We used a thermo-, hygro- and luxmeter (Mavalux Digital, Gossen) at a height of 2 m in the centre of the plot. Temperature and humidity were measured in the shadow and light intensity

in an area receiving full sun. Furthermore we measured the slope of each plot with a clinometer (Suunto PM-5/360 PC) at four distances within each plot MK-8776 and afterwards calculated the average. Statistical analysis In a Spearman’s rank correlation matrix, temperature, humidity and light intensity were collinear (temperature and humidity: N = 86, R = −0.86, *** P < 0.001; temperature and light intensity: N = 67, R = 0.45, *** P < 0.001; humidity and light intensity: N = 66, R = −0.47, *** P < 0.001).

We therefore used a PCA to reduce the total number of variables and extract one main MEK162 ic50 factor (from now on: “climate”), explaining 75% of the total variance to be used as a continuous predictor in the following analysis. We conducted two general linear models (GLM) to identify the factors that structure the pollinator community. The models included number of bee species and number of bee individuals as response variables (log transformed), habitat type and phase as categorical predictors and climate and number and density of flowering plant species as continuous variables. Due to collinearity of density and species richness of flowering plants, we alternated the order of both continuous predictors. Because samples from the same plot in different seasons (phases) were non-independent, plot and phase were included as random effects and plot was nested in habitat type. Post-hoc tests for differences between ioxilan habitat types used Tukey’s unequal N HSD (Honestly Significant

Difference) test. Values per plot and sampling phase of response and predictor variables were used for the statistical analyses. To test whether plant density depends on Tariquidar research buy canopy cover or other plot variables, we conducted a general linear model with plant density as response variable and canopy cover, slope and plot altitude as continuous predictors. We estimated species richness using Michaelis–Menten means (Colwell and Coddington 1994) for each habitat type independent of sample size and calculated the percentage of recorded species from the estimated number of species. We randomly reduced the number of samples for the agroforestry systems to three because we had only three replicates in primary forest and openland. We used the additive partitioning method to test for the contribution of spatial variation in species richness per habitat type (beta-spatial) and temporal variation in species richness per habitat type (beta-temporal) to regional gamma-diversity (Lande 1996; Crist and Veech 2006; Gabriel et al. 2006) such that beta-diversity equals gamma-diversity minus alpha-diversity.