Material and methods

6.2.1 Study area

Kougouleu (0° 24’ 15” N, 9° 53’ 16” E) and the logging concession of Precious Woods Gabon were selected to conduct the study (Figure 1). During numerous decades, forest habitat at Kougouleu has been subject to anthropogenic disturbance related to logging and deforestation of slash and burn farming. Then, food crops, orchards and pioneering trees of young forest constitute the vegetation at Kougouleu. If the population density at Kougouleu was 1.7 inhabitants per km² in 2013, the different crops found in this area were established by the resident populations at Kougouleu but also by those coming from the capital Libreville, where the population density was 3,724.6 inhabitants per km² in 2013 (Direction Générale de la Statistique, 2015). The rainforest constitutes the main vegetation in the logging concession of Precious Woods Gabon, and the population density was 2 inhabitants per km² in 2013 (Direction Générale de la Statistique, 2015). Selective logging is practiced in this concession which is FSC certified (Forest Stewardship Council). The altitude is lower than 150m at Kougouleu, and varies between 250 and 400 m in the logging concession of Precious Woods Gabon.

6.2.2 Nests count and density

Thirty surface transects of 20 x 500 m each were randomly disposed at Kougoulou and at the south of Likokodiba Village (0° 46’ 13” S, 13° 11’ 14” E) in the logging concession of Precious

102 Woods. Highly eusocial bee nests were searched by visual inspection in each transects during the period of flowering and fruit production of many crops and forest plants (Ambougou Atissso, 1991), more precisely from October 2013 to March 2014. The latitude and longitude of each nest found were recorded using a Global Positioning System (Garmin GPSMAP 62).

Nest density per hectare was calculated from nests count per area covers by each transect and all transects according to the study area. Nests density was estimated for all species and for each recorded species. In parallel, trapping and active capture were carried out in 20 sites randomly positioned in each location to compare species richness caught and species richness of nests count.

Figure 1. Study locations in Gabon. (a) Kougouleu whose forest habitat has been disturbed during the last thirty years by food crops. (b) Logging concession of Precious Woods Gabon.

6.2.3 Environmental and anthropogenic data

To characterize nest habitats, three variables were used: distance from nests to the nearest watercourse, altitude and tree canopy cover. The distance from nests to the nearest road and village were used to characterize anthropogenic impact on nesting. For that, three layers (hydrology, road and village) and Shuttle Radar Topographic Mission (SRTM) data version 4.1 were obtained from World Resources Institute (WRI, 2013), map service of Precious Woods

103 Gabon and United States Geological Survey (available on-line from https://earthexplorer.usgs.gov/). Tree canopy cover raster for year 2000 and Forest cover loss raster 2000-2014 were also obtained from Hansen et al. (2013) (available on-line from https://earthenginepartners.appspot.com/science-2013-global-forest/download_v1.2.html).

Tree canopy cover represents the canopy closure for all vegetation taller than 5 m height, the latter is encoded as a percentage per output grid cells (Hansen et al., 2013). While, Hansen et al. (2013) defined forest cover loss as "a stand-replacement disturbance or the complete removal of tree cover canopy", and the cell grids of forest cover losses are encoded as either 1 (loss) or 0 (no loss).

The raster maps of the Euclidean distance were implemented with ARCMAP 10.3 from hydrology, road and village layers for location at Kougouleu and at Likokodiba area. Forest covers loss 2000-2014 data was re-encoded as 0 (loss) and 1 (no loss) and merged with Tree canopy cover data for the year 2000 using Times function of ARCMAP 10.3 to obtain a tree canopy cover data for the year 2014. All Transepts were subdivided into 25 plots 20 x 25 m and a point was positioned at the center of each plot. The distances from nests to the nearest watercourse, road and village were extracted from the respective raster maps of Euclidean distance. As well as, altitude and tree canopy cover of nests were extracted from the respective raster maps of SRTM and tree canopy cover for the year 2014. In the absence of a nest in a plot, the center point of the plot was used to extract the previous data (Figure 2). Thus, the nests were considered presence data and the points at the center of the plots as pseudo-absence data.

Figure 2. Extraction of spatial data from distances, forest cover and altitude.

104 6.2.4 Nests count and detection probability

Three hundred points were randomly disposed at Kougouleu and in the logging concession of Precious Woods Gabon, more precisely south of Baposso village. Honey bee and stingless bee nests were searched by visual inspection for bees in flight and nest entrance on the tree near each point position. Nests observed were counted for each tree. After, the tree was cut down and a new thorough visual inspection was performed to search nest bees that were not discovered during the first visual inspection. Nest bees discovered during the second inspection were counted for each tree. The falling of the trees was carried out according to the conditions of management and exploitation in force in the forest concession of Precious Woods Gabon.

Diameter at breast height (dbh) of each tree was also measured (Larpkern et al., 2011). Then, the presence of nests was estimated from the total number of nests counted and the numbers of trees sampled, and the proportion of detection by the number of nests counted during the first inspection versus the total number of nests counted.

6.2.5 Bee identification

In the case of honey bees, colonies were identified from the bees at the entrance of the nest. In the case of stingless bees, samples were taken mainly after the tree was felled and stored in alcohol at 70°C for identification. This identification was carried out from descriptions by Eardley (2004), Eardley et al. (2010), and Pauly and Fabre Anguilet (2013) and from the comparison with the entomological collection of Gembloux Agro-Bio-Tech, University of Liege.

Data analysis

All the statistical tests were carried out using R 3.1.0 software (R Core Team, 2016). Kruskal- Wallis test was performed to compare the nest density per transect according to study areas.

The presence of nests was analyzed in terms of presence / absence (1/0), with the data of nests as presence and the data in the center of the plots of transects as absence. Given the low number of nest observations, the effect of environmental and anthropogenic factors in the presence of nests was modeled using generalized linear mixed effect models (GLMMs), with binomial family and zero-inflation as a single constant. This model was implemented using glmmADMB function from glmmADMB package (Bolke et al., 2012). Study area, environmental and anthropogenic data were used as fixed effect while transect was used as random effect. Then, the effect of different factors in the presence of the nest was tested for all species and for the

105 species present in the two study areas according to the formula: present ~ location + distance to the watercourse + tree cover canopy + distance to the road + distance to the village +(1/transect).

The nest presence data in tree sampling were analyzed using two approaches. In a first step, the dbh data were classified according to several intervals of diameter: less 20 cm, 20 to 39 cm, 40 to 59 cm and more 60 cm. Then, the presence of the nest was analyzed using generalized linear model (GLM) from RVAideMemoire package (Hervé 2017). This modeling was done according to the formula: presence ~ study area + diameter class, and binomial family. An analysis of deviance was performed with Anova function in the car package (Fox and Weisberg, 2011). This analysis was carried out from the presence data of the species present in the two study areas. In the second step, the probability of detection of a species was determined as the percentage of the nest observed before felling by the number of nests registered after felling.