Spatially explicit ecological modeling improves empirical characterization of plant pathogen dispersal.

Dispersal is a key ecological process, but it remains difficult to measure. By recording numbers of dispersed individuals at different distances from the source, one acquires a dispersal gradient. Dispersal gradients contain information on dispersal, but they are influenced by the spatial extent of the source. How can we separate the two contributions to extract knowledge about dispersal? One could use a small, point-like source for which a dispersal gradient represents a dispersal kernel, which quantifies the probability of an individual dispersal event from a source to a destination. However, the validity of this approximation cannot be established before conducting measurements. This represents a key challenge hindering progress in characterization of dispersal. To overcome it, we formulated a theory that incorporates the spatial extent of sources to estimate dispersal kernels from dispersal gradients. Using this theory, we re-analyzed published dispersal gradients for three major plant pathogens. We demonstrated that the three pathogens disperse over substantially shorter distances compared to conventional estimates. This method will allow the researchers to re-analyze a vast number of existing dispersal gradients to improve our knowledge about dispersal. The improved knowledge has potential to advance our understanding of species' range expansions and shifts, and inform management of weeds and diseases in crops.

How large and diverse are field populations of fungal plant pathogens? The case of Zymoseptoria tritici.

Pathogen populations differ in the amount of genetic diversity they contain. Populations carrying higher genetic diversity are thought to have a greater evolutionary potential than populations carrying less diversity. We used published studies to estimate the range of values associated with two critical components of genetic diversity, the number of unique pathogen genotypes and the number of spores produced during an epidemic, for the septoria tritici blotch pathogen Zymoseptoria tritici. We found that wheat fields experiencing typical levels of infection are likely to carry between 3.1 and 14.0 million pathogen genotypes per hectare and produce at least 2.1-9.9 trillion pycnidiospores per hectare. Given the experimentally derived mutation rate of 3 × 10-10 substitutions per site per cell division, we estimate that between 27 and 126 million pathogen spores carrying adaptive mutations to counteract fungicides and resistant cultivars will be produced per hectare during a growing season. This suggests that most of the adaptive mutations that have been observed in Z. tritici populations can emerge through local selection from standing genetic variation that already exists within each field. The consequences of these findings for disease management strategies are discussed.

Mixed infections alter transmission potential in a fungal plant pathogen.

Infections by more than one strain of a pathogen predominate under natural conditions. Mixed infections can have significant, though often unpredictable, consequences for overall virulence, pathogen transmission and evolution. However, effects of mixed infection on disease development in plants often remain unclear and the critical factors that determine the outcome of mixed infections remain unknown. The fungus Zymoseptoria tritici forms genetically diverse infections in wheat fields. Here, for a range of pathogen traits, we experimentally decompose the infection process to determine how the outcomes and consequences of mixed infections are mechanistically realized. Different strains of Z. tritici grow in close proximity and compete in the wheat apoplast, resulting in reductions in growth of individual strains and in pathogen reproduction. We observed different outcomes of competition at different stages of the infection. Overall, more virulent strains had higher competitive ability during host colonization, and less virulent strains had higher transmission potential. We showed that within-host competition can have a major effect on infection dynamics and pathogen population structure in a pathogen and host genotype-specific manner. Consequently, mixed infections likely have a major effect on the development of septoria tritici blotch epidemics and the evolution of virulence in Z. tritici.

A tradeoff between tolerance and resistance to a major fungal pathogen in elite wheat cultivars.

Tolerance and resistance represent two strategies that hosts evolved to protect themselves from pathogens. Tolerance alleviates the reduction in host fitness due to infection without reducing a pathogen's growth, whereas resistance reduces pathogen growth. We investigated the tolerance of wheat to the major fungal pathogen Zymoseptoria tritici in 335 elite wheat cultivars. We used a novel digital phenotyping approach that included 11 152 infected leaves and counted 2069 048 pathogen fruiting bodies. We discovered a new component of tolerance that is based on the relationship between the green area remaining on a leaf and the number of pathogen fruiting bodies. We found a negative correlation between tolerance and resistance among intolerant cultivars, presenting the first compelling evidence for a tradeoff between tolerance and resistance to plant pathogens. Surprisingly, the tradeoff arises due to limits in the host resources available to the pathogen and not due to metabolic constraints, contrary to what ecological theory suggests. The mechanism underlying this tradeoff may be relevant for many plant diseases in which the amount of host resources available to the pathogen can limit the pathogen population. Our analysis indicates that European wheat breeders may have selected for tolerance instead of resistance to an important pathogen.

In-Field Detection and Quantification of Septoria Tritici Blotch in Diverse Wheat Germplasm Using Spectral-Temporal Features.

Hyperspectral remote sensing holds the potential to detect and quantify crop diseases in a rapid and non-invasive manner. Such tools could greatly benefit resistance breeding, but their adoption is hampered by i) a lack of specificity to disease-related effects and ii) insufficient robustness to variation in reflectance caused by genotypic diversity and varying environmental conditions, which are fundamental elements of resistance breeding. We hypothesized that relying exclusively on temporal changes in canopy reflectance during pathogenesis may allow to specifically detect and quantify crop diseases while minimizing the confounding effects of genotype and environment. To test this hypothesis, we collected time-resolved canopy hyperspectral reflectance data for 18 diverse genotypes on infected and disease-free plots and engineered spectral-temporal features representing this hypothesis. Our results confirm the lack of specificity and robustness of disease assessments based on reflectance spectra at individual time points. We show that changes in spectral reflectance over time are indicative of the presence and severity of Septoria tritici blotch (STB) infections. Furthermore, the proposed time-integrated approach facilitated the delineation of disease from physiological senescence, which is pivotal for efficient selection of STB-resistant material under field conditions. A validation of models based on spectral-temporal features on a diverse panel of 330 wheat genotypes offered evidence for the robustness of the proposed method. This study demonstrates the potential of time-resolved canopy reflectance measurements for robust assessments of foliar diseases in the context of resistance breeding.

Precision Phenotyping Reveals Novel Loci for Quantitative Resistance to Septoria Tritici Blotch.

Accurate, high-throughput phenotyping for quantitative traits is a limiting factor for progress in plant breeding. We developed an automated image analysis to measure quantitative resistance to septoria tritici blotch (STB), a globally important wheat disease, enabling identification of small chromosome intervals containing plausible candidate genes for STB resistance. 335 winter wheat cultivars were included in a replicated field experiment that experienced natural epidemic development by a highly diverse but fungicide-resistant pathogen population. More than 5.4 million automatically generated phenotypes were associated with 13,648 SNP markers to perform the GWAS. We identified 26 chromosome intervals explaining 1.9-10.6% of the variance associated with four independent resistance traits. Sixteen of the intervals overlapped with known STB resistance intervals, suggesting that our phenotyping approach can identify simultaneously (i.e., in a single experiment) many previously defined STB resistance intervals. Seventeen of the intervals were less than 5 Mbp in size and encoded only 173 genes, including many genes associated with disease resistance. Five intervals contained four or fewer genes, providing high priority targets for functional validation. Ten chromosome intervals were not previously associated with STB resistance, perhaps representing resistance to pathogen strains that had not been tested in earlier experiments. The SNP markers associated with these chromosome intervals can be used to recombine different forms of quantitative STB resistance that are likely to be more durable than pyramids of major resistance genes. Our experiment illustrates how high-throughput automated phenotyping can accelerate breeding for quantitative disease resistance.

Hyperspectral Canopy Sensing of Wheat Septoria Tritici Blotch Disease.

Producing quantitative and reliable measures of crop disease is essential for resistance breeding, but is challenging and time consuming using traditional phenotyping methods. Hyperspectral remote sensing has shown potential for the detection of plant diseases, but its utility for phenotyping large and diverse populations of plants under field conditions requires further evaluation. In this study, we collected canopy hyperspectral data from 335 wheat varieties using a spectroradiometer, and we investigated the use of canopy reflectance for detecting the Septoria tritici blotch (STB) disease and for quantifying the severity of infection. Canopy- and leaf-level infection metrics of STB based on traditional visual assessments and automated analyses of leaf images were used as ground truth data. Results showed (i) that canopy reflectance and the selected spectral indices show promise for quantifying STB infections, and (ii) that the normalized difference water index (NDWI) showed the best performance in detecting STB compared to other spectral indices. Moreover, partial least squares (PLS) regression models allowed for an improvement in the prediction of STB metrics. The PLS discriminant analysis (PLSDA) model calibrated based on the spectral data of four reference varieties was able to discriminate between the diseased and healthy canopies among the 335 varieties with an accuracy of 93% (Kappa = 0.60). Finally, the PLSDA model predictions allowed for the identification of wheat genotypes that are potentially more susceptible to STB, which was confirmed by the STB visual assessment. This study demonstrates the great potential of using canopy hyperspectral remote sensing to improve foliar disease assessment and to facilitate plant breeding for disease resistance.

Ranking Quantitative Resistance to Septoria tritici Blotch in Elite Wheat Cultivars Using Automated Image Analysis.

Quantitative resistance is likely to be more durable than major gene resistance for controlling Septoria tritici blotch (STB) on wheat. Earlier studies hypothesized that resistance affecting the degree of host damage, as measured by the percentage of leaf area covered by STB lesions, is distinct from resistance that affects pathogen reproduction, as measured by the density of pycnidia produced within lesions. We tested this hypothesis using a collection of 335 elite European winter wheat cultivars that was naturally infected by a diverse population of Zymoseptoria tritici in a replicated field experiment. We used automated image analysis of 21,420 scanned wheat leaves to obtain quantitative measures of conditional STB intensity that were precise, objective, and reproducible. These measures allowed us to explicitly separate resistance affecting host damage from resistance affecting pathogen reproduction, enabling us to confirm that these resistance traits are largely independent. The cultivar rankings based on host damage were different from the rankings based on pathogen reproduction, indicating that the two forms of resistance should be considered separately in breeding programs aiming to increase STB resistance. We hypothesize that these different forms of resistance are under separate genetic control, enabling them to be recombined to form new cultivars that are highly resistant to STB. We found a significant correlation between rankings based on automated image analysis and rankings based on traditional visual scoring, suggesting that image analysis can complement conventional measurements of STB resistance, based largely on host damage, while enabling a much more precise measure of pathogen reproduction. We showed that measures of pathogen reproduction early in the growing season were the best predictors of host damage late in the growing season, illustrating the importance of breeding for resistance that reduces pathogen reproduction in order to minimize yield losses caused by STB. These data can already be used by breeding programs to choose wheat cultivars that are broadly resistant to naturally diverse Z. tritici populations according to the different classes of resistance.

Emergence of Resistance to Fungicides: The Role of Fungicide Dose.

Resistance to antimicrobial drugs allows pathogens to survive drug treatment. The time taken for a new resistant mutant to reach a population size that is unlikely to die out by chance is called "emergence time." Prolonging emergence time would delay loss of control. We investigate the effect of fungicide dose on the emergence time in fungal plant pathogens. A population dynamical model is combined with dose-response data for Zymoseptoria tritici, an important wheat pathogen. Fungicides suppress sensitive pathogen population. This has two effects. First, the rate of appearance of resistant mutants is reduced, hence the emergence takes longer. Second, more healthy host tissue becomes available for resistant mutants, increasing their chances to invade and accelerates emergence. In theory, the two competing effects may lead to a non-monotonic dependence of the emergence time on fungicide dose that exhibits a minimum. But according to field data, fungicides are unable to reduce the fungicide-sensitive population strongly enough even at high doses. Hence, for full resistance over realistic ranges of pathogen's life history and fungicide dose-response parameters, emergence time decreases monotonically with increasing dose. For partial resistance, there can be cases within a limited parameter range, when emergence decelerates at higher doses.

Invasiveness of plant pathogens depends on the spatial scale of host distribution.

Plant diseases often cause serious yield losses in agriculture. A pathogen's invasiveness can be quantified by the basic reproductive number, R0. Since pathogen transmission between host plants depends on the spatial separation between them, R0 is strongly influenced by the spatial scale of the host distribution. We present a proof of principle of a novel approach to estimate the basic reproductivenumber, R0, of plant pathogens as a function of the size of a field planted with crops and its aspect ratio. This general approach is based on a spatially explicit population dynamical model. The basic reproductive number was found to increase with the field size at small field sizes and to saturate to a constant value at large field sizes. It reaches amaximum in square fields and decreases as the field becomes elongated. This pattern appears to be quite general: it holds for dispersal kernels that decrease exponentially or faster, as well as for fat-tailed dispersal kernels that decrease slower than exponential (i.e., power-law kernels). We used this approach to estimate R0 in wheat stripe rust(an important disease caused by Puccinia striiformis), where we inferred both the transmission rates and the dispersal kernels from the measurements of disease gradients. For the two largest datasets, we estimated R0 of P. striiformis in the limit of large fields to be of the order of 30. We found that the spatial extent over which R0 changes strongly is quite fine-scaled (about 30 m of the linear extension of the field). Our results indicate that in order to optimize the spatial scale of deployment of fungicides or host resistances, the adjustments should be made at a fine spatial scale. We also demonstrated how the knowledge of the spatial dependence of R0 can improve recommendations with regard to fungicide treatment.

An Improved Method for Measuring Quantitative Resistance to the Wheat Pathogen Zymoseptoria tritici Using High-Throughput Automated Image Analysis.

Zymoseptoria tritici causes Septoria tritici blotch (STB) on wheat. An improved method of quantifying STB symptoms was developed based on automated analysis of diseased leaf images made using a flatbed scanner. Naturally infected leaves (n = 949) sampled from fungicide-treated field plots comprising 39 wheat cultivars grown in Switzerland and 9 recombinant inbred lines (RIL) grown in Oregon were included in these analyses. Measures of quantitative resistance were percent leaf area covered by lesions, pycnidia size and gray value, and pycnidia density per leaf and lesion. These measures were obtained automatically with a batch-processing macro utilizing the image-processing software ImageJ. All phenotypes in both locations showed a continuous distribution, as expected for a quantitative trait. The trait distributions at both sites were largely overlapping even though the field and host environments were quite different. Cultivars and RILs could be assigned to two or more statistically different groups for each measured phenotype. Traditional visual assessments of field resistance were highly correlated with quantitative resistance measures based on image analysis for the Oregon RILs. These results show that automated image analysis provides a promising tool for assessing quantitative resistance to Z. tritici under field conditions.

Can high-risk fungicides be used in mixtures without selecting for fungicide resistance?

Fungicide mixtures produced by the agrochemical industry often contain low-risk fungicides, to which fungal pathogens are fully sensitive, together with high-risk fungicides known to be prone to fungicide resistance. Can these mixtures provide adequate disease control while minimizing the risk for the development of resistance? We present a population dynamics model to address this question. We found that the fitness cost of resistance is a crucial parameter to determine the outcome of competition between the sensitive and resistant pathogen strains and to assess the usefulness of a mixture. If fitness costs are absent, then the use of the high-risk fungicide in a mixture selects for resistance and the fungicide eventually becomes nonfunctional. If there is a cost of resistance, then an optimal ratio of fungicides in the mixture can be found, at which selection for resistance is expected to vanish and the level of disease control can be optimized.

Spatial correlations in finite samples revealed by Coulomb explosion.

We demonstrate that fast removal of many electrons uncovers initial correlations of atoms in a finite sample through a pronounced peak in the kinetic-energy spectrum of the exploding ions. This maximum is the result of an intricate interplay between the composition of the system from discrete particles and its boundary. The formation of the peak can be described analytically, accounting for correlations beyond a mean-field reference model. It can be experimentally detected with short and intense light pulses from 4th-generation light sources.

Laser-driven nanoplasmas in doped helium droplets: local ignition and anisotropic growth.

Doping a helium nanodroplet with only a tiny xenon cluster of a few atoms sparks complete ionization of the droplet at laser intensities below the ionization threshold of helium atoms. As a result, the intrinsically inert and transparent droplet turns into a fast and strong absorber of infrared light. Microscopic calculations reveal a two-step mechanism to be responsible for the dramatic change: Avalanchelike ionization of the helium atoms on a femtosecond time scale, driven by field ionization due to the quickly charged xenon core, is followed by resonant absorption enabled by an unusual cigar-shaped nanoplasma within the droplet.

Survival benefits in mimicry: a quantitative framework.

Mimicry is a resemblance between species that benefits at least one of the species. It is a ubiquitous evolutionary phenomenon particularly common among prey species, in which case the advantage involves better protection from predation. We formulate a mathematical description of predation, to investigate benefits and disadvantages of mimicry. The basic setup involves differential equations for quantities representing predator behavior, namely, the probabilities for attacking prey at the next encounter. Using this framework, we present new quantitative results, and also provide a unified description of a significant fraction of the quantitative mimicry literature. The new results include "temporary" mutualism between prey species, and an optimal density at which the survival benefit is greatest for the mimic. The formalism leads naturally to extensions in several directions, such as the interplay of mimicry with population dynamics, studies of spatiotemporal patterns, etc. We demonstrate this extensibility by presenting some explorations on spatiotemporal pattern dynamics.