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. We will take 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. We will collect empirical contact pattern and trait data from plant-pollinator networks to identify aspects of network structure that contribute to disease spread. Through the collection of extensive data on bee traits, floral traits and parasite spread, we will use machine learning techniques to construct and parameterize trait-based models of disease transmission in order to make falsifiable predictions for further testing. We will then test model predictions via whole-community manipulations of bees, parasites and plants in mesocosms. Such whole-community manipulations will offer unparalleled insight into the specific network patterns and traits that shape transmission in multi-host communities.

Public Health Relevance

Pollinators serve a critical role in our native ecosystems as well as agricultural crops, providing billions of dollars in pollination services annually. Recently, parasites have been linked to declines of several pollinator species. Thus, a better understanding of parasite transmission among bees has important conservation and economic implications.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM122062-02
Application #
9355693
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Janes, Daniel E
Project Start
2016-09-21
Project End
2020-07-31
Budget Start
2017-08-01
Budget End
2018-07-31
Support Year
2
Fiscal Year
2017
Total Cost
Indirect Cost
Name
Cornell University
Department
Zoology
Type
Earth Sciences/Resources
DUNS #
872612445
City
Ithaca
State
NY
Country
United States
Zip Code
14850
Adler, Lynn S; Michaud, Kristen M; Ellner, Stephen P et al. (2018) Disease where you dine: plant species and floral traits associated with pathogen transmission in bumble bees. Ecology 99:2535-2545