Introduction
Ecologists recognize that species interactions are a cornerstone in
determining biodiversity, community assembly, and ecosystem functioning
(Bascompte & Jordano 2007; Goudard & Loreau 2008; Tonkin et al.2017). Thus, species interactions have far reaching biological
implications (Tylianakis et al. 2008). For instance, species
interactions are used in the evaluation of community stability (Ives &
Carpenter 2007) and hence are essential for determining community
resilience and resistance to perturbations. In addition, positive
species interactions have been shown to produce species-rich model
communities among competitors supported by a single limiting resource
(Gross 2008), and facilitate increased species richness in harsh
environments (Cavieres & Badano 2009). As well, interactions have been
shown to mediate the negative effects of environmental change (Brooker
2006; Suttle et al. 2007), and hence are likely essential for
continued ecosystem persistence in the face of global change.
While species interactions vary across both space and time (Rasmussenet al. 2013), fewer studies have focused on the changes in
temporal interactions (Alarcón et al. 2008; Thompson et
al. 2012) despite evidence linking them to the coexistence of competing
species (McMeans et al. 2020). More recently, temporal
heterogeneity, in particular seasonality, has been invoked to explain
biodiversity and community structure (Tonkin et al. 2017). For
example, seasonality has been shown to minimize competitive interactions
and help stabilize total species abundances (Shimadzu et al.2013), as well as play a role in maintaining structure and diversity in
communities (e.g., Fitzgerald et al. 2017). Consequently, as many
environments experience seasonal oscillations (Tonkin et al.2017) with different community compositions being favoured across
different seasons, the influence of seasonality on species interactions
is widespread.
Typically, species interactions are analyzed using network theory
(Rasmussen et al. 2013) and seasonality can be incorporated by
constructing and comparing separate networks representing different
seasons. Often these types of ecological interaction networks are
constructed using species co-occurrence methods (Freilich et al.2018). Traditionally, these approaches assume that species with
significant co-occurrence have beneficial interactions while species
with significant co-exclusion have negative interactions (Cazelleset al. 2016). However, co-occurrence data and their corresponding
methods have been criticized for elucidating false interactions and for
failing to detect true pairwise species interactions (Blanchet et
al. 2020). A false interaction may arise due to species responding
similarly to the same environmental factors (Peres-Neto et al.2001) while true interactions may not be detected due to the coarseness
of presence/absence data (Sander et al. 2017). More recently,
joint species distribution models (e.g., Pollock et al. 2014;
Ovaskainen et al. 2016) have been touted as a more robust method
to infer community structure over other co-occurrence methods as they
incorporate abiotic factors into their analysis (D’Amen et al.2018). However, while this approach controls for environmental factors,
it is still likely limited by its reliance on co-occurrence data to
infer species interactions (Blanchet et al. 2020). A promising
approach proposed by Momal et al. (2020) addresses these issues
by utilizing species abundances, instead of presence/absence data, with
a joint species distribution model. Including abundance measures
provides richer information for capturing interactions (Blanchetet al. 2020) while the inclusion of environmental factors help
prevent spurious interactions in the network.
One of the strong utilities of using species interaction networks are
the many developed tools available for comparing networks (e.g., Delmaset al. 2019). Of particular interest are metrics that evaluate
topological differences due to species turnover and interaction rewiring
(i.e. the changes in the interactions between the same species across
space or time despite both species remaining present) (Poisot et
al. 2012). While one of these two processes may be more dominant in a
system than the other, these processes are not mutually exclusive. For
example, the temporal changes in plant-pollinator networks are the
result of both species turnover and rewiring (Alarcón et al.2008; Petanidou et al. 2008; Olesen et al. 2011) and not a
singular process. Identifying the relative contribution(s) of each
process to network topology is important as systems dominated by
rewiring, rather than species turnover, may be more robust to
perturbations (CaraDonna et al. 2017). Additionally, as traits
have been shown to be an important driver of ecological network
structure (Eklöf et al. 2013), determining how traits relate to
rewiring is a critical component for understanding ecosystem dynamics.
Due to the sampling effort required, few systems have the appropriate
biological data needed to produce temporal interaction networks (Alarcónet al. 2008). The difficulty of collecting large spatial or
temporal-scale data primarily limits seasonal network analysis to
physically small organisms such as those that make-up bipartite plant
pollinator networks (e.g., Alarcón et al. 2008; Petanidouet al. 2008; Olesen et al. 2011; Rasmussen et al.2013; Burkle et al. 2016; CaraDonna et al. 2017). However,
notable exceptions include seasonal networks of tropical fish
(Winemiller 1990), frugivorous birds (Carnicer et al. 2009), a
forest predator-prey community (Saavedra et al. 2016), and a
consumer-resource intertidal community (Lopez et al. 2017). Given
the release of a recent time series dataset of stream fish abundances
for sites across the United States (NEON 2020), there is a unique
opportunity to investigate seasonal networks in a freshwater stream
ecosystem. Since stream communities experience regular seasonal
variations from differences in shading, temperature, disturbance, and
productivity (Thompson & Townsend 1999), they are likely a valuable
study system to understand the effects of seasonality on multiplex
networks.
Here, we investigate seasonal changes in a freshwater stream fish
community using NEON data (NEON 2020) and quantify both species turnover
and interaction rewiring therein. Specifically, our objectives are to:
(i) determine if there are measurable differences between Fall and
Spring networks, using modularity and interaction turnover (i.e.
beta-diversity); (ii) quantify the influence of interaction rewiring and
species turnover in these communities; and, (iii) evaluate whether
seasonal changes in species interactions are related to species-specific
traits. To do so, we apply the recently proposed method of Momalet al. (2020) to construct two freshwater species multiplex
networks for Fall and Spring. We find that: (i) Spring interaction
networks have higher modularity than Fall networks, and between seasons,
there is a large amount of interaction turnover; (ii) most topological
change across seasons are the result of species rewiring (c.87%) as
compared to species turnover (c.13%); and (iii) species’ maximum length
and its piscivore/non-piscivore status help explain a species total
number of rewiring connections (adjusted R2=0.34).