Pest and pathogen losses jeopardise global food security and ever since the 19th century Irish famine, potato late blight has exemplified this threat. The causal oomycete pathogen, Phytophthora infestans, undergoes major population shifts in agricultural systems via the successive emergence and migration of asexual lineages. The phenotypic and genotypic bases of these selective sweeps are largely unknown but management strategies need to adapt to reflect the changing pathogen population. Here, we used molecular markers to document the emergence of a lineage, termed 13_A2, in the European P. infestans population, and its rapid displacement of other lineages to exceed 75% of the pathogen population across Great Britain in less than three years. We show that isolates of the 13_A2 lineage are among the most aggressive on cultivated potatoes, outcompete other aggressive lineages in the field, and overcome previously effective forms of plant host resistance. Genome analyses of a 13_A2 isolate revealed extensive genetic and expression polymorphisms particularly in effector genes. Copy number variations, gene gains and losses, amino-acid replacements and changes in expression patterns of disease effector genes within the 13_A2 isolate likely contribute to enhanced virulence and aggressiveness to drive this population displacement. Importantly, 13_A2 isolates carry intact and in planta induced Avrblb1, Avrblb2 and Avrvnt1 effector genes that trigger resistance in potato lines carrying the corresponding R immune receptor genes Rpi-blb1, Rpi-blb2, and Rpi-vnt1.1. These findings point towards a strategy for deploying genetic resistance to mitigate the impact of the 13_A2 lineage and illustrate how pathogen population monitoring, combined with genome analysis, informs the management of devastating disease epidemics.

PCR-based analysis of mononucleotide repeats may be used to detect both intraspecific and interspecific variability in the chloroplast genomes of seed plants. The analysis of polymorphic microsatellites thus provides an important experimental tool to examine a range of issues in plant genetics.

We characterised the spatial structure of soil microbial communities in an unimproved grazed upland grassland in the Scottish Borders. A range of soil chemical parameters, cultivable microbes, protozoa, nematodes, phospholipid fatty acid (PLFA) profiles, community-level physiological profiles (CLPP), intra-radical arbuscular mycorrhizal community structure, and eubacterial, actinomycete, pseudomonad and ammonia-oxidiser 16S rRNA gene profiles, assessed by denaturing gradient gel electrophoresis (DGGE) were quantified. The botanical composition of the vegetation associated with each soil sample was also determined. Geostatistical analysis of the data revealed a gamut of spatial dependency with diverse semivariograms being apparent, ranging from pure nugget, linear and non-linear forms. Spatial autocorrelation generally accounted for 40-60% of the total variance of those properties where such autocorrelation was apparent, but accounted for 97% in the case of nitrate-N. Geostatistical ranges extending from approximately 0.6-6 m were detected, dispersed throughout both chemical and biological properties. CLPP data tended to be associated with ranges greater than 4.5 m. There was no relationship between physical distance in the field and genetic similarity based on DGGE profiles. However, analysis of samples taken as close as 1 cm apart within a subset of cores suggested some spatial dependency in community DNA-DGGE parameters below an 8 cm scale. Spatial correlation between the properties was generally weak, with some exceptions such as between microbial biomass C and total N and C. There was evidence for scale-dependence in the relationships between properties. PLFA and CLPP profiling showed some association with vegetation composition, but DGGE profiling did not. There was considerably stronger association between notional sheep urine patches, denoted by soil nutrient status, and many of the properties. These data demonstrate extreme spatial variation in community-level microbiological properties in upland grasslands, and that despite considerable numeric ranges in the majority of properties, overarching controlling factors were not apparent.

The statistical technique of principal components is used to analyze two sets of near-infrared spectra, wheat flour samples for which % moisture and % protein values are included, and milled barley samples for which hot water extract values are included. The methodology and interpretation of this technique are described within the context of NIR data, and its advantages both in providing insight into the variation of the spectra, and as a method of avoiding the problems caused by highly correlated reflectance energy values in the derivation of calibration equations, are highlighted. In each set of samples the first principal component accounts for the vast majority of the variation. These components also have an almost identical shape, which is interpreted as reflecting particle size. The second wheat component and the third barley component are also almost identical, with a shape very similar to that of the spectrum of water. Both fourth components share peaks at points in the spectrum which are used by fixed-filter instruments to measure protein in cereals.