimpanzee nests

0.2789 −0.3798 0.6747* −1.2972* 0.7740 1.0270* 2.1680*** 0.2030

0.0213** 0.0170**

−0.0228*

* ** ***

log (distance from main forest + 0.01)

0.1156* 0.0085

0.0312*** 1.9142*

0.1819** 0.0005 1.8021*

1.5291** 2.1189***

Distance from populated areas

AICc

Model 2 − p

R2

0.0002

69.11 78.84 77.88 84.54 123.39 108.17 109.33 77.51

***

0.37 0.24 0.11 0.48 0.15 0.18 0.20 0.57

**

***

* ** ***

p < 0.05 p < 0.01 p < 0.001.

cluster size was moderately correlated with transect forest coverage R = (0.43), forest coverage within 500 m (R = 0.42), distance from the main forest (R = −0.38) and distance from populated areas (R = 0.38). Clusters of three to four individuals were observed in small forest fragments, twice in woodlands and once next to buildings. We found chimpanzee nest encounter rates to show a strong correlation with transect forest coverage (R = 0.72), a moderate correlation with the forest coverage within 500 m (R = 0.65) and moderate negative correlation with the distance from the main forest (R = −0.52). Maximum cluster size was highly correlated with the proportion of forest across the transect (R = 0.80) and forest coverage within 500 m (R = 0.74). There was also a moderate negative correlation of maximum cluster size with the distance from the main forest (R = −0.60) and a positive moderate correlation with the distance from populated areas (R = 0.38). Mean cluster size was also shown to be highly correlated with transect forest coverage (R = 0.77) and forest coverage within 500 m (R = 0.73). Also, there was a moderate negative correlation of mean cluster size with the distance from the main forest (R = −0.59) and a positive moderate correlation with the distance from populated areas (R = 0.35). Landscape associations for four primate species Forest coverage within 500 m was the landscape context variable most highly correlated with encounter rate and cluster size for all four primate species. Therefore, in GLMs (together with transect forest coverage, distance from the main forest and distance from populated areas) forest coverage within 500 m explained best encounter rate and mean cluster size (Table 4) variation across the landscape. The transect forest coverage dominated in the landscape models, showing itself relevant on predicting encounter rate for C. ascanius, C. mitis and chimpanzee nests, and cluster size for C. ascanius, C. guereza and chimpanzee nests. In contrast, the distance from populated areas was the least pertinent to primate numbers, being present only in one model (i.e. C. ascanius encounter rate) and representing no significance within it. Forest coverage within 500 m influenced encounter rates for C. ascanius and C. guereza, and cluster size for C. mitis. Distance from the main forest influenced encounter rates for C. mitis and chimpanzee nests, and cluster size for C. ascanius. Chi-squared tests showed three out of four GLMs to be significantly different from the null model for both encounter rate (corresponding to C. ascanius, C. mitis and chimpanzee nests) and cluster size models (corresponding to C. mitis, C. guereza and chimpanzee nests). Among the three significant encounter rate models, the one corresponding to C. ascanius was found to be counter

intuitive, since the ˇ coefficient for the percentage of forest within 500 m was negative. From the remaining five models, the proportion of the variation in encounter rate and cluster size across the study area could only be explained to a reasonable extent by the two models corresponding to chimpanzee nests (R2 = 0.48 for encounter rate, and R2 = 0.57 for cluster size). Discussion Our study confirms that forest primates may display a remarkable adaptability to mixed landscapes, confirming earlier ecological information on the studied primates (e.g. Butynski 1985; Chapman et al. 2000; Onderdonk & Chapman 2000; Tutin et al. 1997; Wieczkowski 2010). Monkeys exhibited considerable population densities and even P. troglodytes were present, although this presence was largely restricted in the studied land-use system to forested areas. C. ascanius densities inside Budongo forest are estimated at 28.4 ind/km2 and C. mitis densities at 50.7 ind/km2 (Babweteera et al. Unpublished results). The densities we found were 16% and 55% lower respectively. In general, the main component of the diet of these guenons consists of fruit (Rudran 1978; Cords 1986; Butynski 1990; Fairgrieve & Muhumuza 2003; Rode et al. 2006). Considering that food is a limiting factor for primate population size (Chapman & Fedigan 1984; Milton 1993; Plumptre & Reynolds 1994; Rode et al. 2006) and given that frugivorous primates face a difficult task having to meet energy needs from fruits and to obtain protein and minerals from alternative foods, frugivores may be more susceptible to habitat modification than folivores (Skorupa 1988). Nevertheless, the considerable diet variability shown by C. mitis within and between sites (Lawes 2002; Fairgrieve & Muhumuza 2003) could suggest a higher ability to cope with modified habitats than species with narrow diets (Harris & Chapman 2007). In fact, C. mitis (although not C. ascanius) were more abundant along the forest boundary than in its interior (Butynski 1985). Tutin et al. (1997) reported several frugivorous primates being found at similar or higher densities in forest patches than in intact forest. For these primates living in forest fragments and edges could be an asset, since neighbouring cropland may provide additional dietary resources (Naughton-Treves 1996). Our results somewhat contrast these studies, but Tutin et al. (1997) surveyed only fragments within 450 m from the forest boundary whereas our survey reached as far as ca. 4000 m. Despite finding guenon densities to be lower in the mixed landscape than inside the forest, the size of these populations outside their natural habitat was remarkable. Estimated densities of C. guereza and P. troglodytes in the landscape mosaic were fairly similar to those in the Budongo forest

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(42.1 ind/km2 and 1.6 ind/km2 respectively for Budongo; Babweteera et al. Unpublished results). Several studies have previously shown colobines to be most successful in disturbed habitats such as degraded forest (Chapman et al. 2000; Chapman & Lambert 2000; Harris & Chapman 2007; Oates 1977; Oates 1996; Struhsaker 1997; Thomas 1991), forest edges (Butynski 1985; Harris & Chapman 2007) and forest fragments (Oates 1996; Onderdonk & Chapman 2000; Chapman et al. 2004). C. guereza numbers are said to grow in altered habitats because of greater availability of food species in secondary successions (Johns & Skorupa 1987; Oates 1977; Plumptre & Reynolds 1994; Struhsaker 1997), higher intake of energy available through higher light levels (Johns & Skorupa 1987), and possibly due to a drop in competition from species negatively affected by forest degradation (von Hippel et al. 2000). In Budongo, their presence beyond the forest boundary (i.e. forest fragments or woodland) might have also been influenced by the presumed absence of P. troglodytes (with the exception of one fragment), for which C. guereza is the main prey in this forest (Newton-Fisher et al. 2002; Reynolds 2005). Furthermore, the relative flexibility of their folivorous diets may presumably allow them to adapt to moderate changes in their environment (Chapman et al. 2000; Chapman et al. 2005). Chapman et al. (2004) even observed C. guereza in a forest fragment neighbouring Kibale National Park, Uganda, to use 9.5% of their feeding time consuming ripe fruit, and especially guava (Psidium gujava), an exotic species from South America. We also sighted C. guereza twice resting in the woodland. All this largely illustrates the considerable resilience that C. guereza can display in anthropogenic habitats in the absence of hunting, and might explain to a considerable extent the high densities displayed in the mixed landscape. The similarity between P. troglodytes density estimates from the studied agroforest matrix and those from inside Budongo forest was not accompanied by a broad use of the mixed landscape by this great ape: chimpanzee nests were not found anywhere beyond the forest boundary, with the exception of one large fragment near main Budongo. However, the systematic distribution of the transects did not allow us to survey in depth the different fragments within the study area and so we could not confirm the absence of P. troglodytes from the large majority of them. Other studies have actually found P. troglodytes to use forest fragments (Tutin et al. 1997; Onderdonk & Chapman 2000). Still, P. troglodytes seemed to make use of areas at or near the forest boundary. Tweheyo & Lye (2005) found Budongo chimpanzees to spend most of their feeding time in forest edge and logged areas. This use of the forest boundary is justified by the convenience of being able to supplement their diet with crops (Naughton-Treves 1996), especially during the dry season when fruits are scarce in the forest (Hockings et al. 2009; Tweheyo et al. 2005; Tweheyo & Lye 2005). Therefore, the availability of seasonal cultivated crops (e.g. mango fruits and pawpaw fruits) and crops that are cultivated all year round, such as sugarcane (Saccharum officinale) (Tweheyo & Lye 2005), may be the reason for the relatively high chimpanzee nest encounter rates at the edge of the forest. Our results showed encounter rates and cluster size for monkeys to be, in general, only moderately related to landscape parameters. Chimpanzee nest encounter rates and cluster size showed considerably high and moderate correlations with most of the landscape variables. GLMs explained approximately half of the variation in chimpanzee nest encounter rate and cluster size, but would only explain 11–24% of that variation for the monkeys. The remaining variation in the data not explained by the models is likely due to smaller scale ecological parameters, (e.g. local food-tree composition, distribution and abundance (Onderdonk & Chapman 2000), access to alternative dietary sources (Riley 2007), presence of logging activities (Chapman et al. 2000), or predation (Irwin et al. 2009).

The increase in encounter rates and cluster size of chimpanzee nests with the proportion of forest along the transect and within the larger landscape may reflect the importance of the size of forested area available for P. troglodytes. Considerable amounts of food resources are necessary to be able to support large body mass (Pusey et al. 2005; Uehara & Nishida 1987) and group size (Boesch 1996; Reynolds 2005) requirements. Together with a large home range (7–26.5 km2 ; Newton-Fisher 2003), larger food availability (Basabose & Yamagiwa 2002; Furuichi & Hashimoto 2004) in addition to a denser canopy cover (McCarthy & Stanford 2012), isolation from human disturbance (McCarthy & Stanford 2012: Plumptre & Reynolds 1997) and protection from wind (McCarthy & Stanford 2012; Samson & Hunt 2012) provided by continuous forest, may partly explain the presumed absence of P. troglodytes from most fragments, especially small ones. Monkeys, on the other hand, did use the fragments and were occasionally even found away from forested areas, hence the only moderate correlation between the proportion of forest and monkey numbers. Results from bivariate correlations suggest that, even though C. guereza show themselves capable of using the agroforest landscape, as forest availability becomes smaller group size decreases as well, perhaps due to reduced availability of food resources (Umapathy et al. 2011). Similarly, Marshall et al. (2010) observed the presence of this species in forest fragments to be most related to forest area. The cluster size in C. mitis was also moderately related to the proportion of forest. Chimpanzee nests followed a similar trend, yet the relationship with the proportion of forest was stronger. It may be that by adapting group size to food availability these primates can minimise intragroup feeding competition (Chapman & Chapman 2000; Clutton-Brock 1974; Mbora et al. 2009; van Schaik & van Hooff 1983). The transect forest coverage was more highly correlated to encounter rate and cluster size than to the forest coverage within 500 m most likely because the large majority of the observations were recorded within ca. 80 m from the transect. In our study area the presence and cluster size of any primate at a given location was more determined by the environment that directly surrounds it (e.g. within 80 m) than by the environment at a much larger scale (e.g. within 500 m), of which it might or might not make use. Additionally, we have to consider the possibility that the actual distribution of the forest may influence the presence and densities of primates. Hence, the fact that the proportion of forest within 500 m does not reflect the distribution of the forest may render this variable less relevant than it could potentially be when trying to reflect the impact of the landscape context. Distance from the main forest and especially distance from populated areas appeared to be not as relevant in defining encounter rates and cluster size across the landscape as the proportion of forest. The distance from the main forest was only related to chimpanzee nest encounter rates and cluster size. Because nearly no observations where made beyond the forest edge for chimpanzee nests and because the distance to the main forest boundary was measured from the centre of each transect, most values recorded beyond ca. 800–1000 m were extremely low or nil. P. troglodytes has nevertheless been observed to use the agricultural matrix and even cross through villages in Guinea-Bissau, where it was not hunted (Karibuhoye 2004). The availability of forest might well be more relevant to primate numbers and distribution across a mixed landscape than human presence. This assumption can be reasonable for regions where primates are generally not hunted. The primate densities reported here may show the principal importance of mixed agroforest systems in the preservation of forest-dwelling primate populations in tropical Africa. This does not mean that mixed landscapes can be a substitute for protected forests though. We do not know whether such a landscape may or may not be able to sustain primate populations in the

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absence of protected areas. However, it seems fairly reasonable to assume that tropical agricultural matrices may support primate populations independently from protected areas, and we therefore advocate inclusion of primates and eventually other large mammals in further studies on the biodiversity of tropical landscapes. This conclusion is supported by recent studies on great apes, which point out the widespread occurrence of chimpanzees inhabiting riverine forest fragments (McLennan & Plumptre 2012) and agroforest matrices (Brnic et al. 2010), and of orang-utans in agroforest systems (Campbell-Smith et al. 2011), all of them outside of protected areas. Our study at least confirms that a mixed landscape has the potential to act effectively as an ecological buffer zone (Mackinnon et al. 1986) next to protected forests, reducing the pressure of socioeconomic activities on biodiversity. Not being in contradiction to the generalized importance of land sparing strategies for large wildlife conservation, a land sharing approach may also bear potential for large mammal conservation. Hence, there is a necessity to incorporate land-sharing practices within integrated protected area management in the form of buffer zones to safeguard primates and large wildlife in general. Further in-depth studies are of course needed to improve our understanding of primate responses to land-use systems, and to make informed decisions on mixed landscape management. Additional detailed analyses will be needed for each species to see how living in such landscapes influences feeding and ranging behaviours, nutritional requirements, activity and stress levels, exposure to disease, social groupings, and final demographic rates. We may nonetheless suggest now that, if protein demand eventually removes its’ burden from primates, population growth and changing land-use are not necessarily inevitable extinction threats for all wild forest primate species. Acknowledgements We thank farmers, field assistants, and particularly Geoffrey Muhanguzi and Budongo Conservation Field Station who made possible fieldwork in Uganda. The works of Matthias Waltert are currently being supported by a grant from Volkswagen Foundation through its Africa Initiative ‘Knowledge for Tomorrow’. Permission to conduct part of this research within Budongo was provided by Uganda National Council for Science and Technology and the National Forest Authority. We are grateful to BIOTA-E02 (T. Lung and G. Schaab) for sharing Budongo land-cover geodata with us, and to Sacha Viquerat for assistance with data analyses. We also want to thank two anonymous reviewers for their constructive comments and assistance in improving our manuscript. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.jnc.2013.04.001. References Abrahamczyk, S., Kessler, M., Putra, D. D., Waltert, M., & Tscharnke, T. (2008). The value of differently managed cacao plantations for forest bird conservation in Sulawesi, Indonesia. Bird Conservation International, 18(4), 349–362. Anderson, J., Rowcliffe, J. M., & Cowlishaw, G. (2007). Does the matrix matter? A forest primate in a complex agricultural landscape. Biological Conservation, 135, 212–222. Ape Alliance. (1998). The African bushmeat trade: A recipe for extinction. Cambridge: Fauna and Flora International. Asensio, N., Arroyo-Rodríguez, V., Dunn, J. C., & Cristóbal-Azkarate, J. (2009). Conservation value of landscape supplementation for howler monkeys living in forest patches. Biotropica, 41, 768–773. Barnes, R. F. W. (2002). The bushmeat boom and bust in West and Central Africa. Oryx, 36, 236–242. Baillie, J., Hilton-Taylor, C., & Stuart, S. N. (2004). IUCN red list of threatened species. A global species assessment. Gland and Cambridge: IUCN.

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