A landscape-scale field survey demonstrates the role of wheat volunteers as a local and diversified source of leaf rust inoculum

Deploying disease-resistant cultivars is one of the most effective control strategies to manage crop diseases such as wheat leaf rust, caused by Puccinia triticina. After harvest, this biotrophic fungal pathogen can survive on wheat volunteers present at landscape scale and constitute a local source of primary inoculum for the next cropping season. In this study, we characterised the diversity of P. triticina populations surveyed on wheat volunteer seedlings for six consecutive years (2007-2012) at the landscape scale. A total of 642 leaf rust samples classified in 52 virulence profiles (pathotypes) were collected within a fixed 5-km radius. The pathotype composition (identity and abundance) of field-collected populations was analyzed according to the distance between the surveyed wheat plots and to the cultivars of origin of isolates. Our study emphasised the high diversity of P. triticina populations on wheat volunteers at the landscape scale. We observed an impact of cultivar of origin on pathogen population composition. Levels of population diversity differed between cultivars and their deployment in the study area. Our results suggest that wheat volunteers could provide a significant though highly variable contribution to the composition of primary inoculum and subsequent initiation of leaf rust epidemics.

Analyzing the Influence of Landscape Aggregation on Disease Spread to Improve Management Strategies

Epidemiological models are increasingly used to predict epidemics and improve management strategies. However, they rarely consider landscape characteristics although such characteristics can influence the epidemic dynamics and, thus, the effectiveness of disease management strategies. Here, we present a generic in silico approach which assesses the influence of landscape aggregation on the costs associated with an epidemic and on improved management strategies. We apply this approach to sharka, one of the most damaging diseases of Prunus trees, for which a management strategy is already applied in France. Epidemic simulations were carried out with a spatiotemporal stochastic model under various management strategies in landscapes differing in patch aggregation. Using sensitivity analyses, we highlight the impact of management parameters on the economic output of the model. We also show that the sensitivity analysis can be exploited to identify several strategies that are, according to the model, more profitable than the current French strategy. Some of these strategies are specific to a given aggregation level, which shows that management strategies should generally be tailored to each specific landscape. However, we also identified a strategy that is efficient for all levels of landscape aggregation. This one-size-fits-all strategy has important practical implications because of its simple applicability at a large scale.

A spatio-temporal individual-based network framework for West Nile virus in the USA: Spreading pattern of West Nile virus

West Nile virus (WNV)—a mosquito-borne arbovirus—entered the USA through New York City in 1999 and spread to the contiguous USA within three years while transitioning from epidemic outbreaks to endemic transmission. The virus is transmitted by vector competent mosquitoes and maintained in the avian populations. WNV spatial distribution is mainly determined by the movement of residential and migratory avian populations. We developed an individual-level heterogeneous network framework across the USA with the goal of understanding the long-range spatial distribution of WNV. To this end, we proposed three distance dispersal kernels model: 1) exponential—short-range dispersal, 2) power-law—long-range dispersal in all directions, and 3) power-law biased by flyway direction —long-range dispersal only along established migratory routes. To select the appropriate dispersal kernel we used the human case data and adopted a model selection framework based on approximate Bayesian computation with sequential Monte Carlo sampling (ABC-SMC). From estimated parameters, we find that the power-law biased by flyway direction kernel is the best kernel to fit WNV human case data, supporting the hypothesis of long-range WNV transmission is mainly along the migratory bird flyways. Through extensive simulation from 2014 to 2016, we proposed and tested hypothetical mitigation strategies and found that mosquito population reduction in the infected states and neighboring states is potentially cost-effective.

The underlying pattern of West Nile virus (WNV) geographic spread across the United States is not completely clear, which is a necessary step for continental or state level mitigation strategies to reduce WNV transmission. We report a network model that explains the geographic spread of WNV in the United States. West Nile virus is a mosquito-borne pathogen that infects many avian species with different movement ranges. From our research, we found that migration patterns and routes play an essential role in the WNV spatial distribution. The virus spreads in all directions at short distances because of local birds and short-distance migratory birds. However, the virus also disperses long distances along the avian migratory routes. Our model is designed to be flexible and therefore can be used to explore spreading patterns of other infectious diseases in other geographic locations.

Kenyan Isolates of Puccinia graminis f. sp. tritici from 2008 to 2014: Virulence to SrTmp in the Ug99 Race Group and Implications for Breeding Programs

Frequent emergence of new variants in the Puccinia graminis f. sp. tritici Ug99 race group in Kenya has made pathogen survey a priority. We analyzed 140 isolates from 78 P. graminis f. sp. tritici samples collected in Kenya between 2008 and 2014 and identified six races, including three not detected prior to 2013. Genotypic analysis of 20 isolates from 2013 and 2014 collections showed that the new races TTHST, TTKTK, and TTKTT belong to the Ug99 race group. International advanced breeding lines were evaluated against an isolate of TTKTT (Sr31, Sr24, and SrTmp virulence) at the seedling stage. From 169 advanced lines from Kenya, 23% of lines with resistance to races TTKSK and TTKST were susceptible to TTKTT and, from two North American regional nurseries, 44 and 91% of resistant lines were susceptible. Three lines with combined resistance genes were developed to facilitate pathogen monitoring and race identification. These results indicate the increasing virulence and variability in the Kenyan P. graminis f. sp. tritici population and reveal vulnerabilities of elite germplasm to new races.

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.

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.

Assessing eradication strategies for rain-splashed and wind-dispersed crop diseases

BACKGROUND

The eradicability of rain-splashed crop diseases was examined by modelling the spread of lupin anthracnose over a spatially heterogeneous landscape. Two hypotheses were investigated: (i) in most cases, rain-splashed diseases are unlikely to be eradicable because spread will be too extensive by the time the disease is detected; (ii) there are recognisable characteristics of an incursion that can be used to identify cases when the disease will be eradicable.

RESULTS

Results indicate that the eradication of a rain-splashed crop disease is heavily dependent on the surveillance effort, on how detectable the disease is and on whether there are susceptible hosts outside the cropping area. These simple indicators can be used to estimate the potential for success of an eradication scheme. Eradication targeting only the crop area is destined to fail, unless it is certain that no susceptible host lies adjacent to the cropping area.

CONCLUSION

A failed eradication attempt can be costly, and a simple set of indicators for the likelihood of success is extremely useful. These indicators can aid decision-makers when faced with a new incursion, identifying when there is little hope of success. © 2012 Society of Chemical Industry.

Analytical methods for quantifying environmental connectivity for the control and surveillance of infectious disease spread

The sustained transmission and spread of environmentally mediated infectious diseases is governed in part by the dispersal of parasites, disease vectors and intermediate hosts between sites of transmission. Functional geospatial models can be used to quantify and predict the degree to which environmental features facilitate or limit connectivity between target populations, yet typical models are limited in their geographical and analytical approach, providing simplistic, global measures of connectivity and lacking methods to assess the epidemiological implications of fine-scale heterogeneous landscapes. Here, functional spatial models are applied to problems of surveillance and control of the parasitic blood fluke Schistosoma japonicum and its intermediate snail host Oncomelania haupensis in western China. We advance functional connectivity methods by providing an analytical framework to (i) identify nodes of transmission where the degree of connectedness to other villages, and thus the potential for disease spread, is higher than is estimated using Euclidean distance alone and (ii) (re)organize transmission sites into disease surveillance units based on second-order relationships among nodes using non-Euclidean distance measures, termed effective geographical distance (EGD). Functional environmental models are parametrized using ecological information on the target organisms, and pair-wise distributions of inter-node EGD are estimated. A Monte Carlo rank product analysis is presented to identify nearby nodes under alternative distance models. Nodes are then iteratively embedded into EGD space and clustered using a k-means algorithm to group villages into ecologically meaningful surveillance groups. A consensus clustering approach is taken to derive the most stable cluster structure. The results indicate that novel relationships between nodes are revealed when non-Euclidean, ecologically determined distance measures are used to quantify connectivity in heterogeneous landscapes. These connections are not evident when analysing nodes in Euclidean space, and thus surveillance and control activities planned using Euclidean distance measures may be suboptimal. The methods developed here provide a quantitative framework for assessing the effectiveness of ecologically grounded surveillance systems and of control and prevention strategies for environmentally mediated diseases.

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 approximately 4,000 host populations across an area of 50 km x 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.