Evaluation of two Fasciola hepatica faecal egg counting protocols in sheep and cattle.

Fascioliasis causes significant economic losses and is a constant challenge to livestock farmers globally. Fluke faecal egg counts (flukeFECs) are a simple, non-invasive method used to detect the presence of patent liver fluke infection. Many flukeFEC techniques exist but they vary in complexity, precision and accuracy. The objective of this study was to evaluate the egg recovery capabilities of two simple flukeFEC methods at different egg concentrations in two ruminant species, using artificially spiked faecal samples. We added Fasciola hepatica eggs to sheep and cattle faeces at 2, 5 10 and 20 epg and utilised the Flukefinder® (FF) and a simple sedimentation method (referred to as the Becker method) to investigate the effects of methods, species and egg density on egg recovery. We calculated the proportion of fluke eggs recovered using each technique, and determined the lowest reliable egg detection threshold of each flukeFEC method. The performance of the flukeFEC methods were also compared using faecal samples collected from naturally infected animals. The egg-spiking study revealed that both FF and the Becker sedimentation method are significantly more likely to recover eggs from cattle faeces than sheep (P < 0.001). Overall, FF recovered more eggs than the Becker method (P < 0.001), and importantly has a reliable low egg detection threshold of 5 epg in sheep and cattle. The kappa coefficient indicated a substantial agreement between FF and the Becker method in naturally infected faecal samples collected from cattle (0.62, P < 0.05) and a moderate agreement in sheep (0.41, P < 0.05). This study demonstrated that FF has a low egg detection threshold and therefore has promising potential for the future of on-farm liver fluke diagnostics.

Genetic and Methylome Variation in Turkish Brachypodium Distachyon Accessions Differentiate Two Geographically Distinct Subpopulations.

Brachypodium distachyon (Brachypodium) is a non-domesticated model grass species that can be used to test if variation in genetic sequence or methylation are linked to environmental differences. To assess this, we collected seeds from 12 sites within five climatically distinct regions of Turkey. Seeds from each region were grown under standardized growth conditions in the UK to preserve methylated sequence variation. At six weeks following germination, leaves were sampled and assessed for genomic and DNA methylation variation. In a follow-up experiment, phenomic approaches were used to describe plant growth and drought responses. Genome sequencing and population structure analysis suggested three ancestral clusters across the Mediterranean, two of which were geographically separated in Turkey into coastal and central subpopulations. Phenotypic analyses showed that the coastal subpopulation tended to exhibit relatively delayed flowering and the central, increased drought tolerance as indicated by reduced yellowing. Genome-wide methylation analyses in GpC, CHG and CHH contexts also showed variation which aligned with the separation into coastal and central subpopulations. The climate niche modelling of both subpopulations showed a significant influence from the "Precipitation in the Driest Quarter" on the central subpopulation and "Temperature of the Coldest Month" on the coastal subpopulation. Our work demonstrates genetic diversity and variation in DNA methylation in Turkish accessions of Brachypodium that may be associated with climate variables and the molecular basis of which will feature in ongoing analyses.

Challenges and priorities for modelling livestock health and pathogens in the context of climate change.

Climate change has the potential to impair livestock health, with consequences for animal welfare, productivity, greenhouse gas emissions, and human livelihoods and health. Modelling has an important role in assessing the impacts of climate change on livestock systems and the efficacy of potential adaptation strategies, to support decision making for more efficient, resilient and sustainable production. However, a coherent set of challenges and research priorities for modelling livestock health and pathogens under climate change has not previously been available. To identify such challenges and priorities, researchers from across Europe were engaged in a horizon-scanning study, involving workshop and questionnaire based exercises and focussed literature reviews. Eighteen key challenges were identified and grouped into six categories based on subject-specific and capacity building requirements. Across a number of challenges, the need for inventories relating model types to different applications (e.g. the pathogen species, region, scale of focus and purpose to which they can be applied) was identified, in order to identify gaps in capability in relation to the impacts of climate change on animal health. The need for collaboration and learning across disciplines was highlighted in several challenges, e.g. to better understand and model complex ecological interactions between pathogens, vectors, wildlife hosts and livestock in the context of climate change. Collaboration between socio-economic and biophysical disciplines was seen as important for better engagement with stakeholders and for improved modelling of the costs and benefits of poor livestock health. The need for more comprehensive validation of empirical relationships, for harmonising terminology and measurements, and for building capacity for under-researched nations, systems and health problems indicated the importance of joined up approaches across nations. The challenges and priorities identified can help focus the development of modelling capacity and future research structures in this vital field. Well-funded networks capable of managing the long-term development of shared resources are required in order to create a cohesive modelling community equipped to tackle the complex challenges of climate change.