Acute toxicity of the fungicide captan to honey bees and mixed evidence for synergism with the insecticide thiamethoxam.

Honey bees are commonly co-exposed to pesticides during crop pollination, including the fungicide captan and neonicotinoid insecticide thiamethoxam. We assessed the impact of exposure to these two pesticides individually and in combination, at a range of field-realistic doses. In laboratory assays, mortality of larvae treated with captan was 80-90% greater than controls, dose-independent, and similar to mortality from the lowest dose of thiamethoxam. There was evidence of synergism (i.e., a non-additive response) from captan-thiamethoxam co-exposure at the highest dose of thiamethoxam, but not at lower doses. In the field, we exposed whole colonies to the lowest doses used in the laboratory. Exposure to captan and thiamethoxam individually and in combination resulted in minimal impacts on population growth or colony mortality, and there was no evidence of synergism or antagonism. These results suggest captan and thiamethoxam are each acutely toxic to immature honey bees, but whole colonies can potentially compensate for detrimental effects, at least at the low doses used in our field trial, or that methodological differences of the field experiment impacted results (e.g., dilution of treatments with natural pollen). If compensation occurred, further work is needed to assess how it occurred, potentially via increased queen egg laying, and whether short-term compensation leads to long-term costs. Further work is also needed for other crop pollinators that lack the social detoxification capabilities of honey bee colonies and may be less resilient to pesticides.

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.

Predicting and Controlling Spillover in Multispecies Disease Transmission Networks: Steady-State Analysis.

AbstractIn multispecies disease systems, pathogen spillover from a "reservoir community" can maintain disease in a "sink community" where it would otherwise die out. We develop and analyze models for spillover and disease spread in sink communities, focusing on questions of control: which species or transmission links are the most important to target to reduce the disease impact on a species of concern? Our analysis focuses on steady-state disease prevalence, assuming that the timescale of interest is long compared with that of disease introduction and establishment in the sink community. We identify three regimes as the sink community R0 scales from 0 to 1. Up to R0≈0.3, overall infection patterns are dominated by direct exogenous infections and one-step subsequent transmission. For R0≈1, infection patterns are characterized by dominant eigenvectors of a force-of-infection matrix. In between, additional network details can be important; we derive and apply general sensitivity formulas that identify particularly important links and species.

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.

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.

Pathogen transport amplifies or dilutes disease transmission depending on the host dose-response relationship.

Pathogen transport by biotic or abiotic processes (e.g. mechanical vectors, wind, rain) can increase disease transmission by creating more opportunities for host exposure. But transport without replication has an inherent trade-off, that creating new venues for exposure decreases the average pathogen abundance at each venue. The host dose-response relationship is therefore required to correctly assess infection risk. We model and analyse two examples-biotic mechanical vectors in plant-pollinator networks, and abiotic-facilitated long-distance pathogen dispersal-to illustrate how oversimplifying the dose-response relationship can lead to incorrect epidemiological predictions. When the minimum infective dose is high, mechanical vectors amplify disease transmission less than suggested by simple compartment models, and may even dilute transmission. When long-distance dispersal leads to infrequent large exposures, models that assume a linear force of infection can substantially under-predict the speed of epidemic spread. Our work highlights an important general interplay between dose-response relationships and pathogen transport.

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.

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.

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.

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.

Codecaying Dark Matter.

We propose a new mechanism for thermal dark matter freeze-out, called codecaying dark matter. Multicomponent dark sectors with degenerate particles and out-of-equilibrium decays can codecay to obtain the observed relic density. The dark matter density is exponentially depleted through the decay of nearly degenerate particles rather than from Boltzmann suppression. The relic abundance is set by the dark matter annihilation cross section, which is predicted to be boosted, and the decay rate of the dark sector particles. The mechanism is viable in a broad range of dark matter parameter space, with a robust prediction of an enhanced indirect detection signal. Finally, we present a simple model that realizes codecaying dark matter.