An improved model for the population dynamics of cattle gastrointestinal nematodes on pasture: parameterisation and field validation for Ostertagia ostertagi and Cooperia oncophora in northern temperate zones.

Gastrointestinal nematodes (GIN) are amongst the most important pathogens of grazing ruminants worldwide, resulting in negative impacts on cattle health and production. The dynamics of infection are driven in large part by the influence of climate and weather on free-living stages on pasture, and computer models have been developed to predict infective larval abundance and guide management strategies. Significant uncertainties around key model parameters limits effective application of these models to GIN in cattle, however, and these parameters are difficult to estimate in natural populations of mixed GIN species. In this paper, recent advances in molecular biology, specifically ITS-2 rDNA 'nemabiome' metabarcoding, are synthesised with a modern population dynamic model, GLOWORM-FL, to overcome this limitation. Experiments under controlled conditions were used to estimate rainfall constraints on migration of infective L3 larvae out of faeces, and their survival in faeces and soil across a temperature gradient, with nemabiome metabarcoding data permitting species-specific estimates for Ostertagia ostertagi and Cooperia oncophora in mixed natural populations. Results showed that L3 of both species survived well in faeces and soil between 0 and 30 °C, and required at least 5 mm of rainfall daily to migrate out of faeces, with the proportion migrating increasing with the amount of rainfall. These estimates were applied within the model using weather and grazing data and use to predict patterns of larval availability on pasture on three commercial beef farms in western Canada. The model performed well overall in predicting the observed seasonal patterns but some discrepancies were evident which should guide further iterative improvements in model development and field methods. The model was also applied to illustrate its use in exploring differences in predicted seasonal transmission patterns in different regions. Such predictive modelling can help inform evidence-based parasite control strategies which are increasingly needed due climate change and drug resistance. The work presented here also illustrates the added value of combining molecular biology and population dynamics to advance predictive understanding of parasite infections.