The Phylogenetic Structure of Plant-Pollinator Networks Increases with Habitat Size and Isolation
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S1 Supporting Information The phylogenetic structure of plant-pollinator networks increases with habitat size and isolation Marcelo A. Aizen, Gabriela Gleiser, Malena Sabatino, Luis J. Gilarranz, Jordi Bascompte, and Miguel Verdú Content Supporting Materials and Methods Tables S1-S14 Figures S1-S5 S2 Supporting Materials and Methods Landscape’s human transformation The fertile Austral Pampas’ region, where the study sierras are located, was effectively colonized by criollos of Spanish descent between 1820 and 1830, and the land divided among the first “estancieros”, whose main activity was cattle-raising. The transformation from pasture to cropland on the plains surrounding the sierras occurred at the end of the 19th century associated with the onset of the big European immigration to Argentina (Barsky & Gelman 2001). As it happened across the Pampas, a relatively diverse agriculture dominated by wheat was replaced, starting in the late seventies, by one monopolized by soybean (Aizen et al. 2009). Today the sierras emerge as true islands of diversity amidst a relatively uniform agriculture matrix (Fig. 1). Threshold distance Functional connectivity depends on the dispersal capacity of individuals. Thus, it is difficult to determine a priori the threshold distance below which two given habitat patches are expected to be "connected" based solely on theoretical expectations, particularly for community attributes. An empirical approach frequently used in landscape ecological studies is to identify the threshold distance that maximizes the variance explained by the correlation between a given connectivity metric and a population/community attribute (e.g. Steffan-Dewenter et al. 2002). We followed this approach by estimating the relation between phylogenetic signals in interactions and estimates of patch betweenness centrality for each of the 12 focal sierras (Table S1), considering threshold distances between 10 and 20 km (Table S2). This range of distances was chosen because distances <10 km resulted in some sierras to be completely "disconnected" from the rest (i.e. their betweenness S3 centrality was undetermined) and distances > 20 km resulted in more than half of the sierras with betweenness centrality values = 0 (i.e. most sierras were connected directly to all sierras). Plant and animal phylogenies Phylogenetic distance estimation for plants Phylogenetic relations among all species were inferred using the Phylomatic v3 online tool, http://phylodiversity.net/phylomatic/ (Webb & Donoghue 2005), choosing the R20120829 source tree, which is based on the work of the Angiosperm Phylogeny Group III (Bremer et al. 2009). As the resulting tree showed some polytomies, we further resolved the phylogenetic relations for some groups using published phylogenies; for Asteraceae we extracted the inferred relations from Funk et al. (2009) and for Iridaceae from Reeves et al. (2001). Specifically, we replaced the polytomies contained in the source tree with resolved sub-trees, which were assembled manually using the topological information contained in the published phylogenies mentioned above. The resulting tree was then calibrated by running the bladj (branch-length-adjusting) algorithm in the Phylocom 4.2 software (Webb et al. 2008), using the estimated node dates from Wikström et al. (2001). This algorithm constrains the ages of the provided nodes and subsequently assigns ages to the remaining nodes evenly in order to produce an ultrametric tree. From this tree, we then obtained a matrix of pairwise phylogenetic distances among all plants by running the phydist command in Phylocom 4.2. Phylogenetic distance estimation for pollinators We constructed a phylogeny for the pollinators observed in all sierras by assembling the information contained in different published sources. The phylogenetic relations among the S4 main groups of insects were extracted from Wiegmann et al. (2009). We then added resolved topologies for each group of the tips of this stem phylogeny from different sources: Brady et al. (2009) for Aculeata, Cardinal et al. (2010) for Apidae, Field et al. (2011) for Sphecidae, Danforth et al. (2008) for Halictidae, Heikkilä et al. (2012) for Lepidoptera, Bertone & Wiegmann (2009) for Diptera, Kutty et al. (2010) for Calyptratae, and McKenna & Farrell (2009) for Coleoptera. From these sources, we extracted the ages of diversification for the main nodes of the phylogeny when available. We used this information to estimate branch lengths for the resulting phylogeny, by applying, as for the plant's phylogeny, the bladj algorithm, but this time using the ages of diversification extracted from the publications mentioned above (e.g. we constructed a customized version of the "ages" example file provided with the Phylocom software, which included known node ages for some pollinator groups). Phylogenetic distances among all pollinators were estimated using the phydist command in Phylocom 4.2. Accounting for topological and chronological uncertainty Both the plants and pollinators phylogenies contained some soft polytomies due to lack of phylogenetic information in the bibliography, or also, in the case of the pollinators, due to incomplete taxonomic identification (see Materials and Methods, Study system and dataset). To account for the effect of the incomplete resolution of the trees, we applied a procedure that simultaneously resolves polytomies and adjusts branch lengths using an evolutionary constant rate birth-death model as an alternative to the bladj algorithm, which does not assume any evolutionary model. This approach consists on first running the PolytomyResolver R script (Kuhn et al. 2011), which produces an input file for the BEAST software (Drummond & Rambaut 2007) that contains topological (known resolved S5 topology) and chronological (known node ages of diversification) constraints. Then, the BEAST software resolves the unresolved portions of the tree randomly, and also dates the rest of the nodes under defined birth-death model priors. We ran, for both plants and flower visitors, the PolytomyResolver R script using the known tree topologies and node ages described above as topological and chronological constraints, and we kept the default settings as described in Kuhn et al. (2011). Then, the resulting input file was read in BEAST v.1.5.4, which performed Markov Chain Monte Carlo (MCMC) analyses for 107 iterations, sampled trees every 103 iterations, and produced an output of 104 alternative trees. Using TreeAnnotator v.1.5.4 (Drummond & Rambaut 2007), we discarded a 25% burnin and we obtained a maximum clade fully-resolved credibility tree. Finally, we compared the resemblance of the matrices of pairwise phylogenetic distances obtained using the bladj vs. the constant rate birth-death model and maximum-credibility tree approach by means of a Mantel correlation test. Spatial autocorrelation Spatial autocorrelation of the residuals of the relations between variables describing phylogenetic structuring of pollination webs as a function of sierra area and connectivity (i.e. betwenness centrality) may increase the probability of Type I error in regression analysis. We checked spatial structure in the residuals of the multiple regressions portrayed in Tables S3 and S4 by means of Moran’s I correlograms (Fortin et al. 2002). Correlograms were made with the software SAM 4.0 (Rangel et al. 2010) by constructing six distance classes as to keep sample sizes per class large enough for meaningful statistical power. We found statistically non-significant Moran’s I values at all regression x distance S6 class combinations (4 x 6) but one (Tables S11 and S12), which might be expected to occur by chance alone given a fixed Type 1 error of 0.05 for each separate test. Supporting references Aizen, M.A., Garibaldi, L.A. & Dondo, M. (2009). Expansión de la soja y diversidad de la agricultura argentina. Ecología Austral, 19, 45-54. Barsky, O. & Gelman, J. (2001). Historia del Agro Argentino. Desde la Conquista Hasta Fines del Siglo XX. Grijalbo-Mondadori, Buenos Aires. Bertone, M.A. & Wiegmann, B.M. (2009). True flies (Diptera). In: The Timetree of Life (eds Hedges S.B. & Kumar, S.). Oxford University Press, Oxford, UK, pp. 270-277. Brady, S.G., Larkin, L. & Danforth, B.N. (2009). Bees, ants, and stinging wasps (Aculeata). In: The Timetree of Life (eds Hedges, S.B. & Kumar, S.). Oxford University Press, Oxford, UK, pp. 264-269. Bremer, B., Bremer, K., Chase, M., Fay, M., Reveal, J., Soltis, D. et al. (2009). An update of the Angiosperm Phylogeny Group classification for the orders and families of flowering plants: APG III. Bot. J. Linn. Soc.,161, 105-121. Cardinal, S., Strakab, J. & Danforth, B.N. (2010). Comprehensive phylogeny of apid bees reveals the evolutionary origins and antiquity of cleptoparasitism. Proc. Natl. Acad. Sci. USA, 107, 16207-16211. Danforth, B.N., Eardley, C., Packer, L., Walker, K., Pauly, A. & Randrianambinintsoa, F.J. (2008). Phylogeny of Halictidae with an emphasis on endemic African Halictinae. Apidologie, 39, 86-101. Drummond, A.J. & Rambaut, A. (2007). BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol. Biol., 7, 214. Field, J., Ohl, M. & Kennedy, M. (2011). A molecular phylogeny for digger wasps in the tribe Ammophilini (Hymenoptera, Apoidea, Sphecidae). Syst. Entomol., 36, 732-740. Fortin, M.J., Dale, M.R. & ver Hoef, J. (2002). Spatial analysis in ecology. In: Encyclopedia of Environmetrics. Volume 4 (eds El-Shaarawi, A.H. & Piegorsch, W.W.). John Wiley & Sons, Chichester, UK, pp. 2051-2058.