Bruce D.L. Fitt

World-Wide Importance of Phoma Stem Canker (Leptosphaeria maculans and L. biglobosa) on Oilseed Rape (Brassica napus)

Phoma stem canker is an internationally important disease of oilseed rape (Brassica napus, canola, rapeseed), causing serious losses in Europe, Australia and North America. UK losses of €56M per season are estimated using national disease survey data and a yield loss formula. Phoma stem canker pathogen populations comprise two main species, Leptosphaeria maculans, associated with damaging stem base cankers, and Leptosphaeria biglobosa, often associated with less damaging upper stem lesions. Both major gene and quantitative trait loci mediated resistance to L. maculans have been identified in B. napus, but little is known about resistance to L. biglobosa.Leptosphaeria maculans, which has spread into areas in North America and eastern Europe where only L. biglobosa was previously identified, now poses a threat to large areas of oilseed rape production in Asia. Epidemics are initiated by air-borne ascospores; major gene resistance to initial infection by L. maculans operates in the leaf lamina of B. napus. It is not clear whether the quantitative trait loci involved in the resistance to the pathogen that can be assessed only at the end of the season operate in the leaf petioles or stems. In countries where serious phoma stem canker epidemics occur, a minimum standard for resistance to L. maculans is included in national systems for registration of cultivars. This review provides a background to a series of papers on improving strategies for managing B. napus resistance to L. maculans, which is a model system for studying genetic interactions between hemi-biotrophic pathogens and their hosts.

Quantitative resistance increases the durability of qualitative resistance to Leptosphaeria maculans in Brassica napus.

It has frequently been hypothesized that quantitative resistance increases the durability of qualitative (R-gene mediated) resistance but supporting experimental evidence is rare. To test this hypothesis, near-isogenic lines with/without the R-gene Rlm6 introduced into two Brassica napus cultivars differing in quantitative resistance to Leptosphaeria maculans were used in a 5-yr field experiment. Recurrent selection of natural fungal populations was done annually on each of the four plant genotypes, using crop residues from each genotype to inoculate separately the four series of field trials for five consecutive cropping seasons. Severity of phoma stem canker was measured on each genotype and frequencies of avirulence alleles in L. maculans populations were estimated. Recurrent selection of virulent isolates by Rlm6 in a susceptible background rendered the resistance ineffective by the third cropping season. By contrast, the resistance was still effective after 5 yr of selection by the genotype combining this gene with quantitative resistance. No significant variation in the performance of quantitative resistance alone was noted over the course of the experiment. We conclude that quantitative resistance can increase the durability of Rlm6. We recommend combining quantitative resistance with R-gene mediated resistance to enhance disease control and crop production.

Pathogenesis, parasitism and mutualism in the trophic space of microbe-plant interactions

Microbe-host interactions can be categorised as pathogenic, parasitic or mutualistic, but in practice few examples exactly fit these descriptions. New molecular methods are providing insights into the dynamics of microbe-host interactions, with most microbes changing their relationship with their host at different life-cycle stages or in response to changing environmental conditions. Microbes can transition between the trophic states of pathogenesis and symbiosis and/or between mutualism and parasitism. In plant-based systems, an understanding of the true ecological niche of organisms and the dynamic state of their trophic interactions with their hosts has important implications for agriculture, including crop rotation, disease control and risk management.

Range and severity of a plant disease increased by global warming

Climate change affects plants in natural and agricultural ecosystems throughout the world but little work has been done on the effects of climate change on plant disease epidemics. To illustrate such effects, a weather-based disease forecasting model was combined with a climate change model predicting UK temperature and rainfall under high- and low-carbon emissions for the 2020s and 2050s. Multi-site data collected over a 15-year period were used to develop and validate a weather-based model forecasting severity of phoma stem canker epidemics on oilseed rape across the UK. This was combined with climate change scenarios to predict that epidemics will not only increase in severity but also spread northwards by the 2020s. These results provide a stimulus to develop models to predict the effects of climate change on other plant diseases, especially in delicately balanced agricultural or natural ecosystems. Such predictions can be used to guide policy and practice in adapting to effects of climate change on food security and wildlife.

Spore dispersal and plant disease gradients: a comparison between two empirical models

Power law and exponential models were fitted to 325 sets of observations which described decreases with distance in deposition of air-borne or splash-borne spores, or pollen, or in amounts of plant disease caused by fungi, bacteria or viruses. There, was generally little difference between the models in the goodness of fit to these data, although deposition gradients for spores borne in splash droplets were fitted better by exponential models and gradients for fungi with air-borne spores less than 10 μm in diameter were fitted better by power law models. The exponential model has the property that the observed variable decreases by half as the distance from the source increases by a constant increment (the half-distance); this provides a measureof the gradient that is more easy to visualize than the exponent in power law model. The half-distances of gradients for air-borne pathogens were greater than those for splash-borne or soil-borne pathogens. The exponential model is easier to incorporate into models of disease development than the power law model because the boundary condition at the source (the estimated number of spores or amount of disease at the source) is finite rather than infinite. However, both these empirical models have limitations and should not be extrapolated to distances outside the observed range.

Are green islands red herrings? Significance of green islands in plant interactions with pathogens and pests

The term green island was first used to describe an area of living, green tissue surrounding a site of infection by an obligately biotrophic fungal pathogen, differentiated from neighbouring yellowing, senescent tissue. However, it has now been used to describe symptoms formed in response to necrotrophic fungal pathogens, virus infection and infestation by certain insects. In leaves infected by obligate biotrophs such as rust and powdery mildew pathogens, green islands are areas where senescence is retarded, photosynthetic activity is maintained and polyamines accumulate. We propose such areas, in which both host and pathogen cells are alive, be termed green bionissia. By contrast, we propose that green areas associated with leaf damage caused by toxins produced by necrotrophic fungal pathogens be termed green necronissia. A range of biotrophic/hemibiotrophic fungi and leaf‐mining insects produce cytokinins and it has been suggested that this cytokinin secretion may be responsible for the green island formation. Indeed, localised cytokinin accumulation may be a common mechanism responsible for green island formation in interactions of plants with biotrophic fungi, viruses and insects. Models have been developed to study if green island formation is pathogen‐mediated or host‐mediated. They suggest that green bionissia on leaves infected by biotrophic fungal pathogens represent zones of host tissue, altered physiologically to allow the pathogen maximum access to nutrients early in the interaction, thus supporting early sporulation and increasing pathogen fitness. They lead to the suggestion that green islands are ‘red herrings’, representing no more than the consequence of the infection process and discrete changes in leaf senescence.

PCR to predict risk of airborne disease

Plant, animal and human diseases spread by microscopic airborne particles have had major economic and social impacts during history. Special air-sampling devices have been used to collect such particles since the 19th century but it has often been impossible to identify them accurately. Exciting new opportunities to combine air sampling with quantitative PCR to identify and count these particles are reviewed, using crop pathogen examples. These methods can be used to predict the risk of unexpected outbreaks of airborne diseases by identifying increases in pathogen inoculum or genetic changes in pathogen populations that render control ineffective. The predictions can provide guidance to policymakers, health professionals or the agricultural industry for the development of strategies to minimise the risk of severe pandemics.

Effector-triggered defence against apoplastic fungal pathogens

Highlights • ETD is triggered by RLPs that engage the receptor-like kinase SOBIR1.• ETD triggers cell wall-related defence responses.• ETD does not eliminate apoplastic pathogens.

North–South divide: contrasting impacts of climate change on crop yields in Scotland and England

Effects of climate change on productivity of agricultural crops in relation to diseases that attack them are difficult to predict because they are complex and nonlinear. To investigate these crop–disease–climate interactions, UKCIP02 scenarios predicting UK temperature and rainfall under high- and low-CO2 emission scenarios for the 2020s and 2050s were combined with a crop-simulation model predicting yield of fungicide-treated winter oilseed rape and with a weather-based regression model predicting severity of phoma stem canker epidemics. The combination of climate scenarios and crop model predicted that climate change will increase yield of fungicide-treated oilseed rape crops in Scotland by up to 0.5 t ha−1 (15%). In contrast, in southern England the combination of climate scenarios, crop, disease and yield loss models predicted that climate change will increase yield losses from phoma stem canker epidemics to up to 50 per cent (1.5 t ha−1) and greatly decrease yield of untreated winter oilseed rape. The size of losses is predicted to be greater for winter oilseed rape cultivars that are susceptible than for those that are resistant to the phoma stem canker pathogen Leptosphaeria maculans. Such predictions illustrate the unexpected, contrasting impacts of aspects of climate change on crop–disease interactions in agricultural systems in different regions.

Effects of Severity and Timing of Stem Canker (Leptosphaeria maculans) Symptoms on Yield of Winter Oilseed Rape (Brassica napus) in the UK

The relationships between yield loss and incidence (% plants with stems affected) or severity (mean stem score, 0–4 scale) of stem canker in winter oilseed rape were analysed using data from experiments at Rothamsted in 1991/92, Withington in 1992/93, Boxworth in 1993/94 and Rothamsted in 1997/98. Critical point models and area under disease progress curve (AUDPC) models were better than multiple point models for describing relationships between yield (t ha−1) and incidence or severity of stem canker for the four experiments. Since yield is influenced by many factors other than disease, % yield loss was calculated and critical point models and AUDPC models relating % yield loss to stem canker were constructed. The critical point models for % yield loss on stem canker incidence for three of the four experiments were similar, but differed from that for Rothamsted in 1991/92. There were also no differences between models of % yield loss on AUDPC of both incidence and severity for these three experiments. Therefore, general models of % yield loss (L) against AUDPC of incidence (X) or severity (S) of stem canker from growth stages 4.8 to 6.4 were derived from the combined data sets for the three experiments: L=−0.76+0.0075X (R2=35%, p<0.001), L=0.26+0.53S (R2=37%, p<0.001). The relationships between % yield loss and % plants with different stem canker severity scores at different growth stages were also analysed; the greatest yield losses were generally associated with the largest severity scores, for plants assessed at the same crop growth stage, and were also associated with the early development of stem lesions. Further analyses showed that % yield loss was related to incidence or severity of both basal stem cankers and upper stem lesions in experiments at Boxworth in 1993/94 and at Rothamsted in 1997/98.