McCarthy, C, Vineer, H R, Morgan, E R & van Dijk, J 2022, ' Predicting the unpredictable? A climate-based model of the timing of peak pasture infectivity for Dictyocaulus viviparus ', Veterinary Parasitology, vol. 309, 109770 . https://doi.org/10.1016/j.vetpar.2022.109770
Outbreaks of cattle lungworm disease (Dictyocaulus viviparus) are explosive and costly. The unpredictability of the disease often encourages farmers to apply blanket anthelmintic treatments to the herd, which impede the acquisition of immunity, increase the risk of drug resistance, and interfere with efforts to reduce anthelmintic use against ubiquitous gastrointestinal nematodes. Improving our understanding of the factors which lead to a high risk of infection with lungworm, (including climatic pressure), would support a more targeted management. We present GLOWORM-FL-DV, the first mathematical model of the free-living stages of D. viviparus. The ecology of D. viviparus is unique compared with other strongylid nematodes due to its relationship with Pilobilus spp. fungi, which enhance the transmission potential. The role of the fungi was therefore incorporated into the model framework, informed by laboratory observations of Pilobolus spp. development and sporulation. The thermal niche of D. viviparus was characterised based on published and laboratory observations. Mortality of parasitic larvae increased significantly below 0 C, and larval development occurred above 1.4 C, whereas the estimated minimum temperature for migration via Pilobolus spp. was 8.8 C. Model predictions were compared with antibody levels in bulk milk tank samples collected at two-weekly intervals from eight dairy herds across Great Britain over two grazing seasons. The model predicted high levels of larval abundance on pasture 46 days (38-52 days) before a rise in antibody levels and 22-26 days before the onset of clinical signs. The model assesses the impact of climate and weather on lungworm larval availability at pasture and provides a framework for the development of a risk forecasting system. This could help to focus vigilance for clinical signs at high-risk times and facilitate the targeted use of anthelmintics to prevent outbreaks, in support of sustainable parasite control.
