Trait-Based Modeling of Multihost Pathogen Transmission: Plant-Pollinator Networks

Abstract

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.

Dominant bee species and floral abundance drive parasite temporal dynamics in plant-pollinator communities

“…This increase in transmission hub abundance may have led to dilution via density, where an increase in the ratio of parasite-free to contaminated hubs resulted from increases in floral abundance compared to bee abundance over time. Theory indicates that increasing flower numbers will reduce parasite transmission in plant-pollinator networks 42 . Whilst our data cannot directly support this finding, we did see that increasing flower numbers reduced the prevalence of parasites on flowers.…”

Flowering plant composition shapes pathogen infection intensity and reproduction in bumble bee colonies

“…Variation in floral traits within and among plant species can change the likelihood of vectoring or transmitting pathogens or parasitic mites (14,15,20,21), and such variation can have consequences for disease transmission dynamics (22). In particular, a recent study found fourfold variation across 14 plant species in transmission of the gut pathogen Crithidia bombi to foraging bumble bees (Bombus impatiens) (20), and defecation on flowers by infected bees varied with plant species (23).…”

Pathways for Novel Epidemiology: Plant–Pollinator–Pathogen Networks and Global Change

“…Floral architecture, by constraining access to pollen and nectar to pollinator species with the requisite morphological adaptations, filters insect visitor interactions and contacts [5]. Plants with structurally simple, large floral displays (e.g., Apiaceae, Rosaceae) attract numerous pollinator species and may serve as hubs facilitating interspecific pathogen transmission [22]. Additionally, plants with a more central position in a plant-pollinator network often contain a high pathogen load [23].…”

“…Highly connected networks with a large diversity or number of interactions may elevate interspecific contact and potential pathogen transmission [46], or decrease pathogen prevalence due to a dilution effect [13], the latter of which may explain lower disease prevalence in diverse pollinator communities [47]. Highly nested networks may result in increased pathogen persistence [22], with generalist species serving as hubs of pathogen spread to specialist species. Increased modularity should buffer against risk of flower-mediated pathogen spillover and spread across entire communities, by compartmentalising pathogens and disease outbreaks within modules [13,48].…”