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Oikos 122: 1643–1648, 2013 doi: 10.1111/j.1600-0706.2013.00562.x © 2013 The Authors. Oikos © 2013 Nordic Society Oikos Subject Editor: Kailen Mooney. Accepted 17 April 2013 Spatial structure of ant–plant mutualistic networks Wesley Dáttilo, Paulo R. Guimarães Jr. and Thiago J. Izzo W. Dáttilo (wdattilo@hotmail.com) and T. J. Izzo, Depto de Ecologia e Botânica, Laboratório de Ecologia de Comunidades, Univ. Federal de Mato Grosso, Cuiabá, Mato Grosso, CEP 78060-900, Brazil. Present address for WD
  1643  Spatial structure of ant – plant mutualistic networks Wesley D á ttilo , Paulo R. Guimar ã es Jr. and Thiago J. Izzo W. D á ttilo (wdattilo@hotmail.com) and T. J. Izzo, Depto de Ecologia e Bot â nica, Laborat ó rio de Ecologia de Comunidades, Univ. Federal de  Mato Grosso, Cuiab á , Mato Grosso, CEP 78060-900, Brazil. Present address for WD: Inst. de Neuroetolog í a, Univ. Veracruzana - UV, Av. Dr. Luiz Castelazo s/n - CP: 91190, Xalapa, Veracruz, Mexico. – P. R. Guimar ã es Jr., Depto de Ecologia, Inst. de Bioci ê ncias, Univ. de S ã o Paulo, S ã o Paulo, S ã o Paulo, CEP 05508-900, Brazil. T e structure of mutualistic networks provides insights into ecological and coevolutionary dynamics of interacting species. However, the spatial e ff  ect has only recently been incorporated as a factor structuring mutualistic networks. In this study,  we evaluated how the topological structure and species turnover of ant – plant mutualistic networks vary over a spatial gradient. We showed that although the ant and plant composition of networks changed over space, the central core of generalist species and the structure of networks remained unaltered on a geographic distance of up to 5099 m in the south-ern Brazilian Amazon. T is finding indicates that independently of variation in local and landscape environmental factors, the nonrandom pattern organization of these interacting assemblages do not change. Finally, we suggest that a stable core can increase the potential for coevolutionary convergence of traits among species from both sides of the interaction within the community. T ese findings contribute to our understanding of the maintenance of biodiversity and coevolutionary processes. T e study of mutualistic networks has provided important insights into the mechanisms that contribute to the struc-tural organization of plant – animal interactions (Rezende et al. 2007, Morales and V á zquez 2008, V á zquez et al. 2009). Using measures of graph theory to characterize the network topology, several studies have found non-random patterns of interaction in a wide range of ecological interactions (Bascompte et al. 2003, Lewinsohn et al. 2006, Guimar ã es et al. 2007, V á zquez et al. 2009). Among their main fea-tures, some of these networks are highly nested and exhibit an asymmetrical pattern of interactions (Lewinsohn et al. 2006, Guimar ã es et al. 2007). T is indicates that species  with few interactions are a subset of highly connected spe-cies (Bascompte et al. 2003, T ompson 2005). Moreover, generalist species are more important for the stability and functioning of the system as a whole than are the peripheral species, mainly because the central core of species interacts  with virtually all species of the network (Bascompte et al. 2003, Guimar ã es et al. 2011). Recently, some theoretical and empirical studies have shown that when new species are introduced into a network, they can influence the ecological dynamics of the entire net- work (Olesen et al. 2002, Memmott et al. 2007, Aizen et al. 2008, D í az-Castelazo et al. 2010). T ese studies have focused on evaluating how the entry of invasive alien species a  ff  ects the structural organization of species interactions. However, because the geographical distributions and abundances of all interacting species of a community rarely coincide, the asso-ciations among them can also change over space ( T ompson 2005). T erefore, in order to understand the consequences of spatial variation in mutualistic networks, it is essential to determine how space modulates the dynamics of interact-ing species (Olesen and Jordano 2002, Burkle and Alarc ó n 2011). A next step in the analysis of species networks is to understand how species turnover a  ff  ect the organization of interacting assemblages.  We used the mutualistic interaction between ants and plants with extrafloral nectaries (EFNs) to evaluate how the structure of mutualistic networks varies through space. In ant – plant mutualistic networks plants produce nutri-tious liquid for ants, which respond by defending the plants against herbivores (Rico-Gray and Oliveira 2007). However, di ff  erent factors can change the nature of ant – plant interac-tions, such as competition (Bl ü thgen and Fiedler 2004), abundance and quality of resources (Rico-Gray and Oliveira 2007), seasonality of nectar production (D í az-Castelazo et al. 2004), and other biotic and abiotic factors (Rico-Gray et al. 2012, D á ttilo et al. 2013), and the spatial distribu-tions of ants on plants can be a  ff  ected by all these factors (Barton 1986, Heil et al. 2000, Apple and Feener 2001). In tropical forests, many plant species are spatially aggregated (Newbery et al. 1986, Condit et al. 2000, K ö hler et al. 2000), and ant workers forage and disperse only on small spatial scales (Fourcassi é et al. 2003). T erefore, is expected that the compositional similarity among plant communities should decrease as the distance between points increases, due to the limited dispersal of organisms and environmental gradients (Chave and Leigh 2002, Gilbert and Lechowicz 2004). Oikos 122: 1643–1648, 2013 doi: 10.1111/j.1600-0706.2013.00562.x © 2013 T e Authors. Oikos © 2013 Nordic Society Oikos Subject Editor: Kailen Mooney. Accepted 17 April 2013  1644 Here we hypothesiszed that the degree of high spatial aggregation of plants in tropical regions and the low mobility of ants would produce a mosaic of interactions with di ff  erent partners over a relatively small geographic space, and this mosaic could generate di ff  erences in the structure of these networks ( T ompson 2005, Morales and V á zquez 2008, Burkle and Alarc ó n 2011). In order to test this hypothesis, we examined 12 ant – plant mutualistic networks in the southern Brazilian Amazon, and analyzed their network topology. Subsequently, we calculated the dissimilarity of network topology over the geographic distance among sampling plots, in order to examine whether: 1) species turnover a  ff  ects the topological structure of ant – plant mutualistic networks, and 2) the core of generalist species remains stable at the geographic scale studied. Material and methods Study area  We conducted this study on S ã o Nicolau Farm (9 °  48 ′  S, 58 °  15 ′   W), located in the municipality of Cotrigua ç u, northern Mato Grosso State, Brazil. According to the K ö ppen classification, the climate is tropical humid (Am). Mean annual temperature is 24 °  C, mean annual relative humidity is 85%, and mean annual rainfall ranges from 2000 – 2300 mm (D á ttilo et al. 2012). Moreover, in this region there are two well-defined seasons, a rainy season from November to April, and a dry season from May to October. T e study area is a dense rainforest in the southern Brazilian  Amazon and recovers ca 7000 ha of continuous forest, sur-rounded by a much larger area of intact forest. T e topogra-phy is undulating, varying in elevation from 200 – 250 m. T e canopy reaches up to 45 m in height and the understory is rel-atively open and dominated by the palm Orbignya phalerata   (Arecaceae). In the Brazilian Amazon, it is usual to find between 18 and 53% of plant species in di ff  erent physio-gnomies having EFNs (extrafloral nectaries), and these plants may reach up to 50% of spatial coverage in a given physiognomy (Rico-Gray and Oliveira 2007). Data collection  All fieldwork was conducted in a site (module) managed by the Brazilian Research Program in Biodiversity (PPBio) (   http://ppbio.inpa.gov.br   ) on S ã o Nicolau Farm. T is PPBio module consists of six parallel north – south trails and two parallel east –  west trails. A permanent plot 250   25 m (6250 m ²  ) is located every 1 km along the trail, with a total of 12 sampling plots in the entire module. We considered each of the 12 sampling plots independent samples of ants and plants, generating 12 di ff  erent ant – plant interaction networks. T is is due to the fact that, ants and plants are sessile organisms, the distance among each sampling plot is enough to guarantee that one species i   found in a plot would never interact with one species  j   on another sampling plot.  We collected the data in December 2010 and January 2011. At each of the 12 sampling plots we looked for EFN plants from 0.5 m to 3 m tall. T is size was used because it is easily accessed by researchers without disturbance. On each plant, we recorded all occurrences of ants collecting liq-uids in EFN. We selected plants at least 10 m distant from each other, in order to minimize the possibility of collecting ants from the same colony that were foraging on di ff  erent plants. Plants and ants were identified to the lowest possible taxonomic level by morphologic comparisons with species deposited in collections from the Entomological Section of the Zoological Collection of the Universidade Federal de Mato Grosso, Brazil (CEMT), and the Herb á rio Centro-Norte Mato-Grossense (CNMT). Moreover, di ff  erent spe-cialists of these institutions also helped us. Data analysis To evaluate how topological properties of ant – plant mutualistic networks vary over space, we calculated the dis-similarity among the 12 sampling plots of the following metrics: connectance, network specialization and nestedness.  We chose these metrics because we consider it a way to com-pare community organization within our study and with others published previously. T e connectance ( C   ) is the pro-portion of possible links that are actually realized (Jordano 1987). We calculated the level of specialization networks using the specialization index ( H    2   ′   ), which ranges from 0 (minimum specialization) to 1 (maximum specialization) using the bipartite package (Dormann et al. 2009) in the R software ver. 2.13.1. T is index is robust to the number of interacting species and to changes in sampling intensity (more details in Bl ü thgen et al. 2006). We estimated the nestedness value of each network using the NODF met-ric (nestedness metric based on overlap and decreasing fill) (Almeida-Neto et al. 2008) in the ANINHADO software (Guimar ã es and Guimar ã es 2006). We tested the nested- ness observed for each network with 1000 networks gener-ated by null model II, in order to assess if the nestedness value observed was higher than that expected by random patterns of interaction. In this null model, we assume that the probability of an interaction occurring is propor-tional to observed number of interactions of both plant and ant species (Bascompte et al. 2003). We also calculated the nestedness value, standardizing the di ff  erence in richness, connectance and heterogeneity of interactions among the sampling plots, using the z-cores to allow cross-network comparisons. z-score is defined as: Z nodf      (x    µ  )/ σ  , where x   NODF value observed, µ     mean NODF value of randomized matrices, and σ     standard deviation of the randomized matrices (Ulrich et al. 2009).  We calculated the additive partitioning of diversity in ant – plant networks (Veech et al. 2002), to assess the spa-tial turnover among the sampling plots, in plant and ant species composition ( β  -diversity). From the total richness of the same trophic level found at two sampling plots ( γ    -  diversity), we calculated the α  -diversity, defined as: α     ( α   1      α   2  )/2, where α   1     species richness at sampling plot 1, and α   2     species richness at sampling plot 2. T en, we calcu-lated the β  -diversity, defined as: β     ( γ       α  ). We also calcu-lated the turnover of β  -diversity only for plants and ants on generalist core species. We defined core or peripheral species components of the networks through: Gc   ( k  i   k  mean )/ σ   k  ,  where k    i       mean number of links for a given plant/ant  1645species, k    mean      mean number of links for all plant/ant species in the network, and σ   k       standard deviation of the number of links for plant/ant species. Gc      1 are species with the larger number of interactions in relation to other spe-cies of the same trophic level, and are therefore considered as species constituting the generalist core. Gc      1 are species  with lower number of interactions in relation to other spe-cies of the same trophic level, and are therefore considered as species constituting the periphery of networks.  We used Mantel tests to determine the existence of a relationship between the turnover in di ff  erent network met-rics described above, and the matrices of geographic distances among all the sampling plots. Mantel tests were done using the vegan package (Oksanen et al. 2007) in the R-project software ver. 2.13.1, using Euclidean distance to calculate the dissimilarity in the metrics and geographic distances among sampling plots. In these analyses, we also tested the correlation coe ffi  cient (r) using this analysis. We com-posed all graphs using the software GraphPad Prism ver. 5.0 (Motulsky 1999). Results In this study, we recorded 70 plant species (or morphospe-cies) with EFNs, belonging to 24 genera and 16 families. T e family Bignoniaceae comprised 26.3% of plant species, followed by 22.8% Mimosaceae and 10.5% Caesalpiniaceae. T e plant species richness per sampling plot was 21.41   3.77 (mean   SD). For ants, we recorded 121 species in 19 genera and eight subfamilies. T e subfamily Myrmicinae comprised 42.28% of ant species, followed by 26.1% for Formicinae and 14.9% for Dolichoderinae. T e ant species richness per sampling plot was 23.16   5.85. A list of all ant – plant interactions recorded can be viewed in the Supplementary material Appendix A1. In general, the mean connectance value of 12 di ff  erent ant – plant networks was 0.140   0.035 (mean   SD) and the network specialization was 0.088   0.049. All networks were significantly nested in comparison to randomized matrices (NODF metric: 21.01   4.40, p   0.05, and z-score 3.63   1.50; null model: 13.76   2.81). On the spatial scale studied, we found no trend in variation in connectance (Mantel statistic r   0.044, p   0.374) and network specialization ( H     2    ’ ) (Mantel statis-tic r   0.004, p   0.457) across communities (Fig. 1A – B). For nestedness, we obtained di ff  erent results before and after accounting for the species richness, connectance and het-erogeneity of interactions. We found no trend in variation in NODF values across space (Mantel statistic r   0.078, p   0.283). However, we observed significant correlations in nestedness calculated by the z-score (Mantel statistic r   0.315, p   0.01) (Fig. 1C – D), indicating after control-ling for other network patterns the degree of nestedness vary across space.  We observed a positive relationship between plant and ant composition and distance ( β  -diversity) (plants: Mantel statistic r   0.401, p   0.01, ants: Mantel statistic r   0.307, p   0.013) (Fig. 2A – B). However, we observed no trend in the turnover in core species composition, for both plants and ants, over the geographic distance (plants: Mantel statistic r   0.007, p   0.437, ants: Mantel statistic r      0.088, p   0.734 (Fig. 2C – D). Indeed, the core species composition  was very stable for both ants and plants. Of 121 ant species,  just one to three species of ants were present in the generalist Figure 1. Relationship among the dissimilarity of network topology: (A) connectance, (B) network specialization, (C) nestedness (NODF metric) and (D) nestedness by z-score metric, with dissimilarity of geographic distance of 12 sampling plots examined on S ã o Nicolau Farm, Mato Grosso State, southern Brazilian Amazon. Correlation coe ffi  cient ( r   ) and significance (p computed using Mantel tests) are also shown (n   66 points in each of the metrics calculated).  1646  Figure 2. Relationship among the dissimilarity of: (A) plant species composition, (B) ant species composition, (C) plant core composition and (D) ant core composition, with dissimilarity of geographic distance of 12 sampling plots examined on S ã o Nicolau Farm, Mato Grosso State, southern Brazilian Amazon. Correlation coe ffi  cient ( r   ) and significance (p computed using Mantel tests) are also shown (n   66 points in each of the metrics calculated). core of networks, and the species  Azteca   sp. 2, Brachymyrmex   sp. 1 and Crematogaster   sp. 8 were present in the core at more than 58% of the sampling plots. For plants, one or two species were present in the generalist core of networks, and Inga   sp. 12,  Mabea   sp. 2, Protium  sp. 1 and Stryphno-dendron  sp. 1 were present in the core in more than 66% of the sampling plots. Discussion Previous studies have shown that disparate mutualistic net- works of free-living species have a nested and asymmetrical pattern, such as plant – pollinator, fruit – frugivore, ant – plant, clownfish – anemone and marine fish cleaning symbioses (reviewed by Hagen et al. 2012). However, few studies have evaluated how the structure of these networks varies over spatial gradients. T e few studies that evaluated the role of spatial variations in mutualistic networks showed that local and landscape environmental factors, as well as spatial aggregation and animal mobility, are important factors that structure the plant – animal interactions (Morales and V á zquez 2008, Burkle and Alarc ó n 2011). In this study, standardizing the network metrics, collection e ff  ort and habitat, we showed that for ant – plant mutualistic networks, the structure remains unaltered over the spatial scale stud-ied. In addition, although the ant and plant composition of networks changes over space, the core of generalist spe-cies remains stable along the 5099 m measured in a dense rainforest in the southern Brazilian Amazon. Studying ant – plant networks on di ff  erent islands, Sugiura (2010) showed that the size of islands strongly influenced the connectance, the nestedness and the number network interactions. However, the islands studied by Sugiura (2010) are quite di ff  erent from the sites we studied, once each island is separated from the other by a impermeable matrix. It makes the landscape much more heterogeneous than a continuous forest or other more connected landscapes. On the other hand, a study made by Chamberlain and Holland (2009) in eight locations of the Sonoran Desert, show that despite the species richness of ant – plant networks vary among sites, the number of interactions per species remains similar. T ese same authors also found that the correlation between the degree and the ant body size also remains stable over all eight communities studied. In our study, within a relatively homo-geneous forest inserted in southern Brazilian Amazon, there are almost no changes in the networks descriptors among the sample points. T erefore, we hypothesize that the topology of ant – plant networks tend to be more stable over homo-geneous environments where there is no direct spatial limi-tation (i.e. deserts and tropical rainforests) than in isolated environments such as islands. In tropical rainforests, the main factors that explain the distribution and diversity of ants are competitive interac-tions, habitat complexity (abundance of food and nesting sites), climate stability and natural barriers that prevent the dispersal of ant queens (Benson and Harada 1988, H ö lldobler and Wilson 1990). T us, along a spatial scale, di ff  erent biotic and abiotic factors can influence the rich-ness and diversity of interactions between ants and plants in several ways (D í az-Castelazo et al. 2004, Rico-Gray and Oliveira 2007, Rico-Gray et al. 2012, Lange et al. 2013). Here, the values of connectance varied widely among the sampling plots, which influenced the values of
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