Data from: Temporal dynamics of direct reciprocal and indirect effects in a host-parasite network

1. 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. 2. We used data on a network of small mammals and their
ectoparasites surveyed over six years to test hypotheses exploring (1) the
temporal variability in direct and indirect influences species exert on
each other in a community, and (2) the differences in temporal variability
of direct/indirect influences between temporally persistent and temporally
intermittent species. 3. We modelled the temporal variation in: (1) direct
reciprocal influence between hosts and parasites (hosts providing
resources to parasites and parasites exploiting the resources of hosts),
using an asymmetry index; and (2) 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. 4. Network dynamics
was determined by temporally persistent 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. 5. We
conclude that the temporal dynamics of host-parasite interactions is
driven by temporally persistent 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.