Why do we need to study various interactions and sub-networks simultaneously?
As argued in the previous section, the quest for the ecological and evolutionary processes shaping interactions within communities has so far been restricted to sub-networks. Although studying a sub-network in isolation from others brings valuable information on various aspects of biological communities, such as for example the links between species traits, phylogeny and network architecture, a full understanding of the ecological and evolutionary dynamic of biological communities would gain much from considering interlinked sub-networks simultaneously. Herein, we develop the hypothesis that sub-networks are not independent. Species are often involved in different sub-networks either simultaneously or at different life stages (e.g. Gilbert 1980). This increases the number of indirect pathways from one species to another, with different types of interactions producing different types of direct and indirect feedbacks (negative for antagonist interactions and positive for mutualistic interactions). The ecological dynamics of a given sub-network should thus not only depend on its own architecture, but also on the architecture of the other sub-networks it is connected to, as well as on the way they are interlinked. For example, mutualistic interactions between ants and aphids can affect the associated aphid–parasitoid network with ants causing a shift from generalist to specialist dominated secondary parasitoid communities (Sanders & van Veen 2010). Moreover, species traits that are important for one interaction are often directly or indirectly affected by another interaction. For example, herbivory can decrease pollen production and lower the attractiveness to pollinators (Strauss 1997) and while the induction of secondary chemicals in plants by herbivores decreases herbivory, it also decreases visitation by pollinators (Strauss 1997). Therefore, the evolutionary dynamics of such traits could be under the influence of the selection processes arising from a variety of ecological interactions. Thus, to understand the ecological and evolutionary processes shaping ecological communities, ecologists should ideally study interconnected interactions and sub-networks rather than restrict their analysis to a single interaction type or sub-network.
Developing approaches to evaluate how the various sub-networks combine into a single interlinked network is highly relevant for applied ecology. Recently restoration ecology has begun to shift in emphasis from restoring species to restoring the interactions between species, and thereby ecosystem functions and services (Henson et al. 2009; Heleno et al. 2010). However, ecosystem services are currently treated as separate entities by ecologists. For example pest control ecologists work on pests and their networks of natural enemies, and pollination ecologists work on crop pollination. However, many parasitoids visit flowers for nectar, these nectar supplies being essential for egg laying (Heimpel & Jervis 2005). Viewing the sub-networks that form particular ecosystem services in isolation is a convenient simplification. In reality agro-ecosystems are very unlikely to consist of a series of neat separate networks and a greater understanding of the linkages between the networks may improve our management and use of them. In agro-environmental schemes, management options that consider optimising a number of ecosystem services rather than on maximising a few of them is obviously attractive, an ecological ‘two or three for the price of one’. For example a field margin could provide flowers for pollinators as well as seeds for endangered farmland birds. Currently though pollen and nectar margins and bird seed margins are different options (Natural England 2008) designed by different groups of ecologists (entomologists vs. ornithologists) with relatively little knowledge or appreciation for each other’s agendas. Management options that consider the whole network could provide forage for both groups in the same margins, effectively increasing the area available for each.
Characterisation and ecological insights of interlinked sub-networks
In this section, we focus on how to describe interlinked bipartite sub-networks and on the potential ecological implications of merging networks in terms of perturbation spread. We define perturbation as any change in the biological attributes of one or more species, such as species abundance, that may cascade through the network. For the sake of simplicity, we will restrict ourselves to binary sub-networks, which do not include information on interaction strength. To study two interlinked bipartite sub-networks we need to focus on the species that link the different sub-networks. Indeed, because they are involved in the two sub-networks, these species should be the ones channelling the effects from one sub-network to the other. In the example illustrated in Fig. 3, plants are central because they are interacting directly with both pollinators and herbivores. The involvement of the linking species in each of the two sub-networks will provide some insights into the strength of the potential effects of one sub-network on the other. In Fig. 3, these effects might be strong because plants are highly connected to both networks with 75% of plants from the pollination network taking part in the herbivory network and 100% from plants of the herbivory network taking part in the pollination network.
Figure 3. Interlinked network of the heathand community linking plant–pollinator and plant–herbivores sub-networks. Each circle represents a species, which are linked by edges when the species interact. This dataset has been assembled from two separate studies performed at the same field site (from Henson et al. 2009 and J. Memmott, unpublished).
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We can extend this approach further focusing on six schematic examples of interlinked networks composed of mutualistic and nested sub-networks, and antagonistic and compartmented sub-networks (Fig. 4). In case of two nested mutualistic sub-networks such as plant–pollinator and plant–seed disperser networks, the linking species, here the plants, can have a similar, an opposite or an unrelated generalism degree in the two sub-networks (Fig. 4a,b). The correlation between the generalism degrees of the linking species in the two sub-networks can then provide a first easy metric to describe the merging of two nested networks. If the correlation is positive (Fig. 4a), species from the two ecosystem services guilds (pollinators and seed dispersers) are linked together by the same generalist species from the linking guild. This implies that any two species from this interlinked network are linked either directly or indirectly through only one other species. Such proximity among species suggests that perturbations should easily cascade through the complete web. Moreover, in this configuration, the same generalist plant species is sustaining both ecosystem services guilds and should be a priority for a conservation programme. On the other hand, if the correlation in generalism degree of the linking species is negative (Fig. 4b), any two species from this interlinked network are linked either directly or indirectly through one or two other species. Perturbations should thus be less likely to spread from one ecosystem service sub-network to the other. Moreover, the identity of the linking species, and thereby the conservation priorities, differs between the two sub-networks. Conserving both ecosystem services will involve protecting more species than in the previous example and considering only one of sub-networks while ignoring the other could lead to wrong conclusions in terms of the conservation of the whole network.
Figure 4. Matrix (left) and network (right) representation of various scenarios of interlinked networks based on: (a) and (b) two-nested sub-networks; (c) and (d) two-compartmented sub-networks; (e) and (f) one-nested and one-compartmented sub-networks.
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In case of two antagonistic compartmented networks, such as a plant–above-ground herbivores network and a plant–root parasite network, the linking species, here the plants, can be part of similar (Fig. 4c) or different (Fig. 4d) compartments in the two sub-webs. Overlap in compartments composition between the two networks can thus quantify the extent to which both sub-networks share similar compartment composition. If the compositions of the compartments are similar (Fig. 4c), perturbations should be contained within these compartments. However, if they differ (Fig. 4d), perturbations could spread throughout the whole interlinked network as compartments of one sub-network are linked together by the compartments of the other sub-network.
Finally, in the case of a mutualistic nested network merged to an antagonistic compartmented network, the most generalist species from the nested network can be part of the same (Fig. 4e) or of different compartments (Fig. 4f). The fact that one of the sub-networks is nested links the different compartments of the other sub-networks. These last configurations (Fig. 4d–f) challenge the assumption that compartments contain the spread of perturbations: taking other types of interactions into account, compartments might actually be connected with each other through other sub-networks, leading to cascading effects of perturbations throughout the complete network.
Of course, the outcome of a perturbation will not only depend on the structure of the sub-networks and on the way these sub-networks are linked but also on the type of interaction. As a first approximation, we can speculate that when two mutualistic networks are linked to each other, a perturbation might be amplified during its propagation through the network because of the presence of positive feedbacks. On the contrary, when the two antagonistic networks are linked to each other, the perturbation could be dampened by negative feedbacks as it propagates through the web. Interestingly, when a mutualistic network is associated with an antagonistic one, the antagonistic network may act as a stabilising entity dampening the perturbation, while this same perturbation would have been amplified when only the mutualistic sub-network would have been considered.
The structure of interlinked networks might also affect other stability measures such as diversity persistence. Current results found in single sub-networks (Bastolla et al. 2009; Thébault & Fontaine 2010) can easily be transferred to interlinked networks when a single interaction type is considered. Interlinked networks made of two mutualistic sub-networks can be expected to sustain higher number of coexisting species when both sub-networks are nested and when the generalism degree of the linking species are positively correlated, making that their overall structure nested (Fig. 4a). Similarly when two antagonistic sub-networks are interlinked, a globally compartmented structure should promote diversity (Fig. 4c). However, when a mutualistic sub-network is interlinked with an antagonistic one, the current theoretical results are insufficient to predict diversity persistence.
Obviously this is simply speculative at this stage and requires further analysis, especially since complex, unexpected dynamics might emerge in interlinked networks (Buldyrev et al. 2010). This very simple framework needs to be further refined, for example by taking weighted interactions into account, or extending it to more than two sub-networks. Other metrics and associated null models (e.g. Melián et al. 2009) may also be needed to better characterise how sub-networks are interlinked and which processes can explain the observed patterns.
Evolutionary insights of merged networks
To study the evolutionary processes taking place within interlinked networks, we believe that focusing on key phenotypic traits, which act as important constraints for the different interactions types, may be an interesting starting point. In this section, we first identify such traits, and then speculate on the implications of interlinked networks for the evolutionary dynamics of these traits. Assuming intraspecific variability in these phenotypic traits and assuming that they are heritable, it is possible to speculate on how merging networks will affect their dynamics, by discussing how it alters the selective pressures acting on them.
Interactions between organisms, allies or enemies, depend crucially on the ability of the two organisms to detect each other. We propose that traits used for signals and detection may provide the ecological glue that merges several networks together. An intuitive example is flower’s corolla. Increasing the size of the corolla will make the flower more detectable not only to pollinators, but also to herbivores (Strauss et al. 2002). A similar argument can be made with plant volatile compounds, whose perfume may be an efficient defence as well as a cue for pollinators (Courtois et al. 2009). These types of interaction while observed infrequently (we would argue because they are not often looked for) should not be seen as exceptions. Moreover, virtually any trait (body size, colour, birdsong, scent) affecting detection of the individual by other organisms can modify simultaneously negative and positive interactions, thereby bridging the different networks.
In addition to the traits used by one organism to detect another, the phenotypic traits that affect the transmission of energy through networks will influence whether or not networks merge. Evolution of plant defences again provides a useful example. Some of these defences involve the production of digestibility reducing compounds, some others, toxicity (Müller-Schärer et al. 2004). In both cases, the trait affects the transmission of energy up the food web by reducing the total consumption of plants by above-ground herbivores. They also inhibit the activity of decomposers so that energy available for the below-ground food web is reduced (e.g. Grimm 1996). Such plant defence-related traits should affect the merging of above-ground and below-ground food webs by reducing resource availability and/or vulnerability in both habitats simultaneously. Again, the argument is not restricted to defences and extends to any trait that redistributes the energy among webs, for example, shoot-root ratio, nectar quantity or stoichiometric ratios.
If phenotypic traits are simultaneously involved in different networks, their evolution depends on selective pressures coming from both the merged webs. It is intriguing to consider the implications of this idea. Going back to the evolution of plant volatile compounds, the diversity of these compounds is very high, with hundreds of molecules identified (Courtois et al. 2009). Such a diversity is also reported in the defence strategies of plants (Strauss et al. 2002). The maintenance of such a high diversity of strategies is counter-intuitive as the defence strategy that has the highest benefit for the least cost should be selected and exclude the other strategies (Müller-Schärer et al. 2004). Maintaining this diversity implies very strong disruptive selection. It is possible that this disruptive selection arises from the presence of several webs, with a benefit in one web (e.g. attracting more pollinators) being counteracted by costs in another web (e.g. attracting more predators). Frequency dependence may arise because a given rare phenotype may be favoured by one of the two interaction type network, and the alternative phenotype favoured by the other network when rare. Analyses that focus on only one type of interaction would therefore likely miss this kind of frequency dependence. At this stage, this should only be considered as an intriguing possibility for the emergence and maintenance of diverse phenotypes. The selection regime depends on many details related to genetic background of the phenotypic traits and trade-offs that are associated to them and much theoretical work is needed to understand the implication of merging networks (see part 3). Pairwise coevolutionary models in which the interaction is a mutualism on one site and an antagonism on the other, have demonstrated the potential of merging interactions in maintaining trait variation (e.g. Nuismer et al. 1999).
Because of conflicting pressures on a given trait from different webs, the consequences for evolutionary dynamics are hard to predict. The first section of this article demonstrated that mutualistic and trophic networks differ in their nestedness and compartmentation. Both these characteristics emerge, at least partially, from the evolution of specialisation. Evolution of specialisation has been most studied in models using only one interaction type (e.g. predation and mutualism, respectively: Egas et al. 2004; Ferrière et al. 2007). Results are diverse, but to illustrate the argument, let us focus on direct feedbacks associated to each interaction. In trophic interactions, specialised predators might deplete their prey, decreasing their frequency, thereby creating selective pressures for less specialisation. In mutualistic interactions, a specialised mutualist might increase the frequency of its partner, thereby creating selective pressures that may favour further specialisation. Now consider the two interlinked networks: once the mutualist increases the frequency of its partner, this increased frequency may well attract predators from the other web, so that the outcome of evolution depends on the relative weight of the two networks, in terms of total density and/or interaction strength. This means that the complete understanding of evolution of specialisation, as well as its structural consequences (nestedness and compartmentation) may require both types of interaction to be considered simultaneously.