John P. Clarkson

Ascospore release and survival in Sclerotinia sclerotiorum

The release and survival of ascospores of a UK Sclerotinia sclerotiorum isolate were studied. Apothecia placed in a spore clock apparatus with different lighting regimes at 15 degrees C released ascospores continuously with an increasing rate for the duration of experiments (72-84 h). Spore release was not confined to light or dark periods in alternating regimes and occurred in continuous dark or light. Ascospores were released in both saturated air (90-95% rh) and at 65-75% rh. High temperature and rh were detrimental to ascospore survival but spore viability was maintained for longer periods than previously reported. The significance of these results in relation to disease control is discussed.

Forecasting Sclerotinia Disease on Lettuce: Toward Developing a Prediction Model for Carpogenic Germination of Sclerotia

ABSTRACT The feasibility of developing a forecasting system for carpogenic germination of Sclerotinia sclerotiorum sclerotia was investigated in the laboratory by determining key relationships among temperature, soil water potential, and carpogenic germination for sclerotia of two S. sclerotiorum isolates. Germination of multiple burials of sclerotia to produce apothecia also was assessed in the field with concurrent recording of environmental data to examine patterns of germination under different fluctuating conditions. Carpogenic germination of sclerotia occurred between 5 and 25 degrees C but only for soil water potentials of >/=-100 kPa for both S. sclerotiorum isolates. Little or no germination occurred at 26 or 29 degrees C. At optimum temperatures of 15 to 20 degrees C, sclerotia buried in soil and placed in illuminated growth cabinets produced stipes after 20 to 27 days and apothecia after 27 to 34 days. Temperature, therefore, had a significant effect on both the rate of germination of sclerotia and the final number germinated. Rate of germination was correlated positively with temperature and final number of sclerotia germinated was related to temperature according to a probit model. Thermal time analysis of field data with constraints for temperature and water potential showed that the mean degree days to 10% germination of sclerotia in 2000 and 2001 was 285 and 279, respecttively, and generally was a good predictor of the observed appearance of apothecia. Neither thermal time nor relationships established in the laboratory could account for a decline in final percentage of germination for sclerotia buried from mid-May compared with earlier burials. Exposure to high temperatures may explain this effect. This, and other factors, require investigation before relationships derived in the laboratory or thermal time can be incorporated into a forecasting system for carpogenic germination.

Forecasting Sclerotinia Disease on Lettuce: A Predictive Model for Carpogenic Germination of Sclerotinia sclerotiorum Sclerotia.

ABSTRACT A predictive model for production of apothecia by carpogenic germination of sclerotia is presented for Sclerotinia sclerotiorum. The model is based on the assumption that a conditioning phase must be completed before a subsequent germination phase can occur. Experiments involving transfer of sclerotia from one temperature regime to another allowed temperature-dependent rates to be derived for conditioning and germination for two S. sclerotiorum isolates. Although the response of each isolate to temperature was slightly different, sclerotia were fully conditioned after 2 to 6 days at 5 degrees C in soil but took up to 80 days at 15 degrees C. Subsequent germination took more than 200 days at 5 degrees C and 33 to 52 days at 20 degrees C. Upper temperature thresholds for conditioning and germination were 20 and 25 degrees C, respectively. A predictive model for production of apothecia derived from these data was successful in simulating the germination of multiple burials of sclerotia in the field when a soil water potential threshold of between -4.0 and -12.25 kilopascals (kPa) was imposed. The use of a germination model as part of a disease forecasting system for Sclerotinia disease in lettuce is discussed.

Development of MILIONCAST, an improved model for predicting downy mildew sporulation on Onions

The effects of temperature and relative humidity on Peronospora destructor sporulation on onion (Allium cepa) leaves were studied under controlled environmental conditions. Sporangia were produced most rapidly at 8 to 12°C after 5 h of high humidity during dark periods. The greatest number of sporangia was produced at 100% relative humidity (RH), and sporulation decreased to almost nil when humidity decreased to 93% RH. A model, named MILIONCAST (an acronym for MILdew on onION foreCAST), was developed based on the data from these controlled environment studies to predict the rate of sporulation in relation to temperature and relative humidity. The accuracy of prediction of sporulation was evaluated by comparing predictions with observations of sporulation on infected plants in pots outdoors. The accuracy of MILIONCAST was compared with the accuracy of existing models based on DOWNCAST. MILIONCAST gave more correct predictions of sporulation than the DOWNCAST models and a random model. All models based on DOWNCAST were more accurate than the random model when compared on the basis of all predictions (including positive and negative predictions), but they gave fewer correct predictions of sporulation than the random model. De Vissers DOWNCAST and ONIMIL improved their accuracy of prediction of sporulation events when the threshold humidity for sporulation was reduced to 92% RH. The temporal pattern of predicted sporulation by MILIONCAST generally corresponded well to the pattern of sporulation observed on the outdoor potted plants at Wellesbourne, UK.

Automatic Detection of Diseased Tomato Plants Using Thermal and Stereo Visible Light Images

Accurate and timely detection of plant diseases can help mitigate the worldwide losses experienced by the horticulture and agriculture industries each year. Thermal imaging provides a fast and non-destructive way of scanning plants for diseased regions and has been used by various researchers to study the effect of disease on the thermal profile of a plant. However, thermal image of a plant affected by disease has been known to be affected by environmental conditions which include leaf angles and depth of the canopy areas accessible to the thermal imaging camera. In this paper, we combine thermal and visible light image data with depth information and develop a machine learning system to remotely detect plants infected with the tomato powdery mildew fungus Oidium neolycopersici. We extract a novel feature set from the image data using local and global statistics and show that by combining these with the depth information, we can considerably improve the accuracy of detection of the diseased plants. In addition, we show that our novel feature set is capable of identifying plants which were not originally inoculated with the fungus at the start of the experiment but which subsequently developed disease through natural transmission.

Registration of thermal and visible light images of diseased plants using silhouette extraction in the wavelet domain

The joint analysis of thermal and visible light images of plants can help to increase the accuracy of early disease detection. Registration of thermal and visible light images is an important pre-processing operation to perform this joint analysis correctly. In the case of diseased plants, registration using common methods based on mutual information is particularly challenging since the plant texture in the thermal image significantly differs from the corresponding texture in the visible light image. Registration methods based on silhouette extraction are therefore more appropriate. This paper proposes an algorithm for registration of thermal and visible light images of diseased plants based on silhouette extraction. The algorithm is based on a novel multi-scale method that employs the stationary wavelet transform to extract the silhouette of diseased plants in thermal images, in which common gradient-based methods usually fail due to the high noise content. Experimental results show that silhouettes extracted using this method can be used to register thermal and visible light images with high accuracy.

A Model for Sclerotinia sclerotiorum Infection and Disease Development in Lettuce, Based on the Effects of Temperature, Relative Humidity and Ascospore Density

The plant pathogen Sclerotinia sclerotiorum can cause serious losses on lettuce crops worldwide and as for most other susceptible crops, control relies on the application of fungicides, which target airborne ascospores. However, the efficacy of this approach depends on accurate timing of these sprays, which could be improved by an understanding of the environmental conditions that are conducive to infection. A mathematical model for S. sclerotiorum infection and disease development on lettuce is presented here for the first time, based on quantifying the effects of temperature, relative humidity (RH) and ascospore density in multiple controlled environment experiments. It was observed that disease can develop on lettuce plants inoculated with dry ascospores in the absence of apparent leaf wetness (required for spore germination). To explain this, the model conceptualises an infection court area containing microsites (in leaf axils and close to the stem base) where conditions are conducive to infection, the size of which is modified by ambient RH. The model indicated that minimum, maximum and optimum temperatures for ascospore germination were 0.0, 29.9 and 21.7°C respectively and that maximum rates of disease development occurred at spore densities >87 spores cm−2. Disease development was much more rapid at 80–100% RH at 20°C, compared to 50–70% RH and resulted in a greater proportion of lettuce plants infected. Disease development was also more rapid at 15–27°C compared to 5–10°C (85% RH). The model was validated by a further series of independent controlled environment experiments where both RH and temperature were varied and generally simulated the pattern of disease development well. The implications of the results in terms of Sclerotinia disease forecasting are discussed.

Population Structure of Sclerotinia subarctica and Sclerotinia sclerotiorum in England, Scotland and Norway

Sclerotinia species are important fungal pathogens of a wide range of crops and wild host plants. While the biology and population structure of Sclerotinia sclerotiorum has been well-studied, little information is available for the related species S. subarctica. In this study, Sclerotinia isolates were collected from different crop plants and the wild host Ranuculus ficaria (meadow buttercup) in England, Scotland, and Norway to determine the incidence of Sclerotinia subarctica and examine the population structure of this pathogen for the first time. Incidence was very low in England, comprising only 4.3% of isolates while moderate and high incidence of S. subarctica was identified in Scotland and Norway, comprising 18.3 and 48.0% of isolates respectively. Characterization with eight microsatellite markers identified 75 haplotypes within a total of 157 isolates over the three countries with a few haplotypes in Scotland and Norway sampled at a higher frequency than the rest across multiple locations and host plants. In total, eight microsatellite haplotypes were shared between Scotland and Norway while none were shared with England. Bayesian and principal component analyses revealed common ancestry and clustering of Scottish and Norwegian S. subarctica isolates while English isolates were assigned to a separate population cluster and exhibited low diversity indicative of isolation. Population structure was also examined for S. sclerotiorum isolates from England, Scotland, Norway, and Australia using microsatellite data, including some from a previous study in England. In total, 484 haplotypes were identified within 800 S. sclerotiorum isolates with just 15 shared between England and Scotland and none shared between any other countries. Bayesian and principal component analyses revealed a common ancestry and clustering of the English and Scottish isolates while Norwegian and Australian isolates were assigned to separate clusters. Furthermore, sequencing part of the intergenic spacer (IGS) region of the rRNA gene resulted in 26 IGS haplotypes within 870 S. sclerotiorum isolates, nine of which had not been previously identified and two of which were also widely distributed across different countries. S. subarctica therefore has a multiclonal population structure similar to S. sclerotiorum, but has a different ancestry and distribution across England, Scotland, and Norway.

Identification of pathogenicity-related genes in Fusarium oxysporum f. sp. cepae

Summary Pathogenic isolates of Fusarium oxysporum, distinguished as formae speciales (f. spp.) on the basis of their host specificity, cause crown rots, root rots and vascular wilts on many important crops worldwide. Fusarium oxysporum f. sp. cepae (FOC) is particularly problematic to onion growers worldwide and is increasing in prevalence in the UK. We characterized 31 F. oxysporum isolates collected from UK onions using pathogenicity tests, sequencing of housekeeping genes and identification of effectors. In onion seedling and bulb tests, 21 isolates were pathogenic and 10 were non‐pathogenic. The molecular characterization of these isolates, and 21 additional isolates comprising other f. spp. and different Fusarium species, was carried out by sequencing three housekeeping genes. A concatenated tree separated the F. oxysporum isolates into six clades, but did not distinguish between pathogenic and non‐pathogenic isolates. Ten putative effectors were identified within FOC, including seven Secreted In Xylem (SIX) genes first reported in F. oxysporum f. sp. lycopersici. Two highly homologous proteins with signal peptides and RxLR motifs (CRX1/CRX2) and a gene with no previously characterized domains (C5) were also identified. The presence/absence of nine of these genes was strongly related to pathogenicity against onion and all were shown to be expressed in planta. Different SIX gene complements were identified in other f. spp., but none were identified in three other Fusarium species from onion. Although the FOC SIX genes had a high level of homology with other f. spp., there were clear differences in sequences which were unique to FOC, whereas CRX1 and C5 genes appear to be largely FOC specific.

Detection of Potato Storage Disease via Gas Analysis: A Pilot Study Using Field Asymmetric Ion Mobility Spectrometry

Soft rot is a commonly occurring potato tuber disease that each year causes substantial losses to the food industry. Here, we explore the possibility of early detection of the disease via gas/vapor analysis, in a laboratory environment, using a recent technology known as FAIMS (Field Asymmetric Ion Mobility Spectrometry). In this work, tubers were inoculated with a bacterium causing the infection, Pectobacterium carotovorum, and stored within set environmental conditions in order to manage disease progression. They were compared with controls stored in the same conditions. Three different inoculation time courses were employed in order to obtain diseased potatoes showing clear signs of advanced infection (for standard detection) and diseased potatoes with no apparent evidence of infection (for early detection). A total of 156 samples were processed by PCA (Principal Component Analysis) and k-means clustering. Results show a clear discrimination between controls and diseased potatoes for all experiments with no difference among observations from standard and early detection. Further analysis was carried out by means of a statistical model based on LDA (Linear Discriminant Analysis) that showed a high classification accuracy of 92.1% on the test set, obtained via a LOOCV (leave-one out cross-validation).