The Eastern Arc mountains has the status of a global biodiversity hot-spot (Pellikka et al., 2013; Adriaensen et al., 2006)1 and it lies in south-eastern Kenya at 03^o20 S, 38^o15 E, about 150 km inland from the coast and covering an area of about 250 km²2(Figure 1). The hills are isolated from other mountainous areas viz the south-east (Shimba Hills), south (Usambara Mountains), south-west (Mt Kilimanjaro), west (Ngulia and Chyulu Hills) and the north-west (Kenyan highlands) by the vast plains of Tsavo 9. The annual rainfall is characterized by two rainy seasons (March- May, September – October) and varies between 480 and 1200 mm in the highlands 14, but much less rainfall (250 mm) (Figure 2) is observed on the surrounding plains of the sparse lowlands 11.

The highlands are considered as high potential agricultural areas which are suitable for agriculture. However, a very small area is available for agricultural purposes due to steep slopes and shallow soils. A total of seven 7 forest fragments are found in this region, and characterized by continuous forest landscapes. Most forests (03°25'S, 38°22'E) are indigenous habitat situated at an altitude of 1,400 m measuring approximately 2 ha 22 and is regarded as part of remnants of the original montane forest, receiving 1700–2400 mm of annual precipitation. Tree species such as Lobelia gibberoa Hemsl, Phoenix sylvestris Sylvestris, Dracaena steudneri Engl. and Cyathea manniana L. are characteristic to this forest. Some isolated forests (03°28'S, 38°28'E) are mixed forest habitats comprising both indigenous and exotic tree species forming dense and continuous canopies, which has a reputation of being the most disturbed forest fragment out of most forest fragments. Tree species predominantly found in the highlands are cypress (Cupressus lusitanica), eucalyptus (Eucalyptus saligna), pines (Pinus caribea, P. patula), Maesopsis eminii and grevillea (Grevillearobusta) and Acacia mearnsii.

In each study site (25 ha), twenty [20) linear transects of 250 m x 20 m each were established using a GPS to mark coordinates with relation to habitat type and altitude. Transects were used as a basic sampling unit for all bees and vegetation data. In addition, the central line transect method was used to represent different habitats of the entire study area and also increase the chances of sampling nesting sites and vegetation types that dominate all study sites.

In order to obtain quantitatively reliable data, each line transect (250m) was divided into five separate sections of 50m x 20m each. The starting point for bee sampling was rotated (starting from the first point and ending at fifth point, then beginning at point 5 during the next sampling round) between the end points of the transects to randomize sample collections thus reducing bias in data collection. Surveys were carried out along four successive different habitats (forest, grassland, bush lands and woodlands); the sites were chosen due to the dissimilarity in habitat structure with key factors such as isolation, geographic location and micro-climates taken into consideration.

Analysis of variance via R statistical package was used to compute the significant effect of habitat type on species abundance. A non-linear regression model such as the species accumulation curve was used to estimate the number of meliponine bee species represented in the whole surveyed area 20. The species accumulation curves was used to estimate species richness and rank abundance of meliponine bee species across varying habitats types 3. Biodiversity indices (species richness, abundance and Shannon index) were computed using the R statistical package called Biodiversity R 8. Species richness, species diversity (using Shannon index and Renyi diversity profiles), and the proportion of habitat type with most abundant meliponine bee species were computed using Renyi diversity profiles 19. Renyi diversity profiles are curves that provide information on richness and evenness, similar to rank abundance curves. Renyi diversity profiles however have the advantage over rank abundance curves because of easy rank ordering from lowest to highest diversity. For this reason, a Renyi diversity profiles was used in this instance 19.

A total of four (4) different meliponine bee species were identified on the basis of morphological differences in the field (Table 2). The four species were determined at species level based on the descriptions from literature of African meliponine bee species 4. All the four species were recorded in all the habitats sampled in the lowlands. (Table 2). Hypotrigona gribodoi was the most dominant species across the two study sites (58.3%) of all recorded nests, followed by Meliponula ferruginea (32.0%). The proportion of Plebeina hildebranti (7.4%) and Hypotrigona ruspolii (2.3%) species revealed that they were the least distributed species. This indicates that all the four (4) species were highly dispersed in distribution across the three habitats (Mixed deciduous woodlands, grasslands and Acacia dominated bush lands) in lowlands sites. The species showed a rank abundance of 1 for Hypotrigona gribodoi, (2) for Melipona ferruginea, (3) for Plebeina hildebrandti and (4) for Hypotrigona ruspolii respectively.

Species number recorded in all sampled sites indicates the composition of meliponine bee species within this hotspot, though this is comparatively less than the species recorded in Kakamega, it unmistakably signifies the effects of habitat fragmentation in determining species composition within an ecosystem5. The majority of H. gribodoi species was more dominant and featured in all habitat types but at variable proportions, which may be attributed to its plasticity in nesting in varying habitat types.

The four species recorded directly from sampling are close to the J Evenness extrapolated predicted value of 4.24. The species accumulation curve indicated approximately 80 sampling points as adequate to recover at least four species.