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Invasive plant diseases can have devastating consequences on the local plant populations, in both agricultural and natural landscapes. Knowledge of the spatial patterns of pathogen spread can be used to guide more time- and cost-effective disease management strategies. Based on disease dispersal principles and consideration of host pattern, an improved plant disease epidemiological model was developed and tested for plant disease mapping. The model is able to characterize the disease dispersal gradient and predict infection risk, with indication of uncertainty, through heterogeneous environments without reference to the source of infection. As a result, sampling methods can be informed by the predicted prevalence map of the disease. In order to better describe the shapes of the dispersal gradients, three different dispersal functions (Exponential, Modified power law, and Cauchy distribution) were considered in the model. Two data sets of disease observations of Huanglongbing (HLB) of citrus in different landscapes (Southern Garden and Devils Garden plantation) in Florida were used to evaluate the performance of the improved method for disease mapping. The results showed that the improved model provided estimates of greater precision for unsampled hosts. With all different dispersal models compared, the exponential dispersal gradient gave the most satisfactory performance. All the determined information can help decision makers understand the spatial aspects of disease processes, and formulate decisions about disease control accordingly.

A modeling framework was used to assess the risk of four metals to UK bat species. Eight species of bats were predicted to be “at risk” from one or more of the metals in over 5% of their ranges. Species differed significantly in their predicted risk. Contamination by Pb was found to pose the greatest risk, followed by Cu, Cd and Zn. A sensitivity analysis identified the proportion of invertebrates ingested as most important in determining the risk. We then compared the model predictions with a large dataset of metals concentrations in the tissues (liver, kidney) of Pipistrellus sp . from across England and Wales. Bats found in areas predicted to be the most “at risk” contained higher metal concentrations in their tissues than those found in areas predicted “not at risk” by the model. Our spatially explicit modeling framework provides a useful tool for further environmental risk assessment studies for wildlife species. [ABSTRACT FROM AUTHOR]

Multiple substrate induced respiration (MSIR) assays which assess the response of soils to carbon source amendment are effective approaches to determine catabolic diversity of soils. Many assays are based on a single short term (<6 h) time point measurement and usually limited to CO2 production only. However, repeated measurements of both CO2 and O2 simultaneously can provide additional valuable information. In this study, a MSIR assay involving eight carbon sources was applied to three hydrocarbon contaminated soils using continuous CO2 and O2 respiration measurements. Based on cumulative CO2 and O2 measurements at 4, 24 and 120 h, the soils were found to be distinct in terms of their catabolic diversity. Most noteworthy, however, was the response to the addition of maleic acid which provided strong evidence of abiotic CO2 efflux to be the overriding process, raising questions about the interpretation of CO2 only responses from organic acid addition in MSIR assays. [Copyright &y& Elsevier]

Abstract: A key challenge for plant pathologists is to develop efficient methods to describe spatial patterns of disease spread accurately from a limited number of samples. Knowledge of disease spread is essential for informing and justifying plant disease management measures. A mechanistic modelling approach is adopted for disease mapping which is based on disease dispersal gradients and consideration of host pattern. The method is extended to provide measures of uncertainty for the estimates of disease at each host location. In addition, improvements have been made to increase computational efficiency by better initialising the disease status of unsampled hosts and speeding up the optimisation process of the model parameters. These improvements facilitate the practical use of the method by providing information on: (a) mechanisms of pathogen dispersal, (b) distance and pattern of disease spread, and (c) prediction of infection probabilities for unsampled hosts. Two data sets of disease observations, Huanglongbing (HLB) of citrus and strawberry powdery mildew, were used to evaluate the performance of the new method for disease mapping. The result showed that our method gave better estimates of precision for unsampled hosts, compared to both the original method and spatial interpolation. This enables decision makers to understand the spatial aspects of disease processes, and thus formulate regulatory actions accordingly to enhance disease control. [Copyright &y& Elsevier]