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Current Biology 17, 341–346, February 20, 2007 ª2007 Elsevier Ltd All rights reserved DOI 10.1016/j.cub.2006.12.039 Report Specialization, Constraints, and Conflicting Interests in Mutualistic Networks Nico Blüthgen,1,* Florian Menzel,1 Thomas Hovestadt,1,2 Brigitte Fiala,1 and Nils Blüthgen3,4 1 Department of Animal Ecology and Tropical Biology University of Würzburg Würzburg, 97074 Germany 2 Field Station Fabrikschleichach University of Würzburg Rauhenebrach, 96181 Germany 3 Institute of Molecular Neurobiology Free University of Berlin Berlin, 14195 Germany Summary The topology of ecological interaction webs holds important information for theories of coevolution, biodiversity, and ecosystem stability [1–6]. However, most previous network analyses solely counted the number of links and ignored variation in link strength. Because of this crude resolution, results vary with scale and sampling intensity, thus hampering a comparison of network patterns at different levels [7–9]. We applied a recently developed [10] quantitative and scale-independent analysis based on information theory to 51 mutualistic plant-animal networks, with interaction frequency as measure of link strength. Most networks were highly structured, deviating significantly from random associations. The degree of specialization was independent of network size. Pollination webs were significantly more specialized than seeddispersal webs, and obligate symbiotic ant-plant mutualisms were more specialized than nectar-mediated facultative ones. Across networks, the average specialization of animal and plants was correlated, but is constrained by the ratio of plant to animal species involved. In pollination webs, rarely visited plants were on average more specialized than frequently attended ones, whereas specialization of pollinators was positively correlated with their interaction frequency. We conclude that quantitative specialization in ecological communities mirrors evolutionary trade-offs and constraints of web architecture. This approach can be easily expanded to other types of biological interactions. Results and Discussion Ecological specialization in a food web or other interaction networks is commonly defined by the number of *Correspondence: [email protected] 4 Present address: Manchester Centre for Integrative Systems Biology (MCISB), Manchester Interdisciplinary Biocentre (MIB), Manchester M1 7ND, United Kingdom. realized ‘‘links.’’ For instance, predators are specialized if they attack only a few prey species, and specialized flowers are those that are visited by few pollinator species only. This concept has been extended to measure the degree of specialization of entire networks (‘‘connectance’’), where associations are classified as ‘‘present’’ or ‘‘absent,’’ but all links are considered equally important [1–3, 6, 8, 11–13]. However, such qualitative measures ignore the importance of variation in interaction strength for community dynamics [5, 14, 15]. Moreover, they are highly sensitive to sampling intensity and network size [7–10, 15]. Therefore, weighted links have been included in quantitative descriptors of different types of webs [5, 14, 16]. In bipartite ecological networks, the frequency of an interaction between two species is a meaningful measure of its strength (Figure 1) and has been shown to represent a suitable surrogate for mutualistic services such as pollination success [17]. In this article, we use two measures inspired by information theory to quantify specialization within and across networks. Technical properties of these indices have been explored in a recent methodology article [10] showing that—in contrast to other quantitative measures—they are scale independent and largely insensitive to sampling effort. Unlike previous measures, we define the overall degree of specialization in each web as the deviation from an expected probability distribution of interactions (evaluated by the standardized two-dimensional entropy H0 2), and individual species’ specialization as the deviation from a conformity expected by the overall utilization of potential partners (standardized Kullback-Leibler distance, d0 i) [10]. The expected null distribution assumes that all species interact with their partners in proportion to their total frequencies, whereas the heterogeneity (evenness) of interactions in previously proposed quantitative metrics such as diversity indices [16, 18] varies with the partner availabilities in an uncontrolled way and is thus less suitable in the context of network analyses (see also [10, 19]). On the basis of these standardized quantitative measures, we explored 51 networks, covering four types of mutualistic plant-animal associations, for patterns of specialization on the level of the entire network [2, 8], the community of each of the two parties (guild level) [5], and the level of species [9]. Network Level Across all networks, the overall degree of specialization (H0 2) covered a broad range (Table S1 in the Supplemental Data available online). All networks showed a significantly higher degree of organization than simulated networks, where partners were associated randomly (all p % 0.001), except for a single network of loosely associated ants and bromeliads [20] (p = 0.31). Pollination mutualisms were significantly more specialized than seed-dispersal mutualisms (Figure 2), corroborating a previous qualitative analysis [2] and expected on the basis of evolutionary considerations [21]. Plants may Current Biology 342 Figure 1. Visualization of Two Quantitative Networks A pollinator web and an ant-plant association are displayed (webs 6 and 37 in Table S1). Widths of links are scaled in relation to interaction frequencies, bar sizes to total interaction frequencies. Both webs are regarded specialized, but the degree of specialization is lower in the pollinator web (H0 2 = 0.46) compared to the myrmecophyte web (H0 2 = 0.84). Note that the former web is asymmetric (more pollinator than plant species), whereas the latter is symmetric. benefit disproportionately more from specialized pollinators, corresponding to the likelihood that each individual pollinator successively visits conspecific plants to maintain both male and female reproductive success of a plant, thereby reducing maladaptive heterospecific pollen transfer ([22, 23], but see [11]). In contrast to pollination, the efficacy of seed dispersal to suitable sites does not depend on the specialization of the dispersal agent [21]. A broader spectrum of seed dispersers may even be profitable from the plant’s perspective to avoid aggregation of seeds [24] and generate fat-tailed dispersal kernels [25], which should be favored by natural selection under many conditions [26]. Correspondingly, obligate specialized mutualisms are known from a number of pollination systems [23] but seem to be largely absent in seed-disperser systems [21]. In ant-plant networks, there is an important distinction between completely facultative associations, based on extrafloral nectaries, and symbiotic associations, where ant colonies, often obligatory, inhabit plants (myrmecophytes) [27, 28]. For obligate and symbiotic mutualisms, a higher degree of specialization is generally expected [4, 29]. This differentiation is supported by our analysis: Ant-plant mutualisms involving myrmecophytes were significantly more specialized than those involving extrafloral nectaries (Figure 2). Obligate associations are common among myrmecophytic associations, sometimes causing irreversible dependence on a single partner species. Myrmecophytes represent a gradient from plants that offer neither specific structures nor specific food rewards to support their facultative ant inhabitants [20, 28] to cases where only few ant species are adapted to actively bite small entrance holes into preformed domatia and where colonies are fully supplied by nutritious plant-produced food bodies and never forage outside their host plants [27, 30]. Obligate-myrmecophytic symbioses represent the most specialized networks across all systems examined in this study. In contrast to other networks, such associations often remain uninterrupted for several generations, opening the opportunity for the evolution of tight specialization. In contrast, extrafloral nectaries usually attract a spectrum of largely opportunistic ants, where the accessible nectaries seem to offer little structural plasticity to facilitate specialization except for some degree of biochemical differentiation [31, 32]. This dissimilarity between the two types of ant-plant mutualisms is particularly evident between nectary-bearing and myrmecophytic species from the same genus [27, 32]. The gradient from facultative to obligate mutualisms is thus largely associated with an increasing H0 2. The degree of specialization did not show a significant trend across networks of different dimensions (Figure 3) (Spearman rank correlations for each of the four network types, all 20.48 % rS % 20.10, p R 0.15). Given that H0 2 is mathematically independent of web size [10], the lack of a correlation between web size and H0 2 indicates that species-rich and species-poor real biological systems (or smaller fractions of a system) do not inherently differ in their degree of specialization between partners. This novel finding contrasts with the hyperbolic decline of Figure 2. Network-Level Specialization Overall specialization (H0 2) in 51 mutualistic networks. Box plots show median, quartiles, and range of the networks analyzed (number of networks in parentheses). Asterisks show significant difference between types according to a t test (*** p < 0.0001, both t R 5.2, Welsh corrected for unequal variances). Mutualistic Interaction Webs 343 Figure 3. Relationship between Network Size and Specialization Overall specialization (H0 2) of 51 networks plotted over network size (plant plus animal species, log scale). Networks include pollination (yellow), seed-dispersal (black), ant-myrmecophyte (green), and ant-nectar plant (red) associations. the qualitative connectance index over increasing network size in different studies [2, 8, 33, 34], a decline that was also found if applied to the dataset used here (see Supplemental Data). Guild Level Within mutualistic networks, differences between the average degree of specialization of both parties (i.e., plants versus animals) could be a consequence of conflicting interests. Consumers would only benefit from increased specialization if this process went along with greater resource-use efficacy and/or reduced interspecific competition, e.g., by improved resource detoxification, reduced handling effort, or specific search images, and outweighed the costs of increased foraging time. If resources were very similar, optimal foraging theory would thus predict selection for generalization in both frugivores and pollinators [11, 21, 23]—the latter conflicting with the plant’s interest in specialized pollinators. However, both parties did not vary independently in their degree of specialization, and average specialization of plants hd0 ii and animals hd0 ji was largely reciprocal (Pearson’s r2 = 0.71, p < 0.0001, n = 51 webs). Moreover, differences between hd0 ii and hd0 ji are strongly predicted by the asymmetry of the matrix (r2 = 0.62, p < 0.0001) (Figure 4). In those webs where animal species were more numerous than plants, animals showed a lower degree of specialization (hd0 ji < hd0 ii) and vice versa. This effect was even stronger (r2 = 0.93) for simulated networks with randomly assigned associations (Figure S2). Pollinator and ant-nectar webs were highly asymmetric, involving a much higher number of Figure 4. Relationship between Network Asymmetry and Specialization Asymmetry of the number of plant (I) and animal (J) species in each web (network asymmetry) is given as (J 2 I) / (I + J) and equals zero for balanced webs (same number of animal and plant species). Specialization asymmetry between plants and animals is given as (hd0 ji – hd0 ii) / (hd0 ii + hd0 ji), based on weighted means across all species (plants i or animals j) in a web. Real networks include pollination (yellow), seed-dispersal (black), ant-myrmecophyte (green), and ant-nectar plant (red) associations. The regression line is plotted for randomly generated networks (fixed total interactions per species, mean values from 100 randomizations per web, r = 20.97). Current Biology 344 pollinator species (usually insects) or ant species than plant species (on average 3.6:1 and 3.8:1, respectively). Consequently, pollinators were significantly less specialized on plants than plants on pollinators, and ants were significantly less specialized on plants with nectaries than vice versa (paired t test; pollinators: t = 3.8, p = 0.001; ants: t = 3.2, p = 0.01). In contrast, networks involving seed dispersers (mostly vertebrates) as well as ant-myrmecophyte associations were usually more symmetric (1.2:1 and 1.6:1, respectively) and did not show significant unequal specialization of both mutualists (both p R 0.38). Hence, the network architecture severely constrains average specialization between two parties irrespective of the type of association, a result that is expected given the mathematical relationships of the indices in their unstandardized form [10]. Such constraints on specialization have been largely overlooked so far, but are important in other network metrics as well, including the ‘‘number of links’’ or quantitative ‘‘dependences’’ used elsewhere [5] (see Supplemental Data). However, with architectural constraints accounted for, residual variation from the linear regression (line shown in Figure 4) depicted differences between networks depending on the type of association. Pollinators were significantly more specialized than expected by the asymmetry (mean residuals > 0, t = 4.7, p < 0.001), whereas ants visiting extrafloral nectaries were more generalized than expected (residuals < 0, t = 22.4, p < 0.05). In seed-dispersal and ant-myrmecophyte networks, differences between animals and plants in specialization did not deviate significantly from the expected on the basis of asymmetry (both p R 0.10). The increased residual specialization observed in pollinators, but not seed dispersers, thus corresponds to the plant’s differential interest in these types of mutualists. Species Level Although the average degree of specialization in a community may be constrained to a large degree, this does not apply to single elements of the network, i.e., the local population of each species. For example, disparities in specialization of pollinators and plants were particularly pronounced for the rarely interacting species in a network. Across pollination networks, there was a significantly positive correlation between pollinator frequency and specialization, but a significant negative correlation between plant frequency and specialization (Table 1). We also found a significantly positive correlation between ant frequency and specialization in ant-myrmecophyte webs but not in any of the other networks investigated. Previous qualitative network analyses showed an invariable negative correlation between frequency and specialization (estimated as the inverse of the number of links), a correlation that can be explained by a null model [9] and is strongly affected by sampling effort. In contrast, our quantitative analysis demonstrates a highly variable relationship between frequency and quantitative specialization, one that differs between network types. Our results suggest that plant populations with low visitation frequencies, presumably those that occur in low densities in a community, have a particularly unconventional spectrum of visitors. Rare plants may be particularly sensitive for two fitness costs: subsequent Table 1. Relationship between Frequency and Specialization of Plant and Animal Species Plants Pollination 20.20* (20.30 2 20.11) (n = 20) Seed dispersal 0.06 (20.19 2 0.23) (n = 7) Ant-myrmecophyte 0.14 (20.16 2 0.40) (n = 14) Ant-nectar plant 20.10 (20.23 2 0.02) (n = 7) Animals 0.27* (0.15 2 0.37) (n = 21) 0.00 (20.27 2 0.24) (n = 8) 0.51* (0.24 2 0.71) (n = 13) 20.04 (20.27 2 0.16) (n = 8) Effect sizes derived from linear correlation coefficients for each network by using meta analysis based on Fisher’s z-transformation. Mean back-transformed r values are shown with range of 95% bootstrap confidence intervals and number of webs (n) in parentheses. Asterisks indicate significant deviation from r = 0. pollen deposition on (more common) plants and clogging of the stigma by pollen from (common) plants [22]. Increased specialization and reduced overlap with visitors of common flowers may reduce such costs. The positive correlation between animal abundance and specialization indicates that resource partitioning is particularly pronounced among the most active species, whereas rarely interacting species use their resources more opportunistically. Conclusions Three general conclusions can be drawn from the results. (1) The network-level specialization is unaffected by network size and form and depicts biologically meaningful system-specific differences. Our results demonstrate that the plant’s interest in specialized pollen transfer but generalized fruit dispersal conformed to the overall specialization of the respective networks. Networks involving facultative associations were less specialized than more obligate ones, particularly in ant-plant webs. (2) The average degree of specialization of both network parties is highly reciprocal, i.e., one party cannot specialize or generalize on the other party without concomitant changes in the specialization within the other party itself. Moreover, differences between network parties are largely driven by constraints in the network architecture (unequal species numbers). Such constraints cause unequal degrees of specialization as well as asymmetric dependences between both parties. Residual differences in specialization still contain meaningful information, e.g., pollinators were more specialized than expected from architectural constraints only. (3) Species-level specialization is less affected by these constraints and may indicate differential roles of rare and common species in a network. Such patterns may potentially unveil density-dependent selection pressures or feedback mechanisms between frequency and specialization. The hypothesis that natural selection drives specialization between interacting mutualists or antagonists has been debated for a long time [4, 11, 35]. Whereas generalists are obviously much less limited by resource or partner availability, specialists are usually better adapted to effectively use their selected resources. For antagonistic relationships (e.g., predator-prey, hostparasite, and plant-herbivore interactions), defensive Mutualistic Interaction Webs 345 mechanisms of hosts or prey substantially constrain the choices of their enemies, enforcing specialization [36]. Trade-offs between specialization and generalization may occur in food webs [6], but are also complex among mutualists [37], where selective pressures on partner choices may be variable and shaped by coevolutionary complementarity or convergence [4]. Refined analyses and more fine-grained empirical data, particularly at the level of individuals, may reveal additional insights into the evolution of a broad spectrum of interaction webs and their ecological fragility. Experimental Procedures We analyzed the degree of specialization for 51 published and unpublished interaction webs that included frequency data, representing a broad range of mutualistic relationships between plant-based resources and their consumers or inhabitants and covering six continents (Supplemental Data). Although all webs were obviously dominated by mutualists, several datasets may contain nonmutualistic species, e.g., nectar robbers and seed predators. Twenty-seven datasets were obtained from the Interaction Web Database (http:// www.nceas.ucsb.edu/interactionweb). For each network containing a total of I plant and J animal species, we obtained the two-dimensional Shannon entropy for the observed association matrix [10] as H2 = 2 I X J X I X J X pij ,ln pij ; with pij = 1: i=1 j=1 pij = aij X I X J aij : i=1 j=1 Our specialization index H0 2 normalized H2 between the minimum and maximum entropy for associations leading to the same matrix row and column totals as H02 = H2max 2 H2 : H2max 2 H2min For quantification of the degree of specialization of each species (say plant i), the proportional distribution of the interactions with each animal (j), p0 ij, was compared with the proportion of the total number of interactions where j was involved, qj, by using the Kullback-Leibler measure di = J X p0ij ,ln j=1 where p0ij = aij = J X p0ij ; qj aij ; thus j=1 I X i=1 Supplemental Data Supplemental Data include additional results and data sources, three figures, and one table and are available with this article online at: http://www.current-biology.com/cgi/content/full/17/4/341/DC1/. i=1 j=1 In this equation, i represents one plant species and j one animal species. The number of interactions between i and j (aij), e.g., the number of recorded visits of pollinator j on plant i, is divided by the total interaction frequencies recorded for the entire web, thus and qj = was compared to a null model (randomly associating all species with the total number of interactions being fixed per species, 104 permutations) by using an established algorithm ([38], see [10]). Fixed marginals have been advocated as suitable constraints for null hypotheses in qualitative webs [6, 39]. In addition, we suggest that total interaction frequencies may better reflect variation in animal activity or plant resource availability for the actual associations than would external estimates of local population densities [10]. Such independent measures of the species’ local abundances for both parties have not been provided by most empirical studies so far. All calculations can be performed online at http://itb.biologie. hu-berlin.de/wnils/stat/. To analyze the relationship between specialization and interaction frequency at the species level, we calculated linear correlation coefficients between log(total number of interactions) and arcsin(O d0 i) across all species of a guild per network and then quantified the combined mean effect size from all networks of the same type by using standard meta-analysis tools (MetaWin 2.0; Fisher’s z-transformation, sample size as number of species, fixed effects); 95% confidence intervals were based on bootstrapping with 999 iterations, bias-corrected. To reduce a bias due to single, very large networks, we removed, prior to analysis, datasets where the number of species was more than twice as large as in the second-largest network (four cases). aij = I X J X J X p0ij = 1; j=1 aij ; thus i=1 j=1 J X qj = 1: j=1 This measure was normalized as d0i = di 2 dmin : dmax 2 dmin Specialization of the plant community (guild level) was obtained as the weighted mean hd0 ii, for which each plant species i was weighted by its total number of interactions. Specialization of animals was calculated in the same way (d0 j and hd0 ji). Maximum and minimum values for H2, di, and dj were computed algorithmically by using the fixed total number of interactions of each species as a constraint [10]. Resulting H0 2, d0 i, and d0 j range between 0.0 for extreme generalization and 1.0 for extreme specialization. For each network, H2 Acknowledgments We thank D. Vázquez, K. Fiedler, and K.E. 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Please cite this article in press as: Blüthgen et al., Specialization, Constraints, and Conflicting Interests in Mutualistic Networks, Current Biology (2007), doi:10.1016/j.cub.2006.12.039 Supplemental Data Specialization, Constraints, and Conflicting Interests in Mutualistic Networks S1 Nico Blüthgen, Florian Menzel, Thomas Hovestadt, Brigitte Fiala, and Nils Blüthgen Supplemental Results and Data Sources All 51 mutualistic networks used in this analysis and their sources are listed in Table S1. Previous analyses invariably demonstrated a hyperbolic decline of the qualitative ‘‘connectance’’ index with increasing network size, a pattern that has considerably constrained the usefulness of this qualitative measure [S1]. Connectance has been defined as the number of realized links divided by the number of cells in the association matrix (plant species 3 animal species) [S2, S3]. For the dataset used here, connectance declined significantly with increasing matrix size within each of the network types (all Spearman rS % 20.64, p % 0.04) except for the small set of ant-nectar webs (rS = 20.59, p = 0.13) (Figure S1). Asymmetries in the average quantitative specialization of plants and animals are a function of the network form (ratio of plant to animal species), an effect that is particularly pronounced in random associations (Figure S2). Neither the reciprocity of average specialization levels between guilds nor the effect of architectural constraints on specialization is confined to the quantitative metrics used here. Specialization as defined in the traditional, qualitative sense (i.e., number of partner species) is strictly dependent on the web architecture: In balanced webs, when an equal number of animal species (J) and plant species (I) is involved (I = J), the mean number of associated partners (links) for each animal (LJ ) equals that for plants (LI ). Consequently, qualitative specialization is a direct function of the network asymmetry, as LJ =LI = I=J. Differences in the number of partner species also affect quantitative ‘‘dependences’’ between plants and animals that were used in previous analyses by Bascompte et al. [S4], an effect that has not been shown previously. The dependence of plant i on animal j (bij) and the reciprocal dependence of j on i (bji) is estimated from of the interaction frequency (aij) between them as bij = aij J P aiq and bji = q=1 aij : I P apj p=1 It can be shown that the average dependence of a guild across all putative interactions (I $ J, including all cases where aij= 0) is a simple function of the number of species, because bij = 1=ðIJÞ 1 = J J and bji = 1=ðIJÞ 1 = : I I Consequently, in square matrices (I = J), expected average differences between the two parties (bij 2 bji) are zero, but become stronger with increasing asymmetry in rectangular networks in both directions (I > J or J > I). This effect is also evident across empirical webs (Figure S3). Supplemental References S1. Blüthgen, N., Menzel, F., and Blüthgen, N. (2006). Measuring specialization in species interaction networks. BMC Ecol. 6, 9. S2. Jordano, P. (1987). Patterns of mutualistic interactions in pollination and seed dispersal: Connectance, dependence asymmetries, and coevolution. Am. Nat. 129, 657–677. S3. Olesen, J.M., and Jordano, P. (2002). Geographic patterns in plant-pollinator mutualistic networks. Ecology 83, 2416–2424. Figure S1. Relationship between Network Size and Connectance Qualitative connectance (proportion of realized links of the total number of possible links) of 51 networks plotted over network size (plant plus animal species, log scale). Networks include pollination (yellow), seed-dispersal (black), ant-myrmecophyte (green), and ant-nectar plant (red) associations. Please cite this article in press as: Blüthgen et al., Specialization, Constraints, and Conflicting Interests in Mutualistic Networks, Current Biology (2007), doi:10.1016/j.cub.2006.12.039 S2 Figure S2. Relationship between Web Asymmetry and Specialization Each point represents the mean value (6 standard deviation [SD]) of 100 randomized networks simulated from the set of 51 natural networks (maintaining the same total interaction frequencies per species). Asymmetry of the number of plant (I) and animal (J) species in each web is given as (J 2 I)/(I + J), asymmetry in specialization between plants i and animals j as (hd0 ji 2 hd0 ii) / (hd0 ji + hd0 ii). Regression line (r = 20.97) was used for Figure 4 in the main text and for calculating residuals of real networks. Figure S3. Relationship between Web Asymmetry and Dependence Asymmetry of the number of plant (I) and animal (J) species in each web is given as (J 2 I)/(I + J), and asymmetry in dependence between plants i and animals j is given as (bji 2 bij)/(bij + bji). Data were obtained from 26 networks [S4], including pollination (yellow) and seed-dispersal (black) associations (means and 95% confidence intervals for all realized interactions). The mean dependence asymmetry across all realized interactions of a web (aij > 0) is strongly linearly predicted by network asymmetry (r = 0.97, p < 0.0001). Please cite this article in press as: Blüthgen et al., Specialization, Constraints, and Conflicting Interests in Mutualistic Networks, Current Biology (2007), doi:10.1016/j.cub.2006.12.039 S3 Table S1. Mutualistic Networks Analyzed Number Reference Taxonomic Focus Location (several families) (several families) (several families) (several families) (several families) (several families) (several families) (several families) (several families) Asclepiadaceae (several families) (several families) Syrphidae (several families) (several families) (several families) (several families) (several families) (several families) (several families) (several families) Canada Sweden Australia Japan Britain Canada USA Azores Mauritius South Africa USA Canada Germany Argentina Argentina Argentina Argentina Argentina Argentina Argentina Argentina Birds Mammals Birds, Mammals Ants Birds, Miconia, Psychotria Birds Birds Birds Papua N.G. Kenya Ivory Coast West Malaysia Panama Plants Animals m hd0 Plantsi hd0 Animalsi H0 2 Pollination 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 Barrett and Helenurm [S5] Elberling and Olesen [S6] Inouye and Pyke [S7] Kato et al. [S8] Memmott [S9] Mosquin and Martin [S10] Motten [S11] Olesen et al. [S12] Olesen et al. [S12] Ollerton et al. [S13] Schemske et al. [S14] Small [S15] Ssymank [S16] Vázquez and Simberloff [S17] Vázquez and Simberloff [S17] Vázquez and Simberloff [S17] Vázquez and Simberloff [S17] Vázquez and Simberloff [S17] Vázquez and Simberloff [S17] Vázquez and Simberloff [S17] Vázquez and Simberloff [S17] 12 23 42 91 25 11 13 10 14 9 7 13 88 10 9 9 10 8 8 7 8 102 118 91 679 79 18 44 12 13 56 32 34 75 29 33 27 29 35 26 24 27 550 383 1459 2392 2183 134 2225 1139 1512 594 299 992 4837 677 613 1130 515 719 286 761 592 0.61 0.48 0.54 0.65 0.27 0.51 0.43 0.50 0.19 0.43 0.33 0.54 0.45 0.65 0.64 0.90 0.56 0.53 0.78 0.65 0.50 0.40 0.29 0.59 0.37 0.16 0.36 0.34 0.46 0.25 0.27 0.18 0.39 0.47 0.63 0.55 0.65 0.39 0.26 0.69 0.84 0.52 0.55 0.33 0.60 0.48 0.24 0.46 0.43 0.53 0.38 0.43 0.34 0.55 0.47 0.71 0.78 0.85 0.60 0.57 0.74 0.79 0.70 31 219 34 33 17 9 33 48 51 20 1189 3730 17575 448 492 0.22 0.20 0.16 0.27 0.16 0.28 0.43 0.15 0.28 0.17 0.26 0.39 0.18 0.24 0.21 65 29 11 14 19 14 2180 19946 7434 0.20 0.21 0.38 0.28 0.29 0.21 0.30 0.30 0.47 4 5 7 8 9 13 5 14 18 51 39 71 111 242 388 0.23 0.16 0.44 0.89 0.51 0.14 0.23 0.23 0.76 0.26 0.23 0.27 0.40 0.89 0.47 Seed dispersal 22 23 24 25 26 Beehler [S18] Engel [S19] Hovestadt [S20] Kaufmann [S21] Poulin et al. [S22] 27 28 29 Snow and Snow [S23] Snow and Snow [S24] Sorensen [S25] Trinidad Britain Britain Ant-myrmecophyte 30 31 32 33 34 Blüthgen et al. [S26] N.B., unpublished data Cabrera and Jaffé [S27] Davidson et al. [S28] Dejean et al. [S29] 35 36 37 38 39 40 41 42 43 Fiala et al. [S30] Fiala et al. [S30] Fiala et al. [S30] Fiala et al. [S30] Fiala et al. [S30] B.F., unpublished data B.F., unpublished data Fonseca and Ganade [S31] Yu and Davidson [S32] Bromeliaceae Melastomataceae Melastomataceae (several families) Bromeliaceae, Orchidaceae Macaranga Macaranga Macaranga Macaranga Macaranga Macaranga Macaranga (several families) Cecropia Venezuela Ecuador Venezuela Peru Mexico Borneo Borneo Borneo Sumatra West Malaysia Borneo Borneo Brazil Peru 9 10 6 7 4 6 7 16 7 7 8 6 4 5 7 5 25 4 349 173 98 78 183 88 88 417 155 0.72 0.74 0.74 0.57 0.71 0.96 0.53 0.72 0.45 0.77 0.87 0.76 0.79 0.77 0.97 0.72 0.78 0.54 0.80 0.86 0.84 0.80 1.00 0.99 0.83 0.80 0.61 Philodendron, Dioclea (several fam.), incl. flowers (several families) (several families) (several families) (several families) Passiflora, Mimosa Euphorbiaceae Venezuela Australia 6 51 53 41 180 644 0.36 0.18 0.11 0.20 0.31 0.13 Borneo Borneo West Malaysia West Malaysia French Guiana Papua N.G. 15 22 11 24 3 5 14 28 16 35 37 17 267 324 121 315 1661 246 0.14 0.34 0.37 0.31 0.24 0.22 0.17 0.21 0.26 0.19 0.08 0.13 0.19 0.23 0.33 0.21 0.23 0.24 Ant-nectar plant 44 45 Blüthgen et al. 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