Plant Diseases Research Articles

The effect of diversity on disease reverses from dilution to amplification in a 22-year biodiversity × N × CO2 experiment

Plant disease often increases with N, decreases with CO2, and increases as biodiversity is lost (i.e., the dilution effect). Additionally, all these factors can indirectly alter disease by changing host biomass and hence density-dependent disease transmission. Yet over long periods of time as communities undergo compositional changes, these biomass-mediated pathways might fade, intensify, or even reverse in direction. Using a field experiment that has manipulated N, CO2, and species richness for over 20 years, we compared severity of a specialist rust fungus (Puccinia andropogonis) on its grass host (Andropogon gerardii) shortly after the experiment began (1999) and twenty years later (2019). Between these two sampling periods, two decades apart, we found that disease severity consistently increased with N and decreased with CO2. However, the relationship between diversity and disease reversed from a dilution effect in 1999 (more severe disease in monocultures) to an amplification effect in 2019 (more severe disease in mixtures). The best explanation for this reversal centered on host density (i.e., aboveground biomass), which was initially highest in monoculture, but became highest in mixtures two decades later. Thus, the diversity-disease pattern reversed, but disease consistently increased with host biomass. These results highlight the consistency of N and CO2 as drivers of plant disease in the Anthropocene and emphasize the critical role of host biomass—despite potentially variable effects of diversity—for relationships between biodiversity and disease.

IDENTIFICATION OF POTATO LEAF DISEASES USING ARTIFICIAL NEURAL NETWORKS WITH EXTREME LEARNING MACHINE ALGORITHM

Potato plants have an important role in providing a source of carbohydrates for society. However, potato production is often threatened by various plant diseases, such as leaf disease, which can cause a decrease in yields. Identification of diseases on potato leaves is currently mostly done by farmers manually, so it is not always efficient and accurate. So the aim of this research is to identify diseases on potato leaves with artificial neural networks using the ELM (Extreme Learning Machine) approach and the GLCM (Gray Level Co-Occurrence Matrix) method for feature extraction. The GLCM approach functions to obtain texture features on objects by measuring how often certain pairs of pixel intensities appear together at various distances and directions in the image. Meanwhile, the ELM algorithm is used for image identification by adopting a one-time training method without iteration, which involves randomly determining weights and biases in hidden layers, thus allowing training to be carried out quickly and efficiently. Evaluation of the model by looking for the level of accuracy produces a value of 84.667%. The results show that the model developed is capable of accurate identification.

Breeding for Resistance against Pest and Diseases in Tomatoes: A Review

Diseases and pests have a substantial effect on tomato production, greatly affecting both the quantity and quality of this crucial vegetable crop. Although fungicides and insecticides have been important in controlling plant diseases and pests, their excessive usage raises significant environmental issues. Vegetable breeders are increasingly concentrating on developing cultivars with natural tolerance to biotic stresses to promote sustainability and environmental friendliness. The change in focus is intended to cultivate tomato cultivars with inherent resistance to diseases and pests, hence decreasing the need for chemical treatments. Advancements in creating high-yielding genetically resistant tomato cultivars are a result of detailed study on the genetic basis of pest and disease resistance in tomato crops, as well as the complex interactions between the host plant and pathogens. For effective breeding programs and pre-breeding activities, scientists and breeders must have access to sources of resistance and a thorough grasp of the genetic complexities involved. This requires examining the genetic composition of both the tomato plants and the different infections that are impacting them. Breeders may generate tomato cultivars with strong resistance to common diseases and pests by using the inherent defensive mechanisms found in certain tomato types via selective crossing. Continuing to study how hosts and pathogens interact and the molecular processes involved in resistance is crucial. This information offers vital insights on how to improve and expand resistance, leading to the creation of cultivars with long-lasting and wide-ranging resistance. Currently, the emphasis on breeding is a proactive and sustainable strategy for transfer of resistances in high yielding tomato cultivars. Researchers aim to develop tomato cultivars that provide high yield and demonstrate tolerance to changing disease and pest stresses by integrating genetic knowledge with sophisticated breeding methods. This comprehensive method protects tomato crops and encourages environmental sustainability by decreasing the need on chemical inputs in agriculture.

Consumers’ Perceptions for an Outdoor Ornamental Plant: Exploring the Influence of Novel Plant Diseases Diagnostics and Sustainable Nurseries Cultivation Management

Consumers’ Perceptions for an Outdoor Ornamental Plant: Exploring the Influence of Novel Plant Diseases Diagnostics and Sustainable Nurseries Cultivation Management

PEMENUHAN GIZI DAN PENINGKATAN PENDAPATAN MASYARAKAT DENGAN BUDIDAYA SAYURAN DI LAHAN PEKARANGAN

Fulfilling people's food needs faces many obstacles, especially due to changes in environmental conditions, such as climate change, land conversion, and the increasing number of cases of pest attacks and plant diseases which cause a decrease in crop yields. The solution offered is to use yard land for cultivating plants. Apart from being able to be used for daily consumption, the harvest from the yard can also be sold as a side business for family members. The community service program aims to assist the target community in increasing their knowledge and skills in optimally utilizing yard land for cultivating vegetable crops. The method used is community empowerment with activities: counseling, training and participatory mentoring of target community groups. The results of the implementation of the community service program show an increase in community knowledge and skills in cultivating vegetable plants in their yards. Planting vegetable plants in the yard can be a source of food, medicine and environmental beauty, as well as increasing people's income.

First Report of Anthracnose Caused by Colletotrichum spaethianum on Zephyranthes candida in China.

Zephyranthes candida, an bulbous perennial plant, are planted in almost every park. In October 2023, anthracnose symptoms were observed on Z. candida leaves in Jiangxi Agricultural University (28.75° N, 115.83°E), Nanchang, Jiangxi Province, China, and the incidence of disease were up to 35% (140 of 400 plants). The lesions extended fromtheleafapextothebase, appearing as a dark brown color, and later changed to yellow and became dry. To isolate the pathogen, 20 symptomatic leaves were collected and cut into small pieces (4×4 mm, one pieces per leave), surface-sterilized with 70% ethanol for 10 s and 1% NaClO for 30 s, rinsed thrice with sterile water, placed onto potato dextrose agar (PDA) plates and incubated at 25℃ for 5 days. Fifteen isolates (15 out of 20) with similar morphological characteristics were obtained. The colonies on PDA presented effuse mycelium, initially white and later pale gray. Conidia were hyaline, curved or slightly curved, aseptate, with a truncate base and acute apex, measuring 17 to 23 × 3 to 6 μm (n = 50), and were matched to Colletotrichum species (Damm et al. 2009). To further confirm species, two representative isolates (JFRL 03-2873 and JFRL 03-2874) were selected for molecular identification. The internal transcribed spacer (ITS), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), chitin synthase (CHS), histone 3 (HIS3), actin (ACT) and β-tubulin 2 (TUB2) regions were amplified and sequenced by using primers sets ITS5/ITS4, Gpd1/Gpd2, CHS-79F/CHS354R, CYLH3F/CYLH3R, ACT-512F/ACT-783R and T1/Bt2b (Tan et al. 2022), respectively. These sequences were deposited into GenBank with accession number PP425890-PP425891 (ITS), PP437551-PP437552 (GAPDH), PP437549-PP437550 (CHS), PP480643-PP480644 (HIS3), PP437547-PP437548 (ACT) and PP437553-PP437554 (TUB2). A BLASTN search revealed high similarity of 99%-100% to ITS (GU227807, 518 nt/519 nt), GAPDH (GU228199, 525 nt/526 nt), CHS (GU228297, 251 nt/251 nt), HIS3 (GU228003, 372 nt/373 nt), ACT (GU227905, 236 nt/237 nt) and TUB2 (GU228101, 490 nt/490 nt) sequences of Colletotrichum spaethianum CBS 167.49. A maximum likelihood phylogenetic tree was constructed by combining ITS, GAPDH, CHS , HIS3, ACT and TUB2 sequences in IQtree web server (Ngugen et al. 2015). The result indicated that the two representative isolates were clustered together with Colletotrichum spaethianum in a clade with 100% bootstrap support. Based on morphologicalobservationandsequenceanalysis, the isolates were identified as C. spaethianum. To confirm pathogenicity, six surface-sterilized leaves of Z. candida were wounded and inoculated with 1 × 106 conidia/ml conidial suspension of JFRL 03-2873, and control leaves were inoculated with sterile water. They were incubated at 25 ℃ with 12 h photoperiod and 80% humidity, theexperimentwasrepeatedtwice. After five days, all leaves inoculated with JFRL 03-2873 displayed anthracnose symptom, whereas the control leaves remained unaffected. We re-isolated C. spaethianum from the symptomatic leaves and identified it based on morphological and molecular characteristics. Previous studies reported that C. spaethianum caused anthracnose on various common herbaceous plants in China (Vieira et al. 2014, Guo et al. 2013), but to our knowledge, this is the first report of C. spaethianum causing anthracnose on Z. candida in China. Anthracnose disease caused great economic loss to the cultivation of landscape plant Z. candida. Therefore, it is necessary to pay more attention to the anthracnose disease of herbaceous plants caused by C. spaethianum and develop appropriate control strategies.

InViTMixup: plant disease classification using convolutional vision transformer with Mixup augmentation

ABSTRACT Computer vision-based research is carried out to enhance the crop yield with automation solutions. In Tamil Nādu, tomato cultivation is carried out in many districts, including Coimbatore, and its contribution to the Indian economy, i.e. GDP growth, is significant. This paper proposes InViT Mixup, a novel approach toward image classification of diseased tomato leaves. The effectiveness of convolutional neural network (CNN) in obtaining spatial information and the attention mechanism of vision transformers, along with Mixup data augmentation, are combined, and the model’s generalization in predicting the diseased tomato based on leaf symptoms is evaluated. The proposed convolutional transformer model improvised the classification results of tomato disease in terms of accuracy and training speed compared to pure CNN or transformer-based models. In the proposed work, 10 tomato leaf disease classes from the PlantVillage dataset are used. Top-1 accuracy of 93.5% and Top-5 accuracy of 99.7% are achieved.

Antibacterial activity of high surface area zinc oxide nanoparticles for controlling black rot disease of Chinese kale

AbstractZinc oxide nanoparticles (ZnO NPs) are inorganic compounds listed as “generally recognized as safe” (GRAS) materials and have been used in plant production as well as for plant disease control. This study investigated the antibacterial efficacy of ZnO NPs with various surface areas against Xanthomonas campestris pv. campestris, assessed the effectiveness of ZnO NPs in controlling black rot disease in Chinese kale, and examined the influence of ZnO NPs application on soil bacterial communities. The results showed that ZnO NPs with high surface area effectively inhibited X. campestris pv. campestris by restraining growth and causing cell damage. Seed treatment and foliar spray application of high surface area ZnO NPs at 250 μg/mL significantly reduced the disease severity of black rot. Furthermore, in the greenhouse experiment, the soil bacterial communities in the treatment of plants applied with ZnO NPs did not differ from those in soil of nontreated plants. Therefore, ZnO NPs have the potential to serve as an alternative substance for plant disease management.

Genomic insights into Streptomyces hygroscopicus subsp. hygroscopicus SRF1: a potential biocontrol agent against fusarium wilt with plant growth-promoting abilities in tomatoes

ABSTRACT Streptomyces sp. isolate SRF1 has previously demonstrated promising results in controlling various plant fungi under in vitro conditions. However, further exploration of its genome and its efficacy in disease control and plant growth promotion under in vivo conditions is warranted. This study aimed to utilise whole genome sequencing (WGS) to classify Streptomyces sp. isolate SRF1 at the species level and predict genes associated with antagonistic ability and plant growth promotion, thereby assessing its potential as a biocontrol agent against fusarium wilt and a promoter of plant growth. WGS was employed to elucidate the taxonomic classification of isolate SRF1 and predict genes involved in disease suppression and plant growth promotion. In vitro assays were conducted to evaluate its antagonistic activity against Fusarium oxysporum f. sp. lycopersici (Fol) and its production of cellulase, amylase, indole-3-acetic acid (IAA), and siderophores. Pot experiments were carried out using SRF1 cell suspension and culture filtrate to assess its efficacy against Fol and its impact on tomato growth. Real-time PCR analysis was performed to correlate gene expression levels with observed activities. The results showed that isolate SRF1 was classified as S. hygroscopicus subsp. hygroscopicus and demonstrated antagonism against Fol. Furthermore, WGS revealed genes or gene clusters associated with disease control, including those encoding antifungal agents such as butyrolactol A, nigericin, and hygromycin A. Additionally, genes responsible for auxin biosynthesis, siderophore production, and carbohydrate-digesting enzymes such as chitinase, cellulase, and amylase were identified, suggesting potential roles in disease suppression and plant growth promotion.

Pan-Genome Analysis and Secondary Metabolic Pathway Mining of Biocontrol Bacterium Brevibacillus brevis

Brevibacillus brevis is one of the most common biocontrol strains with broad applications in the prevention and control of plant diseases and insect pests. In order to deepen our understanding of B. brevis genomes, describe their characteristics comprehensively, and mine secondary metabolites, we retrieved the genomic sequences of nine B. brevis strains that had been assembled into complete genomes from the NCBI database. These genomic sequences were analyzed using phylogenetic analysis software, pan-genome analysis software, and secondary metabolite mining software. Results revealed that the genome size of B. brevis strains ranged from 6.16 to 6.73 Mb, with GC content ranging from 47.0% to 54.0%. Phylogenetic analysis classified the nine B. brevis strains into three branches. The analyses of ANI and dDDH showed that B. brevis NEB573 had the potential to become a new species of Brevibacillus and needed further research in the future. The pan-genome analysis identified 10032 gene families, including 3257 core gene families, 3112 accessory gene families, and 3663 unique gene families. In addition, 123 secondary metabolite biosynthetic gene clusters of 20 classes were identified in the genomes of nine B. brevis strains. The major types of biosynthetic gene clusters were non-ribosomal peptide synthase (NRPS) and transAT polyketide synthase (transAT-PKS). Furthermore, a large number of untapped secondary metabolites were identified in B. brevis. In summary, this study elucidated the pan-genome characteristics of the biocontrol bacterium B. brevis and identified its secondary metabolites, providing valuable insights for its further development and utilization.

Alternaria in berry: Effects on its quality, pathogenic mechanisms and potential control strategies

AbstractIn recent years, the production and demand of berries in the world have gradually increased, but the infection of fungal pathogens has become one of the important factors restricting the production and sales of fresh berries. It has been found that Alternaria can cause a variety of plant diseases and fruit rot worldwide. This review introduces the effect of Alternaria infection on berry quality, including physical, chemical and physiological aspects, and discusses the possible infection mechanisms. In addition, Alternaria produces toxins during infections and seriously threatens the safety of berries and consumer health. Therefore, the prevention and control measures of Alternaria are systematically summarized. This review provides the basis for further optimization of postharvest preservation technology and fruit quality, to prolong the shelf life of berries. We can realistically expect to control plant diseases with increasing efficiency, which is conducive to improving the quality and safety of postharvest berry fruits.

Assessing the Effectiveness of Neem oil and Microalgae in the Management of Cowpea (Vigna unguiculata L.) Leaf Spot Caused by Alternaria spp.

The current study aimed to assess the effectiveness of neem oil and microalgae in controlling leaf spot caused by Alternaria spp. in cowpea (Vigna unguiculataL.). The research was conducted during the Zaid season of 2022 at the Central Research Field of the Department of Plant Pathology, SHUATS, Prayagraj, Uttar Pradesh, using a Randomized Block Design with three replications and seven treatments. Analysis of variance (ANOVA) was utilized to draw conclusions from the data. Leaf spot disease caused by Alternaria spp poses a significant threat to cowpea crops, resulting in yield losses and decreased plant vigor. An in vivo study was carried out to evaluate the impact of different concentrations of microalgae combined with neem oil at 1% on plant growth parameters and Disease Intensity (%). The application of microalgae @540 grams in various splits and neem oil @1% was done. Among all the treatments, T4 (Microalgae 48gms @5 DAS 96gms@15,45 DAS + Neem oil @1% showed promising results. Notably, the T4 treatment significantly increased plant height (84.63 cm) and the number of branches(10.83) at60DAS. Moreover, an increased numberof nodules per plant (21.44) was observed at 75 DAS, indicating improved root health and nitrogen fixation. Additionally, the treatment led to longer pod length (34.44 cm) in three pickings, suggesting enhanced yield potential. Furthermore, the combined application of microalgae and neem oil resulted in a significant reduction in disease intensity, with a notable decrease of 24.47% compared to untreated plants and those treated with chemicals (Bavistin). This indicates that the use of microalgae and neem oil together effectively suppressed leaf spot disease caused by Alternaria spp. in cowpea, thereby improving plant health and productivity. In conclusion, these findings underscore the potential use of eco-friendly alternatives like microalgae and neem oil for managing leaf spot disease in cowpea cultivation. Further research in this area is recommended for sustainable agricultural practices.

AvrSr27 is a zinc-bound effector with a modular structure important for immune recognition.

Effector proteins are central to the success of plant pathogens, while immunity in host plants is driven by receptor-mediated recognition of these effectors. Understanding the molecular details of effector-receptor interactions is key for the engineering of novel immune receptors. Here, we experimentally determined the crystal structure of the Puccinia graminis f. sp. tritici (Pgt) effector AvrSr27, which was not accurately predicted using AlphaFold2. We characterised the role of the conserved cysteine residues in AvrSr27 using in vitro biochemical assays and examined Sr27-mediated recognition using transient expression in Nicotiana spp. and wheat protoplasts. The AvrSr27 structure contains a novel β-strand rich modular fold consisting of two structurally similar domains that bind to Zn2+ ions. The N-terminal domain of AvrSr27 is sufficient for interaction with Sr27 and triggering cell death. We identified two Pgt proteins structurally related to AvrSr27 but with low sequence identity that can also associate with Sr27, albeit more weakly. Though only the full-length proteins, trigger Sr27-dependent cell death in transient expression systems. Collectively, our findings have important implications for utilising protein prediction platforms for effector proteins, and those embarking on bespoke engineering of immunity receptors as solutions to plant disease.

Diagnostics System for Plant Leaf Diseases Using Convergence of Computer Vision and Deep Learning

Early and accurate detection of plant diseases is crucial for maximizing agricultural yield and minimizing economic losses.Traditional approaches, however, frequently depend on specialized knowledge.They are ineffective for large-scale applications since they take a lot of time.The suggested method makes use of deep learning models' capabilities.With Densenet-121, it automatically extracts and gains knowledge of pertinent aspects from leaf photos.To identify a bundle of leaves, it employs a multi-object deep learning model.It can decipher and comprehend visual data from the leaves using computer vision.Accuracy is increased by utilizing deep learning and computer vision. The technique of transfer learning allows the system to recognize more veggies than just those in the training dataset. The results of our experimental evaluation show that our method is effective in precisely recognizing vegetables and diagnosing plant leaf diseases. Our system provides a scalable solution for agricultural management and plant health monitoring by expanding its coverage to include a variety of vegetables and automating disease diagnosis. KEYWORDS: CNN,Densenet-121,multi-object model

Plant Disease Detection Using Convolutional Neural Network

Plant disease detection is critical in agriculture, significantly impacting crop yield and food supply. This paper introduces an innovative approach utilizing deep learning and computer vision techniques to detect and classify plant diseases. The proposed method involves image processing to analyze leaf health conditions using a dataset of varied leaf images exhibiting disease manifestations. The model is trained to recognize distinct patterns associated with different diseases, enabling early detection and intervention. This AI-based system aims to enhance agricultural productivity, minimize crop losses, and contribute to food security. Furthermore, it has potential application in mobile platforms, providing farmers with a user-friendly tool for managing plant diseases. Nonetheless, challenges such as image condition variability and the necessity for comprehensive, diverse datasets for model training require further research and development.

Root exudate-mediated plant–microbiome interactions determine plant health during disease infection

Understanding the interactions between plants and microbes during pathogen infection is crucial for plant health improvement. However, the integrated role of soil nutrients, root exudates, and plant microbiomes in plant health during disease outbreaks remains unclear. In this study, we hypothesized that root exudate-mediated healthy microbiomes (HealthyBiome) would facilitate the escape of soil-borne pathogen infection, whereas a root exudate-mediated disease-conductive microbiome (PathoBiome) would induce disease occurrence. Using watermelon wilt disease caused by Fusarium oxysporum as a model, we investigated these interactions and their implications in plant disease. We examined the pathogen load across plant compartments during the pathogen accumulation (AH) and disease outbreak periods. We analyzed the plant microbiomes and root exudates during the outbreak, which was associated with the emergence in both healthy (BH) and diseased (BD) plants. Compared with AH and BD plants, BH plants had the lowest F. oxysporum density in the root–soil system (0.74–0.84 times that of AH and BD plants). BD plants showed an elevated amino acid metabolism (2.84–3.38 times that of healthy plants), whereas BH plants showed a higher metabolism of phenolic compounds (1.73 times that of diseased plants). BH plants secreted more palmitic acid (1.53 times that of diseased plants) and recruited more nitrogen (N)-fixing bacteria (e.g., Paenarthrobacter, Sphingomonas), which potentially alleviated N scarcity in the soil, promoted plant growth, and improved the plant defense. Structural equation modeling highlighted the interactions among soil nutrients, root metabolites, and plant nutrients in recruiting pro-growth bacteria to the rhizosphere and roots for healthy plant growth. Our findings provide novel evidence of how root exudates influence plant–microbiome interactions during pathogen infections, highlighting the importance of a balanced root exudate profile and its associated “HealthyBiome” in promoting plant health and growth.

Calcium signaling: Unveiling its role in disease control in plants

<p>Calcium signaling is a fundamental regulatory mechanism in plants, governing a myriad of physiological processes crucial for growth, development, and responses to environmental stimuli, including biotic stresses such as pathogen attack. Over the past decades, research into the intricate network of calcium signaling pathways has revealed its pivotal role in orchestrating plant defense mechanisms against a diverse array of pathogens. This review comprehensively examines the multifaceted roles of calcium ions in modulating plant immune responses, elucidating the molecular mechanisms underlying calcium-mediated signal transduction and its integration with other defense signaling pathways. We delve into the intricate crosstalk between calcium signaling and various hormonal pathways, reactive oxygen species (ROS) signaling, and secondary metabolite biosynthesis, highlighting the interconnectedness of these regulatory networks in shaping plant defense strategies. Furthermore, we explore the potential applications of calcium-based strategies for sustainable disease management in agriculture, including the use of calcium channel blockers, calcium chelators, and calcium-rich compounds as novel antimicrobial agents and priming agents for enhancing plant immunity. Challenges and future directions in the field are also discussed, aiming to advance our understanding of calcium signaling in plant-pathogen interactions and harness its potential for developing innovative approaches to combat plant diseases and ensure global food security. This review provides a comprehensive overview of the current state of knowledge regarding calcium signaling in plant disease control, offering valuable insights and perspectives for future research endeavors in this rapidly evolving field.<strong></strong></p>

New reports of pathogen spectrum associated with bulb rot and their interactions during the development of rot in tulip

Bulb rot, a highly damaging disease of tulip plants, has hindered their profitable cultivation worldwide. This rot occurs in both field and storage conditions posing significant challenges. While this disease has been attributed to a range of pathogens, previous investigations have solely examined it within the framework of a single-pathogen disease model. Our study took a different approach and identified four pathogens associated with the disease: Fusarium solani, Penicillium chrysogenum, Botrytis tulipae, and Aspergillus niger. The primary objective of our research was to examine the impact of co-infections on the overall virulence dynamics of these pathogens. Through co-inoculation experiments on potato dextrose agar, we delineated three primary interaction patterns: antibiosis, deadlock, and merging. In vitro trials involving individual pathogen inoculations on tulip bulbs revealed that B. tulipae,was the most virulent and induced complete bulb decay. Nonetheless, when these pathogens were simultaneously introduced in various combinations, outcomes ranged from partial bulb decay to elongated rotting periods. This indicated a notable degree of antagonistic behaviour among the pathogens. While synergistic interactions were evident in a few combinations, antagonism overwhelmingly prevailed. The complex interplay of these pathogens during co-infection led to a noticeable change in the overall severity of the disease. This underscores the significance of pathogen-pathogen interactions in the realm of plant pathology, opening new insights for understanding and managing tulip bulb rot.

Fungal communities shift with soybean cyst nematode abundance in soils

Plant parasitic nematodes pose a significant threat to food security. Management strategies for these pathogens are limited, and additional tools for their control are needed. Some fungi have shown promise for biocontrol, however, their success in the field has varied. In contrast, some fungal plant pathogens form synergistic associations with nematodes resulting in increased plant disease severity. However, how both groups of fungi change with different plant parasitic nematode abundances in the soil is underexplored. In this study, we used the soybean cyst nematode as a model to understand these changes. We sampled soil from 171 Ohio soybean fields in 2019 and 2021 and determined soybean cyst nematode abundance. We identified the fungi in the samples through amplicon sequencing of the 18S-ITS rDNA regions. Edaphoclimatic factors were used to classify samples into geographic regions to account for environmental differences between sampling locations. We hypothesized that fungal communities would be influenced by both region and soybean cyst nematode abundance. K-near neighbor analysis revealed that fungal communities follow regional patterns. We also found that soybean cyst nematode abundance was associated with changes in these communities regardless of the region. Two potential nematophagous fungi were found to be prevalent in Ohio through core community analysis, although they were enriched when soybean cyst nematode abundance was high. Lastly, differential network analysis showed that interactions among fungal community members change when soybean cyst nematode is present in the soil. Together these results suggest that this nematode significantly shifts the fungal community composition in field soils.

Free-Riding in Plant Health: A Social-Ecological Systems Approach to Collective Action.

Plant disease epidemics often transcend land management boundaries, creating a collective-action problem where a group must cooperate in a common effort to maximize individual and group benefits. Drawing upon the social-ecological systems framework and associated design principles, we review variables of resource systems, resource units, actors, and governance systems relevant to collective action in plant health. We identify a need to better characterize how attributes of epidemics determine the usefulness of collective management, what influences actors' decisions to participate, what governance systems fit different plant health threats, and how these subsystems interact to lead to plant health outcomes. We emphasize that there is not a single governance structure that ensures collective action but rather a continuum of structures that depend on the key system variables identified. An integrated social-ecological systems approach to collective action in plant health should enable institutional designs to better fit specific plant health challenges.