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

A Time for Every Purpose: Using Time-Dependent Sensitivity Analysis to Help Understand and Manage Dynamic Ecological Systems

AbstractSensitivity analysis is often used to help understand and manage ecological systems by assessing how a constant change in vital rates or other model parameters might affect the management outcome. This allows the manager to identify the most favorable course of action. However, realistic changes are often localized in time-for example, a short period of culling leads to a temporary increase in the mortality rate over the period. Hence, knowing when to act may be just as important as knowing what to act on. In this article, we introduce the method of time-dependent sensitivity analysis (TDSA) that simultaneously addresses both questions. We illustrate TDSA using three case studies: transient dynamics in static disease transmission networks, disease dynamics in a reservoir species with seasonal life history events, and endogenously driven population cycles in herbivorous invertebrate forest pests. We demonstrate how TDSA often provides useful biological insights, which are understandable on hindsight but would not have been easily discovered without the help of TDSA. However, as a caution, we also show how TDSA can produce results that mainly reflect uncertain modeling choices and are therefore potentially misleading. We provide guidelines to help users maximize the utility of TDSA while avoiding pitfalls.

A time for every purpose: using time-dependent sensitivity analysis to help understand and manage dynamic ecological systems

Sensitivity analysis is often used to help understand and manage ecological systems, by assessing how a constant change in vital rates or other model parameters might affect the management outcome. This allows the manager to identify the most favorable course of action. However, realistic changes are often localized in time-for example, a short period of culling leads to a temporary increase in the mortality rate over the period. Hence, knowing when to act may be just as important as knowing what to act upon. In this article, we introduce the method of time-dependent sensitivity analysis (TDSA) that simultaneously addresses both questions. We illustrate TDSA using three case studies: transient dynamics in static disease transmission networks, disease dynamics in a reservoir species with seasonal life-history events, and endogenously-driven population cycles in herbivorous invertebrate forest pests. We demonstrate how TDSA often provides useful biological insights, which are understandable on hindsight but would not have been easily discovered without the help of TDSA. However, as a caution, we also show how TDSA can produce results that mainly reflect uncertain modeling choices and are therefore potentially misleading. We provide guidelines to help users maximize the utility of TDSA while avoiding pitfalls.

Sunflower plantings reduce a common gut pathogen and increase queen production in common eastern bumblebee colonies

Community diversity can reduce the prevalence and spread of disease, but certain species may play a disproportionate role in diluting or amplifying pathogens. Flowers act as both sources of nutrition and sites of pathogen transmission, but the effects of specific plant species in shaping bee disease dynamics are not well understood. We evaluated whether plantings of sunflower (Helianthus annuus), whose pollen reduces infection by some pathogens when fed to bees in captivity, lowered pathogen levels and increased reproduction in free-foraging bumblebee colonies (Bombus impatiens). Sunflower abundance reduced the prevalence of a common gut pathogen, Crithidia bombi, and reduced infection intensity, with an order of magnitude lower infection intensity at high sunflower sites compared with sites with little to no sunflower. Sunflower abundance was also positively associated with greater queen production in colonies. Sunflower did not affect prevalence of other detected pathogens. This work demonstrates that a single plant species can drive disease dynamics in foraging B. impatiens, and that sunflower plantings can be used as a tool for mitigating a prevalent pathogen while also increasing reproduction of an agriculturally important bee species.

Do pesticide and pathogen interactions drive wild bee declines?

There is clear evidence for wild insect declines globally. Habitat loss, climate change, pests, pathogens and environmental pollution have all been shown to cause detrimental effects on insects. However, interactive effects between these stressors may be the key to understanding reported declines. Here, we review the literature on pesticide and pathogen interactions for wild bees, identify knowledge gaps, and suggest avenues for future research fostering mitigation of the observed declines. The limited studies available suggest that effects of pesticides most likely override effects of pathogens. Bees feeding on flowers and building sheltered nests, are likely less adapted to toxins compared to other insects, which potential susceptibility is enhanced by the reduced number of genes encoding detoxifying enzymes compared with other insect species. However, to date all 10 studies using a fully-crossed design have been conducted in the laboratory on social bees using Crithidia spp. or Nosema spp., identifying an urgent need to test solitary bees and other pathogens. Similarly, since laboratory studies do not necessarily reflect field conditions, semi-field and field studies are essential if we are to understand these interactions and their potential effects in the real-world. In conclusion, there is a clear need for empirical (semi-)field studies on a range of pesticides, pathogens, and insect species to better understand the pathways and mechanisms underlying their potential interactions, in particular their relevance for insect fitness and population dynamics. Such data are indispensable to drive forward robust modelling of interactive effects in different environmental settings and foster predictive science. This will enable pesticide and pathogen interactions to be put into the context of other stressors more broadly, evaluating their relative importance in driving the observed declines of wild bees and other insects. Ultimately, this will enable the development of more effective mitigation measures to protect bees and the ecosystem services they supply.

Floral shape predicts bee-parasite transmission potential

The spread of parasites is one of the primary drivers of population decline of both managed and wild bees. Several bee parasites are transmitted by the shared use of flowers, turning floral resources into potential disease hotspots. However, we know little about how floral morphology and floral species identity affect different steps of the transmission process. Here, we used the gut parasite Crithidia bombi and its primary host, bumble bees (Bombus spp.), to examine whether floral traits or species identity better predict three basic steps of parasite transmission on flowers: feces deposition on flowers, survival of the parasite on flowers, and acquisition by a new host. We also identified which traits and/or species were most strongly associated with each step in the transmission process. We found that both trait‐ and species‐based models fit the data on deposition of feces and survival of C. bombi on flowers, but that species‐based models provided a better fit compared with trait‐based ones. However, trait‐based models were better at predicting the acquisition of C. bombi on flowers. Although different species tended to support higher fecal deposition or parasite survival, we found that floral shape provided explanatory power for each of the transmission steps. When we assessed overall transmission potential, floral shape had the largest explanatory effect, with wider, shorter flowers promoting higher transmission. Taken together, our results highlight the importance of flower species identity and floral traits in disease transmission dynamics of bee parasites, and floral shape as an important predictor of overall transmission potential. Identifying traits associated with transmission potential may help us create seed mix that presents lower parasite transmission risk for bees for use in pollinator habitat.

Honey bees and climate explain viral prevalence in wild bee communities on a continental scale

Viruses are omnipresent, yet the knowledge on drivers of viral prevalence in wild host populations is often limited. Biotic factors, such as sympatric managed host species, as well as abiotic factors, such as climatic variables, are likely to impact viral prevalence. Managed and wild bees, which harbor several multi-host viruses with a mostly fecal-oral between-species transmission route, provide an excellent system with which to test for the impact of biotic and abiotic factors on viral prevalence in wild host populations. Here we show on a continental scale that the prevalence of three broad host viruses: the AKI-complex (Acute bee paralysis virus, Kashmir bee virus and Israeli acute paralysis virus), Deformed wing virus, and Slow bee paralysis virus in wild bee populations (bumble bees and solitary bees) is positively related to viral prevalence of sympatric honey bees as well as being impacted by climatic variables. The former highlights the need for good beekeeping practices, including Varroa destructor management to reduce honey bee viral infection and hive placement. Furthermore, we found that viral prevalence in wild bees is at its lowest at the extreme ends of both temperature and precipitation ranges. Under predicted climate change, the frequency of extremes in precipitation and temperature will continue to increase and may hence impact viral prevalence in wild bee communities.

Mass-flowering monoculture attracts bees, amplifying parasite prevalence

As the global agricultural footprint expands, it is increasingly important to address the link between the resource pulses characteristic of monoculture farming and wildlife epidemiology. To understand how mass-flowering crops impact host communities and subsequently amplify or dilute parasitism, we surveyed wild and managed bees in a monoculture landscape with varying degrees of floral diversification. We screened 1509 bees from 16 genera in sunflower fields and in non-crop flowering habitat across 200 km² of the California Central Valley. We found that mass-flowering crops increase bee abundance. Wild bee abundance was subsequently associated with higher parasite presence, but only in sites with a low abundance of non-crop flowers. Bee traits related to higher dispersal ability (body size) and diet breadth (pollen lecty) were also positively related to parasite presence. Our results highlight the importance of non-crop flowering habitat for supporting bee communities. We suggest monoculture alone cannot support healthy bees.

Temporal metabolite responsiveness of microbiota in the tea plant phyllosphere promotes continuous suppression of fungal pathogens

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Mechanistic insights into host-metabolite-driven microbiota assembly were obtained.

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Tea plants can maintain a functional microbiota during shoot development.

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The main drivers of microbial community assembly were identified.

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Metabolite-responsive microbiota suppresses various tree pathogens in vitro and in vivo.

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Establishment of tea plantations in the proximity of forests was linked to reduced disease incidence.

Introduction

A broad spectrum of rhizosphere bacteria and fungi were shown to play a central role for health, fitness and productivity of their host plants. However, implications of host metabolism on microbiota assembly in the phyllosphere and potential consequences for holobiont functioning were sparsely addressed. Previous observations indicated that tea plants might reduce disease occurrence in various forests located in their proximity; the underlying mechanisms and potential implications of the phyllosphere microbiota remained elusive.

Objectives

This study aimed at deciphering microbiome assembly in the tea plant phyllosphere throughout shoot development as well as elucidating potential implications of host metabolites in this process. The main focus was to explore hidden interconnections between the homeostasis of the phyllosphere microbiome and resistance to fungal pathogens.

Methods

Profiling of host metabolites and microbiome analyses based on high-throughput sequencing were integrated to identify drivers of microbiome assembly throughout shoot development in the phyllosphere of tea plants. This was complemented by tracking of beneficial microorganisms in all compartments of the plant. Synthetic assemblages (SynAss), bioassays and field surveys were implemented to verify functioning of the phyllosphere microbiota.

Results

Theophylline and epigallocatechin gallate, two prevalent metabolites at the early and late shoot development stage respectively, were identified as the main drivers of microbial community assembly. Flavobacterium and Myriangium were distinct microbial responders at the early stage, while Parabacteroides and Mortierella were more enriched at the late stage. Reconstructed, stage-specific SynAss suppressed various tree phytopathogens by 13.0%-69.3% in vitro and reduced disease incidence by 8.24%-41.3% in vivo.

Conclusion

The findings indicate that a functional phyllosphere microbiota was assembled along with development-specific metabolites in tea plants, which continuously suppressed prevalent fungal pathogens. The insights gained into the temporally resolved metabolite response of the tea plant microbiota could provide novel solutions for disease management.

Eristalis flower flies can be mechanical vectors of the common trypanosome bee parasite, Crithidia bombi

Flowers can be transmission platforms for parasites that impact bee health, yet bees share floral resources with other pollinator taxa, such as flies, that may be hosts or non-host vectors (i.e., mechanical vectors) of parasites. Here, we assessed whether the fecal-orally transmitted gut parasite of bees, Crithidia bombi, can infect Eristalis tenax flower flies. We also investigated the potential for two confirmed solitary bee hosts of C. bombi, Osmia lignaria and Megachile rotundata, as well as two flower fly species, Eristalis arbustorum and E. tenax, to transmit the parasite at flowers. We found that C. bombi did not replicate (i.e., cause an active infection) in E. tenax flies. However, 93% of inoculated flies defecated live C. bombi in their first fecal event, and all contaminated fecal events contained C. bombi at concentrations sufficient to infect bumble bees. Flies and bees defecated inside the corolla (flower) more frequently than other plant locations, and flies defecated at volumes comparable to or greater than bees. Our results demonstrate that Eristalis flower flies are not hosts of C. bombi, but they may be mechanical vectors of this parasite at flowers. Thus, flower flies may amplify or dilute C. bombi in bee communities, though current theoretical work suggests that unless present in large populations, the effects of mechanical vectors will be smaller than hosts.

Big bees spread disease: body size mediates transmission of a bumble bee pathogen

Trait variation can have important consequences for the outcomes of species interactions. Even though some traits vary as much within species as across related species, models and empirical studies typically do not consider the role of intraspecific trait variation for processes such as disease transmission. For example, many pollinator species are in decline because of a variety of stressors including pathogens, but the role of intraspecific trait variation in mediating disease dynamics is rarely considered. For example, pollinator body size could affect pathogen transmission via differences in resistance, foraging behavior and physiology. We tested effects of body size on pollinator pathogen transmission using the common eastern bumble bee Bombus impatiens in field tents, introducing an infected "donor" microcolony of large or small workers with an uninfected average-sized "recipient" microcolony and allowing bees to forage for 9-16 d. Small donor bees had nearly 50% higher infection intensity (cells/0.02  μL) than large donor bees, but large donor bees were twice as likely to transmit Crithidia bombi to recipient bees. Both behavioral and physiological mechanisms may underlie this apparent paradox. Compared to small bees, large bees foraged more and produced more feces; simulations showed that foraging and defecation rates together had stronger effects on transmission than did donor infection intensity. Thus, effects of bee size on contact rates and pathogen supply may play significant roles in disease transmission, demonstrating the multifaceted impacts of traits on transmission dynamics.

Evaluating competition for forage plants between honey bees and wild bees in Denmark

A recurrent concern in nature conservation is the potential competition for forage plants between wild bees and managed honey bees. Specifically, that the highly sophisticated system of recruitment and large perennial colonies of honey bees quickly exhaust forage resources leading to the local extirpation of wild bees. However, different species of bees show different preferences for forage plants. We here summarize known forage plants for honey bees and wild bee species at national scale in Denmark. Our focus is on floral resources shared by honey bees and wild bees, with an emphasis on both threatened wild bee species and foraging specialist species. Across all 292 known bee species from Denmark, a total of 410 plant genera were recorded as forage plants. These included 294 plant genera visited by honey bees and 292 plant genera visited by different species of wild bees. Honey bees and wild bees share 176 plant genera in Denmark. Comparing the pairwise niche overlap for individual bee species, no significant relationship was found between their overlap and forage specialization or conservation status. Network analysis of the bee-plant interactions placed honey bees aside from most other bee species, specifically the module containing the honey bee had fewer links to any other modules, while the remaining modules were more highly inter-connected. Despite the lack of predictive relationship from the pairwise niche overlap, data for individual species could be summarized. Consequently, we have identified a set of operational parameters that, based on a high foraging overlap (>70%) and unfavorable conservation status (Vulnerable+Endangered+Critically Endangered), can guide both conservation actions and land management decisions in proximity to known or suspected populations of these species.

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

Multiple global change pressures, and their interplay, cause plant-pollinator extinctions and modify species assemblages and interactions. This may alter the risks of pathogen host shifts, intra- or interspecific pathogen spread, and emergence of novel population or community epidemics. Flowers are hubs for pathogen transmission. Consequently, the structure of plant-pollinator interaction networks may be pivotal in pathogen host shifts and modulating disease dynamics. Traits of plants, pollinators, and pathogens may also govern the interspecific spread of pathogens. Pathogen spillover-spillback between managed and wild pollinators risks driving the evolution of virulence and community epidemics. Understanding this interplay between host-pathogen dynamics and global change will be crucial to predicting impacts on pollinators and pollination underpinning ecosystems and human wellbeing.

Floral traits affecting the transmission of beneficial and pathogenic pollinator-associated microbes

Flowers provide resources for pollinators, and can also be transmission venues for beneficial or pathogenic pollinator-associated microbes. Floral traits could mediate transmission similarly for beneficial and pathogenic microbes, although some beneficial microbes can grow in flowers while pathogenic microbes may only survive until acquired by a new host. In spite of conceptual similarities, research on beneficial and pathogenic pollinator-associated microbes has progressed mostly independently. Recent advances demonstrate that floral traits are associated with transmission of beneficial and pathogenic microbes, with consequences for pollinator populations and communities. However, there is a near-absence of experimental manipulations of floral traits to determine causal effects on transmission, and a need to understand how floral, microbe and host traits interact to mediate transmission.

Understanding host utilization by mosquitoes: determinants, challenges and future directions

Mosquito host utilization is a key factor in the transmission of vector-borne pathogens given that it greatly influences host-vector contact rates. Blood-feeding patterns of mosquitoes are not random, as some mosquitoes feed on particular species and/or individuals more than expected by chance. Mosquitoes use a number of cues including visual, olfactory, acoustic, and thermal stimuli emitted by vertebrate hosts to locate and identify their blood meal sources. Thus, differences in the quality/intensity of the released cues may drive host selection by mosquitoes at both inter- and intra-specific levels. Such patterns of host selection by mosquitoes in space and time can be structured by factors related to mosquitoes (e.g. innate host preference, behavioural plasticity), to hosts (e.g. emission of host-seeking cues, host availability) or to both (e.g. pathogen infection). In this study, we review current evidence, from phenomena to mechanisms, of how these factors influence host utilization by mosquitoes. We also review the methodologies commonly used in this research field and identify the major challenges for future studies. To bridge the knowledge gaps, we propose improvements to strengthen traditional approaches and the use of a functional trait-based approach to infer mosquito host utilization in natural communities.

OneHealth implications of infectious diseases of wild and managed bees

The OneHealth approach aims to further our understanding of the drivers of human, animal and environmental health, and, ultimately, to improve them by combining approaches and knowledge from medicine, biology and fields beyond. Wild and managed bees are essential pollinators of crops and wild flowers. Their health thus directly impacts on human and environmental health. At the same time, these bee species represent highly amenable and relevant model organisms for a OneHealth approach that aims to study fundamental epidemiological questions. In this review, we focus on how infectious diseases of wild and managed bees can be used as a OneHealth model system, informing fundamental questions on ecological immunology and disease transmission, while addressing how this knowledge can be used to tackle the issues facing pollinator health.

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

Pollinator reductions can leave communities less diverse and potentially at increased risk of infectious diseases. Species-rich plant and bee communities have high species turnover, making the study of disease dynamics challenging. To address how temporal dynamics shape parasite prevalence in plant and bee communities, we screened >5,000 bees and flowers over an entire growing season for five common bee microparasites (Nosema ceranae, Nosema bombi, Crithidia bombi, Crithidia expoeki and neogregarines). Over 110 bee species and 89 flower species were screened, revealing that 42% of bee species (12.2% individual bees) and 70% of flower species (8.7% individual flowers) had at least one parasite in or on them, respectively. Some common flowers (for example, Lychnis flos-cuculi) harboured multiple parasite species whilst others (for example, Lythrum salicaria) had few. Significant temporal variation of parasite prevalence in bees was linked to bee diversity, bee and flower abundance and community composition. Specifically, we found that bee communities had the highest prevalence late in the season, when social bees (Bombus spp. and Apis mellifera) were dominant and bee diversity was lowest. Conversely, prevalence on flowers was lowest late in the season when floral abundance was highest. Thus turnover in the bee community impacted community-wide prevalence, and turnover in the plant community impacted when parasite transmission was likely to occur at flowers. These results imply that efforts to improve bee health will benefit from the promotion of high floral numbers to reduce transmission risk, maintaining bee diversity to dilute parasites and monitoring the abundance of dominant competent hosts.

Landscape simplification shapes pathogen prevalence in plant-pollinator networks

Species interaction networks, which play an important role in determining pathogen transmission and spread in ecological communities, can shift in response to agricultural landscape simplification. However, we know surprisingly little about how landscape simplification-driven changes in network structure impact epidemiological patterns. Here, we combine mathematical modelling and data from eleven bipartite plant-pollinator networks observed along a landscape simplification gradient to elucidate how changes in network structure shape disease dynamics. Our empirical data show that landscape simplification reduces pathogen prevalence in bee communities via increased diet breadth of the dominant species. Furthermore, our empirical data and theoretical model indicate that increased connectance reduces the likelihood of a disease outbreak and decreases variance in prevalence among bee species in the community, resulting in a dilution effect. Because infectious diseases are implicated in pollinator declines worldwide, a better understanding of how land use change impacts species interactions is therefore critical for conserving pollinator health.

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

Pathogens pose significant threats to pollinator health and food security. Pollinators can transmit diseases during foraging, but the consequences of plant species composition for infection is unknown. In agroecosystems, flowering strips or hedgerows are often used to augment pollinator habitat. We used canola as a focal crop in tents and manipulated flowering strip composition using plant species we had previously shown to result in higher or lower bee infection in short-term trials. We also manipulated initial colony infection to assess impacts on foraging behavior. Flowering strips using high-infection plant species nearly doubled bumble bee colony infection intensity compared to low-infection plant species, with intermediate infection in canola-only tents. Both infection treatment and flowering strips reduced visits to canola, but we saw no evidence that infection treatment shifted foraging preferences. Although high-infection flowering strips increased colony infection intensity, colony reproduction was improved with any flowering strips compared to canola alone. Effects of flowering strips on colony reproduction were explained by nectar availability, but effects of flowering strips on infection intensity were not. Thus, flowering strips benefited colony reproduction by adding floral resources, but certain plant species also come with a risk of increased pathogen infection intensity.

Integrating data mining and transmission theory in the ecology of infectious diseases

Our understanding of ecological processes is built on patterns inferred from data. Applying modern analytical tools such as machine learning to increasingly high dimensional data offers the potential to expand our perspectives on these processes, shedding new light on complex ecological phenomena such as pathogen transmission in wild populations. Here, we propose a novel approach that combines data mining with theoretical models of disease dynamics. Using rodents as an example, we incorporate statistical differences in the life history features of zoonotic reservoir hosts into pathogen transmission models, enabling us to bound the range of dynamical phenomena associated with hosts, based on their traits. We then test for associations between equilibrium prevalence, a key epidemiological metric and data on human outbreaks of rodent-borne zoonoses, identifying matches between empirical evidence and theoretical predictions of transmission dynamics. We show how this framework can be generalized to other systems through a rubric of disease models and parameters that can be derived from empirical data. By linking life history components directly to their effects on disease dynamics, our mining-modelling approach integrates machine learning and theoretical models to explore mechanisms in the macroecology of pathogen transmission and their consequences for spillover infection to humans.

A spatial model of honey bee colony collapse due to pesticide contamination of foraging bees

We develop a model of honey bee colony collapse based on contamination of forager bees in pesticide contaminated spatial environments. The model consists of differential and difference equations for the spatial distributions of the uncontaminated and contaminated forager bees. A key feature of the model is incorporation of the return to the hive each day of forager bees. The model quantifies colony collapse in terms of two significant properties of honey bee colonies: (1) the fraction of contaminated forager bees that fail to return home due to pesticide contamination, and (2) the fraction of forager bees in the total forager bee population that return to the sites visited on the previous day. If the fraction of contaminated foragers failing to return home is high, then the total population falls below a critical threshold and colony collapse ensues. If the fraction of all foragers that return to previous foraging sites is high, then foragers who visit contaminated sites multiple times have a higher probability of becoming contaminated, and colony collapse ensues. This quantification of colony collapse provides guidance for implementing measures for its avoidance.

Individual Specialization and Multihost Epidemics: Disease Spread in Plant-Pollinator Networks

Many parasites infect multiple species and persist through a combination of within- and between-species transmission. Multispecies transmission networks are typically constructed at the species level, linking two species if any individuals of those species interact. However, generalist species often consist of specialized individuals that prefer different subsets of available resources, so individual- and species-level contact networks can differ systematically. To explore the epidemiological impacts of host specialization, we build and study a model for pollinator pathogens on plant-pollinator networks, in which individual pollinators have dynamic preferences for different flower species. We find that modeling and analysis that ignore individual host specialization can predict die-off of a disease that is actually strongly persistent and can badly over- or underpredict steady-state disease prevalence. Effects of individual preferences remain substantial whenever mean preference duration exceeds half of the mean time from infection to recovery or death. Similar results hold in a model where hosts foraging in different habitats have different frequencies of contact with an environmental reservoir for the pathogen. Thus, even if all hosts have the same long-run average behavior, dynamic individual differences can profoundly affect disease persistence and prevalence.

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

Epidemiological models for multihost 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 susceptible-infectious-susceptible 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 cospecialized 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.