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International Journal of Biodiversity Science, Ecosystem Services & Management Vol. 8, No. 3, September 2012, 190–203 Micro, local, landscape and regional drivers of bee biodiversity and pollination services delivery to coffee (Coffea canephora) in Uganda a,b M.B. Théodore Munyuli * Department of Biology and Environment, National Center for Research in Natural Sciences, CRSN-Lwiro, D.S. Bukavu, Kivu, Democratic Republic of Congo; Centre de Recherche pour la promotion de la Santé, Département de Nutrition et Diététique, Institut Supérieur des Techniques Médicales, ISTM-Bukavu, Sud-Kivu, Democratic Republic of Congo Bee diversity and pollination services delivery in coffee ﬁelds are known to be driven by micro, local, landscape and regional drivers. The purpose of this study was to provide empirical documentation of drivers of bee biodiversity and pollination services delivery to coffee (Coffea canephora) under local conditions in Uganda. On-farm pollination experiments were therefore conducted in 30 small-scale coffee farms with contrasting land-use and management characteristics. The results indicated that coffee ﬂowers were visited by 24–38 bee species with meliponine bee species being the most frequent visitors. The highest fruit set (84%) was recorded in hand cross-pollination followed by open pollination (62%) and bagged ﬂowers (0.8%) treatment. Coffee proportion potential yield and bee contribution to fruit set were positively related to bee abundance, species richness, foraging rate and to the amount of semi-natural habitats available in the surroundings of coffee ﬁelds. Distance to forest/wetland and cultivation intensity were negatively related to coffee proportion potential yield but positively related to coffee pollination limitation. Farmers would beneﬁt from establishing coffee ﬁelds in the vicinity of natural habitats and from adopting pollinator-friendly farming and conservation practices such as increasing the area of semi-natural habitat features as well as promoting high on-farm tree cover to protect good pollinators (e.g. meliponine bees) of coffee in the landscape. Keywords: Coffea canephora; fruit set; measures of pollination service delivery; ecological drivers; stingless bees; Africa Introduction 2010) and its yield in Uganda can be increased through a better understanding of pollination ecology mechanisms. Coffee is Uganda’s most important agricultural cash crop However, knowledge of coffee pollination ecology in sub- and export item (Munyuli 2010). An estimated 2–14% of Saharan Africa (Karanja et al. 2010) and Uganda (Munyuli rural households earn all or most of their cash income from 2010) is scarce and the impact of bees on yield of coffee coffee. There are an estimated 500,000–800,000 small cof- fruit has not been quantiﬁed. fee farms with an average size of less than 1 ha (Munyuli Available studies focus on the effects of bee species 2010) in Uganda. richness and/or the abundance of bees on coffee fruit The genus Coffea (Rubiaceae) is native to tropi- set and production (Klein et al. 2003a; Ricketts 2004). cal and subtropical Africa (Klein et al. 2003a, 2003b). Few studies have examined biophysical variables (drivers) Two coffee species are important crops in many tropi- affecting the delivery of pollination services to coffee cal countries: highland coffee, Coffea arabica L. that is (Klein et al. 2003b). In sub-Saharan Africa and in Uganda, native to the mountains of Ethiopia, and lowland cof- important abiotic and biotic drivers of coffee pollination fee, Coffea canephora Pierre ex Froehner, syn. Coffea and yield remain largely unknown. In Costa Rica, it robusta, originally from the lowlands of Central Africa was found that fruit set of coffee could be predicted by (Democratic Republic of Congo). Coffea canephora covers the number of ﬂower-visiting bee species (not the num- more than 90% (Oryem-Origa 1999) of the land dedi- ber of bees) (Ricketts et al. 2004). In Indonesia, it was cated to coffee production in Uganda. Coffea canephora also observed that the number of social bees decreased plantations are concentrated in Central Uganda around the with forest distance, whereas the number of solitary bees Lake Victoria Arc Zone where coffee is one of the main increased with light intensity and increasing blossoms components of the ‘coffee–banana agroforestry system’ cover of herbs/weeds and coffee (Klein et al. 2003b). (Munyuli 2010). From these studies, it was not clear whether increase Overall, coffee plays a key role not only in small- in fruit set could be predicted by increased amount of scale household economy but also in the national econ- semi-natural habitats in the landscape. In other stud- omy in Uganda (Munyuli 2010). Therefore, information ies, social bees were found to contribute more to the to improve or stabilize the productivity of the crop can explanation of fruit set than solitary bees. Fruit set of inﬂuence rural development policies. Coffea canephora is open pollinated ﬂowers (in contrast to manually cross- a pollinator-dependent crop (Klein et al. 2007; Munyuli *Email: email@example.com; firstname.lastname@example.org ISSN 2151-3732 print/ISSN 2151-3740 online © 2012 Taylor & Francis http://dx.doi.org/10.1080/21513732.2012.682361 http://www.tandfonline.com International Journal of Biodiversity Science, Ecosystem Services & Management 191 pollinated ﬂowers) was positively correlated with the Materials and methods diversity and number of ﬂower-visiting bees (Klein et al. Study area and coffee ﬁeld selection 2003b). This study was conducted from June 2007 to March Potential drivers of pollinator diversity and pollination 2008 by setting pollination experiments with participation services delivery (Kremen et al. 2007; Winfree et al. 2009) of small-scale farmers in 30 different small-scale coffee to coffee in agricultural landscapes are found at micro farms (0.25–15 ha). These coffee farms were selected from (e.g. light intensity, shade cover), local (e.g. abundance of 26 different sites located in different districts (Munyuli blooming ﬂoral resources), landscape (e.g. forest distance) 2012) in the banana–coffee system of Lake Victoria Arc and regional (e.g. land-use intensity) levels. It is not clear Zone in Central Uganda (Figure 1). Farmers participated which drivers are important for coffee fruit set and produc- in the experiments by offering their coffee ﬁelds, by coop- tion in the coffee–banana agroforestry system of Central erating during monitoring and by preventing children and Uganda. Drivers may work alone or in synergy to pro- animals from disturbing pollination experiments. duce negative or positive impacts on coffee fruit set and The Lake Victoria Arc is characterized by ferrisoils yield. with high to medium fertility level and receives on The objective of this study was to identify average 1000–1800 mm of rains on a bimodal pattern microclimatic, local, landscape and regional level factors with 22–28 C and 60–75% of mean annual temperature that affect bee biodiversity and delivery of pollination and relative humidity, respectively (Munyuli et al. 2008; services to coffee. It was hypothesized that bee diversity Munyuli 2011a, 2011b). Several food and cash crops that and pollination services are positively correlated with the are pollinator-dependent crops are grown in small-scale cover of semi-natural habitats, cover of blooming trees monoculture and/or polyculture ﬁelds. Coffee (Coffea and blooming weeds in the coffee farm; and inversely canephora) is the main cash crop and banana is the main correlated with the distance to semi-natural habitats and to staple food crop (Munyuli 2011c). Prior to the selection the overall intensity of land use. NAKASEKE KALIRO KAMULI KAYUNGA LUWEERO Namulekya Naikesa Kimuli IGANGA Kyetume Nonve Bamusuuta Nawangoma Luwunga Segalye JINJA Lukumbi Bukose Kiweebwa Namizi East Kifu MITYANA Namizi west MUBENDE Kimwanyi Lugazi sugarcane Kinoni Kasaku tea Bulyasi KAMPALA Lukalu Mpanga WAKISO MPIGI BABULE MASAKA E KEY Mpugwe Study sites Katwadde Kasaala Kiwaala Open water District boundary 20 0 20 km KALANGALA Figure 1. Location of study sites in the banana–coffee producing areas around Lake Victoria in Uganda, from which coffee farms were selected. 192 M.B.T. Munyuli of experimental ﬁelds, a study tour of different sites was identiﬁed using a previously established bee collection that made and ﬁeld characteristics were noted. For each study is deposited at Makerere University Zoology Museum. site, a coffee ﬁeld was selected to represent all land-use and Prior to the identiﬁcation of bees, I received solid train- environmental variations within the study site. Thus, the ing in bee taxonomy and systematics under guidance of 30 experimental coffee ﬁelds were ﬁnally selected along Connal Eardley. In addition, copies of the specimens were contrasting environmental gradients, local farm manage- sent to him for conﬁrmation of the identity of the bee ment systems, microclimatic characteristics and land-use species. Thus, I am an expert taxonomist familiar with intensity gradients. The study was designed to minimize afrotropical bees (see also Munyuli et al. 2008; Munyuli spatial autocorrelation between local and landscape-scale 2011b; Munyuli et al. 2011; Munyuli 2012). All voucher variables measured within study sites by maintaining a specimens are deposited at Zoology Museum of Makerere minimum distance between coffee ﬁelds of 5–25 km, that University under the folder ‘Uganda coffee bees 2007’. is, beyond the normal foraging range of most pollinator Voucher numbers are composed of the name of collec- species. tor (THEO), the plant (COF) on which the species was recorded, the country and the year of collection (UG07), the blooming season (A, B) and a three-digit collection Pollination experiment and ﬂower visitation censuses number (e.g.THEO/COF/UG07/Season A/001). In each of the 30 selected coffee ﬁelds, 5 coffee trees were randomly selected; and for each of these, 3 branches Measurement of microclimatic, local, landscape and with buds initiating ﬂowers were randomly selected. regional land-use intensity variables Experimental and control coffee branches were tagged for visibility in the ﬁeld. On each experimental coffee branch, Measured microclimatic variables speciﬁc to each coffee individual ﬂowers were counted, labelled and marked using ﬁeld were shade cover at the ground level; ambient tem- coloured ribbons. Following Klein et al. (2003a, 2003b), perature within the tree crown and relative humidity (both each branch was randomly assigned to one of the fol- at 2 m above ground); and light intensity at the surface lowing three pollination treatments: (i) open pollination of blooming ﬂowers on coffee branches. Temperature and (natural pollination), that is, the unrestricted pollination of relative humidity were measured with a mobile digital coffee ﬂowers by insects and wind; (ii) cross-pollination thermo-hygrometer (TESTO 605-H1), while light intensity (artiﬁcial pollination) where pollens from other ﬂowering was measured with a portable luxmeter (HI 97 500; digital coffee plants in the vicinity of the experimental coffee light-gauge range: 0.1–199.9 Klux). For all microclimatic trees were collected and released onto selected coffee ﬂow- variables, 30 measurements were taken per coffee ﬁeld and ers using a camel brush; or (iii) bagging coffee ﬂowers used to calculate the mean of each variable for every coffee (self-pollination), that is, the control treatment where no ﬁeld as recommended (Klein et al. 2003a, 2003b; Munyuli pollination by external vectors was allowed, thereby test- 2010). ing for possible spontaneous self-pollination (autogamy) Measured local variables of coffee ﬂower visitations using very ﬁne white nylon ﬁne mesh (10 µm) imperme- and fruit set were the percentage of young fallows (less able to pollen. Five to six weeks after pollination, fruit set than 2 years) in the vicinity of coffee ﬁelds and the amount was determined by counting the number of green fruits on of coffee and non-coffee ﬂoral resources (percentage cover each treatment branch (Klein et al. 2003a, 2003b; Munyuli of ﬂowering trees, shrubs and herbs). The amount of young 2010). No treatment was done to assess the contribu- fallows found adjacent (2–15 m) to coffee ﬁelds was mea- tion of wind, since previous studies (Klein et al. 2003a, sured using a tape. Coffee ﬂoral resource availability was 2003b) found low contribution of wind to both lowland and measured as the proportion of ﬂowering coffee shrubs rel- highland coffee fruit set (<0.1–1.6% of fruit set). ative to all other ﬂowering plants: in every selected ﬁeld, Insect visits to ﬂowers were observed under typ- 10 quadrats measuring 5 m × 5 m were randomly estab- ical good weather conditions (i.e. sunny and slightly lished to determine the number of plant species. Also in cloudy days with low wind velocity) in each coffee every selected ﬁeld, 10 quadrats measuring 50 m × 50 m ﬁeld in three time intervals (7h00–10h00, 11h00–14h00, were randomly established to determine the percentage 15h00–18h00) for at least 30 minutes per coffee tree cover of herbs and trees/shrubs in bloom. Counts from per observation. Three repeated visits were recorded. the individual quadrats were summed and used to calcu- Observations were restricted to coffee trees in full bloom late a mean number of species and stems of blooming (i.e. >70% of their ﬂowers opened). A visit was deﬁned as plants/crops per coffee ﬁeld. occurring when an insect touched the anthers or stigmas. Landscape-level land-use data were collected within a In addition to visitation censuses, foraging speed (num- 1km site around each selected coffee ﬁeld. Each square ber of ﬂowers visited/minute) and visitation/pollination kilometre was delineated using a global positioning sys- speed (number of seconds spent per ﬂower) were measured tem (GPS) such that the experimental coffee ﬁeld was using a stopwatch. Visitation censuses were conducted dur- located at its centre. Because there were no previously pub- ing the ﬁrst and second blooming seasons. A hand net was lished data on small-scale land-use patterns in the study used to sample ﬂower-visiting bee species for identiﬁcation region, to facilitate basic measurements about different in the laboratory. Collected voucher specimens were land uses the km area was divided into ﬁve transects of International Journal of Biodiversity Science, Ecosystem Services & Management 193 200 m × 1000 m. Here the areas with different land-use It indicates expected maximum yield if all other production types were measured using GPS or a tape in case of small factors are optimally available and if production constraints ﬁelds (<50 m × 50 m). Land-use types were grouped into are minimized in coffee ﬁelds (e.g. no disease leading to major land-use types based on their size and frequency fruit abortion). The proportion bee contribution to fruit set of occurrence in order to calculate the area covered by is the difference between fruit set under open pollination semi-natural habitats, the area covered by crops and the conditions and fruit set when all insects are denied access cover of dependent and non-dependent cultivated crops per to ﬂowers. It is a measure of the approximate contribution km area (Klein et al. 2007). The term semi-natural habi- of bees to the fertilization of coffee ﬂowers. The propor- tats included fallows, hedgerows, ﬁeld margins, grasslands, tion pollination limitation is the difference between fruits roadsides, woodlands, woodlots, track-sides, stream-edges after hand cross-pollination (manual pollination) and fruits and so on. Pollinator-dependent crops are those that require set after open pollination conditions (full access of insect a visit to its ﬂowers by a pollinator to set fruits/seeds (Klein pollinators to ﬂowers). It indicates what additional yield et al. 2007). that can be obtained if the availability of different func- Three landscape variables of ecological importance tional groups and efﬁcient bee species in coffee ﬁelds is (Bolwig et al. 2006) for pollination studies in agricultural maximized from nearby natural and semi-natural habitats. matrices were then calculated for each coffee ﬁeld: (i) the proportion (%) of semi-natural habitats; (ii) the cul- Relationships between pollination service measures, bee tivation intensity, that is, the percentage of the total land diversity, ﬂower visitation frequency, bee foraging area cropped; and (iii) the distance from a given coffee intensity, microclimatic, local and landscape factors ﬁeld to the nearest potential natural pollinators’ source All variables were checked for normality and transformed (forest, wetlands). Distances up to 100 m were measured when necessary, prior to conducting any analysis. Pearson with a tape, otherwise with GPS (Garmin International, correlation analysis was used to determine the suite of Olathe, KS, USA; corrected to ±1 m accuracy with variables most closely (P < 0.05) related to pollination Pathﬁnder v 2.0). service measures at the micro, local and landscape levels. Regional land-use categories were obtained from the Prior to conducting regression analyses, multicollinearity Makerere University Geographic Information Service. was prevented by avoiding the use of independent vari- Broad land uses classiﬁed as low-intensity use includes ables that were strongly intercorrelated (r > 0.60–0.90, areas where at least three-quarters of the land is p < 0.001). If two independent variables were collinear, uncultivated. Medium are managed habitat types where one was discarded. For example, in the microclimatic vari- there is an almost equal distribution of cultivated and ables, the maximum temperature was strongly correlated uncultivated land. High are areas dominated by crops or (r = 0.82, p < 0.01, n = 30) with light intensity; therefore, livestock. Very high represents large monoculture estates light intensity was retained in multiple regression whereas of tea, sugar, coffee and so on (Munyuli 2010). temperature was dropped. Multiple linear regression analysis was conducted to Data analysis identify local and landscape drivers that affected simul- Fruit set and pollination measures taneously coffee fruit set and pollination measures and bee diversity. Differences between the different regres- Coffee fruit set was calculated as a proportion of total ﬂow- sion parameters were assessed with t-tests. All simple and ers that set fruit over the total number of ﬂowers examined multiple regression analyses were conducted in Minitab per experimental coffee branch. Based on three measures version 15.1 (Minitab Inc., New York, NY, USA). of pollination services delivery, the proportion potential To analyse the effects of land-use categories, a gen- yield of coffee (open pollination/cross-pollination), the eral linear model (GLM) analysis of variance (ANOVA) proportion bee contribution to fruit set (open pollination – was conducted with pollination measures as the dependent pollination exclusion) and the proportion pollination limi- variables, and the categorical variables (low, medium and tation (cross-pollination – open exclusion) were calculated high) as ﬁxed factors. ‘Very high’ was not part of this study (Klein et al. 2003a, 2003b; Dafni et al. 2005; Klein et al. since the focus was on small-scale ﬁelds. The least signiﬁ- 2007; Munyuli 2010; Nayak and Davidar 2010; David cant difference tests were used as post hoc tests for multiple Inouye, personal communication 2011). These pollination comparisons of means. service delivery measures were therefore calculated based on total and mean fruit set per treatment per coffee ﬁeld. The three measures (potential yield, bee contribution Results to fruit set and pollination limitation) are classically cal- Effects of microclimatic drivers on bee communities, culated by pollination biologists to measure pollination foraging activities and pollination measures services (for details, see Kearns and Inouye 1993; Dafni and Kevan 2005). Practically, the proportion potential All pollination measures (pollination limitation, propor- yield is the ratio of numbers of fruits set under open tion potential yield and proportion bee contribution to pollination conditions (full access of insects to ﬂowers) and fruit set) were signiﬁcantly (P < 0.05) explained by the hand cross-pollination (manual fertilization of ﬂowers). both light intensity and shade cover (Table 1). Simple 194 M.B.T. Munyuli Table 1. Relationships between measures of coffee pollination service delivery and microclimatic factors (A), local drivers (B), bee foraging intensity variables (C) and landscape drivers (D). Regression parameters in full models Whole models Dependent variables (coffee Regression pollination measures) Independent variables in the model coefﬁcient SE coefﬁcient t-Statistic P-value Multiple R Model F Model P A. Microclimatic factors Df (2,27) Potential yield Constant 0.7690 0.1015 7.58 0.000 Light intensity [Lux = W/m ]atﬂowers 0.002744 0.001148 2.39 0.024 height % Shade at ground of a coffee ﬁeld −0.002820 0.001245 −2.27 0.032 39.1% 8.68 0.001 Bee contribution to fruit set Constant 0.4529 0.1239 3.65 Light intensity [Lux = W/m ]atﬂowers 0.004978 0.001401 3.55 0.001 height % Shade at ground of a coffee ﬁeld −0.003132 0.001520 −2.06 0.049 49.1% 13.03 0.000 Pollination limitation Constant 0.16222 0.07621 2.13 0.043 Light intensity [Lux = W/m ]atﬂowers −0.0021844 0.0008617 −2.53 0.017 height % Shade at ground of a coffee ﬁeld 0.0029736 0.0009346 3.18 0.004 49.4% 13.16 0.000 B. Local drivers Df (3,26) Potential yield Constant 0.60731 0.05423 11.20 0.000 % fallows adjacent to ﬁeld (2–15 m) 0.002661 0.001130 2.35 0.026 % ﬂowering trees/shrubs in ﬁeld 0.005470 0.005544 0.99 0.333 % ﬂowering weeds/herbs in ﬁeld 0.002830 0.001042 2.72 0.012 43.8% 6.76 0.002 Bee contribution to fruit set Constant 0.32960 0.06929 4.76 0.000 % fallows adjacent to ﬁeld (2–15 m) 0.004616 0.001444 3.20 0.004 % ﬂowering trees/shrubs in ﬁeld 0.013535 0.007084 1.91 0.067 % ﬂowering weeds/herbs in ﬁeld 0.001925 0.001331 1.45 0.160 48.5% 8.18 0.001 Pollination limitation Constant 0.32605 0.04293 7.59 0.000 % fallows adjacent to ﬁeld (2–15 m) −0.0017202 0.0008945 −1.92 0.065 % ﬂowering trees/shrubs in ﬁeld −0.007269 0.004389 −1.66 0.110 % ﬂowering weeds/herbs in ﬁeld −0.0024477 0.0008248 −2.97 0.006 48.1% 8.02 0.001 C. Bees and foraging intensity variables Df (5,24) Potential yield Constant 0.2673 0.1179 2.27 0.033 Number of bee species/coffee 0.007948 0.003289 2.42 0.024 tree/180 minutes Bee density (individuals/tree/180 minutes) 0.00001622 0.00000968 1.68 0.107 Foraging rate (number ﬂowers visited/minute) 0.004905 0.001392 3.52 0.002 Visitation speed (seconds spent/ﬂower) −0.0011073 0.0005392 −2.05 0.051 % coffee trees with fresh blossoms in ﬁeld 0.003233 0.001177 2.75 0.011 75.0% 14.43 0.000 International Journal of Biodiversity Science, Ecosystem Services & Management 195 Bee contribution to fruit set Constant 0.3072 0.1735 1.77 0.089 Number of bee species/coffee tree/180 minutes 0.011841 0.004840 2.45 0.022 Bee density (individuals/tree/180 minutes) 0.00003897 0.00001424 2.74 0.012 Foraging rate (number ﬂowers visited/minute) 0.001346 0.002049 0.66 0.518 Visitation speed (seconds spent/ﬂower) −0.0014115 0.0007935 −1.78 0.088 % coffee trees with fresh blossoms in ﬁeld 0.001492 0.001732 0.86 0.397 69.7% 11.02 0.000 Pollination limitation Constant 0.4022 0.1165 3.45 0.002 Number of bee species/coffee tree/180 minutes −0.005998 0.003250 −1.85 0.077 Bee density (individuals/tree/180 minutes) −0.00002241 0.00000956 −2.34 0.028 Foraging rate (number of ﬂowers visited/minute) −0.001800 0.001376 −1.31 0.203 Visitation speed (seconds spent/ﬂower) 0.0009463 0.0005328 1.78 0.088 % coffee trees with fresh blossoms in ﬁeld −0.001460 0.001163 −1.26 0.221 64.0% 8.54 0.000 D. Landscape drivers Df (2,27) D-1. Distance–cultivation intensity Potential yield Constant 1.25901 0.08280 15.21 0.000 Cultivation intensity in km area −0.5715 0.1424 −4.01 0.000 Distance (m) to forests/wetlands −0.00011403 0.00002947 −3.87 0.001 77.1% 45.57 0.000 Bee contribution to fruit set Constant 1.2388 0.1071 11.56 0.000 Cultivation intensity in km area −0.8129 0.1843 −4.41 0.000 Distance (m) to forests/wetlands −0.00014449 0.00003813 −3.79 0.001 78.5% 49.39 0.000 Pollination limitation Constant −0.21449 0.06773 −3.17 0.004 Cultivation intensity in km area 0.4770 0.1165 4.09 0.000 Distance (m) to forests/wetlands 0.00009285 0.00002411 3.85 0.001 77.4% 46.31 0.000 D-2. Distance and semi-natural habitats Potential yield Constant 0.76806 0.08373 9.17 0.000 Distance (m) to forests/wetlands −0.00014516 0.00003433 −4.23 0.000 % Semi-natural habitats in km area 0.003889 0.001863 2.09 0.046 68.6% 29.48 0.000 Bee contribution to fruit set Constant 0.5562 0.1127 4.94 0.000 Distance (m) to forests/wetlands −0.00019313 0.00004618 −4.18 0.000 % Semi-natural habitats in km area 0.005168 0.002507 2.06 0.049 68.1% 28.81 0.000 Pollination limitation Constant 0.19987 0.06849 2.92 0.007 Distance (m) to forests/wetlands 0.00011753 0.00002808 4.19 0.000 % Semi-natural habitats in km area −0.003353 0.001524 −2.20 0.037 69.0% 30.03 0.000 Note: For model P and P-value, bold values in the table indicate statistically signiﬁcant values (P < 0.05). 196 M.B.T. Munyuli linear regression showed strong positive and signiﬁcant the percentage of trees with fresh blossoms in coffee ﬁelds (P < 0.001) relationship between light intensity and (Table 1). Similarly, bee contribution to fruit set was found the number of ﬂower-visiting bee species (R = 0.656, to be signiﬁcantly (P < 0.05) explained by bee species rich- F = 53.38, P < 0.0001), bee density (R = 0.423, ness and bee density. Variations in pollination limitation 1,28 F = 20.54, P < 0.0001) and foraging rate (ﬂowers (pollination deﬁcit) were signiﬁcantly (P < 0.05) predicted 1,28 visited/minute) of all bee species combined (R = 0.197, by bee density only (Table 1). F = 6.87, P = 0.014). There was also a positive rela- Bee contribution to fruit set was signiﬁcantly (P < 1,28 tionship between light intensity and the duration of the visit 0.001) and positively related to both the number of ﬂower- (seconds spent/branch) by Hypotrigona gribodoi Magretti visiting bee species and the density of ﬂower visitors. (R = 0.334, F = 13.77, P = 0.001), indicating that Similarly, the proportion potential yield was signiﬁcantly 1,28 this species increased its foraging time per coffee branch and positively related to the diversity of bee tribes that visit with increasing light intensity. In support, the number of coffee ﬂowers (R = 0.4871, F = 26.59, P < 0.0001). 1,28 bee species per coffee tree was negatively related to shade In contrast, the proportion pollination limitation was cover (R = 0.272, F = 10.46, P = 0.003). negatively and signiﬁcantly related to the density of ﬂower 1,28 visitors, indicating that pollination deﬁcit decreased with increase in the density of pollinating bees. Effects of local drivers on bee foraging variables and The proportion potential yield was also signiﬁcantly pollination measures and positively related to the foraging rates of M. ferruginea (R = 0.1556, F = 5.16, P = 0.031) and M. nebulata Multiple regression analysis showed that the percentage 1,28 (R = 0.1567, F = 5.2, P = 0.030) and to the total for- cover of young fallows adjacent to a coffee ﬁeld and the 1,28 aging rate of all bee species per coffee branch (R = 0.438, percentage cover of ﬂowering weeds/herbs in the ﬁeld were F = 21.8, P < 0.0001). signiﬁcant explanatory variables of proportion potential 1,28 The duration of visit (seconds spent/coffee branch) by coffee yield (Table 1). The percentage of young fallows H. gribodoi was signiﬁcantly and positively related to the in the vicinity of a coffee ﬁeld was positively related percentage cover of coffee trees with open fresh ﬂowers to the proportion of bee contribution to coffee fruit set (R = 0.3745, F = 16.75, P < 0.001) as was the visi- (R = 0.2841, F = 11.11, P = 0.004), suggesting that 1,28 1,28 tation speed (seconds spent/ﬂower) of a given bee species both the proportion potential yield and the proportion con- tribution of bees to coffee fruit increased with increase in (R = 0.189, F = 6.52, P = 0.016). Additionally, the 1,28 the proportion of young fallows in the immediate surround- foraging rate (number of ﬂowers visited/minute) was sig- ings of coffee ﬁelds and with increase in the abundance of niﬁcantly and positively correlated with the proportion ﬂowering weeds/herbs. cover of branches with fresh blossoms per coffee tree The proportion pollination limitation was only related (R = 0.242, F = 8.93, P = 0.006). 1,28 to the percentage cover of ﬂowering weeds/herbs in coffee While exploring the inﬂuences of other cultivated ﬁelds. Simple linear regression indicated that the propor- annual entomophilous crop species on the delivery of tion potential yield was signiﬁcantly and positively related pollination services to coffee, it was found that the propor- to the proportion of young fallows in the vicinity of a cof- tion potential coffee yield was negatively and signiﬁcantly fee ﬁeld (R = 0.2103, F = 7.461, P = 0.026) and with related to both the percentage cover of cultivated crops 1,28 the percentage proportion cover of ﬂowering weeds/herbs that are pollinator dependent (R = 0.295, F = 11.72, 1,28 P = 0.002) and the percentage cover of other non- in the coffee ﬁeld (R = 0.2432, F = 9.00, P = 0.012). 1,28 2 2 coffee ﬁelds in the km area (R = 0.191, F = 6.60, Coffee ﬂowers were visited by 24 bee species (ﬁrst 1,28 P = 0.016). This suggests that the delivery of pollination blooming season) and by 38 bee species (second bloom- services in a coffee ﬁeld by bees may decrease with ing season) (see the Appendix for the list of bee species increase in cultivation of diverse pollinator-dependent crop collected on coffee ﬂowers). The pollination experi- species in the same landscape where coffee ﬁelds are ment showed the highest fruit set (84%) in hand cross- located. pollination treatment followed by open pollination (62%). Fruit set was negligible (0.8%) in controlled pollination. Flower visitations by social bees resulted in 89.7% fruit Effects of landscape drivers on bees and pollination set, and 71.4% for solitary bees. The most effective bee measures species was Meliponula ferruginea Lepeletier (98.0% fruit after single ﬂower visit) followed by Meliponula nebulata Multiple regression models indicated that pollination mea- Smith (97.2%). Hypotrigona gribodoi Magretti was the sures (pollination limitation, bee contribution to fruit set most abundant and important bee species although not the and potential yield) were signiﬁcantly (P < 0.05) predicted most efﬁcient (89.0% fruit after single visit). Meliponini by all landscape drivers tested (cultivation intensity, forest bees were particularly abundant, important and frequent distance and the amount of semi-natural habitats in a km visitors of coffee ﬂowers in Uganda. area; Table 1). Multiple regression analysis revealed that coffee Cultivation intensity was highly negatively related pollination measures were signiﬁcantly (P < 0.05) inﬂu- to both proportion potential yield and proportion bee enced by the number of bee species, bee foraging rate and contribution to coffee fruit. In contrast, the proportion International Journal of Biodiversity Science, Ecosystem Services & Management 197 pollination limitation was positively and signiﬁcantly bees is basic to conservation and sustainable utilization of (P < 0.001) correlated with cultivation intensity. Distance pollinators to increase fruit/seed sets of most crops/plants to forest/wetlands was negatively and signiﬁcantly related (Wang et al. 2009) that are pollinator dependent. to both proportion potential yield and proportion bee con- While in traditional agroforestry systems in Ecuador, tribution to coffee fruit. In contrast, pollination limitation it was found that bee species richness positively increased was positively and signiﬁcantly related to the distance to linearly with shade cover (Veddeler et al. 2006); nega- forest. The proportion (%) of semi-natural habitats was tive and signiﬁcant non-linear relationships between bee found to be positively and signiﬁcantly related to pro- species richness and pollination services delivery and portion potential yield and proportion bee contribution to shade cover (%) were found in this study. The optimal coffee fruit set. Pollination limitation was negatively and shade cover for bee foraging activities ranged between 10% signiﬁcantly related to percentage cover of semi-natural and 50%, meaning that, on overall, the number of bee habitat. It was also observed that the number of ﬂower- species that visited coffee ﬂowers increased with increases visiting bee species was negatively and strongly related in shade cover up to a certain level before starting to to distance to semi-natural habitats (R = 0.5359, F = drop. This ﬁnding is supported by the work of Klein 1,28 32.33, P < 0.0001), indicating that the species richness et al. (2003a) in Indonesia. However, this ﬁnding is not of ﬂower-visiting bee fauna signiﬁcantly declined linearly in line with ﬁndings from coffee plantations in Southern with increasing distance from the nearest bee refugia. Mexico where a signiﬁcantly greater number of visits in Similarly, the number of bee species was negatively and highly shaded coffee habitats were recorded in low-shaded signiﬁcantly related to cultivation intensity (R = 0.3196, coffee habitats for both native and exotic bees (Jha and F = 13.15, P = 0.001), suggesting that bee species Vandermeer 2009a, 2009b). 1,28 richness declined sharply with cropping intensiﬁcation. This study and previous studies found that both light Rates of bee visitation to coffee ﬂowers in relation to intensity and shade cover affected bee species richness; the amount of semi-natural habitats, however, were posi- foraging activities of different bee species are somehow tively and signiﬁcantly correlated (R = 0.2867, F = regulated simultaneously by both factors. Practically, man- 1,28 11.26, P = 0.002), possibly indicating that the species aging coffee ﬁelds with reduced shading may enable light richness of ﬂower visitor bee fauna signiﬁcantly increased to reach blooming plant species located at the ground with increasing amount of semi-natural habitats within the layer. This may stimulate the availability of blooming farm landscape, and/or with increasing percentage cover weeds/herbs providing continuously nectar and pollen of forest and fallow in 1 km agricultural matrices. resources to bees, particularly when coffee blossoms are not available. Consequently, coffee farmers may promote bee abundance and diversity within their own farms by diversifying their shading trees. Farmers may also attract Effects of regional land-use categories on pollination diverse bee species by adopting farm management systems measures (farming practices) allowing trees to age, thus creating a There were signiﬁcant effects of regional land-use cate- mosaic of light gaps and ﬂowering herb patches that will gories on the proportion potential coffee yield, proportion in turn attract a diversity of foraging pollinators to cof- of bees contribution to coffee fruit set and proportion fee (Omoloye and Akinsola 2006; Munyuli 2010). The pollination limitation (Table 2). Pollination services deliv- understanding of activities and performance of native bee ered by bees to coffee were highest in the low land-use species under different shade intensities and temperatures category and least in the high land-use intensity. A simi- is needed in order to assess the effects of habitat deteriora- lar trend was observed for proportion potential coffee yield tion and climate change on these key coffee pollinators in while pollination limitation showed the reverse trend. Uganda. Discussion Inﬂuences of local drivers on pollination services Inﬂuences of microclimatic factors on bee foraging delivery activities The results of this study showed that the proportion poten- In this study, light intensity was found to be positively tial yield was predicted by both the percentage cover of related to bee diversity (species richness) and to the pro- young fallows adjacent to a coffee ﬁeld within 2–15 m dis- portion of bee contribution to fruit set. This result agrees tance and the percentage cover of ﬂowering weeds/herbs with ﬁndings from Indonesia where it was found that diver- in the ﬁeld. Signiﬁcant positive relationships were also sity (species richness of solitary bees) and fruit set of open found between the proportion potential yield and percen- pollination increased positively with light intensity (Klein tage cover of young fallows and the percentage cover of et al. 2003b). Overall, foraging behaviour of bees is known ﬂowering weed/herbs. It was also realized that propor- to be temperature dependent and bees respond similarly tion potential yield was positively related to percentage of to temperature and light intensity (Veddeler et al. 2006). coffee trees with fresh blossoms in a coffee ﬁeld. Thus, an understanding of environmental (microclimatic) The fact that the proportion cover of young fallows factors that affect behaviours of different foraging wild adjacent to a coffee ﬁeld was positively related to the 198 M.B.T. Munyuli Table 2. Effect of regional land-use intensity gradients (levels) on coffee pollination measures. Pollination measures GLM-ANOVA test A. Proportion potential yield F (2, 28) P Regional land-use intensity 31.756 <0.0001 gradients Levels of intensity Mean ± SE High 0.58 ± 0.08 c Medium 0.86 ± 0.15 b Low 0.96 ± 0.02 a B. Bee contribution to fruit set 23.88 <0.0001 High 0.36 ± 0.06 c Medium 0.65 ± 0.09 b Low 0.85 ± 0.17 a C. Proportion pollination limitation 24.22 <0.0001 Low 0.04 ± 0.01 c Medium 0.12 ± 0.06 b High 0.33 ± 0.05 a Note: Within the column (mean ± SE), means followed by the same letters are not signiﬁcantly different at P < 0.05. proportion bee contribution to fruit set indicates that diversity and identity (Lander et al. 2009; Munyuli 2010). young fallows may act as refugia for various pollinating The lack of suitable pollinators together with nutrient bee species (particularly stingless and some solitary bee resources limitation are two important factors tradition- species). In addition, the positive relationship between the ally held responsible for incomplete fruit and seed set proportion of young fallows and the proportion potential (Jacobi and delSarto 2007). This can be further exacerbated yield stresses the importance of having young fallows in by temporary lack of visits because of climate variations, the surrounding of coffee ﬁelds. Consequently, changes in habitat degradation/alterations, soil degradation and nutri- the cover of young fallows in the vicinity of coffee ﬁelds ent deﬁciency and competition with other ﬂoral resources are likely to generate variations in the visitations to cof- (Jacobi and delSarto 2007). For the case of coffee in fee blossoms and yield productivity. It is likely that very Uganda, it seems that high pollination limitation may be good pollinator species (e.g. meliponines) prefer inhabit- primarily linked to cultivation intensity and to the resul- ing the immediate surroundings of coffee ﬁelds (Munyuli, tant decrease in ﬂower visitations by different pollinator personal observation) as compared to other social and soli- species. tary bees (Klein et al. 2003a, 2003b; Ricketts et al. 2008; Munyuli 2010). Inﬂuences of semi-natural habitats on bee communities and pollination services delivered to coffee In this study, it was observed that the proportion of Inﬂuences of landscape drivers semi-natural habitats was positively related to both the Inﬂuences of cultivation intensity on pollinators and proportion potential yield and the proportion bee contri- pollination services delivery bution to fruit set and negatively related to the propor- In this study, it was observed that proportion potential tion pollination limitation. This suggests that pollinator yield and bee contribution to fruit set declined with cul- biodiversity conservation and related ecosystem services tivation intensity and that pollination limitation increased are correlated with retention of native perennial vegetation with cultivation intensity. In addition, it was observed that in Afrotropical mosaic farm landscapes. In support, other species richness declined sharply with cropping intensi- studies have reported a positive relationship between coffee ﬁcation. This may be attributed to limitation in nesting fruit set (%) and the amount (%) of semi-natural habitats in opportunities as bee nesting and foraging habitats are elim- the landscape (Gemmill-Herren and Ochieng 2008; Kasina inated. It is generally accepted that increased cultivation et al. 2009; Otieno et al. 2011) or the proximity of coffee intensity leads to pollinator extirpation/decline (Kremen ﬁelds to forest habitats (Klein et al. 2003a, 2003b; Ricketts et al. 2007; Lonsdorf et al. 2009). Specialized bee species et al. 2004; Veddeler et al. 2008). The positive effect of are the ﬁrst to be lost with increasing cultivation, followed semi-natural habitats on fruit/seed set is always attributed by generalists, especially in the Apoidea group whose to more visitations from a diverse bee community sourc- bee species have different nesting/foraging requirements ing from these semi-natural features (Karanja et al. 2010; (Kremen et al. 2007). Breeze et al. 2011; Winfree et al. 2011). In non-coffee pro- In this study, it was also observed that pollination limi- duction systems, studies have reported negative effects of tation increased with cultivation intensity. Pollination limi- habitat loss on bee species and density (Kremen et al. 2004; tation is a consequence of changes in pollinator abundance, Greenleaf and Kremen 2006a, 2006b). International Journal of Biodiversity Science, Ecosystem Services & Management 199 For a natural/agricultural area to support diverse bee (Klein et al. 2003a). Similar reductions of fruit set in faunas (Cane et al. 2006; Winfree et al. 2008), the land- relation to bee species richness and density and dis- scape must harbour more than 20% natural and semi- tance to forest edge (bee refugia) were observed in this natural habitats (Tscharntke et al. 2005) close enough to study. The results from Indonesia and Uganda indicated crop ﬁelds for bees to reach them. Kremen et al. (2004) that forest patches are valuable sources of crop pollina- recommended that farmers keep at least 30–40% of their tors. Thus, clear management recommendations should be land wild to serve as pollinator reservoirs in order to avoid developed for their conservation (Sande et al. 2009), since pollination deﬁcit and yield reduction while Winfree et al. the distance to nearest pollinator refugia (natural and semi- (2008) recommended that the landscape should be covered natural habitats) is a critical determinant landscape factor by at least 66% wild land. In Uganda, it is therefore pro- in the delivery of pollination services to various pollinator- posed that each farm keeps 10–40% of land uncultivated dependent crops (Martins and Johnson 2009; Carvalheiro (Munyuli 2010); beyond 40% the total productivity et al. 2010). may be jeopardized since too much land may be kept uncultivated. Inﬂuences of regional land-use categories on coffee pollination measures Inﬂuences of the distance to natural habitats (forest In this study, it was observed that pollination services patches) on bee visitation rates delivery to coffee was signiﬁcantly affected by land-use The results of the study indicated that forest/wetland dis- intensity gradients. Similarly, studies conducted elsewhere tance was strongly negatively related to both the proportion on coffee visitation by wild pollinators found negative potential yield and the proportion bee contribution to fruit effects of human land-use systems on bee diversity/density set and positively related to the pollination limitation. and on coffee fruit set (Ricketts 2004; Klein et al. 2007; Ricketts et al. 2008). Both habitat and landscape fragmen- In addition, the species richness of ﬂower-visiting bee tation may affect negatively the dispersal ability of pollina- fauna signiﬁcantly declined linearly with increasing dis- tors searching for coffee blossoms (Winfree et al. 2009) tance from the nearest natural habitats (forest/wetlands). in tropical agricultural matrices. In addition, increased Similarly, the visitation frequencies (abundance) of ﬂower- chemical application of pesticides can lead to erosion visiting bees dropped with forest distance, especially for social bees. Natural habitats are thus important in shap- of specialist pollinators while increasing the prevalence ing the pollinator community and inﬂuence pollination of common and generalist bee species in the landscape services delivery to coffee. These results are consistent (Winfree et al. 2009). With land-use intensiﬁcation, coffee with studies on coffee pollination in tropical Asia and in farmers aim at maximizing yields. Farmers may therefore Neotropical regions where it was observed that nearby adopt all methods to increase the yield, including applica- rainforest promotes coffee pollination by increasing spatio- tion of chemical fertilizers and pesticides, which may cause temporal stability in bee species richness (Veddeler et al. exponential increase in pollination limitation. 2008; Klein 2009; Garibaldi et al. 2011). In Panama, Indonesia, Costa Rica and Brazil, complex agroforestry Conclusions systems were found to support higher bee diversity and bee visits to coffee ﬂowers compared to monocultures or The objective of this study was to identify microclimatic, simple shaded systems (Roubik 2002; Klein et al. 2003a; local, landscape and regional level factors that affect bee DeMarco and Coelho 2004; Ricketts 2004; Ricketts et al. biodiversity and delivery of pollination services to cof- 2004; Veddeler et al. 2006; Klein, Olschewski et al. 2008; fee. At the micro-level, it was observed that bee diversity Vergara and Badano 2009). Similar ﬁndings exist for non- and foraging activities and pollination services delivery coffee crops (Kremen et al. 2004; Chacoff and Aizen 2006; increased linearly with light intensity, whereas shade cover Chacoff et al. 2008). produced a reverse trend. At the local level, the propor- Forest patches in Central Uganda were found to tion contribution of bees to fruit set increased linearly with enhance pollinator activity in surrounding agricultural increases in availability of mass blooming weeds/herbs. ﬁelds, since pronounced reductions in bee populations and At the landscape level, it was found that bee biodiversity in fruit set occurred frequently in coffee ﬁelds located far and pollination services delivery declined steeply with from these natural habitats. In this study, the distance at cultivation intensity and forest distance. Similarly, at the which bee richness and pollination services dropped to half regional level, pollination services declined sharply with of their maximum value was of 500–700 m. Similar ﬁnd- land-use intensity. ings are documented from other tropical regions growing Coffee pollination services provided by native bee coffee (Klein, Cunningham et al. 2008; Klein, Olschewski communities (Munyuli et al. 2011) were strongly depen- et al. 2008; Ricketts et al. 2008). dent on the proportion cover of semi-natural habitat within A maximum fruit set of 85–90% at the forest edge the landscape. Conservation of natural and semi-natural (0–100 m) with 20–30 bee species, and a maximum fruit habitats may thus both serve to promote conservation of set of 50–60% in 1500 m distance with 3–5 bee species bee diversity in the coffee–banana farming systems in were found in coffee agroforestry systems in Indonesia Uganda while simultaneously positively enhancing and 200 M.B.T. Munyuli stabilizing productivity of entomophilous crop species (Hymenoptera: Apiformes) to urban habitat fragmentation. Ecol Appl. 16(2):632–644. (Garibaldi et al. 2011; Rader et al. 2012) such as low- Carvalheiro LG, Seymour CL, Veldtman R, Nicolson SW. 2010. land coffee. Hence, to obtain consistent delivery of optimal Pollination services decline with distance from natural habi- pollination services to coffee and to other crops, it is rec- tat even in biodiversity-rich areas. J Appl Ecol. 47(4): ommended to farmers to adopt pollinator-speciﬁc farm 810–820. management practices/strategies that consider the forag- Chacoff NP, Aizen MA. 2006. Edge effects on ﬂower-visiting insects in grapefruit plantations bordering premontane sub- ing and nesting needs of both native solitary and social tropical forest. J Appl Ecol. 43(1):18–27. bees within the farm landscapes. One of the important ﬁnd- Chacoff NP, Aizen MA, Aschero V. 2008. Proximity to forest ings of this study was the importance of stingless bees in edge does not affect crop production despite pollen limita- coffee pollination. These meliponine bees are particularly tion. Proc R Soc Lond B Biol. 275(1637):907–913. dependent on local nesting and ﬂoral resources found in the Dafni A, Kevan GP, Husband C. 2005. Practical pollination ecology. Cambridge (ON): Enviroquest Ltd. 590 p. vicinity of coffee ﬁelds. DeMarco PJR, Coelho FM. 2004. Services performed by Farmers are likely to enhance coffee yield when they the ecosystem: forest remnants inﬂuence agricultural cul- grow coffee beneath a diversity of shade tree species, but tures’ pollination and production. Biodivers Conserv. also by providing sunlight (e.g. by planting few shad- 13(7):1245–1255. ing tree species at the border of the farm) to promote Garibaldi LA, Steffan-Dewenter I, Kremen C, Morales JM, Bommarco R, Cunningham SA, Carvalheiro LGA, Chacoff ﬂowering herbs and nesting opportunities (Kremen et al. NP, Dudenhöffer JF, Greenleaf SS, et al. 2011. Stability 2002; Ricketts et al. 2004; Kremen et al. 2007; Ricketts of pollination services decreases with isolation from natural et al. 2008; Julier and Roulston 2009; Klein 2009; Hoehn areas despite honey bee visits. Ecol Lett. 14(10):1062–1072. et al. 2010). Farmers are also advised, if possible, to Gemmill-Herren B, Ochieng AO. 2008. Role of native bees and keep 10–40% of their land uncultivated to maximize cof- natural habitats in eggplant (Solanum melongena) pollination in Kenya. Agric Ecosyst Environ. 127(1/2):31–36. fee production and obtain additional beneﬁts, including Greenleaf SS, Kremen C. 2006a. Wild bees enhance honey bees’ other crop production increase and stability over time. pollination of hybrid sunﬂower. Proc Natl Acad Sci USA. Uncultivated areas will always act as reservoirs for diverse 103(37):13890–13895. pollinating agents in the farm landscape. Advisory ser- Greenleaf SS, Kremen C. 2006b. Wild bee species increase vice agents should advise small-scale coffee producers on tomato production and respond differently to surround- ing land-use in Northern California. Biol Conserv. 133(1): how to diversify non-cropped habitats to promote housing 81–87. of diverse good pollinator species. Current global envi- Hoehn P, Steffan-Dewenter I, Tscharntke T. 2010. Relative ronmental changes are expected to have manifold effects contribution of agroforestry, rainforest and openland to on pollination services delivery to coffee (Munyuli 2012) local and regional bee diversity. Biodivers Conserv. 19(8): and other crops. Therefore, there is a need to conduct fur- 2189–2200. Jacobi CM, delSarto MCL. 2007. Pollination of two species of ther researches/studies on impacts of interacting multiple Vellozia (Velloziaceae) from high-altitude quartzitic grass- drivers/stresses on pollination services delivery to coffee. lands, Brazil. Acta Bot Bras. 21(2):325–333. Jha S, Vandermeer JH. 2009a. Contrasting foraging patterns for Africanized honeybees, native bees and native wasps in a Acknowledgements tropical agroforestry landscape. J Trop Ecol. 25(1):13–22. I am very grateful to the Darwin Initiative (Defra, UK; project Jha S, Vandermeer JH. 2009b. Contrasting bee foraging in reference: 14-032) for funding this study under the project response to resource scale and local habitat management. title: Conserving biodiversity in modernized farm landscapes in Oikos. 118(8):1174–1180. Uganda. I am also very grateful to project leaders (Dr Juliet Julier HE, Roulston T. 2009. Wild bee abundance and pollination Vickery, RSPB, Cambridge University, UK, Dr Phil Atkinson, service in cultivated pumpkins: farm management, nest- British Trust for Ornithology, UK and Prof. Derek Pomeroy, ing behavior and landscape effects. J Econ Entomol. Makerere University), scientiﬁc supervisors (Prof. Simon Potts, 102(2):563–573. University of Reading, UK and Prof. Philip Nyeko, Makerere Karanja RHN, Njoroge GN, Gikungu MW, Newton LW. 2010. University, Uganda) and farmers for offering their farms for Bee interactions with wild ﬂora around organic and conven- pollination experiments and for their kind cooperation during data tional coffee farms in Kiambu district, central Kenya. J Pollin collection. I am very grateful to Dr Bwinja M and to Mr Maurice Ecol. 2(2):7–12. Mutabazi (research assistant) for his assistance in the ﬁeld. 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Pollinator limitation and the effect of 80–88. breeding systems on plant reproduction in forest fragments. Winfree R, Williams NM, Gaines H, Ascher JS, Kremen C. 2008. Acta Oecol. 36(2):191–196. Wild bee pollinators provide the majority of crop visitation Omoloye AA, Akinsola PA. 2006. Foraging sources and effects across land-use gradients in New Jersey and Pennsylvania, of selected plant characters and weather variables on the USA. J Appl Ecol. 45(3):793–802. 202 M.B.T. Munyuli Appendix. Bee species collected on coffee ﬂowers during the ﬁrst and second blooming seasons, June 2007–March 2008, Central Uganda. June–August November–December 2007 blooming season 2007 blooming season Social bees Voucher numbers Social bees Voucher numbers Apis mellifera scutellata THEO/COF/UG07/Season Allodapula acutigera THEO/COF/UG07/Season B/006 (Latreille) A/001 (Cockerell, 1936) Apis mellifera adansonii THEO/COF/UG07/Season Apis mellifera adansonia THEO/COF/UG07/Season B/007 (Linnaeus, 1758) A/002 (Linnaeus, 1758) Hypotrigona gribodoi THEO/COF/UG07/Season Apis mellifera scutellata THEO/COF/UG07/Season B/002 (Magretti, 1884) A/003 (Latreille) Meliponula ferruginea THEO/COF/UG07/Season Hypotrigona gribodoi THEO/COF/UG07/Season B/004 (Lepeletier, 1836) A/004 (Magretti, 1884) Meliponula lendliana THEO/COF/UG07/Season Meliponula ferruginea THEO/COF/UG07/Season B/005 (Friese, 1900) A/005 (Lepeletier, 1836) Meliponula nebulata THEO/COF/UG07/Season Meliponula lendliana THEO/COF/UG07/Season B/003 (Smith, 1854) A/006 (Friese, 1900) Meliponula bocandei THEO/COF/UG07/Season Meliponula nebulata THEO/COF/UG07/Season B/001 (Spinola, 1853) A/007 (Smith, 1854) Plebeina hildebrandti THEO/COF/UG07/Season Meliponula bocandei THEO/COF/UG07/Season B/008 (Friese, 1900) A/008 (Spinola, 1853) Solitary bees Solitary bees Allodapula acutigera THEO/COF/UG07/Season Amegilla acraensis THEO/COF/UG07/Season B/013 (Cockerell, 1936) A/009 (Fabricius, 1793) Amegilla acraensis THEO/COF/UG07/Season Amegilla calens THEO/COF/UG07/SeasonB/010 (Fabricius, 1793) A/010 (Lepeletier, 1841) Braunsapis vitrea (Vachal, THEO/COF/UG07/Season Anthophora braunsiana THEO/COF/UG07/SeasonB/011 1903) A/011 (Friese, 1905) Ceratina ruﬁgastra THEO/COF/UG07/Season Braunsapis angolensis THEO/COF/UG07/SeasonB/012 (Cockerell, 1937) A/012 (Cockerell, 1933) Halictus sp. THEO/COF/UG07/Season Braunsapis fascialis THEO/COF/UG07/SeasonB/009 A/013 (Gerstaecker, 1857) Hylaeus sp. THEO/COF/UG07/Season Ceratina (Ctenoceratina) THEO/COF/UG07/SeasonB/039 A/014 sp.2 Lasioglossum THEO/COF/UG07/Season Ceratina nasalis THEO/COF/UG07/SeasonB/015 (Ctenonomia) duponti A/015 (Friese, 1905) (Vachal, 1903) Lasioglossum THEO/COF/UG07/Season Ceratina tanganyicensis THEO/COF/UG07/SeasonB/016 (Ctenonomia) radiatulum A/016 (Strand, 1911) (Cockerell, 1937) Lasioglossum kampalense THEO/COF/UG07/Season Halictus jucundus THEO/COF/UG07/SeasonB/017 (Cockerell, 1945) A/017 (Smith, 1853) Lipotriches sp. THEO/COF/UG07/Season Halictus sp. THEO/COF/UG07/SeasonB/030 A/018 Megachile (Creightonella) THEO/COF/UG07/Season Heriades sp. THEO/COF/UG07/SeasonB/031 erythrura (Pasteels, A/019 1970) Megachile sp. THEO/COF/UG07/Season Hylaeus ruﬁpedoides THEO/COF/UG07/SeasonB/020 A/020 (Strand, 1911) Patellapis sp. THEO/COF/UG07/Season Lasioglossum sp.1 THEO/COF/UG07/Season B/033 A/021 Pseudapis sp. THEO/COF/UG07/Season Lasioglossum sp.2 THEO/COF/UG07/Season B/024 A/022 Sphecodes sp. THEO/COF/UG07/Season Lipotriches sp. THEO/COF/UG07/Season B/035 A/023 Tetraloniella braunsiana THEO/COF/UG07/Season Megachile (Creightonella) THEO/COF/UG07/Season B/036 (Friese, 1905) A/024 globiceps (Pasteels, 1970) Xylocopa inconstans THEO/COF/UG07/Season Megachile (Creightonella) THEO/COF/UG07/Season B/037 (Smith, 1874) A/025 hoplitis (Vachal, 1903) Megachile (Eutricharaea) THEO/COF/UG07/Season B/026 gratiosa (Gerstäcker, 1857) (Continued) International Journal of Biodiversity Science, Ecosystem Services & Management 203 Appendix. (Continued). June–August November–December 2007 blooming season 2007 blooming season Megachile eurimera (Smith, 1854) THEO/COF/UG07/Season B/027 Megachile ruﬁpes (Fabricius, THEO/COF/UG07/Season B/028 1781) Megachile torrida (Smith, 1853) THEO/COF/UG07/Season B/029 Megachile ruﬁpennis (Farbricius, THEO/COF/UG07/Season B/017 1793) Megachile ruﬁventris THEO/COF/UG07/Season B/018 (Guérin-Méneville, 1834) Pseudapis alicea (Cockerell, THEO/COF/UG07/Season B/019 1935) Scrapter ﬂavipes (Friese, 1925) THEO/COF/UG07/Season B/032 Sphecodes sp. THEO/COF/UG07/Season B/021 Tetralonia boharti (Eardley, 1989) THEO/COF/UG07/Season B/022 Xylocopa (Mesotrichia) ﬂavorula THEO/COF/UG07/Season B/023 (De Geer, 1778) Xylocopa caffra (Linnaeus, 1767) THEO/COF/UG07/Season B/034 Xylocopa calens (Lepeletier, THEO/COF/UG07/Season B/038 1841) Xylocopa inconstans (Smith, THEO/COF/UG07/Season B/014 1874) Note: All voucher specimens are deposited at Makerere University Zoology Museum, Kampala, Uganda.
International Journal of Biodiversity Science, Ecosystem Services & Management – Taylor & Francis
Published: Sep 1, 2012
Keywords: Coffea canephora; fruit set; measures of pollination service delivery; ecological drivers; stingless bees; Africa
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