Temporal dynamics of direct reciprocal and indirect effects in a host–parasite network
- Temporal variation in the direct and indirect influence that hosts and parasites exert on each other is still poorly understood. However, variation in species' influence due to species and interactions turnover can have important consequences for host community dynamics and/or for parasite transmission dynamics, and eventually for the risk of zoonotic diseases.
- We used data on a network of small mammals and their ectoparasites surveyed over 6 years to test hypotheses exploring (i) the temporal variability in direct and indirect influences species exert on each other in a community, and (ii) the differences in temporal variability of direct/indirect influences between temporally persistent (TP) and temporally intermittent species.
- We modelled the temporal variation in (i) direct reciprocal influence between hosts and parasites (hosts providing resources to parasites and parasites exploiting the resources of hosts), using an asymmetry index, and (ii) indirect influence among species within a community (e.g. facilitation of parasite infestation by other parasites), using betweenness centrality. We also correlated asymmetry and centrality to examine the relationship between them.
- Network dynamics was determined by TP species but even those species had strong among-species heterogeneity in the temporal variation of the direct/indirect effects they exerted. In addition, there was a significant positive linear correlation between asymmetry and centrality.
- We conclude that the temporal dynamics of host–parasite interactions is driven by TP hosts. However, even within this group of persistent species, some exhibit large temporal variation, such that the functional roles they play (e.g. in promoting parasite transmission) change over time. In addition, parasites having a large negative impact on hosts are also those facilitating the spread of other parasites through the entire host community. Our results provide new insights into community dynamics and can be applied in the management of antagonistic networks aimed at preventing disease outbreaks.