Resurgence of wheat stem rust infections in western Europe: causes and how to curtail them.

Ten years ago, (black) stem rust - the most damaging of wheat (Triticum aestivum) rusts - re-emerged in western Europe. Disease incidences have since increased in scale and frequency. Here, we investigated the likely underlying causes and used those to propose urgently needed mitigating actions. We report that the first large-scale UK outbreak of the wheat stem rust fungus, Puccinia graminis f. sp. tritici (Pgt), in 2022 may have been caused by timely arrival of airborne urediniospores from southwest Europe. The drive towards later-maturing wheat varieties in the UK may be exacerbating Pgt incidences, which could have disastrous consequences. Indeed, infection assays showed that two UK Pgt isolates from 2022 could infect over 96% of current UK wheat varieties. We determined that the temperature response data in current disease risk simulation models are outdated. Analysis of germination rates for three current UK Pgt isolates showed substantial variation in temperature response functions, suggesting that the accuracy of disease risk simulations would be substantially enhanced by incorporating data from prevailing Pgt isolates. As Pgt incidences continue to accelerate in western Europe, we advocate for urgent action to curtail Pgt losses and help safeguard future wheat production across the region.

Genome Sequence Data Reveal at Least Two Distinct Incursions of the Tropical Race 4 Variant of Fusarium Wilt into South America.

The global banana industry is threatened by one of the most devastating diseases: Fusarium wilt of banana. Fusarium wilt of banana is caused by the soilborne fungus Fusarium oxysporum f. sp. cubense (Foc), which almost annihilated the banana production in the late 1950s. A new strain of Foc, known as tropical race 4 (TR4), attacks a wide range of banana varieties, including Cavendish clones, which are the source of 99% of banana exports. In 2019, Foc TR4 was reported in Colombia, and more recently (2021) in Peru. In this study, we sequenced three fungal isolates identified as Foc TR4 from La Guajira (Colombia) and compared them against 19 whole-genome sequences of Foc TR4 publicly available, including four genome sequences recently released from Peru. To understand the genetic relatedness of the Colombian Foc TR4 isolates and those from Peru, we conducted a phylogenetic analysis based on a genome-wide set of single nucleotide polymorphisms (SNPs). Additionally, we compared the genomes of the 22 available Foc TR4 isolates, looking for the presence-absence of gene polymorphisms and genomic regions. Our results reveal that (i) the Colombian and Peruvian isolates are genetically distant, which could be better explained by independent incursions of the pathogen to the continent, and (ii) there is a high correspondence between the genetic relatedness and geographic origin of Foc TR4. The profile of present/absent genes and the distribution of missing genomic regions showed a high correspondence to the clades recovered in the phylogenetic analysis, supporting the results obtained by SNP-based phylogeny.

Climate change and plant pathogens.

Global food security is threatened by climate change, both directly through responses of crop physiology and productivity, and indirectly through responses of plant-associated microbiota, including plant pathogens. While the interactions between host plants, pathogens and environmental drivers can be complex, recent research is beginning to indicate certain overall patterns in how plant diseases will affect crop production in future. Here, we review the results of three methodological approaches: large-scale observational studies, process-based disease models and experimental comparisons of pathosystems under current and future conditions. We find that observational studies have tended to identify rising temperatures as the primary driver of disease impact. Process-based models suggest that rising temperatures will lead to latitudinal shifts in disease pressure, but drying conditions could mitigate disease risk. Experimental studies suggest that rising atmospheric CO2 will exacerbate disease impacts. Plant diseases may therefore counteract any crop yield increases due to climate change.

Emerging health threat and cost of Fusarium mycotoxins in European wheat.

Mycotoxins harm human and livestock health, while damaging economies. Here we reveal the changing threat of Fusarium head blight (FHB) mycotoxins in European wheat, using data from the European Food Safety Agency and agribusiness (BIOMIN, World Mycotoxin Survey) for ten years (2010-2019). We show persistent, high, single- and multi-mycotoxin contamination alongside changing temporal-geographical distributions, indicative of altering FHB disease pressure and pathogen populations, highlighting the potential synergistic negative health consequences and economic cost.

The gap between atmospheric nitrogen deposition experiments and reality.

Anthropogenic activities have dramatically altered the global nitrogen (N) cycle. Atmospheric N deposition, primarily from combustion of biomass and fossil fuels, has caused acidification of precipitation and freshwater, and triggered intense research into ecosystem responses to this pollutant. Experimental simulations of N deposition have been the main scientific tool to understand ecosystem responses, revealing dramatic impacts on soil microbes, plants, and higher trophic levels. However, comparison of the experimental treatments applied in the vast majority of studies with observational and modelled N deposition reveals a wide gulf between research and reality. While the majority of experimental treatments exceed 100 kg N ha-1 y-1, global median land surface deposition rates are around 1 kg N ha-1 y-1 and only exceed 10 kg N ha-1 y-1 in certain regions, primarily in industrialized areas of Europe and Asia and particularly in forests. Experimental N deposition treatments are in fact similar to mineral fertilizer application rates in agriculture. Some ecological guilds, such as saprotrophic fungi, are highly sensitive to N and respond differently to low and high N availability. In addition, very high levels of N application cause changes in soil chemistry, such as acidification, meaning that unrealistic experimental treatments are unlikely to reveal true ecosystem responses to N. Hence, despite decades of research, past experiments can tell us little about how the biosphere has responded to anthropogenic N deposition. A new approach is required to improve our understanding of this important phenomenon. First, characterization of N response functions using observed N deposition gradients. Second, application of experimental N addition gradients at realistic levels over long periods to detect cumulative effects. Third, application of non-linear meta-regressions to detect non-linear responses in meta-analyses of experimental studies.

Taxonomic Revision of the Banana Fusarium Wilt TR4 Pathogen Is Premature.

Taxonomic revisions for pathogens of crops should be based on robust underpinning evidence. Recently, a substantial revision was proposed for the taxonomy of the causative agent of Fusarium wilt on banana. We reanalyzed the data on which this revision was based and discovered that the data do not robustly support the proposals. Several apparent discrepancies and errors in the published phylogenies cast further doubt on the conclusions drawn from them. Although we do not assert that the authors' conclusions are incorrect, we posit that the taxonomic changes are premature, given the data currently in the public domain.[Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Epidemics and the future of coffee production.

In this perspective, we draw on recent scientific research on the coffee leaf rust (CLR) epidemic that severely impacted several countries across Latin America and the Caribbean over the last decade, to explore how the socioeconomic impacts from COVID-19 could lead to the reemergence of another rust epidemic. We describe how past CLR outbreaks have been linked to reduced crop care and investment in coffee farms, as evidenced in the years following the 2008 global financial crisis. We discuss relationships between CLR incidence, farmer-scale agricultural practices, and economic signals transferred through global and local effects. We contextualize how current COVID-19 impacts on labor, unemployment, stay-at-home orders, and international border policies could affect farmer investments in coffee plants and in turn create conditions favorable for future shocks. We conclude by arguing that COVID-19's socioeconomic disruptions are likely to drive the coffee industry into another severe production crisis. While this argument illustrates the vulnerabilities that come from a globalized coffee system, it also highlights the necessity of ensuring the well-being of all. By increasing investments in coffee institutions and paying smallholders more, we can create a fairer and healthier system that is more resilient to future social-ecological shocks.

The persistent threat of emerging plant disease pandemics to global food security.

Plant disease outbreaks are increasing and threaten food security for the vulnerable in many areas of the world. Now a global human pandemic is threatening the health of millions on our planet. A stable, nutritious food supply will be needed to lift people out of poverty and improve health outcomes. Plant diseases, both endemic and recently emerging, are spreading and exacerbated by climate change, transmission with global food trade networks, pathogen spillover, and evolution of new pathogen lineages. In order to tackle these grand challenges, a new set of tools that include disease surveillance and improved detection technologies including pathogen sensors and predictive modeling and data analytics are needed to prevent future outbreaks. Herein, we describe an integrated research agenda that could help mitigate future plant disease pandemics.

Climate-Fungal Pathogen Modeling Predicts Loss of Up to One-Third of Tea Growing Areas.

Climate change will affect numerous crops in the future; however, perennial crops, such as tea, are particularly vulnerable. Climate change will also strongly influence fungal pathogens. Here, we predict how future climatic conditions will impact tea and its associated pathogens. We collected data on the three most important fungal pathogens of tea (Colletotrichum acutatum, Co. camelliae, and Exobasidium vexans) and then modeled distributions of tea and these fungal pathogens using current and projected climates. The models show that baseline tea-growing areas will become unsuitable for Camellia sinensis var. sinensis (15 to 32% loss) and C. sinensis var. assamica (32 to 34% loss) by 2050. Although new areas will become more suitable for tea cultivation, existing and potentially new fungal pathogens will present challenges in these areas, and they are already under other land-use regimes. In addition, future climatic scenarios suitable range of fungal species and tea suitable cultivation (respectively in CSS and CSA) growing areas are Co. acutatum (44.30%; 31.05%), Co. camelliae (13.10%; 10.70%), and E. vexans (10.20%; 11.90%). Protecting global tea cultivation requires innovative approaches that consider fungal genomics as part and parcel of plant pathology.

The problem of scale in predicting biological responses to climate.

Many analyses of biological responses to climate rely on gridded climate data derived from weather stations, which differ from the conditions experienced by organisms in at least two respects. First, the microclimate recorded by a weather station is often quite different to that near the ground surface, where many organisms live. Second, the temporal and spatial resolutions of gridded climate datasets derived from weather stations are often too coarse to capture the conditions experienced by organisms. Temporally and spatially coarse data have clear benefits in terms of reduced model size and complexity, but here we argue that coarse-grained data introduce errors that, in biological studies, are too often ignored. However, in contrast to common perception, these errors are not necessarily caused directly by a spatial mismatch between the size of organisms and the scale at which climate data are collected. Rather, errors and biases are primarily due to (a) systematic discrepancies between the climate used in analysis and that experienced by organisms under study; and (b) the non-linearity of most biological responses in combination with differences in climate variance between locations and time periods for which models are fitted and those for which projections are made. We discuss when exactly problems of scale can be expected to arise and highlight the potential to circumvent these by spatially and temporally down-scaling climate. We also suggest ways in which adjustments to deal with issues of scale could be made without the need to run high-resolution models over wide extents.

Geometry and evolution of the ecological niche in plant-associated microbes.

The ecological niche can be thought of as a volume in multidimensional space, where each dimension describes an abiotic condition or biotic resource required by a species. The shape, size, and evolution of this volume strongly determine interactions among species and influence their current and potential geographical distributions, but the geometry of niches is poorly understood. Here, we analyse temperature response functions and host plant ranges for hundreds of potentially destructive plant-associated fungi and oomycetes. We demonstrate that niche specialization is uncorrelated on abiotic (i.e. temperature response) and biotic (i.e. host range) axes, that host interactions restrict fundamental niche breadth to form the realized niche, and that both abiotic and biotic niches show limited phylogenetic constraint. The ecological terms 'generalist' and 'specialist' therefore do not apply to these microbes, as specialization evolves independently on different niche axes. This adaptability makes plant pathogens a formidable threat to agriculture and forestry.

Threats to global food security from emerging fungal and oomycete crop pathogens.

Emerging fungal and oomycete pathogens infect staple calorie crops and economically important commodity crops, thereby posing a significant risk to global food security. Our current agricultural systems - with emphasis on intensive monoculture practices - and globalized markets drive the emergence and spread of new pathogens and problematic traits, such as fungicide resistance. Climate change further promotes the emergence of pathogens on new crops and in new places. Here we review the factors affecting the introduction and spread of pathogens and current disease control strategies, illustrating these with the historic example of the Irish potato famine and contemporary examples of soybean rust, wheat blast and blotch, banana wilt and cassava root rot. Our Review looks to the future, summarizing what we see as the main challenges and knowledge gaps, and highlighting the direction that research must take to face the challenge of emerging crop pathogens.

Climate change impacts on banana yields around the world.

Nutritional diversity is a key element of food security1-3. However, research on the effects of climate change on food security has, thus far, focussed on the major food grains4-8, while the response of other crops, particularly those that play an important role in the developing world, are poorly understood. Bananas are a staple food and a major export commodity for many tropical nations9. Here we show that for 27 countries - accounting for 86% of global dessert banana production - a changing climate since 1961 has increased yields by an average of 1.37 T.ha-1. Past gains have been largely ubiquitous across the countries assessed and African producers will continue to see yield increases into the future. However, global yield gains could be dampened or disappear in the future, reducing to 0.59 T.ha-1and 0.19 T.ha-1by 2050 under the RCP 4.5 and 8.5 climate scenarios, respectively, driven by declining yields amongst the largest producers and exporters. By quantifying climate-driven and technology-driven influences on yield, we also identify countries at risk from climate change and those capable of mitigating its effects, or capitalising on its benefits.

High-Quality Draft Genome Sequence of the Causal Agent of the Current Panama Disease Epidemic.

We present a high-quality draft genome assembly for Fusarium oxysporum f. sp. cubense tropical race 4 (Fusarium odoratissimum), assembled from PacBio reads and consisting of 15 contigs with a total assembly size of 48.59 Mb. This strain appears to belong to vegetative compatibility group complex 01213/16.

Many unreported crop pests and pathogens are probably already present.

Invasive species threaten global biodiversity, food security and ecosystem function. Such incursions present challenges to agriculture where invasive species cause significant crop damage and require major economic investment to control production losses. Pest risk analysis (PRA) is key to prioritize agricultural biosecurity efforts, but is hampered by incomplete knowledge of current crop pest and pathogen distributions. Here, we develop predictive models of current pest distributions and test these models using new observations at subnational resolution. We apply generalized linear models (GLM) to estimate presence probabilities for 1,739 crop pests in the CABI pest distribution database. We test model predictions for 100 unobserved pest occurrences in the People's Republic of China (PRC), against observations of these pests abstracted from the Chinese literature. This resource has hitherto been omitted from databases on global pest distributions. Finally, we predict occurrences of all unobserved pests globally. Presence probability increases with host presence, presence in neighbouring regions, per capita GDP and global prevalence. Presence probability decreases with mean distance from coast and known host number per pest. The models are good predictors of pest presence in provinces of the PRC, with area under the ROC curve (AUC) values of 0.75-0.76. Large numbers of currently unobserved, but probably present pests (defined here as unreported pests with a predicted presence probability >0.75), are predicted in China, India, southern Brazil and some countries of the former USSR. We show that GLMs can predict presences of pseudoabsent pests at subnational resolution. The Chinese literature has been largely inaccessible to Western academia but contains important information that can support PRA. Prior studies have often assumed that unreported pests in a global distribution database represent a true absence. Our analysis provides a method for quantifying pseudoabsences to enable improved PRA and species distribution modelling.

A new mechanistic model of weather-dependent Septoria tritici blotch disease risk.

We present a new mechanistic model for predicting Septoria tritici blotch (STB) disease, parameterized with experimentally derived data for temperature- and wetness-dependent germination, growth and death of the causal agent, Zymoseptoria tritici. The output of this model (A) was compared with observed disease data for UK wheat over the period 2002-2016. In addition, we compared the output of a second model (B), in which experimentally derived parameters were replaced by a modified version of a published Z. tritici thermal performance equation, with the same observed disease data. Neither model predicted observed annual disease, but model A was able to differentiate UK regions with differing average disease risks over the entire period. The greatest limitations of both models are: broad spatial resolution of the climate data, and lack of host parameters. Model B is further limited by its lack of explicitly defined pathogen death, leading to a cumulative overestimation of disease over the course of the growing season. Comparison of models A and B demonstrates the importance of accounting for the temperature-dependency of pathogen processes important in the initiation and progression of disease. However, effective modelling of STB will probably require similar experimentally derived parameters for host and environmental factors, completing the disease triangle. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'. This issue is linked with the subsequent theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'.

Climate change effects on Black Sigatoka disease of banana.

Climate change has significantly altered species distributions in the wild and has the potential to affect the interactions between pests and diseases and their human, animal and plant hosts. While several studies have projected changes in disease distributions in the future, responses to historical climate change are poorly understood. Such analyses are required to dissect the relative contributions of climate change, host availability and dispersal to the emergence of pests and diseases. Here, we model the influence of climate change on the most damaging disease of a major tropical food plant, Black Sigatoka disease of banana. Black Sigatoka emerged from Asia in the late twentieth Century and has recently completed its invasion of Latin American and Caribbean banana-growing areas. We parametrize an infection model with published experimental data and drive the model with hourly microclimate data from a global climate reanalysis dataset. We define infection risk as the sum of the number of modelled hourly spore cohorts that infect a leaf over a time interval. The model shows that infection risk has increased by a median of 44.2% across banana-growing areas of Latin America and the Caribbean since the 1960s, due to increasing canopy wetness and improving temperature conditions for the pathogen. Thus, while increasing banana production and global trade have probably facilitated Black Sigatoka establishment and spread, climate change has made the region increasingly conducive for plant infection. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'. This issue is linked with the subsequent theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'.