Background/Question/Methods Numerous studies have shown that plant-pollinator interactions vary across geographic and temporal gradients. However, the drivers underlying this flexibility in interaction patterns remain poorly understood. Theory suggests that rewiring, or variation in interaction patterns between species present at multiple sites, is influenced by the biotic and abiotic contexts in which they occur, but few studies have empirically linked plant-pollinator rewiring patterns to specific biotic or abiotic drivers. In this study, we test for effects of specific drivers of rewiring in a subalpine plant-pollinator system comprising 17 sites along a geographic and elevational gradient. We expect that variables including air temperature, soil moisture, community composition, and trait correlation will provide stronger descriptions of rewiring patterns in this system than geographic distance or elevation change. Such patterns would provide evidence of specific ecological processes underlying interaction variation and lay the groundwork for future studies exploring the mechanisms of interaction turnover. Results/Conclusions While data collection and analysis are ongoing for this project, preliminary results confirm that typical geographic metrics of site dissimilarity, such as distance and elevation, poorly predict plant-pollinator interaction rewiring in this system. However, fine-grained ecological data like plant beta diversity shows a statistical relationship with rewiring between sites, suggesting that other fine-grained variables may reveal important relationships between plant-pollinator interaction patterns and the biotic and abiotic contexts in which they occur. We continue to work on analyses disentangling the biotic and abiotic drivers of rewiring in this system, and will present preliminary findings elaborating on this effort here. By linking interaction occurrence to specific environmental and biological contexts, this work will offer new insight into the ecological processes responsible for variation in plant-pollinator interaction patterns. It will also provide valuable information that can be used to predict how ecological communities may respond to global environmental change.
