THE BIOGEOGRAPHY OF SULAWESI REVISITED: IS THERE EVIDENCE FOR A VICARIANT ORIGIN OF TAXA ON WALLACE'S “ANOMALOUS ISLAND”?

Authors


Abstract

Sulawesi, the largest island in the Indonesian biodiversity hotspot region Wallacea, hosts a diverse endemic fauna whose origin has been debated for more than 150 years. We use a comparative approach based on dated phylogenies and geological constraints to test the role of vicariance versus dispersal in the origin of Sulawesi taxa. Most divergence time estimates for the split of Sulawesi lineages from their sister groups postdate relevant tectonic vicariant events, suggesting that the island was predominantly colonized by dispersal. Vicariance cannot be refuted for 20% of the analyzed taxa, though. Although vicariance across Wallace's Line was only supported for one arthropod taxon, divergence time estimates were consistent with a “tectonic dispersal” vicariance hypothesis from the East in three (invertebrate and vertebrate) taxa. Speciation on Sulawesi did not occur before the Miocene, which is consistent with geological evidence for more extensive land on the island from that time. The Pliocene onset of periodic sea-level changes may have played a role in increasing the potential for dispersal to Sulawesi. A more extensive taxon sampling in Wallacea will be crucial for refining our understanding of the region's biogeography and for testing hypotheses on the origin of taxa on its most important island.

We now come to the Island of Celebes, in many respects the most remarkable and interesting in the whole region, or perhaps on the globe, since no other island seems to present so many curious problems for solution. (Wallace 1876, Vol. 1: 426)

The region of oceanic islands in the center of the Indo-Australian Archipelago (IAA) comprising Sulawesi, the Moluccas, and the Lesser Sunda Islands has been dubbed Wallacea by Dickerson (1928). The largely endemic fauna of this biodiversity hotspot (Myers et al. 2000) is sharply distinct from that of the continental islands on the adjacent shelves, the Asian (Sunda) Shelf with Borneo, Java, and Sumatra in the West and the Australian (Sahul) Shelf with New Guinea in the East. The western and eastern limits of Wallacea coincide with Wallace's Line (Wallace 1863) and Lydekker's Line (Lydekker 1896), respectively, which were originally proposed as faunistic boundaries between the Asian and Australian fauna in recognition of the faunal breaks observed by the early naturalists (for a historical overview see Mayr 1944; Simpson 1977). In the North, Wallacea borders on the other large region of oceanic islands in the IAA, the Philippines, which was originally included in Wallacea by Dickerson (1928), but is now generally regarded as a distinct biogeographic entity (but see Cox 2001; Michaux 2010), as it has a largely unique fauna of its own (see, e.g., Heaney et al. 2005) and is regarded as a biodiversity hotspot as well (Myers et al. 2000). The biogeography of the IAA in general and Wallacea in particular has been a major research issue ever since the seminal work by Wallace (1869). Recently, that is, during the last 10–20 years, the widespread application of molecular phylogenetics to the region's taxa and an increasingly detailed understanding of its complex geology have provided a boost to respective studies (see review by Lohman et al. 2011). Today, most of the Philippine and Wallacean islands are generally regarded as true oceanic islands that have had no terrestrial connection to any surrounding area since their emergence (van Oosterzee 1997).

THE BIOGEOGRAPHY OF SULAWESI

Sulawesi, the former Celebes, is the largest and possibly oldest island in Wallacea and as such harbors the most diverse fauna (see, e.g., Whitten et al. 2002). Wallace's difficulties in the taxonomic assignment of some characteristic taxa of Sulawesi, such as the well-known babirusa (Babyrousa babyrussa Perry 1811), to living relatives within their family led him to regard it as comprising “remnants of an exceedingly ancient land” (Wallace 1876) or simply an “anomalous island” (Wallace 1880). Soon and perhaps not very surprisingly given geographic position, it had been realized that Sulawesi's fauna is predominantly of Asian origin (Sarasin and Sarasin 1901; Wallace 1910; de Beaufort 1926; Stresemann 1939; Whitmore 1987; Holloway 1990; Whitten et al. 2002). This notion is supported by most recent molecular phylogenies involving taxa from across the Wallace Line that have also identified (comparatively) recent Miocene to Pleistocene dispersal as the most likely mechanism for the origin of these taxa on Sulawesi (see, e.g., Butlin et al. 1998; Evans et al. 1999; Inger and Voris 2001; Evans et al. 2003a; Mercer and Roth 2003; Alfaro et al. 2008; Merker et al. 2009). Nevertheless, the origin of some endemic taxa on Sulawesi is still controversial, particularly with regard to the role of vicariance and dispersal in bringing about current distribution patterns (Lohman et al. 2011). The complex geological history of Sulawesi offers the theoretical possibility for a vicariant origin of taxa both from across the Wallace Line, that is, from Sundaland, and from New Guinea/Australia (see below for details), and several biogeographers or geologists have suggested respective scenarios (Burrett et al. 1991; Michaux 1991, 1994, 1996; De Boer and Duffels 1996; Moss and Wilson 1998). Consequently, in several recent molecular phylogenetic studies estimating divergence times of Sulawesi taxa, a vicariant origin has been suggested or at least considered (from Asia: Walton et al. 1997; Maekawa et al. 2001; Takehana et al. 2005; Clouse and Giribet 2010; from Australia: Glaubrecht and Rintelen 2003; Sparks and Smith 2004; Ruedas and Morales 2005; von Rintelen and Glaubrecht 2005). Recently, Michaux (2010) analyzed the topologies from 33 molecular phylogenies comprising Sulawesi taxa, albeit without considering divergence time estimates, and suggested an vicariant origin of a considerably higher number of groups on Sulawesi, either across Wallace's Line (23 taxa) or from Australia (9 taxa).

THE GEOLOGY OF SULAWESI

Geologically, the IAA is highly complex and the site of an ongoing collision between two plates (Asia and Australia) that began in the Miocene involving numerous allochthonous continental fragments (terranes) (Hall 2009a). Plate tectonic reconstructions of the geological evolution of the IAA through the Cenozoic have been constantly refined (Hall 1996, 1997, 2002, 2011; Hall 2001) but many uncertainties remain. The complex history is mirrored in the geology of Sulawesi, which is a composite island at the center of this collision zone. For many years, Sulawesi has been divided into three major tectonic provinces, the (1) West Sulawesi Plutono-Volcanic Arc, the (2) East Sulawesi Ophiolite Belt, the (3) Central Sulawesi Metamorphic Belt, with two smaller continental fragments, Banggai-Sula in the northeast and the Tukang Besi Block in the southeast (e.g., Hamilton 1979; Moss and Wilson 1998; Fig. 1). This has led to the view that Sulawesi represents the collision between an active volcanic margin and multiple microcontinental fragments sliced from New Guinea that arrived at different times since the Early Miocene.

Figure 1.

The tectonic history of Sulawesi and geological constraints on vicariant events. (A) Schematic summary of the geographic connections and the timing of separation or collision of the different parts of Sulawesi shown in the sketch map of the island. See text for details. Blue tinting indicates presumably submerged areas, see legend for details. Red arrows indicate area vicariance (1, 2) or fusion (3). Based on Moss and Wilson (1998), Hall (2009b), and Spakman and Hall (2010). (B and C) Hypothetical phylogenies of Sulawesi taxa (B) derived from Asia and (C) from Australia. The black triangles indicate (1) the split of the Sulawesi lineage from its sister taxon and (2) the first diversification event on Sulawesi. Under a vicariance scenario, the timing of these splits is constrained by the geological events indicated in panel A. (D) Tectonic reconstructions of the Asia-Australia collision zone. Modified from Spakman and Hall (2010).

Recent work has considerably changed this simple picture (Hall 2009a, 2011; Spakman and Hall 2010). By the Late Cretaceous West Sulawesi was part of the Asian margin and became separated from it in the Eocene (about 45 Mya) by rifting that led to formation of the Makassar Straits (Fig. 1, red arrow no. 1). West Sulawesi was not the site of significant active magmatism but there was a volcanic arc in the North Arm of Sulawesi from the Eocene. In the Early Miocene (about 23–20 Mya), a large promontory, the Sula Spur, which was the continuation of the Australian continental margin in New Guinea, collided with the North Sulawesi volcanic arc resulting in emplacement of ophiolites, and probably resulting in emergence of much of east and southeast Sulawesi. Subduction continued at the Java Trench and this subduction rolled back into the Banda oceanic embayment south of the Sula Spur from the Middle Miocene leading to extension of Sulawesi and the Sula Spur. It was this extension that formed the microcontinental fragments, such as Banggai-Sula and Tukang Besi, separated by young oceanic crust of the North and later South Banda Seas. This extensional fragmentation began about 15 Mya (Fig. 1, red arrow no. 2). The Banda volcanic arc formed as the subduction rolled back eastwards into the Banda embayment, and this collided with the southern continental margin of the embayment in Timor at about 4 Mya. The movie in Spakman and Hall (2010; http://searg.rhul.ac.uk/pubs/spakman_hall_2010_banda) shows this geological history that, although very different from the traditional view of slicing of fragments from New Guinea and their translation westwards as potential arks (cf. Michaux 2010), did lead to fragmentation that could account for vicariant biogeographic hypotheses. It is still unclear at what time the collision of the Sula Spur with the North and West arms of Sulawesi led to the establishment of a (subaerial) connection between these and the fragments of the island derived from the Sula Spur (Fig. 1, red arrow no. 3), which is reflected in a wide time range for this event (10–20 Mya).

THE IMPORTANCE OF PALAEGEOGRAPHY FOR BIOGEOGRAPHERS

For biogeographers, the plate tectonic reconstructions of the IAA through the Cenozoic provide an indispensable framework for discussing the plausibility of dispersal and vicariance scenarios in the region. However, at least as important in this context is the past distribution of land and sea. If continental fragments are to act as “rafts” for terrestrial or limnic fauna (dubbed “tectonic dispersal” by Michaux 2010), they must obviously have been subaerial throughout their existence as separate entities. Although the formation of present-day Sulawesi is not due to the subsequent collision of microcontinental fragments with an Australian origin as previously believed (see above), the basic requirement of the continuous presence of land holds also true in the case of a single collision of the Sula Spur. For Sulawesi (and Wallacea in general), there is apparently little evidence for the relevant areas being continuously subaerial, with the best chance for the continuous existence of at least small amounts of land in Southwest Sulawesi (Fig. 1; see Hall et al. 2001; Hall 2009b, but Moss and Wilson 1998). If true, this would make any vicariance scenario at least by “tectonic dispersal” to Sulawesi from the East a priori impossible. However, dynamic land connections, that is, land is continuously present but fragmented and with shifting boundaries, which would require terrestrial or limnic organisms to migrate continuously to keep afloat, seem possible. The past emergence of smaller islands is notoriously difficult to infer, as characteristic signatures of larger land areas such as fluvial sediments will not be present, and the existence of land is largely inferred from negative evidence such as gaps in sediments of marine origin (Hall 2009b). There remains a token of hope for biogeographers, thus, and it may be expected that future work is likely to lead to considerable revisions of paleogeographic maps of these critical regions (Hall 2009b).

A COMPARATIVE APPROACH TO SULAWESI'S BIOGEOGRAPHY

Molecular phylogenetic data are now available for a wide range of Sulawesi taxa and most respective studies also discuss their biogeographic origin against a geological background. However, a comparative assessment of biogeographic patterns for Sulawesi taxa utilizing the temporal dimension, that is, divergence time estimates, is still lacking. Here, we reanalyze published molecular data for (mostly nonflying) terrestrial or limnic taxa using standardized molecular clock analyses to test the compatibility of divergence time estimates with dispersal and vicariance hypotheses as proposed originally. We also explore the validity of our results with respect to the uncertainties inherent in molecular clock analyses and the vagaries of interpreting dated phylogenies in biogeographic terms.

Materials and Methods

ANALYZED STUDIES

Altogether 27 individual datasets (alignments of individual genes) from 25 publications covering 20 different taxa were analyzed (Tables 1 and 2). In several of the original studies, multiple markers were analyzed, but not all single markers contained in these datasets could be included in the analyses as for some mtDNA and nuDNA datasets there were no external rates or calibration data available. Datasets containing multiple markers (see Tables 1 and 2) were analyzed both jointly and separately, with the exception of the mite harvestmen dataset, which was only analyzed jointly.

Table 1.  Sulawesi taxa included in this study with a summary of divergence times and biogeographic hypotheses as originally published. For comparison, the hypotheses supported in this study and the maximum mean age of the time to the most recent common ancestor of a Sulawesi taxon (TMRCA) are also provided, see Tables 2 and S1 for details). If Sulawesi was colonized multiple times by a taxon (see respective column) only the age of the oldest lineage on the island is given here, see Figures 2–4 and Table S1 for all divergence time estimates. The individual genes contained in multigene datasets were analyzed separately here (see Material and Methods), the numbers of the respective dataset correspond to those used in Figures 2–5 and Tables 2 and 3. D—dispersal; DV—mixed dispersal/vicariance model; V—in column “source”: vicariance hypothesis proposed or considered; in column “this study”: vicariance hypothesis compatible with geological constraints as shown in Fig. 1; V?—vicariance hypothesis not supported if TMRCA estimates based on second substitution rate are used. Gray shading indicates major conflict between data or hypotheses from the source publication with the results from this study.
Taxon1Biogeographic hypothesisAge of Sulawesi taxon (TMRCA, My)2Colonizations of SulawesiGenes analyzedDatasets
SourceThis studySourceThis study
  1. 1Dataset: aClouse and Giribet (2010); bBalke et al. (2004); cKöhler and Glaubrecht (2010); dSchubart and Ng (2008); eEsselstyn et al. (2009); fRuedi et al. (1998); gEvans et al. (1999); hEvans et al. (2003b); iRuedas and Morales (2005); kSparks and Smith (2004); lMaekawa et al. (2001); mMercer and Roth (2003); nBirks and Edwards (2002); oEvans et al. (2003a); pMerker et al. (2009); qWalton et al. (1997); rTakehana et al. (2005); scombination of Kikkawa et al. (1997), Hassanin and Douzery (1999), Schreiber et al. (1999), and Hassanin and Ropiquet (2004); tEvans et al. (2003c); uAlfaro et al. (2008); vvon Rintelen and Glaubrecht (2006); wLarson et al. (2007).

  2. 2Age of Sulawesi taxon: TMRCA of oldest lineage; 95% confidence intervals are given in brackets. Inferred divergence times separated by vertical lines if different rates were used.

  3. 3 Köhler and Glaubrecht (2010) do not discuss the biogeography of the Sulawesi lineage included in their study, but vicariance has been explicitly assumed by Glaubrecht and von Rintelen (2003) and von Rintelen and Glaubrecht (2005) based on molecular phylogenetic data without divergence time estimates.

  4. 4Topology conflict in the reduced concatenated dataset, but no support in basal splits. The number of colonizations inferred in the source publication is highlighted in bold type.

  5. 5Based on evidence from several nuclear markers, Sulawesi macaques may form a monophyletic group (Tosi et al. 2003; Evans et al. 2010).

  6. 6Differs between datasets in this study (topology conflict among both rates used in 12S, but no support for three lineages; two colonizations in 16S and in the concatenated dataset). The number of colonizations inferred in the source publication is highlighted in bold type.

  7. 7Transitory land bridge also considered in original study.

  8. 8A priori assumption in original study, not based on divergence time estimate.

  9. 9Topology of tree in our study would suggest an origin of the Asian clade on Sulawesi, but has been ignored here because of the complete lack of nodal support (see Figs. S22 and S27).

  10. 10No divergence time estimate attempted or discussed in original study, minimum age based on fossil record of close relative in Europe.

Mite harvestmenaV (Asia)V (Asia)About 5060.8 (43.9, 77.4)2Concatenated dataset1
      (16S, 18S, COI, H3, H4, 28S) 
Water beetlesbD (Asia)D (Asia)About 4337.5 (−) | 24.9 (−)2Cyt b2
    15.7 (−) | 2.1 (−) COI9
     3.8 (−) 16S22
    10.2 (−) Cyt b + COI + 16S2 + 9 + 22
Freshwater snailscV (Australia)10V (Australia)c. 1630.5 (20.0, 42.0)116S3
Freshwater crabsd D (Asia) 26.0 (12.1, 42.4)116S4
Shrewse,fD (Asia)D (Asia)Pliocene25.9 (19.3, 32.7) | 10.5 (7.9, 13.2)2Cyt b5
   Pleistocene21.4 (16.9, 26.1) | 8.7 (7.0, 10.5)  6
Macaquesg,hD (Asia)D (Asia)3–4.619.5 (12.6, 27.2) | 8.1 (5.4, 11.3)2–3(2)4,5NADH7
   Pleistocene 3.7 (2.5, 5.0) | 1.5 (1.0, 2.0) 12S24
    18.6 (10.8, 29.0) | 7.6 (4.4, 11.8) NADH + 12S7 + 24
PhalangeridsiDV (Australia)V (Australia)?21.1–23.318.8 (14.1, 24.5) | 7.6 (5.6, 10.0)116S8
Sailfin silversideskV (Australia)V (Australia)?25–3015.2 (8.1, 23.4) | 12.5 (6.4, 19.3)1ND510
CockroacheslV (Asia)D (Asia)24–5614.0 (10.9, 17.3)2COII11
SquirrelsmD (Asia)D (Asia)1112.6 (−)116S12
    11.8 (−) 12S13
    11.1 (−) 16S + 12S12 + 13
MegapodesnD (Australia) 9.9 (6.6, 13.8)1ND214
Fanged frogs°D (Asia)D (Asia | Philippines)Pliocene 9.6 (6.9, 12.5) | 5.7 (3.8, 7.3)2–3(2)616S15
     9.2 (−) | 5.6 (−) 12S16
     9.4 (6.91, 11.7) | 5.7 (4.2, 7.2) 16S + 12S15 + 16
TarsierspD (Asia)8D (Asia)119 8.3 (3.9, 16.7) | 3.9 (1.6, 9.4)1Cyt b17
GrasshoppersqV (Asia)D (Asia)7–147.9 (2.1, 8.0)1COI18
RicefishrV (Asia)D (Asia)29–327.1 (4.4, 10.2) | 5.7 (3.6, 8.1)116S19
    3.8 (2.6, 5.1) | 3.0 (2.0, 4.0) 12S23
    4.9 (3.6, 6.5) | 3.9 (2.8, 5.1) 16S + 12S19 + 23
Bovidss D (Asia)2.234.3 (3.2, 5.5) | 1.8 (1.3, 2.3)17Cyt b20
ToadstD (Asia)D (Asia)Pleistocene4.2 (2.6, 5.9) | 2.6 (1.5, 3.6)112S21
Water snakesuD (Asia)D (Asia)0.5–4.52.1 (1.1, 3.4)1Cyt b25
Freshwater bivalvesvD (Asia)D (Asia)1.2 (−) | 1.4 (0.9, 1.9)3COI26
PigswD (Asia)D (Asia)Pleistocene30.9 (−)37CR27
Table 2.  Datasets studied including test results and parameters for the molecular clock analysis. Estimated clock (clock.rate and ucld.mean) rates are per My. Abbreviations used: Pinv—proportion of invariant sites; na—not applicable; BF(DHM, Mr Bayes)—Bayes factors of the Mr Bayes analyses using the difference of the harmonic means (see also text); log10BF—log10Bayes factors comparing relaxed and strict tree likelihoods of Tracer.
TaxonDataset(s)GenePinvBF Mr Bayes (DHM)Strict clock rejected? DHMlog10BF BEAST | COV (coefficient of variation)Substitution modelClock rate (rate 1 | rate 2) or calibration (My ± SD) used → source6Estimated clock rate | ESS
  1. According to Kass and Raftery (1995): 1= positive support for null hypothesis, BF values 0–3; 2= strong support for null hypothesis, BF values 3–6; 3= decisive support for null hypothesis (relaxed clock), BF values > 6; .

  2. 4Concatenated multiple gene dataset, see Table 1.

  3. 5Also analyzed as concatenated dataset with same priors (clock model, substitution model, clock rate). In water beetles (datasets 2, 9, and 22), the concatenated dataset was analyzed using the priors: GTR + I + G, relaxed clock, clock rate: 0.023.

  4. 6Rates (clock.rate and ucld.mean set in BEAUTi) and calibration points were derived from these sources: (1) Brower (1994), genetic distance in butterflies; (2) Schubart et al. (1998), 16S, freshwater crabs; (3) Mercer and Roth (2003), used with SD ± 1; (4) Echelle et al. (2005), ND2, cyprinodont fish; (5) Pesole et al. (1999), see also Esselstyn et al. (2009), mammalian mtDNA; (6) Pereira and Baker (2006), ND2, galliform birds; (7) Crawford (2003), ND2, leptodactylid frogs; (8) Köhler and Glaubrecht (2010), 16S, freshwater snails; (9) Alfaro et al. (2008), calibration point from fossil data; (10) Wilke et al. (2009), COI, mean ± SD from two different gastropod datasets; (11) Clouse and Giribet (2010), calibration point from fossil data corresponds to root height; (12) Pons et al. (2010), cyt b and COI, beetles; (13) Tamura und Nei (1993); control region, primates.

Mite harvestmen1Multiple4NANANA158.6803| 0.581GTR + I + G425 ± 1 → 110.001 | 21
Water beetles52Cyt b0.088−6.28No6.8793| 0.215HKY + I + G0.023 | 0.0343 → 1 / 12
 9COI0.26330.283Yes8.4333| 0.313GTR + I + G0.023 | 0.17212 → 1 / 12
 2216S0.36545.263Yes7.0203| 0.391GTR + I + G0.023 → 1
Freshwater snails316S0.42821.943Yes7.1553| 0.385GTR + I + G0.01 → 8
Freshwater crabs416S0.5982.321Yes1.9881| 0.729GTR + I + G0.0088 → 2
Shrews5Cyt b0.561−7.56No0.2191| 0.113GTR + I + G0.00562 | 0.01385 → 5
 6Cyt b0.554−30.28No0.5181| 0.008GTR + I + G0.00562 | 0.01385 → 5
Macaques57NADH0.601179.743Yes41.7273| 0.840GTR + I + G0.00562 | 0.01385 → 5
 2412S0.683−94.10No1.1881| 0.366HKY + I + G0.00562 | 0.01385 → 5
Phalangerids816S0.686168.403Yes0.4381| 0.204GTR + G0.00562 | 0.01385 → 5
Sailfin silversides10ND50.4645.602Yes1.5071| 0.274GTR + I + G0.025 | 0.031 → 4
Cockroaches11COII0.0681.001Yes3.7262| 0.223GTR + I + G0.023 → 1
Squirrels51216S0.01259.083Yes14.6133| 0.369GTR + I + G36 ± 1 → 30.0110 | 1251
 1312S0.019114.923Yes22.0113| 0.390GTR + I + G36 ± 1 → 30.0132 | 541
Megapodes14ND20.46547.523Yes11.4663| 0.615GTR + I + G0.0179 → 6
Fanged frogs51516S0.068168.403Yes36.3633| 0.447GTR + I + G0.0148 | 0.0245 → 7
 1612S0.00474.843Yes19.1873| 0.447HKY + I + G0.0148 | 0.0245 → 7
Tarsiers17Cyt b0.103−15.12No1.8791| 2.621GTR + G0.00562 | 0.01385 → 5
Grasshoppers18COI0.738−9.92No8.4013| 1.739GTR + I + G0.023 → 1
Ricefish51916S0.00418.823Yes5.3642| 0.600GTR + I + G0.025 | 0.031 → 4
 2312S0.492−28.38No0.9771| 0.280GTR + I + G0.025 | 0.031 → 4
Bovids20Cyt b0.064−14.36No17.9303| 0.266GTR + I + G0.00562 | 0.01385 → 5
Toads2112S0.711−44.14No0.0801| 0.341GTR + I + G0.0148 | 0.0245 → 7
Water snakes25Cyt b0.44624.703Yes4.2472| 0.272GTR + I + G36 ± 19 → 90.0239 | 1096
Freshwater bivalves26COI0.042−135.98No5.5952| 0.740HKY + I + G0.0148 | 0.016 → 10
Pigs27CR0.216−35.18No19.8033| 1.183GTR + I + G0.05 → 13

MOLECULAR CLOCK ANALYSES

Sequences were obtained from GenBank or were copied from the published alignment; base ambiguity codes (R, Y, M, K, W, S) were replaced by N to avoid an overestimation of the number of haplotypes due to potential sequencing errors. Noncoding sequences were aligned using MAFFT (Katoh and Toh 2008; default settings: gap open = 1.53, gap extension = 0.123, perform FFTS = localpair) and corrected by eye. All datasets were reduced to unique haplotypes (sequences containing more than 30% of N were deleted a priori) using DAMBE version 5.1.1 (Xia and Xie 2001) and were subsequently tested for nucleotide substitution saturation using the test by Xia and Xie (2001) as implemented in DAMBE including the estimation of the proportion of invariant sites (Pinv; Pinv tree-building algorithms: FastME, default settings). For the included datasets, tests revealed no significant saturation (P > 0.05), except for the assumption of an (unlikely) completely asymmetrical tree in some datasets (2, 5, 9, 10, 14, 22–24). Substitution models for Bayesian inference (BI) analyses and molecular clock analyses were estimated using the Akaike Information Criterion in jModelTest (Posada 2008; three substitution schemes, i.e., 24 models) (Table 2).

Molecular clock analyses can be performed using either strict or relaxed clock conditions, that is, under the assumption of homogeneous and heterogeneous substitution rates among branches, respectively. As a comparative approach, two different analysis methods have been conducted to test for the applicability of a strict clock. First, BI analyses were performed using Mr Bayes version 3.1.2 (Ronquist and Huelsenbeck 2003; parameters: ngen = 500,000–5,000,000, samplefreq = 10–100 [depending on ngen size], nchains = 4, burnin value = ngen-x = 15,000 [samples used for analyses]); a second BI run was performed (ngenstrict= ngendefault) for all datasets including a strict clock model (both sump burnin outputs were saved). Second, strict and relaxed lognormal molecular clock analyses were performed using BEAST version 1.5.3 (Drummond and Rambaut 2007; parameters used: Speciation: Yule process, ngen = 20,000,000, log = 400, burnin value = 35,001, calibration point setting for datasets 1, 10, 14, 25: normal distribution, that is, this prior—in comparison to a lognormal distribution—allows a younger age of the respective fossil used for the calibration). For two datasets (mite harvestmen and freshwater snails), strict and relaxed clock analyses were additionally performed under a birth–death (BD) process to assess the potential influence of extinction. Effective sampling size (ESS) values never dropped below 200 except for dataset 1 (see also below; Table 2). Specific clock rates and calibration points, respectively, are given in Table 2. Whenever possible, a taxon- and gene-specific external rate or calibration point was used, but for some datasets this was not possible and “general” substitution rates were used (see Table 2). Unspecific rates were taken from the next relative of the target taxon for which a rate was available, at best from the same family, but more frequently from the same major clade, for example, amphibians or insects (see Table 2 for details).

Bayes factor analyses for all datasets were conducted to test whether a strict clock could be accepted a priori by using harmonic mean values (the total value of run 1 and run 2 from both default and strict-enforced clock runs obtained from the sump output in Mr Bayes) and calculating twice the difference (delta harmonic mean [DHM]). For the BEAST runs, a Bayes factor analysis (log10 Bayes factors) was conducted as a post hoc test using tree likelihoods of both strict and relaxed lognormal clock analyses in Tracer version 1.5 (Rambaut and Drummond 2007; 1000 bootstrap replicates) to assess support for a strict versus a relaxed clock. Negative Bayes factor values are linked to a higher probability of the null hypothesis (i.e., strict-clock enforced runs in Mr Bayes and strict clock models in BEAST) and vice versa (Kass and Raftery 1995; see also Suchard et al. 2001). Kass and Raftery (1995) also suggest thresholds for deciding in favor of or against the null hypothesis: 0–3 (positive support), 3–6 (strong support), and > 6 (decisive support; see also Table 2).

Coefficients of variation (COV; obtained from the log files imported in Tracer) of the relaxed clock BEAST analyses were also considered as this parameter represents an indication for rate heterogeneity across the tree. Small COV values close to zero denote a clock-like evolution among lineages, whereas larger values indicate a higher degree of rate heterogeneity and may give a higher plausibility to perform relaxed clock analyses (e.g., Drummond et al. 2006).

Results

In the Bayes factor analyses using Mr Bayes, a strict clock was rejected for 15 of 27 datasets (Table 2). The Beast analyses comparing a strict to a relaxed clock yielded always positive values, that is, a strict clock was rejected for all datasets (Table 2). No clear link between Bayes factor support thresholds and COV values was found, though.

Node ages inferred by a relaxed clock differed by less than 10% from those estimated in the strict clock analyses in 56% (n= 15) of the datasets, with 22% (n= 6) differing by more then 20%. The tarsiers (dataset 17) are the only group showing a striking difference (> 50%) between the TMRCAs obtained from the two clock analyses, with a strong decrease of the TMRCA from 26.7 (rate 1) and 11.2 (rate 2) Mya (strict) to 8.3 and 3.9 Mya, respectively (relaxed). The following results are exclusively based on the relaxed clock analyses.

The topology of the individual calibrated phylogenies for the 20 taxa (27 datasets) analyzed reveals an Asian (mainland and/or Larger Sunda Isles) origin for 15 taxa, a Philippine origin for one taxon, and an Australian (or New Guinea) origin for four taxa (Figs. 2–4 and Table 1). In this respect, different datasets ( = different genes) for the same taxon (e.g., for water beetles, datasets 2, 9, 22 or squirrels, datasets 12, 13) did not reveal any differences if node support is taken into consideration (Figs. S1–S27). The term origin is used in a loose sense here also for cases where Sulawesi taxa are merely sister group to an Asian or Australian lineage as, for example, the pachychilid snails (Fig. 2, dataset 3), which is no evidence for an origin of the Sulawesi taxon from the respective area. Given the overall topologies of the trees (see also Supporting information), the origin ascribed here to a Sulawesi taxon seems nevertheless valid, if read as a metaphor for closest relatives being in Asia or Australia.

Figure 2.

Calibrated phylogenies of Sulawesi taxa (1) arranged in approximately chronological order with decreasing age. Numbers refer to the respective dataset (Table 1). Diversification time is indicated by scale(s) beneath trees for each rate/calibration. Red—Sulawesi taxa; blue—Asian taxa; green—Australian (including New Guinea) taxa; gray = Philippines, Moluccas, Lesser Sunda Islands East of Bali. Arrows indicate the timing of Sulawesi diversification (black) and the split from the MRCA (gray). Horizontal bars at nodes show the 95% confidence interval of diversification time.

Figure 3.

Calibrated phylogenies of Sulawesi taxa (2) arranged in approximately chronological order with decreasing age. Numbers refer to the respective dataset (Table 1). Diversification time is indicated by scale(s) beneath trees for each rate/calibration. Red—Sulawesi taxa; blue—Asian taxa; green—Australian (including New Guinea) taxa.

Figure 4.

Calibrated phylogenies of Sulawesi taxa (3) arranged in approximately chronological order with decreasing age. Numbers refer to the respective dataset (Table 1). Diversification time is indicated by scale(s) beneath trees for each rate/calibration. Red—Sulawesi taxa; blue—Asian taxa; green—Australian (including New Guinea) taxa.

The analysis of concatenated multigene datasets for the respective taxa revealed no topological conflicts to the single gene analyses if node support is considered (Figs. S1–S27). For a few taxa with an origin in Asia, multiple independent colonizations of Sulawesi have been shown, for example, for shrews or fanged frogs (Table 1 and Figs. 2–5). The topologies reconstructed in our study generally match the phylogenetic patterns described in the source publications (see Table 1), at least with respect to the position of the Sulawesi taxa. Topological conflicts between our results and the original publications or between different datasets here had either no effect on the conclusions (see Discussion) or were not supported at all, as in two of the three single gene water beetle datasets (Bayesian posterior probabilities below 0.5).

Figure 5.

Summary of the geographic and temporal origin of Sulawesi taxa. The bars on the geological scales in each panel indicate the timing of (bottom) the split of the Sulawesi lineage from its sister taxon and of (top) the first diversification event on Sulawesi. See Table 1 for reference. (A and B) Distribution of mean age, general patterns. Green bars indicate Sahul Shelf relationships and black bars Sunda Shelf relationships of Sulawesi taxa (sister group); pink arrows show sea-level lowstands (see panel C for details). (A) Based on substitution rate 1 only; (B) Based on substitution rate 2 only (if applicable; gray bars show rate 1 pattern, see panel A). (C) Sea-level fluctuations in Southeast Asia, redrawn from Mercer and Roth (2003) and Haq et al. (1987); the red arrows denotes the onset of cyclic sea-level changes enabling Sulawesi colonization. (D) Distribution of mean age of limnic taxa. In panels E and F, taxa are color-coded and numbers on bars refer to the respective taxon (Table 1). (E) Distribution of mean age of datasets revealing multiple colonizations of Sulawesi. (F) Distribution of mean age patterns when the same substitution rate is applied to different mitochondrial markers.

The estimation of divergence times, which are based on the results from the relaxed clock analyses, reveals a wide range from the Paleocene to the Pleistocene for the time to the most recent common ancestor (TMRCA; stem age, Crisp et al. 2011) of Sulawesi taxa, that is, the split from their sister group outside of Sulawesi (Fig. 5A and Tables 1 and S1). Except for two taxa, the mite harvestmen and water beetles (Fig. 2, datasets 1 and 2), the oldest TMRCA of any Sulawesi taxon is the early Oligocene, though, which narrows the range by about 25 My. The time range estimated for the earliest speciation event on Sulawesi (crown age of Sulawesi lineages; Crisp et al. 2011) is less wide, the oldest split being in the early Miocene (about 20 Mya). For several datasets the TMRCA was also estimated using an alternative substitution rate. In most cases, the results of both analyses differed considerably (Table 1 and Figs. 2–4, 5 B).

The additional application of a BD process model to the mite harvestmen and pachychilid snail datasets revealed a decrease in node age for the older lineage in mite harvestmen by 9 My to 51.8 Mya in comparison to the TMRCA estimate of 60.8 Mya under a Yule process, but for the younger lineage the TRMCA estimate increased to 54.0 Mya (BD) from 50.96 (Yule). For the pachychilid snails, the difference between TMRCA estimates under the two models was negligible (Yule: 30.5 vs. BD: 29.9 Mya).

Discussion

THE ORIGIN OF SULAWESI TAXA

The divergence time estimates for Sulawesi taxa can be used to test vicariance hypotheses by contrasting the TMRCA with the temporal constraints on vicariance events derived from geology (Fig. 1). The TMRCA for a Sulawesi taxon must predate (but compare discussion below) or coincide with a vicariant event such as the opening of the Makassar Strait or the separation of the relevant areas of the Sula Spur from New Guinea (compare Fig. 1, red arrows labeled 1 and 2), otherwise a vicariance hypothesis is falsified (but see below for caveats). For the purpose of this discussion, the oldest time estimate has always been used, even though there is some evidence that the alternative rate may be the better choice, see discussion on the reliability on the molecular clock analyses below. Similarly, in case of multiple colonizations, the older TMRCA was used (see Fig 5D). Confidence intervals of divergence time estimates have been considered in all cases here, that is, the interpretation is not only based on the mean TMRCA (compare Table 1 and Figs. 2–4). By applying these constraints, a different pattern emerges for taxa of Asian and Australian origin, respectively.

All post-Eocene TMRCA estimates of Sulawesi taxa postdate the opening of the Makassar Strait about 45 Mya (see Introduction) and are thus not compatible with the assumption of a vicariance hypothesis for an origin of Sulawesi taxa from the West (Fig. 5A and Table 1). Three of the four studies on Sulawesi taxa of Asian origin that have considered a vicariance hypothesis involving the opening of the Makassar Strait have also found a post-Eocene TMRCA for the respective taxon (grasshoppers, Walton et al. 1997; ricefish, Takehana et al. 2005; cockroaches, Maekawa et al. 2001), and support for a vicariance scenario was solely based on a somewhat relaxed interpretation of the time frame for the separation of Borneo and Sulawesi. Trans-Makassar Strait vicariance is only supported for one of the four taxa for whom a respective hypothesis has been suggested, namely mite harvestmen (Clouse and Giribet 2010), as the TMRCA estimates of about 51 and 61 Mya found in our analyses for the two lineages on Sulawesi fit the geological time frame (Fig. 2 and Table 1). However, poor node support and low ESS values were found in both the strict and relaxed clock analyses, which might indicate general issues with the dataset as analyzed.

Vicariant “tectonic dispersal” hypotheses to account for the origin of taxa from the East on the Sula Spur have been proposed or at least discussed for three of the four Sulawesi taxa with Australian affinities considered here (pachychilid snails, Glaubrecht and Rintelen 2003; von Rintelen and Glaubrecht 2005; Köhler and Glaubrecht 2010; sailfin silversides, Sparks and Smith 2004; phalangerids, Ruedas and Morales 2005). The TMRCA for all three taxa either clearly predates (pachychilid snails) or rather closely matches (sailfin silversides, phalangerids) the beginning separation of the Sula Spur parts that collided and remained with Sulawesi from New Guinea-Australia about 15 Mya (compare Fig. 1, red arrow no. 2; Figs. 2–4 and Table 1). For the latter two taxa, this only holds true for one of the two substitution rates used for estimating divergence times, though, as the second rate yields a younger TMRCA in both cases (if confidence intervals are considered, the divergence time of sailfin silversides matches the timing of Sula-Australia split also assuming the faster rate). The TMRCA of the fourth Sulawesi taxon with closest relatives in Australia, the megapodes, clearly postdates the relevant vicariant events. However, the Sulawesi megapodes are probably the best dispersers among these taxa, because they are capable of flying at least short distances, depending on the taxon (Dekker 2007).

In summary, the divergence time estimates derived by application of a standardized molecular clock approach are not compatible with a vicariance hypothesis in 80% of the taxa analyzed and suggest dispersal as the predominant process in the origin of Sulawesi taxa. This pattern becomes even more pronounced if the faster of two external rates used for some datasets should turn out to be more appropriate for the respective taxon (Fig. 5B, here called rate 2; compare also discussion below). However, our data are prima facie also compatible with vicariance hypotheses in 20% of the analyzed taxa. This proportion is 75% for taxa of Australian origin/affinities, but this can hardly be regarded as representative given the low number of respective datasets included.

Vicariance hypotheses involving Sulawesi taxa are at odds with the current paleogeographic reconstructions of Wallacea, that is, the past distribution of land and sea during the Cenozoic. Recent updates of the respective models suggest that both in West Sulawesi and on the Sula Spur larger areas may have been subaerial since the Eocene than previously assumed (compare Hall 2009b; Lohman et al. 2011). There is as yet no evidence for the existence of a subaerial link between the Sula Spur and Australia-New Guinea during the critical time period from the Oligocene to the early Miocene, though. Such a link is an essential assumption for matching a vicariance hypothesis with the respective dated phylogenies, for example, for the pachychilid snails. However, as emphasized above, reconstructions of the past distribution of land based on geological data tend to be biased towards detecting larger areas of land (Hall 2009b), and it should also be noted that the intervals of 5 or 10 My for which reconstructions are available for the last 40 My are rather coarse. We thus suggest not to reject a vicariance hypothesis for explaining the origin of Sulawesi taxa merely because it is not consistent with paleogeographic reconstructions, which are subject to at least as much uncertainty as the biological data. It seems fair to assume “bottlenecks” in the area of available land both in Southwest Sulawesi and on the Sula Spur, though, under a vicariance scenario. Interestingly, the two taxa where there seems to be least temporal uncertainty about the compatibility with a vicariance scenario, the mite harvestmen and pachychilid snails, show an extreme time lag between their respective TMRCA and the first speciation event on Sulawesi (Fig. 5A). This pattern matches the assumption of the respective lineage persisting on very little land during the Miocene both in Southwest Sulawesi at around 20 Mya and on the Sula Spur until about 20 Mya, which would presumably constrain the potential for speciation in both taxa by limiting population size, habitat diversity, and the possibility for allopatry. However, a long time lag between the TMRCA and speciation on Sulawesi might also be, for example, caused by insufficient taxonomic coverage or extinction. A role of the latter is possibly supported by the lower TMRCA found for the older Sulawesi lineage of mite harvestmen assuming a BD process, which also considers extinction in node age estimation. Nevertheless, a sampling bias in terms of taxa and area coverage is quite likely as well for a number of the invertebrates considered here, given the low number of sample sites on Sulawesi, but also other areas in some cases, for example, the mite harvestmen (compare Clouse and Giribet 2010; Fig. 1).

The earliest speciation event on Sulawesi in any taxon included in our study dates from the early Miocene about 20 Mya (Fig. 5A). This might be linked to two geological/climatological events: (1) the availability of more land on Sulawesi from about that time as a consequence of the collision of the Sula Spur with the North and West arms of the island (Hall 2009b; Spakman and Hall 2010; compare Fig. 1) and (2) the climate-induced start of periodic sea-level lowstands in the Mid-Miocene with inverse peaks in the late Miocene and Pliocene (see Haq et al. 1987; De Graciansky et al. 1998; Fig. 5C: pink arrow). Both factors would not only increase the potential of speciation on Sulawesi by providing, for example, opportunities for creating new ecological niches or allopatric differentiation, they would also increase the likelihood for chance dispersal, for example, across Makassar Strait being successful by providing a larger “target” at a lower distance from the source area. A link between Miocene colonization of Sulawesi and sea-level changes was already proposed for squirrels by Mercer and Roth (2003). The effect of sea-level lowstand(s) on decreasing the maximum open water distances between Sulawesi and the potential source areas would probably have been greatest from the Pliocene onwards (Hall 2009b). The splits of Sulawesi taxa from their sister groups and the earliest speciation events on Sulawesi are particularly densely clustered in the Pliocene/Pleistocene. If this is not merely a sampling artifact, it would support the assumption of an important role for sea-level lowstands coupled with the increasing extent of land area on Sulawesi for dispersal onto the island.

Several taxa have colonized Sulawesi more than once (see Table 1), and the time between independent colonization events ranges from almost instantly in geological terms (≤ 0.5 My) to about 10 My (Fig. 5E). The timing of the two colonization events is not correlated to the two geological/climatological events discussed above, that is, both colonization events in each taxon occurred either in the Miocene or Pliocene. The water beetles (black bars in Fig. 5E) are an exception here, but this might be an effect of substitution rate issues (see below) rather than indicative of very differently timed colonizations of Sulawesi. There is also no clear pattern of exclusive distribution of the two Sulawesi lineages in the respective taxa, which might be taken as indicative of the colonization of different potentially separated paleoislands of Sulawesi. In some taxa, different colonizations resulted in a largely or completely overlapping distribution of the Sulawesi clades, for example, in fanged frogs (Evans et al. 2003a) or shrews (Ruedi et al. 1998), whereas the two lineages of mite harvestmen, for example, are found in Southwest and North Sulawesi, respectively (Clouse and Giribet 2010). However, here as well as in some other cases (Ruedi et al. 1998), the geographic coverage of sampling on Sulawesi is poor. In taxa such as the macaques or the wild boar, the different Sulawesi lineages do not occur sympatrically, but it is doubtful whether monophyly of the Sulawesi taxa can be ruled out, either because there is insufficient support of the respective nodes (wild boars, Larson et al. 2005) or because nuclear data are in conflict with the mtDNA gene trees on this point (macaques, Tosi et al. 2003; Evans et al. 2010). Not only is there no obvious respective pattern in the distribution of lineages that have colonized Sulawesi independently, there is in our opinion also little hard evidence for any Sulawesi clade being confined to part of the island only, which might suggest a link of its origin on Sulawesi and tectonic processes. For most invertebrates, the poor sampling prevents any respective statement, and for vertebrates we are not aware of any recent example (and the fossil record is rather limited, see van den Bergh et al. 2001). Michaux (2010) has proposed that Sulawesi should not be employed as an area of endemism in biographic analyses in its entirety, but that rather parts of different geological or tectonic origin such as West Sulawesi or the Banggai-Sula block should be considered separately. This is not supported by our data.

However, subsequent to colonization of the island, geology, or climate-related changes in paleogeography seem to have played an important role in driving diversification processes on Sulawesi. A pronounced and at least partly congruent geographic structure of Sulawesi populations or species has been found in all taxa that have been sufficiently extensively sampled on the island (see, e.g., Bridle et al. 2001; Evans et al. 2003c, 2008; Larson et al. 2005; Brown et al. 2010; T. von Rintelen et al. unpubl. data). Although not the focus of this study, it would be highly interesting to test for temporal congruence in intra-Sulawesi diversification as well.

The datasets analyzed in this study comprise (mostly nonflying) terrestrial and limnic taxa. The distribution of TMRCA estimates of limnic taxa does not deviate from the general pattern (Fig. 5D), suggesting that freshwater organisms are not necessarily suffering “unique biogeographic constraints” (Unmack 2001). It rather seems that the capability for dispersal varies as much as in terrestrial taxa, and the limnic candidate taxa for a vicariant origin on Sulawesi, for example, comprise taxa absolutely confined to freshwater such as viviparous snails (the pachychilid Tylomelania, von Rintelen and Glaubrecht 2005) and a group of secondary freshwater fish with brackish water species (telmatherinids, Sparks and Smith 2004).

RELIABILITY OF DIVERGENCE TIME ESTIMATES

The validity of our results and their interpretation in terms of biogeographic hypotheses is strongly dependent on the quality of the divergence time estimates. This is not an exclusive issue here, as the usage of substitution rates or fossil calibrations for divergence time estimations is crucial in all molecular clock analyses (e.g., Graur and Martin 2004; Pulquério and Nichols 2007; Ho et al. 2008; Wilke et al. 2009; Wertheim and Sanderson 2010). Fossil record data might be imprecise and only give minimum estimates for the age of a group (e.g., Benton and Donoghue 2007; Donoghue and Benton 2007); the application of substitution rates derived from nonclosely related organisms and/or for different genes is fraught with difficulties (Thomas et al. 2006; Wilke et al. 2009). The latter point is of particular relevance here, because only a subset (n= 10) of the datasets in this study could be analyzed using a taxon-specific substitution rate or calibration point, respectively (see Table 1 for details and references). The majority of analyses were conducted using a rather unspecific substitution rate (compare Table 1), for example, for insects (general mtDNA rate), mammals (general mtDNA rate), fish (ND2 rate for cyprinodont fish), and frogs/toads (ND2 rate for leptodactylid frogs).

Precise calibration points and specific substitution rates are strong priors for the estimation of divergence times and if a large error in either calibration method is unrecognized this makes the resulting divergence time estimates unreliable to an unknown degree (Pulquério and Nichols 2007). Our study provides striking examples of the issues involved. For the ricefish, for example, Takehana et al. 2005 suggested a TMRCA of 29–32 Mya for the Sulawesi lineage compared to the 3–7 Mya estimated here. This discrepancy is largely due to their use of a substitution rate for Antarctic notothenioid fish (Bargelloni et al. 2000), which is an order of magnitude slower than the rates usually reported for vertebrates (Near 2004) and does seem an unusual choice for small tropical freshwater fish. Divergence time estimates also almost always differed considerably if different genes were analyzed for the same taxon (Fig. 5F and Table 1). The water beetle dataset is rather illustrative here, comprising data from three different gene fragments, which were both independently and jointly (see below) analyzed here (datasets 2, 9, 22) using the same rate (“general” insect mtDNA rate of 2.3%/My; Brower 1994). This approach resulted in extremely varying divergence times for the same Sulawesi taxon (the older of the two lineages found) from its sister group ranging from the Oligocene to the Pleistocene for the individual genes (datasets 2, 9, 22, summarized in Fig. 5F). In a recent study by Pons et al. (2010), beetle-specific rates for protein-coding mitochondrial genes have been estimated using a calibration derived from fossils. The rates for some individual genes differ considerably from the 2.3% average of the general insect mtDNA rate. For cytochrome c oxidase I (COI, dataset 9, Fig. 2), the specific rate is more than eightfold higher than the general insect rate and for cytochrome b the rate is more than 30% faster than the general rate (Cyt b, dataset 2, Fig. 2). More specific rates thus do not necessarily result in better congruence among the divergence times estimated from different genes (see Table 1). Using the new rates, the age of the studied water beetle lineages does decrease considerably for COI, shifting the TMRCA from the Mid-Miocene to the Pleistocene (Figs. 2–4 and Table 1). Pons et al. (2010) also show that the average rates between beetle taxa vary considerably, indicating that a beetle rate may be little better than a general insect rate for estimating divergence times in some lower rank taxa of beetles, which is perhaps not totally surprising in this old and megadiverse “superradiation” (Hunt et al. 2007). However, other recent studies have demonstrated a good fit between divergence time estimates derived from multiple calibration points and a general rate (Jønsson et al. 2011). In our study, the results from some vertebrate datasets suggest that the use of unspecific rates is not necessarily misleading. For the squirrels (datasets 12, 13), for example, the calibration point used by Mercer and Roth (2003) was employed and the resulting estimated clock rates (Table 2) approximately fit the faster of two published rates for mammals. These were used for all other mammal datasets in this study, such as the tarsiers (dataset 3). Here, the slower mammalian mtDNA rate 1 seems more appropriate, with the mean estimated TMRCA of 8.3 My (see Table 2) roughly (considering confidence intervals) fitting the split from T. bancanus about 11 Mya derived from fossil data (Merker et al. 2009), whereas the faster rate (rate 2 in our study) yields a much younger TMRCA. The substitution rates in primates have long been known to be lower than those of other mammals, particularly rodents (Li et al. 1987) and among primates the lemurs, which are comparatively closely related to tarsiers, have the lowest rates (Hasegawa et al. 1990), so these differences in rate applicability between mammal datasets should perhaps be expected. The application of external, that is, non-taxon specific rates in the absence of fossil calibrations is not necessarily misleading, but as it is hard to assess the reliability without external reference, the results must be interpreted with extreme caution.

With the exception of the mite harvestmen, datasets comprising multiple gene fragments were not only analyzed separately for each gene but also as a concatenated alignment using one rate (fanged frogs, macaques, ricefish, water beetles). As expected, the resulting TMRCA was intermediate to the estimates gained from the separate analyses (see Table 1). For the macaques and, to a lesser degree, also the water beetles, the TMRCAs from the separate analyses differ strongly and the TMRCA estimated from the concatenated dataset is very far from the mean of the two single gene TMRCAs, even though the same external rate was used in all analyses (Table 1). We suspect that this might be an effect of length differences between the individual gene fragments. For the macaques, for example, the TMRCA of the combined analysis is 18.6 Mya, which is quite close to the 19.5 Mya estimated for the NADH dataset alone, which has a length of 1325 bp and very different from the 3.7 Mya for the 12S dataset with only 413 bp length.

Although it has been suggested that a strict clock must be employed in molecular dating analyses using external rates (e.g., Wilke et al. 2009), the latter can also be employed under relaxed clock models as implemented in BEAST (Drummond and Rambaut 2007). The effect of the choice of molecular clock model (strict vs. relaxed clock) on the estimated TMRCAs seems secondary, though, if compared to the differences stemming from applying different substitution rates to the same gene or the same rate for different genes (see results; compare Tables 1 and S2). Interestingly, the differences between node age estimates are not linked to the rejection of a strict clock by the Bayes factor analyses in both approaches (compare Tables 2 and S2); for the tarsiers, for example, a strict clock was not rejected by the Bayes factor values of the DHM analysis or was the Bayes factor (BEAST) remarkably high (Table 2). The choice of clock model clearly matters and a strict clock model should not be used when rejected by Bayes factors, but we suggest that the choice of substitution rate and calibration is generally more important with respect to the robustness of divergence time estimates. Remarkably, the biogeographic implications of our analyses remain valid under each model or rate used.

BIOGEOGRAPHIC INTERPRETATION OF MOLECULAR DIVERGENCE TIME ESTIMATES

Our study essentially relies on comparing divergence times of Sulawesi taxa with vicariant events derived from geology (Fig. 1). This approach has lead to a “resurrection of oceanic dispersal” (de Queiroz 2005; Cowie and Holland 2006; Heaney 2007) in historical biogeography by using divergence time estimates to falsify vicariance hypotheses by demonstrating that the phylogenetic splits in question postdate a vicariance event (e.g., Knapp et al. 2005; Heinicke et al. 2007; Michalak et al. 2010; Schweizer et al. 2010; Bartish et al. 2011). Crisp et al. (2011) have recently argued to use calibrated trees in biogeography in a more explicitly hypothesis-driven way instead of an inductive “pattern first, process later” procedure of fitting some geological data to the tree. Overall, our study provides ample evidence in justification of their argument. Crisp et al. (2011: Box 1) have also suggested that a vicariance scenario is only supported by the data if the timing of respective phylogenetic splits and their confidence is overlapping with the time frame of the vicariant events. Although we acknowledge that this approach is rigorous and objective, we would suggest that a TMRCA predating a vicariant event, as in the case of the mite harvestmen (Fig. 2, dataset 1) in this study, does not automatically falsify an involvement of vicariance in causing the phylogenetic split. The mite harvestmen are probably representative for many groups of small tropical invertebrates in having a high likelihood of being both taxonomically and geographically undersampled (compare Clouse and Giribet 2010; Fig. 1). Extinction could also cause a time lag between the split recovered in a phylogeny of extant taxa and the vicariant event. It might be argued that the risk of extinction is particularly high in Wallacea and adjacent regions where ongoing tectonic processes entail frequent local submergence and uplift events.

Although stem age estimates are widely used to test vicariance hypotheses in biogeography (see Crisp et al. 2011 and above), this approach has recently been heavily criticized (Heads, 2005, 2009, 2011; Nelson and Ladiges 2009), particularly the use of divergence time estimates as maximum ages for phylogenetic splits. If a molecular clock has been calibrated using fossils or external rates ultimately derived from fossil calibrations, any resulting age estimate will be a minimum age (e.g., Donoghue and Benton 2007). Using this age estimate as a de facto maximum age that is then employed in testing congruence between the age of a vicariant event and a specific phylogenetic split can indeed create problems if the TMRCA of a lineage is much older than the fossil used to define its age or if the difference between the age geological vicariant event and the phylogenetic split is small (though confidence intervals in a properly conducted molecular clock analysis would then probably overlap with the time range assigned to the vicariant event). Heads (2009, 2011) suggested to rely on tectonic calibrations and use well-established vicariance events instead of fossils for fixing node age. We consider this proposal even more problematic than the issue it tries to overcome, mainly for two reasons: (1) age estimates for vicariant events can be imprecise as well, as also acknowledged by Heads (2011), for example, for the Panama Isthmus, and, more importantly, (2) using them would either lead to circular reasoning if the aim of a study is to test for a role of vicariant events in the evolution of a taxon (see also Kodandaramaiah 2011) or imply the a priori acceptance of a vicariance model, which would make biogeography less of a science and more of a religion. Although naive dispersal concepts have been rightly criticized (e.g., Croizat et al. 1974), which paved the way for vicariance biogeography (e.g., Wiley 1988), it seems futile to deny a role of either dispersal (long-distance dispersal) and vicariance in biogeography (for details see Thornton 1983).

CONCLUSIONS

Sulawesi has a fauna of mixed geographic and temporal origin. Given the patterns discussed here, it is little surprising that Wallace had difficulties in understanding the biogeography of this “anomalous island” (Wallace 1876). Dispersal seems the primary mechanism of bringing taxa to the island and a standardized molecular clock approach has let to the falsification of vicariance hypotheses for some Sulawesi taxa of Asian origin. The divergence time estimates presented here are compatible with a vicariance hypothesis in 10–20% of the studied taxa, though, depending on the choice of substitution rate in two cases. A vicariance scenario is dependent on accurate tectonic and paleogeographical data, but evidence for vicariance can at the same time inform geology and, for example, increase the probability for assuming land in certain parts of Wallacea or suggest where geological evidence for this might be sought. Sulawesi has a rich fauna (and flora), and we have used but a few taxa for which the respective data were available for this review of the biogeography of the island. Despite the methodological problems inherent to a molecular clock approach and the by necessity poor taxon coverage, we believe our results are generally robust. However, more data are needed to gain a truly comprehensive understanding of Sulawesi's biogeography, including intra-island diversification. Biogeographers in the pre-molecular and precladistic age compiled many fascinating distribution patterns involving Sulawesi, and the simplified “origin from Asia” or “origin from Australia” dichotomy this study is largely confined to will surely become more complex. Recently, for example, the dating of phylogenetic splits in two genera of nymphalid butterflies has revealed sister groups of the Sulawesi lineages from Timor and the Moluccas, respectively (Müller and Beheregaray 2010; Müller et al. 2010). The time frame of Sulawesi colonization for the butterflies (Miocene) and speciation on the island (Miocene?/Pliocene) is fully compatible with the data presented here, though, and suggests that Sulawesi's fauna was decisively shaped by events in the Miocene and Pliocene. The need for new data also applies to geology. Biogeographers must acknowledge the limitations of tectonic or paleogeographic reconstructions (Hall 2009a), but they should also use them explicitly to derive temporal constraints for biogeographic hypotheses of vicariance, as for example, exemplarily done for the Madagascan fauna by Warren et al. (2010). This will only work in conjunction with molecular divergence time estimates. Again, these are not the Holy Grail but biological data with a confidence margin and should be treated accordingly, but ignoring them with a vague reference to paleoendemics (Michaux 2010) is not an option in our opinion.


Associate Editor: B. Fitzpatrick

ACKNOWLEDGMENTS

We thank J. A. Esselstyn (Kansas, USA) and R. Clouse (New York, USA) for providing sequence data. R. Schultheiß (Giessen, Germany) provided helpful comments on the manuscript. Remarks by two anonymous reviewers helped to greatly improve critical aspects of this manuscript. This study was financed by German Research Council (DFG) grants AL 1076/1–1 and RI 1738/4–1 to CA and TvR, respectively.

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