Mutualism-network studies assume that all interacting species are mutualistic partners and consider that all links are of one kind. However, the influence of different types of links, such as cheating links, on network organization remains unexplored. We studied two flower-visitation networks (Malpighiaceae and Bignoniaceae and their flower visitors), and divide the types of link into cheaters (i.e. robbers and thieves of flower rewards) and effective pollinators. We investigated if there were topological differences among networks with and without cheaters, especially with respect to nestedness and modularity. The Malpighiaceae network was nested, but not modular, and it was dominated by pollinators and had much fewer cheater species than Bignoniaceae network (28% versus 75%). The Bignoniaceae network was mainly a plant–cheater network, being modular because of the presence of pollen robbers and showing no nestedness. In the Malpighiaceae network, removal of cheaters had no major consequences for topology. In contrast, removal of cheaters broke down the modularity of the Bignoniaceae network. As cheaters are ubiquitous in all mutualisms, the results presented here show that they have a strong impact upon network topology.
Species are organized into complex networks through their interactions or links (e.g. Bascompte 2009). In almost all studies, links are only of one kind, e.g. predatory or parasitic (but see Melián et al. 2009). In mutualism networks, we assume that all interactions are beneficial, e.g. the exchange of pollination service for floral rewards (e.g. Elberling & Olesen 1999). In plant–animal mutualisms, rewards and services are also exploited by non-mutualistic species. In pollination networks, ‘pollinator’ is used as a generic term for all kinds of flower visitor (Elberling & Olesen 1999). This is a common simplification. However, animals visit flowers for many reasons, e.g. florivory, ovule/seed predation and nectar and pollen harvesting with or without pollen transfer (e.g. Inouye 1983; Strauss & Whittall 2006).
The implications of this link diversity to the structure, stability and dynamics of networks are unknown. Melián et al. (2009), for example, found that strong links and a high ratio of mutualistic : consumer/resource links were important for the diversity of networks. In food webs, parasites are major determinants of stability (Lafferty et al. 2006), strongly affecting network features. Among network features recorded in mutualistic networks are nestedness, in which the links of specialists are subsets of the links of more generalized species (Bascompte et al. 2003), and modularity, in which species are organized into small, strongly linked groups or modules, which are loosely interlinked (Olesen et al. 2007).
We investigated two flower-visitation networks: a set of Malpighiaceae and Bignoniacaeae species and their flower visitors. For both, visitors were categorized as pollinators, cheaters or both. A flower visitor was a pollinator, if it contacted anthers and stigma, or a cheater, if it obtained its reward without any contact (Inouye 1983). In the Bignoniaceae network, cheaters were further categorized as either nectar or pollen robbers. First, the topology of networks was described and then cheaters were removed to analyse their influence. If cheaters interact with generalist plants, their removal may reduce nestedness and increase modularity by reducing the number of across-module interactions (Olesen et al. 2007). If cheaters interact with different sets of related species, their elimination may decrease modularity. We addressed the questions: what are the differences and similarities in topology of the visitation, pollination and plant–cheater networks, especially with respect to modularity and nestedness? And how are cheater species distributed within networks?
2. Material and methods
(a) Study site and dataset
We used data from literature to identify the types of interaction between flower visitors and (i) Malpighiaceae (Sigrist 2001; Sigrist & Sazima 2004) and (ii) Bignoniaceae (Amaral 1992) in a 250 ha fragment of semi-deciduous, seasonal forest (Santa Genebra Municipal Reserve, SGR), Campinas, SE Brazil (22°49′ S, 47°7′ W).
The networks are presented as adjacency matrices depicting plant–animal interactions, in which matrix element rij =1, if plant species i interacts with animal species j and zero otherwise (e.g. Bascompte et al. 2003). Each visitation network was split into two submatrices, viz. a pollination matrix of plants and pollinators and a plant–cheater matrix (electronic supplementary material). Additionally, we operated with two kinds of cheater for Bignoniaceae, viz. nectar and pollen robbers, and split the plant–cheater matrix into a pollen robbery matrix of plants and pollen robbers, and a nectar robbery matrix of plants and nectar robbers.
(b) Network statistics
The linkage level L of a species denotes its number of links (Olesen et al. 2007). The level of nestedness n was estimated using the NODF metric (Almeida-Neto et al. 2008) and the software Aninhado (Guimarães & Guimarães 2006). Significance of NODF was tested using a null model described in Bascompte et al. (2003).
To estimate the level of modularity, module number and species network role, we used an algorithm by Guimerà & Amaral (2005; see the electronic supplementary material, Olesen et al. 2007). Among-module distribution of cheater species was assessed by a co-occurrence analysis (electronic supplementary material).
The topology of the visitation networks clearly looks different in the two families (figure 1). The Malpighiaceae network had fewer species but more links and thus a higher connectance than the Bignoniaceae network (table 1). The Malpighiaceae network was nested, but not modular, whereas the Bignoniaceae network was modular, but not nested (table 1). Removing cheaters from the visitation networks had completely different outcomes in the two networks: it did not affect nestedness in the Malpighiaceae network, but destroyed modularity in the Bignoniaceae network (table 1). Thus, pollinators caused nestedness in the Malpighiaceae network, and cheaters caused modularity in the Bignoniaceae network. These differences may be due to a smaller proportion of cheaters in the Malpighiaceae network (28% versus 75%, table 1) and/or a higher food–plant specificity among Bignoniaceae cheaters (1.3 versus 3.1, table 1).
The visitation matrix of Bignoniaceae had nine modules, each with 2–14 species. Most species were peripherals (N = 62%, figure 2), i.e. with only one to two links to other species, but three species were connectors between modules, viz. the nectar-robbing bees Xylocopa suspecta, Oxaea flavescens and Euglossa annectans. The plants Adenocalyma bracteatum and Anemopaegma chamberlaynii were module hubs, whereas Arrabidea triplinervia was a network hub (figure 2). Cheaters show much less co-occurrence among modules in the Bignoniaceae visitation matrix than expected by chance (C score = 0.86, P = 0.0002), indicating that cheaters were overdispersed among modules. However, one module was composed exclusively of cheaters and Lundia obliqua. The plant–cheater matrix of Bignoniaceae had seven non-connected modules with 3–12 peripheral species. Here, the same bee species as in the visitation matrix were classified as connectors, whereas A. bracteatum and A. triplinervia were module hubs. The pollen robbery matrix had five modules with two to six species, and all animals were peripherals (Lm = 1.0, table 1).
The Bignoniaceae and Malpighiaceae visitation networks have similar number of plants, whereas flower-visiting animal species are twice as frequent in the Bignoniaceae network. This is due to the cheaters, which are three times as common in the Bignoniaceae network. Thus, the Malpighiaceae network is essentially a pollination network, whereas the Bignoniaceae network is a plant–cheater network. As in other pollination networks (Bascompte et al. 2003), the Malpighiaceae network is nested, but also non-modular, which may reflect its small size (Olesen et al. 2007). Another Malpighiaceae–bee network was strongly nested, but, in contrast to ours, showed a low degree of modularity (Bezerra et al. 2009).
The Bignoniaceae networks had a completely different topology from our Malpighiaceae networks. In the Bignoniaceae networks, connectance is only half of that in the Malpighiaceae networks, indicating that the Bignoniaceae networks are more specialized. In addition, the Bignoniaceae visitation network was non-nested, but modular. The reason for this was the many specialized links (i.e. one or two animals visiting one plant species only) between pollen robbers and their plants. Pollen robbery is similar to herbivory, and herbivory networks are strongly modular (Lewinsohn et al. 2006). An increase in cheating in visitation networks may decrease nestedness, but increase modularity. Large pollination networks are modular (Olesen et al. 2007). However, this assumes that all links are pollinatory. We show that, besides phylogenetic clustering and trait convergence (Olesen et al. 2007), consumer/resource links, such as cheating links, also contribute to modularity.
Malpighiaceae is pan(sub)tropical with ca 1250 species, with oil flower species restricted to the neotropics. Female bees collect pollen and oil, which is used as larval provision and for nest cell walls (Sigrist & Sazima 2004). In this study, 26 of the 29 bee visitor species were known oil collectors (Vogel 1974). The oil-collecting species made 93–96% of all links in the matrices. Thus, the Malpighiaceae visitation and pollination networks are bee pollination–floral-oil systems, and the small cheating networks are a floral-oil harvesting system. However, floral resources in Malpighiaceae species seem difficult to exploit by visiting animals without pollinating. As Bezerra et al. (2009) indicated, the phylogenetic and ecological similarity among partners results in a high nested pattern in this flower-oil system.
Bignoniaceae is a predominantly neotropical family with ca 800 species pollinated by insects, birds and bats (Gentry 1980). Bignoniaceae flowers are much more diverse than Malpiguiaceae, allowing a much larger array of flower visitors the access to their flower rewards. In the network, 54 per cent, 23 per cent and 23 per cent of the animals were bees, butterflies and others (Diptera, Coleoptera and hummingbirds), respectively. Bees were mainly pollinators or nectar robbers; butterflies and hummingbirds were mainly nectar robbers, and most of the ‘others’ were pollen robbers. Nectar robbers act as the glue of the Bignoniaceae network, connecting the modules and increasing the cohesiveness of the network, while pollen robbers show more specialized links. The distribution of cheaters among modules might imply that robbers are overdispersed in niche space leading to the observed modular pattern, and/or may be a result of flower features such as calyx thickness and pubescence and corolla shape, which constrains the foraging of bees. Finally, the proportion of cheaters was three times higher in Bignoniaceae networks than in Malpighiaceae networks. The reasons might be that (i) the adaptations of the morphology and behaviour of visitors to flowers of the Malpighiaceae are much tighter, making it more difficult to harvest rewards without pollinating, and (ii) the oil resource is more costly than nectar to produce, resulting in a more specialized, conservative flower-pollination system, and the adapted visitors morphology and behaviour on Malpighiaceae flowers.
Nestedness adds robustness to a network and shortens the distance between species (Bascompte et al. 2003). Thus, cheaters, such as Bignonaceae pollen robbers, may have a strong impact upon network stability by destroying nestedness and enforcing modularity. The importance of modularity for networks is less known, although a modular structure may slow down the spread of disturbances (Olesen et al. 2007). Cheaters are a ubiquitous part of maybe all mutualisms, and they seem to be important to the overall stability and integration of natural systems.
This study is supported by a FAPESP fellowship (J.G.), CNPq and FAPESP (P.G., L.P.C.M.) and FNU (J.M.O.). We are most grateful to M. E. Amaral and M. R. Sigrist for data access.
- Received December 8, 2009.
- Accepted December 18, 2009.
- © 2010 The Royal Society