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

Climate change impacts on plant pathogens, food security and paths forward

Plant disease outbreaks pose significant risks to global food security and environmental sustainability worldwide, and result in the loss of primary productivity and biodiversity that negatively impact the environmental and socio-economic conditions of affected regions. Climate change further increases outbreak risks by altering pathogen evolution and host-pathogen interactions and facilitating the emergence of new pathogenic strains. Pathogen range can shift, increasing the spread of plant diseases in new areas. In this Review, we examine how plant disease pressures are likely to change under future climate scenarios and how these changes will relate to plant productivity in natural and agricultural ecosystems. We explore current and future impacts of climate change on pathogen biogeography, disease incidence and severity, and their effects on natural ecosystems, agriculture and food production. We propose that amendment of the current conceptual framework and incorporation of eco-evolutionary theories into research could improve our mechanistic understanding and prediction of pathogen spread in future climates, to mitigate the future risk of disease outbreaks. We highlight the need for a science-policy interface that works closely with relevant intergovernmental organizations to provide effective monitoring and management of plant disease under future climate scenarios, to ensure long-term food and nutrient security and sustainability of natural ecosystems.

More Than the Sum of Its Parts: Unlocking the Power of Network Structure for Understanding Organization and Function in Microbiomes

Plant and soil microbiomes are integral to the health and productivity of plants and ecosystems, yet researchers struggle to identify microbiome characteristics important for providing beneficial outcomes. Network analysis offers a shift in analytical framework beyond "who is present" to the organization or patterns of coexistence between microbes within the microbiome. Because microbial phenotypes are often significantly impacted by coexisting populations, patterns of coexistence within microbiomes are likely to be especially important in predicting functional outcomes. Here, we provide an overview of the how and why of network analysis in microbiome research, highlighting the ways in which network analyses have provided novel insights into microbiome organization and functional capacities, the diverse network roles of different microbial populations, and the eco-evolutionary dynamics of plant and soil microbiomes.

Plant Disease Models and Forecasting: Changes in Principles and Applications Over the Last 50 Years

This review gives a perspective of selected advances made since the middle of the 20th century in plant disease modeling, and the associated increase in the number of models published during that time frame. This progress can be mainly attributed to advances in (i) sensors and automatic environmental data collection technology, (ii) instrumentation and methods for studying botanical epidemiology, and (iii) data analytics and computer science. We review the evolution of techniques for developing data-based (empirical) models and process-based (mechanistic) models using the wheat rusts as a case study. We also describe the increased importance of knowledge about biological processes for plant disease modeling by using apple scab as a second case study. For both wheat rusts and apple scab, we describe how the models have evolved over the last 50 years by considering certain milestones that have been achieved in disease modeling. Finally, we describe how plant disease models are used as part of a multi-modeling approach to develop decision-making tools in the application of integrated pest management.

Direct and indirect viral associations predict coexistence in wild plant virus communities

Viruses are a vastly underestimated component of biodiversity that occur as diverse communities across hierarchical scales from the landscape level to individual hosts. The integration of community ecology with disease biology is a powerful, novel approach that can yield unprecedented insights into the abiotic and biotic drivers of pathogen community assembly. Here, we sampled wild plant populations to characterize and analyze the diversity and co-occurrence structure of within-host virus communities and their predictors. Our results show that these virus communities are characterized by diverse, non-random coinfections. Using a novel graphical network modeling framework, we demonstrate how environmental heterogeneity influences the network of virus taxa and how the virus co-occurrence patterns can be attributed to non-random, direct statistical virus-virus associations. Moreover, we show that environmental heterogeneity changed virus association networks, especially through their indirect effects. Our results highlight a previously underestimated mechanism of how environmental variability can influence disease risks by changing associations between viruses that are conditional on their environment.

Translating virome analyses to support biosecurity, on-farm management, and crop breeding

Virome analysis via high-throughput sequencing (HTS) allows rapid and massive virus identification and diagnoses, expanding our focus from individual samples to the ecological distribution of viruses in agroecological landscapes. Decreases in sequencing costs combined with technological advances, such as automation and robotics, allow for efficient processing and analysis of numerous samples in plant disease clinics, tissue culture laboratories, and breeding programs. There are many opportunities for translating virome analysis to support plant health. For example, virome analysis can be employed in the development of biosecurity strategies and policies, including the implementation of virome risk assessments to support regulation and reduce the movement of infected plant material. A challenge is to identify which new viruses discovered through HTS require regulation and which can be allowed to move in germplasm and trade. On-farm management strategies can incorporate information from high-throughput surveillance, monitoring for new and known viruses across scales, to rapidly identify important agricultural viruses and understand their abundance and spread. Virome indexing programs can be used to generate clean germplasm and seed, crucial for the maintenance of seed system production and health, particularly in vegetatively propagated crops such as roots, tubers, and bananas. Virome analysis in breeding programs can provide insight into virus expression levels by generating relative abundance data, aiding in breeding cultivars resistant, or at least tolerant, to viruses. The integration of network analysis and machine learning techniques can facilitate designing and implementing management strategies, using novel forms of information to provide a scalable, replicable, and practical approach to developing management strategies for viromes. In the long run, these management strategies will be designed by generating sequence databases and building on the foundation of pre-existing knowledge about virus taxonomy, distribution, and host range. In conclusion, virome analysis will support the early adoption and implementation of integrated control strategies, impacting global markets, reducing the risk of introducing novel viruses, and limiting virus spread. The effective translation of virome analysis depends on capacity building to make benefits available globally.

Where to Invest Project Efforts for Greater Benefit: A Framework for Management Performance Mapping with Examples for Potato Seed Health

Policymakers and donors often need to identify the locations where technologies are most likely to have important effects, to increase the benefits from agricultural development or extension efforts. Higher-quality information may help to target the high-benefit locations, but often actions are needed with limited information. The value of information (VOI) in this context is formalized by evaluating the results of decision making guided by a set of specific information compared with the results of acting without considering that information. We present a framework for management performance mapping that includes evaluating the VOI for decision making about geographic priorities in regional intervention strategies, in case studies of Andean and Kenyan potato seed systems. We illustrate the use of recursive partitioning, XGBoost, and Bayesian network models to characterize the relationships among seed health and yield responses and environmental and management predictors used in studies of seed degeneration. These analyses address the expected performance of an intervention based on geographic predictor variables. In the Andean example, positive selection of seed from asymptomatic plants was more effective at high altitudes in Ecuador. In the Kenyan example, there was the potential to target locations with higher technology adoption rates and with higher potato cropland connectivity, i.e., a likely more important role in regional epidemics. Targeting training to high management performance areas would often provide more benefits than would random selection of target areas. We illustrate how assessing the VOI can contribute to targeted development programs and support a culture of continuous improvement for interventions.[Formula: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Identifying Associations in Minimum Inhibitory Concentration Values of Escherichia coli Samples Obtained From Weaned Dairy Heifers in California Using Bayesian Network Analysis

Objective

Many antimicrobial resistance (AMR) studies in both human and veterinary medicine use traditional statistical methods that consider one bacteria and one antibiotic match at a time. A more robust analysis of AMR patterns in groups of animals is needed to improve on traditional methods examining antibiotic resistance profiles, the associations between the patterns of resistance or reduced susceptibility for all isolates in an investigation. The use of Bayesian network analysis can identify associations between distributions; this investigation seeks to add to the growing body of AMR pattern research by using Bayesian networks to identify relationships between susceptibility patterns in Escherichia coli (E. coli) isolates obtained from weaned dairy heifers in California.

Methods

A retrospective data analysis was performed using data from rectal swab samples collected from 341 weaned dairy heifers on six farms in California and selectively cultured for E. coli. Antibiotic susceptibility tests for 281 isolates against 15 antibiotics were included. Bayesian networks were used to identify joint patterns of reduced susceptibility, defined as an increasing trend in the minimum inhibitory concentration (MIC) values. The analysis involved learning the network structure, identifying the best fitting graphical mode, and learning the parameters in the final model to quantify joint probabilities.

Results

The graph identified that as susceptibility to one antibiotic decreases, so does susceptibility to other antibiotics in the same or similar class. The following antibiotics were connected in the final graphical model: ampicillin was connected to ceftiofur; spectinomycin was connected with trimethoprim-sulfamethoxazole, and this association was mediated by farm; florfenicol was connected with tetracycline.

Conclusions

Bayesian network analysis can elucidate complex relationships between MIC patterns. MIC values may be associated within and between drug classes, and some associations may be correlated with farm of sample origin. Treating MICs as discretized variables and testing for joint associations in trends may overcome common research problems surrounding the lack of clinical breakpoints.

Whole genome sequences reveal the Xanthomonas perforans population is shaped by the tomato production system

Modern agricultural practices increase the potential for plant pathogen spread, while the advent of affordable whole genome sequencing enables in-depth studies of pathogen movement. Population genomic studies may decipher pathogen movement and population structure as a result of complex agricultural production systems. We used whole genome sequences of 281 Xanthomonas perforans strains collected within one tomato production season across Florida and southern Georgia fields to test for population genetic structure associated with tomato production system variables. We identified six clusters of X. perforans from core gene SNPs that corresponded with phylogenetic lineages. Using whole genome SNPs, we found genetic structure among farms, transplant facilities, cultivars, seed producers, grower operations, regions, and counties. Overall, grower operations that produced their own transplants were associated with genetically distinct and less diverse populations of strains compared to grower operations that received transplants from multiple sources. The degree of genetic differentiation among components of Florida's tomato production system varied between clusters, suggesting differential dispersal of the strains, such as through seed or contaminated transplants versus local movement within farms. Overall, we showed that the genetic variation of a bacterial plant pathogen is shaped by the structure of the plant production system.

Ecological Dynamics and Microbial Treatments against Oomycete Plant Pathogens

In this review, we explore how ecological concepts may help assist with applying microbial biocontrol agents to oomycete pathogens. Oomycetes cause a variety of agricultural diseases, including potato late blight, apple replant diseases, and downy mildew of grapevine, which also can lead to significant economic damage in their respective crops. The use of microbial biocontrol agents is increasingly gaining interest due to pressure from governments and society to reduce chemical plant protection products. The success of a biocontrol agent is dependent on many ecological processes, including the establishment on the host, persistence in the environment, and expression of traits that may be dependent on the microbiome. This review examines recent literature and trends in research that incorporate ecological aspects, especially microbiome, host, and environmental interactions, into biological control development and applications. We explore ecological factors that may influence microbial biocontrol agents’ efficacy and discuss key research avenues forward.

The phytosanitary risks posed by seeds for sowing trade networks

When successful, the operation of local and international networks of crop seed distribution or “seed systems” ensures farmer access to seed and impacts rural livelihoods and food security. Farmers are both consumers and producers in seed systems and benefit from access to global markets. However, phytosanitary measures and seed purity tests are also needed to maintain seed quality and prevent the spread of costly weeds, pests and diseases, in some countries regulatory controls have been in place since the 1800s. Nevertheless, seed contaminants are internationally implicated in between 7% and 37% of the invasive plant species and many of the agricultural pests and diseases. We assess biosecurity risk across international seed trade networks of forage crops using models of contaminant spread that integrate network connectivity and trade volume. To stochastically model hypothetical contaminants through global seed trade networks, realistic dispersal probabilities were estimated from quarantine weed seed detections and incursions from border security interception data in New Zealand. For our test case we use contaminants linked to the global trade of ryegrass and clover seed. Between 2014 and 2018 only four quarantine weed species (222 species and several genera are on the quarantine schedule) warranting risk mitigation were detected at the border. Quarantine weeds were rare considering that average import volumes were over 190 tonnes for ryegrass and clover, but 105 unregulated contaminant species were allowed in. Ryegrass and clover seed imports each led to one post-border weed incursion response over 20 years. Trade reports revealed complex global seed trade networks spanning >134 (ryegrass) and >110 (clover) countries. Simulations showed contaminants could disperse to as many as 50 (clover) or 80 (ryegrass) countries within 10 time-steps. Risk assessed via network models differed 18% (ryegrass) or 48% (clover) of the time compared to risk assessed on trade volumes. We conclude that biosecurity risk is driven by network position, the number of trading connections and trade volume. Risk mitigation measures could involve the use of more comprehensive lists of regulated species, comprehensive inspection protocols, or the addition of field surveillance at farms where seed is planted.

The Role of Plant-Associated Bacteria, Fungi, and Viruses in Drought Stress Mitigation

Drought stress is an alarming constraint to plant growth, development, and productivity worldwide. However, plant-associated bacteria, fungi, and viruses can enhance stress resistance and cope with the negative impacts of drought through the induction of various mechanisms, which involve plant biochemical and physiological changes. These mechanisms include osmotic adjustment, antioxidant enzyme enhancement, modification in phytohormonal levels, biofilm production, increased water and nutrient uptake as well as increased gas exchange and water use efficiency. Production of microbial volatile organic compounds (mVOCs) and induction of stress-responsive genes by microbes also play a crucial role in the acquisition of drought tolerance. This review offers a unique exploration of the role of plant-associated microorganisms-plant growth promoting rhizobacteria and mycorrhizae, viruses, and their interactions-in the plant microbiome (or phytobiome) as a whole and their modes of action that mitigate plant drought stress.

Harnessing Eco-Evolutionary Dynamics of Xanthomonads on Tomato and Pepper to Tackle New Problems of an Old Disease

Bacterial spot is an endemic seedborne disease responsible for recurring outbreaks on tomato and pepper around the world. The disease is caused by four diverse species, Xanthomonas gardneri, Xanthomonas euvesicatoria, Xanthomonas perforans, and Xanthomonas vesicatoria. There are no commercially available disease-resistant tomato varieties, and the disease is managed by chemical/biological control options, although these have not reduced the incidence of outbreaks. The disease on peppers is managed by disease-resistant cultivars that are effective against X. euvesicatoria but not X. gardneri. A significant shift in composition and prevalence of different species and races of the pathogen has occurred over the past century. Here, I attempt to review ecological and evolutionary processes associated with the population dynamics leading to disease emergence and spread. The goal of this review is to integrate the knowledge on population genomics and molecular plant-microbe interactions for this pathosystem to tailor disease management strategies.

Assessing Changes and Associations in the Xanthomonas perforans Population Across Florida Commercial Tomato Fields Via a Statewide Survey

Before 1991, Xanthomonas euvesicatoria was the causal agent of bacterial spot of tomato in Florida but was quickly replaced by X. perforans. The X. perforans population has changed in genotype and phenotype despite lack of a clear selection pressure. To determine the current Xanthomonas population in Florida, we collected 585 Xanthomonas strains from 70 tomato fields, representing 22 farms across eight counties, in the Florida tomato production region. Strains were isolated from 23 cultivars across eight seed producers and were associated with eight transplant facilities during the fall 2017 season. Our collection was phenotypically and genotypically characterized. Only X. perforans was identified, and all strains except one (99.8%) were tolerant to copper sulfate and 25% of strains were resistant to streptomycin sulfate. Most of the strains (99.3%) that were resistant to streptomycin sulfate were sequence type 1. The X. perforans population consisted of tomato races 3 (8%) and 4 (92%) and all three previously reported sequence types, ranging from 22 to 46% frequency. Approximately half of all strains, none of which were sequence type 2, produced bacteriocins against X. euvesicatoria. Effector profiles were highly variable among strains, which could impact the strains' host range. The effector xopJ4, which was previously thought to be conserved in X. perforans tomato pathogens, was absent in 19 strains. Nonmetric multidimensional scaling and network analyses show how strains and strain traits were associated with production system variables, including anonymized farms and transplant facilities. These analyses show that the composition of the Florida X. perforans population is diverse and complex.

Global Advances in Tomato Virome Research: Current Status and the Impact of High-Throughput Sequencing

Viruses cause a big fraction of economically important diseases in major crops, including tomato. In the past decade (2011–2020), many emerging or re-emerging tomato-infecting viruses were reported worldwide. In this period, 45 novel viral species were identified in tomato, 14 of which were discovered using high-throughput sequencing (HTS). In this review, we first discuss the role of HTS in these discoveries and its general impact on tomato virome research. We observed that the rate of tomato virus discovery is accelerating in the past few years due to the use of HTS. However, the extent of the post-discovery characterization of viruses is lagging behind and is greater for economically devastating viruses, such as the recently emerged tomato brown rugose fruit virus. Moreover, many known viruses still cause significant economic damages to tomato production. The review of databases and literature revealed at least 312 virus, satellite virus, or viroid species (in 22 families and 39 genera) associated with tomato, which is likely the highest number recorded for any plant. Among those, here, we summarize the current knowledge on the biology, global distribution, and epidemiology of the most important species. Increasing knowledge on tomato virome and employment of HTS to also study viromes of surrounding wild plants and environmental samples are bringing new insights into the understanding of epidemiology and ecology of tomato-infecting viruses and can, in the future, facilitate virus disease forecasting and prevention of virus disease outbreaks in tomato.

"Breaking through the 40% adoption ceiling: Mind the seed system gaps." A perspective on seed systems research for development in One CGIAR

Seed systems research is central to achieving the United Nations Sustainable Development Goals. Improved varieties with promise for ending hunger, improving nutrition, and increasing livelihood security may be released, but how do they reach and benefit different types of farmers? Without widespread adoption the genetic gains achieved with improved crop varieties can never be actualized. Progress has been made toward demand responsive breeding, however the draft CGIAR 2030 Research and Innovation Strategy fails to recognize the complexity of seed systems and thus presents a narrow vision for the future of seed systems research. This points to the lack of evidence-based dialogue between seed systems researchers and breeders. This perspective paper presents findings from an interdisciplinary group of more than 50 CGIAR scientists who used a suite of seed systems tools to identify four knowledge gaps and associated insights from work on the seed systems for vegetatively propagated crops (VPCs), focusing on bananas (especially cooking bananas and plantains), cassava, potato, sweetpotato, and yam. We discuss the implications for thinking about and intervening in seed systems using a combined biophysical and socioeconomic perspective and how this can contribute to increased varietal adoption and benefits to farmers. The tools merit wider use, not only for the seed systems of VPCs, but for the seed of crops facing similar adoption challenges. We argue for deeper collaboration between seed systems researchers, breeders and national seed system stakeholders to address these and other knowledge gaps and generate the evidence and innovations needed to break through the 40% adoption ceiling for modern varieties, and ensure good quality seed once the new varieties have been adopted. Without this, the achievements of breeders may remain stuck in the seed delivery pipeline.

Institutional approaches for plant health provision as a collective action problem

The provision of plant health has public good attributes when nobody can be excluded from enjoying its benefits and individual benefits do not reduce the ability of others to also benefit. These attributes increase risk of free-riding on plant health services provided by others, giving rise to a collective action problem when trying to ensure plant health in a region threatened by an emerging plant disease. This problem has traditionally been addressed by government intervention, but top-down approaches to plant health are often insufficient and are increasingly combined with bottom-up approaches that promote self-organization by affected individuals. The challenge is how to design plant health institutions that effectively deal with the spatial and temporal dynamics of plant diseases, while staying aligned with the preferences, values and needs of affected societies. Here, we illustrate how Ostrom’s design principles for collective action can be used to guide the incorporation of bottom-up approaches to plant health governance in order to improve institutional fit. Using the ongoing epidemic of huanglongbing (HLB) as a case study, we examine existing institutions designed to ensure citrus health under HLB in Brazil, Mexico, the United States and Argentina, and discuss potential implications of Ostrom’s design principles for the collective provision of plant health under HLB and other plant diseases that are threatening food security worldwide. The discussion leads to an outline for the interdisciplinary research agenda that would be needed to establish the link between institutional approaches and plant health outcomes in the context of global food security.

Global Cropland Connectivity: A Risk Factor for Invasion and Saturation by Emerging Pathogens and Pests

The geographic pattern of cropland is an important risk factor for invasion and saturation by crop-specific pathogens and arthropods. Understanding cropland networks supports smart pest sampling and mitigation strategies. We evaluate global networks of cropland connectivity for key vegetatively propagated crops (banana and plantain, cassava, potato, sweet potato, and yam) important for food security in the tropics. For each crop, potential movement between geographic location pairs was evaluated using a gravity model, with associated uncertainty quantification. The highly linked hub and bridge locations in cropland connectivity risk maps are likely priorities for surveillance and management, and for tracing intraregion movement of pathogens and pests. Important locations are identified beyond those locations that simply have high crop density. Cropland connectivity risk maps provide a new risk component for integration with other factors-such as climatic suitability, genetic resistance, and global trade routes-to inform pest risk assessment and mitigation.

One model to rule them all? Modelling approaches across OneHealth for human, animal and plant epidemics

One hundred years after the 1918 influenza outbreak, are we ready for the next pandemic? This paper addresses the need to identify and develop collaborative, interdisciplinary and cross-sectoral approaches to modelling of infectious diseases including the fields of not only human and veterinary medicine, but also plant epidemiology. Firstly, the paper explains the concepts on which the most common epidemiological modelling approaches are based, namely the division of a host population into susceptible, infected and removed (SIR) classes and the proportionality of the infection rate to the size of the susceptible and infected populations. It then demonstrates how these simple concepts have been developed into a vast and successful modelling framework that has been used in predicting and controlling disease outbreaks for over 100 years. Secondly, it considers the compartmental models based on the SIR paradigm within the broader concept of a 'disease tetrahedron' (comprising host, pathogen, environment and man) and uses it to review the similarities and differences among the fields comprising the 'OneHealth' approach. Finally, the paper advocates interactions between all fields and explores the future challenges facing modellers. 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'.

Effective Altruism as an Ethical Lens on Research Priorities

Effective altruism is an ethical framework for identifying the greatest potential benefits from investments. Here, we apply effective altruism concepts to maximize research benefits through identification of priority stakeholders, pathosystems, and research questions and technologies. Priority stakeholders for research benefits may include smallholder farmers who have not yet attained the minimal standards set out by the United Nations Sustainable Development Goals; these farmers would often have the most to gain from better crop disease management, if their management problems are tractable. In wildlands, prioritization has been based on the risk of extirpating keystone species, protecting ecosystem services, and preserving wild resources of importance to vulnerable people. Pathosystems may be prioritized based on yield and quality loss, and also factors such as whether other researchers would be unlikely to replace the research efforts if efforts were withdrawn, such as in the case of orphan crops and orphan pathosystems. Research products that help build sustainable and resilient systems can be particularly beneficial. The "value of information" from research can be evaluated in epidemic networks and landscapes, to identify priority locations for both benefits to individuals and to constrain regional epidemics. As decision-making becomes more consolidated and more networked in digital agricultural systems, the range of ethical considerations expands. Low-likelihood but high-damage scenarios such as generalist doomsday pathogens may be research priorities because of the extreme potential cost. Regional microbiomes constitute a commons, and avoiding the "tragedy of the microbiome commons" may depend on shifting research products from "common pool goods" to "public goods" or other categories. We provide suggestions for how individual researchers and funders may make altruism-driven research more effective.[Formula: see text] Copyright © 2020 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Network Analysis of the Papaya Orchard Virome from Two Agroecological Regions of Chiapas, Mexico

Virus-virus interactions in plants can modify host symptoms. As a result, disease management strategies may be unsuccessful if they are based solely on visual assessment and diagnostic assays for known individual viruses. Papaya ringspot virus is an important limiting factor for papaya production and likely has interactions with other viruses that are not yet known. Using high-throughput sequencing, we recovered known and novel RNA and DNA viruses from papaya orchards in Chiapas, Mexico, and categorized them by host and, in the case of papaya, symptom type: asymptomatic papaya, papaya with ringspot virus symptoms, papaya with nonringspot symptoms, weeds, and insects. Using network analysis, we demonstrated virus associations within and among host types and described the ecological community patterns. Recovery of viruses from weeds and asymptomatic papaya suggests the need for additional management attention. These analyses contribute to the understanding of the community structure of viruses in the agroecological landscape.

The study of complex ecological interactions, such as those among host, pathogen, and vector communities, can help to explain host ranges and the emergence of novel pathogens. We evaluated the viromes of papaya orchards, including weed and insect viromes, to identify common viruses in intensive production of papaya in the Pacific Coastal Plain and the Central Depression of Chiapas, Mexico. Samples of papaya cultivar Maradol, susceptible to papaya ringspot virus (PRSV), were categorized by symptoms by local farmers (papaya ringspot symptoms, non-PRSV symptoms, or asymptomatic). These analyses revealed the presence of 61 viruses, where only 4 species were shared among both regions, 16 showed homology to known viruses, and 36 were homologous with genera including Potyvirus, Comovirus, and Tombusvirus (RNA viruses) and Begomovirus and Mastrevirus (DNA viruses). We analyzed the network of associations between viruses and host-location combinations, revealing ecological properties of the network, such as an asymmetric nested pattern, and compared the observed network to null models of network association. Understanding the network structure informs management strategies, for example, revealing the potential role of PRSV in asymptomatic papaya and that weeds may be an important pathogen reservoir. We identify three key management implications: (i) each region may need a customized management strategy; (ii) visual assessment of papaya may be insufficient for PRSV, requiring diagnostic assays; and (iii) weed control within orchards may reduce the risk of virus spread to papaya. Network analysis advances understanding of host-pathogen interactions in the agroecological landscape.

IMPORTANCE Virus-virus interactions in plants can modify host symptoms. As a result, disease management strategies may be unsuccessful if they are based solely on visual assessment and diagnostic assays for known individual viruses. Papaya ringspot virus is an important limiting factor for papaya production and likely has interactions with other viruses that are not yet known. Using high-throughput sequencing, we recovered known and novel RNA and DNA viruses from papaya orchards in Chiapas, Mexico, and categorized them by host and, in the case of papaya, symptom type: asymptomatic papaya, papaya with ringspot virus symptoms, papaya with nonringspot symptoms, weeds, and insects. Using network analysis, we demonstrated virus associations within and among host types and described the ecological community patterns. Recovery of viruses from weeds and asymptomatic papaya suggests the need for additional management attention. These analyses contribute to the understanding of the community structure of viruses in the agroecological landscape.

Next generation microbiome applications for crop production - limitations and the need of knowledge-based solutions

Plants are associated with highly diverse microbiota, which are crucial partners for their host carrying out important functions. Essentially, they are involved in nutrient supply, pathogen antagonism and protection of their host against different types of stress. The potential of microbial inoculants has been demonstrated in numerous studies, primarily under greenhouse conditions. However, field application, for example, as biofertilizer or biocontrol agent, is still a challenge as the applied microorganisms often are not provided in sufficiently high cell numbers, are rapidly outcompeted and cannot establish or require specific conditions to mediate the desired effects. We still have limited understanding on the fate of inoculants and on holobiont interactions, that is, interactions between plants, micro-biota and macro-biota and the environment, under field conditions. A better understanding will provide the basis for establishing models predicting the behaviour of strains or consortia and will help identifying microbiome members being able to establish and to mediate desired effects under certain conditions. Such models may also inform about the best management practices modulating microbiota in a desired way. Also, smart delivery approaches of microbial inoculants as well as the selection or breeding of plant genotypes better able to interact with microbiota may represent promising avenues.

Modeling Epidemics in Seed Systems and Landscapes To Guide Management Strategies: The Case of Sweet Potato in Northern Uganda

Seed systems are critical for deployment of improved varieties but also can serve as major conduits for the spread of seedborne pathogens. As in many other epidemic systems, epidemic risk in seed systems often depends on the structure of networks of trade, social interactions, and landscape connectivity. In a case study, we evaluated the structure of an informal sweet potato seed system in the Gulu region of northern Uganda for its vulnerability to the spread of emerging epidemics and its utility for disseminating improved varieties. Seed transaction data were collected by surveying vine sellers weekly during the 2014 growing season. We combined data from these observed seed transactions with estimated dispersal risk based on village-to-village proximity to create a multilayer network or "supranetwork." Both the inverse power law function and negative exponential function, common models for dispersal kernels, were evaluated in a sensitivity analysis/uncertainty quantification across a range of parameters chosen to represent spread based on proximity in the landscape. In a set of simulation experiments, we modeled the introduction of a novel pathogen and evaluated the influence of spread parameters on the selection of villages for surveillance and management. We found that the starting position in the network was critical for epidemic progress and final epidemic outcomes, largely driven by node out-degree. The efficacy of node centrality measures was evaluated for utility in identifying villages in the network to manage and limit disease spread. Node degree often performed as well as other, more complicated centrality measures for the networks where village-to-village spread was modeled by the inverse power law, whereas betweenness centrality was often more effective for negative exponential dispersal. This analysis framework can be applied to provide recommendations for a wide variety of seed systems.[Formula: see text] Copyright © 2019 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Rootstocks Shape the Rhizobiome: Rhizosphere and Endosphere Bacterial Communities in the Grafted Tomato System

Root-associated microbes are critical to plant health and performance, although understanding of the factors that structure these microbial communities and the theory to predict microbial assemblages are still limited. Here, we use a grafted tomato system to study the effects of rootstock genotypes and grafting in endosphere and rhizosphere microbiomes that were evaluated by sequencing 16S rRNA. We compared the microbiomes of nongrafted tomato cultivar BHN589, self-grafted BHN589, and BHN589 grafted to Maxifort or RST-04-106 hybrid rootstocks. Operational taxonomic unit (OTU)-based bacterial diversity was greater in Maxifort compared to the nongrafted control, whereas bacterial diversity in the controls (self-grafted and nongrafted) and the other rootstock (RST-04-106) was similar. Grafting itself did not affect bacterial diversity; diversity in the self-graft was similar to that of the nongraft. Bacterial diversity was higher in the rhizosphere than in the endosphere for all treatments. However, despite the lower overall diversity, there was a greater number of differentially abundant OTUs (DAOTUs) in the endosphere, with the greatest number of DAOTUs associated with Maxifort. In a permutational multivariate analysis of variance (PERMANOVA), there was evidence for an effect of rootstock genotype on bacterial communities. The endosphere-rhizosphere compartment and study site explained a high percentage of the differences among bacterial communities. Further analyses identified OTUs responsive to rootstock genotypes in both the endosphere and rhizosphere. Our findings highlight the effects of rootstocks on bacterial diversity and composition. The influence of rootstock and plant compartment on microbial communities indicates opportunities for the development of designer communities and microbiome-based breeding to improve future crop production.IMPORTANCE Understanding factors that control microbial communities is essential for designing and supporting microbiome-based agriculture. In this study, we used a grafted tomato system to study the effect of rootstock genotypes and grafting on bacterial communities colonizing the endosphere and rhizosphere. To compare the bacterial communities in control treatments (nongrafted and self-grafted plants) with the hybrid rootstocks used by farmers, we evaluated the effect of rootstocks on overall bacterial diversity and composition. These findings indicate the potential for using plant genotype to indirectly select bacterial taxa. In addition, we identify taxa responsive to each rootstock treatment, which may represent candidate taxa useful for biocontrol and in biofertilizers.