RETRACTED
This article has been retracted as it was found to include erroneous data. Its findings and conclusion cannot be relied on.

Psyche

Volume 2013 (2013), Article ID 564528, 38 pages

http://dx.doi.org/10.1155/2013/564528

Research Article

Climatic, Regional Land-Use Intensity, Landscape, and Local Variables Predicting Best the Occurrence and Distribution of Bee Community Diversity in Various Farmland Habitats in Uganda

M. B. Théodore Munyuli1,2,3,4

¹Academic Affairs and Research Program, Cinquantenaire University (UNI-50/Lwiro), D.S. Bukavu, South-Kivu Province, Democratic Republic of Congo

²Department of Biological, Agricultural and Environment Sciences, National Center for Research in Natural Sciences (CRSN/Lwiro), D.S. Bukavu, South-Kivu Province, Democratic Republic of Congo

³Centre of research for health promotion (CRPS), Department of Nutrition and Dietetics, Bukavu Higher Institute of Medical Techniques (ISTM/Bukavu), P.O. Box 3036, Bukavu, South-Kivu Province, Democratic Republic of Congo

⁴Department of Environmental and Natural Resource Economics, Faculty of Natural Resources and Environmental Sciences, Namasagali Campus, Busitema University, P.O. Box 236, Tororo, Uganda

Received 8 September 2012; Revised 14 December 2012; Accepted 17 December 2012

Academic Editor: Fernando B. Noll

Copyright © 2013 M. B. Théodore Munyuli. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

This study was conducted in 2006 in central Uganda to provide baseline data on relationships between bee community variables and local, climatic, landscape and regional drivers affecting bee community abundance and diversity in agricultural landscapes. Bee abundance and species richness increased significantly () with increase in percent cover of semi-natural habitats and the abundance of wild and cultivated floral resources in the landscape. There were strong linear declines () in bee species richness and abundance with cultivation intensity. Bee species richness declined very steeply with forest distance. Bee species richness and abundance were negatively affected by land-use intensity (). Bee species richness and abundance were strongly negatively correlated () with increase in mean annual temperatures in the previous years than in current years indicating potential vulnerability of local bee species to future climate changes. The percent cover of semi-natural habitats and natural in the farmland predicted best the occurrence and distribution in central Uganda. It is therefore recommended to policy-makers and to farmers to invest in the protection of forest fragments (and related semi-natural habitats) acting as buffer in the mitigation of negative effects of climate change on bee biodiversity and pollination services delivery.

1. Introduction

Pollinators provide a crucial ecosystem service through their role in the sexual reproduction of both wild plants and crops [1–3]. Pioneering works highlighted the fact that wild bees are by far the most important providers of vital pollination services in the world [4–7]. Their ongoing decline and potential ecological and economic consequences are therefore of major concern [8–11]. Long-term losses of certain pollinator species may threaten future ability of rural landscapes to maintain current levels of crop production.

There exist multitude factors (pressures), but currently suspected drivers (working alone or in synergy to produce negative or positive impacts) with potential effects (e.g., likely causing decline) in bees include land use change, use of pesticides (pesticide exposure) and reductions in population genetic diversity, farming and farm management practices changes, habitat loss and fragmentation, introduction of non native invasive species, species competition for resources, parasites and pathogen spread, heavy metal pollution, and climate change [9, 11–13]. Interactions between these multiple factors and various other factors are likely; for example, nutritional stress, due to a lack of floral resources or their poor quality, may lower the tolerance of pollinators to pesticides and diseases [14].

With an estimated 20,000–30,000 species worldwide [15, 16], bees are a useful group for the study of biodiversity and ecosystem services delivery in farmland habitats [17–21]. Bees are therefore important bioindicators of ecosystem health [19, 22–24] and environmental quality in different land-uses and ecosystems [24] since they reflect ecological changes by their richness and related parameters [25] and are sensitive to environmental alterations.

Animal pollinators contribute to approximately one-third of global food production [1], and pollination by bees and other insects is responsible for as much as 70%–84% of the 264 crop species grown in the world. Globally, the economics gains from crop pollination by free ecological services provided by wild bees, honeybees, and other insect pollinators are high and have been estimated to be worth several billions US dollars per year [2, 6]. The value of pollination to agricultural production worldwide is currently estimated to be worth US$226 billion (153 billion) per year or approximately 39% of the world crop production value (625 billion) from the total value of 46 insect pollinated direct crop species [25, 26]. Although one-third of the world’s food production relies on animals for pollination, it is, however, projected that insect pollinators (bees) may be responsible of more than one-third US$1 trillion in annual sales of agricultural products worldwide [27–30]. Challenges related to the conservation of bee faunas in tropical agricultural landscapes include the absence of basic knowledge about their biology, natural history, vulnerability to climate change and spatio-temporal distribution of communities (abundance, richness, diversity) in agricultural landscapes. In addition, factors driving the distribution of different bee species in different localities in farmlands of Uganda are not documented.

There exist several land-use pressures and land degradation problems in central Uganda; and agroforestry systems were developed and disseminated in rural areas of central Uganda as a way of maximizing yields on small scale farms [30]. In the coffee-banana agroforestry system of central Uganda, small-scale farmers grow and rely (for their livelihoods) on several foods (e.g., banana) and food crops (e.g., coffee) that highly require animal pollination to set fruit/seeds [27]. Due to negative effects of various drivers, decline in solitary and social wild bees (that are important pollinators of wild plant species and many insect-pollinated crops) has been predicted worldwide. Decline in pollinator availability may be caused to a greater extent by variation in the abundance of generalist rather than specialist bee pollinators [31, 32]. Decline in growth rates of bees may be likely affected by both abiotic factors and biotic factors.

Although pollination is a critical ecosystem service and that bees are the most important pollinators, they are endangered by intensive agricultural practices. Knowledge, on the relationships between insect pollinators and landscape structure/composition, land use change and habitat quality in Uganda, is still limited. While landscape context and habitat quality are known to influence species richness and abundance of bees [17], there is lack of information of the influences of climate factors on bee species richness and abundance. Climate change and variability may be contributing in boosting declining bee populations including afrotropical bees. Worldwide, empirical studies that take climatic variables into account are rare. However, there exist scantly speculative literature on potential effects climate change on plant-pollinator interactions and the consequence for pollination services delivery [33]. No previous studies have examined experimentally the combined (simultaneous) effects of micro, local, landscape, land-use intensity and regional and climatic factors on abundance and species richness bees in agricultural landscapes in Uganda and in Sub-Saharan Africa. However such studies are important to help in developing strategies to prevent future decline in bee species and guaranteeing future stability of pollinator-dependent crop yields and for food security of human communities depending on these crops. It is still not clear how in the future local, landscape and climatic factors will simultaneously affect the pattern of bee species richness and abundance in agricultural landscapes in Uganda.

Climate warming may interact to disrupt this crucial mutualism by altering plant chemistry in ways that alter floral attractiveness or even nutritional rewards for bees [33]. One possible effect of climate change is the generation of a mismatch in the seasonal timing of interacting organisms, owing to species-specific shifts in phenology. Local environmental conditions are the primary determinants of emergence phenology in bees and their food plants. In other words, phenology of plants and bees is regulated in similar ways by temperature, but that plants are more likely than bees to advance phenology in response to springtime warming. Different responses of bees towards climate change may lead to an increasing asynchrony in the life cycles of bees and flowering plants [29]. With the predicted climate change in interaction with land-use change and habitat alteration, bee species richness and abundance are expected to change patterns in occurrence with the resultant negative effect on pollination services delivery. Understanding how landscape characteristics (composition, structure, and configuration) affect bee biodiversity patterns and ecological processes at local and landscape scales will be critical for mitigating future negative effects of global environmental change [33] on bee biodiversity.

Climate change is only starting to shape the pollination service research agenda. The complexity of the impact of this phenomenon on bee biodiversity and pollination services delivery remains largely unveiled particularly in Sub-Saharan Africa. Future climate change is expected to have different scenarios (rate of shifts in distribution range, or rate of extirpation, rate of decline or disappearance in the landscape) upon bees. Responses of different species to change in climatic conditions are predicted to range from thriving (i.e., species capable of living under the new set of conditions) to adapting (i.e., species capable of surviving a change in global conditions by changing their ecology, physiology, and/or distribution) and going extinct.

Responses of different bee species to climate change are not experimentally documented in Uganda and in East Africa. It may be important to identify how different bee species will adapt to future climatic change as well predicting how bee species will disappear. However, global environmental changes (driven by multiple interacting drivers/pressures) are expected to have manifold effects (and unanticipated outcomes) on bee species richness and populations and on pollination services [28]. Global warming or climate change (changes in temperature and rainfall) is likely to have significant impacts on bee species richness and populations across different locations in eastern and central Africa. Specialized species may be vulnerable and reach high levels of risk of extinction in the landscapes.

Likely impacts of climate change on bee species richness may be linked to life history traits of different bee species (phenology, sociality, and bee-host plant synchronization). Trait-based approaches to predict and analyze the effects of climate change in interaction with other local and landscape drivers have been suggested by scientists. Responses of bees may vary among taxa. Life-history traits are related to the specialization of the bee species (nesting guide and feeding habit) and to sensitivity lower/higher risk of harm from various threats.

Changing flowering phenologies under climate change is well documented in temperature regions. The impact of climate change on plant-pollinator mutualisms is little understood or well predicted in Africa. Despite the enormous economic and ecological importance of bees as pollinators, currently there are no studies investigating the interaction between bee species and abundance and historical/current climatic factors (rainfall and temperature) in Uganda and in eastern and central Africa. However, such studies are also important to monitor and prevent decline in species richness and in pollination services delivery. Now that climate change is a reality in Africa, there is an urgent need to investigate its potential impact on bee species richness and abundance to foster to speculation on potential consequences of climate change on bee richness and pollination service delivery for food security and livelihoods of people.

Overall, there is a need to know the degree at which different environmental factors may affect bee communities to better plan conservation strategies of these pollinating service agents and prevent their decline in face of future climate changes, thus guaranteeing yield stability of pollinator-dependent crops while improving small-scale farmers’ food security and livelihoods. Speculating on bee species vulnerability to future changes in environmental conditions in which they are found, and modeling (predicting/forecasting) future changes in species populations may be an approach to inform conservation policy.

Factors influencing patterns of occurrence [30, 34] of bee communities in relationship to climate factors have not yet been studied in Uganda. Understanding how land-use intensity, climatic, landscape and local level-factors influence the presence/absence of different bee faunal species in different localities of an agricultural landscape can be very useful in influencing rural development policies about defining strategies to prevent future decline in face global environmental change threat.

Current bee abundance and richness may be the result of the simultaneous cumulative effects of local, landscape composition and regional/climatic factors over the recent years. Thus accounting for the recent of weather variability may be relevant in predicting future response of bee communities to climatic changes.

The general objective of this study was therefore to determine the relationships between bee community variables (abundance, species richness) and climatic, regional, landscape and local variables. The overall goal was to determine the degree to which these different variables can powerfully predict future patterns of bee communities in farmlands of central Uganda. It was hypothesized that bee abundance and species richness on agricultural landscapes in central Uganda are related to local and landscape variables, but not to regional climatic variables. The sub-hypothesis tested also whether precipitations/temperatures (climatic factors) in the current and previous year were associated with current bee abundance and richness, since precipitation is a primary driver of plant population dynamics and of bee emergence dynamics in most agricultural and natural regions [17, 18].

2. Material and Methods

2.1. Study Area

This study was conducted in the banana-coffee system of Lake Victoria Arc covering several districts of central Uganda (Figure 1). The study zone (average latitude: 0.5°31′22′′; longitude: 31°11′71′′; altitude: 1080–1325 m) is characterized by ferrisoils with high to medium fertility level and receives on average 1000–1800 mm of rainfall per annum on a bimodal pattern (rainy seasons: March-May, September-November; semi-dry to dry seasons: June-August, December-February) with °C and % of mean annual temperature and relative humidity respectively [29, 35]. But the rainfall amounts and patterns are unpredictable. The study zone belong to the Lake Victoria phytochorion [19–21] with shrubs of Acacia spp., legume trees, melliferous plant species, Papyrus and palms ranging from 2 to 15 m high dominating the remnant secondary vegetation [28]. In this study region, coffee (Coffea canephora Pierre ex Froehner) is the main cash crop and banana the main staple food crop. Several pollinator-dependent food and cash crops are grown in small-scale monoculture and/or polyculture fields that are integrated into this coffee-banana agroforestry system including home-gardens. There were no standard crops per study sites but most crops were found grown in almost all study sites. Crops grown as sole or in association with coffee and or banana include cassava (Manihot esculentum L.), sweet-potato, (Ipomoea batatus L.), maize (Zea mays L.), beans (Phaseolus vulgaris L.), groundnut (Arachis hypogea L.), tomato (Lycopersicon esculentum L.), watermelon (Citrullus lanatus L.), pumpkin (Cucurbita moschata L.), cucumber (Cucumis sativus L.), melon (Cucumis melo L.), chilies (Capsicum spp.); and several other fruits, vegetables and horticultural crops (cabbage, onion, etc., egg plants, sim-sim, etc.). The majority of these crops are grown in small-scale monoculture and or polyculture fields that are integrated into the coffee-banana agroforest production systems. The agroforestry system is also dominated by several native/indigenous, fruit and agroforestry tree species [27–30, 35].

Figure 1: Location of study sites in which bee fauna survey was conducted in the banana-coffee growing area around Lake Victoria in Uganda in 2006.

Rural central Uganda is mosaic landscape where “islands” of patches of natural habitats (forest fragments, forest reserves, wetlands, woodlands) and linear (e.g., hedgerows) and non-linear (fallow fields, grasslands, woodlots, cattle pastures or rangelands) features of semi-natural habitats [27, 28] that serve as “field boundaries” of the variety of small-scale fields; are found scattered within agricultural matrices. Compared to other regions (districts) of the country, the study area (central Uganda) is also characterized by high demographic pressure, limited access to arable lands, continuous cultivation and over-exploited lands under unrevised land policies [18].

2.2. Study Sites

In this study region, data was collected in 26 different study sites (1  size each) with different environmental characteristics (Figure 1). The 26 study sites were chosen distant one from another to reduce on confounding factors. Prior to the selection of different study sites, a study tour of different sites was made, and sites characteristics were noted. Thus, the 26 study sites were selected along contrasting environmental gradients, farm management systems, agroecological, semi-natural habitats, vegetation characteristics, and land-use intensity gradients. The study was designed to minimize spatial autocorrelation between local and landscape-scale variables measured within study sites by maintaining a minimum distance between study sites and clusters (each cluster was composed of 2 or 4 sites located in the same zone with similar general vegetation and environmental characteristics). The minimum distance between two study sites selected within a cluster was of 2–25 km (which is beyond the normal foraging range of most pollinator species), and the minimum distance between clusters was of 50–250 km. All 26 agricultural field sites had also some forest remnant tree species retained within them, ranging from 1 to 175 trees/ha found both in crop fields as well inside remnant natural vegetations scattered inside the forest. The distance between a study site and the nearest forest fragment/wetland varied from 2 to >2000 m and the size of the forest fragments found in the vicinity of crop fields varied from very small fragments of 0.1 ha to large forests greater than 850 ha in size. Shade cover within the coffee-banana agroforests ranged from 10% to 92%, and shade tree density was of more than 5–500 trees (all species combined) per hectare, excluding the density of other agroforestry and fruit tree species. Overall, the species richness of typical coffee shade trees, agroforestry trees, forest remnant trees, and fruit trees varied from 1.23 to 15.45 species per hectare.

There exist in this study region some large monoculture plantations (sugar cane plantations, coffee plantations, tea plantations, etc.). However, compared to large scale plantations, study sites that were covered by typical banana-coffee agroforests mixed with semi-natural habitats (e.g., young fallows) and related small-scale farms were of 95%, whereas study sites located within or in the proximity of large scale plantations covered approximately 5% of the farm-landscape studied. Most study sites were less similar in terms of altitude (altitude: 1080–1325 m) and in terms of type of semi-natural habitats surrounding all 1 km² study sites selected. However, few study sites (16 sites) had in their immediate vicinity natural forests and/or large wetlands.

2.3. Sampling Design and Bee Sampling Methods

Bees were sampled in each of the 26 study sites. Each study site was divided into five linear transects of  m each. Transects were used as basic units for bee sampling. They were also used as basic units for measuring all land-uses, habitats, and vegetation data. In each round of data collection, one transect per site was used. Thus, bees were sampled on one central transect (200 m wide 1000 m long) per study site. A sampling belt (20 m wide 1000 m long) was selected in the middle of each central transect as recommended [17, 18, 30] to reduce bias in bee sampling. Using a tape measure, the sampling belt of each selected central transect was divided into 10 sections (sampling plots) of the same size (20 m wide 100 m long each). The 10 sampling plots of each sampling belt were visited at each sampling date.

Bee sampling with each of the three sampling methods was concentrated in the sampling belt. Bees were sampled using three complementary sampling methods: hand-netting, transect walk-and-counts, and pan-trapping. Multiple trapping methods are recommended to be used to catch a wide array of bees as each sampling method has their own biases [17].

Transects walks-and-counts (standardized transect walks) method was used to sample foraging bees in the sampling belt by two observers per site as recommended [29, 30, 36]. Transects walks-and-counts are one of the standard protocols for visual observations and presence recording of bees in the fields. Observations of foraging activities and bee counts were done on flowering patches found within different habitats and land-uses while walking along transects at a slow speed (<0.25 km per hour). Observations were made under conditions favorable for bee flights: sunny or cool weather and weak wind. Hand-netting (“sweep-net” sampling) of bees with a hand-net (30 cm diameter) was conducted in patches of fresh flowers in each plot immediately after visual censuses. Transects were sampled from 08 h00 to 17 h00 local time to capture bees on wild blooming plants as recommended [11, 17, 19, 34, 37, 38]. Only hand-net bee samples were transferred to zip lock plastic bags and placed in a portable cooler.

Pan traps have been used to sample (bees and other insects) for almost 2 decades [9, 11, 17]. In the pan-trapping method, a single trapping point was established within each plot. Pan traps were constructed from 236.5 mL white, blue and yellow plastic bowls [1, 9, 10, 39, 40]. The bowls (brand “Solo” supplied by Prof Simon Potts from the University of Reading, UK) were sprayed with ultra-violet bright paint colors (blue, yellow and white). Pantraps were filled with water and a small amount of detergent to reduce surface tension to help insect sink and increase UV-light reflection [28, 29], thereby attracting bees and other insects, which fly into the water and drown.

At each sampling point, three pan traps (blue, yellow and white) were spaced 5 m apart on the ground as recommended [35]. Pan traps were placed elevated in the plant canopy at the height of flowers easily visible by flying insects. Bowls were hung on plant branches or on stakes fixed in the soil. In each pan trap, unscented, biodegradable liquid detergent was poured into pan traps (2.5 mL detergent/liter water). Pan-traps were left in place for about two days before collecting samples to reduce bias in the sampling procedure [11, 40]. Most pans used were generally placed far from tracks to reduce disturbance by curious un-informed villagers or school children. A limited number of traps were found disturbed (i.e., empty pan traps, pan-traps taken away, etc.) during the second and the fourth rounds of data collection, resulting in a few missing data points. During the rainy seasons, holes were added in the upper zone of each pan-trap to allow excess water to drain without washing away bee samples.

For each of the three sampling methods, transect walk- and counts, hand-netting, and pan-trapping [10, 18], bees were sampled during five consecutive rounds from January 2006 to December 2006 (Round 1: January-April, Round 2: May-June, Round 3: July-August, Round 4: September-October and Round 5: November-December). Sampling was conducted consistently across crop growing seasons in order to compare sample yields (bee species richness and individuals estimates) and between rainy (wet) and dry seasons. Bee fauna surveys were conducted across months of highest (September-May) and lowest (July-August) bee abundance and species richness [17]. Each round of data collection lasted 5 to 7 weeks. Across sampling methods, bees were sampled for 30-min period per sampling plot between 8 h00–17 h00. Although being are that some bees start foraging [22, 30] even before sunrise and stop soon afterwards (example: crepuscular bee species representing less than 0.1% of bee species found in central Uganda and that may be lost during bee sampling), bees were sampled between 8 h00 and 17 h00 due to time constraint and depending on local light conditions and ambient temperature. Bee specimens from each sampling method (hand-nets, pan-traps) were strained into plastic vials containing alcohol (70% ethanol) and taken to the laboratory for identification to the highest possible taxonomic level. Bee samples were sorted out at Makerere University (Zoology department museums). The majority (95% of bee samples) were identified up to species levels at bio-systematic division of the plant protection research institute (Pretoria- South Africa). Other minor identifications were conducted at Natural History Museums-London (UK), Smithsonian Tropical Research Institute-Panama, and University of Reading (UK). The established reference collection of bees from central Uganda is deposited at Makerere University Zoology Museum.

Surveys on bee food plants and bee nest trees were conducted parallel to bee faunistic surveys. Data on different types and size of semi-natural habitats/land-uses was also collected parallel to bee surveys. During bee surveys, the number of nests and nesting sites for most bee species were counted in these different habitats/land-use types. Bee food plants and Bee nest were identified in the field with author experience. However, when in doubt, bee food plant specimens were collected and identifications to species-level were confirmed at Makerere University Herbarium.

2.4. Measurement of Correlative (Factors) Variables

In this study, different metrics (local, landscape, regional factors) affecting pollinator populations and species richness in farm-landscapes of central Uganda were measured. In this study, local variables (farm-level variables) referred to factors affecting bee communities at a local scale (1–500 m radius). Local factors are limited at an individual plot/land-use level [30]. Landscape-scale factors were referred to factors operating at a large scale level (>0.5–1 km radius) covering flight range of various typical farmland native bee species [29]. Regional variables referred to broad scaled factors that may have an indirect effect on bee activities at local level from very far. These are factors operating at a larger scale level (>1–100 km radius). Thus, few local, landscape, regional and climatic factors of importance for bees were measured at different scale levels.

Local and landscape variables were measured following approaches by previous workers [41–45]. Measured local variables included the amount of floral resources [29]. These included the percent cover of wild flowering plants (trees, shrubs and herbs) or the mass flowering of wild plants, the number of wild blooming plant species, and the percent cover of cultivated floral resources (percent of cultivated pollinator-dependent and non-pollinator dependent crops) per 1 km² area (site). Data on herbs were collected from ten quadrats measuring (25 ) while those on shrub and trees were collected for in twenty quadrats measuring (500 ). All trees with stems greater than 10 cm diameter at breast height (dbh) were recorded. Data on the number of fresh flowers were also recorded in “plots” of  m (25 ) dimension [46]. The quadrats (plots) were randomly established in each study site as recommended [22, 23] to determine the number of plant species. Measurement of wild floral resources focused only on plant in bloom. Wild blooming floral resources were measured in five samples (five rounds) as the bee data.

The percentage cover (area covered in hectares) of cultivated floral resources was determined based on land-use data collected about the proportion cover of each type of crops cultivated [1] in a 1 km² site area. Later, all cultivated crops were grouped in two subcategories [17, 18, 29] and their respective proportion determined based on area (ha) covered by (i) pollinator-dependent crops and (ii) by non-pollinator-dependent crops. Therefore, crops were categorized into two subgroups based on the dependence ratios used by Klein et al. [1].

Landscape-level land-use data were collected within a 1 km² site. Each square kilometre was delineated using a global positioning system (GPS). Because there were no previously published data on small-scale land-use patterns in the study region, to facilitate basic measurements about different land uses, the km² area was divided into five transects of  m [18, 28]. Here, the areas with different land-use types were measured using GPS or a tape in case of small fields ( m and less). Land-use types were grouped into major land-use types based on their size (0.06 ha–1 ha to 9.95–16.45 ha) and frequency of occurrence in order to calculate the area covered by semi-natural habitats, the area covered by crops and the cover of dependent and nondependent cultivated crops per km² area [22, 27, 30, 35]. The term semi-natural habitats included all linear (hedgerows, field margins, roadsides, track-sides, stream edges, etc.) and non-linear (fallow fields, grasslands, woodlands, woodlots, etc.) semi-natural habitats [47–49] of ecological importance for pollinators living in farm-landscapes [27, 28, 35]. These semi-natural habitats play an important role in the maintenance of many bee species in rural landscapes in Uganda [27, 29]. Semi-natural habitats have been shown to be important in structuring bee communities in agricultural landscapes [22]. Young fallow fields play a particular role as foraging habitats and bee refugia and as reservoir of other ecosystem services delivery agents in rural landscapes [22, 23, 27–30, 35]. Pollinator-dependent crops are those that require a visit to its flowers by a pollinator to set fruits/seeds [1, 29].

Three landscape variables of ecological importance [50] for bee biodiversity and pollination studies [29] in agricultural matrices were then calculated for each 1 km² study site: (i) the percent of semi-natural habitats; (ii) the cultivation intensity, that is, the percentage of the total land area cropped; (iii) the distance from a given study site to the nearest potential natural pollinators’ source (forest and wetlands)—the distance from a large (defined) forest fragment/natural wetland [22, 30]. Distances up to 100 m were measured with a tape, otherwise with GPS (corrected to 1 m accuracy with Pathfinder v 2.0).

Regional land-use categories (gradients) were obtained from the Makerere University Geographic Information Services [23, 28]. Broad land uses classified as low-intensity use include areas where at least three-quarters of the land is uncultivated. Medium are managed habitat types where there is an almost equal distribution of cultivated and uncultivated land. High are areas dominated by crops or livestock (such as only one-quarter of the total land is used for other purposes rather crops and livestock). Very high represents large monoculture estates of tea, sugar, coffee, and so on [22, 23, 27–30].

Preliminary analysis of relationships between butterfly community variables (species richness and abundance) and weather/climatic factors (rainfalls, temperature, etc.) in previous years (10 years, 2 years before) and current years revealed potential influences of climate change on the distribution, occurrence, and activities of different butterfly species [17, 18]. Thus, this study aimed at verifying if this trend was the same for all farmland bee species. Therefore, data on regional climatic factors were obtained from meteorological stations located in the study area including Kamenyamigo meteorological station covering Masaka cluster (sites: Kasaala, Katwadde, Kiwaala, and Mpugwe) in Masaka district; Entebbe meteorological station covering Kalagi (sites: Bamusuuta, Kifu, Kimwanyi, and Kiweebwa), Lugazi (sites: Kasaku and Sugar), Mpigi (sites: Lukalu and Mpanga) and Mabira (sites: Bulyasi and Kinoni) clusters in Mukono district; Jinja meteorological station covering Kamuli (sites: Naikesa and Namulekya), and Bujjagali (sites: Bukose, Namizi-East, Namizi West, and Nawangoma) clusters in Kamuli district and Kiige meteorological stations covering Nakaseke (sites: Kimuli, Kyetume, Lukumbi, and Segalye) and Kaweri coffee plantation (sites: Nomve and Luwunga) clusters in Mubende district. From the raw data obtained from the different meteorological stations, a monthly mean for 10 years (1998 to 2007) of temperatures and rainfalls was calculated to see the trends in the rainfall patterns and temperature since such oscillations can affect the patterns of pollinator communities in rural landscapes. Other variables (helping in detecting current patterns of bees in relationship to past climatic events) calculated included (i) the overall mean rains (means/month/10 years); (ii) the overall daily mean minimum temperature (mean of 10 years); (iii) the overall daily mean maximum temperature (mean 10 years); (iv) the mean monthly rainfalls (2007); (v) the mean monthly maximum temperature (2005); the mean monthly maximum temperature (2006); (vi) the mean monthly maximum temperature (2007); (vii) the mean monthly minimum temperature (2005); (iix) the mean monthly minimum temperature (2006); (ix) the mean monthly minimum temperature (2007); (x) the mean monthly rainfalls (2005) and (xi) the mean monthly rainfalls (2006).

2.5. Data Analysis

Although collections were not similar, data from the three sources (transect walk-and-counts, pan-traps, and hand-nets) were pooled as recommended [18, 36] to provide total bee abundance and species richness estimates per transect/study site/sampling round. In fact, bee abundances (χ² 2 df = 26.78, ) and species richness (χ² 2 df = 12.56, ) were significantly different among the three sampling methods, but pooling data from the three different sampling methods was still conducted and motivated by the fact the interests was estimates of overall abundance and specie richness and not on comparing the efficiency of the three different sampling methods. In addition, each of three sampling methods applied can be associated with bias towards number of species and individuals detectable by the sampling method [18].

Genera-tribe richness, species richness, abundance and dominance were calculated to highlight (indicate) the structure/characteristic of bee communities studied; they were expected to be driven by various local, landscape, regional and climatic factors. Thus, species richness of some taxa (e.g., genera, tribe) was calculated as the number of species belonging to that taxa.

Bee abundance was calculated as the total number of individuals recorded per transect each sampling day. The species richness was calculated as the total numbers of bee species recorded per transect per study site each sampling day. Species dominance () was calculated according to Munyuli [18] and Magurran [51]: D = (abundance of a species/total abundances recorded) × 100. If %, the species was termed a dominant species. Species accumulation and estimation curves were constructed/generated using the Jacknife-1 estimator [51, 52].

To determine “indicator species” or “characteristic species” of pollinator communities from farmland habitats of central Uganda, indicator (IndVal) method of Dufrêne and Legendre was adopted and used in this study in a modified form as recommended by Munyuli [22] to identify indicator. Indicator species are ecological “characteristic species” or ubiquitous/common species of bee communities inhabiting certain type of habitats of a given landscape [22]. Indicator species are potentially effective pollinator species delivering pollination services to wild and cultivated crops in the landscape [18, 30]. Knowledge of indicator species is important to acquire since it gives an idea on reliable spatio-temporal pollination services delivery agent species in the rural landscapes. Such knowledge may help in predicting/speculating on responses of different bee species to various drivers.

The correlation of independents variables (e.g., meteorological variables, local and landscape variables) characterizing the 26 study sites with dependent variables (bee species richness, bee abundance) were tested using Pearson correlation. The derived correlation coefficients and -values associated with the paired variables were presented in matrix of correlation. Correlation analysis was used to determine the suite of variables most closely related to bee species richness and abundances measured. The different independents variables were checked to prevent collinearity following approaches previously described by Munyuli [30]. Based on the correlation matrix of all variables measured, only independents variables that were significantly () related to dependent variables (bee communities) were chosen for further analyses in simple regression analyses. These illustrated the trends and magnitude of the effects of independent factors expected to affect bee communities (abundance and or species richness).

Prior for conducting analyses, Kolmogorov-Smirnov test was used to check if variables were normally distributed. Data was transformed if found necessary to meet the assumption of normality and homogeneity of variances. Variable data expressed as percentages were arcsine square-root (+0.5) transformed. The number of species and of individuals were square root + 1 transformed and log-transformed using ln (), respectively. Back-transformed data are reported [22, 23, 30].

Simple regression methods were mainly applied to explore relationships between local (abundance and richness of wild and cultivated floral resources) and landscape (cultivation intensity, amount of semi-natural habitats, and forest distance) variables and the abundance and species richness of bees. Scatter plots were used to illustrate scale dependency of bees on different local and landscape variables measured. Therefore, for all simple regression models obtained, the coefficient of determinations () (that measures the proportion of the total variance of observed data explained by predicted data) was calculated to demonstrate the level of influence of the type of variables that correlate with the abundance and species richness of bees. In other words, the coefficient of determination () was used for determining the proportion (%) of influence of all different variables on the abundance and species richness of bees. Relationship between the density of nests and bee abundance/species was explored using simple regression analysis in Minitab15. All simple regression analyses were conducted in Minitab version-15 and the results plotted.

When interested in distinguishing/exploring the combined (simultaneous) effects of multiple predictor variables (local, landscape, and climatic variables) on bee communities (species richness and abundance), generalized linear models (GLMs) were performed. Models were computed including (i) local, landscape, and climatic variables as predictor variables; (ii) bee species richness or abundance as continuous/response variable. Generalized linear models (GLMs), with normal error distribution and log-link function, followed by a likelihood ratio test, and with three iteration levels, were fitted given the type of measured response (which changes may change scale from discrete counts to continuous frequency). The generalized linear modelling (GLM) framework was constructed in STATA version 8 for windows. Models were simplified using the Akaike’s Information Criterion (AIC) and a drop function of variables for collinearity reasons (if any). The significance of the simultaneous effects of different variables was tested using test.

The effects of categorical predictors (land-use intensity gradient categories) on bee species richness and abundance were analyzed by applying a general linear model (GLM). Analysis of variance (ANOVA) in Minitab statistical software version-15 was conducted with bee community variables (abundance and species richness) as the dependent variables, and the categorical variables (low, medium, high, and very high) as fixed factors. The Tukey tests were used as post hoc tests at . Differences between means were inspected using Tukey’s honestly significant difference (HSD).

3. Results

3.1. Characteristics of Bee Assemblages

In total, 652 species, (Table 8) representing 76 genera were recorded, and these comprised a total of 80883 individuals recorded. For data collection, three bee sampling methods of different efficiency and accuracy levels were employed in this study. Most bees Tables 8 and 9 were recorded through transect counts (85% of total individuals), and very few individuals were captured by hand-net (8%) and pan-traps (7%). Although hand-net had significantly high bee species than pan-traps, the two methods recorded almost equal number of bee individuals. Total of 59, 314, and 559 bee species were recorded in transect counts, pan-traps, and hand-nets, respectively. Thus, transect count was not accurate in estimating species richness as compared to hand-net and pan-trap. The majority of bee species registered were native. There was no managed bee species. Population of honey bees encountering foraging during field is a population from wild established colonies and not from colonies established in hives.

Overall, bee species richness assessment was incomplete as taxon accumulation curves did not achieve asymptotes. Species richness was still increasing at the end of the sampling period and never reached the asymptote. The number of observed species was 652 species, whereas the average projected true species richness estimated was of 931 species according to Jacknife-1 estimator. Estimates of the expected richness indicated that 70% of the species present at the sampling sites during the period of study were found.

The most species-rich genera were Megachile (12.5% of total species recorded), Lasioglossum (8.5%), Lipotriches (6.2%), Patellapis (5.8%), Scrapter (5.7%), Nomia (5.4%) and Ceratina (5.1%). Similarly, Megachilini (14.42%), Halictini (11.7%), Anthophorni (8.2%), Allodapini (7.94%), Ceratini (7.18%), Anthidiini (6.35%), Eucerini (5.05%), and Xylocopini (5.05%) tribes were the most species-rich and abundant taxa. The abundant and most widespread (>5% of total individuals recorded) bee species were Apis mellifera adansonii Linnaeus (23.20%), Hypotrigona gribodoi Magretti (18.89%), Meliponula ferruginea Lepeletier (12.54%), Lasioglossum ugandicum Cockerell 6.90%), Apis mellifera scutellata Latreille (5.92%), Allodapula acutigera Cockerell (5.89%), Ceratina rufigastra Cockerell (5.60%), Braunsapis angolensis Cockerell (5.29%), and Seladonia jucundus Smith (5.02%). Most bee species observed were polylectic; that is, they forage for pollen on a diverse array of plant species, and few of them were oligolectic bees. Most species were short-tongued species. Moreover, the community was dominated by ground-nesting species, whereas above-ground nesting species were rare.

Approximately 17 bee species were identified as ubiquitous and “ecologically characteristic” of the coffee-banana agroforests of central Uganda. These included Apis mellifera adansonii, Hypotrigona gribodoi, Meliponula ferruginea, Lasioglossum trichardti Cockerell, Apis mellifera scutellata, Lipotriches dentipes Friese, Lasioglossum ugandicum, Braunsapis angolensis, Heriades speculiferus Cockerell, Seladonia jucundus Smith, Meliponula nebulata Smith, Ceratina rufigastra Cockerell, Ceratina tanganyicensis Strand, Allodapula acutigera, Nomia atripes Friese, Allodape microsticta Cockerell, and Halictus frontalis Smith.

Although the different bee sampling methods used prevent direct comparison, the data indicate that the study area harbours one of the most diverse bee faunas in central and East Africa. Factors likely favouring the high bee diversity in this area include moderated climate and diversity of land use and related semi-natural habitats within agricultural matrix.

3.2. Individual and Combined Effects of Various Factors on Bee Community Parameters

Climatic factors (rainfall, minimum and maximum temperatures) were cross-correlated with bee community parameters (abundance and species richness) and with several local and landscape variables measured. The correlation between rainfalls/temperatures of years 2004, 2005 and 2006 with current abundance and species richness was determined since it has previously been observed that, most often, current afrotropical pollinator communities structures are also the reflect of variability in previous climatic events [17, 18, 53].

Thus, bee community was strongly correlated with some climatic variables. In fact, rainfalls of year 2005 was found to be negatively correlated with both species richness (, , ) and abundance (, , ) of bees. Thus, bee species richness and abundance were significantly and negatively associated with precipitation in previous (not the current) year. Previous year is likely to be the stronger predictor of bee species richness (not bee abundance) in central Uganda. In other words, cumulative precipitation in the previous year is likely to be a good predictor of bee richness in the current year. Also, the mean rainfall of year 2006 was negatively correlated to species richness (, , ) abundance (, , ) of bees. By the contrast, the abundance and species richness of bees were not significantly () associated with the mean rainfall of year 2007 and with the overall mean rainfall of 10 years (Table 1). In addition bee abundance was negatively correlated with both the mean maximum temperature of 10 years (, , ), and mean maximum temperature of year 2005 (, , ); but positively correlated with both the mean minimum temperature of 10 years (, , ), and with the mean minimum temperature of year 2006 (, , ; Table 1).

Table 1: Cross-correlation matrix showing naïve multiple correlations of Apoidea (bee) variables with environmental, local, and landscape variables (different levels of significance of Spearman rank of correlation coefficients:*P <0.05; **P < 0.001; otherwise not significant when no value given).

O
P
Q
R
S
T
U
V
W
X

B

0.49*

0.49*

C

−0.51*

−0.56**

E

−0.52*

J
−0.40*

0.51**

0.55**
0.39*
0.59**
0.41*
−0.58**
−0.67**

L

0.51**
0.46*
−0.61**
−0.44*
−0.56**
0.52**

M

0.44*
0.51**
−0.50**
−0.46*
−0.57**
0.57**

N
0.43*
−0.65**
−0.62**
0.426*

−0.70**

0.525*

O

−0.60**
−0.68**
0.93**
0.78**
−0.66**

0.57**
0.55**

P

0.84**
−0.58**
−0.61**
0.88**

−0.68**
−0.71**

Q

−0.62**
−0.78**
0.94**

0.44*
−0.95**
−0.98**

R

0.82**
−0.76**
0.51*

0.56**
0.52*

S

0.68*

0.71**
0.78**

Legend: A = overall mean rainfalls (means/month/10 years); B = overall daily mean minimum temperature (mean 10 years); C = overall daily mean maximum temperature (mean 10 years ); D = mean monthly rainfalls (2007); E = mean monthly maximum temperature (2005); F = mean monthly maximum temperature (2006); H = mean monthly maximum temperature (2007); I = mean monthly minimum temperature (2005); J = mean monthly minimum temperature (2006); K = mean monthly minimum temperature (2007); L = mean monthly rainfalls (2005); M = mean monthly rainfalls (2006); N = wild flowering plant species richness; O = density of plant species; P = human population density; Q = proportion cultivation intensity; R = mean bee species richness; S = mean bee abundances; T = forest distance (m); U = cultivated with floral resources (pollinator-dependent crops); V = cultivated without resources (pollinator nondependent crops); W = % seminatural habitats/site; X = % young fallows per site.

Results of the generalized linear model (GLM), applied to explore the simultaneous of multiple factors, revealed few significant () predictor variables with combined and or interactive effects on bee species richness. Variables with combined negative/positive effects included the distance to forest, overall 10-years monthly mean rainfalls, mean monthly rainfalls of year 2006 and mass flowering wild plant resources. Variables with significant combined negative/positive effects on bee abundance included (i) overall daily mean minimum temperature mean of 10 years, (ii) mean monthly rainfalls 2007, (iii) mean monthly rainfalls 2006, (iv) number of flowering plant species, (v) mass blooming wild plant species, (vi) overall daily mean minimum temperature mean of 10 years (vii) proportion cultivation intensity, (iix) mean monthly maximum temperature, (ix) cultivated floral resources with positive, (x) mean monthly minimum temperature of year 2006 with, the percent cover of semi-natural habitats, and so forth (Table 2). These GLMs also indicated that current trend in occurrence of bee species richness and abundance is the consequence of various interacting factors operating at different scale levels but with simultaneous negative/positive effects.

Table 2: Generalized linear models (GLMs) testing the effects of local, landscape, and climatic factors on bee abundance and species richness in farmlands of central Uganda.

Bee species richness
Coef.
Std. Err.

95% Conf. Interval

Landscape variables
Forest distance
−0.3040771
0.0372303
−8.17
0.000
−0.3770472
−0.2311071

Proportion cultivation intensity
−0.0417633
0.0184095
−2.27
0.023
−0.0778453
−0.0568113

% seminatural habitats
0.0260201
0.0098786
2.63
0.008
0.0066585
0.0453817

Human population density (inhabitants/Km²)
0.0136742
0.0417017
0.33
0.743
−0.0680597
0.0954081

    Climatic variables
Overall daily mean minimum temperature (mean 10 years)
−2.36636
2.509198
−0.94
0.346
−7.284298
2.551578

Overall daily mean maximum temperature (mean 10 years )
−0.8994122
0.3635115
−2.47
0.013
−1.611882
−0.1869429

Mean monthly maximum temperature (2005)
−5.517305
5.026883
−1.1
0.272
−15.36982
4.335206

Mean monthly maximum temperature (2006)
−0.0751644
0.1294036
0.561
0.561
−0.3287908
0.178462

Mean monthly minimum temperature (2006)
0.0237176
0.0198609
1.19
0.232
−0.015209
0.0626443

Overall mean rainfall (means/month/10 years)
0.8934966
0.4196737
2.13
0.033
0.0709512
1.716042

Mean monthly rainfalls (2007)
−5.14455
5.405548
−0.95
0.341
−15.73923
5.450129

Mean monthly rainfalls (2006)
−0.9998961
0.3946438
−2.53
0.011
−1.773384
−0.2264084

    Local variables
Density of wild plants (weeds/herbs/trees)
0.6940608
0.3129488
2.22
0.027
0.0806924
1.307429

Number of flowering wild plant species (weeds/herbs/trees)
−2.874384
0.8531344
−3.37
0.001
−4.546496
−1.202271

% of cultivated floral resources (pollinator-dependent crops)
0.4345841
2.249894
0.19
0.847
−3.975128
4.844296

% cultivated floral resources (nonpollinator-dependent crops)
0.8652652
2.129935
0.41
0.685
−3.309331
5.039861

Constant
548.8108
500.5592
1.1
0.273
−432.2671
1529.889

Other statistics: Log likelihood = −124.727761; AIC (Akaike's Information Criterion) = 12.70252; BIC (Schwarz's Bayesian Criterion) = −21.62718472

Bee abundance

Landscape variables
Forest distance
0.0039313
0.0054335
0.72
0.4669
−716.9601
0.0145808

Proportion cultivation intensity
−613.0046
180.3214
−3.4
0.001
−966.4281
−259.5811

% of seminatural habitats
0.001616
0.0001599
10.10
0.000
0.0013026
0.0019295

Overall monthly mean rainfalls (means of 10 years)
26.26025
2.734129
9.6
0.000
−19.34479
31.61905

    Climatic variables
Overall daily mean minimum temperature (mean of 10 years)
−377.1344
18.25537
−20.66
0.000
−9.023715
−341.3545

Overall daily mean maximum temperature (mean of 10 years )
−383.902
169.9307
−2.26
0.024
−716.9601
−50.84403

Mean monthly minimum temperature (2006)
−263.8625
35.75006
−7.38
0.000
−333.9313
−193.7937

Overall daily mean minimum temperature mean 10 years
5.595728
0.3103008
18.03
0.000
4.98755
6.203907

Mean monthly maximum temperature (2005)
−66.88049
32.75358
−2.04
0.041
−131.0763
−2.684648

Mean monthly rainfalls (2007)
−14.75942
2.339518
−6.31
0.000
−19.34479
−10.17404

Mean monthly rainfalls (2006)
−5.413767
1.841844
−2.94
0.003
−9.023715
−1.803819

Mean number of flowering wild plant species
−4.652631
4.882926
−0.95
0.341
−14.22299
4.917729

    Local variables
Mean density of wild flowering plants
1.686491
0.1404906
12
0.000
1.411134
1.961847

Cultivated floral resources (pollinator dependent crops)
−192.0184
16.07023
−11.95
0.000
−223.5154
−160.5213

Cultivated—without resources (nonpollinator-dependent crops)
0.1851433
0.1925751
0.9611
0.336
−0.192297
0.5625835

Constant
36034.16
3331.619
10.82
0.0000
29504.3
42564.01

Other statistics: Log likelihood = −390.1545969; AIC (Akaike's Information Criterion) = 36.83224; BIC (Schwarz's Bayesian Criterion ) = 568.065439

Simple linear regression analysis revealed that floral resources exhibited positive and significant relationships with bee abundance and species richness. Bee species richness and abundance were related to richness and abundance of wild blooming plants. Bee species richness was also related to the abundance cultivated floral resources. The list of pollinator-dependent and non-pollinator dependent crops grown in Uganda is presented in Table 6. Several wild blooming plant species were registered during the course of the study (Table 7). In addition, there was a seasonal variability in richness and abundance of wild blooming plant species (Figure 2). Across the five rounds of data collection, there was variability in cultivated and noncultivated floral resources following different environmental conditions found in the different study sites.

Figure 2: Trends in blooming wild plant species richness and abundance in relationships to rainfall and maximum temperature patterns in central Uganda. Blooming plant species richness and abundance had a significant correlation (, ) with rainfall. The minor wet season (September–November) to early dry season (December-January) had more diverse blooming plant species with greater availability of herbs/weeds than trees and shrubs. However, both trees/shrubs and herbs/weeds blooming groups were least in June during the major wet season. Blooming tree/shrub richness showed a highly significant inverse correlation (, ) with rainfall patterns. They declined a month ago after the start of the minor and major rainy seasons (September–March) and increased quickly two months later. This indicated that the two wild plant groups were not all in bloom at the same moment, and this is interesting for bees that need cover of floral resources around the year. Most (90%) blooming herbs and weeds were in full bloom towards the end of the major wet season and peaked in June-July when the maximum temperature was not high. The number of fresh flowers per 25 m square was not correlated to neither rainfall (, ) nor to maximum temperature ().

Species richness (not the abundance) of cultivated crops decreased linearly (, , ) with cultivation intensity. A reverse trend (, , ) was observed for wild blooming plants (herbs, weeds, etc.). Species richness (not the abundance) of wild blooming plants increased linearly with increase in % cover of semi-natural habitats (, , ) but declined linearly with forest distance (, , ). These results indicated that isolated sites or overcultivated sites were associated with low species richness in flowering plant species although, at some times, abundant mass blooming crops could be observed in overcultivated areas or in areas located very far from forests. On the contrast, areas that were covered by a high proportion of semi-naturals were also associated with high species richness of wild blooming plants and not necessarily with abundant blooming plant populations.

The results of simple linear and non-linear regressions (quadratic regressions) indicated that percent of wild floral resources (blossom cover) was significant and positively related to both species richness (Figure 3(a)) and abundance (Figure 3(b)) of bees. This result suggested that the amount of wild floral resources played a critical role in shaping flower-visiting bee communities in agricultural landscapes of central Uganda. Overall, wild blossoms (from wild blooming plants) cover explained 86% and 61% of variation in bee species richness and in bee abundance, respectively. Interestingly, flowering wild plant species richness was significantly () related to bee species richness (Figure 3(c)) but was not significantly () related to bee abundance (Figure 3(d)). The number of wild blooming plant species accounted for 19.6% of variation in bee species richness. These results indicated that increase in the diversity of flowering plant species can attract a high number of bee species; however, beyond 30 plant species, bee species richness can start to drop. The drop in bee species richness was unexpected and may be difficult to explain since at some points there is an expected plateau due to carrying capacity. In other words, bee species richness response to richness of wild floral resources richness is not necessarily linear under local conditions in Uganda.

(a)

(b)

(c)

(d)

(e)

(f)

Figure 3: Relationship between (i) the abundance of wild floral resources (% blossoms cover), (ii) the abundance of cultivated floral resources (% of cultivated pollinator-dependent crops: all annual, bi-annual, perennial entomophilous crop species potentially offering pollen-nectar to bees), (iii) the number of flowering plant species (weeds, herbs, trees, and shrubs), and the number of bee species (a, c, and e) and bee abundance (b, d, and e) per study site.

As expected, the cover of cultivated non-pollinator-dependent floral resources was not significantly (, , ) related to either bee species richness or to bee abundance. Yet, the percentage cover of cultivated non-pollinator-dependent crops decreased as bee species richness and/or abundance increased. Conversely, the number of species of cultivated non-pollinator-dependent floral resources was not related () to bee species richness (Figure 3(e)) and/or abundance (Figure 3(f)) under local conditions in central Uganda. Practically, cultivated pollinator-dependent floral resources were significantly () and positively related to bee species richness (Figure 3(e)) and bee abundance (Figure 3(f)). The proportion of cultivated pollinator-dependent crops explained 26% and 47% of the variation in bee species and in bee abundance, respectively (Figure 3). This indicates that cultivated pollinator-dependent crops species provide sufficient pollen resources to bee communities visiting crop fields in the agricultural mosaics of central Uganda. In other worlds, species richness and population density of bees in the agricultural landscapes are also influenced by the abundance of cultivated pollinator-dependent crop floral resources. Although no clear relationships between cover of cultivated and cover of noncultivated pollinator-dependent crops in the local farm-landscape were detected, when farmers increased the land area dedicated to pollinator-dependent crops, cover of non-pollinator-dependent crops reduced in terms of area coverage. Overall, species richness and population density of bees in the agricultural landscapes were found to be largely influenced (were more predicted) by the percent cover of bloom of wild plant species than by the proportion of cultivated crops that are pollinator dependent.

The regression analysis revealed that some landscape factors showed highly significant () associations with bee community parameters. Cultivation intensity was significantly () and negatively related to both bee species richness (Figure 4(a)) and population density (Figure 4(b)). The proportion cultivation intensity explained 38% and 62% of variation in bee species richness and in bee abundance, respectively. These results suggested a “steep” (abrupt) “decline” (reduction) in bee species richness and abundance with cultivation intensity. The proportion of semi-natural habitats in a 1 km² area was significantly () and positively related to both species richness (Figure 4(c)) and abundance (Figure 4(d)) of bees. The proportion of semi-natural habitats accounted for 31% and 50% of variation in bee species richness and in bee abundance respectively. This result suggested that any increase in amount of semi-natural habitats in the landscape was likely to lead to an increase in the number of bee species and individuals in the agricultural matrices. Surprisingly, forest distance was significantly () and negatively related to bee species richness (Figure 4(e)) but was not significantly () related to bee abundance (Figure 4(f)). Forest distance explained 58% of the variation in bee species (Figure 4). This result suggested also that there was a strong decline in bee species with forest distance. In other words, study sites that were riparian of forest reserves harbored high species richness than did sites that were isolated or located far way from forest reserves.

(a)

(b)

(c)

(d)

(e)

(f)

Figure 4: Relationship between landscape context variables (cultivation intensity, distance to forest margins, and % of semi-natural habitats) per Km²-area and bee species richness (a, c, and e) and population density (b, d, and f) per study site.

3.3. Effects of Regional Land-Use Intensity Factors

There were significant () effects of the different land-use categories on bee species richness (Figure 5(a)) and population density (Figure 5(b)). Bee species richness was on average significantly () greater in study sites of low to medium land-use intensity categories compared to study sites under high to very high land-use categories (Figure 5(a)). Bee species richness and abundance were significantly higher in low land-use intensity than in all the other land-use categories. Similarly, bee population density was on average significantly () two to three times greater in study sites with low to medium land-use intensity gradients compared to study sites with high to very high land-use intensity gradients (Figure 5(b)).

(a) [GLM-ANOVA, , , ]

(b) [GLM-ANOVA, , , ]

Figure 5: Effects of regional land-use intensity on the species richness (a) and abundances (b) of bees recorded per study site in farmlands of central Uganda. Error bars are SE. Means () followed by different letters are significantly different at according to Tukey test.

3.4. Nest Density and Bee Attraction to Various Types of Habitats/Land-Uses

Few environmental factors (land-uses/habitats) were observed to be significantly () associated with high species richness and abundance of bees (Table 3). They were of high value for bees. Across study sites and sampling rounds, some habitats attracted a high number of bee species and individuals both during rainy and dry seasons: fallows, forest plantations, and woodlands. Count of nesting sites was conducted concurrently to bee surveys. Across study sites and sampling rounds, nest density (mean number of nests/0.5 ha transect/site) was significantly and positively related to bee species richness (, = 8.97, ) and to bee abundance (, = 5.67, ). There was a variation in the density of nests per nesting type per land-use/habitat type (Table 4). Different bee species used a variety of tree species (fruit and agroforestry tree species) as nest trees (Table 5). In other words, apart from establishing nests in various semi-natural habitats surrounding fields, some bee species (e.g., eusocial bees) managed to establish their nests in hallows of living trees, meaning that one should know these tree species and maintain them in the farm-landscape to increase nesting sites opportunities of good pollinators living within agricultural matrices.

Table 3: Richness and abundance of bees attracted to different habitats (land uses) frequently observed in the coffee-banana farming systems of central Uganda. Most attractive habitats were those with > 20% of weeds/crops/grass/wild plant species blooming at the time of visit.

Foraging habitats: seminatural habitat types/crop-field habitat types
Habitat frequency
Habitat size range

Bee species

(X ± SE)
Bee abundance

(X ± SE)

Undisturbed field margins associated with or without termite mounds and shrubs/trees/grass species
125
0.05–0.5
159
22.45 ±3.45 a
223.80 ± 35.67 d

Field boundaries/hedgerows/track-sides
100
0.01–0.05
150
24.32 ±5.23 a
256.56 ± 25.78 d

Forest remnants/forest patches
35
0.5–40
45
16.54 ± 2.41 b
169.65 ± 19.21 d

Woodlots (pines, etc.)
20
0.5–40
30
14.22 ± 2.17 b
87.981 ± 11.23 f

Woodlots (Eucalyptus blooming)
15
0.5–30
22
5.291 ± 1.45 d
876.12 ± 125.7 b

Agroforestry tree species (Moringa sp., Sesbania sp., Ricinus communis,Leucaena leucocephala,Calliandra sp, Cassia sp., Sena spectabilis, etc.)
35
0.05–2
45
14.12 ± 2.31 b
259.54 ± 35.78 c

Grazed fields (Pasturelands), grasslands
115
0.05–10
190
13.76 ± 1.21 b
47.891 ± 7.782 f

Swampy (marshland) habitats, and (streams-sides)
54
0.3–5
89
21.45 ±2.31 a
211.45 ± 34.51 d

Abandoned gardens
45
0.02–1
258
14.23 ± 1.13 c
233.67 ± 15.67 d

Fallows (weedy, grassy, bushy, woody, grassy-bushy, bush-woody, scrubby)
122
0.03–3
356
15.21 ± 1.16 b
176.56 ± 13.76 d

Herbaceous crop-habitats(unweeded fields) and weedy harvested fields
135
0.02–3
367
19.23 ±1.27 a
284.56 ± 22.67 c

Perennial crops blooming (coffee)
106
0.05–12
600
19.54 ±1.61 a
876.23 ± 68.98 b

Perennial crops blooming (banana)
112
0.05–15
789
11.12 ± 1.93 c
251.12 ± 31.76 c

Perennial fruit crops blooming (avocado, mango, citrus, orange, lemon, tangerines, guava, papaya, etc.)
62
0.03–10
98
4.783 ± 1.32 e
1986.5 ±398.6 a

Perennial fruit crops blooming (passion fruits)
15
0.01–3
22
12.76 ± 1.93 c
76.456 ± 10.43 f

Home gardens of annual vegetable crop species blooming (pumpkin, watermelon, Amaranthus, Cleome, bitter Solanum, onion, cucumber, garlic, lettuce, etc.)
59
0.01–0.5
79
12.56 ± 1.35 c
367.65 ± 51.73 c

Annual commercial/cash vegetable crops (tomato, eggplant, chiles, pepper)
88
0.02–6
121
8.514 ± 1.25 d
34.125 ± 8.911 f

Annual commercial/cash crops (sim-sim, sun flower)
25
0.02–10
48
11.34 ± 2.11 c
113.32 ± 11.74 e

Annual cereals (maize) mixed with pulse crops (beans, cowpea, greengram, soybean, groundnut): maize blooming
91
0.04–7
421
2.125 ± 2.11 f
198.23 ± 23.56 d

Annual cereals (maize, sorghum, millet) mixed with pulse crops (beans, cowpea, greengram, soybean, groundnut): sorghum-millet blooming mixed crops
33
0.03–6
259
5.126 ± 1.45 d
59.546 ± 14.65 f

Annual cereals (maize, sorghum, millet) mixed with pulse crops (beans, soybean, groundnut): beans-cowpea blooming mixed crops
45
0.02–5
342
4.211 ± 1.13 e
19.176 ± 1.542 f

Annual cereals (maize, sorghum, millet) mixed with pulse crops (beans, soybean, groundnut): groundnut blooming
65
0.02–6
403
3.214 ± 0.91 e
14.548 ± 3.541 f

Bi-annual crops (cassava blooming)
71
0.02–8
198
1.897 ± 0.25 f
57.459 ± 15.21 f

Annual crops (sweet-potato blooming)
78
0.02–3
400
5.128 ± 1.27 d
157.67 ± 12.67 d

Annual cereals (rice blooming)
38
0.05–15
55
3.459 ± 0.87 e
21.235 ± 3.563 f

Annual crops (Irish potato blooming)

0.05–12
43
7.126 ± 1.41 d
28.659 ± 6.124 f

Habitat frequency = number of observation cases or number of times the habitat type was encountered across all 26 study sites and all sampling rounds.Habitat size range (ha) = the data show the minimum and the maximum size of the type of habitat encountered during butterfly faunistic surveys. = number of samples (bees species and individuals) recorded in five sampling rounds across the 26 study sites in 2006.Within columns, different letters show significant differences of the means at according to Tukey test performed after Kruskall-Walis ANOVA test indicating that the habitat type was significant () for the number of species and individuals attracted.

Table 4: Density (x ± sd) of bee nests (number of individual bee nests counted during transects walks and counts different farmland habitats/land-uses) per nesting type.

Types of seminatural habitats/land-uses

(bee reservoirs/refugia)
Habitat size (ha)
Bee hives
Foliage nests
Ground-nests
Termite mounds
House-wall nests
Wood/tree nests

Roadsides/track-sides

12.21 ± 4.5 (n** = 125)
125.7 ± 78

(n*** = 15)
2.57 ± 0.78 (n* = 5)

Hedgerows, field boundaries (field margins)
0.21 ± 0.05
1.00 ± 0.00 ()

12.7 ± 7.8 (n**** = 45)
125.7 ± 87 ()

2.5 ± 1.8 ()

Small tropical forest remnants/forest patches
7.22 ± 21.6
5.7 ± 3.4 ()
1.5 ± 1.1 ()
18.1 ± 4.6 ()
9.1 ± 4.6 ()

15.7 ± 3.1 ()

Ecotones/edge of forest reserves
1.84 ± 0.21

2.2 ± 1.4 ()
2.2 ± 1.4 ()
16.2 ± 1.4 ()
725.7 ± 278 ()
35.7 ± 13.1 ()

Woodlots (pines/eucalyptus) and woodlands
9.45 ± 34.78
3.2 ± 1.4 ()

43.2 ± 21.4 ()
23.2 ± 11.6 ()
25.3 ± 2.8 ()
53.2 ± 31.4 ()

Edge of wetlands/streams
0.91 ± 2.12

1.00 ± 0.0 ()
19.34 ± 12.5 ()
2.2 ± 1.1 ()

3.2 ± 1.1 ()

Abandoned gardens
0.06 ± 0.04
1.2 ± 0.7 ()

9.4 ± 5.5 ()
4.2 ± 1.2 ()
421.7 ± 367 ()
11.57 ± 1.78 ()

Fenced cattle keeping fields, large pasturelands
9.43 ± 4.91

13.1 ± 3.5 ()
10.2 ± 7.2 ()

27.7 ± 12.7 ()

Lantanacamara/Erlangea 

tomentosa fallows
0.45 ± 1.29

53.1 ± 13.5 ()
17.2 ± 4.2 ()

7.2 ± 2.1 ()

Swampy fallows (different ages)
4.45 ± 1.29

4.2 ± 1.4 ()
3.1 ± 1.5 ()
3.1 ± 1.2 ()

17.2 ± 12.1 ()

Forest fallows (>5–7 years)
4.32 ± 24.31
11.2 ± 3.4 ()
1.00 ± 0.00 ()
45.1 ± 21.5 ()
13.1 ± 7.1 ()

47.2 ± 32.1 ()

Young fallow (<1-2 years aged)
0.06 ± 0.67

39.3 ± 11.5 ()
4.1 ± 2.1 ()

11.2 ± 2.1 ()

Small and large grasslands
0.43 ± 4.91

25.3 ± 5.6 ()
2.1 ± 1.1 ()

2.2 ± 1.1 ()

Small grazing fields (for small ruminants)
0.37 ± 3.51

5.3 ± 2.6 ()
1.7 ± 0.86 ()

1.2 ± 0.5 ()

Simple agroforestry systems

(agroforestry trees + fruits)
0.32 ± 1.13
3.8 ± 1.4 ()

2.3 ± 1.2 ()
1.1 ± 0.46 ()
321.7 ± 167 ()
21.2 ± 10.5 ()

Complex agroforests (agroforest trees + fruits + native trees)
0.72 ± 1.35
5.2 ± 2.4 ()

3.1 ± 1.3 ()
2.5 ± 1.2 ()
821.7 ± 461 ()
29.1 ± 20.5 ()

Perennial crops associated headed by coffee + banana
0.87 ± 5.62
3.2 ± 1.1 ()

12.3 ± 5.6 ()
1.8 ± 1.2 ()
456.6 ± 121 ()
21.2 ± 11.5 ()

Homegardens with annual vegetable crop species
0.34 ± 0.291

1.0 ± 0.0 ()
1.0 ± 0.0 ()
399.7 ± 111 ()
1.2 ± 0.5 ()

Marshland habitats and reclaimed wetlands
0.99 ± 6.97

4.1 ± 1.2 ()
1.0 ± 0.0 ()

1.9 ± 0.7 ()

Bi-annual root crops (Cassava) fields
0.79 ± 5.32

1.1 ± 0.1 ()
2.00 ± 0.0 ()
323.1 ± 145 ()
1.00 ± 0.00 ()

Annual root/tuber crops (sweet potato) fields
0.24 ± 1.97

7.1 ± 2.1 ()
2.00 ± 0.0 ()
723.1 ± 345 ()
3.9 ± 1.7 ()

Annual cereal (maize, sorghum, rice) + legume (bean, groundnut) crops
0.41 ± 2.765

1.6 ± 0.6 ()
1.0 ± 0.0 ()
412.1 ± 156 ()
2.2 ± 1.2 ()

The density is the number of nests counted per nesting site per 5 ha-transect. The data reflect the number of observations or number of time this nest was recorded in five rounds of data collection conducted during 2006 in central Uganda. = the number of time the individual bee nest was recorded in a particular land-use/habitat type during five rounds of data collection across 26 study sites. Number of individual bee nests included all type of solitary and social bee species and all type of bee nests location combined.n**** = number of active ground nets recorded in that habitat; n** = number of active termite mounds recorded in that habitat; n*** = number of active nests counted on house wall (old houses, nests, livestock houses, abandoned or not) established near the habitat described; n* = number of active wood/tree nests seen and counted during transect walks.

Table 5: Density (number of trees/5 ha) of nest tree species recorded in farmlands of central Uganda during bee faunistic surveys conducted in 2006. Data are means of 26 study sites and five sampling rounds conducted in 2006.

Family
Species name
Density of trees

(Mean ± SD)

Caesalpiniaceae
Senna occidentalis
236.92 ± 34.76

Bignoniaceae
Markhamia lutea
188.82 ± 4.11

Myrsinaceae
Maesa lanceolata
146.222 ± 34.67

Asteraceae
Vernonia amygdalina
133.23 ± 76.1

Myrtaceae
Psidium guajava
109.06 ± 45.7

Moraceae
Ficus saussureana
87.31 ± 5.87

Lauraceae
Persea americana
87.31 ± 56.91

Anacardiaceae
Mangifera indica
77.34 ± 6.76

Myrtaceae
Eucalyptus grandis
67.97 ± 5.67

Tiliaceae
Theobroma cacao
67.97 ± 45.12

Papilionaceae
Erythrina abyssinica
24.47 ± 10.11

Proteaceae
Grevillea robusta
19.33 ± 8.98

Solanaceae
Solanum wrightii
19.33 ± 5.67

Caesalpiniaceae
Cassia spectabilis
14.80 ± 65.2

Cupressaceae
Cupressus lusitanica
14.80 ± 9.56

Moraceae
Ficus mucuso
14.80 ± 7.54

Mimosaceae
Leucaena leucocephala
10.87 ± 13.89

Bignoniaceae
Spathodea campanulata
10.87 ± 8.98

Rutaceae
Citrus lemon
7.55 ± 7.65

Apocynaceae
Funtumia africana
7.55 ± 5.43

Mimosaceae
Calliandra calothyrsus
4.83 ± 3.45

Bignoniaceae
Jacaranda mimosifolia
4.83 ± 3.12

Euphorbiaceae
Bridelia micrantha
2.71 ± 1.12

Moraceae
Ficus thonningii
2.72 ± 2.34

Bignoniaceae
Kigelia africana
2.71 ± 5.12

Euphorbiaceae
Macaranga schweinfurthii
2.71 ± 5.12

Papilionaceae
Sesbania sesban
2.71 ± 3.45

Mimosaceae
Albizia chinensis
1.21 ± 1.23

Mimosaceae
Albizia coriaria
1.20 ± 4.54

Rubiaceae?
Coffeacanephora
1.20 ± 5.65

Caesalpiniaceae
Senna spectabilis
1.20 ± 1.23

Mimosaceae
Acrocarpus fraxinifolius
1.33 ± 5.12

Mimosaceae
Albizia glaberrima
1.50 ± 2.54

Mimosaceae
Albizia grandibracteata
1.90 ± 4.12

Mimosaceae
Albizia gummifera
1.30 ± 5.32

Moraceae
Artocarpus heterophyllus
1.30 ± 2.12

Meliaceae
Azadirachta indica
1.30 ± 3.45

Caesalpiniaceae
Cassia siamea
1.30 ± 3.23

Ulmaceae
Celtis africana
0.90 ± 2.12

Ulmaceae
Celtis mildbraedii
0.83 ± 3.12

Rutaceae
Citrus sinensis
0.81 ± 3.12

Palmae
Elaeis guineensis
0.70 ± 2.43

Moraceae
Ficus asperifolia
0.68 ± 3.55

Moraceae
Ficus barteri
0.67 ± 3.23

Moraceae
Ficus benjamina
0.62 ± 1.56

Moraceae
Ficus brachypoda
0.57 ± 2.32

Moraceae
Ficus cyathistipula
0.50 ± 1.14

Moraceae
Ficus dicronystilla
0.46 ± 1.23

Moraceae
Ficus exasperata
0.40 ± 1.54

Moraceae
Ficus natalensis
0.36 ± 1.45

Moraceae
Ficus ottoniifolia
0.35 ± 0.97

Moraceae
Ficus polita
0.34 ± 0.76

Moraceae
Ficus pseudomangifera
0.33 ± 6.71

Moraceae
Ficus stipulifera
0.31 ± 4.56

Moraceae
Ficus sur
0.30 ± 2.78

Moraceae
Ficus sycomorus
0.30 ± 4.56

Moraceae
Ficus valifolia
0.29 ± 5.76

Moraceae
Ficus vasta
0.28 ± 3.61

Mimosaceae
Grilicia sepium
0.27 ± 3.11

Ulmaceae
Holopteria grandis
0.26 ± 2.71

Rhamnaceae
Maesopsis eminii
0.24 ± 1.51

Moringaceae
Moringa oleifera
0.21 ± 3.19

Pinaceae
Pinus carribaea
0.20 ± 1.12

Podocarpaceae
Podocarpus milinjuanus
0.19 ± 3.13

Rosaceae
Prunus africana
0.18 ± 2.13

Myrtaceae
Syzygium cuminii
0.16 ± 0.91

Combretaceae
Terminalia sperba
0.15 ± 0.45

Ulmaceae
Trema orientalis
0.13 ± 0.30

Table 6: List of pollinator-dependent crops and nonpollinator crops grown in Uganda (the crop species are presented per dependency status, crop category, common names, and scientific names).

Dependency status
Crop category
Common names
Scientific name

Nonpollinator-dependent crop
Banana
Plantains
Musa sp.

Nonpollinator-dependent crop
Cereal crops
Finger millet
Eleusine sp.

Nonpollinator-dependent crop
Cereal crops
Maize
Zea maize

Nonpollinator-dependent crop
Cereal crops
Sorghum
Sorgum bicolor

Nonpollinator-dependent crop
Cereal crops
Rice
Oryza sativa

Nonpollinator-dependent crop
Cereal crops
Wheat
Triticum sp.

Nonpollinator-dependent crop
Root/tuber crops
Sweet potatoes
Ipomoea batatus

Nonpollinator-dependent crop
Root/tuber crops
Potatoes
Solanum tuberusum

Nonpollinator-dependent crop
Root/tuber crops
Cassava
Manihot esculentum

Pollinator-dependent crop
Pulse crops
Beans
Phaseolus vulgarus

Pollinator-dependent crop
Pulse crops
Field peas
Pisum arvense

Pollinator-dependent crop
Pulse crops
Cowpeas
Vigna unguiculata

Pollinator-dependent crop
Pulse crops
Greengram seeds
Vigna radiata

Pollinator-dependent crop
Pulse crops
Peageon peas
Cajanus cajan

Pollinator-dependent crop
Pulse crops
Bambaranut
Vigna subterranea

Pollinator-dependent crop
Pulse crops
Groundnut
Arachis hypogea

Pollinator-dependent crop
Industrial crops/edible oils
Soy beans
Glycine max

Pollinator-dependent crop
Industrial crops/edible oils
Sim-Sim seeds
Sesamum indicum

Pollinator-dependent crop
Industrial crops/edible oils
Sun-flower seeds
Helianthus annus

Pollinator-dependent crop
Industrial crops/edible oils
Coffee beans
Coffea canephora/arabica

Pollinator-dependent crop
Industrial crops/edible oils
Cotton seed
Gossypium sp.

Pollinator-dependent crop
Industrial crops/edible oils
Tobacco seed
Nicotiana tabacum

Pollinator-dependent crop
Industrial crops/edible oils
Tea
Camelia sinsensis

Pollinator-dependent crop
Industrial crops/edible oils
Sugar
Sugar cane

Pollinator-dependent crop
Industrial crops/edible oils
Cocoa
Theobroma cacao

Pollinator-dependent crop
Industrial crops/edible oils
Coconut
Cocos nucifera

Pollinator-dependent crop
Fruit crops
Avocado
Persea americana

Pollinator-dependent crop
Fruit crops
Mangos
Mangifera indica

Pollinator-dependent crop
Fruit crops
Orange and tangerine
Citrus myrtifolia/reticulata

Pollinator-dependent crop
Fruit crops
Grapefruits
Citrus grandis

Pollinator-dependent crop
Fruit crops
Passion fruits
Passiflora edulis

Pollinator-dependent crop
Fruit crops
Papaw
Carica papaya

Pollinator-dependent crop
Fruit crops
Guavas
Psidium guajava

Pollinator-dependent crop
Fruit crops
Apples
Malus domestica

Pollinator-dependent crop
Fruit crops
Jackfruit
Artocarpus heterophyllus

Pollinator-dependent crop
Vegetable crops
Tomato fruits
Lycopersicon esculentum

Pollinator-dependent crop
Vegetable crops
Eggplants
Solanum melongena

Pollinator-dependent crop
Vegetable crops
Pepper fruits
Capsicum fruitescens

Pollinator-dependent crop
Vegetable crops
Mustard seeds
Brassica alba

Pollinator-dependent crop
Vegetable crops
Okra, Gumbo
Abelmoschus esculentus

Pollinator-dependent crop
Vegetable crops
Pumpkin
Cucurbita maxima/moschata

Pollinator-dependent crop
Vegetable crops
Squash
Cucurbitamixta

Pollinator-dependent crop
Vegetable crops
Gourde
Lagenaria siceraria

Pollinator-dependent crop
Vegetable crops
Watermelon
Citrullus lunatus

Pollinator-dependent crop
Vegetable crops
Cucumber
Cucumis sativus

Pollinator-dependent crop
Spices and condiments
Vanilla
Vanilla planifolia

Pollinator-dependent crop
Medicinal plants
Moringa
Moringa oleifera

Table 7: List of common wild flowering plant species visited by bees during floral resources collection in the coffee-banana farming system of central Uganda. Species are arranged per family, life cycle type, and type of flower colour/shape. These are plant species met in blooming periods and that were observed being visited by different bees species during transect surveys.

Family
Species
Lifecycle
Flower color/shape

Mimosaceae
Acacia hockii De Wild
Tree
Yellow

Mimosaceae
Acacia gerradii Benth
Tree
Dirty/white

Mimosaceae
Acacia zanziberica
Shrub
Yellow

Euphorbiaceae
Acalypha bipartita Muell. Arg.
Shrub
Green

Euphorbiaeae
Acalypha ornata A.Rich.
Shrub
Red

Acanthaceae
Acanthus pubescens Engl
Shrub
Pink

Asteraceae
Acmella caulirhiza Delile
Herb
Yellow

Asteraceae
Ageratum conyzoides L.
Herb
White

Mimosaceae
Albizzi grandibracteata Taub
Tree
Pale/green

Mimosaceae
Albizzia glaberrima (Schumach. and Thonn.) Benth
Tree
White

Mimosaceae
Albizzia adianthifolia (Schumach.) W.F. Wight
Tree
White

Mimosaceae
Albizzia coriaria Oliv.
Tree
White

Scrophulariaceae
Alectra sessiliflora (Vahl) Kuntze
Herb
Yellow

Amaranthaceae
Amaranthus dubius Thell
Herb
Green

Amaranthaceae
Amaranthus hybridus L.subsp.hybridus
Herb
Green

Aristolochiaceae
Aristolochia elegans Mast.
Herb
Reddish/purple

Asteraceae
Aspilia africana (Pers) C.D.Adams
Herb
Yellow

Acanthaceae
Asystasia gangetica (L.) T.Andersson
Herb
White

Acanthaceae
Asystasia mysorensis (Roth) T.Anderson
Herb
White

Acanthaceae
Barleria spinisepale
Herb
Purple

Asteraceae
Berkheya spekeana Oliv.
Herb
Yellow

Asteraceae
Bidens pilosa L.
Herb
White/yellow

Oxalidaceae
Biophytum abyssinicum A.Rich.
Herb
Yellow

Euphorbiaceae
Bridelia micrantha (Hochst) Baill
Tree
Greenish/yellow

Caesalpiniaceae
Caesalpinia decapetala (Roth) Alston
Shrub
Yellow

Papilionaceae
Cajanus cajan (L.) Millsp
Shrub
Yellow

Mimosaceae
Calliandra calothyrsus Meissner
Shrub
Red

Myrtaceae
Callistemon lanceolatus DC.
Tree
Red

Theaceae
Camellia sinensis (L.) O.Ktze.
Tree
White/cream

Pailionaceae
Canavalia africana Dunn
Herb
Purple

Pailionaceae
Canavalia virosa (Roxb.) Wight
Herb
Purple

Solanaceae
Capsicum annum L.
Herb
Green

Sapindaceae
Cardiospermum halicacabum L.
Herb
White

Caesalpiniaceae
Cassia hirsuta L.
Herb
Yellow

Caesalpiniaceae
Cassia kirkii Oliv.
Herb
Yellow

Meliaceae
Cedrella odorata L.
Tree
Yellow/greenish

Rutaceae
Citrus aurantifolia Swingle
Tree
White

Rutaceae
Citrus lemon (L.) Burm.f.
Tree
White

Rutaceae
Citrus reticulata Blanco
Tree
White

Rutaceae
Citrus sinensis (L.) Osbeck
Tree
White

Capparaceae
Cleome gynandra (L.) Briq.
Herb
White

Capparaceae
Cleome monophylla L.
Herb
Purple

Verbenaceae
Clerodendrum myricoides (Hochct.) Vodke
Shrub
Blue

Verbenaceae
Clerodendrum rotundifolium Oliv.
Herb
White

Palmae
Cocos nucifera L.
Tree
Orange/yellow

Commelinaceae
Commelina benghalensis L.
Herb
Yellow

Commelinaceae
Commelina africana L.
Herb
Yellow

Asteraceae
Crassocephalum montuosum (S. Moore) Milne-Redh.
Herb
Red

Asteraceae
Crassocephalum vitellinum (Benth.) S. moore
Herb
Yellow

Papilionaceae
Crotalaria incana L.
Herb
Yellow

Papilionaceae
Crotalaria brevidens Benth.var. Intermedia (Kotschy) Polh.
Herb
Yellow

Papilionaceae
Crotalaria laburnifolia L.
Herb
Yellow

Papilionaceae
Crotalaria natalica Meisn
Herb
Yellow

Vitaceae
Cyphostemma adenocaule (A.Rich.) Willd. and Drummond
Herb
Red

Papilionaceae
Desmodium Salicifolium (Poir.) DC.
Herb
Pink/purple

Papilionaceae
Desmodium tortusum (Sw.) DC.
Herb
Pink

Asteraceae
Dichrocephala integrifolia (L.f.) O.Ktze
Herb
Greenish/whitish

Acanthaceae
Dyschoriste radicans Nees
Herb
Yellow

Palmae
Elaeis huineensis Jacq
Tree
Yellow

Asteraceae
Emilia javanica (Burm.f.) C.B. Rob.
Herb
Red

Papilionaceae
Eriosema psoraleoides (Lam.) G.Don
Herb
Yellow

Asteraceae
Erlangea cordifolia (Oliv) S. Moore
Herb
Purple

Asteraceae
Erlangea tomentosa S. Moore
Herb
Light purple

Asteraceae
Erlangea ugandensis S. Moore
Shurb
Blue

Cruciferae
Erucastrum arabicum Fisch and Mey
Herb
Yellow

Papilionaceae
Erythrina abyssinica DC
Tree
Red

Myrtaceae
Eucalyptus grandis Marden
Tree
Pale

Myrtaceae
Eucalyptus camaldulensis
Tree
Cream

Euphorbiaceae
Euphorbia heterophylla L.
Herb
Cream

Euphorbiaceae
Euphorbia hirta L.
Herb
Green/purple

Asteraceae
Galisonga parviflora Cav.
Herb
White/yellow

Papilionaceae
Gliricidia sepium (Jacq.) Walp.
Tree
NIL

Papilionaceae
Glycine wightii (Wight and Arn.) Verdc. var.longicanda (Scheinf.) Verdc.
Herb
Pale/grey

Asteraceae
Guizotia scabra (Vis.) Chiov.
Herb
Yellow

Asteraceae
Helianthus annua L.
Herb
Yellow

Convulvulaceae
Hewittia sublobata (L.f.) O.Ktze.
Herb
Yellow

Malvaceae
Hibiscus surrantensis L.
Herb
Yellow

Malvaceae
Hibiscus diversifolius Jacq
Shrub
Yellow

Malvaceae
Hibiscus ludwigii Eckl. and Zeyh.
Shrub
Yellow

Lamiaceae
Hoslundia opposita Vahl
Herb
Yellowish

Papilionaceae
Indiofera spicata Forssk.
Herb
Red

Convulvulaceae
Ipomoea cairica (L.) Sweet
Herb
Purple

Convulvulaceae
Ipomoea hederifolia L.
Herb
Red

Convulvulaceae
Ipomoea obscura (L.) Ker-Gawl
Herb
Yellow

Convulvulaceae
Ipomoea purpurea (L.) Roth
Herb
Purple

Convulvulaceae
Ipomoea wightii (Wall.) Choisy
Herb
Purple

Acanthaceae
Justicia flava (Forsk) Vahl
Herb
Yellow

Acanthaceae
Justicia heterocarpa T.Andersson
Herb
Pink

Cyperaceae
Kyllinga bulbosa P.Beauv.
Sedge
White

Verbenaceae
Lantana camara L.
Shrub
Pink

Verbenaceae
Lantana trifolia L.
Shrub
Light purple

Lamiaceae
Leonotis nepetifolia (L.) Ait.f.
Herb
Red

Mimosaceae
Leucaena leucocephala (Lam.) De Wit
Tree
Red

Lamiaceae
Leucas deflexa Hook.f.
Herb
White

Verbenaceae
Lippia abyssinica (Otto and Diebr) Cuf.
Herb
White/yellow

Lamiaceae
Luecas martinicensis (Jacq) Ait.f.
Herb
White

Myrsinaceae
Maesa lanceolata Forsk
Tree
Green

Anacardiaceae
Mangifera indica L.
Tree
Cream

Bignoniaceae
Markhamia lutea K.Schum.
Tree
Yellow

Asteraceae
Melanthera scandens (Schumach and Thonn) Roberty
Herb
Yellow

Asteraceae
Microglossa pyrifolia (Lam.) O.Ktze.
Shurb
Pale/yellow

Moraceae
Milicia excelsa (Welw.) C.C. Berg.
Tree
Greenish

Mimosaceae
Mimosa pigra L.
Shrub
Pink

Mimosaceae
Mimosa pudica L.
Herb
Purple

Rubiaceae
Mitracarpus villosus (S.W.) DC
Herb
White

Cucurbitaceae
Momordica foetida Schumach.
Herb
Yellow

Moringaceae
Moringa oleifera Lam.
Tree
White/cream

Moraceae
Morus alba L.
Tree
Nil

Musaceae
Musa paradisiaca L.
Tree
Red/brown/purple

Musaceae
Musa sapienthum
Tree
Red/brown/purple

Lamiaceae
Ocimum gratissimum ( L.) O.Suave
Herb
White

Lamiaceae
Ocimum gratissimum var.rutshuruensis De Wild
Herb
White

Lamiaceae
Ocimum suave Willd.
Herb
White

Oxalidaceae
Oxalis corniculata L.
Herb
Yellow

Oxalidaceae
Oxalis latifolia L.
Herb
Pink

Polygonaceae
Oxygonum snuatum (Meisn.) Dammer
Herb
Pale/pink

Passifloraceae
Passiflora edulis Sims
Tree
Purple

Lauraceae
Persea americana Mill.
Tree
Cream

Solanaceae
Physalis peruviana L.
Herb
Yellow

Phytolacaceae
Phytolacca dodecandra L'Herit
Herb
Greenish/white

Lamiaceae
Plastostoma africanum P. Beauv.
Herb
White

Lamiaceae
Plectranthus barbatus Andr.
Herb
Purple

Rubiaceae
Pnetas parvifolia
Herb
Red/marron

Polygalaceae
Polygala pygmaea Gurke
Herb
Yellow

Polygonaceae
Polygonum setosulum A.Rich.
Herb
Pink

Papilionaceae
Pseudarthria hookeri Wight and Arn.
Herb
Pink

Myrtaceae
Psidium guajava L.
Tree
White

Euphorbiceae
Ricinus communis L.
Tree
Yellowish

Polygonaceae
Rumex abyssinicus Jacq
Herb
Greenish

Polygonaceae
Rumex bequaertii De Wild
Herb
Greenish

Asteraceae
Senecio discifolius Oliv.
Herb
Yellow

Caesalpiniaceae
Senna didimobotrya L.
Shrub
Yellow

Pedaliaceae
Sesamum angolense Welw
Herb
Purple

Malvaceae
Sida acuta Burm.f.
Herb
Cream/pale

Malvaceae
Sida cordifolia L.
Herb
Yellow

Malvaceae
Sida cuneifolia Roxb.
Herb
Yellow

Malvaceae
Sida rhombifolia L.
Herb
Whitish/yellowish

Asteraceae
Siegesbeckia abyssinica (Ch. Bip.) Oliv. and Hiern
Herb
Yellow

Asteraceae
Siegesbeckia orientalis L.
Herb
Yellow

Solanaceae
Solanum nigrum L.
Herb
White/yellow

Solanaceae
Solanum aculeastrum Dunal
Shrub
White

Solanaceae
Solanum anguivii Lam.
Herb
White

Solanaceae
Solanum florulentum Bitt.
Herb
White

Solanaceae
Solanum incanum L.
Herb
Purple

Solanaceae
Solanum macrocarpon L.
Herb
Purple

Solanaceae
Solanum mauritianum Scop
Shrub
Purple

Bignoniaceae
Spathodea nilotica Seem
Herb
Red

Rubiaceae
Spermacoce princeae (K. Schum.)
Herb
White

Verbenaceae
Stachytarpheta cayennensis (Rich.) Vahl
Herb
Blue

Papilionaceae
Tephrosia rhodesia Bak.f.
Herb
Purple

Papilionaceae
Tephrosia vogelii Hook.f.
Shrub
Purple/white

Apocynaceae
Thevetia pereviana (Pob) K.Schum.
Shrub
Yellow

Acanthaceae
Thumbergia holstii
Herb
Purple

Acanthaceae
Thunbergia alata Sims
Herb
Yellow

Asteraceae
Tridax procumbens
Herb
Yellow

Tiliaceae
Triumfetta tomenosa Boj.
Shrub
Yellow

Tiliaceae
Triumfetta rhomboidea Jacq
Herb
Yellow

Tiliaceae
Triumfetta trichocarpa A.Rich.
Herb
Cream

Typhaceae
Typha domingensis Pers
Sedge
Brown

Malvaceae
Urena lobata L.
Herb
Pink

Asteraceae
Vernonia cinerea (L.) Less.
Herb
Purple

Asteraceae
Vernonia amygdalina Del.
Tree
White/cream

Asteraceae
Vernonia auriculifera Hiern
Tree
Purple

Asteraceae
Vernonia auriculifera Hiern
Tree
Cream/white

Asteraceae
Vernonia campanea S. moore
Herb
Purple

Asteraceae
Vernonia kirungei
Shrub
Purple

Asteraceae
Vernonia lasiopus O.Haffm.
Herb
Purple

Papilionaceae
Vigna vexillata (L.) A.Rich.
Herb
Purple

Table 8: List of bee species collected in coffee-banana agroforestry systems in central Uganda in 2006.

Family
Species
Family
Species

Andrenidae
Andrena africana (Friese, 1909)
Halictidae
Lasioglossum somereni (Cockerell, 1945)

Andrenidae
Andrena notophila (Cockerell)
Halictidae
Lasioglossum stellatifrons (Cockerell, 1945)

Andrenidae
Melitturga penrithorum (Eardley, 1991)
Halictidae
Lasioglossum trichardti (Cockerell)

Andrenidae
Meliturgula braunsi (Friese, 1903)
Halictidae
Lasioglossum ugandicum (Cockerell, 1937)

Andrenidae
Meliturgula eardleyana (Patiny, 2000)
Halictidae
Lasioglossum zonaturum (Cockerell)

Andrenidae
Meliturgula flavida (Friese, 1913)
Halictidae
Lassioglossum simulator (Cockerell, 1935)

Andrenidae
Meliturgula rozeni (Eardley, 1991)
Halictidae
Lipotriches ablusa (Cockerell)

Andrenidae
Meliturgula scriptifrons (Walker, 1871)
Halictidae
Lipotriches amatha (Cockerell, 1935)

Andrenidae
Meliturgula wilmattae (Cockerell, 1932)
Halictidae
Lipotriches angustifrons (Cockerell)

Apidae
Afromelecta bicuspis (Stadelmann, 1898)
Halictidae
Lipotriches armatipes (Friese, 1930)

Apidae
Allodape armatipes (Friese, 1924)
Halictidae
Lipotriches aureotecta (Cockerell, 1931)

Apidae
Allodape brachycephala (Michener, 1971)
Halictidae
Lipotriches aurifrons (Smith, 1853)

Apidae
Allodape ceratinoides (Gribodo, 1884)
Halictidae
Lipotriches brevipennis (Friese, 1915)

Apidae
Allodape collaris (Vachal, 1903)
Halictidae
Lipotriches clavata (Cockerell)

Apidae
Allodape exoloma (Strand, 1915)
Halictidae
Lipotriches collaris (Vachal)

Apidae
Allodape friesei (Strand, 1915)
Halictidae
Lipotriches cubitalis (Vachal)

Apidae
Allodape interrupta (Vachal, 1903)
Halictidae
Lipotriches dentipes (Friese, 1930)

Apidae
Allodape macula (Strand, 1912)
Halictidae
Lipotriches digitata (Friese, 1909)

Apidae
Allodape microsticta (Cockerell, 1934)
Halictidae
Lipotriches ethioparca (Cockerell, 1935)

Apidae
Allodape punctata (Lepeletier and Audinet-Serville, 1825)
Halictidae
Lipotriches flavitarsis (Friese)

Apidae
Allodape quadrilineata (Cameron, 1905)
Halictidae
Lipotriches friesei (Magretti, 1899)

Apidae
Allodape rufogastra (Lepeletier and Audinet-Serville, 1825)
Halictidae
Lipotriches gratiosa (Strand)

Apidae
Allodape tridentipes (Cockerell, 1933)
Halictidae
Lipotriches guluensis (Cockerell)

Apidae
Allodapula acutigera (Cockerell, 1936)
Halictidae
Lipotriches hirsutula (Cockerell)

Apidae
Allodapula hessei (Michener)
Halictidae
Lipotriches inaequalis (Cockerell)

Apidae
Allodapula jucunda (Smith, 1879)
Halictidae
Lipotriches kampalana (Cockerell, 1935)

Apidae
Allodapula maculithorax (Michener, 1971)
Halictidae
Lipotriches longipes (Strand)

Apidae
Allodapula melanopus (Cameron, 1905)
Halictidae
Lipotriches macropus (Friese)

Apidae
Allodapula monticola (Cockerell, 1933)
Halictidae
Lipotriches meadewaldoi (Brauns, 1912)

Apidae
Allodapula palliceps (Friese, 1924)
Halictidae
Lipotriches natalensis (Cockerell, 1916)

Apidae
Allodapula rozeni (Michener, 1975)
Halictidae
Lipotriches notabilis (Schletterer)

Apidae
Allodapula variegata (Smith, 1854)
Halictidae
Lipotriches nubecula (Smith, 1875)

Apidae
Amegilla acraensis (Fabricius, 1793)
Halictidae
Lipotriches oberthurella (Saussure)

Apidae
Amegilla africana (Friese, 1905)
Halictidae
Lipotriches orientalis (Friese, 1909)

Apidae
Amegilla albocaudata (Dours, 1869)
Halictidae
Lipotriches patellifera (Westwood, 1875)

Apidae
Amegilla atrocincta (Lepeletier, 1841)
Halictidae
Lipotriches picardi (Gribodo)

Apidae
Amegilla bothai (Friese)
Halictidae
Lipotriches reichardia (Strand, 1911)

Apidae
Amegilla calens (Lepeletier, 1841)
Halictidae
Lipotriches rubella (Smith)

Apidae
Amegilla capensis (Friese)
Halictidae
Lipoiriches rufipes (Smith, 1875)

Apidae
Amegilla eritrina (Friese, 1915)
Halictidae
Lipotriches ruwenzorica (Cockerell, 1935)

Apidae
Amegilla fallax (Smith, 1879)
Halictidae
Lipotriches sessensis (Cockerell)

Apidae
Amegilla madecassa (Saussure)
Halictidae
Lipotriches sjoestedti (Friese, 1909)

Apidae
Amegilla mimadvena (Cockerell, 1916)
Halictidae
Lipotriches speculina (Cockerell, 1942)

Apidae
Amegilla natalensis (Friese, 1922)
Halictidae
Lipotriches spinulifera (Cockerell)

Apidae
Amegilla nila (Eardley, 1994)
Halictidae
Lipotriches tanganyicensis (Strand, 1913)

Apidae
Amegilla niveata (Friese, 1905)
Halictidae
Lipotriches viciniformis (Cockerell, 1939)

Apidae
Amegilla nubica (Lepeletier, 1841)
Halictidae
Lipotriches vulpina (Gerstäcker, 1857)

Apidae
Amegilla obscuriceps (Friese, 1905)
Halictidae
Lipotriches welwitschi (Cockerell, 1908)

Apidae
Amegilla penicula (Eardley, 1994)
Halictidae
Lipotriches whitfieldi (Cockerell, 1942)

Apidae
Amegilla punctifrons (Walker, 1871)
Halictidae
Nomia amabilis (Cockerell, 1908)

Apidae
Amegilla rapida (Smith, 1879)
Halictidae
Nomia atripes (Friese, 1909)

Apidae
Amegilla regalis (Cockerell, 1946)
Halictidae
Nomia bouyssoui (Vachal, 1903)

Apidae
Amegilla rufipes (Lepeletier, 1841)
Halictidae
Nomia brevipes (Friese, 1914)

Apidae
Amegilla sierra (Eardley, 1994)
Halictidae
Nomia candida (Smith, 1875)

Apidae
Amegilla somalica (Magretti)
Halictidae
Nomia chandleri (Ashmead, 1899)

Apidae
Amegilla terminata (Smith, 1879)
Halictidae
Nomia clavicauda (Cockerell)

Apidae
Anthophora vestita (Smith, 1854)
Halictidae
Nomia elephas (Strand, 1911)

Apidae
Anthophora armata (Friese, 1905)
Halictidae
Nomia ethiopica (Pauly, 2000)

Apidae
Anthophora basalis (Smith)
Halictidae
Nomia felina (Cockerell)

Apidae
Anthophora braunsiana (Friese, 1905)
Halictidae
Nomia forbesii (W. F. Kirby, 1900)

Apidae
Anthophora diversipes (Friese, 1922)
Halictidae
Nomia garambensis (Pauly, 2000)

Apidae
Anthophora glaucopis (Friese, 1905)
Halictidae
Nomia granulata (Vachal, 1903)

Apidae
Anthophora rufolanata (Dours)
Halictidae
Nomia lutea (Warncke, 1976)

Apidae
Anthophora rufovestita (Cockerell)
Halictidae
Nomia maculata (Friese)

Apidae
Anthophora schultzei (Friese, 1909)
Halictidae
Nomia marginata (Pauly, 1990)

Apidae
Anthophora strucki (Eardley and Brooks, 1989)
Halictidae
Nomia nigrociliata (Cockerell, 1932)

Apidae
Anthophora wartmanni (Friese, 1905)
Halictidae
Nomia politula (Cockerell)

Apidae
Anthophora xanthostoma (Cockerell, 1932)
Halictidae
Nomia postscutellaris (Strand, 1914)

Apidae
Apis mellifera adansonii (Linnaeus, 1758)
Halictidae
Nomia pretoriensis (Cockerell, 1946)

Apidae
Apis mellifera scutellata (Latreille, 1804)
Halictidae
Nomia rozeni (Pauly, 2000)

Apidae
Braunsapis facialis (Gerstäcker, 1857)
Halictidae
Nomia rufosuffusa (Cockerell, 1935)

Apidae
Braunsapis albipennis (Friese, 1909)
Halictidae
Nomia senticosa (Vachal, 1897)

Apidae
Braunsapis albitarsis (Friese, 1924)
Halictidae
Nomia somalica (Friese, 1908)

Apidae
Braunsapis angolensis (Cockerell, 1933)
Halictidae
Nomia stageri (Pauly, 2000)

Apidae
Braunsapis bouyssoui (Vachal, 1903)
Halictidae
Nomia theryi (Gribodo, 1894)

Apidae
Braunsapis flavitarsis (Gerstaecker)
Halictidae
Nomia viridicincta (Meade-Waldo)

Apidae
Braunsapis foveata (Smith, 1854)
Halictidae
Nomia whiteana (Cameron, 1905)

Apidae
Braunsapis gorillarum (Cockerell, 1936)
Halictidae
Nomia zonaria (Walker, 1871)

Apidae
Braunsapis leptozonia (Vachal)
Halictidae
Nomioides micheneri Pesenko and Pauly

Apidae
Braunsapis minutula (Friese, 1914)
Halictidae
Patellapis aberdarica (Cockerell, 1945)

Apidae
Braunsapis natalica (Michener, 1970)
Halictidae
Patellapis albofasciata (Smith, 1879)

Apidae
Braunsapis neavei (Vachal, 1910)
Halictidae
Patellapis benoiti (Pauly)

Apidae
Braunsapis rhodesi (Cockerell, 1936)
Halictidae
Patellapis bilineata (Friese)

Apidae
Braunsapis strandi (Masi, 1930)
Halictidae
Patellapis communis (Smith, 1879)

Apidae
Braunsapis vitrea (Vachal, 1903)
Halictidae
Patellapis disposita (Cameron, 1905)

Apidae
Ceratina aliceae (Cockerell, 1937)
Halictidae
Patellapis flavofasciata (Friese, 1915)

Apidae
Ceratina armata (Smith, 1854)
Halictidae
Patellapis flavorufa (Cockerell, 1937)

Apidae
Ceratina braunsi (Eardley and Daly, 2007)
Halictidae
Patellapis glabra (Pauly, 1989)

Apidae
Ceratina excavata (Cockerell)
Halictidae
Patellapis gowdeyi (Cockerell, 1937)

Apidae
Ceratina labrosa (Friese, 1905)
Halictidae
Patellapis hargreavesi (Cockerell)

Apidae
Ceratina lineola (Vachal, 1903)
Halictidae
Patellapis harunganae (Pauly, 1989)

Apidae
Ceratina lunata (Friese, 1905)
Halictidae
Patellapis kivuensis (Pauly, 1989)

Apidae
Ceratina minuta (Friese, 1905)
Halictidae
Patellapis macrozonia (Cockerell)

Apidae
Ceratina moerenhouti (Vachal)
Halictidae
Patellapis minima (Friese, 1909)

Apidae
Ceratina nasalis (Friese, 1905)
Halictidae
Patellapis minutior (Friese, 1909)

Apidae
Ceratina nigriceps (Friese, 1905)
Halictidae
Patellapis mosselina (Cockerell)

Apidae
Ceratina nilotica (Cockerell, 1937)
Halictidae
Patellapis neavei (Cockerell, 1946)

Apidae
Ceratina paulyi (Daly, 1988)
Halictidae
Patellapis nomioides (Friese, 1909)

Apidae
Ceratina penicillata (Friese, 1905)
Halictidae
Patellapis obscurescens (Cockerell)

Apidae
Ceratina rufigastra (Cockerell, 1937)
Halictidae
Patellapis partita (Cockerell, 1933)

Apidae
Ceratina ruwenzorica (Cockerell)
Halictidae
Patellapis patriciformis (Cockerell, 1933)

Apidae
Ceratina speculifrons (Cockerell, 1920)
Halictidae
Patellapis perineti (Benoist, 1954)

Apidae
Ceratina tanganyicensis (Strand, 1911)
Halictidae
Patellapis perpansa (Cockerell, 1933)

Apidae
Ceratina viridifrons (Cockerell, 1934)
Halictidae
Patellapis pondoensis (Cockerell)

Apidae
Ceratina viridis (Guérin-Méneville, 1844)
Halictidae
Patellapis retigera (Cockerell)

Apidae
Ceratina whiteheadi (Eardley and Daly, 2007)
Halictidae
Patellapis ruwensorensis (Strand, 1911)

Apidae
Cleptotrigona cubiceps (Friese, 1912)
Halictidae
Patellapis schultzei (Friese, 1909)

Apidae
Compsomelissa nigrinervis (Cameron, 1905)
Halictidae
Patellapis spinulosa (Cockerell)

Apidae
Compsomelissa nigrinervis (Cameron, 1905)
Halictidae
Patellapis terminalis (Smith, 1853)

Apidae
Ctenoplectra albolimbata (Magretti)
Halictidae
Patellapis tshibindica (Cockerell)

Apidae
Ctenoplectra antinorii (Gribodo, 1884)
Halictidae
Patellapis vittata (Smith, 1853)

Apidae
Ctenoplectra armata (Magretti, 1895)
Halictidae
Pseudapis aliceae (Cockerell, 1935)

Apidae
Ctenoplectra polita (Strand, 1912)
Halictidae
Pseudapis anomala (W. F. Kirby, 1900)

Apidae
Ctenoplectra terminalis (Smith, 1879)
Halictidae
Pseudapis anthidioides (Gerstäcker, 1857)

Apidae
Ctenoplectra ugandica (Cockerell, 1944)
Halictidae
Pseudapis armata (Olivier, 1812)

Apidae
Ctenoplectrina politula (Cockerell, 1930)
Halictidae
Pseudapis flavicarpa (Vachal)

Apidae
Dactylurina schmidti (Stadelmann, 1895)
Halictidae
Pseudapis kenyensis (Pauly, 1990)

Apidae
Dactylurina staudingeri (Gribodo)
Halictidae
Pseudapis patellata (Magretti, 1884)

Apidae
Epeolus amabilis (Gerstäcker, 1869)
Halictidae
Pseudapis rhodocantha (Cockerell)

Apidae
Epeolus corniculatus (Bischoff)
Halictidae
Pseudapis rugiventris (Friese, 1930)

Apidae
Epeolus friesei (Brauns, 1903)
Halictidae
Pseudapis schubotzi (Strand)

Apidae
Epeolus natalensis (Smith, 1879)
Halictidae
Seladonia africana (Friese)

Apidae
Hypotrigona gribodoi (Magretti, 1884)
Halictidae
Seladonia jucundus (Smith)

Apidae
Liotrigona bottegoi (Magretti, 1895)
Halictidae
Seladonia jucundus (Smith, 1853)

Apidae
Macrogalea candida (Smith, 1879)
Halictidae
Seladonia valligensis (Cockerell, 1937)

Apidae
Meliponula bocandei (Spinola, 1853)
Halictidae
Spatunomia filifera (Cockerell)

Apidae
Meliponula ferruginea (Lepeletier, 1836)
Halictidae
Sphecodes abyssinicus (Sichel)

Apidae
Meliponula lendliana (Friese, 1900)
Halictidae
Sphecodes braunsi (Blüthgen)

Apidae
Meliponula nebulata (Smith, 1854)
Halictidae
Sphecodes centralis (Cockerell)

Apidae
Nomada africana (Friese, 1911)
Halictidae
Sphecodes dilutus (Cockerell)

Apidae
Nomada aurantifascia (Eardley and Schwarz, 1991)
Halictidae
Sphecodes fimbriatus (Blüthgen)

Apidae
Nomada eximia (Eardley and Schwarz, 1991)
Halictidae
Sphecodes hagensi (Ritsema)

Apidae
Nomada gigas (Friese, 1905)
Halictidae
Sphecodes luteiventris (Friese)

Apidae
Nomada whiteheadi (Eardley and Schwarz, 1991)
Halictidae
Sphecodes punctatus (Sichel, 1865)

Apidae
Pachymelus abessinicus (Friese, 1913)
Halictidae
Sphecodes punctiscutum (Eardley and Urban)

Apidae
Pachymelus bettoni (Cockerell)
Halictidae
Sphecodes ugandae (Blüthgen, 1928)

Apidae
Pachymelus ciliatus (Friese, 1922)
Halictidae
Sphecodes woodi (Cockerell)

Apidae
Pachymelus claviger (Benoist, 1962)
Halictidae
Thrinchostoma bequaerti (Blüthgen)

Apidae
Pachymelus conspicuus (Smith, 1879)
Halictidae
Thrinchostoma emini (Blüthgen, 1930)

Apidae
Pachymelus festivus (Dours, 1869)
Halictidae
Thrinchostoma mwangai (Blüthgen)

Apidae
Pachymelus reichardti (Stadelmann, 1898)
Halictidae
Thrinchostoma sjoestedti (Friese, 1909)

Apidae
Pasites appletoni (Cockerell, 1910)
Halictidae
Thrinchostoma torridum (Smith)

Apidae
Pasites barkeri (Cockerell, 1919)
Halictidae
Thrinchostoma ugandae (Blüthgen, 1930)

Apidae
Pasites braunsi (Bischoff, 1923)
Halictidae
Thrinchostoma umtaliellus (Cockerell, 1937)

Apidae
Pasites carnifex (Gerstäcker, 1869)
Halictidae
Thrinchostoma wissmanni (Blüthgen, 1930)

Apidae
Pasites dichroa (Smith, 1854)
Halictidae
Systropha ugandensis (Cockerell)

Apidae
Pasites friesei (Cockerell, 1910)
Megachilidae
Afranthidium braunsi (Friese, 1904)

Apidae
Pasites humecta (Eardley, 1997)
Megachilidae
Afranthidium junodi (Friese, 1904)

Apidae
Pasites jenseni (Friese, 1915)
Megachilidae
Afranthidium sjoestedti (Friese, 1909)

Apidae
Pasites jonesi (Cockerell, 1921)
Megachilidae
Afranthidium tanganyicola (Strand, 1911)

Apidae
Pasites rotundiceps (Bischoff, 1923)
Megachilidae
Afroheriades larvatus (Friese, 1909)

Apidae
Pasites rufipes (Friese, 1915)
Megachilidae
Afroheriades reicherti (Brauns, 1929)

Apidae
Pasites somalicus (Eardley, 1997)
Megachilidae
Anthidiellum bipectinatum (Pasteels, 1984)

Apidae
Plebeina hildebrandti (Friese, 1900)
Megachilidae
Anthidiellum eritrinum (Friese, 1915)

Apidae
Sphecodopsis aculeata (Friese, 1922)
Megachilidae
Anthidiellum rubellum (Friese, 1917)

Apidae
Sphecodopsis capensis (Friese, 1915)
Megachilidae
Anthidium abjunctum (Cockerell, 1936)

Apidae
Sphecodopsis capicola (Strand, 1911)
Megachilidae
Anthidium basale (Pasteels, 1984)

Apidae
Sphecodopsis minutissima (Cockerell, 1933)
Megachilidae
Anthidium cordiforme (Friese, 1922)

Apidae
Sphecodopsis vespericena (Eardley, 1997)
Megachilidae
Anthidium niveocinctum (Gerstäcker, 1857)

Apidae
Tetralonia boharti (Eardley, 1989).
Megachilidae
Anthidium pontis (Cockerell, 1933)

Apidae
Tetralonia caudata (Friese, 1905)
Megachilidae
Anthidium severini (Vachal, 1903)

Apidae
Tetralonia macrognatha (Gerstäcker, 1870)
Megachilidae
Coelioxys aurifrons (Smith)

Apidae
Tetralonia obscuriceps (Friese, 1916)
Megachilidae
Coelioxys caffra (Friese)

Apidae
Tetralonia penicillata (Friese, 1905)
Megachilidae
Coelioxys cherenensis (Friese)

Apidae
Tetralonia ruficollis (Friese, 1911)
Megachilidae
Coelioxys foveolata (Smith)

Apidae
Tetralonia trichardti (Cockerell, 1933)
Megachilidae
Coelioxys nasuta (Friese)

Apidae
Tetraloniella apicalis (Friese, 1905)
Megachilidae
Coelioxys natalensis (Cockerell, 1920)

Apidae
Tetraloniella aurantiflava (Eardley, 1989)
Megachilidae
Coelioxys odin (Strand, 1912)

Apidae
Tetraloniella braunsiana (Friese, 1905)
Megachilidae
Coelioxys recusata (Schulz)

Apidae
Tetraloniella brevikeraia (Eardley, 1989)
Megachilidae
Coelioxys torrida (Smith)

Apidae
Tetraloniella capensis (Lepeletier, 1841)
Megachilidae
Coelioxys ultima (Pasteels)

Apidae
Tetraloniella elsei (Eardley, 1989)
Megachilidae
Coelioxys verticalis (Smith, 1854)

Apidae
Tetraloniella friesei (Meade-Waldo, 1914)
Megachilidae
Eoanthidium rothschildi (Vachal)

Apidae
Tetraloniella junodi (Friese, 1909)
Megachilidae
Euaspis abdominalis (Fabricius)

Apidae
Tetraloniella katangensis (Cockerell, 1930)
Megachilidae
Euaspis abdominalis (Fabricius, 1773)

Apidae
Tetraloniella michaelseni (Friese, 1916)
Megachilidae
Heriades arnoldi (Friese)

Apidae
Tetraloniella minuta (Friese, 1905)
Megachilidae
Heriades bequerti (Cockerell)

Apidae
Tetraloniella nanula (Cockerell, 1932)
Megachilidae
Heriades bouyssoui (Vachal, 1903)

Apidae
Tetraloniella paulyi (Eardley, 2001)
Megachilidae
Heriades capicola (Strand, 1912)

Apidae
Tetraloniella sierranila (Eardley, 1989)
Megachilidae
Heriades eximius (Friese)

Apidae
Tetraloniella simpsoni (Meade-Waldo, 1914)
Megachilidae
Heriades fumipennis (Cockerell)

Apidae
Tetraloniella sjoestedti (Friese, 1909)
Megachilidae
Heriades humilis (Cockerell)

Apidae
Tetraloniella whiteheadi (Eardley, 1989)
Megachilidae
Heriades rufifrons (Cockerell, 1932)

Apidae
Thyreus abyssinicus (Radoszkowski, 1873)
Megachilidae
Heriades scutellatus (Friese, 1922)

Apidae
Thyreus albomaculatus (DeGeer, 1778)
Megachilidae
Heriades speculiferus (Cockerell)

Apidae
Thyreus axillaris (Vachal, 1903)
Megachilidae
Hoplitis infrapicta (Cockerell, 1916)

Apidae
Thyreus bouyssoui (Vachal, 1903)
Megachilidae
Lithurgus pullatus (Vachal, 1903)

Apidae
Thyreus calceatus (Vachal, 1903)
Megachilidae
Lithurgus spiniferus (Cameron)

Apidae
Thyreus delumbatus (Vachal, 1903)
Megachilidae
Lithurgus spiniferus (Cameron, 1905)

Apidae
Thyreus interruptus (Vachal, 1903)
Megachilidae
Megachile abessinica (Friese, 1915)

Apidae
Thyreus meripes (Vachal)
Megachilidae
Megachile accraensis (Friese, 1903)

Apidae
Thyreus neavei (Cockerell, 1933)
Megachilidae
Megachile aculeata (Vachal, 1910)

Apidae
Thyreus niloticus (Cockerell, 1937)
Megachilidae
Megachile admixta (Cockerell, 1931)

Apidae
Thyreus oxaspis (Cockerell, 1936)
Megachilidae
Megachile afra (Pasteels, 1965)

Apidae
Thyreus pretextus (Vachal)
Megachilidae
Megachile albocincta (Radoszkowski, 1874)

Apidae
Thyreus scotaspis (Vachal, 1903)
Megachilidae
Megachile aliceae (Cockerell, 1932)

Apidae
Thyreus somalicus (Strand, 1911)
Megachilidae
Megachile altera (Vachal)

Apidae
Thyreus stellifera (Cockerell)
Megachilidae
Megachile apiformis (Smith, 1853)

Apidae
Xylocopa africana (Fabricius, 1781)
Megachilidae
Megachile attenuata (Vachal, 1910)

Apidae
Xylocopa albiceps (Fabricius, 1804)
Megachilidae
Megachile aurifera (Cockerell)

Apidae
Xylocopa apicalis (Smith, 1854)
Megachilidae
Megachile basalis (Smith, 1853)

Apidae
Xylocopa braunsi (Dusmet and Y Alonso, 1924)
Megachilidae
Megachile battorensis (Meade-Waldo, 1912)

Apidae
Xylocopa caffra (Linnaeus, 1767)
Megachilidae
Megachile beniticola (Strand, 1912)

Apidae
Xylocopa calcarata (Le Veque, 1928)
Megachilidae
Megachile bilobata (Friese, 1915)

Apidae
Xylocopa calens (Lepeletier, 1841)
Megachilidae
Megachile boswendica (Cockerell)

Apidae
Xylocopa erythrina (Gribodo, 1894)
Megachilidae
Megachile burungana (Cockerell)

Apidae
Xylocopa flavicollis (DeGeer, 1778)
Megachilidae
Megachile capitata (Smith, 1853)

Apidae
Xylocopa flavorufa (DeGeer, 1778)
Megachilidae
Megachile chrysopogon (Vachal)

Apidae
Xylocopa gaullei (Vachal, 1898)
Megachilidae
Megachile cincta (Fabricius)

Apidae
Xylocopa gribodoi (Magretti, 1892)
Megachilidae
Megachile cognata (Smith, 1853)

Apidae
Xylocopa hottentota (Smith, 1854)
Megachilidae
Megachile congruens (Friese)

Apidae
Xylocopa imitator (Smith, 1854)
Megachilidae
Megachile coniformis (Friese, 1922)

Apidae
Xylocopa inconstans (Smith, 1874)
Megachilidae
Megachile cornigera (Friese, 1904)

Apidae
Xylocopa lateritia (Smith, 1854)
Megachilidae
Megachile crassitarsis (Cockerell, 1920)

Apidae
Xylocopa mixta (Radoszkowski, 1881)
Megachilidae
Megachile curtula (Gerstaecker, 1857)

Apidae
Xylocopa modesta (Smith, 1854)
Megachilidae
Megachile devexa (Vachal, 1903)

Apidae
Xylocopa nigrita (Fabricius, 1775)
Megachilidae
Megachile digiticauda (Cockerell, 1937)

Apidae
Xylocopa olivacea (Fabricius, 1778)
Megachilidae
Megachile discolor (Smith)

Apidae
Xylocopa praeusta (Smith, 1854)
Megachilidae
Megachile dolichognatha (Cockerell)

Apidae
Xylocopa pubescens (Spinola, 1838)
Megachilidae
Megachile dorsata (Smith, 1853)

Apidae
Xylocopa senior (Vachal, 1899)
Megachilidae
Megachile edwardsiana (Friese, 1925)

Apidae
Xylocopa torrida (Westwood, 1838)
Megachilidae
Megachile ekuivella (Cockerell, 1909)

Apidae
Xylocopa ustulata (Smith, 1854)
Megachilidae
Megachile erythrura (Pasteels, 1970)

Apidae
Xylocopa varipes (Smith, 1854)
Megachilidae
Megachile eupyrrha (Cockerell, 1937)

Apidae
Xylocopa villosa (Friese, 1909)
Megachilidae
Megachile eurymera (Smith, 1864)

Apidae
Xylocopa wellmani (Cockerell, 1906)
Megachilidae
Megachile excavata (Cockerell)

Colletidae
Colletes eardleyi (Kuhlmann)
Megachilidae
Megachile fastigiata (Vachal)

Colletidae
Colletes opacicollis (Friese)
Megachilidae
Megachile felina (Gerstäcker, 1857)

Colletidae
Colletes reginae (Cockerell)
Megachilidae
Megachile fimbriata (Smith, 1853)

Colletidae
Colletes rothschildi (Vachal)
Megachilidae
Megachile flavipennis (Smith, 1853)

Colletidae
Colletes rufitarsis (Friese)
Megachilidae
Megachile fulva (Smith, 1853)

Colletidae
Colletes schultzei (Friese)
Megachilidae
Megachile fulvitarsis (Friese, 1910)

Colletidae
Colletes somereni (Cockerell)
Megachilidae
Megachile fulvohirta (Friese, 1904)

Colletidae
Hyaeus tinctulus (Cockerell)
Megachilidae
Megachile funebris (Radoszkowski, 1874)

Colletidae
Hylaeus alfkeni (Friese, 1913)
Megachilidae
Megachile garambana (Pasteels)

Colletidae
Hylaeus braunsi (Alfken, 1905)
Megachilidae
Megachile gastracantha (Cockerell)

Colletidae
Hylaeus fortis (Cockerell)
Megachilidae
Megachile globiceps (Pasteels)

Colletidae
Hylaeus heraldicus (Smith, 1853)
Megachilidae
Megachile gowdeyi (Cockerell, 1931)

Colletidae
Hylaeus lineaticeps (Friese, 1913)
Megachilidae
Megachile gratiosa (Gerstäcker, 1857)

Colletidae
Hylaeus magrettii (Vachal)
Megachilidae
Megachile griseola (Cockerell)

Colletidae
Hylaeus neavei (Cockerell, 1942)
Megachilidae
Megachile hecate (Vachal)

Colletidae
Hylaeus scutispinus (Alfken)
Megachilidae
Megachile hirticauda (Cockerell)

Colletidae
Hylaeus subfortis (Cockerell)
Megachilidae
Megachile hopilitis (Vachal, 1903)

Colletidae
Hylaeus ugandicus (Cockerell, 1939)
Megachilidae
Megachile ikuthaensis (Friese)

Colletidae
Scrapter albitarsis (Friese, 1909)
Megachilidae
Megachile invenita (Pasteels)

Colletidae
Scrapter algoensis (Friese, 1925)
Megachilidae
Megachile junodi (Friese, 1904)

Colletidae
Scrapter amplispinatus (Eardley, 1996)
Megachilidae
Megachile laminata (Friese)

Colletidae
Scrapter amplitarsus (Eardley, 1996)
Megachilidae
Megachile leucospila (Cockerell, 1933)

Colletidae
Scrapter armatipes (Friese, 1913)
Megachilidae
Megachile lineofasciata (Pasteels, 1965)

Colletidae
Scrapter aureiferus (Cockerell, 1932)
Megacliilidae
Megachile luteociliata (Pasteels)

Colletidae
Scrapter avius (Eardley, 1996)
Megachilidae
Megachile mabirensis (Cockerell)

Colletidae
Scrapter basutorum (Cockerell, 1915)
Megachilidae
Megachile mackieae (Cockerell, 1937)

Colletidae
Scrapter bicolor (Lepeletier and Audinet-Serville, 1825)
Megachilidae
Megachile maculosella (Pasteels, 1965)

Colletidae
Scrapter caesariatus (Eardley, 1996)
Megachilidae
Megachile manyara (Eardley and Urban)

Colletidae
Scrapter calx (Eardley, 1996)
Megachilidae
Megachile masaiella (Cockerell, 1930)

Colletidae
Scrapter capensis (Friese, 1909)
Megachilidae
Megachile meadewaldoi (Brauns, 1912)

Colletidae
Scrapter catoxys (Davies, 2005)
Megachilidae
Megachile mimetica (Cockerell)

Colletidae
Scrapter chloris (Eardley, 1996)
Megachilidae
Megachile mixtura (Eardley and R. P. Urban, 2005)

Colletidae
Scrapter chrysomastes (Davies, 2005)
Megachilidae
Megachile nasalis (Smith, 1879)

Colletidae
Scrapter erubescens (Friese, 1925)
Megachilidae
Megachile natalica (Cockerell, 1920)

Colletidae
Scrapter flavipes (Friese, 1925)
Megachilidae
Megachile neavei (Vachal, 1910)

Colletidae
Scrapter flavostictus (Cockerell, 1934)
Megachilidae
Megachile nigroaurea (Pasteels)

Colletidae
Scrapter glarea (Davies, 2005)
Megachilidae
Megachile niveicauda (Cockerell, 1920)

Colletidae
Scrapter heterodoxus (Cockerell, 1921)
Megachilidae
Megachile niveofasciata (Friese, 1904)

Colletidae
Scrapter leonis (Cockerell, 1934)
Megachilidae
Megachile panda (Cockerell)

Colletidae
Scrapter luridus (Eardley, 1996)
Megachilidae
Megachile paupera (Pasteels, 1965)

Colletidae
Scrapter niger (Lepeletier and Audinet-Serville, 1825)
Megachilidae
Megachile perfimbriata (Cockerell, 1920)

Colletidae
Scrapter nitidus (Friese, 1909)
Megachilidae
Megachile postnigra (Cockerell)

Colletidae
Scrapter pallidipennis (Cockerell, 1920)
Megachilidae
Megachile pulvinata (Vachal)

Colletidae
Scrapter pruinosus (Davies, 2006)
Megachilidae
Megachile pyrrhothorax (Schletterer, 1891)

Colletidae
Scrapter pyretus (Davies, 2006)
Megachilidae
Megachile rosarum (Cockerell)

Colletidae
Scrapter rufescens (Friese, 1913)
Megachilidae
Megachile rufa (Friese, 1903)

Colletidae
Scrapter ruficornis (Cockerell, 1916)
Megachilidae
Megachile rufigaster (Cockerell, 1945)

Colletidae
Scrapter striatus (Smith, 1853)
Megachilidae
Megachile rufipennis (Fabricius, 1793)

Colletidae
Scrapter viciniger (Davies, 2006)
Megachilidae
Megachile rufipes (Fabricius, 1781)

Colletidae
Scrapter whiteheadi (Eardley, 1996)
Megachilidae
Megachile scindularia (du Buysson)

Halictidae
Ceylalictus muiri (Cockerell)
Megachilidae
Megachile selenostoma (Cockerell)

Halictidae
Eupetersia similis (Benoist)
Megachilidae
Megachile semiflava (Cockerell, 1937)

Halictidae
Evylaeus kampalensis (Cockerell)
Megachilidae
Megachile silverlocki (Meade-Waldo)

Halictidae
Evylaeus latesellatus (Cockerell)
Megachilidae
Megachile simulator (Cockerell)

Halictidae
Evylaeus microsellatus (Cockerell)
Megachilidae
Megachile sinuata (Friese, 1903)

Halictidae
Evylaeus nigritulinus (Cockerell)
Megachilidae
Megachile striatula (Cockerell, 1931)

Halictidae
Evylaeus semilucidus (Cockerell)
Megachilidae
Megachile torrida (Smith, 1853)

Halictidae
Halictus bidens (Cameron)
Megachilidae
Megachile truncaticeps (Friese)

Halictidae
Halictus chalybaeus (Friese, 1908)
Megachilidae
Megachile ungulata (Smith, 1853)

Halictidae
Halictus fascialis (Smith)
Megachilidae
Megachile utra (Vachal)

Halictidae
Halictus frontalis (Smith, 1853)
Megachilidae
Megachile venustella (Cockerell)

Halictidae
Halictus harveyi (Cockerell)
Megachilidae
Megachile vittatula (Cockerell, 1920)

Halictidae
Halictus jonesi (Cockerell)
Megachilidae
Megachile wahlbergi (Friese, 1901)

Halictidae
Halictus obscurifrons (Cockerell, 1945)
Megachilidae
Megachile waterbergensis (Strand, 1911)

Halictidae
Halictus picaninus (Cockerell)
Megachilidae
Noteriades tricarinatus (Bingham)

Halictidae
Halictus placatus (Cockerell)
Megachilidae
Othinosmia braunsiana (Friese)

Halictidae
Halictus rugicollis (Friese)
Megachilidae
Othinosmia globicola (Stadelmann, 1892)

Halictidae
Halictus zonatus (Friese)
Megachilidae
Othinosmia nitidula (Cockerell)

Halictidae
Lasioglossum aethiopicum (Cameron, 1905)
Megachilidae
Pachyanthidium apicatum (Smith)

Halictidae
Lasioglossum bouyssoui (Vachal)
Megachilidae
Pachyanthidium benguelense (Vachal, 1903)

Halictidae
Lasioglossum candidicinctum (Cockerell, 1945)
Megachilidae
Pachyanthidium bicolor (Lepeletier, 1841)

Halictidae
Lasioglossum choronotum (Cockerell)
Megachilidae
Pachyanthidium bouyssoui (Vachal, 1903)

Halictidae
Lasioglossum cinctulum (Cockerell)
Megachilidae
Pachyanthidium cordatum (Smith, 1854)

Halictidae
Lasioglossum claripenne (Cockerell)
Megachilidae
Pachyanthidium micheneri (Pasteels)

Halictidae
Lasioglossum duponti (Vachal, 1903)
Megachilidae
Pachyanthidium obscurum (Pasteels)

Halictidae
Lasioglossum entebbianum (Cockerell, 1945)
Megachilidae
Pachyanthidium paulinieri (Guérin-Méneville)

Halictidae
Lasioglossum flavolineatum (Cockerell)
Megachilidae
Pachyanthidium rufescens (Friese, 1915)

Halictidae
Lasioglossum geteinum (Cockerell, 1945)
Megachilidae
Pseudoanthidium lanificum (Smith, 1879)

Halictidae
Lasioglossum gossypiellum (Cockerell)
Megachilidae
Pseudoanthidium truncatum (Smith, 1854)

Halictidae
Lasioglossum griseocinctum (Cockerell)
Megachilidae
Pseudoanthidium tuberculiferum (Brauns, 1905)

Halictidae
Lasioglossum hancocki (Cockerell)
Megachilidae
Pseudoheriades moricei (Friese, 1897)

Halictidae
Lasioglossum macrurops (Cockerell, 1937)
Megachilidae
Pseudoheriades pellucidus (Cockerell)

Halictidae
Lasioglossum masaiense (Cockerell)
Megachilidae
Serapista denticulata (Smith, 1854)

Halictidae
Lasioglossum michaelseni (Friese, 1916)
Megachilidae
Serapista rufipes (Friese, 1904)

Halictidae
Lasioglossum moderatum (Benoist, 1962)
Megachilidae
Stenoheriades braunsi (Cockerell, 1932)

Halictidae
Lasioglossum modestum (Benoist)
Megachilidae
Stenoheriades mackieae (Cockerell, 1936)

Halictidae
Lasioglossum nairobicum (Cockerell, 1945)
Megachilidae
Stenoheriades truncaticeps (Friese, 1922)

Halictidae
Lasioglossum nairobiense (Cockerell, 1945)
Melittidae
Capicola braunsiana (Friese, 1911)

Halictidae
Lasioglossum namaense (Friese, 1909)
Melittidae
Capicola micheneri (Michez, 2007)

Halictidae
Lasioglossum natense (Cockerell, 1935)
Melittidae
Haplomelitta atra (Michener, 1981)

Halictidae
Lasioglossum nudatum (Benoist, 1962)
Melittidae
Meganomia andersoni (Meade-Waldo, 1916)

Halictidae
Lasioglossum nyasense (Cockerell, 1945)
Melittidae
Meganomia binghami (Cockerell, 1909)

Halictidae
Lasioglossum pachyacanthum (Cockerell, 1937)
Melittidae
Melitta albida (Cockerell, 1935)

Halictidae
Lasioglossum pellitosum (Cockerell, 1934)
Melittidae
Melitta arrogans (Smith, 1879)

Halictidae
Lasioglossum pernotescens (Cockerell, 1934)
Melittidae
Melitta danae (Eardley, 2006)

Halictidae
Lasioglossum plicatinum (Cockerell)
Melittidae
Melitta katherinae (Eardley, 2006)

Halictidae
Lasioglossum radiatulum (Cockerell, 1937)
Melittidae
Melitta schultzei (Friese, 1909)

Halictidae
Lasioglossum rubricaude (Cameron, 1905)
Melittidae
Melitta whiteheadi (Eardley, 2006)

Halictidae
Lasioglossum rubritarse (Cockerell)
Melittidae
Rediviva colorata (Michener, 1981)

Halictidae
Lasioglossum rufomarginatum (Smith, 1853)
Melittidae
Rediviva emdeorum (Vogel and Michener, 1985)

Halictidae
Lasioglossum semidiversum (Cockerell, 1940)
Melittidae
Redivivoides simulans (Michener, 1981)

Table 9: Morpho species and doubtful identification.

Family
Morpho species
Family
Morpho species

Andrenidae
Andrena sp.1
Halictidae
Nomia sp.2

Andrenidae
Andrena sp.2
Halictidae
Nomia (Leuconomia) sp.

Andrenidae
Melitturga sp.2
Halictidae
Nomia (Acunomia) sp.

Andrenidae
Melitturga sp.1
Halictidae
Nomia (Crocisapidia) sp.

Andrenidae
Melitturgula sp.1
Halictidae
Nomia sp.1

Andrenidae
Meliturgula sp.2
Halictidae
Nomioides sp.

Apidae
Afromelecta sp.
Halictidae
Patellapis sp.3

Apidae
Allodape sp.1
Halictidae
Patellapis (Archihalictus) sp.

Apidae
Allodape sp.2
Halictidae
Patellapis (Chaetalictus) sp.

Apidae
Allodapula sp.1
Halictidae
Patellapis (Lomatalictus) sp.

Apidae
Amegilla sp.1
Halictidae
Patellapis (Zonalictus) sp.1

Apidae
Ammobates sp.
Halictidae
Patellapis (Zonalictus) sp.2

Apidae
Anthophora sp.1
Halictidae
Patellapis sp.1

Apidae
Anthophora sp.2
Halictidae
Patellapis sp.2

Apidae
Braunsapis sp.
Halictidae
Patellapis sp.4

Apidae
Ceratina (Ctenoceratina) sp.1
Halictidae
Pseudapis sp.1

Apidae
Ceratina (Ctenoceratina) sp.2
Halictidae
Pseudapis sp.2

Apidae
Ceratina (Neoceratina) sp.
Halictidae
Sphecodes sp.

Apidae
Ceratina (Pithitis) sp.
Halictidae
Thrinchostoma sp.

Apidae
Ceratina sp.1
Halictinae
Halictus sp.1

Apidae
Ceratina sp.2
Halictinae
Halictus sp.2

Apidae
Ceratina sp.3
Megachilidae
Afranthidium sp.

Apidae
Cleptotrigona sp.
Megachilidae
Afroheriades sp.1

Apidae
Compsomelissa sp.1
Megachilidae
Anthidiellum sp.1.

Apidae
Ctenoplectra sp.2
Megachilidae
Anthidiellum sp.2.

Apidae
Ctenoplectra sp.1
Megachilidae
Anthidium (Severanthidium) sp.1

Apidae
Ctenoplectrina sp.
Megachilidae
Anthidium sp.

Apidae
Dactylurina sp.
Megachilidae
Dianthidium sp.1

Apidae
Epeolus sp.
Megachilidae
Euasapis sp

Apidae
Liotrigona sp.
Megachilidae
Fidelia sp

Apidae
Melecta sp.
Megachilidae
Heriades sp.1

Apidae
Nomada sp.
Megachilidae
Heriades (Amboheriades) sp.

Apidae
Pachymelus sp.1
Megachilidae
Heriades (Pachyheriades) sp.

Apidae
Pachymelus sp.2
Megachilidae
Heriades sp.2

Apidae
Pachymelus sp.2
Megachilidae
Hoplitis sp.1

Apidae
Pasites sp.2
Megachilidae
Hoplitis sp.2

Apidae
Pasites sp.1
Megachilidae
Lithurge sp

Apidae
Sphecodopsis sp.
Megachilidae
Lithurgus sp

Apidae
Tetralonia sp.1
Megachilidae
Megachile (Amegachile) sp.

Apidae
Tetralonia sp.2
Megachilidae
Megachile (Creightonella) sp.1

Apidae
Tetralonia (Eucara) sp.1
Megachilidae
Megachile (Creightonella) sp.2

Apidae
Tetralonia (Eucara) sp.2
Megachilidae
Megachile (Creightonella) sp.3

Apidae
Tetralonia sp.3
Megachilidae
Megachile (Eutricharaea) sp.1

Apidae
Tetraloniella sp.4
Megachilidae
Megachile (Eutricharaea) sp.2

Apidae
Tetraloniella sp.1
Megachilidae
Megachile (Paracella) sp.

Apidae
Tetraloniella sp.2
Megachilidae
Megachile (Xeromegachile) sp.

Apidae
Tetraloniella sp.3
Megachilidae
Megachile sp.1

Colletidae
Colletes sp.1
Megachilidae
Megachile sp.2

Colletidae
Colletes sp.2
Megachilidae
Noteriades sp.

Colletidae
Hylaeus sp.1
Megachilidae
Osmia sp.1

Colletidae
Hylaeus sp.2
Megachilidae
Osmia sp.2

Colletidae
Scrapter sp.
Megachilidae
Pachyanthidium sp.

Colletidae
Scrapter sp.1
Megachilidae
Pseudoanthidium sp.

Colletidae
Scrapter sp.2
Megachilidae
Pseudoheriades sp.

Halictidae
Cellariella sp.1
Megachilidae
Serapista sp.1

Halictidae
Cellariella sp.2
Megachilidae
Serapista sp.2

Halictidae
Ceylalictus sp
Megachilidae
Stenoheriades sp.

Halictidae
Lasioglossum sp.1
Melittidae
Capicola sp

Halictidae
Lasioglossum sp.2
Melittidae
Capicola sp

Halictidae
Lasioglossum sp.3
Melittidae
Haplomelitta sp.

Halictidae
Lipotriches sp.1
Melittidae
Meganomia sp.

Halictidae
Lipotriches (Afronomia) sp
Melittidae
Melitta sp.2

Halictidae
Lipotriches (Macronomia) sp.
Melittidae
Melitta sp.1

Halictidae
Lipotriches (Trinomia) sp.
Melittidae
Rediviva sp.1

Halictidae
Lipotriches sp.2
Melittidae
Rediviva sp.2

Melittidae
Redivivoides sp.

4. Discussion

4.1. Effects of Climatic Factors on Bee Communities

Results from this study indicated that bee species richness and abundance were affected by climatic factors. In fact, species richness and abundance of bees were correlated with 10 years average temperature, as well as with temperature in the current year and two years prior to the study. Thus, temperature was found to be a very good and significant predictor of bee species in previous and current years. In climatic regions with strong wet-dry seasonality and low cold-hot seasonality, the main factor influencing occurrence, temporal distribution of different foraging bee species is temperature [18]. Even when there is a great variability in humidity and solar radiation (light intensity) along the year and the day, their influences on bee foraging seem to be small at the regional level although the influence at the microlevel may be higher. Temperature plays therefore a crucial role in occurrence and emergence of different adult bee species. Temperature is a key determinant of phenology of insect pollinated plants in natural and agricultural landscapes. Temperature is expected to be of primary importance in regulating phenology of pollinators, with other factors playing a secondary role [32, 54, 55]. The temperature seems to be responsible for adult appearance of bees in the environment, and this strong dependence may be expected because the average daily temperatures matters in the foraging behavior of many bee species. Multi-years monitoring data showed that climatic fluctuations are primarily responsible for the interannual variability in appearance phenology of bee species belonging to several functional groups [18].

While for some bee species (such as Apis mellifera), occurrence (presence/absence) may be related to increasing temperature, for most bee species, some factors (microhabitats, farming practices, and land-use intensity) may influence the occurrence/appearance and phenology. Although bee species richness and abundance were observed to be negatively and significantly correlated to maximum temperature and minimum temperature one to two years prior to the study, at the moment there exists no clear explanation for such pattern. The fact that the species richness and abundance of bees were strongly correlated with mean annual temperature in the previous years than in current years indicated potential vulnerability of local/native bee species to global environment and climatic changes [18]. Consequently, various bee species may be at risk of disappearance (extinction) in face of future climate change and variability in central Uganda. Such patterns were previously predicted for other pollinating species such as butterflies, flies, and beetles [18] in central Uganda.

Strong associations between previous (not current) year precipitation and the abundance/richness of bees were found in this study.

These results are consistent with the observation that previous year precipitation cues bee emergence in agricultural regions of Sub-Saharan Africa including Uganda [18]. In West Indies, the effects of climate on the plant-pollinator communities were studied, and it was found that rainfall and temperature affected richness and importance of the different pollinator functional groups and species. It was found in that study that rainfall explained most of the variation in pollinator richness and relative importance. However, effect of climate on other insect pollinator groups was more obscure [55]. Bees were strongly negatively affected by rainfall. Bee variation along the climate gradient could therefore be largely explained by their physiological capabilities to respond to rainfall and temperature [55].

While comparing historical pollination rates to present rates of visitations by pollinators to an orchid plant species in RSA, a decline in the pollination and in pollinator species richness and abundance was found [32]. Similarly, in a recent study in the USA, climate data suggested that patterns of precipitation in the current and previous year climate change drove variation in bee abundance because of its effects on cues for bee emergence in the current year and on the abundance of floral resources in the previous year [32, 55]. Thus, it is likely that accounting for the cumulative effects of climatic variables may be relevant for studying and predicting the future of bee communities in rural landscapes under climate change scenarios in Uganda.

Temporal and spatial fluctuations in the frequency of occurrence of different bee species can be associated with local climatic variation. In particular, bee abundance appeared in this study to be influenced positively by precipitation during previous years. The effects of the previous-year precipitation is likely indirect; greater precipitation tends to increase flower production in different bee food plants, which in turn may increase the food supply and reproductive success of generalist and specialist bees. This has consequence on the reproduction, emergence, phenology, and seasonality of bees, particularly for univoltine specialist solitary bees. Population dynamic in oligolectic bees (as opposed to polylectic bees) in response to year-to-year variation in floral resources may be also linked to previous climatic factors than to current ones [17]. Time lag in the population dynamics of bees in response to their food plants may also help in explaining in part dramatic fluctuations that can be observed in the visitation rates of different bee species to crop and wild blooming plant species.

In this study, it was also observed that rains two years prior to sampling had an effect on current bee populations. This was an indication of a potential long-term factor effects in shaping bee communities from rural landscapes of central Uganda. Although the average lifetime of the bee is much less than 1-2 years, it may appear controversial that past rainfall and temperature correlate with current/existing bee richness and abundance. In addition, such significant correlations may provide an indication of what has been happening as well as enabling speculative prediction of the future of species richness and abundance of bees in face of variability in climatic factors and the consequence on food security and livelihoods of people. Thus, studying separately (alone) the effects of current (specific period) weather factors on bee species richness and abundance may not provide an idea on the vulnerability of different bee species to future climate change. However, correlating weather data collected 2 to ≥10 years prior to the current period of the study is preliminary proven to be indicative in terms of predicting the vulnerability and risk of extension of various bee species to climate change and global environmental change.

Results of this study indicated variability in 10-year average temperature and rainfall. This variability was due to fact that the study area is located around the Equator. Previous observations of weather data in Uganda indicated a high variability in micro- and macroclimatic factors at a small-scaled scale [18]. Thus, this may also explain variability in response of bee species and individuals to variability in rainfall and temperature among study sites when these study sites cover a relatively small geographic region in Uganda (12% of the national land area).

In this study, it was also observed that bee species richness was correlated with previous climatic factors than current. Variability in climatic factors is expected over large areas (separated by more than 50 Km) as the data from the current study indicated. This variability in climatic factors has got consequences on occurrence/disappearance, seasonality, and voltinism of certain bee species found in central Uganda. Although there are almost no studies from East Africa and from Uganda predicting future impacts of climate change and variability on bee species richness and abundance, this study yielded preliminary results indicating potential vulnerability of bee biodiversity and its future consequence on yield stability and food security in the country.

Much remains to be learned about how bee species richness and abundance will respond to future climate change in different landscapes in Sub-Saharan Africa and in Uganda. There may be various mechanism responses of different bee species to climatic variability: delaying foraging dates and times and phenology shifts. Variability in terms of extinction or disappearance of certain functional groups may be catastrophic for certain groups of crops/plant species. The climatic variability may lead to increased pollen limitation in crop species [18]. Such situation may in turn lead to crop yield failure and food and livelihood insecurities of people, particularly rural communities where high reliance to pollination services is observed in most Sub-Saharan African countries. Hegland et al. [31] found that species phenological responses to climate warming often seem to occur at parallel magnitudes in plants and pollinators, sometimes resulting in temporal mismatches among these mutualistic partners. It has been also found that climate change-induced phenological shifts may reduce the floral resources available to different bee species by 17%–50%, and this situation increases thereby extinction risks and disrupting crop/plant-pollinator interactions.

Central Uganda is the second African hotspot for bee biodiversity [22]. Future climate change is likely to particularly affect negatively and increase the risk of extinction of a number of bee species of local and unique Apoidea fauna found in central Uganda. Rising temperatures and altered rainfall regimes potentially have a huge impact on bee functional groups and life history traits like sociality in halictid bees [56] and host plant synchronization and, thus in turn, on pollination services and crop reproductive success.

In Uganda, future potential reductions in number of bee species and abundance were also predicted in this study. Novel mitigation mechanisms are highly needed to prevent decline in bee species and population in face of future climate change that may also alter crop/plant-pollinator mutualisms. For example, maintaining the spatio-temporal stability of bees (enhancing the persistence of populations) and their pollination services in the landscapes may require the establishment of observatories in rural landscapes.

For successful conservation of bee biodiversity under climate change-induced habitat fragmentation/loss and availability of migration corridors and reserves, dispersal rates and colonization ability may be crucial factors to enhance in rural landscapes of central Uganda. Understanding and monitoring the effects of rising temperatures and changing precipitation regimes on bee species richness and population dynamics may be vital for the development of efficient landscape and habitat conservation strategies in rural landscapes of Uganda to enhance the delivery of pollination services to various pollinator-dependent crops/wild plants.

Given the relevance of crop/plant-pollinator mutualisms for biodiversity and ecosystem integrity and crop productivity in farmland of central Uganda, it is crucial that future in-deep studies/investigations on the impact of climate change (in interaction with various abiotic/biotic drivers) are conducted on crop-animal interactions in different ecological zones of Uganda, with a particular focus on vital pollination function and service stability enhancement.

Anthropogenic, environmental, and climatic changes and the introduction of alien species have been predicted to affect plant-pollinator interactions [57] and delivery of pollination services to crops at the global level. In addition prediction in parallel declines in bee species richness and insect-pollinated plants indicated a potential reduction in pollination services and/or in available flower resources for flower-visiting insects. Bees are important plant pollinators, but they are among biota that are very sensitive to disturbance; particularly to anthropogenic activities, intensification in land-use systems and change in farming practices. Any decline in numbers or species or functional groups of bees, due to anthropogenic disturbances in interaction with variability in climatic factors, has consequence for crop productivity in Uganda since 75% of crops grown by farmers are pollinator-dependent crops [18]. Thus, the predicted population declines and species extinctions constitutes a significant threat both to biological diversity and their ecosystem services and to whole agricultural economics. The impacts of climate change on pollination services delivery may be more destructive in sub-Saharan Africa and in Uganda where there is a high livelihood dependency of human being to pollination services [18]. Thus the need to set and implement in advance climate-friendly conservation strategies before pollinators can disappear.

4.2. Effects of Regional Land-Use Intensity Factors on Bee Species Richness and Abundance

In this study, bee species richness and abundance were also found to be affected by regional land-use intensity variables as it was found in studies conducted elsewhere [1–3]. Results obtained from this study and those from other studies confirmed that the intensification of farmland management poses a threat to bee diversity [44, 58] and thus may reduce pollination services delivery to crops in rural landscapes. As human land-use intensifies, wild bees are exposed to habitat degradation/loss; exotic species (e.g., viruses, mites, and parasitoids) and spatial dissociation of food and nesting resources [1, 23, 33], including native bees [58, 59]. In rural landscapes of Uganda, bee communities are generally contingent on land-use, with solitary being more sensitive to anthropogenic activities than social bees [18]. Less anthropized areas generally harbor a greater richness and number of rare (singletons, doubletons) bee species while more intensively managed land-use types harbor higher population densities.

Land-use intensity may have indirect (reducing the diversity and cover of insect-pollinated plants, and thereby floral resources) negative effects on bee communities in rural landscapes. In fact, less intensively managed crop fields are expected to support more stable pollinator communities as a result of the higher availability of food resources. Higher stability of pollinators in rural landscapes has considerable effects on pollination success (e.g., reduced pollination limitation) and plant reproduction. Because bees are responsible for the pollination of many cultivated crops and wild plants, the decreased stability of bee communities (abundance and species richness), as well as the consequent decreased pollination services as a result of the negative impacts of land-use intensity (crop cultivation intensification) on bees, could have serious ecological and economical consequences [1, 58] in pollinator-dependent crop production systems of Uganda. Chronic pollen limitation caused by reduced pollinator availability is expected to result in strong crop yield failure/reduction and increase the likelihood of food insecurity of human communities.

From an ecological point of view, habitat loss, fragmentation (disruptions of habitat configuration and modification) are the major drivers of species extinction in the Anthropocene [60, 61] in the tropics. Their effects are exacerbated when interaction with climatic factors-stress related. Landscape disturbance primarily influences three components of pollination interactions: pollinator density, movement, and plant demography. The effects of habitat loss on each of these components are likely to differ substantially from the effects of fragmentation, which is likely to be more complex and may influence each pollination component in contrasting ways [60].

4.3. Effects of Local and Landscape Variables on Bee Communities

In this study, variations in bee abundance and species richness among different study locations were hypothesized to be related to landscape variables, but not to climatic and local variables. The results indicated the opposite. In fact, bee species richness and abundance were predicted by local factors (abundance and richness of floral resources) as well as by landscape and climatic factors. Thus, flowering (wild and cultivated) plants play significant role in structuring bee communities, particularly as sources of pollen and other floral resources needed for their survival in rural landscapes of central Uganda. In fact more than 24 flowering crop species (Table 6) and more than 50 wild blooming plant species (Table 7) were recorded during transect walk-and-counts in this study. Obviously, floral and nesting resources are critical for the occurrence/survival of diverse bee communities in natural and man-made landscapes [18]. Resource availability is a critical factor determining the dynamics of populations over space and time [62]. Several other studies have demonstrated that species richness of wild floral resources and mass flowering of both wild plants and crop species actually influence both bee density and diversity that occur in farmed landscape [63–67].

Landscape variables (% cultivation intensity, % semi-natural habitats, and forest distance) were observed to be powerful predictive variables of the variation in abundance, richness, and diversity of bees; few farm-scaled local variables (% of wild floral resources) were equally powerful, whereas most of the local variables were significant but had weaker to nonsignificant effects.

In other words, landscape variables explained more variation in population density and species richness of bees than did local factors. In fact, bee species richness and abundance declined with forest distance. Geographic scales at which landscape effects on bee faunas are most pronounced remain largely unsurveyed in Sub-Saharan Africa. However, bee communities were observed to be affected by food resources within 0–200 m of their nesting sites [18]. As previously mentioned, forging habitat and nectar/pollen sources (located at <2000 m far from bee refugia) are critical ecological resources for maintaining pollination services by native bees in agricultural landscapes [7, 59]. Recently, in coffee-agroforest systems in India, it was found that the abundance of Apis dorsata decreased with increasing distance from a neighbouring forest patch, but this distance effect was reduced with an increase in size of the nearby forest [66], and this result indicated that justifying the conservation of large forest remnants may be problematic unless more studies are conducted to account for the direct effect of forest on crop fruit/seed set in most agricultural landscapes of the world [66].

Bee species and abundance were found to be linearly related to cover of semi-natural habitats although some studies [22–24, 27–30, 35] have found that bee species richness may be positively but non necessarily being linearly related to semi-natural habitat area. Studies conducted elsewhere stressed that landscape factors (% cover of semi-natural habitats, forest distance, and habitat fragmentation/isolation) are more important in determining the rule of occurrence [34] of Apoidea communities in rural landscapes [65–69]. Also, while investigating the effect of the quantity of surrounding natural habitat, organic management, and strips of semi-natural vegetation on flower visitation frequency of wild and managed bees in intensive agricultural landscapes in USA, it was realized that wild bee species visited almond flowers but only in orchards with adjacent semi-natural habitat or vegetation strips [1].

Linear remnants of native vegetation and related semi-natural habitats are known to contribute to bee assemblage heterogeneity by adding unique species to the regional pool [70]. In fact, semi-natural habitats and related noncrop habitats provide dispersal corridors and “habitat islands” required by many bee species as refuges and feeding areas [71]. Thus, an increase in amount of semi-natural habitats may lead to a more diverse flora in fields, providing valuable nesting and foraging resources for bees and other pollinators.

Although some farmland habitats may provide sufficient floral resources over the year, they may not be good nesting sites for bees. Short-distance to bee refugia (forest, wetlands, and remnant vegetation) and the percentage cover of semi-natural habitats are attributes that contribute to bee diversity in rural landscapes. Bee species richness and diversity are expected to increase with increase in the amount of semi-natural habitats in the landscapes [72, 73] because of availability of floral/nesting opportunities in such habitats [22, 27]. Semi-natural habitats are known to positively affect pollinators in the surrounding agricultural landscape presumably through contributing both nest sites and forage resources. Other non-crop areas such as field margins may also be beneficial provided that they are rich in flower resources. Non-crop and semi-natural areas add heterogeneity to the farm-landscape. These non-crop habitats often provide a continuous supply of nectar and pollen which bees can utilize. They can provide suitable habitats for bees to nest and have been shown to contain higher densities of bee nests in this study. Hence, they may promote pollinator abundance and species richness in agricultural landscapes of Uganda, even if measures promoting pollinators may not necessarily benefit pollination of wild plants, because species may vary in their effectiveness as pollinators. Therefore, supporting wild pollinators and crop production in agricultural landscape requires the maintenance of mosaics of natural/semi-natural features and remnant vegetation in agricultural landscapes [70]. Networks of natural and semi-natural areas (hedgerows, grasslands, fallows, woodlands, riparian corridors, and road-sides) in the farmland can therefore be beneficial to agricultural production [9–11] although the effects of increasing semi-natural in the landscape may be taxon dependent [74] since some species richness may found beneficial while other many find these habitats hostile [74]. Because bees are among the important pollinator guilds in rural landscape, protection of the remaining natural habitat and vegetation in close proximity to farmland habitats is an imperative conservation strategy in host sport bee biodiversity regions.

Natural habitats, semi-natural habitats, and vegetation structure/composition generally explain most of the variance for the species richness and abundance of bees in agricultural landscapes [18]. There are a number of semi-natural habitats in the farmlands, but old fallows are generally associated with the highest species richness of bees. Other semi-natural habitats may harbor bee communities of similar species richness and composition [18]. In farm-landscapes with 20%–30% of habitats kept uncultivated as reservoirs for bees, crop yields are expected to be stable over time and space [22]. Thus, conservation programs aiming at enhancing pollination services in the farm-landscape should concentrate on strategies to maintain/improve/increase the amount of semi-natural habitats in the surrounding of crop fields to guarantee spatio-temporal availability of floral resources for beneficial insects including bees.

4.4. Contrasting Effects of Local and Landscape Factors on Bee Abundance, and Richness in Rural Landscapes

Occurrence, prevalence, abundance and richness of bees in rural landscapes may be linked to various local and landscapes. The degree at which different environmental characteristics influence bee communities is still subject of debate by scientists. Some studies have shown a relative high importance of landscape-scaled variables [16, 19] as compared to local-scale variables [18]. Overall, some studies stress that landscape variables are more determinants than local variables. Other studies indicate the importance of local variables in shaping bee communities variables. Within tropical regions, some studies often find that species richness and abundance are determined by local drivers, whereas studies from temperate regions report that landscape drivers are more determinants for bees in agricultural landscapes. For example, recently, Féon et al. [10] found that the abundance of bees (solitary bees) in fields increased positively with the increase in the proportion cover of semi-natural habitats (grasslands) than with increasing amount of flowering resources in an agricultural area of western France.

While in central Uganda both landscape and local factors had significant effect of bee abundance and diversity, in Mexico [75], it was recently found that local habitat factors, managed within agroforestry systems, had strong impacts on local bee abundance and species richness, more than landscape-level factors (e.g., % cover of forests). Both the local and landscape factors affected bee communities in Uganda and this is different from what is commonly reported from elsewhere. For instance, there is no clear explanation for such pattern and difference. Explanations may be linked to the bee community composition found in central Uganda. The community composition plays a significant role in explaining this pattern since different bee species may respond differently at different local and landscape scaled factors [23, 30]. In addition, in central Uganda, there coexist a diversity of bees with different nesting affinities and foraging ranges. There are evidences for existence of a high diversity of nesting resources and floral resources on which bees may depend on for pollen and nectar in farmlands of central Uganda [18]. Central Uganda had a variety of floral resources to support rich and diverse bee fauna. Some few common species may thus be affected (positively/negatively) by local factors (e.g., species flying at around 500 m from their nests), whereas others may only be affected by landscape factors (example species foraging up to 2000 m beyond their nests). Also, the difference in results between Uganda, and Mexico may be explained by the difference in local management strategies of fields. The diversity of floral resources combined to the diversity of nesting sites and to habitat heterogeneity was found to be likely explaining the survival and coexistence of great number of bee species with various ecological affinities in farmlands of central Uganda.

In brief, there is a need to conserve biodiversity to ensure the provision of ecosystem services in rural landscapes [75, 76] of Uganda and Sub-Saharan Africa. The conservation of pollinators and pollination services will play a significant role in a long-term viability of food supplies, in the livelihood security/improvement of smallholder farmers, in commercial agricultural enterprises, in generation of household and national revenues, and in production of diverse products to satisfy food, fibre, and fuel demands of expanding rural-urban populations.

5. Conclusion and Recommendations

The overall goal of this work was to understand how environmental characteristics operating at different scales affect the occurrence, distribution, and diversity of bee communities in farmlands of central Uganda. Understanding the response of species to various drivers is essential to designing conservation management, especially in mosaic agricultural landscapes. Aspects of the farm, surrounding landscape, and regional and climatic factors were found to be potentially useful predictors of bee abundance and species richness.

Overall, conservation, management, and policy efforts aiming at increasing ecological intensification of agricultural production systems and stabilizing food productivity in central Uganda should (i) first preserve and prevent degradation of remaining forest fragments, forest fallows, and wetlands: reducing natural and semi-natural vegetation clearance (retaining and maintaining the current status of natural forest patches and wetlands is important since these ecosystems increase the bee species richness in riparian agricultural matrices); (ii) secondly, strongly encouraging small-scale farmers to maintain higher cover of multipurpose agroforestry tree species and good proportion of linear and non-linear features of semi-natural habitats; (iii) mimicking natural vegetation or natural ecosystems through promoting establishment of related natural habitats (woodlots of eucalyptus/pines) and community village forestry in the rural landscapes.

The most critical point and exciting finding from this study was to find out that species richness/abundance correlated with mean annual temperatures in previous years. It was also found in this study that availability of semi-natural habitats, abundance of wild and cultivated floral resources in the landscape, and distance to the closest forest also influence critically the bee communities. Thus, changes in these landscape variables along the years are expected to affect bee communities as the change in average temperature/rainfall does. Hence, if landscape and land-use change data is found available (registered) in the region, further research should focus on potential influence of landscape changes on occurrence, distribution, and current community structure of bees. In fact, it is expected that the change in landscape variables may influence the change (variability) in climatic factors, and the lack of stability in climatic factors may have strong negative effects [18] on the spatio-temporal occurrence and distribution and distribution of bee communities in the agricultural landscape.

There is a need for future research to be conducted in many parts of the world to get more evidence of the role played by forest fragments, wetlands and related semi-natural habitats (e.g., forest fallows) in agricultural landscapes to provide a variety of ecosystem services [66]. There is also a need to increase scientific ability to define and experimentally measure pollination resilience, determine under which conditions there will be pollinator population stability, as well as increase the understanding of the factors shaping this parameter to be able to support efforts to forecast the impact of climate change on the delivery of pollination services to pollinator-dependent crops [77, 78]. There is also a scientific need to estimate cost-benefits of conserving bee biodiversity and pollination services in natural and agricultural landscapes in [79] Uganda and in Sub-Saharan Africa. Findings from such studies may enable the development of opportunities to use semi-natural features in adaptation and mitigation activities related to future climate change.

Conflict of Interests

I declare having no conflict of interest with MINITAB Company. TheStatistical software used for data analysis (MINITAB-15, Englishversion) was officially purchased as single license use for onecomputer by the leader of the Darwin project (Dr Phil Atkinson fromBritish trust for ornithology) while in UK attending a training indata analysis between March and May 2007.

Acknowledgments

The author is very grateful to Darwin Initiative (Defra, UK; project reference: 14–032; project title: Conserving biodiversity in modernized farm landscapes in Uganda) for funding this study. Mr. Maurice Mutabazi (research assistant) is acknowledged for for his assistance in the field. He is also very grateful to Professor Simon Potts and Professor Philip Nyeko (scientific supervisors), as well as Dr. Juliet Vickery, Dr. Phil Atkinson, and Professor Derek Pomeroy (project scientific advisors) for all kind of help given. He is also very grateful to all bee taxonomists (Dr. Connal Eardley fromf ARC-RSA, Dr. David Roubik from Smithsonian Tropical Research Institute, Dr David Notton from the department of Entomology of Landon History Museums, UK, and Robert Stuart from the University of Reading, UK) who helped with proper identification and validation of names of different bee species collected during the course of this study.

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