Modeling plant diseases under climate change: evolutionary perspectives

Yang, Lina; Ren, Maozhi; Zhan, Jiasui

doi:10.1016/j.tplants.2022.12.011

Cited by 17 publications

(6 citation statements)

References 62 publications

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“…Predictive models for a variety of agricultural pests and diseases, both resident and invasive, could, in the future, be used operationally as part of a national biovigilance strategy to anticipate and provide early-warning of unanticipated and emerging threats to agricultural production systems across Canada (Newlands, 2018;2023). The performance of predictive models of disease risk in relation to different weather and climate data spatial and temporal scale, and accuracy needs to be more comprehensively investigated (Yang et al, 2022). This is because there exists a wide variety of available weather and climate information onhistorical climate (e.g., observation weather and climate station-based, model-interpolated, data-model climate reanalysis) andfuture weather and climate (e.g., meteorological forecast model, regionallydownscaled global climate model scenario projections).…”

Section: Prediction Power Of Trajectory Simulations In Revealing the ...mentioning

confidence: 99%

Geographical and seasonal dynamics and air trajectory modeling of air-borne cereal rust fungi: a case study in western Canada

Chen

Newlands

Hambleton

et al. 2023

Preprint

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Cereal rust diseases are caused by Puccinia spp. urediniospores that travel by air currents to cause losses in wheat, oat, barley, and rye. In western Canada, inoculum can arrive from the USA through the Great Plains or via a Pacific corridor west of the Rocky Mountains. Moreover, climate and land use are known factors in Puccinia dissemination, but their relative effects remain poorly understood. We investigated aeromycobiota in western Canada by weekly air-sampling over four growing seasons (2015-2018) three mixed-crop sites in British Columbia (BC) and one season (2018) at five sites in southern Alberta (AB). ITS2-based metabarcoding and novel bioinformatic comparative analyses to known, especially curated cereal rust fungal sequences, was used for species identification. The overall aeromycobiota and rust fungal community diversity was higher west than east of the Canadian Rockies. This mountain range delineates climate and land use and also creates a barrier to wind flow to prevent the spread of rusts and other plant pathogens (e.g., Bipolaris, Blumeria). We recovered seven major cereal rusts and revealed their geographic and seasonal dynamics at the eight sampling sites. Forward and reverse trajectory HYSPLIT model simulations predicted the potential sources of rust urediniospores and their pathways of movement modulated by air currents, through which some pronounced changes in the abundance of wheat rust pathogens were explained. This study paves the way for a potential application for pathogen monitoring and disease risk forecasting as part of the design and development of an early-warning system for enhanced biovigilance against crop diseases.

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Section: Prediction Power Of Trajectory Simulations In Revealing the ...mentioning

confidence: 99%

Geographical and seasonal dynamics and air trajectory modeling of air-borne cereal rust fungi: a case study in western Canada

Chen

Newlands

Hambleton

et al. 2023

Preprint

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“…As a result of climate change, plants become more susceptible to phytopathogen attacks due to three primary factors: (i) changes in phytopathogen population dynamics, (ii) evolutionary adaptations of pathogens to evade plant systemic responses, and (iii) disruptions in plant systemic response pathways [9]. Currently, around 13-22% of crop production is lost due to phytopathogen outbreaks, and this is projected to rise due to climate change [27]. In this context, it is crucial to develop alternatives that enhance crop resilience to adapt to changing environmental conditions to ensure food security.…”

Section: Plant Defense Responses Under Climate Change Scenariosmentioning

confidence: 99%

The Mitigation of Phytopathogens in Wheat under Current and Future Climate Change Scenarios: Next-Generation Microbial Inoculants

Campos-Avelar,

Montoya-Martínez,

Villa-Rodríguez

et al. 2023

Sustainability

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Wheat production worldwide faces numerous challenges linked to climate change, exponential population growth, nutrient depletion in agricultural soils, and the increasing threat of phytopathogen occurrence. The application of beneficial microorganisms is a promising strategy for crop management as it favorizes nutrient uptake, improves soil fertility, and increases plant resilience. Therefore, this approach facilitates the transition to more sustainable agricultural practices while reducing the dependence on agrochemicals. The valuable beneficial impacts of bioinoculant application include the enrichment of agricultural soils’ ecosystems by restoring microbial populations and interactions that have been lost through the years due to decades of intensive agricultural practices and the massive application of pesticides. Furthermore, beneficial microorganisms constitute a remarkable tool for combating biotic threats, specifically fungal pathogens, whose proliferation and emergence are predicted to increase due to global warming. To optimize their beneficial impact, bioinoculant development requires an extensive study of microbial interactions with plants and their surrounding ecosystem, to improve their composition, mode of action, and stability through application. The use of innovative tools, such as omic sciences, facilitates the elucidation of these mechanisms. Finally, bioprospection and bioformulation must be consciously executed to guarantee the application and persistence of adapted microorganisms and/or their bioactive molecules.

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“…Plant diseases brought on by biotic factors such as fungi, bacteria, viruses, and insects can result in substantial yield losses and pose a significant threat to global food security ( Yi et al., 2023 ). Effective disease management requires an accurate and rapid diagnosis of plant diseases, but traditional methods such as visual observation, microscopy, and culture-based techniques can be time-consuming, labor-intensive, and may require specialized knowledge and apparatus ( Yang et al., 2023 ). In recent years, the introduction of advanced omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, has brought about a revolutionary shift in our ability to investigate plant-pathogen interactions at the molecular level ( Shen et al., n.d ).…”

Section: Introductionmentioning

confidence: 99%

Deep learning for plant bioinformatics: an explainable gradient-based approach for disease detection

Shoaib,

Shah,

Sayed

et al. 2023

Front. Plant Sci.

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Emerging in the realm of bioinformatics, plant bioinformatics integrates computational and statistical methods to study plant genomes, transcriptomes, and proteomes. With the introduction of high-throughput sequencing technologies and other omics data, the demand for automated methods to analyze and interpret these data has increased. We propose a novel explainable gradient-based approach EG-CNN model for both omics data and hyperspectral images to predict the type of attack on plants in this study. We gathered gene expression, metabolite, and hyperspectral image data from plants afflicted with four prevalent diseases: powdery mildew, rust, leaf spot, and blight. Our proposed EG-CNN model employs a combination of these omics data to learn crucial plant disease detection characteristics. We trained our model with multiple hyperparameters, such as the learning rate, number of hidden layers, and dropout rate, and attained a test set accuracy of 95.5%. We also conducted a sensitivity analysis to determine the model’s resistance to hyperparameter variations. Our analysis revealed that our model exhibited a notable degree of resilience in the face of these variations, resulting in only marginal changes in performance. Furthermore, we conducted a comparative examination of the time efficiency of our EG-CNN model in relation to baseline models, including SVM, Random Forest, and Logistic Regression. Although our model necessitates additional time for training and validation due to its intricate architecture, it demonstrates a faster testing time per sample, offering potential advantages in real-world scenarios where speed is paramount. To gain insights into the internal representations of our EG-CNN model, we employed saliency maps for a qualitative analysis. This visualization approach allowed us to ascertain that our model effectively captures crucial aspects of plant disease, encompassing alterations in gene expression, metabolite levels, and spectral discrepancies within plant tissues. Leveraging omics data and hyperspectral images, this study underscores the potential of deep learning methods in the realm of plant disease detection. The proposed EG-CNN model exhibited impressive accuracy and displayed a remarkable degree of insensitivity to hyperparameter variations, which holds promise for future plant bioinformatics applications.

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Modeling plant diseases under climate change: evolutionary perspectives

Cited by 17 publications

References 62 publications

Geographical and seasonal dynamics and air trajectory modeling of air-borne cereal rust fungi: a case study in western Canada

Geographical and seasonal dynamics and air trajectory modeling of air-borne cereal rust fungi: a case study in western Canada

The Mitigation of Phytopathogens in Wheat under Current and Future Climate Change Scenarios: Next-Generation Microbial Inoculants

Deep learning for plant bioinformatics: an explainable gradient-based approach for disease detection

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