Quantifying airborne dispersal routes of pathogens over continents to safeguard global wheat supply

Population genetics of plant fungal threats: Insights from wheat powdery mildew

Population genetics studies of microbial pathogens are demonstrating the potential of evolutionary theory to inform decisions that impact society. A recent study on wheat powdery mildew fungus in PLOS Biology uses genomic data to reveal the pathogen's population structure and to predict its continental-scale dispersion routes.

Migration of wheat stripe rust from the primary oversummering region to neighboring regions in China

Changing climate and changes in cropping systems have greatly affected outbreaks of plant diseases. Wheat stripe rust is a disease posing a threat to global wheat production, caused by Puccinia striiformis f. sp. tritici (Pst). Pst oversummering regions play a crucial role in the emergence of new races in China. To unveil the migration pattern of oversummering to adjacent regions, we develop a set of KASP-SNP marker from 28 Pst whole-genome sequences to investigate the population structure in the oversummering and its adjacent regions. A set of 19 Chinese wheat differentials is used to characterize the virulence patterns of 308 sampled Pst isolates. By integrating virulence characterization, population genetic analysis, air trajectory simulation and field disease monitoring, two main Pst dispersal routes are identified. Inocula from Eastern Qinghai are dispersed to Western and Eastern Liupan Mountain, and reach Guanzhong Plain. The second route originates from Middle Gansu, then through Longnan, and reaches the Guanzhong Plain via Eastern Liupan Mountain. Both dispersal routes result in Pst inoculum spreading to the Huang-Huai-Hai region, the main wheat-growing region in China. The proposed migration routes can be used to develop disease management strategies at a regional and national scale.

A framework for modelling desert locust population dynamics and large-scale dispersal

There is an urgent need for mathematical models that can be used to inform the deployment of surveillance, early warning and management systems for transboundary pest invasions. This is especially important for desert locust, one of the most dangerous migratory pests for smallholder farmers. During periods of desert locust upsurges and plagues, gregarious adult locusts form into swarms that are capable of long-range dispersal. Here we introduce a novel integrated modelling framework for use in predicting gregarious locust populations. The framework integrates the selection of breeding sites, maturation through egg, hopper and adult stages and swarm dispersal in search of areas suitable for feeding and breeding. Using a combination of concepts from epidemiological modelling, weather and environment data, together with an atmospheric transport model for swarm movement we provide a tool to forecast short- and long-term swarm movements. A principal aim of the framework is to provide a practical starting point for use in the next upsurge.

Monitoring yellow rust progression during spring critical wheat growth periods using multi-temporal Sentinel-2 imagery

BACKGROUND

Yellow rust (Puccinia striiformis f. sp. tritici) is a devastating hazard to wheat production, which poses a serious threat to yield and food security in the main wheat-producing areas in eastern China. It is necessary to monitor yellow rust progression during spring critical wheat growth periods to support its prediction by providing timely calibrations for disease prediction models and timely green prevention and control.

RESULTS

Three Sentinel-2 images for the disease during the three wheat growth periods (jointing, heading, and filling) were acquired. Spectral, texture, and color features were all extracted for each growth period disease. Then three period-specific feature sets were obtained. Given the differences in field disease epidemic status in the three periods, three period-targeted monitoring models were established to map yellow rust damage progression in spring and track its spatiotemporal change. The models' performance was then validated based on the disease field truth data during the three periods (87 for the jointing period, 183 for the heading period, and 155 for the filling period). The validation results revealed that the representation of the wheat yellow rust damage progression based on our monitoring model group was realistic and credible. The overall accuracy of the healthy and diseased pixel classification monitoring model at the jointing period reached 87.4%, and the coefficient of determination (R2) of the disease index regression monitoring models at the heading and filling periods was 0.77 (heading period) and 0.76 (filling period). The model-group-result-based spatiotemporal change detection of the yellow rust progression across the entire study area revealed that the area proportions conforming to the expected disease spatiotemporal development pattern during the jointing-to-heading period and the heading-to-filling period reached 98.2% and 84.4% respectively.

CONCLUSIONS

Our jointing, heading, and filling period-targeted monitoring model group overcomes the limitations of most existing monitoring models only based on single-phase remote sensing information. It performs well in revealing the wheat yellow rust spatiotemporal epidemic in spring, can timely update disease trends to optimize disease management, and provide a basis for disease prediction to timely correct model. © 2024 Society of Chemical Industry.

MaPom1, a Dual-Specificity Tyrosine Phosphorylation-Regulated Kinase, Positively Regulates Thermal and UV-B Tolerance in Metarhizium acridum

Fungi play irreplaceable roles in the functioning of natural ecosystems, but global warming poses a significant threat to them. However, the mechanisms underlying fungal tolerance to thermal and UV-B stresses remain largely unknown. Dual-specificity tyrosine phosphorylation-regulated kinase (DYRK) Pom1 is crucial for fungal growth, conidiation, and virulence. However, its role in stress tolerance within kingdom fungi has not been explored. In this study, we analyzed the function of MaPom1 (a Pom1 homologous gene) in the entomopathogenic fungus Metarhizium acridum and its regulatory roles in stress tolerance. Conidial thermal and UV-B tolerance significantly decreased in the MaPom1 disruption strain (ΔMaPom1), whereas conidial yield and virulence were unaffected. RNA-Seq analysis indicated that the differentially expressed genes (DEGs) were primarily related to amino sugar, nucleotide sugar metabolism, cell wall components, growth and development, and stress response pathways. Under heat shock treatment, the expression levels of heat shock protein genes decreased significantly, leading to reduced thermotolerance. Moreover, under UV-B treatment, MaPom1 expression and the enzyme activity significantly changed, indicating its involvement in regulating UV-B tolerance. The percentage of nuclear damage in ΔMaPom1 under UV-B treatment was higher than that in the wild-type strain (WT) and the complementary strain (CP). Additionally, the transcription levels of DNA damage-related genes significantly decreased, whereas those of several genes involved in the DNA damage repair response increased significantly. Overall, MaPom1 contributed to thermal and UV-B tolerance by regulating the expression of heat shock protein genes and DNA damage repair genes.

Economical-epidemiological analysis of the coffee trees rust pandemic

Coffee leaf rust is a prevalent botanical disease that causes a worldwide reduction in coffee supply and its quality, leading to immense economic losses. While several pandemic intervention policies (PIPs) for tackling this rust pandemic are commercially available, they seem to provide only partial epidemiological relief for farmers. In this work, we develop a high-resolution spatiotemporal economical-epidemiological model, extending the Susceptible-Infected-Removed model, that captures the rust pandemic's spread in coffee tree farms and its associated economic impact. Through extensive simulations for the case of Colombia, a country that consists mostly of small-size coffee farms and is the second-largest coffee producer in the world, our results show that it is economically impractical to sustain any profit without directly tackling the rust pandemic. Furthermore, even in the hypothetical case where farmers perfectly know their farm's epidemiological state and the weather in advance, any rust pandemic-related efforts can only amount to a limited profit of roughly 4% on investment. In the more realistic case, any rust pandemic-related efforts are expected to result in economic losses, indicating that major disturbances in the coffee market are anticipated.

Developing Predictive Models and Early Warning Systems for Invading Pathogens: Wheat Rusts

Innovations in aerobiological and epidemiological modeling are enabling the development of powerful techniques to infer connectivity networks for transboundary pathogens in ways that were not previously possible. The innovations are supported by improved access to historical and near real-time highly resolved weather data, multi-country disease surveillance data, and enhanced computing power. Using wheat rusts as an exemplar, we introduce a flexible modeling framework to identify characteristic pathways for long-distance spore dispersal within countries and beyond national borders. We show how the models are used for near real-time early warning systems to support smallholder farmers in East Africa and South Asia. Wheat rust pathogens are ideal exemplars because they continue to pose threats to food security, especially in regions of the world where resources for control are limited. The risks are exacerbated by the rapid appearance and spread of new pathogenic strains, prodigious spore production, and long-distance dispersal for transboundary and pandemic spread.

A Simulation Study to Reveal the Epidemiology and Aerosol Transmission Characteristics of Botrytis cinerea in Grape Greenhouses

Most previously studies had considered that plant fungal disease spread widely and quickly by airborne fungi spore. However, little is known about the release dynamics, aerodynamic diameter, and pathogenicity threshold of fungi spore in air of the greenhouse environment. Grape gray mold is caused by Botrytis cinerea; the disease spreads in greenhouses by spores in the air and the spore attaches to the leaf and infects plant through the orifice. In this study, 120 μmol/L propidium monoazide (PMA) were suitable for treatment and quantitation viable spore by quantitative real-time PCR, with a limit detection of 8 spores/mL in spore suspension. In total, 93 strains of B. cinerea with high pathogenicity were isolated and identified from the air samples of grapevines greenhouses by a portable sampler. The particle size of B. cinerea aerosol ranged predominately from 0.65-3.3 μm, accounting for 71.77% of the total amount. The B. cinerea spore aerosols were infective to healthy grape plants, with the lowest concentration that could cause disease being 42 spores/m3. Botrytis cinerea spores collected form six greenhouse in Shandong Province were quantified by PMA-qPCR, with a higher concentration (1182.89 spores/m3) in May and June and a lower concentration in July and August (6.30 spores/m3). This study suggested that spore dispersal in aerosol is an important route for the epidemiology of plant fungal disease, and these data will contribute to the development of new strategies for the effective alleviation and control of plant diseases.

The atmosphere: a transport medium or an active microbial ecosystem?

The atmosphere may be Earth's largest microbial ecosystem. It is connected to all of Earth's surface ecosystems and plays an important role in microbial dispersal on local to global scales. Despite this grand scale, surprisingly little is understood about the atmosphere itself as a habitat. A key question remains unresolved: does the atmosphere simply transport microorganisms from one location to another, or does it harbour adapted, resident, and active microbial communities that overcome the physiological stressors and selection pressures the atmosphere poses to life? Advances in extreme microbiology and astrobiology continue to push our understanding of the limits of life towards ever greater extremes of temperature, pressure, salinity, irradiance, pH, and water availability. Earth's atmosphere stands as a challenging, but potentially surmountable, extreme environment to harbour living, active, resident microorganisms. Here, we confront the current understanding of the atmosphere as a microbial habitat, highlighting key advances and limitations. We pose major ecological and mechanistic questions about microbial life in the atmosphere that remain unresolved and frame the problems and technical pitfalls that have largely hindered recent developments in this space, providing evidence-based insights to drive future research in this field. New innovations supported by rigorous technical standards are needed to enable progress in understanding atmospheric microorganisms and their influence on global processes of weather, climate, nutrient cycling, biodiversity, and microbial connectivity, especially in the context of rapid global change.

Computing Geographical Networks Generated by Air-Mass Movement

As air masses move within the troposphere, they transport a multitude of components including gases and particles such as pollen and microorganisms. These movements generate atmospheric highways that connect geographic areas at distant, local, and global scales that particles can ride depending on their aerodynamic properties and their reaction to environmental conditions. In this article we present an approach and an accompanying web application called tropolink for measuring the extent to which distant locations are potentially connected by air-mass movement. This approach is based on the computation of trajectories of air masses with the HYSPLIT atmospheric transport and dispersion model, and on the computation of connection frequencies, called connectivities, in the purpose of building trajectory-based geographical networks. It is illustrated for different spatial and temporal scales with three case studies related to plant epidemiology. The web application that we designed allows the user to easily perform intensive computation and mobilize massive archived gridded meteorological data to build weighted directed networks. The analysis of such networks allowed us for example, to describe the potential of invasion of a migratory pest beyond its actual distribution. Our approach could also be used to compute geographical networks generated by air-mass movement for diverse application domains, for example, to assess long-term risk of spread from persistent or recurrent sources of pollutants, including wildfire smoke.

Understanding atmospheric intercontinental dispersal of harmful microorganisms

The atmosphere is a major route for microbial intercontinental dispersal, including harmful microorganisms, antibiotic resistance genes, and allergens, with strong implications in ecosystem functioning and global health. Long-distance dispersal is facilitated by air movement at higher altitudes in the free troposphere and is affected by anthropogenic forcing, climate change, and by the general atmospheric circulation, mainly in the intertropical convergence zone. The survival of microorganisms during atmospheric transport and their remote invasive potential are fundamental questions, but data are scarce. Extreme atmospheric conditions represent a challenge to survival that requires specific adaptive strategies, and recovery of air samples from the high altitudes relevant to study harmful microorganisms can be challenging. In this paper, we highlight the scope of the problem, identify challenges and knowledge gaps, and offer a roadmap for improved understanding of intercontinental microbial dispersal and their outcomes. Greater understanding of long-distance dispersal requires research focus on local factors that affect emissions, coupled with conditions influencing transport and survival at high altitudes, and eventual deposition at sink locations.

Global dispersal and diversity of rust fungi in the context of plant health

Long-distance dispersal of plant pathogens at the continental scale may have strong implications on plant health, in particular when incursions result in spread of disease to new territories where the disease was previously absent or insignificant. These dispersions may be caused by airborne transmission of spores or accidental spread via human travel and trade. Recent surveillance efforts of cereal rust fungi have demonstrated that incursion of new strains with superior fitness into areas where the disease is already established may have similar implications on plant health. Since dispersal events are highly stochastic, irrespective of transmission mechanism, critical mitigation efforts include preparedness by coordinated pathogen surveillance activities, host crop diversification, and breeding for disease resistance with low vulnerability to sudden changes in the pathogen population.

Wheat Stem Rust Detection and Race Characterization in Tunisia

Climate changes over the past 25 years have led to conducive conditions for invasive and transboundary fungal disease occurrence, including the re-emergence of wheat stem rust disease, caused by Puccinia graminis f.sp. tritici (Pgt) in East Africa, Europe, and the Mediterranean basin. Since 2018, sporadic infections have been observed in Tunisia. In this study, we investigated Pgt occurrence at major Tunisian wheat growing areas. Pgt monitoring, assessment, and sampling from planted trap nurseries at five different locations over two years (2021 and 2022) revealed the predominance of three races, namely TTRTF (Clade III-B), TKKTF (Clade IV-F), and TKTTF (Clade IV-B). Clade III-B was the most prevalent in 2021 as it was detected at all locations, while in 2022 Pgt was only reported at Beja and Jendouba, with the prevalence of Clade IV-B. The low levels of disease incidence during these two years and Pgt population diversity suggest that this fungus most likely originated from exotic incursions and that climate factors could have caused disease establishment in Tunisia. Further evaluation under the artificial disease pressure of Tunisian wheat varieties and weather-based modeling for early disease detection in the Mediterranean area could be helpful in monitoring and predicting wheat stem rust emergence and epidemics.

Demystifying ecological connectivity for actionable spatial conservation planning

Connectivity underpins the persistence of life; it needs to inform biodiversity conservation decisions. Yet, when prioritising conservation areas and developing actions, connectivity is not being operationalised in spatial planning. The challenge is the translation of flows associated with connectivity into conservation objectives that lead to actions. Connectivity is nebulous, it can be abstract and mean different things to different people, making it difficult to include in conservation problems. Here, we show how connectivity can be included in mathematically defining conservation planning objectives. We provide a path forward for linking connectivity to high-level conservation goals, such as increasing species' persistence. We propose ways to design spatial management areas that gain biodiversity benefit from connectivity.

Virulence Survey of Puccinia striiformis in Israel Revealed Considerable Changes in the Pathogen Population During the Period 2001 to 2019

A total of 353 urediniospore isolates of Puccinia striiformis f. sp. tritici (Pst) collected in Israel during 2001 to 2019 were analyzed. Pst pathogenicity was studied with a set of 20 differentials (17 Avocet and 3 other lines). Three periods were compared: 2001 to 2007, 2009 to 2016, and 2017 to 2019. No virulence to Yr5 or Yr15 was detected. Virulence frequencies on Yr4, Yr10, Yr24, and YrSp genes rose to the moderate level (0.28 to 0.44) in 2017 to 2019. Virulence frequencies to Yr2 and Yr9 decreased. One Pst phenotype was identified in all three periods, but its frequency drastically decreased from 0.74 in 2001 to 2016 to 0.21 in 2017 to 2019. The most probable scenario of emergence of wheat yellow rust in Israel is wind dissemination of Pst urediniospores from the Horn of Africa. Variability of the Pst population increased amid considerable evolution with two major transformations in 2009 and 2017. The first modification can be attributed to changes in wheat genetic background in Israel due to deployment of new cultivars resistant to yellow rust since 2004. The second shift in 2017 can be primarily explained by intensive deployment of wheat cultivars resistant to the stem rust race Ug99 in the 2010s in the Horn of Africa. This led to changing genetic backgrounds of the cultivated wheats in the donor region and development and long-distance spread of new Pst phenotypes to Israel. Two singular multivirulent Pst phenotypes were identified in 2019, one of them being closely related to the aggressive Warrior race. Such phenotypes may potentially defeat existing resistances.

Variable climate suitability for wheat blast (Magnaporthe oryzae pathotype Triticum) in Asia: results from a continental-scale modeling approach

Crop fungal diseases constitute a major cause of yield loss. The development of crop disease monitoring and forecasting tools is an important effort to aid farmers in adapting to climate variability and change. Recognizing weather as a main driver of fungal disease outbreaks, this work assesses the climate suitability for wheat blast (Magnaporthe oryzae pathotype Triticum, MoT) development in Asian wheat-producing countries. MOT was reported for the first time in Bangladesh in 2016 and could spread to other countries, provided that environmental conditions are suitable to spore development, distribution, and infection. With results from a generic infection model driven by air temperature and humidity, and motivated by the necessity to assess the potential distribution of MoT based on the response to weather drivers only, we quantify potential MOT infection events across Asia for the period 1980-2019. The results show a potential higher incidence of MOT in Bangladesh, Myanmar, and some areas of India, where the number of potential infection (NPI) events averaged up to 15 during wheat heading. Interannual trends show an increase in NPI over those three countries, which in turns show their higher interannual variability. Cold/dry conditions in countries such as Afghanistan and Pakistan appear to render them unlikely candidates for MOT establishment. The relationship between seasonal climate anomalies and NPI suggests a greater association with relative humidity than with temperature. These results could help to focus future efforts to develop management strategies where weather conditions are conducive for the establishment of MOT.

Puccinia graminis f. sp. tritici Population Causing Recent Wheat Stem Rust Epidemics in Kazakhstan Is Highly Diverse and Includes Novel Virulence Pathotypes

Wheat stem rust, caused by Puccinia graminis f. sp. tritici (Pgt), is a reemerging disease that caused severe epidemics in northern Kazakhstan and western Siberia in the period of 2015 to 2019. We analyzed 51 stem rust samples collected between 2015 and 2017 in five provinces in Kazakhstan. A total of 112 Pgt races were identified from 208 single-pustule isolates. These races are phenotypically and genotypically diverse, and most of them are likely of sexual origin. No differentiation of phenotypes and single-nucleotide polymorphism genotypes was observed between isolates from Akmola and North Kazakhstan provinces, supporting the idea of a wide dispersal of inoculum in the northern regions of the country. Similarities in virulence profiles with Pgt races previously reported in Siberia, Russia, suggest that northern Kazakhstan and western Siberia constitute a single stem rust epidemiological region. In addition to the races of sexual origin, six races reported in Europe, the Caucasus, and East Africa were detected in Kazakhstan, indicating that this epidemiological region is not isolated, and spore inflow from the west occurs. Virulence alone or in combination to several genes effective against the Ug99 race group was detected, including novel virulence on Sr32 + Sr40 and Sr47. The occurrence of a highly diverse Pgt population with virulence to an important group of Sr genes demonstrated the importance of the pathogen's sexual cycle in generating new and potentially damaging virulence combinations.

Life is in the air: An expedition into the Amazonian atmosphere

Biological particles suspended in the atmosphere have a crucial role in the dynamics of the biosphere underneath. Although much attention is paid for the chemical and physical properties of these particles, their biological taxonomic identity, which is relevant for ecological research, remains little studied. We took air samples at 300 meters above the forest in central Amazonia, in seven periods of 7 days, and used high-throughput DNA sequencing techniques to taxonomically identify airborne fungal and plant material. The use of a molecular identification technique improved taxonomic resolution when compared to morphological identification. This first appraisal of airborne diversity showed that fungal composition was strikingly different from what has been recorded in anthropogenic regions. For instance, basidiospores reached 30% of the OTUs instead of 3–5% as found in the literature; and the orders Capnodiales and Eurotiales—to which many allergenic fungi and crop pathogens belong—were much less frequently recorded than Pleosporales, Polyporales, and Agaricales. Plant OTUs corresponded mainly to Amazonian taxa frequently present in pollen records such as the genera Helicostilys and Cecropia and/or very abundant in the region such as Pourouma and Pouteria. The origin of extra-Amazonian plant material is unknown, but they belong to genera of predominantly wind-pollinated angiosperm families such as Poaceae and Betulaceae. Finally, the detection of two bryophyte genera feeds the debate about the role of long distance dispersal in the distribution of these plants.

Climate Change Effects on Pathogen Emergence: Artificial Intelligence to Translate Big Data for Mitigation

Plant pathology has developed a wide range of concepts and tools for improving plant disease management, including models for understanding and responding to new risks from climate change. Most of these tools can be improved using new advances in artificial intelligence (AI), such as machine learning to integrate massive data sets in predictive models. There is the potential to develop automated analyses of risk that alert decision-makers, from farm managers to national plant protection organizations, to the likely need for action and provide decision support for targeting responses. We review machine-learning applications in plant pathology and synthesize ideas for the next steps to make the most of these tools in digital agriculture. Global projects, such as the proposed global surveillance system for plant disease, will be strengthened by the integration of the wide range of new data, including data from tools like remote sensors, that are used to evaluate the risk ofplant disease. There is exciting potential for the use of AI to strengthen global capacity building as well, from image analysis for disease diagnostics and associated management recommendations on farmers' phones to future training methodologies for plant pathologists that are customized in real-time for management needs in response to the current risks. International cooperation in integrating data and models will help develop the most effective responses to new challenges from climate change.

Addressing biohazards to food security in primary production

This review addresses ways to prepare for and to mitigate effects of biohazards on primary production of crops and livestock. These biohazards can be natural or intentional introductions of pathogens, and they can cause major economic damage to farmers, the agricultural industry, society, and international trade. Agroterrorism is the intentional introduction of animal or plant pathogens into agricultural production systems with the intention to cause socioeconomic harm and generate public fear. Although few acts of agroterrorism are reported, the threat of agroterrorism in Europe is real. New concerns about threats arise from the rapid advancements in biotechnology and emerging technologies. FORSA, an analytical framework for risk and vulnerability analysis, was used to review how to prepare for and mitigate the possible effects of natural or intentional biohazards in agricultural production. Analyzing the effects of a biohazard event involves multiple scientific disciplines. A comprehensive analysis of biohazards therefore requires a systems approach. The preparedness and ability to manage events are strengthened by bolstered farm biosecurity, increased monitoring and laboratory capacity, improved inter-agency communication and resource allocation. The focus of this review is on Europe, but the insights gained have worldwide applications. The analytical framework used here is compared to other frameworks. With climate change, Covid-19 and the war in Ukraine, the supply chains are challenged, and we foresee increasing food prices associated with social tensions. Our food supply chain becomes more fragile with more unknowns, thereby increasing the needs for risk and vulnerability analyses, of which FORSA is one example.

Development and Validation of a Mechanistic, Weather-Based Model for Predicting Puccinia graminis f. sp. tritici Infections and Stem Rust Progress in Wheat

Stem rust (or black rust) of wheat, caused by Puccinia graminis f. sp. tritici (Pgt), is a re-emerging, major threat to wheat production worldwide. Here, we retrieved, analyzed, and synthetized the available information about Pgt to develop a mechanistic, weather-driven model for predicting stem rust epidemics caused by uredospores. The ability of the model to predict the first infections in a season was evaluated using field data collected in three wheat-growing areas of Italy (Emilia-Romagna, Apulia, and Sardinia) from 2016 to 2021. The model showed good accuracy, with a posterior probability to correctly predict infections of 0.78 and a probability that there was no infection when not predicted of 0.96. The model's ability to predict disease progress during the growing season was also evaluated by using published data obtained from trials in Minnesota, United States, in 1968, 1978, and 1979, and in Pennsylvania, United States, in 1986. Comparison of observed versus predicted data generated a concordance correlation coefficient of 0.96 and an average distance between real data and the fitted line of 0.09. The model could therefore be considered accurate and reliable for predicting epidemics of wheat stem rust and could be tested for its ability to support risk-based control of the disease.

OUP accepted manuscript

The atmosphere connects habitats across multiple spatial scales via airborne dispersal of microbial cells, propagules and biomolecules. Atmospheric microorganisms have been implicated in a variety of biochemical and biophysical transformations. Here, we review ecological aspects of airborne microorganisms with respect to their dispersal, activity and contribution to climatic processes. Latest studies utilizing metagenomic approaches demonstrate that airborne microbial communities exhibit pronounced biogeography, driven by a combination of biotic and abiotic factors. We quantify distributions and fluxes of microbial cells between surface habitats and the atmosphere and place special emphasis on long-range pathogen dispersal. Recent advances have established that these processes may be relevant for macroecological outcomes in terrestrial and marine habitats. We evaluate the potential biological transformation of atmospheric volatile organic compounds and other substrates by airborne microorganisms and discuss clouds as hotspots of microbial metabolic activity in the atmosphere. Furthermore, we emphasize the role of microorganisms as ice nucleating particles and their relevance for the water cycle via formation of clouds and precipitation. Finally, potential impacts of anthropogenic forcing on the natural atmospheric microbiota via emission of particulate matter, greenhouse gases and microorganisms are discussed.

A general trait-based modelling framework for revealing patterns of airborne fungal dispersal threats to agriculture and native flora

Fungal plant pathogens are of economic and ecological importance to global agriculture and natural ecosystems. Long-distance atmospheric dispersal of fungal spores (LAD) can pose threats to agricultural and native vegetation lands. An understanding of such patterns of fungal spore dispersal and invasion pathways can provide valuable insights into plant protection. Spore traits affect their dispersal abilities. We propose a general trait-based framework for modelling LAD to reveal dispersal patterns and pathways, and assess subsequent threats of arrival (TOA) quantitatively in the context of biosecurity. To illustrate the framework, we present a study of Australia and its surrounding land masses. The overall dispersal pattern covered almost the entire continent of Australia. Fungal spores in the size class of 10 and 20 µm (aerodynamic diameter) posed the greatest TOA. Our study shows the effects of morphological traits on these potential TOA, and how they varied between source regions, size classes, and seasons. Our framework revealed spore dispersal patterns and pathways. It also facilitates comparisons of spatio-temporal dispersal dynamics among fungal classes, gaining insights into atmospheric long-distance dispersal of fungi as a whole, and provides a basis for assessing fungal pest threats in potential source regions based on easily measured spore characteristics.

Aeciospore ejection in the rust pathogen Puccinia graminis is driven by moisture ingress

Fungi have evolved an array of spore discharge and dispersal processes. Here, we developed a theoretical model that explains the ejection mechanics of aeciospore liberation in the stem rust pathogen Puccinia graminis. Aeciospores are released from cluster cups formed on its Berberis host, spreading early-season inoculum into neighboring small-grain crops. Our model illustrates that during dew or rainfall, changes in aeciospore turgidity exerts substantial force on neighboring aeciospores in cluster cups whilst gaps between spores become perfused with water. This perfusion coats aeciospores with a lubrication film that facilitates expulsion, with single aeciospores reaching speeds of 0.053 to 0.754 m·s^-1. We also used aeciospore source strength estimates to simulate the aeciospore dispersal gradient and incorporated this into a publicly available web interface. This aids farmers and legislators to assess current local risk of dispersal and facilitates development of sophisticated epidemiological models to potentially curtail stem rust epidemics originating on Berberis.

Regional Relative Risk, a Physics-Based Metric for Characterizing Airborne Infectious Disease Transmission

Airborne infectious disease transmission events occur over a wide range of spatial scales and can be an important means of disease transmission. Physics- and biology-based models can assist in predicting airborne transmission events, overall disease incidence, and disease control strategy efficacy. We describe a new theory that extends current approaches for the case in which an individual is infected by a single airborne particle, including the scenario in which numerous infectious particles are present in the air but only one causes infection. A single infectious particle can contain more than one pathogenic microorganism and be physically larger than the pathogen itself. This approach allows robust relative risk estimates even when there is wide variation in (i) individual exposures and (ii) the individual response to that exposure (the pathogen dose-response function can take any mathematical form and vary by individual). Based on this theory, we propose the regional relative risk-a new metric, distinct from the traditional relative risk metric, that compares the risk between two regions. In theory, these regions can range from individual rooms to large geographic areas. In this paper, we apply the regional relative risk metric to outdoor disease transmission events over spatial scales ranging from 50 m to 20 km, demonstrating that in many common cases minimal input information is required to use the metric. Also, we demonstrate that the model predictions are consistent with data from prior outbreaks. Future efforts could apply and validate this theory for other spatial scales, such as transmission within indoor environments. This work provides context for (i) the initial stages of an airborne disease outbreak and (ii) larger-scale disease spread, including unexpected low-probability disease "sparks" that potentially affect remote populations, a key practical issue in controlling airborne disease outbreaks. IMPORTANCE Airborne infectious disease transmission events occur over a wide range of spatial scales and can be important to disease outbreaks. We describe a new physics- and biology-based theory for the important case in which individuals are infected by a single airborne particle (even though numerous infectious particles can be emitted into the air and inhaled). Based on this theory, we propose a new epidemiological metric, regional relative risk, that compares the risk between two geographic regions (in theory, regions can range from individual rooms to large areas). Our modeling of transmission events predicts that for many scenarios of interest, minimal information is required to use this metric for locations 50 m to 20 km downwind. This prediction is consistent with data from prior disease outbreaks. Future efforts could apply and validate this theory for other spatial scales, such as indoor environments. Our results may be applicable to many airborne diseases a priori, as these results depend on the physics of airborne particulate dispersion.

Quantifying Airborne Dispersal Route of Corynespora cassiicola in Greenhouses

Target leaf spot (TLS), caused by Corynespora cassiicola, is an emerging and high-incidence disease that has spread rapidly on the global scale. Aerospores released by infected plants play a significant role in the epidemiology of cucumber TLS disease; however, no data exist concerning the infectiousness and particle size of C. cassiicola aerospores, and the experimental evidence for the aerospores transmission was lacking. In the present study, highly effective approaches to collect and quantify aerospores were developed for exposure chamber and greenhouse studies. Quantifiable levels of C. cassiicola aerospores were detected in 27 air samples from nine naturally infested greenhouses, ranging from 198 to 5,969 spores/m³. The C. cassiicola strains isolated from air samples were infective to healthy cucumber plants. Exposure chambers were constructed to study the characteristics of C. cassiicola aerospores released by artificially infested cucumber plants. The particle size of C. cassiicola ranged predominately from 2.1 to 4.7 μm, accounting for 71.97% of the total amount. In addition, the transmission dynamics of C. cassiicola aerospores from donor cucumber plants to recipient cucumber plants were confirmed in exposure chambers and greenhouses. The concentration of C. cassiicola aerospores was positively associated with cucumber TLS disease severity. This study suggested that aerospore dispersal is an important route for the epidemiology of plant fungal disease, and these data will contribute to the development of new strategies for the effective alleviation and control of plant diseases.

The Role of Earth Observation in Achieving Sustainable Agricultural Production in Arid and Semi-Arid Regions of the World

Crop production is a major source of food and livelihood for many people in arid and semi-arid (ASA) regions across the world. However, due to irregular climatic events, ASA regions are affected commonly by frequent droughts that can impact food production. In addition, ASA regions in the Middle East and Africa are often characterised by political instability, which can increase population vulnerability to hunger and ill health. Remote sensing (RS) provides a platform to improve the spatial prediction of crop production and food availability, with the potential to positively impact populations. This paper, firstly, describes some of the important characteristics of agriculture in ASA regions that require monitoring to improve their management. Secondly, it demonstrates how freely available RS data can support decision-making through a cost-effective monitoring system that complements traditional approaches for collecting agricultural data. Thirdly, it illustrates the challenges of employing freely available RS data for mapping and monitoring crop area, crop status and forecasting crop yield in these regions. Finally, existing approaches used in these applications are evaluated, and the challenges associated with their use and possible future improvements are discussed. We demonstrate that agricultural activities can be monitored effectively and both crop area and crop yield can be predicted in advance using RS data. We also discuss the future challenges associated with maintaining food security in ASA regions and explore some recent advances in RS that can be used to monitor cropland and forecast crop production and yield.

The persistent threat of emerging plant disease pandemics to global food security

Plant disease outbreaks are increasing and threaten food security for the vulnerable in many areas of the world. Now a global human pandemic is threatening the health of millions on our planet. A stable, nutritious food supply will be needed to lift people out of poverty and improve health outcomes. Plant diseases, both endemic and recently emerging, are spreading and exacerbated by climate change, transmission with global food trade networks, pathogen spillover, and evolution of new pathogen lineages. In order to tackle these grand challenges, a new set of tools that include disease surveillance and improved detection technologies including pathogen sensors and predictive modeling and data analytics are needed to prevent future outbreaks. Herein, we describe an integrated research agenda that could help mitigate future plant disease pandemics.

Potential Infection Risks of the Wheat Stripe Rust and Stem Rust Pathogens on Barberry in Asia and Southeastern Europe

Barberry (Berberis spp.) is an alternate host for both the stripe rust pathogen, Puccinia striiformis f. sp. tritici (Pst), and the stem rust pathogen, P. graminis f. sp. tritici (Pgt), infecting wheat. Infection risk was assessed to determine whether barberry could be infected by either of the pathogens in Asia and Southeastern Europe, known for recurring epidemics on wheat and the presence of barberry habitats. For assessing infection risk, mechanistic infection models were used to calculate infection indices for both pathogens on barberry following a modeling framework. In East Asia, Bhutan, China, and Nepal were found to have low risks of barberry infection by Pst but high risks by Pgt. In Central Asia, Azerbaijan, Iran, Kazakhstan, southern Russia, and Uzbekistan were identified to have low to high risks of barberry infection for both Pst and Pgt. In Northwest Asia, risk levels of both pathogens in Turkey and the Republic of Georgia were determined to be high to very high. In Southwest Asia, no or low risk was found. In Southeastern Europe, similar high or very high risks for both pathogens were noted for all countries. The potential risks of barberry infection by Pst and/or Pgt should provide guidelines for monitoring barberry infections and could be valuable for developing rust management programs in these regions. The framework used in this study may be useful to predict rust infection risk in other regions.

Multi-Scale Airborne Infectious Disease Transmission

Airborne disease transmission is central to many scientific disciplines including agriculture, veterinary biosafety, medicine, and public health. Legal and regulatory standards are in place to prevent agricultural, nosocomial, and community airborne disease transmission. However, the overall importance of the airborne pathway is underappreciated, e.g.,, US National Library of Medicine's Medical Subjects Headings (MESH) thesaurus lacks an airborne disease transmission indexing term. This has practical consequences as airborne precautions to control epidemic disease spread may not be taken when airborne transmission is important, but unrecognized. Publishing clearer practical methodological guidelines for surveillance studies and disease outbreak evaluations could help address this situation.To inform future work, this paper highlights selected, well-established airborne transmission events - largely cases replicated in multiple, independently conducted scientific studies. Methodologies include field experiments, modeling, epidemiology studies, disease outbreak investigations and mitigation studies. Collectively, this literature demonstrates that airborne viruses, bacteria, and fungal pathogens have the capability to cause disease in plants, animals, and humans over multiple distances - from near range (< 5 m) to continental (> 500 km) in scale. The plausibility and implications of undetected airborne disease transmission are discussed, including the notable underreporting of disease burden for several airborne transmitted diseases.

Wheat rust epidemics damage Ethiopian wheat production: A decade of field disease surveillance reveals national-scale trends in past outbreaks

Wheat rusts are the key biological constraint to wheat production in Ethiopia-one of Africa's largest wheat producing countries. The fungal diseases cause economic losses and threaten livelihoods of smallholder farmers. While it is known that wheat rust epidemics have occurred in Ethiopia, to date no systematic long-term analysis of past outbreaks has been available. We present results from one of the most comprehensive surveillance campaigns of wheat rusts in Africa. More than 13,000 fields have been surveyed during the last 13 years. Using a combination of spatial data-analysis and visualization, statistical tools, and empirical modelling, we identify trends in the distribution of wheat stem rust (Sr), stripe rust (Yr) and leaf rust (Lr). Results show very high infection levels (mean incidence for Yr: 44%; Sr: 34%; Lr: 18%). These recurrent rust outbreaks lead to substantial economic losses, which we estimate to be of the order of 10s of millions of US-D annually. On the widely adopted wheat variety, Digalu, there is a marked increase in disease prevalence following the incursion of new rust races into Ethiopia, which indicates a pronounced boom-and-bust cycle of major gene resistance. Using spatial analyses, we identify hotspots of disease risk for all three rusts, show a linear correlation between altitude and disease prevalence, and find a pronounced north-south trend in stem rust prevalence. Temporal analyses show a sigmoidal increase in disease levels during the wheat season and strong inter-annual variations. While a simple logistic curve performs satisfactorily in predicting stem rust in some years, it cannot account for the complex outbreak patterns in other years and fails to predict the occurrence of stripe and leaf rust. The empirical insights into wheat rust epidemiology in Ethiopia presented here provide a basis for improving future surveillance and to inform the development of mechanistic models to predict disease spread.

Metapopulation Structure Predicts Population Dynamics in the Cakile maritima-Alternaria brassicicola Host-Pathogen Interaction

AbstractIn symbiotic interactions, spatiotemporal variation in the distribution or population dynamics of one species represents spatial and temporal heterogeneity of the landscape for the other. Such interdependent demographic dynamics result in situations where the relative importance of biotic and abiotic factors in determining ecological processes is complicated to decipher. Using a detailed survey of three metapopulations of the succulent plant Cakile maritima and the necrotrophic fungus Alternaria brassicicola located along the southeastern Australian coast, we developed a series of statistical analyses-namely, synchrony analysis, patch occupancy dynamics, and a spatially explicit metapopulation model-to understand how habitat quality, weather conditions, dispersal, and spatial structure determine metapopulation dynamics. Climatic conditions are important drivers, likely explaining the high synchrony among populations. Host availability, landscape features facilitating dispersal, and habitat conditions also impact the occurrence and spread of disease. Overall, we show that the collection of extensive data on host and pathogen population dynamics, in combination with spatially explicit epidemiological modeling, makes it possible to accurately predict disease dynamics-even when there is extreme variability in host population dynamics. Finally, we discuss the importance of genetic information for predicting demographic dynamics in this pathosystem.

Detection, forecasting and control of infectious disease epidemics: modelling outbreaks in humans, animals and plants

The 1918 influenza pandemic is one of the most devastating infectious disease epidemics on record, having caused approximately 50 million deaths worldwide. Control measures, including prohibiting non-essential gatherings as well as closing cinemas and music halls, were applied with varying success and limited knowledge of transmission dynamics. One hundred years later, following developments in the field of mathematical epidemiology, models are increasingly used to guide decision-making and devise appropriate interventions that mitigate the impacts of epidemics. Epidemiological models have been used as decision-making tools during outbreaks in human, animal and plant populations. However, as the subject has developed, human, animal and plant disease modelling have diverged. Approaches have been developed independently for pathogens of each host type, often despite similarities between the models used in these complementary fields. With the increased importance of a One Health approach that unifies human, animal and plant health, we argue that more inter-disciplinary collaboration would enhance each of the related disciplines. This pair of theme issues presents research articles written by human, animal and plant disease modellers. In this introductory article, we compare the questions pertinent to, and approaches used by, epidemiological modellers of human, animal and plant pathogens, and summarize the articles in these theme issues. We encourage future collaboration that transcends disciplinary boundaries and links the closely related areas of human, animal and plant disease epidemic modelling. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'. This issue is linked with the subsequent theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'.

Expecting the unexpected: factors influencing the emergence of fungal and oomycete plant pathogens

In recent years, the number of emergent plant pathogens (EPPs) has grown substantially, threatening agroecosystem stability and native biodiversity. Contributing factors include, among others, shifts in biogeography, with EPP spread facilitated by the global unification of monocultures in modern agriculture, high volumes of trade in plants and plant products and an increase in sexual recombination within pathogen populations. The unpredictable nature of EPPs as they move into new territories is a situation that has led to sudden and widespread epidemics. Understanding the underlying causes of pathogen emergence is key to managing the impact of EPPs. Here, we review some factors specifically influencing the emergence of oomycete and fungal EPPs, including new introductions through anthropogenic movement, natural dispersal and weather events, as well as genetic factors linked to shifts in host range.

Presence of a Sexual Population of Puccinia graminis f. sp. tritici in Georgia Provides a Hotspot for Genotypic and Phenotypic Diversity

Wheat stem rust, caused by Puccinia graminis f. sp. tritici, is a re-emerging disease exemplified by recent epidemics caused by new virulent races. Understanding the sources and origins of genetic variations in the pathogen populations globally can facilitate the development of better strategies in disease management. We analyzed 68 wheat stem rust samples collected between 2013 and 2015 from Georgia where stem rust incidences are frequent and the alternate host, common barberry, is present. A total of 116 single-pustule isolates were derived and evaluated on stem rust differential lines to determine the virulence phenotypes and 23 races were identified, many of which were detected for the first time. Unique virulence combinations including, Sr22+Sr24 and Sr13b+Sr35+Sr37 were detected. These virulence combinations pose new challenges to breeding programs because many of these genes are used in breeding for resistance to the Ug99 race group. Sixty-one isolates were genotyped using a custom single-nucleotide polymorphism chip and 17 genotypes were identified. The 2013 isolates contained 11 multilocus genotypes compared with isolates of 2014 and 2015, with five and three genotypes, respectively. The higher levels of virulence and genotypic diversity observed in the 2013 samples strongly indicated that sexual recombination occurs in the Georgian P. graminis f. sp. tritici population, and that the Caucasus region of Eurasia may be an important source of new races.[Formula: see text] Copyright © 2019 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

An early warning system to predict and mitigate wheat rust diseases in Ethiopia

Wheat rust diseases pose one of the greatest threats to global food security, including subsistence farmers in Ethiopia. The fungal spores transmitting wheat rust are dispersed by wind and can remain infectious after dispersal over long distances. The emergence of new strains of wheat rust has exacerbated the risks of severe crop loss. We describe the construction and deployment of a near realtime early warning system (EWS) for two major wind-dispersed diseases of wheat crops in Ethiopia that combines existing environmental research infrastructures, newly developed tools and scientific expertise across multiple organisations in Ethiopia and the UK. The EWS encompasses a sophisticated framework that integrates field and mobile phone surveillance data, spore dispersal and disease environmental suitability forecasting, as well as communication to policy-makers, advisors and smallholder farmers. The system involves daily automated data flow between two continents during the wheat season in Ethiopia. The framework utilises expertise and environmental research infrastructures from within the cross-disciplinary spectrum of biology, agronomy, meteorology, computer science and telecommunications. The EWS successfully provided timely information to assist policy makers formulate decisions about allocation of limited stock of fungicide during the 2017 and 2018 wheat seasons. Wheat rust alerts and advisories were sent by short message service and reports to 10 000 development agents and approximately 275 000 smallholder farmers in Ethiopia who rely on wheat for subsistence and livelihood security. The framework represents one of the first advanced crop disease EWSs implemented in a developing country. It provides policy-makers, extension agents and farmers with timely, actionable information on priority diseases affecting a staple food crop. The framework together with the underpinning technologies are transferable to forecast wheat rusts in other regions and can be readily adapted for other wind-dispersed pests and disease of major agricultural crops.

Plant-Pathogen Warfare under Changing Climate Conditions

Global environmental changes caused by natural and human activities have accelerated in the past 200 years. The increase in greenhouse gases is predicted to continue to raise global temperature and change water availability in the 21^st century. In this Review, we explore the profound effect the environment has on plant diseases - a susceptible host will not be infected by a virulent pathogen if the environmental conditions are not conducive for disease. The change in CO₂ concentrations, temperature, and water availability can have positive, neutral, or negative effects on disease development, as each disease may respond differently to these variations. However, the concept of disease optima could potentially apply to all pathosystems. Plant resistance pathways, including pattern-triggered immunity to effector-triggered immunity, RNA interference, and defense hormone networks, are all affected by environmental factors. On the pathogen side, virulence mechanisms, such as the production of toxins and virulence proteins, as well as pathogen reproduction and survival are influenced by temperature and humidity. For practical reasons, most laboratory investigations into plant-pathogen interactions at the molecular level focus on well-established pathosystems and use a few static environmental conditions that capture only a fraction of the dynamic plant-pathogen-environment interactions that occur in nature. There is great need for future research to increasingly use dynamic environmental conditions in order to fully understand the multidimensional nature of plant-pathogen interactions and produce disease-resistant crop plants that are resilient to climate change.

Cooperation and Coordination in Plant Disease Management

Scaling of management efforts beyond the boundaries of individual farms may require that individuals act collectively. Such approaches have been suggested several times in plant pathology contexts but rarely have been implemented, in part because the institutional structures that enable successful collective action are poorly understood. In this research, we conducted in-depth interviews with hop producers in Oregon and Washington State to identify their motivations for and barriers to collective action regarding communication of disease levels, coordination of management practices, and sharing of best management practices and other data for powdery mildew (caused by Podosphaera macularis). Growers were generally open to and engaged in communication with neighbors and others on disease status in their hop yards and some evidence of higher levels of information sharing on management practices was found. However, growers who had developed extensive knowledge and databases were reluctant to share information viewed as proprietary. Relationships, trust, and reciprocity were facilitating factors for communication and information sharing, whereas lack of these factors and social norms of independence and pride in portions of the grower community were identified as impediments. Given the heterogeneity of trust, lack of confidence in reciprocity, and weak shared norms, communication of disease risk and coordinated management may be most successful if directed at a smaller scale as a series of neighborhood-based partnerships of growers and their immediate neighbors. Developing a disease reporting system and coordinated disease management efforts with more producers and at larger spatial extents would require formalized structures and rules that would provide assurance that there is consistency in disease data collection and reporting, reciprocation, and sanctions for those who use the information for marketing purposes against other growers. Given the analyses presented here, we believe there is potential for collective action in disease management but with limitations on the scope and nature of the actions.

MARPLE, a point-of-care, strain-level disease diagnostics and surveillance tool for complex fungal pathogens

Background

Effective disease management depends on timely and accurate diagnosis to guide control measures. The capacity to distinguish between individuals in a pathogen population with specific properties such as fungicide resistance, toxin production and virulence profiles is often essential to inform disease management approaches. The genomics revolution has led to technologies that can rapidly produce high-resolution genotypic information to define individual variants of a pathogen species. However, their application to complex fungal pathogens has remained limited due to the frequent inability to culture these pathogens in the absence of their host and their large genome sizes.

Results

Here, we describe the development of Mobile And Real-time PLant disEase (MARPLE) diagnostics, a portable, genomics-based, point-of-care approach specifically tailored to identify individual strains of complex fungal plant pathogens. We used targeted sequencing to overcome limitations associated with the size of fungal genomes and their often obligately biotrophic nature. Focusing on the wheat yellow rust pathogen, Puccinia striiformis f.sp. tritici (Pst), we demonstrate that our approach can be used to rapidly define individual strains, assign strains to distinct genetic lineages that have been shown to correlate tightly with their virulence profiles and monitor genes of importance.

Conclusions

MARPLE diagnostics enables rapid identification of individual pathogen strains and has the potential to monitor those with specific properties such as fungicide resistance directly from field-collected infected plant tissue in situ. Generating results within 48 h of field sampling, this new strategy has far-reaching implications for tracking plant health threats.

Electronic supplementary material

The online version of this article (10.1186/s12915-019-0684-y) contains supplementary material, which is available to authorized users.

Prediction of Spread and Regional Development of Hop Powdery Mildew: A Network Analysis

Dispersal is a fundamental aspect of epidemic development at multiple spatial scales, including those that extend beyond the borders of individual fields and to the landscape level. In this research, we used the powdery mildew of the hop pathosystem (caused by Podosphaera macularis) to formulate a model of pathogen dispersal during spring (May to June) and early summer (June to July) at the intermediate scale between synoptic weather systems and microclimate (mesoscale) based on a census of commercial hop yards during 2014 to 2017 in a production region in western Oregon. This pathosystem is characterized by a low level of overwintering of the pathogen as a result of absence of the ascigerious stage of the fungus and consequent annual cycles of localized survival via bud perennation and pathogen spread by windborne dispersal. An individual hop yard was considered a node in the model, whose disease status in a given month was expressed as a nonlinear function of disease incidence in the preceding month, susceptibility to two races of the fungus, and disease spread from other nodes as influenced by their disease incidence, area, distance away, and wind run and direction in the preceding month. Parameters were estimated by maximum likelihood over all 4 years but were allowed to vary for time transition periods from May to June and from June to July. The model accounted for 34 to 90% of the observed variation in disease incidence at the field level, depending on the year and season. Network graphs and analyses suggest that dispersal was dominated by relatively localized dispersal events (<2 km) among the network of fields, being mostly restricted to the same or adjacent farms. When formed, predicted disease attributable to dispersal from other hop yards (edges) associated with longer distance dispersal was more frequent in the June to July time transition. Edges with a high probability of disease transmission were formed in instances where yards were in close proximity or where disease incidence was relatively high in large hop yards, as moderated by wind run. The modeling approach provides a flexible and generalizable framework for understanding and predicting pathogen dispersal at the regional level as well as the implications of network connectivity on epidemic development.

A new identified suppressor of Cdc7p/SepH kinase, PomA, regulates fungal asexual reproduction via affecting phosphorylation of MAPK-HogA

The septation initiation network (SIN), composed of a conserved SepH (Cdc7p) kinase cascade, plays an essential role in fungal cytokinesis/septation and conidiation for asexual reproduction, while the mitogen-activated protein kinase (MAPK) pathway depends on successive signaling cascade phosphorylation to sense and respond to stress and environmental factors. In this study, a SepH suppressor-PomA in the filamentous fungus A. nidulans is identified as a negative regulator of septation and conidiation such that the pomA mutant is able to cure defects of sepH1 in septation and conidiation and overexpression of pomA remarkably suppresses septation. Under the normal cultural condition, SepH positively regulates the phosphorylation of MAPK-HogA, while PomA reversely affects this process. In the absence of PbsB (MAPKK, a putative upstream member of HogA), PomA and SepH are unable to affect the phosphorylation level of HogA. Under the osmostress condition, the induced phosphorylated HogA is capable of bypassing the requirement of SepH, a key player for early events during cytokinesis but not for MobA/SidB, the last one in the core SIN protein kinase cascade, indicating the osmotic stimuli-induced septation is capable of bypassing requirement of SepH but unable to bypass the whole SIN requirement. Findings demonstrate that crosstalk exists between the SIN and MAPK pathways. PomA and SepH indirectly regulate HogA phosphorylation through affecting HogA-P upstream kinases.

Three phylogenetic groups have driven the recent population expansion of Cryptococcus neoformans

Cryptococcus neoformans (C. neoformans var. grubii) is an environmentally acquired pathogen causing 181,000 HIV-associated deaths each year. We sequenced 699 isolates, primarily C. neoformans from HIV-infected patients, from 5 countries in Asia and Africa. The phylogeny of C. neoformans reveals a recent exponential population expansion, consistent with the increase in the number of susceptible hosts. In our study population, this expansion has been driven by three sub-clades of the C. neoformans VNIa lineage; VNIa-4, VNIa-5 and VNIa-93. These three sub-clades account for 91% of clinical isolates sequenced in our study. Combining the genome data with clinical information, we find that the VNIa-93 sub-clade, the most common sub-clade in Uganda and Malawi, was associated with better outcomes than VNIa-4 and VNIa-5, which predominate in Southeast Asia. This study lays the foundation for further work investigating the dominance of VNIa-4, VNIa-5 and VNIa-93 and the association between lineage and clinical phenotype.

Movement Ecology of Pest Helicoverpa: Implications for Ongoing Spread

The recent introduction and spread of Helicoverpa armigera throughout South America highlight the invasiveness and adaptability of moths in the Helicoverpa genus. Long-range movement in three key members, H. armigera, H. zea, and H. punctigera, occurs by migration and international trade. These movements facilitate high population admixture and genetic diversity, with important economic, biosecurity, and control implications in today's agricultural landscape. This is particularly true for the spread of resistance alleles to transgenic crops expressing Bacillus thuringiensis (Bt) toxins that are planted over vast areas to suppress Helicoverpa spp. The ability to track long-distance movement through radar technology, population genetic markers, and/or long-distance dispersal modeling has advanced in recent years, yet we still know relatively little about the population trajectories or migratory routes in Helicoverpa spp. Here, we consider how experimental and theoretical approaches can be integrated to fill key knowledge gaps and assist management practices.

Microsatellite Analysis and Urediniospore Dispersal Simulations Support the Movement of Puccinia graminis f. sp. tritici from Southern Africa to Australia

The Australian wheat stem rust (Puccinia graminis f. sp. tritici) population was shaped by the introduction of four exotic incursions into the country. It was previously hypothesized that at least two of these (races 326-1,2,3,5,6 and 194-1,2,3,5,6 first detected in 1969) had an African origin and moved across the Indian Ocean to Australia on high-altitude winds. We provide strong supportive evidence for this hypothesis by combining genetic analyses and complex atmospheric dispersion modeling. Genetic analysis of 29 Australian and South African P. graminis f. sp. tritici races using microsatellite markers confirmed the close genetic relationship between the South African and Australian populations, thereby confirming previously described phenotypic similarities. Lagrangian particle dispersion model simulations using finely resolved meteorological data showed that long distance dispersal events between southern Africa and Australia are indeed possible, albeit rare. Simulated urediniospore transmission events were most frequent from central South Africa (viable spore transmission on approximately 7% of all simulated release days) compared with other potential source regions in southern Africa. The study acts as a warning of possible future P. graminis f. sp. tritici dispersal events from southern Africa to Australia, which could include members of the Ug99 race group, emphasizing the need for continued surveillance on both continents.

Assessing the Aerial Interconnectivity of Distant Reservoirs of Sclerotinia sclerotiorum

Many phytopathogenic fungi are disseminated as spores via the atmosphere from short to long distances. The distance of dissemination determines the extent to which plant diseases can spread and novel genotypes of pathogens can invade new territories. Predictive tools including models that forecast the arrival of spores in areas where susceptible crops are grown can help to more efficiently manage crop health. However, such models are difficult to establish for fungi with broad host ranges because sources of inoculum cannot be readily identified. Sclerotinia sclerotiorum, the pandemic agent of white mold disease, can attack >400 plant species including economically important crops. Monitoring airborne inoculum of S. sclerotiorum in several French cropping areas has shown that viable ascospores are present in the air almost all the time, even when no susceptible crops are nearby. This raises the hypothesis of a distant origin of airborne inoculum. The objective of the present study was to determine the interconnectivity of reservoirs of S. sclerotiorum from distant regions based on networks of air mass movement. Viable airborne inoculum of S. sclerotiorum was collected in four distinct regions of France and 498 strains were genotyped with 16 specific microsatellite markers and compared among the regions. Air mass movements were inferred using the HYSPLIT model and archived meteorological data from the global data assimilation system (GDAS). The results show that up to 700 km could separate collection sites that shared the same haplotypes. There was low or no genetic differentiation between strains collected from the four sites. The rate of aerial connectivity between two sites varied according to the direction considered. The results also show that the aerial connectivity between sites is a better indicator of the probability of the incoming component (PIC) of inoculum at a given site from another one than is geographic distance. We identified the links between specific sites in the trajectories of air masses and we quantified the frequencies at which the directional links occurred as a proof-of-concept for an operational method to assess the arrival of airborne inoculum in a given area from distant origins.

Network Analysis: A Systems Framework to Address Grand Challenges in Plant Pathology

Plant pathology must address a number of challenges, most of which are characterized by complexity. Network analysis offers useful tools for addressing complex systems and an opportunity for synthesis within plant pathology and between it and relevant disciplines such as in the social sciences. We discuss applications of network analysis, which ultimately may be integrated together into more synthetic analyses of how to optimize plant disease management systems. The analysis of microbiome networks and tripartite phytobiome networks of host-vector-pathogen interactions offers promise for identifying biocontrol strategies and anticipating disease emergence. Linking epidemic network analysis with social network analysis will support strategies for sustainable agricultural development and for scaling up solutions for disease management. Statistical tools for evaluating networks, such as Bayesian network analysis and exponential random graph models, have been underused in plant pathology and are promising for informing strategies. We conclude with research priorities for network analysis applications in plant pathology.