“Trait-based modeling of multi-host pathogen transmission: Plant-pollinator networks”

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Lauren L. Truitt, Scott H. McArt, Andrew H. Vaughn, and Stephen P. Ellner (June 2019)

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A new modeling approach for disease spread in plant-pollinator networks suggests new ideas for controlling bee diseases

Which interactions matter most for disease spread in a plant-pollinator network?

Honey bee (Apis mellifera) and wild bee (Halictus sp.) on a cone flower (Echinacea sp.). Pollinators interact indirectly in bee-flower visitation networks, including by sharing pathogens. In species-rich communities, traits can be used to predict superspreaders and disease hotspots in networks.
(Credit: Emma Mullen)

A bee visiting a flower to gather nectar might pick up an unwelcome extra: a virus (or another infectious disease organism) deposited there by a previous visitor. Because infectious disease is one of the factors implicated in recent declines of bees and other natural pollinators, there is increasing interest in finding practical ways to reduce the spread of these diseases. Mathematical models have often been helpful for identifying “disease hotspots” and designing effective disease control strategies in humans and wildlife. Disease spread in species-rich plant-pollinator interaction networks poses the challenge that a model describing each of the many distinct interactions (among multiple pollinator and flower species) would require an enormous number of parameters and an enormous amount of data to estimate those parameters.

In a new paper in The American Naturalist, Truitt et al. propose an alternative trait-based approach to modeling disease spread in complex plant-pollinator networks. As one example, many such networks are “nested”: non-choosy pollinators visit all flowers, and more choosy ones visit smaller and smaller subsets of those visited by the less choosy. Truitt et al. model a nested network in terms of two traits: pollinator “choosiness” and flower “attractiveness”, where choosiness determines how much a pollinator concentrates on the more attractive flowers. Given the pollinator and flower trait distributions, one parameter (maximum choosiness) specifies the relative risk of disease spread in different interactions. Mathematical and computational studies of this model (mostly by Lauren Truitt and co-author Andrew Vaughn), and another where bees preferentially visit flowers of a size matching their own, demonstrate that the key interactions are those involving flowers visited out of proportion to their abundance, and pollinators which preferentially visit those flowers. This finding suggests strategies, based on feasible data gathering, to reduce disease spread by changing the abundances of different plant species. Such strategies could be implemented in wildflower plantings whose goal is to improve pollinator health.

The paper is an extension of Truitt’s undergraduate honors thesis at Cornell (directed by co-authors McArt and Ellner), where she co-majored in biology and mathematics. Vaughn is currently a Cornell senior majoring in mathematics, and Truitt is interning at NIH.


Epidemiological models for multi-host pathogen systems often classify individuals taxonomically and use species-specific parameter values, but in species-rich communities, that approach may require intractably many parameters. Trait-based epidemiological models offer a potential solution, but have not accounted for within-species trait variation or between-species trait overlap. Here, we propose and study trait-based models with host and vector communities represented as trait distributions without regard to species identity. To illustrate this approach, we develop SIS models for disease spread in plant-pollinator networks with continuous trait distributions. We model trait-dependent contact rates in two common scenarios: nested networks, and specialized plant-pollinator interactions based on trait matching. We find that disease spread in plant-pollinator networks is impacted the most by selective pollinators, universally attractive flowers, and co-specialized plant-pollinator pairs. When extreme pollinator traits are rare, pollinators with common traits are most important for disease spread, whereas when extreme flower traits are rare, flowers with uncommon traits impact disease spread the most. Greater nestedness and specialization both typically promote disease persistence. Given recent pollinator declines caused in part by pathogens, we discuss how trait-based models could inform conservation strategies for wild and managed pollinators. Furthermore, while we have applied our model to pollinators and pathogens, its framework is general and can be transferred to any kind of species interactions, in any community.