Introduction
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1 Journal of Biogeography SUPPORTING INFORMATION Diversity versus disparity and the role of ecological opportunity in a continental bird radiation Manuel Schweizer, Stefan T. Hertwig and Ole Seehausen Appendix S1 Phylogenetic analyses and species sampled. Materials and methods Nucleotide sequences of 133 of 164 described species were collated from GenBank. Our sampling thus included more than 81% of all described Arini species and all of the 29 recognized genera following the taxonomy of Forshaw (2010), except the monotypic genus Ognorhynchus (Table S1). We moreover included four taxa of the African Psittacini (Psittacus erithacus and three species of the genus Poicephalus), the sister group of Arini (Schweizer et al., 2010, 2011). Melopsittacus undulatus was used as an outgroup. We used partial sequences of the comparatively slowly evolving nuclear exon RAG-1 (Groth & Barrowclough, 1999) and of the faster evolving mitochondrial genes COI (cytochrome c oxidase subunit I), cytb (cytochrome b) and ND2 (NADH dehydrogenase subunit 2) (Table S1). We used BEAST 1.7.5 (Drummond & Rambaut, 2007) to simultaneously estimate divergence times and establish a phylogenetic hypothesis for Arini. We applied a relaxed molecular clock with an uncorrelated lognormal distribution of branch lengths and a Yule tree prior. We used PARTITIONFINDER 1.0.1 (Lanfear et al., 2012) to select the best-fitting partitioning scheme and models of nuclear evolution using the greedy algorithm with unlinked branch lengths corresponding to separate models with varying base frequencies, rate matrix, shape parameters and proportion of invariable sites for the different genes and/or their codon positions. The Bayesian information criterion (BIC) was used as optimality criterion for model selection. We incorporated a secondary calibration point in our dating analyses based on the results of Schweizer et al. (2011): a mean estimate of 35 Ma (millions of years ago) for the split between Arini and Psittacini (based on a normal distribution with a standard deviation of 6.5 Ma). The 95% confidence interval of this normal distribution included the 95% highest posterior density (HPD) interval for this split as estimated in Schweizer et al. (2011). Default prior distributions were chosen for all other parameters and we ran three independent chains of Markov chain Monte Carlo (MCMC) with 25 million generations sampled every 1000 generations. TRACER 1.5 (Rambaut & Drummond, 2007) was used to confirm appropriate burn-in, confirm that the effective sample sizes of the posterior distribution for each run were adequate, and to assess convergence among runs by comparing the likelihoods and posterior distributions of all parameters. The resulting trees and 95% posterior distribution of each estimated node were analysed with FIGTREE 1.2.1 (Rambaut, 2008). The outgroup and taxa of the African Psittacini were pruned from the trees before further analyses. We moreover employed a maximum-likelihood search using RAXML 7.0.4 (Stamatakis, 2006) on the web server with 100 rapid bootstrap inferences (Stamatakis et al., 2008), with all free model parameters estimated by the software (substitution rates, gamma shape 2 parameter and base frequencies), based on the best partitioning scheme identified by PARTITIONFINDER (see above). In the MEDUSA analysis, species missing from our sample, and for which no genetic data were available, were added to terminal branches using taxonomic information or plumage similarities taken from various sources (Collar, 1997; Arndt, 2006; Forshaw, 2010). Amazona mercenaria was added to the terminal branch leading to Amazona farinosa, and Amazona xantholora was added to Amazona albifrons. Ara ambiguus, Ara militaris and Ara rubrogenys were added to the terminal branch leading to Ara severus. Aratinga frontalis had been formerly been treated as a subspecies of Aratinga wagleri (Collar, 1997) and was hence added to the terminal branch leading to the latter. Aratinga erythrogenys was also included in Aratinga wagleri. Aratinga hockingi is part of the Aratinga mitrata complex (Arndt, 2006) and was added to the terminal branch leading to the latter. Aratinga strenua was added to Aratinga holochlora, of which it has previously been treated as a subspecies (Collar, 1997). The three missing species in the genus Bolborhynchus (B. auriforns, B. orbignesius and B. ferrugineifrons) were included in Bolborhynchus lineola. Enicognathus ferrugineus was added to the terminal branch leading to Enicognathus leptorhynchus. Ognorhynchus icterotis was added to Rhynchopsitta pachyrhyncha. Pyrrhura calliptera was included in Pyrrhura lepida. Pyrrhura caeruleiceps, Pyrrhura subandida and Pyrrhura lucianii were formerly treated as subspecies of Pyrrhura picta and were added to the latter’s terminal branch. Pyrrhura devillei forms a superspecies with P. frontalis and was hence added to the latter. Pyrrhura egregia and Pyrrhura hoematotis were added to the terminal branch leading to Pyrrhura lepida. Pyrrhura parvifrons was included in Pyrrhura roseifrons. Pyrrhura viridicata was included in Pyrrhura rhodocephala. Rhynchopsitta terresi was added to the terminal branch leading to Rhynchopsitta pachyrhyncha. The missing species in the genus Touit (T. costaricensis, T. dilectissima, T. melanotus, T. huetii, T. stictoptera and T. surda) were added to Touit batavica. Phylogenetic results The final alignment was 5506 bp long and consisted of 622 bp of COI, 1140 bp of cytb, 1041 bp of ND2 and 2703 bp of RAG-1. It contained no indels. PARTITIONFINDER identified two subsets as the best-fitting partitioning scheme with a HKY+I+G substitution model for the first and second codon positions of the three mitochondrial genes as well as for the nuclear exon, and a GTR+I+G substitution model for the third codon position of the three mitochondrial genes. The comparison of the three independent runs with BEAST revealed high convergence among the different parameters and effective sample sizes were > 250 for all parameters. The three runs were combined, with a burn-in of 2.5 million generations for each. The maximum clade credibility tree was then estimated from 67,503 trees. The topology of the best-scoring tree of the ML analysis with RAXML was highly congruent with the maximum clade credibility tree of the BEAST analysis and there was no conflict between supported nodes (Fig. S1). They only differed in the positions of Amazona agilis, Amazona albifrons, Amazona barbadensis, Amazona farinosa, Amazona kawalli, Aratinga brevipes, Aratinga finschii, Brotogeris sanctithomae, Brotogeris tirica, Pyrrhura rupicola and the clade consisting of Amazona xanthops and Graydidascalus brachyurus. Moreover, two basal clades 3 within Aratinga were not revealed as monophyletic in the ML analyses, in contrast to the BEAST analyses (Fig. 1, Fig. S1). Comparison of topology with recent molecular phylogenies of Neotropical parrots We compared the topology of the maximum clade credibility tree from the BEAST analyses with recently published molecular phylogenies of Arini, although comparisons, especially of the basal splits, are hampered by the fact that no previous work used a comparably broad taxon sampling. The phylogenetic relationships found among genera were highly congruent with the supported nodes in Tavares et al. (2006), with the exception of the clade consisting of Brotogeris and Myopsitta, which was there revealed as the sister group to the taxa in our clade 5. There was also no conflict with the supported nodes in Wright et al. (2008), except the position of the clade consisting of Deroptyus and Pionites; this was the sister group to the remaining taxa of clade 2 in our study, whereas in Wright et al. (2008) it was nested within the taxa in our clade 2. Within Brotogeris, we recovered the same basal clades as Ribas et al. (2009) and there was no conflict between supported nodes. The topology within Pyrrhura was also broadly congruent with the results of Ribas et al. (2006) for supported nodes, with the exception of the positions of P. picta and P. emma. The relationships among the analysed species of Aratinga were congruent with Ribas & Miyaki (2004) and Kirchman et al. (2012). Some differences from Smith et al. (2013) were revealed in the topology within Forpus. That study, however, included different subspecies and, in some cases, several samples per species. Additionally, the relationships found within Pionus were congruent with the supported nodes in Ribas et al. (2007). Amazona xanthops did not cluster with the other members of Amazona as also observed in other studies (Russello & Amato, 2004; Tavares et al., 2006). The relationships within Amazona were found to be consistent overall with the supported nodes in Russello & Amato (2004), with the exception of the position of A. amazonica. The relationships we found within Gypopsitta were identical to those in Ribas et al. (2005). As in that study, Hapalopsittaca was found to be the sister group to Gypopsitta. Testing the assignment of unsampled Pyrrhura species in the MEDUSA analysis The clade consisting of all Pyrrhura species except Pyrrhura cruentata was found to be unexpectedly species-rich in the MEDUSA analysis. Nine unsampled Pyrrhura species were added to terminal branches of the sampled species contained in this clade based on taxonomic information and plumage similarities (see above), which may have influenced this result. We therefore collapsed this clade and added the missing species either to this clade or to the terminal branch leading to Pyrrhura cruentata, although the available taxonomic evidence (see above) makes it unlikely that they are more closely related to this species than to the remaining Pyrrhura species. When 5–9 of the unsampled species were added to Pyrrhura cruentata (the remaining 0–4 unsampled species were included in the other Pyrrhura lineage), MEDUSA found a rate shift on the branch leading to all Pyrrhura species. When fewer than five of the unsampled species were added to the terminal branch of Pyrrhura cruentata, the rate shift was identified on the branch leading to the remaining Pyrrhura species as in the initial analyses. Hence, the allocation of the unsampled species had only a marginal effect on the overall results. 4 Table S1 Species sampled and GenBank accession numbers for the four genes analysed for this study. Amazona aestiva Amazona agilis Amazona albifrons Amazona amazonica Amazona arausiaca Amazona autumnalis Amazona barbadensis Amazona brasiliensis Amazona collaria Amazona dufresniana Amazona farinosa Amazona festiva Amazona finschi Amazona guildingii Amazona imperialis Amazona kawalli Amazona leucocephala Amazona ochrocephala ochrocephala Amazona pretrei Amazona rhodocorytha Amazona tucumana Amazona ventralis Amazona versicolor Amazona vinacea Amazona viridigenalis Amazona vittata Amazona xanthops Anodorhynchus hyacinthinus Anodorhynchus leari Ara ararauna Ara chloropterus Ara glaucogularis Ara macao Ara severus Aratinga acuticaudata Aratinga aurea Aratinga auricapillus Aratinga branickii Aratinga brevipes Aratinga cactorum Aratinga canicularis Aratinga chloroptera Aratinga euops Aratinga finschi Aratinga holochlora Aratinga jandaya Aratinga leucophthalma Aratinga mitrata COI cytb ND2 RAG1 EU340705 AY301426 AY301427 AY194399 AY301431 AY194379 AY301436 AY301437 AY301438 AY301439 AY301443 AY301444 AY301445 AY301446 AY301447 JQ235567 AY301449 AY194397 AY301457 AY301458 AY301459 AY301460 AY301461 AY301462 AY301463 AY301464 AY301465 AF370738 AF370736 AY301467 — GU826174 EU621598 AF370748 NC_020325 AF370746 AY301466 EU621626 GU826177 AF370750 HQ629753 GU826178 GU826179 GU826180 GU826181 — AF370749 GU826183 AY194404 AY283489 AY283506 AY283516 AY283465 AY283456 AY283462 — AY283493 AY283454 FJ899168 — AY283461 AY283460 AY283458 JQ235589 AY283518 AY194411 — — — AY283474 AY283466 — — AY283514 DQ143293 AF346370 AF370763 DQ150994 AF346367 — AF346366 AF370766 NC_020325 U70762 AY208239 — — AY281254 — — — — — AY208244 AY281253 — AY194434 — HQ629715 AY194466 — AY194446 — — — — AY194461 — — — — — — AY194460 — — — — — — EU327598 — AY669485 DQ143311 AY669446 DQ143315 — HQ270481 EU327601 — NC_020325 DQ143321 HQ270483 EU327630 — — HQ629718 HQ270484 HQ270485 HQ270486 HQ270487 — Q143298 HQ270489 JF807980 — — — — — — — — JF807981 DQ143346 — — — — — — — JF807982 — — — — — — — DQ143345 DQ143329 — — — DQ143340 — — DQ143341 — — — — — — — — — — DQ143331 — 5 Aratinga nana Aratinga nenday Aratinga pertinax Aratinga rubritorquis Aratinga solstitialis Aratinga wagleri Aratinga weddelli Bolborhynchus aymara Bolborhynchus lineola Brotogeris chiriri Brotogeris chrysoptera Brotogeris cyanoptera Brotogeris jugularis Brotogeris pyrrhoptera Brotogeris sanctithomae Brotogeris tirica Brotogeris versicolurus Cyanoliseus patagonus Cyanopsitta spixii Deroptyus accipitrinus Diopsittaca nobilis Enicognathus leptorhynchus Forpus coelestis Forpus conspicillatus Forpus cyanopygius Forpus passerinus Forpus sclateris(=modestus) Forpus xanthops Forpus xanthopterygius Graydidascalus brachyurus Guaruba guarouba Gypopsitta aurantiocephala Gypopsitta barrabandi Gypopsitta caica Gypopsitta haematotis Gypopsitta pulchra Gypopsitta pyrilia Gypopsitta vulturina Hapalopsittaca amazonina Hapalopsittaca fuertesi Hapalopsittaca melanotis Hapalopsittaca pyrrhops Melopsittacus undulatus Myiopsitta monachus Nannopsittaca dachilleae Nannopsittaca panychlora Orthopsittaca manilata Pionites leucogaster Pionites melanocephalus COI cytb ND2 RAG1 GU826184 EU621632 EU621597 — GU826185 GU826186 — HQ629774 GU826187 — HQ629756 NC_015530 EU621601 — — — AF370755 GQ232191 EU621610 EU621613 AF370752 EU621616 — — — EU621621 — — — AY301468 AF370741 — AY661216 AY661221 AY661225 AY661231 — AY661213 EU621625 — GU826192 — EU327633 EU621631 — EU621633 EU621640 — EU621644 — AY219915 JX877361 — DQ143285 — AY669401 — DQ143291 FJ652859 FJ652893 FJ652871 FJ652910 FJ652865 FJ652902 FJ652849 FJ652853 GQ232267 DQ823368 DQ150992 AF370769 — JX877284 JX877337 JX877356 JX877308 JX877272 JX877314 DQ143294 AY669439 AF346371 AY669407 AY669424 AY669419 AY669433 AY669427 AY669426 AY669412 AY669443 JN393252 — JN393246 EU621629 AF346378 — — DQ150991 DQ143288 — FJ361233 AY274066 EU327600 JX477130 DQ143317 HQ270492 AY669445 HQ629738 DQ143319 FJ652922 FJ652957 FJ652938 FJ652973 FJ652928 FJ652965 FJ652911 FJ652917 EU327613 EU327614 EU327617 EU327618 EU327620 JX877369 JX877387 JX877421 EU327625 JX877411 JX877378 DQ143300 AY669484 DQ143309 AY669451 AY669468 EU327649 AY669477 AY669472 AY669470 AY669453 EU327629 JN393236 HQ270498 JN393230 DQ143295 EU327635 DQ143320 EU327637 EU327644 DQ143312 EU327648 — DQ143326 — — DQ143330 — — — DQ143350 DQ143348 — — — — — — — DQ143334 DQ143336 DQ143338 DQ143335 DQ143332 — — — — — — DQ143325 DQ143344 DQ143333 — DQ143349 — — — — — — — — — DQ143354 DQ143328 DQ143352 — DQ143337 DQ143342 — 6 Pionopsitta pileata Pionus chalcopterus Pionus fuscus Pionus maximiliani Pionus menstruus Pionus senilis Pionus seniloides Pionus sordidus Pionus tumultuosus Poicephalus gulielmi Poicephalus rufiventris Poicephalus senegalus Primolius auricollis Primolius couloni Primolius maracana Psittacus erithacus Pyrrhura albipectus Pyrrhura amazonum Pyrrhura cruentata Pyrrhura eisenmanni Pyrrhura emma Pyrrhura frontalis Pyrrhura griseipectus Pyrrhura hoffmanni Pyrrhura lepida Pyrrhura leucotis Pyrrhura melanura Pyrrhura molinae Pyrrhura orcesi Pyrrhura perlata Pyrrhura peruviana Pyrrhura pfrimeri Pyrrhura picta Pyrrhura rhodocephala Pyrrhura roseifrons Pyrrhura rupicola Pyrrhura snethlageae Rhynchopsitta pachyrhyncha Touit batavicus Touit purpuratus Triclaria malachitacea COI cytb ND2 RAG1 AY661227 AY661232 — — AY301469 GU826194 — — — — — HQ629771 AF370753 AF370762 AF370754 EU621657 HQ629779 — — — — — — HQ629780 — — — — — GU826196 — — EU621660 — — — — EU621661 EU621666 HQ629781 HQ629783 AY669438 EF517621 EF517636 EF517622 EF517611 EF517618 EF517616 EF517634 EF517616 AY283498 — — DQ150995 AF370780 AF370775 AY082076 AY751640 AY751616 AY751658 AY751599 AY751626 AY751643 AY751630 AY751654 AY751645 AY751633 AY751652 AY751642 AY751636 AY751646 AY751583 AY751620 AY751602 AY751638 AY751596 AY751656 AY751612 DQ143297 — — AY669442 AY669479 EF517656 EF517671 EF517662 EF517637 EF517653 EF517651 EF517669 EF517649 — — — DQ143302 HQ270501 — GU816826 — — — — — — — HQ629744 — DQ143307 — — — HQ270502 — — AY669444 — — — — DQ143303 EU327670 HQ629745 AY669486 DQ143351 — — DQ143347 JF807987 — — — — JF807988 GQ505226 GQ505227 DQ143343 — — EF517674 — — — — — AY233360 — — — DQ143353 — — — — — — — — — — — DQ143339 — — DQ143327 7 Figure S1 Best-scoring maximum likelihood tree using RAXML for Neotropical parrots (Arini) and outgroup species. Supported nodes (bootstrap values ≥ 70%) are marked with black circles, while moderately supported nodes (0.5 ≤ posterior probability ≤ 0.7) are marked with grey circles. 8 Appendix S2 Testing the influence of incomplete taxon sampling on diversification rate estimates. To test the influence of incomplete taxon sampling and the robustness of our results, we simulated 1000 trees under a constant-rate birth–death model using the parameters as estimated with a pure-birth model (Rabosky, 2006) based on our data. These phylogenies were simulated to 164 tips representing the current species diversity of Arini and then randomly pruned to our taxon sampling of 133 species. To control for the potentially phylogenetically overdispersed sampling, we implemented the method of Brock et al. (2011) using different values for the scaling parameter α. The root node of the simulated trees was then set to 25.719 Ma (mean value recovered). We then compared the distribution of the differences in AIC scores between the best-fitting rate-variable and the best-fitting rate-constant model of the simulated trees and the 1000 last trees of the BEAST analyses with the root node set to 25.719 Ma (see below). A positive difference in AIC scores indicates that the rate-variable model fits the data better than the rate-constant model. Only the following models could be compared in the LASER package: pure birth, constant-rate birth-death, yule2rate, DDX and DDL. We often found positive differences in AIC scores between the best-fitting variablerate and the best-fitting constant-rate model for the 1000 trees simulated under a birth–death model (Fig. S2). When completely random or low-levels of overdispersed sampling were assumed, this null-distribution peaked below zero, but broadly overlapped with the distribution of the differences in AIC score for the 1000 last trees of the BEAST analyses. However, when higher levels of overdispersed sampling were considered, the null-distribution shifted to even more positive differences in AIC scores. Hence, the observed distribution cannot be distinguished from a constant-rate diversification process. Figure S2 Distribution of ΔAIC between the best-fitting constant-rate and the best-fitting variable-rate diversification model calculated from the posterior distribution of the BEAST analysis (grey) and from a null distribution of phylogenies simulated under a constant-rate model (white). We controlled for potentially phylogenetically overdispersed sampling using different values of the scaling parameter α. 9 Appendix S3 Additional figures on the analyses of morphological evolution. Figure S3 (a) Maximum clade credibility tree of the dating analysis using BEAST of Neotropical parrots (Arini) with the colours of branches representing the reconstructed evolution of shape PC1 along lineages. (b) Disparity-through-time plot for shape PC1 of Arini. The solid line indicates the mean subclade disparity through time based on the maximum clade credibility tree of the BEAST analyses, while the dashed line shows the median expected subclade disparity derived from 10,000 simulations under Brownian motion evolution. The area shaded in grey indicates the 95% range for the simulated data. 10 Figure S4 (a) Maximum clade credibility tree of the dating analysis using BEAST of Neotropical parrots (Arini) with the colours of branches representing the reconstructed evolution of shape PC2 along lineages. (b) Disparity-through-time plot for shape PC2 of Arini. The solid line indicates the mean subclade disparity through time based on the maximum clade credibility tree of the BEAST analyses, while the dashed line shows the median expected subclade disparity derived from 10,000 simulations under Brownian motion evolution. 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