Laurel wilt kills members of the Lauraceae plant family in the southeastern United States. It is caused by Raffaelea lauricola T.C. Harr., Fraedrich and Aghayeva, a nutritional fungal symbiont of an invasive Asian ambrosia beetle, Xyleborus glabratus Eichhoff, which was detected in Port Wentworth, Georgia, in 2002. The beetle is the primary vector of R. lauricola in forests along the southeastern coastal plain of the United States, but other ambrosia beetle species that obtained the pathogen after the initial introduction may play a role in the avocado (Persea americana Miller) pathosystem. Susceptible taxa are naïve (new-encounter) hosts that originated outside Asia. In the southeastern United States, over 300 million trees of redbay (P. borbonia (L.) Spreng.) have been lost, and other North American endemics, non-Asian ornamentals and avocado—an important crop that originated in MesoAmerica—are also affected. However, there are no reports of laurel wilt on the significant number of lauraceous endemics that occur in the Asian homeland of R. lauricola and X. glabratus; coevolved resistance to the disease in the region has been hypothesized. The rapid spread of laurel wilt in the United States is due to an efficient vector, X. glabratus, and the movement of wood infested with the insect and pathogen. These factors, the absence of fully resistant genotypes, and the paucity of effective control measures severely constrain the disease’s management in forest ecosystems and avocado production areas.

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.

Peronospora effusa is an obligate oomycete that causes downy mildew of spinach. Downy mildew threatens sustainable production of fresh market organic spinach in California, and routine fungicide sprays are often necessary for conventional production. In this study, airborne P. effusa spores were collected using rotating arm impaction spore trap samplers at four sites in the Salinas Valley between late January and early June in 2013 and 2014. Levels of P. effusa DNA were determined by a species-specific quantitative polymerase chain reaction assay. Peronospora effusa was detected prior to and during the growing season in both years. Nonlinear time series analyses on the data suggested that the within-season dynamics of P. effusa airborne inoculum are characterized by a mixture of chaotic, deterministic, and stochastic features, with successive data points somewhat predictable from the previous values in the series. Analyses of concentrations of airborne P. effusa suggest both an exponential increase in concentration over the course of the season and oscillations around the increasing average value that had season-specific periodicity around 30, 45, and 75 days, values that are close to whole multiples of the combined pathogen latent and infectious periods. Each unit increase in temperature was correlated with 1.7 to 6% increased odds of an increase in DNA copy numbers, while each unit decrease in wind speed was correlated with 4 to 12.7% increased odds of an increase in DNA copy numbers. Disease incidence was correlated with airborne P. effusa levels and weather variables, and a receiver operating characteristic curve analysis suggested that P. effusa DNA copy numbers determined from the spore traps nine days prior to disease rating could predict disease incidence.

For an ordinary least-squares regression model, the coefficient of determination (R²) describes the proportion (or percentage) of variance of the response variable explained by the model, and is a widely accepted summary measure of predictive power. A number of R²-analogues are available as summary measures of predictive power associated with logistic regression models, including models of disease risk. Tjur’s R² and McFadden’s R² are of particular interest in this context. Both of these metrics have transparent derivations, which reveal that they apply to different aspects of model evaluation. Tjur’s R² is a measure of separation between (known) actual states (e.g., gold standard determinations of “healthy” or “diseased” status) whereas McFadden’s R² is a measure of separation between predicted states (e.g., forecasts of disease status based on models of disease risk). This clarifies their interpretation in the context of evaluation of logistic regression models of disease risk. In addition, versions of both Tjur’s R² and McFadden’s R² may be obtained from analyses of disease risk that are not preceded by logistic regression analysis. Tjur’s R² and McFadden’s R² are shown to be useful, distinct summary measures of predictive power for epidemiological models of disease risk.

Disaster plant pathology addresses how natural and human-driven disasters impact plant diseases and the requirements for smart management solutions. Local to global drivers of plant disease change in response to disasters, often creating environments more conducive to plant disease. Most disasters have indirect effects on plant health through factors such as disrupted supply chains and damaged infrastructure. There is also the potential for direct effects from disasters, such as pathogen or vector dispersal due to floods, hurricanes, and human migration driven by war. Pulse stressors such as hurricanes and war require rapid responses, whereas press stressors such as climate change leave more time for management adaptation but may ultimately cause broader challenges. Smart solutions for the effects of disasters can be deployed through digital agriculture and decision support systems supporting disaster preparedness and optimized humanitarian aid across scales. Here, we use the disaster plant pathology framework to synthesize the effects of disasters in plant pathology and outline solutions to maintain food security and plant health in catastrophic scenarios. We recommend actions for improving food security before and following disasters, including (i) strengthening regional and global cooperation, (ii) capacity building for rapid implementation of new technologies, (iii) effective clean seed systems that can act quickly to replace seed lost in disasters, (iv) resilient biosecurity infrastructure and risk assessment ready for rapid implementation, and (v) decision support systems that can adapt rapidly to unexpected scenarios. [ ] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY 4.0 International license .

Seedborne pathogens and pests limit production in many agricultural systems. Quarantine programs help prevent the introduction of exotic pathogens into a country, but few regulations directly apply to reducing the reintroduction and spread of endemic pathogens. Use of phytosanitary thresholds helps limit the movement of pathogen inoculum through seed, but the costs associated with rejected seed lots can be prohibitive for voluntary implementation of phytosanitary thresholds. In this paper, we outline a framework to optimize thresholds for seedborne pathogens, balancing the cost of rejected seed lots and benefit of reduced inoculum levels. The method requires relatively small amounts of data, and the accuracy and robustness of the analysis improves over time as data accumulate from seed testing. We demonstrate the method first and illustrate it with a case study of seedborne oospores of Peronospora effusa, the causal agent of spinach downy mildew. A seed lot threshold of 0.23 oospores per seed could reduce the overall number of oospores entering the production system by 90% while removing 8% of seed lots destined for distribution. Alternative mitigation strategies may result in lower economic losses to seed producers, but have uncertain efficacy. We discuss future challenges and prospects for implementing this approach.