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Dispersal by grazing and browsing animals has the potential to structure plant-associated microbes in plant communities. Bison are one of the largest grazing animals in North America and charismatic symbols of the Great Plains. They have an important role in maintaining plant communities in tallgrass prairie and may have played a significant role in microbial dispersal across the Great Plains, along with other grazers and herbivores in general. To determine whether bison saliva could maintain viable fungal propagules, we sampled saliva from bison at Konza Prairie Biological Station. Fifty-nine internal transcribed spacer sequences were recovered from distinct fungal colonies on media selective for Fusarium species and Macrophomina phaseolina, representing at least 26 fungal species. Most sequences were identified as originating from potential plant pathogens, indicating that bison may play a role in spreading plant pathogens throughout the prairie. Given their historic numbers and extensive migration across North America, bison may have had a role in establishing the current structure of the prairie microbiome. [Figure: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

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.

For farmers to adopt and maintain sustainable farming practices, they must have the resources and network to succeed with this work and must realize a positive impact on their business model. As a food system is ultimately made up of the people, organizations, and institutions that grow, move, buy and sell food, we must understand who is at the center of this network, who is well-connected, and who is peripheral. Within a particular regional food system in a highly productive southeastern U.S. state, the network of local producers interested in sustainable production, including environmental and economic components, seems to be growing. However, it is unclear who benefits from this system and whether this system is growing in a way that encourages and enhances the benefits for sustainable agriculture. Existing evidence for the network size and its vulnerabilities has been anecdotal, from Extension agents and their contacts with individual producers, rather than based on systematic research. We used social network analysis to understand the status of the system and its constituents. Connections between producers appear to be weak overall with potential fragmentation, suggesting a fragility that could easily derail efforts to increase sustainable production in the region.

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.

This white paper presents a series of perspectives on current and future phytobiome management, discussed at the Wild and Tamed Phytobiomes Symposium in University Park, PA, U.S.A., in June 2018. To enhance plant productivity and health, and to translate lab- and greenhouse-based phytobiome research to field applications, the academic community and end-users need to address a variety of scientific, practical, and social challenges. Prior discussion of phytobiomes has focused heavily on plant-associated bacterial and fungal assemblages, but the phytobiomes concept covers all factors that influence plant function. Here we discuss various management considerations, including abiotic conditions (e.g., soil and nutrient applications), microorganisms (e.g., bacterial and fungal assemblages, bacterial and fungal inoculants, and viruses), macroorganisms (e.g., arthropods and plant genetics), and societal factors (e.g., communication approaches and technology diffusion). An important near-term goal for this field should be to estimate the potential relative contribution of different components of the phytobiome to plant health, as well as the potential and risk of modifying each in the near future.

ABSTRACT 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.

ABSTRACT 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.

Abstract Understanding soil systems is critical because they form the structural and nutritional foundation for plants and thus every terrestrial habitat and agricultural system. In this paper, we encourage increased use of mathematical models to drive forward understanding of interactions in soil ecological systems. We discuss several distinctive features of soil ecosystems and empirical studies of them. We explore some perceptions that have previously deterred more extensive use of models in soil ecology and some advances that have already been made using models to elucidate soil ecological interactions. We provide examples where mathematical models have been used to test the plausibility of hypothesized mechanisms, to explore systems where experimental manipulations are currently impossible, or to determine the most important variables to measure in experimental and natural systems. To aid in the development of theory in this field, we present a table describing major soil ecology topics, the theory previously used, and providing key terms for theoretical approaches that could potentially address them. We then provide examples from the table that may either contribute to important incremental developments in soil science or potentially revolutionize our understanding of plant–soil systems. We challenge scientists and mathematicians to push theoretical explorations in soil systems further and highlight three major areas for the development of mathematical models in soil ecology: theory spanning scales and ecological hierarchies, processes, and evolution.

Cassava is one of the most important annual crops in Southeast Asia, and faces increasing seed borne pest and disease pressures. Despite this, cassava seed systems have received scant research attention. In a first analysis of Vietnamese and Cambodian cassava seed systems, we characterized existing cassava seed systems in 2016–2017 through a farmer survey based approach at both national and community scales, with particular focus on identifying seed system actors, planting material management, exchange mechanisms, geographies, and variety use, and performed a network analysis of detected seed movement at the provincial level. Despite their status as self-organized “informal” networks, the cassava seed systems used by farmers in Vietnam and Cambodia are complex, connected over multiple scales, and include links between geographically distant sites. Cassava planting material was exchanged through farmer seed systems, in which re-use of farm-saved supply and community-level exchanges dominated. At the national level, use of self-saved seed occurred in 47 and 64% of seed use cases in Cambodia and Vietnam, respectively. Movement within communes was prevalent, with 82 and 78% of seed provided to others being exchanged between family and acquaintances within the commune in Cambodia and Vietnam, respectively. Yet, meaningful proportions of seed flows, mediated mostly by traders, also formed inter-provincial and international exchange networks, with 20% of Cambodia's seed acquisitions imported from abroad, especially neighboring Vietnam and Thailand. Dedicated seed traders and local cassava collection points played important roles in the planting material distribution network at particular sites. Sales of planting material were important means of both acquiring and providing seed in both countries, and commercial sale was more prevalent in high-intensity than in low-intensity production sites. Considerable variability existed in local seed networks, depending on the intensity of production and integration with trader networks. Adapted innovations are needed to upgrade cassava seed systems in the face of emerging pests and diseases, taking into account and building on the strengths of the existing systems; including their social nature and ability to quickly and efficiently distribute planting materials at the regional level.

Temperature response curves under diurnal oscillating temperatures differ from those under constant conditions for all stages of the Phytophthora infestans infection cycle on potatoes. We developed a mechanistic model (BLIGHTSIM) with an hourly time step to simulate late blight under fluctuating environmental conditions and predict late blight epidemics in potato fields. BLIGHTSIM is a modified susceptible (S), latent (L), infectious (I) and removed (R) compartmental model with hourly temperature and relative humidity as driving variables. The model was calibrated with growth chamber data covering one infection cycle and validated with field data from Ecuador. The model provided a good fit to all data sets evaluated. There was a significant interaction between average temperature and amplitude in their effects on the area under the disease progress curve (AUDPC) as predicted from growth chamber data on a single infection cycle. BLIGHTSIM can be incorporated in a potato growth model to study effects of diurnal temperature range on late blight impact under climate change scenarios.

Including food production in non-food systems, such as rubber plantations and biofuel or bioenergy crops, may contribute to household food security. We evaluated the potential for planting rice, mungbean, rice cultivar mixtures, and rice intercropped with mungbean in young rubber plantations in experiments in the Arakan Valley of Mindanao in the Philippines. Rice mixtures consisted of two- or three-row strips of cultivar Dinorado, a cultivar with higher value but lower yield, and high-yielding cultivar UPL Ri-5. Rice and mungbean intercropping treatments consisted of different combinations of two- or three-row strips of rice and mungbean. We used generalized linear mixed models to evaluate the yield of each crop alone and in the mixture or intercropping treatments. We also evaluated a land equivalent ratio for yield, along with weed biomass (where Ageratum conyzoides was particularly abundant), the severity of disease caused by Magnaporthe oryzae and Cochliobolus miyabeanus, and rice bug (Leptocorisa acuta) abundance. We analyzed the yield ranking of each cropping system across site-year combinations to determine mean relative performance and yield stability. When weighted by their relative economic value, UPL Ri-5 had the highest mean performance, but with decreasing performance in low-yielding environments. A rice and mungbean intercropping system had the second highest performance, tied with high-value Dinorado but without decreasing relative performance in low-yielding environments. Rice and mungbean intercropped with rubber have been adopted by farmers in the Arakan Valley.

The challenge of maintaining sufficient food, feed, fiber, and forests, for a projected end of century population of between 9–10 billion in the context of a climate averaging 2–4 °C warmer, is a global imperative. However, climate change is likely to alter the geographic ranges and impacts for a variety of insect pests, plant pathogens, and weeds, and the consequences for managed systems, particularly agriculture, remain uncertain. That uncertainty is related, in part, to whether pest management practices (e.g., biological, chemical, cultural, etc.) can adapt to climate/CO2 induced changes in pest biology to minimize potential loss. The ongoing and projected changes in CO2, environment, managed plant systems, and pest interactions, necessitates an assessment of current management practices and, if warranted, development of viable alternative strategies to counter damage from invasive alien species and evolving native pest populations. We provide an overview of the interactions regarding pest biology and climate/CO2; assess these interactions currently using coffee as a case study; identify the potential vulnerabilities regarding future pest impacts; and discuss possible adaptive strategies, including early detection and rapid response via EDDMapS (Early Detection & Distribution Mapping System), and integrated pest management (IPM), as adaptive means to improve monitoring pest movements and minimizing biotic losses while improving the efficacy of pest control.

Given the wide range of scales and mechanisms by which pest or disease agents disperse, it is unclear whether there might exist a general relationship between scale of host heterogeneity and spatial spread that could be exploited by available management options. In this model-based study, we investigate the interaction between host distributions and the spread of pests and diseases using an array of models that encompass the dispersal and spread of a diverse range of economically important species: a major insect pest of coniferous forests in western North America, the mountain pine beetle (Dendroctonus ponderosae); the bacterium Pseudomonas syringae, one of the most-widespread and best-studied bacterial plant pathogens; the mosquito Culex erraticus, an important vector for many human and animal pathogens, including West Nile Virus; and the oomycete Phytophthora infestans, the causal agent of potato late blight. Our model results reveal an interesting general phenomenon: a unimodal ('humpbacked') relationship in the magnitude of infestation (an index of dispersal or population spread) with increasing grain size (i.e., the finest scale of patchiness) in the host distribution. Pest and disease management strategies targeting different aspects of host pattern (e.g., abundance, aggregation, isolation, quality) modified the shape of this relationship, but not the general unimodal form. This is a previously unreported effect that provides insight into the spatial scale at which management interventions are most likely to be successful, which, notably, do not always match the scale corresponding to maximum infestation. Our findings could provide a new basis for explaining historical outbreak events, and have implications for biosecurity and public health preparedness.

Surveying invasive species can be highly resource intensive, yet near-real-time evaluations of invasion progress are important resources for management planning. In the case of the soybean rust invasion of the United States, a linked monitoring, prediction, and communication network saved U.S. soybean growers approximately $200 M/yr. Modeling of future movement of the pathogen (Phakopsora pachyrhizi) was based on data about current disease locations from an extensive network of sentinel plots. We developed a dynamic network model for U.S. soybean rust epidemics, with counties as nodes and link weights a function of host hectarage and wind speed and direction. We used the network model to compare four strategies for selecting an optimal subset of sentinel plots, listed here in order of increasing performance: random selection, zonal selection (based on more heavily weighting regions nearer the south, where the pathogen overwinters), frequency-based selection (based on how frequently the county had been infected in the past), and frequency-based selection weighted by the node strength of the sentinel plot in the network model. When dynamic network properties such as node strength are characterized for invasive species, this information can be used to reduce the resources necessary to survey and predict invasion progress.

Environmental variables such as moisture availability are often important in determining species prevalence and intraspecific diversity. The population genetic structure of dominant plant species in response to a cline of these variables has rarely been addressed. We evaluated the spatial genetic structure and diversity of Andropogon gerardii populations across the U.S. Great Plains precipitation gradient, ranging from approximately 48 cm/year to 105 cm/year.Genomic diversity was evaluated with AFLP markers and diversity of a disease resistance gene homolog was evaluated by PCR-amplification and digestion with restriction enzymes. We determined the degree of spatial genetic structure using Mantel tests. Genomic and resistance gene homolog diversity were evaluated across prairies using Shannon's index and by averaging haplotype dissimilarity. Trends in diversity across prairies were determined using linear regression of diversity on average precipitation for each prairie. We identified significant spatial genetic structure, with genomic similarity decreasing as a function of distance between samples. However, our data indicated that genome-wide diversity did not vary consistently across the precipitation gradient. In contrast, we found that disease resistance gene homolog diversity was positively correlated with precipitation.Prairie remnants differ in the genetic resources they maintain. Selection and evolution in this disease resistance homolog is environmentally dependent. Overall, we found that, though this environmental gradient may not predict genomic diversity, individual traits such as disease resistance genes may vary significantly.