Plant-animal mutualistic networks are interaction webs consisting of two sets of entities, plant and animal species, whose evolutionary dynamics are deeply influenced by the outcomes of the interactions, yielding a diverse array of coevolutionary processes. These networks are two-mode networks sharing many common properties with others such as food webs, social, and abiotic networks. Here we describe generalized patterns in the topology of 29 plant-pollinator and 24 plant-frugivore networks in natural communities. Scale-free properties have been described for a number of biological, social, and abiotic networks; in contrast, most of the plant-animal mutualistic networks (65.6%) show species connectivity distributions (number of links per species) with a power-law regime but decaying as a marked cut-off, i.e. truncated power-law or broad-scale networks and few (22.2%) show scale-invariance. We hypothesize that plant-animal mutualistic networks follow a build-up process similar to complex abiotic nets, based on the preferential attachment of species. However, constraints in the addition of links such as morphological mismatching or phenological uncoupling between mutualistic partners, restrict the number of interactions established, causing deviations from scale-invariance. This reveals generalized topological patterns characteristic of self-organized complex systems. Relative to scale-invariant networks, such constraints may confer higher robustness to the loss of keystone species that are the backbone of these webs.

We present a comprehensive approach to detect pattern in assemblages of plant and animal species linked by interactions such as pollination, frugivory or herbivory. Simple structural models produce gradient, compartmented or nested patterns of interaction; intermediate patterns between a gradient and compartments are possible, and nesting within compartments produces a combined model. Interaction patterns can be visualized and analyzed either as matrices, as bipartite networks or as multivariate sets through correspondence analysis. We argue that differences among patterns represent outcomes of distinct evolutionary and ecological processes in these highly diversified assemblages. Instead of choosing one model a priori, assemblages should be probed for a suite of patterns. A plant Á /pollinator assemblage exemplifies a simple nested pattern, whereas a plant Á / herbivore assemblage illustrates a compound pattern with nested structures within compartments. Compartmentation should reflect coevolutionary histories and constraints, whereas differences in species abundance or dispersal may generate nestedness.

Summary1. Understanding the structure of ecological networks is a crucial task for interpreting community and ecosystem responses to global change. 2. Despite the recent interest in this subject, almost all studies have focused exclusively on one specific network property. The question remains as to what extent different network properties are related and how understanding this relationship can advance our comprehension of the mechanisms behind these patterns. 3. Here, we analysed the relationship between nestedness and modularity, two frequently studied network properties, for a large data set of 95 ecological communities including both plant-animal mutualistic and host-parasite networks. 4. We found that the correlation between nestedness and modularity for a population of random matrices generated from the real communities decreases significantly in magnitude and sign with increasing connectance independent of the network type. At low connectivities, networks that are highly nested also tend to be highly modular; the reverse happens at high connectivities. 5. The above result is qualitatively robust when different null models are used to infer network structure, but, at a finer scale, quantitative differences exist. We observed an important interaction between the network structure pattern and the null model used to detect it. 6. A better understanding of the relationship between nestedness and modularity is important given their potential implications on the dynamics and stability of ecological communities.

Recent reviews of plant–pollinator mutualistic networks showed that generalization is a common pattern in this type of interaction. Here we examine the ecological correlates of generalization patterns in plant–pollinator networks, especially how interaction patterns covary with latitude, elevation, and insularity. We review the few published analyses of whole networks and include unpublished material, analyzing 29 complete plant–pollinator networks that encompass arctic, alpine, temperate, Mediterranean, and subtropical–tropical areas. The number of interactions observed (I) was a linear function of network size (M) the maximum number of interactions: ln I = 0.575 + 0.61 ln M; R2 = 0.946. The connectance (C), the fraction of observed interactions relative to the total possible, decreased exponentially with species richness, the sum of animal and plant species in each community (A + P): C = 13.83 exp[−0.003(A + P)]. After controlling for species richness, the residual connectance was significantly lower in highland (>1500 m elevation) than in lowland networks and differed marginally among biogeographic regions, with both alpine and tropical networks showing a trend for lower residual connectance. The two Mediterranean networks showed the highest residual connectance. After correcting for variation in network size, plant species were shown to be more generalized at higher latitude and lowland habitats, but showed increased specialization on islands. Oceanic island networks showed an impoverishment of potential animal pollinators (lower ratio of animal to plant species, A : P, compared to mainland networks) associated with this trend of increased specialization. Plants, but not their flower‐visiting animals, supported the often‐repeated statements about higher specificity in the tropics than at higher latitudes. The pattern of interaction build‐up as diversity increases in pollination networks does not differ appreciably from other mutualisms, such as plant–seed disperser networks or more complex food webs.

Ecological networks are complexes of interacting species, but not all potential links among species are realized. Unobserved links are either missing or forbidden. Missing links exist, but require more sampling or alternative ways of detection to be verified. Forbidden links remain unobservable, irrespective of sampling effort. They are caused by linkage constraints. We studied one Arctic pollination network and two Mediterranean seed-dispersal networks. In the first, for example, we recorded flower-visit links for one full season, arranged data in an interaction matrix and got a connectance C of 15 per cent. Interaction accumulation curves documented our sampling of interactions through observation of visits to be robust. Then, we included data on pollen from the body surface of flower visitors as an additional link 'currency'. This resulted in 98 new links, missing from the visitation data. Thus, the combined visit-pollen matrix got an increased C of 20 per cent. For the three networks, C ranged from 20 to 52 per cent, and thus the percentage of unobserved links (100 2 C) was 48 to 80 per cent; these were assumed forbidden because of linkage constraints and not missing because of under-sampling. Phenological uncoupling (i.e. non-overlapping phenophases between interacting mutualists) is one kind of constraint, and it explained 22 to 28 per cent of all possible, but unobserved links. Increasing phenophase overlap between species increased link probability, but extensive overlaps were required to achieve a high probability. Other kinds of constraint, such as size mismatch and accessibility limitations, are briefly addressed.