Transmission networks in trait-based communities: Implications for disease in bees
The complexity of ecological communities creates challenges to understanding multi-host parasite transmission. Pronounced heterogeneity in transmission among individuals, species and across space is the rule rather than the exception. Community ecologists are beginning to make great strides in predicting multi-species interactions using a trait-based rather than taxonomic approach, identifying key functional attributes of organisms and environments that are important to understanding the system. At the same time, disease ecologists generally use network modeling to understand parasite transmission in complex communities. Yet the merging of a trait-based approach with network modeling to understand multi-host transmission across space and time is in its infancy. For this project, we are taking advantage of a highly tractable system – diverse communities of bees that transmit parasites via networks of flowering plants – to merge trait-based theory with network modeling, introducing a novel theoretical framework for multi-host parasite transmission in complex communities.
Unsustainable losses of honey bees, and range contractions of wild bees, both of which are related to pathogen pressure.
This project will be starting in earnest in summer 2017 with the arrival of a postdoc in my lab, Peter Graystock. However, my current PhD student is making great strides on this topic for her dissertation. Laura has collected empirical contact pattern and trait data from 11 plant-pollinator networks, screening 865 bees and 153 flowers for 4 common bee pathogens using PCR. She is currently identifying aspects of network structure that are associated with disease prevalence, and is beginning to use machine learning techniques to construct and parameterize trait-based models of disease transmission in order to make falsifiable predictions for further testing. Other collaborators on the project (Pete Graystock, Lynn Adler, Bekcy Irwin) will then test model predictions via whole-community manipulations of bees, parasites and plants in mesocosms.
Preliminary results from this study have been shared with NYSDAM, NYSDEC and the NYS Governor’s office, who were especially interested in the pathogen data on flowers, which they may consider when designing bee-friendly wildflower mixtures in restoration efforts.
- McArt, Scott
- McArt, Scott
- United States of America
United States focus:
- New York