Modelling Parasite Transmission in a Grazing System: The Importance of Host Behaviour and Immunity

Abstract

Parasitic helminths present one of the most pervasive challenges to grazing herbivores. Many macro-parasite transmission models focus on host physiological defence strategies, omitting more complex interactions between hosts and their environments. This work represents the first model that integrates both the behavioural and physiological elements of gastro-intestinal nematode transmission dynamics in a managed grazing system. A spatially explicit, individual-based, stochastic model is developed, that incorporates both the hosts’ immunological responses to parasitism, and key grazing behaviours including faecal avoidance. The results demonstrate that grazing behaviour affects both the timing and intensity of parasite outbreaks, through generating spatial heterogeneity in parasite risk and nutritional resources, and changing the timing of exposure to the parasites’ free-living stages. The influence of grazing behaviour varies with the host-parasite combination, dependent on the development times of different parasite species and variations in host immune response. Our outputs include the counterintuitive finding that under certain conditions perceived parasite avoidance behaviours (faecal avoidance) can increase parasite risk, for certain host-parasite combinations. Through incorporating the two-way interaction between infection dynamics and grazing behaviour, the potential benefits of parasite-induced anorexia are also demonstrated. Hosts with phenotypic plasticity in grazing behaviour, that make grazing decisions dependent on current parasite burden, can reduce infection with minimal loss of intake over the grazing season. This paper explores how both host behaviours and immunity influence macro-parasite transmission in a spatially and temporally heterogeneous environment. The magnitude and timing of parasite outbreaks is influenced by host immunity and behaviour, and the interactions between them; the incorporation of both regulatory processes is required to fully understand transmission dynamics. Understanding of both physiological and behavioural defence strategies will aid the development of novel approaches for control.

Key challenges and priorities for modelling European grasslands under climate change

“…Under conditions where the interaction between animal behaviour and the environment have severe impacts on the sward, the effects on both grassland and livestock become a function of management choices, as grazing pressure is reduced or animals are moved off the pasture. In turn, sward composition, plant cover and condition directly affect feed availability and digestibility (Hopkins and Wilkins, 2006), while external conditions, grazing behaviour and management choices can all affect the disease and parasite risk from the grassland environment (Fox et al, 2013;Smith et al, 2009). Models need to capture such relationships in order to identify the best animal species, breeds and management regimes to maximise the efficiency of grassland-based production under climate change in different environments.…”

Modeling European ruminant production systems: Facing the challenges of climate change

“…At the farm level, husbandry has a dominant influence on disease transmission (Fox et al, 2013;Smith et al, 2009); long term predictive models therefore need to incorporate the effects of management responses to climate change. An optimal modeling approach is likely to combine mechanistic processes and physiological thresholds with correlative bioclimatic modeling, incorporating changes in livestock husbandry and disease control.…”

Cattle and Nematodes Under Global Change: Transmission Models as an Ally

“…Garnier et al [37] built further on the moment closure approach of Grenfell et al [38] and fitted their immune response to data of trickle infection experiments. More mechanistic approaches have also made contributions in the efforts to capture the role of acquired immunity [39,40]. Specific mechanistic understanding and parameter estimation for cattle, however, are not so well developed.…”