Discussion
The role of seasonality in shaping species interaction networks remains
largely unexplored. While few studies have shown seasonality in
small-size organismal bipartite networks (e.g., plant-pollinator
networks), limited evidence exists on how seasonality shapes multiplex
networks across communities of larger species. Despite increasing
evidence that topological changes arise due to rewiring and species
turnover (Alarcón et al. 2008; Petanidou et al. 2008;
Olesen et al. 2011; Rasmussen et al. 2013; Lopez et
al. 2017), as of yet, identifying which process is dominant and more
critically, quantifying the contributions of each process, is rarely
done across seasons and for multiplex networks. Here, we provide a study
on seasonal multiplex networks that shows strong evidence of seasonal
change using both modularity scores and interaction turnover, and
further show that seasonal rewiring is the main driving process of
community changes in stream fish communities.
Given the long appreciated influence of temporal oscillations in
theoretical ecology (e.g., Hutchinson 1961), as well as the strong
evidence of seasonality in stream fish networks (e.g., Thompson &
Townsend 1999), unsurprisingly, we found evidence that seasonality
influenced our network structure. In particular, the arrangement of
species into close knit groups (i.e. modules), was higher in the Spring
than the Fall underscoring that species are assembling differently
across seasons. Specifically, in the Spring, species on average confine
their interactions more often to a subset of species (i.e. those in
their module) as compared to species in the Fall. This may signify that
communities in the Spring are more robust to perturbations as the
effects of these perturbations may be confined to specific modules
rather than spreading to the entire community (Gilarranz et al.2017). The differences in network structure between seasons is also
exemplified in the degree of its interaction turnover across seasons. As
a score of zero indicates networks are identical and a score of one
indicates that networks have no common interactions, our average score
of \({\overset{\overline{}}{\beta}}_{\text{WN}}=0.60\), indicates a
relatively high differentiation between our seasonal networks. Taken
together, both modularity and total interaction turnover metrics
provides strong evidence that network topology is indeed changing across
seasons.
Beyond classifying network change, identifying the primary drivers of
species interactions is essential for predicting community structure
(McLeod et al. 2020). In our study, we found that seasonal
topological changes to our inferred network were primarily driven by
interaction rewiring (c.87%) with a small contribution by species
turnover (c.13%). Moreover, the contribution of species turnover to
seasonal topological change
(βST/βWN ) was always smaller than
the overall number of species that contributed to turnover;\(7/32\approx 0.22\). Generally, this low value for species turnover
indicates that species only observed in a single season are not highly
connected to other species. Our results contribute to the small but
growing recognition that rewiring occurs at seasonal scales, and that a
significant portion of interactions in an ecological network are
transient (Carnicer et al. 2009; CaraDonna et al. 2017;
Lopez et al. 2017).
Despite highlighting the need to resolve networks along a temporal
gradient, our results regarding the potential dominance of rewiring
across seasonal networks also provides a general prediction for how
these seasonal communities may respond to disturbances. If species
subject to seasonality are more strongly driven by rewiring, we may also
expect these species to be more robust when subject to other types of
disturbances (CaraDonna et al. 2017). For example, Kaiser-Bunburyet al. (2010) found that in plant-pollinator networks, rewiring
increased community robustness when faced with community species loss,
and Saavedra et al. (2016) found that seasonal interactions play
a key role in maintaining the homeostatic state of ecological
communities. Indeed, it would be of interest to conservation managers to
determine if their systems are robust to future perturbations, given
that their system also undergoes seasonal rewiring. However, while in
general we expect rewiring to have a stabilizing effect, rewiring has
been shown in some cases to have a negative effect on the persistence of
both natural and computer generated food webs (Gilljam et al.2015). Hence, future studies should explore whether stability due to
rewiring holds for different disturbance types, and under which
conditions it switches from a stabilizing to a destabilizing effect.