Bees are important pollinators globally that have experienced population declines linked to pathogens. However, we have a limited understanding of how pathogens spread between bees through shared use of flowers and how this impacts entire bee communities. Here I developed a mechanistic understanding of bee pathogen transmission on flowers and assessed how these patterns scaled to the community level. In Chapter 1, using bumble bees as a model system, I experimentally evaluated bee pathogen deposition, persistence, and acquisition on flowers, finding differences among flower species, flower parts, and environmental conditions. I found that bumble bees frequently defecated on plants while foraging and did so more when infected with a gut pathogen. In Chapter 2 I combined empirical data with mathematical modeling to evaluate how pathogen prevalence in plant-pollinator networks was affected by landscape simplification. I found that landscape simplification indirectly shaped pathogen prevalence via the diet breadth of the dominant bee species. Moreover, I found that network connectance was a key parameter driving disease spread and prevalence at the community level. In Chapter 3 I evaluated whether bee functional traits explained the variance in prevalence found in the bee communities. I found that functional traits, namely nesting location and intraspecific body size for the dominant species, were linked to prevalence of certain pathogen groups but not others. Lastly, in Chapter 4 I experimentally assessed whether two solitary bee species could become infected with a gut pathogen known to infect bumble bees, and evaluated whether diet played a role in infection dynamics and host survival. Both solitary bee species presented evidence of pathogen replication, regardless of whether they had access to pollen. These results expand the current understanding of bee epidemiology beyond honey bees and bumble bees, highlighting the need to evaluate the host range and impact of pathogens on wild bee communities. Overall, these findings expand our understanding of bee pathogen transmission dynamics. Furthermore, insights from this work can help inform the development of wildflower mixes that maximize forage while minimizing disease spread in pollinator communities.