Samuel Soubeyrand

Flight behavior and pheromone changes associated to Nosema ceranae infection of honey bee workers (Apis mellifera) in field conditions

Parasites are known to cause the loss of individuals in social insects. In honey bee colonies the disappearance of foragers is a common factor of the wide extended colony losses. The emergent parasite of the European honey bee Nosema ceranae has been found to reduce homing and orientation skills and alter metabolism of forager bees. N. ceranae-infected bees also show changes in Ethyl Oleate (EO) levels, which is so far the only primer pheromone identified in workers that is involved in foraging behavior. Thus, we hypothesized that N. ceranae (i) modifies flight activity of honey bees and (ii) induces EO changes that can alter foraging behavior of nestmates. We compared flight activity of infected bees and non-infected bees in small colonies using an electronic optic bee counter during 28 days. We measured EO levels by gas chromatography-mass spectrometry and spore-counts. Bee mortality was estimated at the end of the experiment. Infected bees showed precocious and a higher flight activity than healthy bees, which agreed with the more elevated EO titers of infected bees and reduced lifespan. Our results suggest that the higher EO levels of infected bees might delay the behavioral maturation of same age healthy bees, which might explain their lower level of activity. We propose that delayed behavioral maturation of healthy bees might be a protective response to infection, as healthy bees would be performing less risky tasks inside the hive, thus extending their lifespan. We also discuss the potential of increased flight activity of infected bees to reduce pathogen transmission inside the hive. Further research is needed to understand the consequences of host behavioral changes on pathogen transmission. This knowledge may contribute to enhance natural colony defense behaviors through beekeeping practices to reduce probability of colony losses.

A Bayesian Inference Framework to Reconstruct Transmission Trees Using Epidemiological and Genetic Data

The accurate identification of the route of transmission taken by an infectious agent through a host population is critical to understanding its epidemiology and informing measures for its control. However, reconstruction of transmission routes during an epidemic is often an underdetermined problem: data about the location and timings of infections can be incomplete, inaccurate, and compatible with a large number of different transmission scenarios. For fast-evolving pathogens like RNA viruses, inference can be strengthened by using genetic data, nowadays easily and affordably generated. However, significant statistical challenges remain to be overcome in the full integration of these different data types if transmission trees are to be reliably estimated. We present here a framework leading to a bayesian inference scheme that combines genetic and epidemiological data, able to reconstruct most likely transmission patterns and infection dates. After testing our approach with simulated data, we apply the method to two UK epidemics of Foot-and-Mouth Disease Virus (FMDV): the 2007 outbreak, and a subset of the large 2001 epidemic. In the first case, we are able to confirm the role of a specific premise as the link between the two phases of the epidemics, while transmissions more densely clustered in space and time remain harder to resolve. When we consider data collected from the 2001 epidemic during a time of national emergency, our inference scheme robustly infers transmission chains, and uncovers the presence of undetected premises, thus providing a useful tool for epidemiological studies in real time. The generation of genetic data is becoming routine in epidemiological investigations, but the development of analytical tools maximizing the value of these data remains a priority. Our method, while applied here in the context of FMDV, is general and with slight modification can be used in any situation where both spatiotemporal and genetic data are available.

Spatiotemporal Structure of Host‐Pathogen Interactions in a Metapopulation

The ecological and evolutionary dynamics of species are influenced by spatiotemporal variation in population size. Unfortunately, we are usually limited in our ability to investigate the numerical dynamics of natural populations across large spatial scales and over long periods of time. Here we combine mechanistic and statistical approaches to reconstruct continuous‐time infection dynamics of an obligate fungal pathogen on the basis of discrete‐time occurrence data. The pathogen, Podosphaera plantaginis, infects its host plant, Plantago lanceolata, in a metapopulation setting where the presence of the pathogen has been recorded annually for 6 years in ∼4,000 host populations across an area of 50 km × 70 km in Finland. The dynamics are driven by strong seasonality, with a high extinction rate during winter and epidemic expansion in summer for local pathogen populations. We are able to identify with our model the regions in the study area where overwintering has been most successful. These overwintering sites represent foci that initiate local epidemics during the growing season. There is striking heterogeneity at the regional scale in both the overwintering success of the pathogen and the encounter intensity between the host and the pathogen. Such heterogeneity has profound implications for the coevolutionary dynamics of the interaction.

A Bayesian approach for inferring the dynamics of partially observed endemic infectious diseases from space-time-genetic data

We describe a statistical framework for reconstructing the sequence of transmission events between observed cases of an endemic infectious disease using genetic, temporal and spatial information. Previous approaches to reconstructing transmission trees have assumed all infections in the study area originated from a single introduction and that a large fraction of cases were observed. There are as yet no approaches appropriate for endemic situations in which a disease is already well established in a host population and in which there may be multiple origins of infection, or that can enumerate unobserved infections missing from the sample. Our proposed framework addresses these shortcomings, enabling reconstruction of partially observed transmission trees and estimating the number of cases missing from the sample. Analyses of simulated datasets show the method to be accurate in identifying direct transmissions, while introductions and transmissions via one or more unsampled intermediate cases could be identified at high to moderate levels of case detection. When applied to partial genome sequences of rabies virus sampled from an endemic region of South Africa, our method reveals several distinct transmission cycles with little contact between them, and direct transmission over long distances suggesting significant anthropogenic influence in the movement of infected dogs.

Emigration of the plant pathogen Pseudomonas syringae from leaf litter contributes to its population dynamics in alpine snowpack

The recently discovered ubiquity of the plant pathogen Pseudomonas syringae in headwaters and alpine ecosystems worldwide elicits new questions about the ecology of this bacterium and subsequent consequences for disease epidemiology. Because of the major contribution of snow to river run-off during crop growth, we evaluated the population dynamics of P.syringae in snowpack and the underlying leaf litter during two years in the Southern French Alps. High population densities of P.syringae were found on alpine grasses, and leaf litter was identified as the main source of populations of P.syringae in snowpack, contributing more than the populations arriving with the snowfall. The insulating properties of snow foster survival of P.syringae throughout the winter in the 10 cm layer of snow closest to the soil. Litter and snowpack harboured populations of P.syringae that were very diverse in terms of phenotypes and genotypes. Neither substrate nor sampling site had a marked effect on the structure of P.syringae populations, and snow and litter had genotypes in common with other non-agricultural habitats and with crops. These results contribute to the mounting evidence that a highly diverse P.syringae metapopulation is disseminated throughout drainage basins between cultivated and non-cultivated zones.

Long-distance wind-dispersal of spores in a fungal plant pathogen: Estimation of anisotropic dispersal kernels from an extensive field experiment

Given its biological significance, determining the dispersal kernel (i.e., the distribution of dispersal distances) of spore-producing pathogens is essential. Here, we report two field experiments designed to measure disease gradients caused by sexually- and asexually-produced spores of the wind-dispersed banana plant fungus Mycosphaerella fijiensis. Gradients were measured during a single generation and over 272 traps installed up to 1000 m along eight directions radiating from a traceable source of inoculum composed of fungicide-resistant strains. We adjusted several kernels differing in the shape of their tail and tested for two types of anisotropy. Contrasting dispersal kernels were observed between the two types of spores. For sexual spores (ascospores), we characterized both a steep gradient in the first few metres in all directions and rare long-distance dispersal (LDD) events up to 1000 m from the source in two directions. A heavy-tailed kernel best fitted the disease gradient. Although ascospores distributed evenly in all directions, average dispersal distance was greater in two different directions without obvious correlation with wind patterns. For asexual spores (conidia), few dispersal events occurred outside of the source plot. A gradient up to 12.5 m from the source was observed in one direction only. Accordingly, a thin-tailed kernel best fitted the disease gradient, and anisotropy in both density and distance was correlated with averaged daily wind gust. We discuss the validity of our results as well as their implications in terms of disease diffusion and management strategy.

Sharka Epidemiology and Worldwide Management Strategies: Learning Lessons to Optimize Disease Control in Perennial Plants

Many plant epidemics that cause major economic losses cannot be controlled with pesticides. Among them, sharka epidemics severely affect prunus trees worldwide. Its causal agent, Plum pox virus (PPV; genus Potyvirus), has been classified as a quarantine pathogen in numerous countries. As a result, various management strategies have been implemented in different regions of the world, depending on the epidemiological context and on the objective (i.e., eradication, suppression, containment, or resilience). These strategies have exploited virus-free planting material, varietal improvement, surveillance and removal of trees in orchards, and statistical models. Variations on these management options lead to contrasted outcomes, from successful eradication to widespread presence of PPV in orchards. Here, we present management strategies in the light of sharka epidemiology to gain insights from this worldwide experience. Although focused on sharka, this review highlights more general levers and promising approaches to optimize disease control in perennial plants.

Autoinfection in wheat leaf rust epidemics

Autoinfection (within-host inoculum transmission) allows plant pathogens locally to increase their density on an infected host. Estimating autoinfection is of particular importance in understanding epidemic development in host mixtures. More generally, autoinfection influences the rate of host colonization by the pathogen, as well as pathogen evolution. Despite its importance in epidemiological models, autoinfection has not yet been directly quantified. It was measured here on wheat (Triticum aestivum) leaves infected by a pathogenic fungus (Puccinia triticina). Autoinfection was measured either on inoculated leaves or by assessing the local progeny of spontaneous infections, and was described by a model of the form y = microx(alpha), where alpha accounts for host saturation and micro represents the pathogen multiplication rate resulting from autoinfection. It was shown that autoinfection resulted in typical patterns of disease aggregation at the leaf level and influenced lesion distribution in the crop during the first epidemic stages. The parameter micro was calculated by taking overdispersion of the data and density dependence into account. It was found that a single lesion produced between 50 and 200 offspring by autoinfection, within a pathogen generation. By taking into account environmental variability, it was possible to estimate autoinfection under optimal conditions for epidemic development.

OutbreakTools: A new platform for disease outbreak analysis using the R software

The investigation of infectious disease outbreaks relies on the analysis of increasingly complex and diverse data, which offer new prospects for gaining insights into disease transmission processes and informing public health policies. However, the potential of such data can only be harnessed using a number of different, complementary approaches and tools, and a unified platform for the analysis of disease outbreaks is still lacking. In this paper, we present the new R package OutbreakTools, which aims to provide a basis for outbreak data management and analysis in R. OutbreakTools is developed by a community of epidemiologists, statisticians, modellers and bioinformaticians, and implements classes and methods for storing, handling and visualizing outbreak data. It includes real and simulated outbreak datasets. Together with a number of tools for infectious disease epidemiology recently made available in R, OutbreakTools contributes to the emergence of a new, free and open-source platform for the analysis of disease outbreaks.

Anisotropy, in Density and in Distance, of the Dispersal of Yellow Rust of Wheat: Experiments in Large Field Plots and Estimation

ABSTRACT Long-distance dispersal of spores generally presents anisotropy. This anisotropy can appear in the mean number of spores deposited along a given direction (anisotropy in density) and in the mean distance that a spore travels in a given direction (anisotropy in distance). Specific experiments together with a statistical methodology are proposed to study this effect. The experiments are based on the use of a point source of a traceable inoculum and susceptible trap plots in large resistant field plots. The anisotropy is characterized by two functions: a directional density function and a mean distance function which are related with the anisotropies in density and distance, respectively. A nonparametric approach is developed to estimate these functions and to help in choosing a parametric model. Then, the parametric model is estimated. In two field experiments, migrations up to 175 and 225 m from the source were detected, with approximately 25% of the trap plots infected. Whatever the experiment, the two estimated anisotropies presented different shapes (i.e., the number of spores dispersed in a given direction was not proportional to the mean distance travelled by these spores).