# The Effect of Land Cover on the Nectar Collection by Honeybee Colonies in Urban and Rural Areas
## 3 Results
### Multivariate Relationships Among Landscape Variables Based on PCA  
To explore multivariate relationships among landscape features in greater detail, a principal component analysis (PCA) was performed. The first two principal components explained a total of 59.97% of the variance in land cover data, with 32.14% and 27.83% accounted for by PC1 and PC2, respectively. Figure 5A,B show the distribution of individual locations in the PCA space along with variable vectors (loadings). The first principal component (PC1) is predominantly associated with the agricultural land-use gradient. The highest positive loadings were observed for agricultural areas (0.47) and grasslands (0.346), while strong negative loadings were recorded for coniferous forests (\(-\)0.392) and anthropogenic areas (\(-\)0.383). This finding indicates that PC1 can be used as a differentiating factor between agriculturally transformed landscapes and more natural, moist, and shrub-covered fallow areas. In summary, highly negative PC1 values are indicative of wild, wetland-like environments, while highly positive PC1 values reflect areas extensively used for agriculture. The second principal component (PC2) is related to the urbanisation gradient. The strongest positive influence on PC2 is exhibited by anthropogenic areas (0.495), while broad-leaved forests (\(-\)0.492) and coniferous forests (\(-\)0.313) demonstrate the most significant negative contributions. Therefore, it can be interpreted that PC2 contrasts urbanised areas with forested environments, particularly broad-leaved forests. The plots presented in Figure S3A-H demonstrate the relationship between gross hive weight gain (Gross Gain) and key environmental variables derived from the PCA related to different types of land cover. The employment of non-linear regression curves (third-degree polynomial) in conjunction with 95% confidence intervals indicates the presence of significant, frequently non-linear relationships between Gross Gain and various land cover types. The presence of both increasing and decreasing trends, in addition to inflection points, indicates a multifaceted relationship between the proportion of specific land cover types and the productivity of bee foraging habitats. Groupings of apiaries based on landscape variables (PC1 and PC2) were also performed using the k-means algorithm, three clusters (Figure 6), as well as three clusters using the DBSCAN method. The analysis revealed no significant associations between groupings and variations in hive weight gain. For instance, in the three-cluster k-means solution, the average annual nectar yields were 67.2 kg, 78.7 kg, and 77.9 kg, with no statistically significant differences observed (Kruskal-Wallis test: H (2, N = 10) = 0.272, \(p\) = 0.87). A similar outcome was observed when the data were divided into two clusters, with average yields of 78.6 kg and 67.2 kg, respectively, also showing no significant difference (Mann-Whitney U test: U = 10.0, \(p\) = 0.67). In summary, no distinct groups of apiaries with consistently high or low honey production could be identified based on the landscape composition. The distribution of apiaries, both urban and rural, across the PCA space indicates the potential for attaining high nectar yields in a range of landscape configurations. Figure 5: Principal component analysis (PCA) of land cover structure surrounding beekeeping sites. (**A**) Coordinates of urban and rural apiaries in the PCA space. The plot displays the distribution of beekeeping sites based on the first two principal components (PC1 and PC2), which together explain a substantial portion of the variance (PC1: 32.16%, PC2: 27.85%). Each point represents an individual site, grouped by location and type (urban or rural). (**B**) Environmental variable vectors in the PC1–PC2 plane. Arrows indicate the direction and strength (loadings) of each land cover variable’s contribution to the principal components.