Threat of establishment of non-indigenous potato blackleg and tuber soft rot pathogens in Great Britain under climate change.

Potato blackleg and soft rot caused by Pectobacterium and Dickeya species are among the most significant bacterial diseases affecting potato production globally. In this study we estimate the impact of future temperatures on establishment of non-indigenous but confirmed Pectobacterium and Dickeya species in Great Britain (GB). The calculations are based on probabilistic climate change data and a model fitted to disease severity data from a controlled environment tuber assay with the dominant potato blackleg and soft rot-causing species in GB (P. atrosepticum), and three of the main causative agents in Europe (P. carotovorum subsp. brasiliense, P. parmentieri, Dickeya solani). Our aim was to investigate if the European strains could become stronger competitors in the GB potato ecosystem as the climate warms, on the basis of their aggressiveness in tubers at different temperatures. Principally, we found that the tissue macerating capacity of all four pathogens will increase in GB under all emissions scenarios. The predominant Pectobacterium and Dickeya species in Europe are able to cause disease in tubers under field conditions currently seen in GB but are not expected to become widely established in the future, at least on the basis of their aggressiveness in tubers relative to P. atrosepticum under GB conditions. Our key take-home messages are that the GB potato industry is well positioned to continue to thrive via current best management practices and continued reinforcement of existing legislation.

Crop connectivity under climate change: future environmental and geographic risks of potato late blight in Scotland.

The impact of climate change on dispersal processes is largely ignored in risk assessments for crop diseases, as inoculum is generally assumed to be ubiquitous and nonlimiting. We suggest that consideration of the impact of climate change on the connectivity of crops for inoculum transmission may provide additional explanatory and predictive power in disease risk assessments, leading to improved recommendations for agricultural adaptation to climate change. In this study, a crop-growth model was combined with aerobiological models and a newly developed infection risk model to provide a framework for quantifying the impact of future climates on the risk of disease occurrence and spread. The integrated model uses standard meteorological variables and can be easily adapted to various crop pathosystems characterized by airborne inoculum. In a case study, the framework was used with data defining the spatial distribution of potato crops in Scotland and spatially coherent, probabilistic climate change data to project the future connectivity of crop distributions for Phytophthora infestans (causal agent of potato late blight) inoculum and the subsequent risk of infection. Projections and control recommendations are provided for multiple combinations of potato cultivar and CO2 emissions scenario, and temporal and spatial averaging schemes. Overall, we found that relative to current climatic conditions, the risk of late blight will increase in Scotland during the first half of the potato growing season and decrease during the second half. To guide adaptation strategies, we also investigated the potential impact of climate change-driven shifts in the cropping season. Advancing the start of the potato growing season by 1 month proved to be an effective strategy from both an agronomic and late blight management perspective.

Invasion of Phytophthora infestans at the landscape level: how do spatial scale and weather modulate the consequences of spatial heterogeneity in host resistance?

Strategic spatial patterning of crop species and cultivars could make agricultural landscapes less vulnerable to plant disease epidemics, but experimentation to explore effective disease-suppressive landscape designs is problematic. Here, we present a realistic, multiscale, spatiotemporal, integrodifference equation model of potato late blight epidemics to determine the relationship between spatial heterogeneity and disease spread, and determine the effectiveness of mixing resistant and susceptible cultivars at different spatial scales under the influence of weather. The model framework comprised a landscape generator, a potato late blight model that includes host and pathogen life cycles and fungicide management at the field scale, and an atmospheric dispersion model that calculates spore dispersal at the landscape scale. Landscapes consisted of one or two distinct potato-growing regions (6.4-by-6.4-km) embedded within a nonhost matrix. The characteristics of fields and growing regions and the separation distance between two growing regions were investigated for their effects on disease incidence, measured as the proportion of fields with ≥1% severity, after inoculation of a single potato grid cell with a low initial level of disease. The most effective spatial strategies for suppressing disease spread in a region were those that reduced the acreage of potato or increased the proportion of a resistant potato cultivar. Clustering potato cultivation in some parts of a region, either by planting in large fields or clustering small fields, enhanced the spread within such a cluster while it delayed spread from one cluster to another; however, the net effect of clustering was an increase in disease at the landscape scale. The planting of mixtures of a resistant and susceptible cultivar was a consistently effective option for creating potato-growing regions that suppressed disease spread. It was more effective to mix susceptible and resistant cultivars within fields than plant some fields entirely with a susceptible cultivar and other fields with a resistant cultivar, at the same ratio of susceptible to resistant potato plants at the landscape level. Separation distances of at least 16 km were needed to completely prevent epidemic spread from one potato-growing region to another. Effects of spatial placement of resistant and susceptible potato cultivars depended strongly on meteorological conditions, indicating that landscape connectivity for the spread of plant disease depends on the particular coincidence between direction of spread, location of fields, distance between the fields, and survival of the spores depending on the weather. Therefore, in the simulation of (airborne) pathogen invasions, it is important to consider the large variability of atmospheric dispersion conditions.