- Original Scientific Article
- Open Access
Coevolution in Multispecific Interactions among Free-Living Species
© Springer Science+Business Media, LLC 2009
- Published: 29 December 2009
Ecological interactions among species are the backbone of biodiversity. Interactions take a tremendous variety of forms in nature and have pervasive consequences for the population dynamics and evolution of species. A persistent challenge in evolutionary biology has been to understand how coevolution has produced complex webs of interacting species, where a large number of species interact through mutual dependences (e.g., mutualisms) or influences (e.g., predator–prey interactions in food webs). Recent work on megadiverse species assemblages in ecological communities has uncovered interesting repeated patterns that emerge in these complex networks of multispecies interactions. They include the presence of a core of super- generalists, proper patterns of interaction (that resemble nested chinese boxes), and multiple modules that act as the basic blocks of the complex network. The structure of multispecies interactions resembles other complex networks and is central to understanding its evolution and the consequences of species losses for the persistence of the whole network. These patterns suggest both precise ways on how coevolution goes on beyond simple pairwise interactions and scales up to whole communities.
- Multispecies interactions
- Complex networks
- Seed dispersal
We shouldn’t be surprised that empirical naturalists remain skeptical about the new insights that network theory and the formal analysis of complexity bring to the study of biodiversity (Weitz et al. 2007). First, it might be difficult to incorporate research agendas that include a vast amount of multidisciplinary approaches that are far away from our expertise. Second, we need to fully understand how abstract representations like network graphs can be embedded with important natural history details of species and their interactions. The analysis of ecological networks is a formal way to visualize, explore, and address the shared patterns that lie beyond the myriad interactions involved in megadiverse multispecies assemblages like tropical forests, coral reefs or soil microorganisms, and plants. We have a formal tool, with a solid multidisciplinary knowledge base, to dissect the complexity of ecological systems by moving from the reductionist analysis of their component parts (e.g., pairwise interactions) to the analysis of their macroscopic properties (Bascompte and Jordano 2007). This essay briefly reviews the principles of network theory applied to ecological systems and considers the new insights gained about the coevolution of megadiverse assemblages of interacting species.
The variety of antagonistic, predator–prey, or competitive effects portrayed in food web analysis are among the multiple types of interactions that occur in natural systems (Thompson 1982) and not necessarily the most important ones. Think for example about the keystone relevance of plant–pollinator and plant–frugivore interactions for tropical forests where up to 95% of the trees and subcanopy shrubs need these animals for effective pollen transfer and successful regeneration (Bawa 1990; Jordano 2000). Forest regeneration would simply collapse without the intervention of animal mutualists. Mutualistic webs of interaction are best depicted as bipartite graphs (Fig. 1a, 2b), where the mutual dependences of each pairwise interaction can be represented. The two distinct sets of species (animals and plants) are linked through coevolved interactions of mutual dependence (Fig. 1b) that depict the reciprocal consequences of their interaction. In the same way as traditional food webs portray the patterns of energy transfer in ecosystems (Fig. 2a), bipartite graphs capture the main elements of coevolved interactions in these multispecific assemblages of species: generalization, asymmetry of mutual dependences, compartments, etc. (Fig. 2b, c). Thus the net outcomes of these interactions, as well as their overall complexity in form (topology) and structure, can be analyzed. Network topology refers mainly to its size (number of nodes) and the form and density of links among them (relative to the maximum possible), as well as their distribution. Two networks might differ in the intensity of mutual effects among species, yet share the same topology. Network structure conveys information about the identity of the nodes: who eats whom and whether distinct subsets of nodes more linked among them exist.
Recent analyses indicate that mutualistic networks have specific signatures in their topology and structure (i.e., the way species are interconnected through mutual dependences) that confer more robustness and stability than expected for randomly assembled interactions (Bascompte and Jordano 2007). Resembling other complex networks (both biological and nonbiotic; Amaral et al. 2000), mutualistic webs are characterized by their heterogeneity (Fig. 2b), with a number of nodes having a high number of interactions and a high number of nodes with few interactions. That is, there are a few super-generalist species that form a well-connected core of the network and many other species with few interactions (Jordano et al. 2003). A randomly assembled web would, in contrast, have a more even distribution of interactions among species, as null models of mutualistic webs indicate (Vázquez and Aizen 2003). An interesting property of heterogeneous networks is that they are very robust to random disturbances (loss of a node) but very sensitive to selective losses of nodes at the core (Albert et al. 2000). Therefore, mutualistic networks of interaction organized around a distinct subset of super generalists can be reasonably robust to disturbances not directed to this central backbone of their structure.
Second, the wireframe of interactions among species is built on asymmetric and weak reciprocal dependences (Jordano 1987; Bascompte et al. 2006). Just realize that the zero to one records (presence–absence) for the pairwise interactions can take the form of quantitative estimates of the actual strength of dependence of each partner. In Fig. 1b, the strength of mutual dependences between Euterpe edulis and two of its major seed dispersers in the Atlantic rainforest of SE Brazil vary significantly. While the palm is quite dependent on the toucan for successful fruit removal, the fruits are a minor part of the toucan generalist diet; in contrast, the jacutinga relies extensively on the palm fruits, but due to low abundance and infrequent visitation to the fruiting palms, it plays a secondary role as seed disperser. This pattern is extremely common in plant–animal mutualisms: most interactions are weak; and the few of them that are stronger tend to be quite asymmetric, with, e.g., the plant depending heavily on a pollinator or frugivore species but the animal being a super-generalist that relies only marginally on that plant. If we sum the total dependences that a plant or an animal species has in a network, we get a measure of the species strength (the quantitative analog to the number of interactions per species in the zero to one matrix). Again, the distribution of strength values is extremely skewed across mutualistic species (Bascompte et al. 2006), with only a few generalists concentrating most of the dependencies for the pollination or seed dispersal services of the rest of the community. Central species thus combine a high number of interactions and a high value of summed dependences (strength) of partner species thus being pivotal for the functioning of the network.
Third, the networks show distinct modules or compartments, i.e., distinct subsets of species that interact more strongly among themselves than with other modules. Olesen et al. (2007) showed that most pollination networks are modular, with distinct subsets of plant–pollinator groups, such as butterfly-pollinated plants or those visited predominantly by hummingbirds. Modules are the basic blocks that structure these networks, analogous to the different walls that made up a building. Individual species can have different roles in this scenario: while some species only interact heavily with species of their own modules, other super-generalists “glue” together all the modules by showing extremely generalized interactions. These interactions tie together peripheral species and can be extremely important for maintaining the cohesiveness of the network. For instance, invasive species can be peripheral in the network during the early stages of invasion but quickly increase in strength and get to the core of the network, displacing native species (Aizen et al. 2008).
All these main properties of the mutualistic networks appear to be omnipresent in nature, independent of the type of interaction or the specific ecosystem or community we study. While enormous progress has been made in recent years to understand the basic patterns and modes of interaction (Bascompte and Jordano 2007), the challenge to understand how these megadiverse assemblages coevolve remains, i.e., how pairwise interactions add up to modules of tightly interacting species to whole communities as diversified as those we can document in tropical forests.
What is the basic process assembling these megadiverse networks? Ultimately, the role of each species in the network depends on the number of interactions it establishes with the potential partner species. Established interactions are thus, like the basic blocks that form larger modules that in turn made up the whole complex architecture of the network. These basic blocks are called interaction motifs (Milo et al. 2002), or repeated patterns or forms of interaction, that occur in the network (Fig. 2b, c). Depending on the relative frequency of these different motifs, the overall aspect of the whole network can be very different: more modular and specialized if built predominantly on specialized motifs (e.g., Fig. 2e, top) or more nested and generalized if nonspecialized motifs are dominant (e.g., Fig. 2e, bottom). Data from empirical networks of plant–pollinator and plant–frugivore interactions shows that local abundance has pervasive influence on these patterns, but other important species–specific traits (size, phenology, color) also restrict the range of partners each species interacts with (Jordano 1987; Jordano et al. 2003; Vázquez et al. 2007), determining the type of motifs contributed. A future challenge would be to explore how species–specific traits mold the pattern of interaction and add up to generate these network-wide patterns.
It is far from clear how coevolved selection pressures contribute to the emergence of highly nested patterns of interaction, given the omnipresence of asymmetry of mutual dependencies (and, presumably, asymmetry of phenotypic selection intensities; Jordano 1987; Bascompte et al. 2006). It is expected that the selection pressures originating from pairwise interactions should be more symmetric in antagonistic interactions or in mutualisms with a high degree of intimacy and specialization (like ant-plants; Fig. 2c; Guimarães et al. 2007), resulting in more modular networks with distinct groups of coevolving species. The asymmetric pattern of interaction that pervades mutualistic networks of free-living species favors the diversification and growth of the web by adding new species that link with the core of super-generalists. For instance, rare plant species can probably persist and have a functional service of pollen transfer or seed dispersal by depending strongly on generalist animal partners that, in turn, only marginally rely on the plant resources.
We might expect a variety of influences of the plants’ and animals’ evolutionary history in shaping network patterns (Fig. 3b). As new species add up in the network, the overall levels of phenotypic convergence and complementarity would increase. For instance, new frugivores would tend to converge (be more similar in morphology) with preexisting frugivore species and share codispersed plants; in turn, selection pressures would increase the phenotypic matching of animal traits (body parts, phenology) and fruit traits, increasing complementarity between interacting partners. Convergence would tend to facilitate the persistence of a given species within a multispecific mutualism; complementarity would tend to facilitate how the species efficiently uses the mutualistic services provided by the partners. The interaction pattern will then reflect the phylogenies of the two groups of species (Fig. 3b, top) and markedly deviate from the checkerspot pattern (Fig. 3b, bottom) of interactions expected in the absence of phylogenetic signal. Significant influences of the evolutionary history of only one of the species groups would mean that the group has driven the evolution of the network (Fig. 3b, middle): closely related plants showing a trend to interact with similar sets of frugivore species, but these animal species being not phylogenetically related. A given plant would be using the dispersal services of a wide array of frugivore clades, but a given frugivore species would tend to exploit a subset of phylogenetically restricted fruit species.
When convergence and increased complementarity remain restricted to distinct subsets of species, then modularity will increase through a disproportionate growth of specialized motifs, creating vortexes of coevolutionary change (Thompson 2005, 2006). We can expect these trends for highly intimate mutualistic associations such as ant-plants, symbioses, and high-specificity antagonisms such as host–parasite interactions. In contrast, super generalists are expected to evolve and coevolve within megadiversified webs of interactions among free-living species mainly by evolving abilities to interact with multiple, distinct groups of partners. A characteristic pattern in nested networks of mutualists (Olesen et al. 2007) is that the super-generalists are true hubs in the network, adding interactions that connect different modules. To some extent, the evolution of the supergeneralist lifestyle allows the gluing together of the diverse bricks and blocks that make up the fascinating architecture of these ecological services and their biodiversity.
The recent development of network-based tools applied to the study of complex patterns of ecological interactions bridges multidisciplinary approaches from statistical mechanics in physics, biocomplexity, ecological modeling, and basic natural history. It is probably the only approach that can successfully decipher the simple, general patterns that lie behind the extreme complexity of interaction webs in ecosystems. A fascinating aspect of these webs is their similarity and analogy to other complex networks, spanning biotic (e.g., gene regulation, cell metabolic reactions) and abiotic (e.g., the internet) scenarios. The multidisciplinary integrative approach to the study of complex networks can be a key to developing early-warning diagnostic criteria to identify critical situations of disturbance in natural areas well before the functional aspects of key ecosystem services, like animal-mediated pollination or seed dispersal, reach a no-return point for their successful restoration. On top of these applied objectives, research on complex ecological networks has taken the first steps to a fuller understanding of how coevolution drives megadiverse assemblages of mutualistic species, which are the backbone of ecosystems like the tropical rainforest.
Understanding complex coevolving networks is important because species and their interactions do not exist in an ecological vacuum. By using new multidisciplinary approaches, we aim to better forecast the risks of losing a single species or collapsing a single interaction to the persistence of the whole ecosystem. We know that these complex systems are more than the sum of their parts, so the consequences of losing one of the parts may extend well beyond its immediate influence. Thus, this understanding will help us to be better prepared to effectively restore the key functions and services needed to rebuild disturbed ecosystems. We need a solid scientific theory of conservation with a knowledge and understanding of complex patterns of biodiversity that at first sight appear impossible to handle and analyze (the “entangled bank”). Coevolving networks of multispecies interactions underpin this entangled bank, and we are just starting to grasp the fine details of their coevolution.
I appreciate the invitation by John N. Thompson and Rodrigo Medel to contribute to this special issue on coevolution. Their advice and comments, together with one anonymous referee, helped to improve a draft of this paper. Over the years, my ideas on networks have benefited from discussions and collaborations with Jordi Bascompte, Jens M. Olesen, Paulo R. Guimarães Jr., John N. Thompson, Thomas Lewinsohn, and people at the Integrative Ecology Group (Sevilla), especially Alfredo Valido, Carlos Melián, Miguel A. Fortuna, and Jofre Carnicer. My work has been supported by grants from the Spanish Ministry of Science (MICINN; CGL2006-00373) and Junta de Andalucía (P07-RNM2824). This paper is dedicated to Myriam Márquez, for her birthday and the suggestions to design Figs. 1 and 3.
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