Introduction
Species represent the main building blocks of ecosystems and are
connected in webs of positive and negative interactions, which shape
ecosystem processes and functioning (Thompson et al. 2012). Given
the central role of interactions among species for energy and matter
flow between ecosystem compartments (Barnes et al. 2018),
studying the structure of ecological networks helps us understand how
ecosystem functioning might be disrupted by global changes (Petcheyet al. 1999; Tylianakis et al. 2008). The wiring among
interacting species is hardly random but rather governed by ecological
rules (Bascompte 2010; Laigle et al. 2018). The strength of
interactions between species may depend on the degree of matching
between functional traits, which are shaped through co-evolutionary
processes (Rausher 2001; Laigle et al. 2018). In turn, rules of
functional matching might be influenced by variation in environmental
conditions, such as temperature (Sentis et al. 2014; Gounandet al. 2016), or by climatic stability (Dalsgaard et al.2011). By inducing changes in species composition, ecological gradients
can be associated with shifts in species co-occurrence and their ability
to form stable links (Welti & Joern 2015; Pellissier et al.2018). Moreover, shifting environmental conditions might influence
interactions among species even when they are steadily co-occurring
(Tylianakis & Morris 2017). As a result, interactions shifts along
climatic clines can lead to changes in the structure of networks (Welti
& Joern 2015). Nevertheless, the geographic variation in networks is
poorly studied, owing to the difficulty of documenting multiple
ecological networks along climatic gradients.
There are major challenges to the large-scale study of ecological
networks that relate to the documentation of interactions and the
methods used to perform network comparisons at the landscape scale
(Pellissier et al. 2018). The study of ecological networks along
environmental gradients has so far been limited by the difficulty of
observing comparable interactions simultaneously at multiple locations.
Novel DNA metabarcoding methods, which are increasingly cheaper, faster
and more comprehensive, have opened such opportunities (Kaartinenet al. 2010; Roslin et al. 2019). Deagle et al.(2007) were among the first to develop a DNA metabarcoding protocol to
reconstruct the trophic regime of the macaroni penguins on Heard Island
in the Indian Ocean. Since then, the study of entire ecological networks
has been facilitated through the adaptation of DNA metabarcoding
techniques to different sample sources, which enables the collection of
many samples over a short period of time (Roslin et al. 2019).
For instance, Pornon et al. (2016) developed a protocol to
quantify plant–pollinator interactions from pollen samples in the
French Central Pyrenees, while Ibanez et al. (2013) applied this
approach to insect feces for studying the diet of insect herbivores.
However, most protocols for network reconstruction were not designed for
studies with large spatial scales, and not all were aimed at
species-level resolution. In addition, a complete description of the wet
lab and bioinformatic procedures is not always accessible, limiting the
adaptability and reproducibility of the techniques used to document
species interactions. Scaling up the utilization of DNA metabarcoding to
entire landscapes, while also sharing methodological workflows as
detailed and user-friendly protocols, can spur advances in the study of
species interactions along environmental gradients.
From the wide range of natural gradients impacting species distribution
and interaction patterns, montane clines represent optimal natural
laboratories to understand how species and their interactions vary over
environmental gradients (Körner 2003). Changes in climate – most
notably temperature – along elevation gradients cause strong
environmental filtering in communities (Rahbek 1995; Hodkinson 2005) and
can therefore also be expected to influence the structure of ecological
networks (O’Connor et al. 2009; Welti & Joern 2015). The
structure of ecological networks along environmental gradients can
change as a result of two main processes: (i) a turnover of the species
in the network, or (ii) a turnover of the links in the network, in which
co-occurring species rewire their interactions along the gradient
(Gravel et al. 2019). In particular, the steady decrease in
temperature with increasing elevation has been associated with changes
in species richness and abundance (Rahbek 1995; Hodkinson 2005;
Descombes et al. 2017), likely influencing the networks of
species interactions (Adedoja et al. 2018; Pellissier et
al. 2018). Therefore, studying changes in the architecture of species
networks along elevation gradients contributes to evaluations of the
effect of temperature on community structure and stability.
Changes in species’ interactions within networks can be summarized by a
set of indicators relating to the degree of structuration and complexity
of the network (Delmas et al. 2019), including connectance
(Martinez 1992), generality (Bersier et al. 2002), nestedness
(Bascompte et al. 2003) and robustness (Dunne et al.2002). Metrics of network structure can also quantify the resilience of
the networks to environmental disturbances (Thebault & Fontaine 2010).
Specialized networks have been found to be associated with lower
robustness against species extinction (Lafferty & Kuris 2009;
Tylianakis & Morris 2017; but see May 1973; McCann 2000). This is the
result of the existence of keystone species (Paine 1969), which are
nodes of interactions on which relies the system stability (Poweret al. 1996). The importance and identity of keystone species,
but also general structural properties involved in network resilience,
may reshuffle along elevation clines. Three main non-exclusive
hypotheses have been proposed to support this pattern: (i) at higher
elevations the environment is expected to be less predictable (Barry
2008), and survival under these conditions necessitates the evolution of
a broader diet breadth (Macarthur & Levins 1967); (ii) more intense
competition at low elevations is predicted to select for more
specialized diets to decrease niche overlap (Macarthur & Levins 1967;
Hodkinson 2005); and, more closely linked to plant–herbivore
interactions, (iii) a decline in the capacity of plants to resist
herbivore attack at higher elevations is expected to facilitate a larger
diet breadth of herbivores (Pellissier et al. 2012a; Rasmannet al. 2014; Moreira et al. 2018). In contrast to the
expectation of increased herbivore generality at higher elevations,
where plant communities are less diverse, it has been proposed that
higher plant species richness could benefit insect generalists simply by
increasing the availability of species to feed on (Unsicker et
al. 2008, Welti et al. 2017).
The comparison of ecological networks along environmental gradients has
the inherent methodological difficulties of network comparison
(Pellissier et al. 2018). Effective analyses of network structure
isolate the influence of real interaction patterns on network structural
indices from the effects of network size or sampling design
(Banašek-Richter et al. 2004). Studying ecological networks along
large-scale environmental clines is challenging in this regard, as the
decline in species richness at the extremes of the gradient might result
in significant variation in species richness, in turn affecting measures
of structural indices sensitive to matrix size (Trøjelsgaard & Olesen
2016; Pellissier et al. 2018). For instance, the number of links
per species inevitably declines from large to small networks, as larger
networks include more possible links. Several strategies have been
developed to alleviate the confounding effects of comparing networks of
different sizes (see Pellissier et al. 2018 for a review of these
approaches). The most commonly used approach is the null model, where
networks of randomly distributed interactions are generated and compared
with the empirical patterns. This method has been established to isolate
the role of observed interaction patterns on the network structure from
the effect of matrix size variation when comparing networks along
environmental gradients (Vázquez & Aizen 2003).
In this study, we investigated the variation in the structure of
plant–orthoptera ecological networks along elevation gradients.
Orthoptera are among the most abundant herbivorous arthropods in
semi-natural grasslands of the European Alpine system, and they strongly
impact the functioning of these ecosystems (Blumer & Diemer 1996). We
optimized a protocol for plant DNA metabarcoding applied to orthopteran
feces in order to reconstruct plant–orthoptera bipartite networks
across 48 study sites situated along six elevation transects in the
Swiss Alps. We then applied null models to explore the structural
variation in plant–orthoptera bipartite networks and determine if lower
levels of network organization and increased robustness are associated
with the low temperatures of the alpine environment. Specifically, we
proposed the following three hypotheses:
- Levels of generality in orthoptera should increase in colder
environment (higher elevation) according to the following lines of
argument: generalist feeders are better equipped to compensate for
higher environmental uncertainty; lower interspecific competition
attenuates positive selection for specialization; and the reduced
plant chemical defences typically found at higher elevations offer
more dietary opportunities for insects.
- The robustness of the network after simulated plant primary
extinctions should be higher in colder environments (at high
elevations). The increase in insect generalist feeding behavior
predicted in the first hypothesis should allow networks of alpine
communities to better compensate for possible plant species loss.
- If more generalist insect herbivores are present and the network is
more robust in colder environment, the removal of plant species should
induce fewer extinctions within orthopteran communities than at low
elevation so that plants have lower keystone weights.