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.

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.

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.

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.

Efficiency of insect-proof net tunnels in reducing virus-related seed degeneration in sweet potato.

Virus-related degeneration constrains production of quality sweet potato seed, especially under open field conditions. Once in the open, virus-indexed seed is prone to virus infection leading to decline in performance. Insect-proof net tunnels have been proven to reduce virus infection under researcher management. However, their effectiveness under farmer-multiplier management is not known. This study investigated the ability of net tunnels to reduce degeneration in sweet potato under farmer-multiplier management. Infection and degeneration were assessed for two cultivars, Kabode and Polista, grown in net tunnels and open fields at two sites with varying virus pressures. There was zero virus incidence at both sites during the first five generations. Sweet potato feathery mottle virus and sweet potato chlorotic stunt virus were present in the last three generations, occurring singly or in combination to form sweet potato virus disease. Virus infection increased successively, with higher incidences recorded at the high virus pressure site. Seed degeneration modelling illustrated that for both varieties, degeneration was reduced by the maintenance of vines under net tunnel conditions. The time series of likely degeneration based on a generic model of yield loss suggested that, under the conditions experienced during the experimental period, infection and losses within the net tunnels would be limited. By comparison, in the open field most of the yield could be lost after a small number of generations without the input of seed with lower disease incidence. Adopting the technology at the farmer-multiplier level can increase availability of clean seed, particularly in high virus pressure areas.

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.

Epidemic Network Analysis for Mitigation of Invasive Pathogens in Seed Systems: Potato in Ecuador.

Seed systems have an important role in the distribution of high-quality seed and improved varieties. The structure of seed networks also helps to determine the epidemiological risk for seedborne disease. We present a new approach for evaluating the epidemiological role of nodes in seed networks, and apply it to a regional potato farmer consortium (Consorcio de Productores de Papa [CONPAPA]) in Ecuador. We surveyed farmers to estimate the structure of networks of farmer seed tuber and ware potato transactions, and farmer information sources about pest and disease management. Then, we simulated pathogen spread through seed transaction networks to identify priority nodes for disease detection. The likelihood of pathogen establishment was weighted based on the quality or quantity of information sources about disease management. CONPAPA staff and facilities, a market, and certain farms are priorities for disease management interventions such as training, monitoring, and variety dissemination. Advice from agrochemical store staff was common but assessed as significantly less reliable. Farmer access to information (reported number and quality of sources) was similar for both genders. However, women had a smaller amount of the market share for seed tubers and ware potato. Understanding seed system networks provides input for scenario analyses to evaluate potential system improvements. [Formula: see text] Copyright © 2017 The Author(s). This is an open access article distributed under the CC BY 4.0 International license .

Resistance Genes in Global Crop Breeding Networks.

Resistance genes are a major tool for managing crop diseases. The networks of crop breeders who exchange resistance genes and deploy them in varieties help to determine the global landscape of resistance and epidemics, an important system for maintaining food security. These networks function as a complex adaptive system, with associated strengths and vulnerabilities, and implications for policies to support resistance gene deployment strategies. Extensions of epidemic network analysis can be used to evaluate the multilayer agricultural networks that support and influence crop breeding networks. Here, we evaluate the general structure of crop breeding networks for cassava, potato, rice, and wheat. All four are clustered due to phytosanitary and intellectual property regulations, and linked through CGIAR hubs. Cassava networks primarily include public breeding groups, whereas others are more mixed. These systems must adapt to global change in climate and land use, the emergence of new diseases, and disruptive breeding technologies. Research priorities to support policy include how best to maintain both diversity and redundancy in the roles played by individual crop breeding groups (public versus private and global versus local), and how best to manage connectivity to optimize resistance gene deployment while avoiding risks to the useful life of resistance genes. [Formula: see text] Copyright © 2017 The Author(s). This is an open access article distributed under the CC BY 4.0 International license .

A Risk Assessment Framework for Seed Degeneration: Informing an Integrated Seed Health Strategy for Vegetatively Propagated Crops.

Pathogen buildup in vegetative planting material, termed seed degeneration, is a major problem in many low-income countries. When smallholder farmers use seed produced on-farm or acquired outside certified programs, it is often infected. We introduce a risk assessment framework for seed degeneration, evaluating the relative performance of individual and combined components of an integrated seed health strategy. The frequency distribution of management performance outcomes was evaluated for models incorporating biological and environmental heterogeneity, with the following results. (1) On-farm seed selection can perform as well as certified seed, if the rate of success in selecting healthy plants for seed production is high; (2) when choosing among within-season management strategies, external inoculum can determine the relative usefulness of 'incidence-altering management' (affecting the proportion of diseased plants/seeds) and 'rate-altering management' (affecting the rate of disease transmission in the field); (3) under severe disease scenarios, where it is difficult to implement management components at high levels of effectiveness, combining management components can be synergistic and keep seed degeneration below a threshold; (4) combining management components can also close the yield gap between average and worst-case scenarios. We also illustrate the potential for expert elicitation to provide parameter estimates when empirical data are unavailable. [Formula: see text] Copyright © 2017 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Microbiome Networks: A Systems Framework for Identifying Candidate Microbial Assemblages for Disease Management.

Network models of soil and plant microbiomes provide new opportunities for enhancing disease management, but also challenges for interpretation. We present a framework for interpreting microbiome networks, illustrating how observed network structures can be used to generate testable hypotheses about candidate microbes affecting plant health. The framework includes four types of network analyses. "General network analysis" identifies candidate taxa for maintaining an existing microbial community. "Host-focused analysis" includes a node representing a plant response such as yield, identifying taxa with direct or indirect associations with that node. "Pathogen-focused analysis" identifies taxa with direct or indirect associations with taxa known a priori as pathogens. "Disease-focused analysis" identifies taxa associated with disease. Positive direct or indirect associations with desirable outcomes, or negative associations with undesirable outcomes, indicate candidate taxa. Network analysis provides characterization not only of taxa with direct associations with important outcomes such as disease suppression, biofertilization, or expression of plant host resistance, but also taxa with indirect associations via their association with other key taxa. We illustrate the interpretation of network structure with analyses of microbiomes in the oak phyllosphere, and in wheat rhizosphere and bulk soil associated with the presence or absence of infection by Rhizoctonia solani.

History, Epidemic Evolution, and Model Burn-In for a Network of Annual Invasion: Soybean Rust.

Ecological history may be an important driver of epidemics and disease emergence. We evaluated the role of history and two related concepts, the evolution of epidemics and the burn-in period required for fitting a model to epidemic observations, for the U.S. soybean rust epidemic (caused by Phakopsora pachyrhizi). This disease allows evaluation of replicate epidemics because the pathogen reinvades the United States each year. We used a new maximum likelihood estimation approach for fitting the network model based on observed U.S. epidemics. We evaluated the model burn-in period by comparing model fit based on each combination of other years of observation. When the miss error rates were weighted by 0.9 and false alarm error rates by 0.1, the mean error rate did decline, for most years, as more years were used to construct models. Models based on observations in years closer in time to the season being estimated gave lower miss error rates for later epidemic years. The weighted mean error rate was lower in backcasting than in forecasting, reflecting how the epidemic had evolved. Ongoing epidemic evolution, and potential model failure, can occur because of changes in climate, host resistance and spatial patterns, or pathogen evolution.

Meta-analysis and other approaches for synthesizing structured and unstructured data in plant pathology.

The term data deluge is used widely to describe the rapidly accelerating growth of information in the technical literature, in scientific databases, and in informal sources such as the Internet and social media. The massive volume and increased complexity of information challenge traditional methods of data analysis but at the same time provide unprecedented opportunities to test hypotheses or uncover new relationships via mining of existing databases and literature. In this review, we discuss analytical approaches that are beginning to be applied to help synthesize the vast amount of information generated by the data deluge and thus accelerate the pace of discovery in plant pathology. We begin with a review of meta-analysis as an established approach for summarizing standardized (structured) data across the literature. We then turn to examples of synthesizing more complex, unstructured data sets through a range of data-mining approaches, including the incorporation of 'omics data in epidemiological analyses. We conclude with a discussion of methodologies for leveraging information contained in novel, open-source data sets through web crawling, text mining, and social media analytics, primarily in the context of digital disease surveillance. Rapidly evolving computational resources provide platforms for integrating large and complex data sets, motivating research that will draw on new types and scales of information to address big questions.

Transvaginal sacrospinous rectopexy: initial clinical experience.

BACKGROUND: There is a wide range of surgical procedures available to treat rectal prolapse that differ in approach as well as in principle. The current perineal approaches available involve mucosal or full thickness resection. There are currently no accepted procedures combining rectal fixation without resection using the perineal approach. We present our initial report of transvaginal sacrospinous rectopexy for the treatment of rectal prolapse. METHODS: A longitudinal incision was made in the posterior wall of the vagina. The rectum and sacrospinous ligament were identified. Two sutures were placed in the sacrospinous ligament and brought through a piece of Surgisis mesh previously anchored to the anterior surface of the rectum. This was performed bilaterally. These sutures were tied to complete the rectal suspension, and the posterior wall of the vagina was closed. RESULTS: Transvaginal sacrospinous rectopexy was performed in all seven cases. In the first two cases, a Delorme procedure was performed concurrently. Two patients had rubber band ligation for symptomatic internal hemorrhoids, one patient had a sphincter plication, and one patient had an anal encirclement procedure with Surgisis. Six of the seven patients had concomitant urologic procedures. The average operative time was 163 min, and the average blood loss was 107 mL. None of the cases required conversion to an open procedure. There was one full thickness recurrence at 18 weeks. CONCLUSION: Transvaginal sacrospinous rectopexy is a safe, minimally invasive, technically feasible technique for the treatment of rectal prolapse.

Relatedness of Macrophomina phaseolina isolates from tallgrass prairie, maize, soybean and sorghum.

Agricultural and wild ecosystems may interact through shared pathogens such as Macrophomina phaseolina, a generalist clonal fungus with more than 284 plant hosts that is likely to become more important under climate change scenarios of increased heat and drought stress. To evaluate the degree of subdivision in populations of M. phaseolina in Kansas agriculture and wildlands, we compared 143 isolates from maize fields adjacent to tallgrass prairie, nearby sorghum fields, widely dispersed soybean fields and isolates from eight plant species in tallgrass prairie. Isolate growth phenotypes were evaluated on a medium containing chlorate. Genetic characteristics were analysed based on amplified fragment length polymorphisms and the sequence of the rDNA-internal transcribed spacer (ITS) region. The average genetic similarity was 58% among isolates in the tallgrass prairie, 71% in the maize fields, 75% in the sorghum fields and 80% in the dispersed soybean fields. The isolates were divided into four clusters: one containing most of the isolates from maize and soybean, two others containing isolates from wild plants and sorghum, and a fourth containing a single isolate recovered from Solidago canadensis in the tallgrass prairie. Most of the sorghum isolates had the dense phenotype on media containing chlorate, while those from other hosts had either feathery or restricted phenotypes. These results suggest that the tallgrass prairie supports a more diverse population of M. phaseolina per area than do any of the crop species. Subpopulations show incomplete specialization by host. These results also suggest that inoculum produced in agriculture may influence tallgrass prairie communities, and conversely that different pathogen subpopulations in tallgrass prairie can interact there to generate 'hybrids' with novel genetic profiles and pathogenic capabilities.

A benefit of high temperature: increased effectiveness of a rice bacterial blight disease resistance gene.

*Continuous planting of crops containing single disease resistance (R) genes imposes a strong selection for virulence in pathogen populations, often rendering the R gene ineffective. Increasing environmental temperatures may complicate R-gene-mediated disease control because high temperatures often promote disease development and reduce R gene effectiveness. Here, performance of one rice bacterial blight disease R gene was assessed in field and growth chamber studies to determine the influence of temperature on R gene effectiveness and durability. *Disease severity and virulence of Xanthomonas oryzae pv. oryzae (Xoo) populations were monitored in field plots planted to rice with and without the bacterial blight R gene Xa7 over 11 yr. The performance of Xa7 was determined in high- and low-temperature regimes in growth chambers. *Rice with Xa7 exhibited less disease than lines without Xa7 over 11 yr, even though virulence of Xoo field populations increased. Xa7 restricted disease more effectively at high than at low temperatures. Other R genes were less effective at high temperatures. *We propose that Xa7 restricts disease and Xoo population size more efficiently in high temperature cropping seasons compared with cool seasons creating fluctuating selection, thereby positively impacting durability of Xa7.

Beyond yield: plant disease in the context of ecosystem services.

The ecosystem services concept provides a means to define successful disease management more broadly, beyond short-term crop yield evaluations. Plant disease can affect ecosystem services directly, such as through removal of plants providing services, or indirectly through the effects of disease management activities, including pesticide applications, tillage, and other methods of plant removal. Increased plant biodiversity may reduce disease risk if susceptible host tissue becomes less common, or may increase risk if additional plant species are important in completing pathogen life cycles. Arthropod and microbial biodiversity may play similar roles. Distant ecosystems may provide a disservice as the setting for the evolution of pathogens that later invade a focal ecosystem, where plants have not evolved defenses. Conversely, distant ecosystems may provide a service as sources of genetic resources of great value to agriculture, including disease resistance genes. Good policies are needed to support conservation and optimal use of genetic resources, protect ecosystems from exotic pathogens, and limit the homogeneity of agricultural systems. Research is needed to provide policy makers, farmers, and consumers with the information required for evaluating trade-offs in the pursuit of the full range of ecosystem services desired from managed and native ecosystems.

Intraspecific functional diversity in hosts and its effect on disease risk across a climatic gradient.

The effects of host biodiversity on disease risk may vary greatly depending on host population structure and climatic conditions. Agricultural diseases such as potato late blight, caused by Phytophthora infestans, provide the opportunity to study the effects of intraspecific host diversity that is relatively well-defined in terms of disease resistance phenotypes and may have functional impacts on disease levels. When these systems are present across a climatic gradient, it is also possible to study how season length and conduciveness of the environment to disease may influence the effects of host diversity on disease risk. We developed a simple model of epidemic progress to evaluate the effects on disease risk of season length, environmental disease conduciveness, and host functional divergence for mixtures of a susceptible host and a host with some resistance. Differences in disease levels for the susceptible vs. resistant genotypes shifted over time, with the divergence in disease levels first increasing and then decreasing. Disease reductions from host diversity were greatest for high host divergence and combinations of environmental disease conduciveness and season length that led to moderate disease severity. We also compared the effects of host functional divergence on potato late-blight risk in Ecuador (long seasons), two sites in Peru (intermediate seasons) in El Niño and La Niña years, and the United States (short seasons). There was some evidence for greater disease risk reduction from host diversity where seasons were shorter, probably because of lower regional inoculum loads. There was strong evidence for greater disease reduction when host functional divergence was greater. These results indicate that consideration of season length, environmental conduciveness to disease, and host functional divergence can help to explain the variability in disease response to host diversity.

Limiting temperatures for urediniospore germination are low in a systemic rust fungus of tallgrass prairie.

Potential responses of plant disease phenology to climate change have been addressed primarily in agricultural systems. As a first step toward understanding the phenology of Uropyxis petalostemonis, a rust fungus commonly infecting the legume Dalea candida in U.S.A. tallgrass prairie, we evaluated the effects of temperature on urediniospore germination. While urediniospore germination for many rust fungi has been reported to decline only when temperatures are well above 25 degrees C, in vitro germination of U. petalostemonis dropped sharply at this temperature. Responses observed on water agar, potato dextrose agar and lima bean agar were similar, although lima bean agar supported a higher percentage germination overall. The low limiting temperatures suggest that most epidemically important new infections by U. petalostemonis occur in spring. High summer temperatures in tallgrass prairie might push infection by this rust fungus species to earlier in the year and select for stronger systemic growth characteristics.

Climate change effects on plant disease: genomes to ecosystems.

Research in the effects of climate change on plant disease continues to be limited, but some striking progress has been made. At the genomic level, advances in technologies for the high-throughput analysis of gene expression have made it possible to begin discriminating responses to different biotic and abiotic stressors and potential trade-offs in responses. At the scale of the individual plant, enough experiments have been performed to begin synthesizing the effects of climate variables on infection rates, though pathosystem-specific characteristics make synthesis challenging. Models of plant disease have now been developed to incorporate more sophisticated climate predictions. At the population level, the adaptive potential of plant and pathogen populations may prove to be one of the most important predictors of the magnitude of climate change effects. Ecosystem ecologists are now addressing the role of plant disease in ecosystem processes and the challenge of scaling up from individual infection probabilities to epidemics and broader impacts.