Data

Table 1. The first 5 rows and 5 columns of data table showing bee community data after being normalized for the number of days each trap was set for.

Community and environmental data was consolidated into a table with the plot number (trap locations) as rows and bee species and environmental variables as columns (tables 1 & 2). The environmental variables included percent land cover for each spatial scale and climate variables.

Table 2. The first 8 rows and 13 columns of the data table showing environental variables after scaling.

There were nearly 16,000 individual specimens captured and identified, spanning 219 unique species. The most common species was Bombus mixtus, it was captured at 60 of the 76 traps for a total count of 2,458 individuals.

Using the Bray-curtis distance method on the community data was found to create significant outliers likely due to the high number of zeros within the dataset. To handle this, euclidean distance was used instead. Additionally, plot number 36 was found to be a significant outlier and was therefore removed from all analyses. To simplify the statistical models only the species that made up the top 90% of bee occurances were used. This included the 22 most common species, although this means rare species were disregarded from the analysis, the results varied very little when only 22 species were used versus all of them, therefore, I only used 22 species to keep the models cleaner.

Principal Component Analysis of Environmental Drivers with Shannon Diversity Index

Figure 6. Principal component analysis biplot showing the environmental variables at the 1000m scale as vectors. Each trap location represents a dot and is coloured according to the shannon diveristy index calculated at that trap.

In order to get a better idea of how each landcover type and climate variable was impacting the community I performed a PCA (figure 6). The long vectors of climate variables highlights how important climate is in shaping the community. This analysis also helps show that there are no variables very highly correlated. The three separate clusters of sites coloured by their shannon diversity index show the three main climate clusters of Alberta. The cluster on the top right which includes sites with some of the highest shannon diversity indices would represent areas with high precipitation likely around the Rocky Mountains.

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