Modelling nematode infections in sheep and parasite control strategies

Abstract

Gastrointestinal parasitism in grazing lambs adversely affects animal performance and welfare, causing significant production losses for the sheep industry. Control of gastrointestinal parasitism using chemotherapeutic treatment is under threat due to the emergence of anthelmintic resistance, thus stimulating research into alternative control strategies. Whilst investigating control strategies experimentally can be costly and time consuming, using a mathematical modelling approach can reduce such constraints. A previously developed model which describes the impact of host nutrition, genotype and gastrointestinal parasitism in a growing lamb, provided an appropriate starting point to explore control strategies and their impact on host-parasite interactions. Two contrasting mechanisms have previously been proposed to account for the occurrence of anorexia during parasitism. These were reductions in either intrinsic growth rate or relative food intake. Thus, the existing individual lamb model was modified to evaluate these mechanisms by exploring the relationship between anorexia and food composition (Chapter 2). For foods that did not constrain food intake, published data was found to be consistent with the predictions that arose from anorexia being modelled as a reduction in relative food intake. Reported genetic parameter estimates for resistance and performance traits appear to vary under differing production environments. In order to explore the impact of epidemiological effects and anthelmintic input on genetic parameter estimates the model was extended to simulate a population of lambs in a grazing scenario (Chapter 3). Whilst estimates of heritabilities and genetic correlations for drenched lambs remained constant, for lambs given no anthelmintic treatment, the heritability of empty body weight (EBW) reduced and the genetic correlation between faecal egg count (FEC) and EBW became increasingly negative with increasing exposure to infective larvae. Thus differences in anthelmintic input and pasture larval contamination (PC) may provide plausible causes for the variation in genetic parameter estimates previously reported. To investigate the interactions between host resistance and epidemiology (Chapter 4) a population of 10,000 lambs were simulated and FEC predictions used to assign the 1,000 lambs with the highest and lowest predicted FEC to ‘susceptible’ (S) and ‘resistant’ (R) groups, respectively. R and S groups were then simulated to graze separate pastures over 3 grazing seasons. The average FEC and PC predictions of these groups diverged during the first 2 grazing seasons and stabilised during the third, such that the difference in FEC predictions between R and S groups were double those predicted when grazed with the population. This was found to be consistent with experimental data. Further, anthelmintic treatment and grazing strategies were predicted to have no impact on the EBW of resistant lambs, suggesting that control strategies should be targeted towards susceptible animals. Targeted selective anthelmintic treatment (TST) has been proposed to reduce risks of anthelmintic resistance with minimal impacts on performance. To describe the short- and long-term impacts of TST and drenching frequency on sheep production and the emergence of anthelmintic resistance, the model was extended to include a description of anthelmintic resistance genotypes within the nematode population (Chapter 5). Reducing the proportion of treated animals was predicted to increase the duration of anthelmintic efficacy, whilst reducing the drenching frequency increased the long-term benefits of anthelmintic on sheep production. Various determinant criteria for use in TST regimes were compared (Chapter 5) including performance traits such as live weight and growth rate, and parasitological traits such as FEC. Using FEC as the TST criterion was predicted to allow the greatest reduction in the number of anthelmintic treatments administered whilst maintaining the highest average EBW, whilst live weight and growth rate were predicted to give little to no improvement in comparison to selecting animals at random for TST. Using estimated breeding values (EBVs) for FEC as the determinant criterion for TST regimes was compared to using measured FEC (Chapter 6). The EBV for true FEC across the entire growth period, akin to perfect genomic selection, was predicted to be a better criterion than measured time-specific FEC (including a sampling error) for a TST regime. EBVs calculated using measured time-specific FEC showed little benefit compared to measured FEC. The information gained from these simulation studies increases our understanding of control strategies and their impact on host-parasite interactions under various scenarios that may not have been possible using experimental methods. It is important to remember that the aim of alternative or complimentary control strategies is to maintain the sustainability of sheep production systems, and as such the production gain of any control strategy needs to be weighed against the financial, labour and time costs involved in implementation.