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The digenean trematode Alaria alata, an intestinal parasite of wild canids is widely distributed in Europe. The recent finding of the mesocercarial life cycle stage in the paratenic wild boar host suggests that it may potentially infect humans Mohl et al. (Parasitol Res 105:1-15, 2009). Over 500 foxes were examined during a wildlife survey for zoonotic diseases in 2009 and 2010. The prevalence of A. alata ranged from 21% to 26% in 2009 and 2010, and the intensity of infection varied, with the majority of foxes having between one and ten trematodes, but a small number of animals had parasitic burdens greater than 500. The location of foxes was geo-referenced and mapped using a geographic information system. The results of the spatial analysis suggest that A. alata may have a limited distribution being confined mainly to areas of pasture especially in the central plain and north Munster. Hot spot analysis indicated a clustering and that the level of parasitism was greatest in foxes from those areas where the prevalence of infection was highest.

Natural and semi-natural habitats in agricultural landscapes are likely to come under increasing pressure with the global population set to exceed 9 billion by 2050. These non-cropped habitats are primarily made up of trees, hedgerows and grassy margins and their amount, quality and spatial configuration can have strong implications for the delivery and sustainability of various ecosystem services. In this study high spatial resolution (0.5 m) colour infrared aerial photography (CIR) was used in object based image analysis for the classification of non-cropped habitat in a 10,029 ha area of southeast England. Three classification scenarios were devised using 4 and 9 class scenarios. The machine learning algorithm Random Forest (RF) was used to reduce the number of variables used for each classification scenario by 25.5 % ± 2.7%. Proportion of votes from the 4 class hierarchy was made available to the 9 class scenarios and where the highest ranked variables in all cases. This approach allowed for misclassified parent objects to be correctly classified at a lower level. A single object hierarchy with 4 class proportion of votes produced the best result (kappa 0.909). Validation of the optimum training sample size in RF showed no significant difference between mean internal out-of-bag error and external validation. As an example of the utility of this data, we assessed habitat suitability for a declining farmland bird, the yellowhammer (Emberiza citronella), which requires hedgerows associated with grassy margins. We found that ∼22% of hedgerows were within 200 m of margins with an area >183.31 m2. The results from this analysis can form a key information source at the environmental and policy level in landscape optimisation for food production and ecosystem service sustainability.

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