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.

Alternative Tissue Sampling for Improved Detection of Candidatus Liberibacter asiaticus.

Early detection and prompt response are key factors in the eradication of 'huanglongbing' (HLB) in California. Currently, qPCR testing of leaf tissue guides the removal of infected trees. However, because of the uneven distribution of 'Candidatus Liberibacter asiaticus' (CLas) in an infected tree and asymptomatic infection, selecting the best leaves to sample, from a mature tree with more than 200,000 estimated leaves, is a major hurdle for timely detection. The goal of this study was to address this issue by testing alternative tissues that might improve the CLas detection rate. Using two years of field data, old and young leaves, peduncle bark of fruit, and feeder roots were evaluated for the presence of CLas. Quadrant-peduncle (Q-P) tissue sampling consistently resulted in better CLas detection than any other tissue type. Q-P samples had a 30% higher qPCR positivity rate than quadrant-leaf (Q-L) samples. No significant seasonal patterns were observed. Roots and single peduncles had similar detection rates; both were higher than single leaves or Q-L samples. If symptoms were used to guide sampling, 30% of infected trees would have been missed. Taken together, these results suggest that Q-P tissue sampling is the optimal choice for improved CLas detection under California growing conditions.

The value of volunteer surveillance for the early detection of biological invaders.

Early detection of invaders requires finding small numbers of individuals across large landscapes. It has been argued that the only feasible way to achieve the sampling effort needed for early detection of an invader is to involve volunteer groups (citizen scientists, passive surveyors, etc.). A key concern is that volunteers may have a considerable false-positive and false-negative rate. The question then becomes whether verification of a report from a volunteer is worth the effort. This question is the topic of this paper. Since we are interested in early detection we calculate the Z% upper limit of the one sided confidence interval of the incidence (fraction infected) and use the term maximum expected plausible incidence for this. We compare the maximum plausible incidence when the expert samples on their own, qE∼, and the maximum plausible incidence when the expert only verifies cases reported by the volunteer surveyor to be infected, qV∼. The maximum plausible incidences qE∼ and qV∼. are related as, qV∼=θfp1-θfnqE∼ where θfp and θfn are the false positive and false negative rate of the volunteer surveyor, respectively. We also show that the optimal monitoring programme consists of verifying only the cases reported by the volunteer surveyor if, TXTN<θfp1-θfn, where TN is the time needed for a sample taken by the expert and TX is the time needed for an expert to verify a case reported by a volunteer surveyor. Our results can be used to calculate the maximum plausible incidence of a plant disease based on reports of passive surveyors that have been verified by experts and data from experts sampling on their own. The results can also be used in the development phase of a surveillance project to assess whether including passive surveyor reports is useful in the early detection of exotic invaders.

Spatio-Temporal Spread of Grapevine Leafroll Disease in Washington State Vineyards.

The spread of grapevine leafroll disease (GLD) to vineyards planted with certified planting stock is of significant concern to grape growers. In this study, the spatial and temporal spread of GLD was examined in three vineyard blocks planted with virus-tested wine grape (Vitis vinifera) cultivars adjacent to vineyard blocks heavily infected with GLD in two geographic locations in eastern Washington State. During each season, the position of vines showing GLD symptoms was recorded in a matrix representing the planting lattice. Symptomatic vines were positive only for Grapevine leafroll-associated virus 3 (GLRaV-3), the most common virus species consistently associated with GLD in Washington vineyards. The results from multiple seasons showed a gradual increase in disease incidence over time in all three blocks. Spatial and temporal mapping of GLD indicated a disease gradient in which the highest percentage of symptomatic vines was in rows proximal to infected old blocks. Spatial autocorrelation analysis using Moran's I values suggested random patterns of symptomatic vines in the three blocks during initial years, indicating primary spread of the virus not related to infected vines within the block. Clustering at the scale of neighboring vines during subsequent years suggested secondary spread within the block. Results of quadrat-based spatial analyses of GLD incidence were compared with previously reported data obtained from California and elsewhere for an improved understanding of the dynamics of GLD spread to facilitate area-wide disease management strategies.

Perceived Vulnerability and Propensity to Adopt Best Management Practices for Huanglongbing Disease of Citrus in California.

Huanglongbing (HLB) disease of citrus, which is associated with the bacterium 'Candidatus Liberibacter asiaticus', has been confined to residential properties in Southern California 8 years after it was first detected in the state. To prevent the spread of HLB to commercial citrus groves, growers have been asked to adopt a portfolio of voluntary best management practices. This study evaluates the citrus industry's propensity to adopt these practices using surveys and a novel multivariate ordinal regression model. We estimate the impact on adoption of perceived vulnerability to HLB, intentions to stay informed and communicate about the disease and various socio-economic factors, and reveal what practices are most likely to be jointly adopted as an integrated approach to HLB. Survey participants were in favor of scouting and surveying for HLB symptoms, but they were reluctant to test trees, use early detection technologies (EDTs), and install barriers around citrus groves. Most practices were perceived as complementary, particularly visual inspections and some combinations of preventive practices with tests and EDTs. Participants who felt more vulnerable to HLB had a higher propensity to adopt several practices, as did those who intended to stay informed and communicate with the coordinators of the HLB control program, although this effect was modulated by the perceived vulnerability to HLB. Communication with neighbors and the size of citrus operations also influenced practice adoption. Based on these results, we provide recommendations for outreach about HLB management in California and suggest future directions for research about the adoption of plant disease management practices.[Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Information Graphs Incorporating Predictive Values of Disease Forecasts.

Diagrammatic formats are useful for summarizing the processes of evaluation and comparison of forecasts in plant pathology and other disciplines where decisions about interventions for the purpose of disease management are often based on a proxy risk variable. We describe a new diagrammatic format for disease forecasts with two categories of actual status and two categories of forecast. The format displays relative entropies, functions of the predictive values that characterize expected information provided by disease forecasts. The new format arises from a consideration of earlier formats with underlying information properties that were previously unexploited. The new diagrammatic format requires no additional data for calculation beyond those used for the calculation of a receiver operating characteristic (ROC) curve. While an ROC curve characterizes a forecast in terms of sensitivity and specificity, the new format described here characterizes a forecast in terms of relative entropies based on predictive values. Thus it is complementary to ROC methodology in its application to the evaluation and comparison of forecasts.

Mutual Information as a Performance Measure for Binary Predictors Characterized by Both ROC Curve and PROC Curve Analysis.

The predictive receiver operating characteristic (PROC) curve differs from the more well-known receiver operating characteristic (ROC) curve in that it provides a basis for the evaluation of binary diagnostic tests using metrics defined conditionally on the outcome of the test rather than metrics defined conditionally on the actual disease status. Application of PROC curve analysis may be hindered by the complex graphical patterns that are sometimes generated. Here we present an information theoretic analysis that allows concurrent evaluation of PROC curves and ROC curves together in a simple graphical format. The analysis is based on the observation that mutual information may be viewed both as a function of ROC curve summary statistics (sensitivity and specificity) and prevalence, and as a function of predictive values and prevalence. Mutual information calculated from a 2 × 2 prediction-realization table for a specified risk score threshold on an ROC curve is the same as the mutual information calculated at the same risk score threshold on a corresponding PROC curve. Thus, for a given value of prevalence, the risk score threshold that maximizes mutual information is the same on both the ROC curve and the corresponding PROC curve. Phytopathologists and clinicians who have previously relied solely on ROC curve summary statistics when formulating risk thresholds for application in practical agricultural or clinical decision-making contexts are thus presented with a methodology that brings predictive values within the scope of that formulation.

Canine Olfactory Detection of a Non-Systemic Phytobacterial Citrus Pathogen of International Quarantine Significance.

For millennia humans have benefitted from application of the acute canine sense of smell to hunt, track and find targets of importance. In this report, canines were evaluated for their ability to detect the severe exotic phytobacterial arboreal pathogen Xanthomonas citri pv. citri (Xcc), which is the causal agent of Asiatic citrus canker (Acc). Since Xcc causes only local lesions, infections are non-systemic, limiting the use of serological and molecular diagnostic tools for field-level detection. This necessitates reliance on human visual surveys for Acc symptoms, which is highly inefficient at low disease incidence, and thus for early detection. In simulated orchards the overall combined performance metrics for a pair of canines were 0.9856, 0.9974, 0.9257 and 0.9970, for sensitivity, specificity, precision, and accuracy, respectively, with 1-2 s/tree detection time. Detection of trace Xcc infections on commercial packinghouse fruit resulted in 0.7313, 0.9947, 0.8750, and 0.9821 for the same performance metrics across a range of cartons with 0-10% Xcc-infected fruit despite the noisy, hot and potentially distracting environment. In orchards, the sensitivity of canines increased with lesion incidence, whereas the specificity and overall accuracy was >0.99 across all incidence levels; i.e., false positive rates were uniformly low. Canines also alerted to a range of 1-12-week-old infections with equal accuracy. When trained to either Xcc-infected trees or Xcc axenic cultures, canines inherently detected the homologous and heterologous targets, suggesting they can detect Xcc directly rather than only volatiles produced by the host following infection. Canines were able to detect the Xcc scent signature at very low concentrations (10,000× less than 1 bacterial cell per sample), which implies that the scent signature is composed of bacterial cell volatile organic compound constituents or exudates that occur at concentrations many fold that of the bacterial cells. The results imply that canines can be trained as viable early detectors of Xcc and deployed across citrus orchards, packinghouses, and nurseries.

Characterization of Pathogen Airborne Inoculum Density by Information Theoretic Analysis of Spore Trap Time Series Data.

In a previous study, air sampling using vortex air samplers combined with species-specific amplification of pathogen DNA was carried out over two years in four or five locations in the Salinas Valley of California. The resulting time series data for the abundance of pathogen DNA trapped per day displayed complex dynamics with features of both deterministic (chaotic) and stochastic uncertainty. Methods of nonlinear time series analysis developed for the reconstruction of low dimensional attractors provided new insights into the complexity of pathogen abundance data. In particular, the analyses suggested that the length of time series data that it is practical or cost-effective to collect may limit the ability to definitively classify the uncertainty in the data. Over the two years of the study, five location/year combinations were classified as having stochastic linear dynamics and four were not. Calculation of entropy values for either the number of pathogen DNA copies or for a binary string indicating whether the pathogen abundance data were increasing revealed (1) some robust differences in the dynamics between seasons that were not obvious in the time series data themselves and (2) that the series were almost all at their theoretical maximum entropy value when considered from the simple perspective of whether instantaneous change along the sequence was positive.

Development of an IPM Strategy for Thrips and Tomato spotted wilt virus in Processing Tomatoes in the Central Valley of California.

Tomato spotted wilt virus (TSWV; species Tomato spotted wilt orthotospovirus; genus Orthotospovirus; family Tospoviridae) is a thrips-transmitted virus that can cause substantial economic losses to many crops, including tomato (Solanum lycopersicum). Since 2005, TSWV emerged as an economically important virus of processing tomatoes in the Central Valley of California, in part due to increased populations of the primary thrips vector, western flower thrips (WFT; Frankliniella occidentalis). To develop an understanding of the epidemiology of TSWV in this region, population densities of WFT and incidence of TSWV were monitored in California's processing tomato transplant-producing greenhouses and associated open fields from 2007 to 2013. Thrips were monitored with yellow sticky cards and in tomato flowers, whereas TSWV incidence was assessed with indicator plants and field surveys for virus symptoms. All thrips identified from processing tomato fields were WFT, and females were three-fold more abundant on sticky cards than males. Symptoms of TSWV infection were observed in all monitored processing tomato fields. Incidences of TSWV ranged from 1 to 20%, with highest incidence found in late-planted fields. There was no single primary inoculum source, and inoculum sources for thrips/TSWV varied depending on the production region. These results allowed us to develop a model for TSWV infection of processing tomatoes in the Central Valley of California. The model predicts that low levels of primary TSWV inoculum are amplified in early-planted tomatoes and other susceptible crops leading to highest levels of infection in later-planted fields, especially those with high thrips populations. Based upon these findings, an integrated pest management (IPM) strategy for TSWV in processing tomatoes in California was devised. This IPM strategy focuses on strategic field placement (identification of high-risk situations), planting TSWV- and thrips-free transplants, planting resistant varieties, monitoring for TSWV symptoms and thrips, roguing infected plants, thrips management targeting early generations, extensive sanitation after harvest, and strategic cropping to avoid overlap with winter bridge crops.

Using Game Theory to Understand Systemic Acquired Resistance as a Bet-Hedging Option for Increasing Fitness When Disease Is Uncertain.

Systemic acquired resistance (SAR) is a mechanism through which plants may respond to initial challenge by a pathogen through activation of inducible defense responses, thereby increasing resistance to subsequent infection attempts. Fitness costs are assumed to be incurred by plants induced for SAR, and several studies have attempted to quantify these costs. We developed a mathematical model, motivated by game-theoretic concepts, to simulate competition between hypothetical plant populations with and without SAR to examine conditions under which the phenomenon of SAR may have evolved. Data were gathered from various studies on fitness costs of induced resistance on life history traits in different plant hosts and scaled as a proportion of the values in control cohorts in each study (i.e., healthy plants unprimed for SAR). With unprimed healthy control plants set to a fitness value of 1, primed healthy plants incurred a fitness cost of about 10.4% (0.896, n = 157), primed diseased plants incurred a fitness cost of about 15.5% (0.845, n = 54), and unprimed diseased plants incurred a fitness cost of about 28.9% (0.711, n = 69). Starting from a small proportion of the population (0.5%) and competing against a population with constitutive defenses alone in stochastic simulations, the SAR phenotype almost always dominated the population after 1000 generations when the probability of disease was greater than or equal to 0.5 regardless of the probability for priming errors.

Using models to provide rapid programme support for California's efforts to suppress Huanglongbing disease of citrus.

We describe a series of operational questions posed during the state-wide response in California to the arrival of the invasive citrus disease Huanglongbing. The response is coordinated by an elected committee from the citrus industry and operates in collaboration with the California Department of Food and Agriculture, which gives it regulatory authority to enforce the removal of infected trees. The paper reviews how surveillance for disease and resource allocation between detection and delimitation have been addressed, based on epidemiological principles. In addition, we describe how epidemiological analyses have been used to support rule-making to enact costly but beneficial regulations and we highlight two recurring themes in the programme support work: (i) data are often insufficient for quantitative analyses of questions and (ii) modellers and decision-makers alike may be forced to accept the need to make decisions on the basis of simple or incomplete analyses that are subject to considerable uncertainty. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'. This theme issue is linked with the earlier issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'.

The global burden of pathogens and pests on major food crops.

Crop pathogens and pests reduce the yield and quality of agricultural production. They cause substantial economic losses and reduce food security at household, national and global levels. Quantitative, standardized information on crop losses is difficult to compile and compare across crops, agroecosystems and regions. Here, we report on an expert-based assessment of crop health, and provide numerical estimates of yield losses on an individual pathogen and pest basis for five major crops globally and in food security hotspots. Our results document losses associated with 137 pathogens and pests associated with wheat, rice, maize, potato and soybean worldwide. Our yield loss (range) estimates at a global level and per hotspot for wheat (21.5% (10.1-28.1%)), rice (30.0% (24.6-40.9%)), maize (22.5% (19.5-41.1%)), potato (17.2% (8.1-21.0%)) and soybean (21.4% (11.0-32.4%)) suggest that the highest losses are associated with food-deficit regions with fast-growing populations, and frequently with emerging or re-emerging pests and diseases. Our assessment highlights differences in impacts among crop pathogens and pests and among food security hotspots. This analysis contributes critical information to prioritize crop health management to improve the sustainability of agroecosystems in delivering services to societies.

Multiple origins of downy mildews and mito-nuclear discordance within the paraphyletic genus Phytophthora.

Phylogenetic relationships between thirteen species of downy mildew and 103 species of Phytophthora (plant-pathogenic oomycetes) were investigated with two nuclear and four mitochondrial loci, using several likelihood-based approaches. Three Phytophthora taxa and all downy mildew taxa were excluded from the previously recognized subgeneric clades of Phytophthora, though all were strongly supported within the paraphyletic genus. Downy mildews appear to be polyphyletic, with graminicolous downy mildews (GDM), brassicolous downy mildews (BDM) and downy mildews with colored conidia (DMCC) forming a clade with the previously unplaced Phytophthora taxon totara; downy mildews with pyriform haustoria (DMPH) were placed in their own clade with affinities to the obligate biotrophic P. cyperi. Results suggest the recognition of four additional clades within Phytophthora, but few relationships between clades could be resolved. Trees containing all twenty extant downy mildew genera were produced by adding partial coverage of seventeen additional downy mildew taxa; these trees supported the monophyly of the BDMs, DMCCs and DMPHs but suggested that the GDMs are paraphyletic in respect to the BDMs or polyphyletic. Incongruence between nuclear-only and mitochondrial-only trees suggests introgression may have occurred between several clades, particularly those containing biotrophs, questioning whether obligate biotrophic parasitism and other traits with polyphyletic distributions arose independently or were horizontally transferred. Phylogenetic approaches may be limited in their ability to resolve some of the complex relationships between the "subgeneric" clades of Phytophthora, which include twenty downy mildew genera and hundreds of species.

A Framework for Optimizing Phytosanitary Thresholds in Seed Systems.

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.

Analysis of the Spatial Pattern of Strawberry Angular Leaf Spot in California Nursery Production.

In California, angular leaf spot (ALS) is a common disease in strawberry nursery production, and a major concern for nurseries wishing to export plants. As the spatial pattern of a disease can offer insight into pathogen source, mode of dissemination, and how current crop management practices affect epidemic development, an understanding of the spatial pattern of ALS would allow nursery growers to make informed decisions regarding disease management. Ninety-seven field assessments of disease incidence were performed at different nursery locations in 2014 and 2015 to quantify ALS spatial pattern under commercial conditions. Both point-pattern and geostatistical statistical procedures were used to analyze the data. The spatial pattern of ALS was characterized by a high degree of heterogeneity, as indicated by high median values of the beta-binomial distribution's theta parameter (0.643), and the index of dispersion, D (4.218). The binary power law provided a robust description of the data with estimated slope and intercept parameters significantly greater than 1 and 0, respectively (P < 0.001). Spatial analysis by distance indices (SADIE) detected significant nonrandom spatial arrangements for 64% of the data sets. Analysis of directional disease spread showed a strong spatial association between sampling units along the same planting row. This suggests that recurrent crop operations during the growing season play a significant role in ALS spread and should be taken into account to improve disease control.

Coordination of Diagnostic Efforts in the Great Plains: Wheat Virus Survey and Modeling of Disease Onset.

Following the discovery of two new wheat virus diseases in the United States, the Great Plains region (Colorado, Kansas, Montana, Nebraska, North Dakota, Oklahoma, South Dakota, Texas, and Wyoming) of the National Plant Diagnostic Network (NPDN) initiated a project to measure the prevalence of five wheat diseases using indirect ELISA. Wheat streak mosaic virus (WSMV), Wheat mosaic virus (WMoV), and Triticum mosaic virus (TriMV) were found in all nine states. WSMV was the most prevalent, averaging 23 to 47% of samples each year. TriMV and WMoV were detected with WSMV (in up to 76% of the samples). All three mite-transmitted viruses were present in 26% or fewer of the samples. Aphid-transmitted viruses in the barley yellow dwarf complex Barley yellow dwarf virus, and Cereal yellow dwarf virus-RPV were less frequent (fewer than 65% of the samples). This paper presents the first case-control methodology paper using plant diagnostic laboratory data and the first signed diagnostic data-sharing agreement between the NPDN and its regulatory stakeholders. Samples collected when <700 cumulative degree-days base 0°C, were twice as likely to be virus negative. This proof-of-concept effort highlights the potential of the NPDN and its National Data Repository to develop knowledge about emerging diseases.

Coupling Spore Traps and Quantitative PCR Assays for Detection of the Downy Mildew Pathogens of Spinach (Peronospora effusa) and Beet (P. schachtii).

Downy mildew of spinach (Spinacia oleracea), caused by Peronospora effusa, is a production constraint on production worldwide, including in California, where the majority of U.S. spinach is grown. The aim of this study was to develop a real-time quantitative polymerase chain reaction (qPCR) assay for detection of airborne inoculum of P. effusa in California. Among oomycete ribosomal DNA (rDNA) sequences examined for assay development, the highest nucleotide sequence identity was observed between rDNA sequences of P. effusa and P. schachtii, the cause of downy mildew on sugar beet and Swiss chard in the leaf beet group (Beta vulgaris subsp. vulgaris). Single-nucleotide polymorphisms were detected between P. effusa and P. schachtii in the 18S rDNA regions for design of P. effusa- and P. schachtii-specific TaqMan probes and reverse primers. An allele-specific probe and primer amplification method was applied to determine the frequency of both P. effusa and P. schachtii rDNA target sequences in pooled DNA samples, enabling quantification of rDNA of P. effusa from impaction spore trap samples collected from spinach production fields. The rDNA copy numbers of P. effusa were, on average, ≈3,300-fold higher from trap samples collected near an infected field compared with those levels recorded at a site without a nearby spinach field. In combination with disease-conducive weather forecasting, application of the assays may be helpful to time fungicide applications for disease management.

Considerations of scale in the analysis of spatial pattern of plant disease epidemics.

Scale is an important but somewhat neglected subject in plant pathology. Scale serves as an abstract concept, providing a framework for organizing observations and theoretical models, and plays a functional role in the organization of ecological communities and physical processes. Rich methodological resources are available to plant pathologists interested in considering either or both aspects of scale in their research. We summarize important concepts in both areas of the literature, particularly as they apply to the spatial pattern of plant disease, and highlight some new results that emphasize the importance of scaling on the emergence of different types of probability distribution in empirical observation. We also highlight the important links between heterogeneity and scale, which are of central importance in plant disease epidemiology and the analysis of spatial pattern. We consider statistical approaches that are available, where actual physical scale is known, and for more conceptual research on hierarchies, where scale plays a more abstract role, particularly for field-based research. For the latter, we highlight methods that plant pathologists could consider to account for the effect of scale in the design of field studies.

The use and role of predictive systems in disease management.

Disease predictive systems are intended to be management aids. With a few exceptions, these systems typically do not have direct sustained use by growers. Rather, their impact is mostly pedagogic and indirect, improving recommendations from farm advisers and shaping management concepts. The degree to which a system is consulted depends on the amount of perceived new, actionable information that is consistent with the objectives of the user. Often this involves avoiding risks associated with costly disease outbreaks. Adoption is sensitive to the correspondence between the information a system delivers and the information needed to manage a particular pathosystem at an acceptable financial risk; details of the approach used to predict disease risk are less important. The continuing challenge for researchers is to construct tools relevant to farmers and their advisers that improve upon their current management skill. This goal requires an appreciation of growers' decision calculus in managing disease problems and, more broadly, their overall farm enterprise management.