3.22: Sympatric speciation - Biology

3.22: Sympatric speciation - Biology

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While the logic and mechanisms of allopatric speciation are relatively easy to grasp (we hope), there is a second type of speciation, known as sympatric speciation, which was originally more controversial. One involves host selection104. Mutations that reinforce an initial, perhaps weak, mating preference can lead to what known as reproductive isolation - as we will see this is a simple form of sexual selection105. One population has become two distinct, reproductively independent populations, one species has become two.

November 2017

Tequila BA Incipient w/ Blueberries, Limes, & Salt(Collaboration w/ Tanglewood Winery)

Base Beer: Golden Sour
Grist: Pilsner, Wheat, Oats
Vessel: Tequila Barrels (2nd use)
Brewed: 6/2/17
Bottled: 8/28/17
Release Date: 11/11/17
Fruit: 2lbs/gallon Blueberries from Tanglewood Winery
Original Gravity: 1056
Gravity at Bottling: 1001
ABV: 7.5%
pH: 3.2
IBU: 0
# of Bottles: 792
Distribution: Brewery only

Trace ElementsWild Grisette/Pilsner Hybrid

Base Beer: Grisette
Grist: Pilsner
Vessel: Stainless
Brewed: 8/16/17
Bottled: 9/12/17
Release Date: 11/11/17
Dry Hopped w/ Aged Saaz & Ultra hops
Original Gravity: 1035
Gravity at Bottling: 1001
ABV: 4.5
pH: 4.0
IBU: 35
# of Bottles: 660
Distribution: West MI only

Magic TraitSour w/ Blackberries & Black Currant - Double Fruit

Base Beer: Golden Sour
Grist: Pilsner, Wheat, Oats
Vessel: Stainless
Brewed: 8/21/17
Bottled: 9/25/17
Release Date: 11/11/17
Fruit: 10 gallons/8 bbls Concentrate from Coloma Frozen Foods
Original Gravity: 1052
Gravity at Bottling: 1002
ABV: 7.6
pH: 3.33
IBU: 0
# of Bottles: 1056
Distribution: West MI only

Morphology - Dry Hopped Berliner Weisse (East Side of MI only)(Collaboration w/ M4 CIC, our East Side Distributor)

Base Beer: Berliner Weisse
Grist: Pilsner, Wheat, Oats
Vessel: Stainless
Brewed: 8/30/17
Bottled: 10/3/17
Release Date: 11/11/17
Dry Hops: Mandarina Bavaria, Medusa, Mosaic, Motueka (2.5lb/bbl total)
Original Gravity: 1032
Gravity at Bottling: 1000
ABV: 4.2
pH: 3.64
IBU: 0
# of bottles: 924
Distribution: Cultured Club and East Side of MI Only

Differences in spawning time drive cryptic speciation in the coral Acropora divaricata

Most of the reef-building Acropora corals around Okinawa Island spawn in May and/or June. We found that two morphotypes of Acropora divaricata spawned in August and/or September. The “robust” and “slender” morphotypes differed in branch width and in the diameter of axial corallites. Histological analyses showed that the onset of gamete production/maturation occurred from June onwards. Most of the other Acropora species spawned from late May to early June. A. florida, which spawned in May/June, initiated gamete production in early April. We assumed that the two morphotypes of A. divaricata were reproductively isolated from most of the other Acropora species because of differences in the spawning seasons. We found that the spawning seasons of the two morphotypes slightly overlapped in 2015 but not in 2019, and inter-morphotype gamete compatibility was high. However, population genetics analyses and a phylogeny of the mitochondrial d -loop region showed that they were genetically distinct and rarely hybridized. Thus, the broadcast-spawning coral A. divaricata might have speciated when there might have been only a low possibility of interspecific gamete interaction.

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Transitions between phases of genomic differentiation during stick-insect speciation

Speciation can involve a transition from a few genetic loci that are resistant to gene flow to genome-wide differentiation. However, only limited data exist concerning this transition and the factors promoting it. Here, we study phases of speciation using data from >100 populations of 11 species of Timema stick insects. Consistent with early phases of genic speciation, adaptive colour-pattern loci reside in localized genetic regions of accentuated differentiation between populations experiencing gene flow. Transitions to genome-wide differentiation are also observed with gene flow, in association with differentiation in polygenic chemical traits affecting mate choice. Thus, intermediate phases of speciation are associated with genome-wide differentiation and mate choice, but not growth of a few genomic islands. We also find a gap in genomic differentiation between sympatric taxa that still exchange genes and those that do not, highlighting the association between differentiation and complete reproductive isolation. Our results suggest that substantial progress towards speciation may involve the alignment of multi-faceted aspects of differentiation.

Speciation involves genetic differentiation 1–3 . In the absence of gene flow, genome-wide differentiation can readily build by selection and drift. Differentiation with gene flow is potentially more complex, as the homogenizing effects of gene flow must be countered 1–3 . The genic model of speciation proposes that specific genetic regions subject to strong divergent natural or sexual selection become resistant to gene flow (that is, exhibit ‘reproductive isolation’) before others 4,5 . This model thus predicts localized, and potentially few, regions of accentuated differentiation or ‘genomic islands’ at the initiation of speciation 1,6 . It also predicts that genes subject to divergent selection reside in regions of accentuated differentiation. Consistent with such patterns, colour-pattern differences between subspecies of crows and races of butterflies map to a few localized peaks of genetic differentiation 7–9 .


The genome comparison conducted in this study suggests that the recent emergence of M. graminicola as a specialized wheat pathogen was associated with genomic changes involving mainly DNA located on the smallest chromosomes (<1 Mb). The remainder of the genome has maintained a high degree of identity between M. graminicola and S1. This genome comparison included only two fungal individuals. It is well known that M. graminicola exhibits not only intra-specific presence/absence polymorphism in the dispensable chromosomes, but also size polymorphisms [8], [18]. The lack of synteny and the different size distribution of the smallest chromosomes in S1 suggest that this set of chromosomes is not entirely homologous to the small chromosomes in M. graminicola. The small chromosomes instead show a fragmented pattern of aligned sequences that have evolved in a different manner compared to the essential chromosomes. So far we do not know whether the seven small chromosomes in S1 are dispensable, but PFGE analyses have revealed size polymorphisms among the small chromosomes in other S1 isolates. More intra-specific data will be needed from both species to better understand the karyotype-level structural changes that occurred during speciation.

Our data suggests that the structural changes between S1 and M. graminicola have exceeded the molecular evolution at the nucleotide level. It was previously reported that rates of structural and molecular evolution are correlated in vertebrates, nematodes and arthropods [19]–[21]. However, the present study as well as previous genome comparisons [3], [22], [23] illustrate that rates of structural evolution can be accelerated relative to rates of molecular evolution in fungi.

The speciation of M. graminicola and S1 occurred 10–11,000 years ago representing approximately 10,000 pathogen generations however this significant differentiation of genomes may have occurred over a longer period of time or as a consequence of host domestication and a very rapid split between pathogen lineages as M. graminicola co-evolved with wheat to become host-specialized. Our pathogenicity tests show that S1 causes less disease and forms many fewer reproductive structures on wheat, demonstrating that specialized adaptation to wheat is a trait that emerged in M. graminicola after the split between the two pathogen species. This host specialization may have been a driving factor in the process of speciation. The average genome divergence of 7% is consistent with a speciation time of 11,000 years as previously estimated using the Isolation with Migration (IM) coalescence model [5], [24]. The divergence time is the speciation time plus average time to coalescence in the common ancestral species [25]. The mean coalescence time in the ancestral species is 2N generations, where N is the effective population size of the ancestral species. Assuming an effective population size in the ancestral species of 15,000 (Stukenbrock et al, unpubl) we find that the average divergence time is 11,000 years plus 2 * 15,000 = 41,000 years (assuming on average one generation per year). This would correspond to an average substitution rate of 0.07/82,000 = 8.5×10 −7 nucleotide substitutions per generation consistent with experimentally measured mutation rates in yeast [26].

A striking finding of our genome comparison is the highly different patterns of nucleotide evolution on the essential and dispensable chromosomes. The dispensable chromosomes share several features characteristic of B-chromosomes in plants, including more structural changes and an accumulation of repetitive DNA and pseudogenes [27]. Previous studies have reported an irregular meiotic behavior of this group of chromosomes as well as a different recombination pattern caused by the absence of pairings between homologous chromosomes in crosses involving isolates with different chromosome combinations [8]. We believe that the different evolutionary patterns of essential and dispensable chromosomes observed here are due to the irregular transmission of the dispensable chromosomes. Chromosomes that exist in the pathogen population at lower frequencies will as a consequence have a lower effective population size compared to the essential chromosomes that are always present in all individuals. If the small chromosomes were also dispensable in the common ancestor of M. graminicola and S1, they would similarly have had a lower effective population size. In the modern species this would be observed as less average divergence, which is indeed the pattern we observe when comparing essential and dispensable chromosomes. Distinct evolutionary patterns of essential and dispensable chromosomes have also been demonstrated for another fungal plant pathogen. In a comparison of Fusarium genomes it was also shown that dispensable chromosomes have different sequence characteristics including a higher content of unique genes and a different codon usage compared to core chromosomes of the pathogens [11]. The authors suggested that horizontal transfer of chromosomes between Fusarium species led to the emergence of new host specific lineages by the acquisition of new pathogenicity related genes. Genome plasticity of M. graminicola and closely related species may similarly play a role in the evolution of different host specificities and horizontal gene transfer from distantly related species has been proposed as an origin for the dispensable chromosomes [6]. Sequencing of additional isolates and species will enable us to further investigate the origin and evolution of the dispensable chromosomes in Mycosphaerella.

On the essential chromosomes we find significantly higher Ks compared to intergenic substitution rates. The intergenic chromosome regions were on average only 70% as diverged as synonymous sites in exons. The fact that our alignment did not include rapidly evolving repetitive DNA may affect our average estimates of intergenic substitution rates. However, our findings suggest that a large fraction of the intergenic DNA on the essential chromosomes evolves under selective constraints. The importance of natural selection on genes also manifests itself as a lower synonymous rate in gene dense regions, suggesting a combination of background selection and selective sweeps. On the contrary, Ks on the dispensable chromosomes was significantly lower than Ks on the essential chromosomes. This does not necessarily imply a lower mutation rate on the dispensable chromosomes but, as mentioned above, is likely a consequence of a smaller effective population size for the small chromosomes. In the ancestral species of M. graminicola and S1 this would result in less polymorphism on the dispensable chromosomes compared to the essential chromosomes and thus also less average divergence. In the dispensable chromosomes we additionally find that Ks and the intergenic substitution rate were of the same size, most likely reflecting the neutral evolution of the large number of pseudogenes.

Our analysis of gene evolution shows that ∼70% of genes on the dispensable chromosomes are putative pseudogenes. Because the present study does not include an out-group, we are not able to say if the genes are evolving to become pseudogenes only in M. graminicola while maintaining function in S1. Future studies will elucidate the role of these genes in S1. Among the 11 genes on the dispensable chromosomes that show evidence of positive selection, only two were supported by EST data. This distribution of non-functional genes strongly supports the hypothesis that a large fraction of the DNA present on the eight small chromosomes is redundant i.e. not playing an essential role in fitness of the pathogen. However the persistence of these chromosomes in the genomes of the pathogens indicates that some of the genes on these chromosomes may still play an important role for the pathogen species.

We found that measures of genome variation including intergenic substitution rates, Ks and Ka were unevenly distributed along chromosomes, with sequence divergence increasing towards the chromosome ends. Similar patterns were found in yeast using whole genome oligonucleotide arrays including both coding and non-coding DNA [28]. Genes with increased Ka/Ks ratios were also located closer to chromosome ends compared to more conserved genes, suggesting that the chromosomal location of a gene can influence its evolutionary potential.

While the infection biology of S1 has not yet been studied intensively, we believe M. graminicola and S1 are likely to share many features in their disease cycles because they are so closely related, differing mainly in their specific interactions with particular host molecules. Extensive gene loss has been reported in other comparative genome studies of fungi [3], [29] and we hypothesize that gene loss also played a significant role in the genome divergence of S1 and M. graminicola. 10% of the genes annotated in M. graminicola were not aligned by S1 contigs and we likewise could not align all assembled S1 contigs to the M. graminicola genome, demonstrating the presence of similar S1 specific sequences. We identified 43 genes with a signature of positive selection. Our stringent significance thresholds for Ka>Ks could lead us to miss genes with less dramatic signatures of positive selection. For example our approach to test Ka>Ks may not be sensitive enough to detect positive selection that affects only a few codons in a gene. The set of genes we identified in this analysis have significantly diverged between M. graminicola and S1. None of these 43 positively selected genes has a known protein function. Only one of these genes encodes a secreted protein and has the characteristics expected for a possible effector molecule. The pooled set of signal peptides has an increased ratio of KaKs demonstrating an increased evolutionary rate compared to the remaining fraction of protein coding genes. Interestingly the vast majority of signal peptides are located on the essential chromosomes, suggesting an essential function for the proteins encoded by these genes.

The process of speciation between S1 and M. graminicola included the evolution of reproductive barriers between the two pathogens. The intra and interspecific comparisons reported here and in our previous study of S1 and M. graminicola provide no evidence for contemporary genetic exchange between the pathogens [5]. Though many genes are known to be involved in ascomycete mating, we focused on the gene encoding the mating type protein MAT1-1. The mating type gene is located in a chromosomal region of lower diversity compared to the general chromosome divergence. Low levels of diversity at the mating type loci and flanking regions have been reported in other fungi as a consequence of less recombination at the MAT locus in heterothallic fungi [30], [31]. While the MAT1-1 locus appears to be conserved across species borders, our ongoing characterization of the MAT1-2 gene in the two species suggests that reproductive barriers can be mediated at least partially through a high number of amino acid changes in the MAT1-2 protein (Stukenbrock et al, in prep).

This is the first comparative genome analysis of plant pathogens that have recently diverged in response to host domestication. Although we found a high degree of nucleotide similarity between the two genomes, our findings suggest that the divergence of these pathogens has been accompanied by structural rearrangements in their genomes and strong positive selection on a small set of genes with unknown function. Ongoing resequencing of additional S1 and M. graminicola strains will allow us to more precisely differentiate intraspecific versus interspecific diversity at the genome level. Genome sequencing of “S2”, another Mycosphaerella species infecting wild grasses in the Middle East, will provide additional opportunities to understand the origin of dispensable chromosomes as well as to determine how “natural” speciation between S1 and S2 affected genome structure compared to the “human mediated” speciation that led to M. graminicola.


Darwin thought of evolution as a gradual accumulation of small changes. This proposal is a major component and one of the most controversial of Darwin's theories. He repeated several times the Latin phrase “natura non-falcit saltum” (nature does not make laps). The punctuationists believe that all are species have their own history, appearing and then disappearing, whereas gradualists consider species with much less interest, as a concept of convenience. Like Lamarck, Darwin believed that species changed gradually by undergoing changes and modifications over time without sharp changes. Because the evolutionarily relevant mutations are supposed to have infinitesimally small fitness effects, the Darwinian model of evolution inevitably leads to the concept of gradual progressive improvement (Darwin, 1859). This vision comes from his early training as a geologist who intentionally ignored disasters and catastrophic events in evolution. We know that this view is false in both geology and biology. Rather than small, gradual changes, massive events occur that affect living beings. Thus, because a gene must be present or absent to produce an inherited effect, Mendel assumed that the appearance of a new function would occur at once rather than gradually, as Darwin imagined. Later the zoologist Ernst Mayr showed that new species generally appears in geographic isolation and undergo a true “revolution” that rapidly transforms their gene pool. Studies on the frequency and geographical distribution of chronological horse fossils show that species evolution is not linear but consist of periods of stasis (gradual changes) interspersed with 𠇌rises,” which lead to sudden extinctions and the appearance of new species. Indeed, different species could coexist with their original species while that ancestor remained unchanged, and there have even been reversals in evolutionary characteristics. These are all different evolutionary phenomena that explain the diversity of fossils and constitute a direct rebuttal to the principle of gradualism.

Moreover, Darwin's principle has been challenged by the Birth, Death, and Innovation Model of gene family evolution (Karev et al., 2002). In this model, duplication and lateral gene transfer give 𠇋irth” to new paralogous genes, �th” refers to gene elimination, and innovation corresponds to the acquisition of a new gene family via duplication and rapid evolution or via de novo creation. These events induce large and profound variations in genome size and gene repertoire (Figure 3). Thus, bacterial lineages that are specialized, including those with an obligatory intracellular lifestyle, show a repeated pattern of reduction in genome size through gene loss (Andersson and Kurland, 1998 Merhej et al., 2009). Bacterial genomes expand through lateral gene transfer and duplication. As a result, a considerable proportion (up to 14% of the ORFs) of most bacterial genomes consists of horizontally acquired genes (Nakamura et al., 2004). Lateral transfer allows for the acquisition of xenobiotic functions (Treangen and Rocha, 2011). Lederberg's work in microbiology showed that these alterations can be transmitted in a heritable manner (Lederberg, 1949). Plasmids of several hundred kilobases can be transferred, as can bacteriophages, in bacteria. This phenomenon also occurs in eukaryotes. The virus HHV6 can integrate into the genome of humans and be transferred to their children (Arbuckle et al., 2010 Raoult, 2011). Additionally, the entire genome of the intracellular bacterium, Wolbachia was found to be integrated into the genome of its host (Dunning Hotopp et al., 2007 McNulty et al., 2010). Some of these inserted sequences are transcribed within eukaryotic cells, indicating that they may be functionally relevant to the evolution of the microbe's host. Finally, bacterial genomes exhibit a significant number of paralogous genes due to duplication (Fitch, 1970), ranging from 7% in Rickettsia conorii to 41% in Streptomyces coelicolor A3 (Gevers et al., 2004). Gene duplication represents an important path to the evolution of new biological functions via neo-functionalization (Ohno, 1970 Innan and Kondrashov, 2010). Clearly, loss, the lateral acquisition of genes, and the emergence of a new gene as a result of duplication or de novo creation are far from being “infinitesimal” changes, and if such large events occur, they are too abrupt so that the gradualist paradigm is not valid.

Figure 3. Dynamic entity of the bacterial genomes.

Materials and Methods

Specimen collection

Specimens of Palythoa species were collected in the intertidal zone from several sites in the Ryukyu Archipelago, including Okinawa-jima Island, Yoron-to Island, Okinoerabu-jima Island, and Tokunoshima Island ( Fig. 1 , Table 2 ) between March 2010 to October 2012. All specimens were stored in 99.5% ethanol for DNA analyses or 5% formalin-SW solution for morphological and anatomical analyses.

Table 2

Specimen codeLocation/regionGPS codeSpecies IDDate (m/d/y)Collected byFixed bymt COImt 16S-rDNAITS-rDNAALG11
2PtOkOdOdo/Okinawa1P. tuberculosaAug 18. 09MM*199.5% EtOHNANANA <"type":"entrez-nucleotide","attrs":<"text":"KX389373","term_id":"1159852495","term_text":"KX389373">> KX389373
4PtOkOdOdo/Okinawa1P. tuberculosaAug 23. 09MM99.5% EtOHNA <"type":"entrez-nucleotide","attrs":<"text":"KX389335","term_id":"1159852457","term_text":"KX389335">> KX389335 NA <"type":"entrez-nucleotide","attrs":<"text":"KX389374","term_id":"1159852497","term_text":"KX389374">> KX389374
5PtOkOdOdo/Okinawa1P. tuberculosaAug 23. 09MM99.5% EtOHNANANA <"type":"entrez-nucleotide","attrs":<"text":"KX389375","term_id":"1159852499","term_text":"KX389375">> KX389375
37PtYoMaMaehama/Yoron2P. tuberculosaMar 03. 10JDR* 2 99.5% EtOHNA <"type":"entrez-nucleotide","attrs":<"text":"KX389336","term_id":"1159852458","term_text":"KX389336">> KX389336 NA <"type":"entrez-nucleotide","attrs":<"text":"KX389376","term_id":"1159852501","term_text":"KX389376">> KX389376
39PtYoUkUkachi/Yoron3P. tuberculosaMar 04. 10MM99.5% EtOHNA <"type":"entrez-nucleotide","attrs":<"text":"KX389337","term_id":"1159852459","term_text":"KX389337">> KX389337 <"type":"entrez-nucleotide","attrs":<"text":"KX389459","term_id":"1159852667","term_text":"KX389459">> KX389459 <"type":"entrez-nucleotide","attrs":<"text":"KX389377","term_id":"1159852503","term_text":"KX389377">> KX389377
40PtYoUkUkachi/Yoron3P. tuberculosaMar 04. 10MM99.5% EtOHNANANA <"type":"entrez-nucleotide","attrs":<"text":"KX389378","term_id":"1159852505","term_text":"KX389378">> KX389378
49PtYoUkUkachi(West)/Yoron4P. tuberculosaMar 04. 10MM99.5% EtOHNANANA <"type":"entrez-nucleotide","attrs":<"text":"KX389379","term_id":"1159852507","term_text":"KX389379">> KX389379
63PtErYaYakomo/Okinoerabu5P. tuberculosaMar 05. 10MM99.5% EtOHNA <"type":"entrez-nucleotide","attrs":<"text":"KX389338","term_id":"1159852460","term_text":"KX389338">> KX389338 NA <"type":"entrez-nucleotide","attrs":<"text":"KX389380","term_id":"1159852509","term_text":"KX389380">> KX389380
65PtErYaYakomo/Okinoerabu5P. tuberculosaMar 05. 10MM99.5% EtOHNANANA <"type":"entrez-nucleotide","attrs":<"text":"KX389381","term_id":"1159852511","term_text":"KX389381">> KX389381
91PtToYoYonama/Tokunoshima6P. tuberculosaMar 08. 10MM99.5% EtOHNANANA <"type":"entrez-nucleotide","attrs":<"text":"KX389382","term_id":"1159852513","term_text":"KX389382">> KX389382
98PtToKaKaminomine/Tokunoshima7P. tuberculosaMar 09. 10MM99.5% EtOHNANANA <"type":"entrez-nucleotide","attrs":<"text":"KX389383","term_id":"1159852515","term_text":"KX389383">> KX389383
100PtToKaKaminomine/Tokunoshima7P. tuberculosaMar 09. 10MM99.5% EtOHNA <"type":"entrez-nucleotide","attrs":<"text":"KX389339","term_id":"1159852461","term_text":"KX389339">> KX389339 NA <"type":"entrez-nucleotide","attrs":<"text":"KX389384","term_id":"1159852517","term_text":"KX389384">> KX389384
358PtOkAkAkazaki/Okinawa8P. tuberculosaJun 24. 12MM99.5% EtOHNA <"type":"entrez-nucleotide","attrs":<"text":"KX389340","term_id":"1159852462","term_text":"KX389340">> KX389340 NA <"type":"entrez-nucleotide","attrs":<"text":"KX389385","term_id":"1159852519","term_text":"KX389385">> KX389385
361PtOkOkOku/Okinawa9P. tuberculosaJun 25. 12MM99.5% EtOHNANANA <"type":"entrez-nucleotide","attrs":<"text":"KX389386","term_id":"1159852521","term_text":"KX389386">> KX389386
371PtZaAmAma/Zamami10P. tuberculosaJul 16. 12YM*399.5% EtOHNA <"type":"entrez-nucleotide","attrs":<"text":"KX389341","term_id":"1159852463","term_text":"KX389341">> KX389341 NA <"type":"entrez-nucleotide","attrs":<"text":"KX389387","term_id":"1159852523","term_text":"KX389387">> KX389387
3PyOkOdOdo/Okinawa1P. sp. yoronAug 18. 09MM99.5% EtOH <"type":"entrez-nucleotide","attrs":<"text":"KX389439","term_id":"1159852627","term_text":"KX389439">> KX389439 <"type":"entrez-nucleotide","attrs":<"text":"KX389342","term_id":"1159852464","term_text":"KX389342">> KX389342 <"type":"entrez-nucleotide","attrs":<"text":"KX389460","term_id":"1159852668","term_text":"KX389460">> KX389460 <"type":"entrez-nucleotide","attrs":<"text":"KX389388","term_id":"1159852525","term_text":"KX389388">> KX389388
14PyOkOdOdo/Okinawa1P. sp. yoronAug 23. 09MM99.5% EtOH <"type":"entrez-nucleotide","attrs":<"text":"KX389440","term_id":"1159852629","term_text":"KX389440">> KX389440 <"type":"entrez-nucleotide","attrs":<"text":"KX389343","term_id":"1159852465","term_text":"KX389343">> KX389343 <"type":"entrez-nucleotide","attrs":<"text":"KX389472","term_id":"1159852680","term_text":"KX389472">> KX389472 <"type":"entrez-nucleotide","attrs":<"text":"KX389389","term_id":"1159852527","term_text":"KX389389">> KX389389
15PyOkOdOdo/Okinawa1P. sp. yoronSep 05. 09MM99.5% EtOH <"type":"entrez-nucleotide","attrs":<"text":"KX389441","term_id":"1159852631","term_text":"KX389441">> KX389441 <"type":"entrez-nucleotide","attrs":<"text":"KX389344","term_id":"1159852466","term_text":"KX389344">> KX389344 <"type":"entrez-nucleotide","attrs":<"text":"KX389461","term_id":"1159852669","term_text":"KX389461">> KX389461 <"type":"entrez-nucleotide","attrs":<"text":"KX389390","term_id":"1159852529","term_text":"KX389390">> KX389390
16PyOkOdOdo/Okinawa1P. sp. yoronSep 05. 09MM99.5% EtOHNA <"type":"entrez-nucleotide","attrs":<"text":"KX389345","term_id":"1159852467","term_text":"KX389345">> KX389345 <"type":"entrez-nucleotide","attrs":<"text":"KX389462","term_id":"1159852670","term_text":"KX389462">> KX389462 <"type":"entrez-nucleotide","attrs":<"text":"KX389391","term_id":"1159852531","term_text":"KX389391">> KX389391
43PyYoUkUkachi/Yoron3P. sp. yoronMar 04. 10MM99.5% EtOH <"type":"entrez-nucleotide","attrs":<"text":"KX389442","term_id":"1159852633","term_text":"KX389442">> KX389442 <"type":"entrez-nucleotide","attrs":<"text":"KX389346","term_id":"1159852468","term_text":"KX389346">> KX389346 <"type":"entrez-nucleotide","attrs":<"text":"KX389470","term_id":"1159852678","term_text":"KX389470">> KX389470 <"type":"entrez-nucleotide","attrs":<"text":"KX389392","term_id":"1159852533","term_text":"KX389392">> KX389392
44PyYoUkUkachi/Yoron3P. sp. yoronMar 04. 10MM99.5% EtOHNA <"type":"entrez-nucleotide","attrs":<"text":"KX389347","term_id":"1159852469","term_text":"KX389347">> KX389347 <"type":"entrez-nucleotide","attrs":<"text":"KX389471","term_id":"1159852679","term_text":"KX389471">> KX389471 <"type":"entrez-nucleotide","attrs":<"text":"KX389393","term_id":"1159852535","term_text":"KX389393">> KX389393
51PyYoUkUkachi(West)/Yoron4P. sp. yoronMar 04. 10MM99.5% EtOH <"type":"entrez-nucleotide","attrs":<"text":"KX389443","term_id":"1159852635","term_text":"KX389443">> KX389443 <"type":"entrez-nucleotide","attrs":<"text":"KX389348","term_id":"1159852470","term_text":"KX389348">> KX389348 <"type":"entrez-nucleotide","attrs":<"text":"KX389466","term_id":"1159852674","term_text":"KX389466">> KX389466 <"type":"entrez-nucleotide","attrs":<"text":"KX389394","term_id":"1159852537","term_text":"KX389394">> KX389394
53PyYoUkUkachi(West)/Yoron4P. sp. yoronMar 04. 10MM99.5% EtOHNA <"type":"entrez-nucleotide","attrs":<"text":"KX389349","term_id":"1159852471","term_text":"KX389349">> KX389349 NA <"type":"entrez-nucleotide","attrs":<"text":"KX389395","term_id":"1159852539","term_text":"KX389395">> KX389395
81PyErYaYakomo/Okinoerabu5P. sp. yoronMar 05. 10MM99.5% EtOH <"type":"entrez-nucleotide","attrs":<"text":"KX389444","term_id":"1159852637","term_text":"KX389444">> KX389444 <"type":"entrez-nucleotide","attrs":<"text":"KX389350","term_id":"1159852472","term_text":"KX389350">> KX389350 <"type":"entrez-nucleotide","attrs":<"text":"KX389463","term_id":"1159852671","term_text":"KX389463">> KX389463 <"type":"entrez-nucleotide","attrs":<"text":"KX389396","term_id":"1159852541","term_text":"KX389396">> KX389396
83PyErYaYakomo/Okinoerabu5P. sp. yoronMar 05. 10MM99.5% EtOHNA <"type":"entrez-nucleotide","attrs":<"text":"KX389351","term_id":"1159852473","term_text":"KX389351">> KX389351 <"type":"entrez-nucleotide","attrs":<"text":"KX389464","term_id":"1159852672","term_text":"KX389464">> KX389464 <"type":"entrez-nucleotide","attrs":<"text":"KX389397","term_id":"1159852543","term_text":"KX389397">> KX389397
85PyErYaYakomo/Okinoerabu5P. sp. yoronMar 05. 10MM99.5% EtOH <"type":"entrez-nucleotide","attrs":<"text":"KX389445","term_id":"1159852639","term_text":"KX389445">> KX389445 <"type":"entrez-nucleotide","attrs":<"text":"KX389352","term_id":"1159852474","term_text":"KX389352">> KX389352 <"type":"entrez-nucleotide","attrs":<"text":"KX389465","term_id":"1159852673","term_text":"KX389465">> KX389465 <"type":"entrez-nucleotide","attrs":<"text":"KX389398","term_id":"1159852545","term_text":"KX389398">> KX389398
87PyErYaYakomo/Okinoerabu5P. sp. yoronMar 05. 10MM99.5% EtOHNA <"type":"entrez-nucleotide","attrs":<"text":"KX389353","term_id":"1159852475","term_text":"KX389353">> KX389353 NA <"type":"entrez-nucleotide","attrs":<"text":"KX389399","term_id":"1159852547","term_text":"KX389399">> KX389399
105PyToKaKaminomine/Tokunoshima7P. sp. yoronMar 09. 10MM99.5% EtOH <"type":"entrez-nucleotide","attrs":<"text":"KX389446","term_id":"1159852641","term_text":"KX389446">> KX389446 <"type":"entrez-nucleotide","attrs":<"text":"KX389354","term_id":"1159852476","term_text":"KX389354">> KX389354 <"type":"entrez-nucleotide","attrs":<"text":"KX389467","term_id":"1159852675","term_text":"KX389467">> KX389467 <"type":"entrez-nucleotide","attrs":<"text":"KX389400","term_id":"1159852549","term_text":"KX389400">> KX389400
107PyToKaKaminomine/Tokunoshima7P. sp. yoronMar 09. 10MM99.5% EtOH <"type":"entrez-nucleotide","attrs":<"text":"KX389447","term_id":"1159852643","term_text":"KX389447">> KX389447 <"type":"entrez-nucleotide","attrs":<"text":"KX389355","term_id":"1159852477","term_text":"KX389355">> KX389355 <"type":"entrez-nucleotide","attrs":<"text":"KX389468","term_id":"1159852676","term_text":"KX389468">> KX389468 <"type":"entrez-nucleotide","attrs":<"text":"KX389401","term_id":"1159852551","term_text":"KX389401">> KX389401
109PyToKaKaminomine/Tokunoshima7P. sp. yoronMar 09. 10MM99.5% EtOHNA <"type":"entrez-nucleotide","attrs":<"text":"KX389356","term_id":"1159852478","term_text":"KX389356">> KX389356 <"type":"entrez-nucleotide","attrs":<"text":"KX389469","term_id":"1159852677","term_text":"KX389469">> KX389469 NA
359PyOkAkAkazaki/Okinawa8P. sp. yoronJun 24. 12MM99.5% EtOH <"type":"entrez-nucleotide","attrs":<"text":"KX389448","term_id":"1159852645","term_text":"KX389448">> KX389448 <"type":"entrez-nucleotide","attrs":<"text":"KX389357","term_id":"1159852479","term_text":"KX389357">> KX389357 NA <"type":"entrez-nucleotide","attrs":<"text":"KX389402","term_id":"1159852553","term_text":"KX389402">> KX389402
42PmYoUkUkachi/Yoron3P. mutukiMar 04. 10MM99.5% EtOHNA <"type":"entrez-nucleotide","attrs":<"text":"KX389366","term_id":"1159852488","term_text":"KX389366">> KX389366 <"type":"entrez-nucleotide","attrs":<"text":"KX389488","term_id":"1159852696","term_text":"KX389488">> KX389488 <"type":"entrez-nucleotide","attrs":<"text":"KX389403","term_id":"1159852555","term_text":"KX389403">> KX389403
61PmYoUkUkachi/Yoron3P. mutukiMar 04. 10JDR99.5% EtOHNANANA <"type":"entrez-nucleotide","attrs":<"text":"KX389404","term_id":"1159852557","term_text":"KX389404">> KX389404
73PmErYaYakomo/Okinoerabu5P. mutukiMar 05. 10MM99.5% EtOHNANA <"type":"entrez-nucleotide","attrs":<"text":"KX389484","term_id":"1159852692","term_text":"KX389484">> KX389484 <"type":"entrez-nucleotide","attrs":<"text":"KX389405","term_id":"1159852559","term_text":"KX389405">> KX389405
75PmErYaYakomo/Okinoerabu5P. mutukiMar 05. 10MM99.5% EtOHNA <"type":"entrez-nucleotide","attrs":<"text":"KX389367","term_id":"1159852489","term_text":"KX389367">> KX389367 <"type":"entrez-nucleotide","attrs":<"text":"KX389482","term_id":"1159852690","term_text":"KX389482">> KX389482 <"type":"entrez-nucleotide","attrs":<"text":"KX389406","term_id":"1159852561","term_text":"KX389406">> KX389406
77PmErYaYakomo/Okinoerabu5P. mutukiMar 05. 10MM99.5% EtOHNANA <"type":"entrez-nucleotide","attrs":<"text":"KX389481","term_id":"1159852689","term_text":"KX389481">> KX389481 <"type":"entrez-nucleotide","attrs":<"text":"KX389407","term_id":"1159852563","term_text":"KX389407">> KX389407
93PmToYoYonama/Tokunoshima6P. mutukiMar 08. 10MM99.5% EtOHNANANA <"type":"entrez-nucleotide","attrs":<"text":"KX389408","term_id":"1159852565","term_text":"KX389408">> KX389408
94PmToYoYonama/Tokunoshima6P. mutukiMar 08. 10MM99.5% EtOHNANANA <"type":"entrez-nucleotide","attrs":<"text":"KX389409","term_id":"1159852567","term_text":"KX389409">> KX389409
95PmToYoYonama/Tokunoshima6P. mutukiMar 08. 10MM99.5% EtOHNA <"type":"entrez-nucleotide","attrs":<"text":"KX389368","term_id":"1159852490","term_text":"KX389368">> KX389368 <"type":"entrez-nucleotide","attrs":<"text":"KX389487","term_id":"1159852695","term_text":"KX389487">> KX389487 NA
216PmOkOdOdo/Okinawa1P. mutukiMay 04. 11MM99.5% EtOHNA <"type":"entrez-nucleotide","attrs":<"text":"KX389369","term_id":"1159852491","term_text":"KX389369">> KX389369 NA <"type":"entrez-nucleotide","attrs":<"text":"KX389410","term_id":"1159852569","term_text":"KX389410">> KX389410
218PmOkOdOdo/Okinawa1P. mutukiMay 04. 11MM99.5% EtOHNANA <"type":"entrez-nucleotide","attrs":<"text":"KX389483","term_id":"1159852691","term_text":"KX389483">> KX389483 <"type":"entrez-nucleotide","attrs":<"text":"KX389411","term_id":"1159852571","term_text":"KX389411">> KX389411
220PmOkOdOdo/Okinawa1P. mutukiMay 04. 11MM99.5% EtOHNANA <"type":"entrez-nucleotide","attrs":<"text":"KX389489","term_id":"1159852697","term_text":"KX389489">> KX389489 <"type":"entrez-nucleotide","attrs":<"text":"KX389412","term_id":"1159852573","term_text":"KX389412">> KX389412
222PmOkOdOdo/Okinawa1P. mutukiMay 04. 11MM99.5% EtOHNANA <"type":"entrez-nucleotide","attrs":<"text":"KX389485","term_id":"1159852693","term_text":"KX389485">> KX389485 <"type":"entrez-nucleotide","attrs":<"text":"KX389413","term_id":"1159852575","term_text":"KX389413">> KX389413
240PmErSuSumiyoshi/Okinoerabu11P. mutukiJun 18. 11MM99.5% EtOHNANANA <"type":"entrez-nucleotide","attrs":<"text":"KX389414","term_id":"1159852577","term_text":"KX389414">> KX389414
280PmToKaKaminomine/Tokunoshima7P. mutukiOct 05. 11MM99.5% EtOHNANANA <"type":"entrez-nucleotide","attrs":<"text":"KX389415","term_id":"1159852579","term_text":"KX389415">> KX389415
316PmOkKoKomesu/Okinawa12P. mutuki ?Feb 25. 12MM99.5% EtOHNA <"type":"entrez-nucleotide","attrs":<"text":"KX389370","term_id":"1159852492","term_text":"KX389370">> KX389370 <"type":"entrez-nucleotide","attrs":<"text":"KX389480","term_id":"1159852688","term_text":"KX389480">> KX389480 <"type":"entrez-nucleotide","attrs":<"text":"KX389416","term_id":"1159852581","term_text":"KX389416">> KX389416
319PmOkMiMizugama/Okinawa13P. mutuki ?Mar 29. 12MM99.5% EtOHNA <"type":"entrez-nucleotide","attrs":<"text":"KX389371","term_id":"1159852493","term_text":"KX389371">> KX389371 <"type":"entrez-nucleotide","attrs":<"text":"KX389486","term_id":"1159852694","term_text":"KX389486">> KX389486 <"type":"entrez-nucleotide","attrs":<"text":"KX389417","term_id":"1159852583","term_text":"KX389417">> KX389417
320PmOkMiMizugama/Okinawa13P. mutukiMar 29. 12MM99.5% EtOHNANANA <"type":"entrez-nucleotide","attrs":<"text":"KX389418","term_id":"1159852585","term_text":"KX389418">> KX389418
323PmOkTeTeniya/Okinawa14P. mutukiApr 05. 12MM99.5% EtOHNANANA <"type":"entrez-nucleotide","attrs":<"text":"KX389419","term_id":"1159852587","term_text":"KX389419">> KX389419
324PmOkTeTeniya/Okinawa14P. mutukiApr 05. 12MM99.5% EtOHNANANA <"type":"entrez-nucleotide","attrs":<"text":"KX389420","term_id":"1159852589","term_text":"KX389420">> KX389420
349PmOkShShioya Bay/Okinawa15P. mutukiJun 17. 12MM99.5% EtOHNANANA <"type":"entrez-nucleotide","attrs":<"text":"KX389421","term_id":"1159852591","term_text":"KX389421">> KX389421
362PmOkOkOku/Okinawa9P. mutukiJun 25. 12MM99.5% EtOHNANANA <"type":"entrez-nucleotide","attrs":<"text":"KX389422","term_id":"1159852593","term_text":"KX389422">> KX389422
155PamErYaYakomo/Okinoerabu5P. aff. mutukiJuly 25. 10MM70% EtOH <"type":"entrez-nucleotide","attrs":<"text":"KX389449","term_id":"1159852647","term_text":"KX389449">> KX389449 <"type":"entrez-nucleotide","attrs":<"text":"KX389358","term_id":"1159852480","term_text":"KX389358">> KX389358 <"type":"entrez-nucleotide","attrs":<"text":"KX389473","term_id":"1159852681","term_text":"KX389473">> KX389473 <"type":"entrez-nucleotide","attrs":<"text":"KX389423","term_id":"1159852595","term_text":"KX389423">> KX389423
159PamToKaKaminomine/Tokunoshima7P. aff. mutukiJuly 28. 10MM70% EtOHNANANA <"type":"entrez-nucleotide","attrs":<"text":"KX389424","term_id":"1159852597","term_text":"KX389424">> KX389424
229PamErYaYakomo/Okinoerabu5P. aff. mutukiJun 17. 11MM99.5% EtOH <"type":"entrez-nucleotide","attrs":<"text":"KX389450","term_id":"1159852649","term_text":"KX389450">> KX389450 <"type":"entrez-nucleotide","attrs":<"text":"KX389359","term_id":"1159852481","term_text":"KX389359">> KX389359 <"type":"entrez-nucleotide","attrs":<"text":"KX389474","term_id":"1159852682","term_text":"KX389474">> KX389474 NA
231PamErYaYakomo/Okinoerabu5P. aff. mutukiJun 17. 11MM99.5% EtOH <"type":"entrez-nucleotide","attrs":<"text":"KX389451","term_id":"1159852651","term_text":"KX389451">> KX389451 <"type":"entrez-nucleotide","attrs":<"text":"KX389360","term_id":"1159852482","term_text":"KX389360">> KX389360 <"type":"entrez-nucleotide","attrs":<"text":"KX389475","term_id":"1159852683","term_text":"KX389475">> KX389475 <"type":"entrez-nucleotide","attrs":<"text":"KX389425","term_id":"1159852599","term_text":"KX389425">> KX389425
233PamErYaYakomo/Okinoerabu5P. aff. mutukiJun 17. 11MM99.5% EtOH <"type":"entrez-nucleotide","attrs":<"text":"KX389452","term_id":"1159852653","term_text":"KX389452">> KX389452 <"type":"entrez-nucleotide","attrs":<"text":"KX389361","term_id":"1159852483","term_text":"KX389361">> KX389361 <"type":"entrez-nucleotide","attrs":<"text":"KX389476","term_id":"1159852684","term_text":"KX389476">> KX389476 <"type":"entrez-nucleotide","attrs":<"text":"KX389426","term_id":"1159852601","term_text":"KX389426">> KX389426
237PamErSuSumiyoshi/Okinoerabu11P. aff. mutukiJun 18. 11MM99.5% EtOH <"type":"entrez-nucleotide","attrs":<"text":"KX389453","term_id":"1159852655","term_text":"KX389453">> KX389453 <"type":"entrez-nucleotide","attrs":<"text":"KX389362","term_id":"1159852484","term_text":"KX389362">> KX389362 <"type":"entrez-nucleotide","attrs":<"text":"KX389479","term_id":"1159852687","term_text":"KX389479">> KX389479 NA
248PamToKaKaminomine/Tokunoshima7P. aff. mutukiJun 21. 11MM99.5% EtOH <"type":"entrez-nucleotide","attrs":<"text":"KX389454","term_id":"1159852657","term_text":"KX389454">> KX389454 <"type":"entrez-nucleotide","attrs":<"text":"KX389363","term_id":"1159852485","term_text":"KX389363">> KX389363 <"type":"entrez-nucleotide","attrs":<"text":"KX389478","term_id":"1159852686","term_text":"KX389478">> KX389478 <"type":"entrez-nucleotide","attrs":<"text":"KX389427","term_id":"1159852603","term_text":"KX389427">> KX389427
250PamToKaKaminomine/Tokunoshima7P. aff. mutukiJun 21. 11MM99.5% EtOH <"type":"entrez-nucleotide","attrs":<"text":"KX389455","term_id":"1159852659","term_text":"KX389455">> KX389455 <"type":"entrez-nucleotide","attrs":<"text":"KX389364","term_id":"1159852486","term_text":"KX389364">> KX389364 <"type":"entrez-nucleotide","attrs":<"text":"KX389477","term_id":"1159852685","term_text":"KX389477">> KX389477 <"type":"entrez-nucleotide","attrs":<"text":"KX389428","term_id":"1159852605","term_text":"KX389428">> KX389428
328PamOkTeTeniya/Okinawa14P. aff. mutukiApr 05. 12MM99.5% EtOH <"type":"entrez-nucleotide","attrs":<"text":"KX389456","term_id":"1159852661","term_text":"KX389456">> KX389456 NANA <"type":"entrez-nucleotide","attrs":<"text":"KX389429","term_id":"1159852607","term_text":"KX389429">> KX389429
364PamOkOkOku/Okinawa9P. aff. mutukiJun 25. 12MM99.5% EtOH <"type":"entrez-nucleotide","attrs":<"text":"KX389457","term_id":"1159852663","term_text":"KX389457">> KX389457 <"type":"entrez-nucleotide","attrs":<"text":"KX389365","term_id":"1159852487","term_text":"KX389365">> KX389365 NA <"type":"entrez-nucleotide","attrs":<"text":"KX389430","term_id":"1159852609","term_text":"KX389430">> KX389430
215PsOkIkIkei E/Okinawa16Palythoa sp. sakurajimensisApr 29. 11MM99.5% EtOHNA <"type":"entrez-nucleotide","attrs":<"text":"KX389372","term_id":"1159852494","term_text":"KX389372">> KX389372 <"type":"entrez-nucleotide","attrs":<"text":"KX389491","term_id":"1159852699","term_text":"KX389491">> KX389491 <"type":"entrez-nucleotide","attrs":<"text":"KX389431","term_id":"1159852611","term_text":"KX389431">> KX389431
1595 a Wanli Tung/Taiwan2Palythoa sp. sakurajimensisSep. 09JDR99.5% EtOH <"type":"entrez-nucleotide","attrs":<"text":"KF499697","term_id":"556699330","term_text":"KF499697">> KF499697 <"type":"entrez-nucleotide","attrs":<"text":"KF499661","term_id":"556699284","term_text":"KF499661">> KF499661 <"type":"entrez-nucleotide","attrs":<"text":"KX389490","term_id":"1159852698","term_text":"KX389490">> KX389490 <"type":"entrez-nucleotide","attrs":<"text":"KX389432","term_id":"1159852613","term_text":"KX389432">> KX389432
1597 a Wanli Tung/Taiwan1Palythoa sp. sakurajimensisSep. 09JDR99.5% EtOH <"type":"entrez-nucleotide","attrs":<"text":"KF499696","term_id":"556699328","term_text":"KF499696">> KF499696 <"type":"entrez-nucleotide","attrs":<"text":"KF499662","term_id":"556699285","term_text":"KF499662">> KF499662 <"type":"entrez-nucleotide","attrs":<"text":"KF499778","term_id":"556699478","term_text":"KF499778">> KF499778 <"type":"entrez-nucleotide","attrs":<"text":"KX389433","term_id":"1159852615","term_text":"KX389433">> KX389433
1635 a Bitouchiao/Taiwan8Palythoa sp. sakurajimensisSep. 09JDR99.5% EtOH <"type":"entrez-nucleotide","attrs":<"text":"KF499735","term_id":"556699406","term_text":"KF499735">> KF499735 <"type":"entrez-nucleotide","attrs":<"text":"KF499652","term_id":"556699275","term_text":"KF499652">> KF499652 <"type":"entrez-nucleotide","attrs":<"text":"KF499783","term_id":"556699483","term_text":"KF499783">> KF499783 <"type":"entrez-nucleotide","attrs":<"text":"KX389434","term_id":"1159852617","term_text":"KX389434">> KX389434
321PhOkMiMizugama/Okinawa13P. heliodiscusMar 29. 12MM99.5% EtOH <"type":"entrez-nucleotide","attrs":<"text":"KX389458","term_id":"1159852665","term_text":"KX389458">> KX389458 NANA <"type":"entrez-nucleotide","attrs":<"text":"KX389435","term_id":"1159852619","term_text":"KX389435">> KX389435
TN116Mizugama/Okinawa13P. heliodiscusAug 19. 10TN*499.5% EtOHNANANA <"type":"entrez-nucleotide","attrs":<"text":"KX389436","term_id":"1159852621","term_text":"KX389436">> KX389436
TN119Mizugama/Okinawa13P. heliodiscusJul 4. 12TN99.5% EtOHNANANA <"type":"entrez-nucleotide","attrs":<"text":"KX389437","term_id":"1159852623","term_text":"KX389437">> KX389437
TN121Mizugama/Okinawa13P. heliodiscusJul 4. 12TN99.5% EtOHNANANA <"type":"entrez-nucleotide","attrs":<"text":"KX389438","term_id":"1159852625","term_text":"KX389438">> KX389438

Each specimen was identified according to morphological classification methodology (Pax, 1910), supplemented with a key to field identification (Reimer, 2010), and ecological and morphological aspects of P. sp. yoron (Shiroma & Reimer, 2010). Characters employed for identification of Palythoa species were environment (habitat), coenenchyme development, polyp structure, number of polyps per colony, and numbers of tentacles per polyp. All specimens were identified preliminarily as Palythoa tuberculosa ( Fig. 2A ), P. mutuki ( Fig. 2B ) and P. sp. yoron ( Fig. 2C ). During collection, it was noticed that certain specimens had a similar external appearance with P. mutuki but with less well developed marginal ridges and larger polyp sizes. Such specimens were found sympatrically with other specimens, and these were designated as P. aff. mutuki ( Fig. 2D ). In addition, spawning timing investigations for all species groups were carried out between June 2010 to December 2010, and from June 2011 to February 2012 at Kaminomine, Tokunoshima, Kagoshima (27끆�′N, 129뀂�𠌮) by monthly sampling. In particular, for collecting P. tuberculosa, investigation was conducted in a wide area from lagoon tide pools to the outer reef in 2010. However, in 2011� investigations were conducted only in tide pools due to rough sea conditions. At least five different colonies of approximately ten polyps for each species were collected in whole or partially.

Morphological analyses

External anatomy

Fixed specimens were cut horizontally at the oral disk height by surgical knife and tweezers under stereomicroscope (S8APO, Leica, Tokyo) and the number of tentacles, which is one of the characters for Palythoa species (e.g., Ryland & Lancaster, 2003), were counted ( Table 3 ). To eliminate pseudo-replication in comparison among species, a single polyp was chosen with the table of random number from each colony. The mean numbers of tentacles per polyp for each species pair were compared using Mann–Whitney U test with Bonferroni correction.

Table 3

SpeciesP. tuberculosaP. sp. yoronP. mutukiP. aff. mutuki
Mann–Whitney U test
P. tuberculosa31.6 ±ਃ.4 (N =ꀑ)π.001π.001π.001
P. sp. yoron 40.5 ±ਂ.56 (N =ਈ)π.001π.001
P. mutuki 54.4 ±ਇ.43 (N =ਇ)π.001
P. aff. mutuki 71 ±਄.14 (N =ਈ)


Cnidae analyses were conducted using undischarged nematocysts from the tentacles, column, pharynx, and mesenteriel filaments of polyps (n =ਃ/species group) under a Nikon Eclipse80i stereomicroscope (Nikon, Tokyo). Cnidae sizes were measured using ImageJ v1.45s (Rasband, 2012). Cnidae classification followed England (1991) and Ryland & Lancaster (2004 see also Table 4 ).

Table 4

Frequency: relative abundance of cnidae type in decreasing order numerous, common, occasional, rare, very rare (N = number of specimens found/total specimens examined).

Palythoa aff. mutukiPalythoa mutukiPalythoa sp. yoronPalythoa tuberculosa
Length × width (µm)FrequencyLength × width (µm)FrequencyLength × width (µm)FrequencyLength × width (µm)Frequency
Spirocysts12� × 3𠄸Numerous (3/3)13� × 2𠄸Numerous (3/3)11� × 2𠄶Common (3/3)17� × 3𠄷Numerous (3/3)
Basitrichs16� × 4𠄷Common (3/3)14� × 3𠄸Numerous (3/3)25� × 2𠄹Numerous (3/3)25� × 4𠄶Common (3/3)
Holotrichs small15� × 5𠄹Rare (1/3)000
Holotrichs large35� × 19�Occasional (2/3)39� × 18�Numerous (3/3)47� × 21�Numerous (3/3)28� × 17�Occasional (2/3)
P-mastigophores25� × 5�Common (3/3)15 × 4Very rare (single specimen)26� × 5𠄶Occasional (2/3)46� × 6𠄸Rare (1/3)
Spirocysts00016� × 3𠄶Rare (1/3)
Basitrichs21� × 5𠄷Occasional (2/3)25� × 5𠄹Common (3/3)025� × 4�Common (3/3)
Holotrichs small21 × 7Very rare (single specimen)19� × 8Rare (1/3)00
Holotrichs large32� × 15�Numerous (3/3)24� × 17�Numerous (3/3)39� × 18�Numerous (3/3)34� × 14�Numerous (3/3)
P-mastigophores21� × 6𠄸Rare (1/3)0052� × 7𠄸Occasional (2/3)
Spirocysts018� × 4𠄶Occasional (2/3)16� × 3𠄸Occasional (2/3)19� × 4𠄷Rare (1/3)
Basitrichs19� × 4�Numerous (3/3)16� × 3𠄸Numerous (3/3)17� × 3𠄹Numerous (3/3)22� × 3�Numerous (3/3)
Holotrichs small19� × 7𠄸Rare (1/3)000
Holotrichs large34� × 18�Numerous (3/3)34� × 4�Numerous (3/3)38� × 10�Common (3/3)40� × 18�Numerous (3/3)
P-mastigophores29� × 7�Rare (1/3)021� × 6𠄷Occasional (2/3)28� × 5𠄸Rare (1/3)
Mesenteries filaments
Spirocysts15 × 24Very rare (single specimen)0028 × 8Very rare (single specimen)
Basitrichs25� × 4�Numerous (3/3)41� × 5�Numerous (3/3)33� × 4𠄹Numerous (3/3)24� × 5𠄹Numerous (3/3)
Holotrichs small0000
Holotrichs large37� × 22�Numerous (3/3)44� × 21�Numerous (3/3)51� × 21�Numerous (3/3)45� × 22�Numerous (3/3)
P-mastigophores27� × 5�Occasional 2/321 × 6Very rare (single specimen)21� × 4𠄸Common (3/3)21� × 5�Occasional (2/3)

Spawning period investigation

Ovary development of all preserved colonies was observed via cross sections made by cutting polyps vertically through the mouth located at the center of oral disk under a stereomicroscope. During anthozoans’ oogenesis, oocytes form a single-layered germinal ribbon down the mesoglea of the central third of the septa. Subsequently, the germinal ribbon develops a sequence of swollen nodes where the septum folds locally in an S and the layers fuse (Ryland, 1997 Ryland, 2000). When we observed a germinal ribbon in a polyp, we counted the polyp as “possessing developing ovaries”, and the number of polyps possessing developing ovaries were totaled. To evaluate the spawning period of each species, the ratio of the number of polyps possessing developing and/or developed ovaries to the total number of polyps examined was calculated over time. When the calculated proportion of developed/developing ovaries dropped dramatically, we designated this as the start of the estimated spawning period. The end of the estimated spawning period was defined as the point where the number of developed/developing ovaries reached 0%.

Molecular analyses

DNA extraction, PCR amplification and direct sequencing

DNA from each specimen was extracted using a DNeasy Blood and Tissue Kit (QIAGEN, Tokyo, Japan) according to the manufacturer’s instructions. A small amount of tissue from each specimen was removed using a surgical knife sterilized by open flame. Extracted DNA was subsequently stored at � ଌ, and then we amplified target sequences via polymerase chain reaction (PCR).

Three molecular markers that have previously been used for differentiation of Palythoa were chosen (1) the mitochondrial 16S of ribosomal DNA (mt 16S-rDNA), (2) the mitochondrial cytochrome c oxidase subunit I (mtCOI), and (3) the internal transcribed spacer region of nuclear ribosomal DNA (ITS-rDNA) (Reimer et al., 2004 Sinniger et al., 2005 Reimer et al., 2007a, etc.). Furthermore, a nuclear housekeeping gene, (4) asparagine-linked glycosylation 11 protein (ALG11) region, was also examined for the first time in zoantharians. This marker has been found to be more informative than mtCOI in examining sponge relationships and succeeded in solving previously debated nodes (Hill et al., 2013) and has also been considered to be useful for resolving cnidarian relationships (Belinky et al., 2012).

Thermal cycler programs were set to the following conditions: (1) mt 16S-rDNA an initial denaturing step at 94 ଌ for 2 min, followed by 40 cycles of 30 s 94 ଌ, 1 min annealing at 52 ଌ and 2 min extension at 72 ଌ, followed by 5 min final elongation at 72 ଌ with Zoantharia-specific primer set 16Sant1a (5′-GCC ATG AGT ATA GAC GCA CA-3′) and 16SbmoH (5′-CGA ACA GCC AAC CCT TGG-3′) (Sinniger et al., 2005) (2) mtCOI 1 min at 95 ଌ, then 35 cycles: 1 min at 95 ଌ, 1 min at 40 ଌ and 90 s at 72 ଌ, followed by 7 min at 72 ଌ with the universal primers HCO2198 (5′-TAA ACT TCA GGG TGA CCA AAA AAT CA-3′) and LCO1490 (5′-TAA ACT TCA GGG TGA CCA AAA AAT CA-3′) (Folmer et al., 1994) and (3) ITS-rDNA 1 min at 95 ଌ , then 35 cycles of 1 min at 94 ଌ, 1 min at 50 ଌ, and 2 min at 72 ଌ, followed by 10 min at 72 ଌ with Zoantharia-specific primers Zoan-f (5′-CTT GAT CAT TTA GAG GGA GT-3′) and Zoan-r (5′-CGG AGA TTT CAA ATT TGA GCT-3′) (Reimer et al., 2007a).

Amplification for the remaining coding region (ALG11) was performed by touch-down PCR and nested PCR because of low numbers of copies in the whole genome as this is a single-copy gene. For ALG11, although we basically followed the original protocols (Sperling, Pisani & Peterson, 2007 Belinky et al., 2012), some modifications were required to fit the thermal cycler we used, and the conditions were as follows: (4) ALG11 first touchdown, 2 min at 95 ଌ, then 13 cycles of 1 min at 95 ଌ, 1 min at 52-40 ଌ (dropping one degree for each cycle), 1.5 min at 72 ଌ followed by 20 cycles of 1 min at 95 ଌ, 1 min at 52 ଌ, 1.5 min at 72 ଌ lastly 5 min at 72 ଌ with primers ALG11-D1 (5′-TTY CAY CCN TAY TGY AAY GCN GGN GG-3′) and ALG11-R1 (5′-ATN CCR AAR TGY TCR TTC CAC AT-3′), and (5) MAT-f (5′-GGN GAR GGN CAY CCN GAY AA-3′). In the second touchdown procedure an amplicon of the first touchdown was utilized as the template, followed by 2 min at 95 ଌ, then 35 cycles of 1 min at 95  ଌ, 1 min at 52 ଌ, and 1.5 min at 72 ଌ. In the end, nested PCR was performed with 2 min at 95 ଌ, and then 35 cycles of 1 min at 95 ଌ, 1 min at 52 ଌ, and 1.5 min at 72 ଌ with primers ALG11-D2 (5′-TGY AAY GCN GGN GGN GGN GGN GA-3′) and ALG11-R2 (5′-CCR AAR TGY TCR TTC CAC ATN GTR TG-3′).

Amplicons were outsourced for sequencing to a private sequencing company (Fasmac Co., Ltd., Kanagawa, Japan) on an Applied Biosystems 3730xl DNA sequencer, using BigDye Terminator V3.1 and the same primer sets as for PCR as described above. Sequence data were edited using BioEdit v.7.2.0 (Hall, 1999).

Sequence alignment

The total number of novel sequences obtained from specimens in this study were (1) mt 16S-rDNA 38 (2) mtCOI 20 (3) ITS-rDNA 35 and (4) ALG11 65, respectively. Obtained sequences were aligned by BioEdit v7.2.0 (Hall, 1999) with other sequences deposited in GenBank ( Table 5 ).

Table 5

Sequence codeSpeciesmtCOI accession numbermt 16S-rDNA accession numberITS-rDNA accession numberReference
PtEW3P. tuberculosaNANA <"type":"entrez-nucleotide","attrs":<"text":"DQ997902","term_id":"119887939","term_text":"DQ997902">> DQ997902 Reimer et al. (2007a)
PtAT1P. tuberculosa <"type":"entrez-nucleotide","attrs":<"text":"AB219195","term_id":"82175241","term_text":"AB219195">> AB219195 NANAReimer, Takishita & Maruyama (2006)
PtAT2P. tuberculosa <"type":"entrez-nucleotide","attrs":<"text":"AB219196","term_id":"82175243","term_text":"AB219196">> AB219196 NA <"type":"entrez-nucleotide","attrs":<"text":"DQ997897","term_id":"119887934","term_text":"DQ997897">> DQ997897 Reimer, Takishita & Maruyama (2006)
PtBA1P. tuberculosa <"type":"entrez-nucleotide","attrs":<"text":"AB219197","term_id":"82175246","term_text":"AB219197">> AB219197 NANAReimer, Takishita & Maruyama (2006)
PtWK1P. tuberculosa <"type":"entrez-nucleotide","attrs":<"text":"AB219198","term_id":"82175248","term_text":"AB219198">> AB219198 NANAReimer, Takishita & Maruyama (2006)
PtYS1P. tuberculosa <"type":"entrez-nucleotide","attrs":<"text":"AB219200","term_id":"82175253","term_text":"AB219200">> AB219200 NANAReimer, Takishita & Maruyama (2006)
PtMil1P. tuberculosa <"type":"entrez-nucleotide","attrs":<"text":"AB219199","term_id":"82175250","term_text":"AB219199">> AB219199 <"type":"entrez-nucleotide","attrs":<"text":"AB219218","term_id":"82175297","term_text":"AB219218">> AB219218 NAReimer, Takishita & Maruyama (2006)
PtIsK3P. tuberculosa <"type":"entrez-nucleotide","attrs":<"text":"AB219203","term_id":"82175260","term_text":"AB219203">> AB219203 NANAReimer, Takishita & Maruyama (2006)
PtEO1P. tuberculosa <"type":"entrez-nucleotide","attrs":<"text":"AB219205","term_id":"82175265","term_text":"AB219205">> AB219205 NANAReimer, Takishita & Maruyama (2006)
PtKK1P. tuberculosa <"type":"entrez-nucleotide","attrs":<"text":"AB219206","term_id":"82175269","term_text":"AB219206">> AB219206 NANAReimer, Takishita & Maruyama (2006)
PtIsK2P. tuberculosa <"type":"entrez-nucleotide","attrs":<"text":"AB219207","term_id":"82175273","term_text":"AB219207">> AB219207 NANAReimer, Takishita & Maruyama (2006)
PtYS4P. tuberculosaNANA <"type":"entrez-nucleotide","attrs":<"text":"DQ997903","term_id":"119887940","term_text":"DQ997903">> DQ997903 Reimer, Takishita & Maruyama (2006)
PtIrHo16P. tuberculosaNANA <"type":"entrez-nucleotide","attrs":<"text":"DQ997909","term_id":"119887946","term_text":"DQ997909">> DQ997909 Reimer, Takishita & Maruyama (2006)
PtCN1P. tuberculosaNANA <"type":"entrez-nucleotide","attrs":<"text":"DQ997896","term_id":"119887933","term_text":"DQ997896">> DQ997896 Reimer, Takishita & Maruyama (2006)
PtCN14P. tuberculosaNANA <"type":"entrez-nucleotide","attrs":<"text":"DQ997933","term_id":"119887970","term_text":"DQ997933">> DQ997933 Reimer, Takishita & Maruyama (2006)
PtIsO1P. tuberculosa <"type":"entrez-nucleotide","attrs":<"text":"AB219202","term_id":"82175257","term_text":"AB219202">> AB219202 NANAReimer, Takishita & Maruyama (2006)
PtIsO13P. tuberculosaNANA <"type":"entrez-nucleotide","attrs":<"text":"DQ997919","term_id":"119887956","term_text":"DQ997919">> DQ997919 Reimer, Takishita & Maruyama (2006)
PtIsO11P. tuberculosaNANA <"type":"entrez-nucleotide","attrs":<"text":"DQ997929","term_id":"119887966","term_text":"DQ997929">> DQ997929 Reimer, Takishita & Maruyama (2006)
PtIsrael13P. tuberculosaNANA <"type":"entrez-nucleotide","attrs":<"text":"DQ997931","term_id":"119887968","term_text":"DQ997931">> DQ997931 Reimer, Takishita & Maruyama (2006)
PtOtsFu11P. tuberculosaNANA <"type":"entrez-nucleotide","attrs":<"text":"DQ997945","term_id":"119887982","term_text":"DQ997945">> DQ997945 Reimer et al. (2007a)
PtIrHo11P. tuberculosaNANA <"type":"entrez-nucleotide","attrs":<"text":"DQ997914","term_id":"119887951","term_text":"DQ997914">> DQ997914 Reimer et al. (2007a)
PtOtsNi3P. tuberculosaNANA <"type":"entrez-nucleotide","attrs":<"text":"DQ997939","term_id":"119887976","term_text":"DQ997939">> DQ997939 Reimer, Takishita & Maruyama (2006)
PtIrHo13P. tuberculosaNANA <"type":"entrez-nucleotide","attrs":<"text":"DQ997911","term_id":"119887948","term_text":"DQ997911">> DQ997911 Reimer et al. (2007a)
PtL1P. tuberculosaNA <"type":"entrez-nucleotide","attrs":<"text":"EU333661","term_id":"168824201","term_text":"EU333661">> EU333661 NAReimer & Todd (2009)
PtK2P. tuberculosaNA <"type":"entrez-nucleotide","attrs":<"text":"EU333654","term_id":"168824194","term_text":"EU333654">> EU333654 NAReimer & Todd (2009)
PtL3P. tuberculosaNA <"type":"entrez-nucleotide","attrs":<"text":"EU333662","term_id":"168824202","term_text":"EU333662">> EU333662 NAReimer & Todd (2009)
PtK7P. tuberculosaNA <"type":"entrez-nucleotide","attrs":<"text":"EU333657","term_id":"168824197","term_text":"EU333657">> EU333657 NAReimer & Todd (2009)
PtYoS1P. sp. yoron <"type":"entrez-nucleotide","attrs":<"text":"AB219204","term_id":"82175262","term_text":"AB219204">> AB219204 <"type":"entrez-nucleotide","attrs":<"text":"AB219219","term_id":"82175299","term_text":"AB219219">> AB219219 <"type":"entrez-nucleotide","attrs":<"text":"DQ997921","term_id":"119887958","term_text":"DQ997921">> DQ997921 Reimer et al. (2007a)
PmATP. mutuki <"type":"entrez-nucleotide","attrs":<"text":"AB219209","term_id":"82175277","term_text":"AB219209">> AB219209 NANAReimer, Takishita & Maruyama (2006)
PmPM2P. mutuki <"type":"entrez-nucleotide","attrs":<"text":"AB219210","term_id":"82175279","term_text":"AB219210">> AB219210 NANAReimer, Takishita & Maruyama (2006)
Pm1162P. mutuki <"type":"entrez-nucleotide","attrs":<"text":"JF419796","term_id":"380709832","term_text":"JF419796">> JF419796 NANAReimer et al. (2011)
Pm1163P. mutuki <"type":"entrez-nucleotide","attrs":<"text":"JF419788","term_id":"380709824","term_text":"JF419788">> JF419788 NANAReimer et al. (2011)
PmBA1P. mutuki <"type":"entrez-nucleotide","attrs":<"text":"AB219215","term_id":"82175290","term_text":"AB219215">> AB219215 NANAReimer, Takishita & Maruyama (2006)
PmYS1P. mutuki <"type":"entrez-nucleotide","attrs":<"text":"AB219213","term_id":"82175286","term_text":"AB219213">> AB219213 NANAReimer, Takishita & Maruyama (2006)
PmIrHo1P. mutukiNANA <"type":"entrez-nucleotide","attrs":<"text":"DQ997888","term_id":"119887925","term_text":"DQ997888">> DQ997888 Reimer et al. (2007a)
PmYS2P. mutukiNANA <"type":"entrez-nucleotide","attrs":<"text":"DQ997892","term_id":"119887929","term_text":"DQ997892">> DQ997892 Reimer et al. (2007a)
PpAT1P. mutuki <"type":"entrez-nucleotide","attrs":<"text":"AB219211","term_id":"82175281","term_text":"AB219211">> AB219211 <"type":"entrez-nucleotide","attrs":<"text":"AB219220","term_id":"82175301","term_text":"AB219220">> AB219220 <"type":"entrez-nucleotide","attrs":<"text":"DQ997891","term_id":"119887928","term_text":"DQ997891">> DQ997891 Reimer et al. (2007a)
PmMil1P. mutuki <"type":"entrez-nucleotide","attrs":<"text":"AB219217","term_id":"82175295","term_text":"AB219217">> AB219217 <"type":"entrez-nucleotide","attrs":<"text":"AB219225","term_id":"82175310","term_text":"AB219225">> AB219225 <"type":"entrez-nucleotide","attrs":<"text":"DQ997889","term_id":"119887926","term_text":"DQ997889">> DQ997889 Reimer et al. (2007a)
PmEs1P. mutukiNANA <"type":"entrez-nucleotide","attrs":<"text":"DQ997894","term_id":"119887931","term_text":"DQ997894">> DQ997894 Reimer et al. (2007a)
PpAT2P. mutuki <"type":"entrez-nucleotide","attrs":<"text":"AB219212","term_id":"82175284","term_text":"AB219212">> AB219212 <"type":"entrez-nucleotide","attrs":<"text":"AB219221","term_id":"82175302","term_text":"AB219221">> AB219221 NAReimer, Takishita & Maruyama (2006)
PpYS1P. mutukiNA <"type":"entrez-nucleotide","attrs":<"text":"AB219222","term_id":"82175304","term_text":"AB219222">> AB219222 NAReimer, Takishita & Maruyama (2006)
PamTOB51P. aff. mutukiNA <"type":"entrez-nucleotide","attrs":<"text":"GQ464873","term_id":"300498422","term_text":"GQ464873">> GQ464873 <"type":"entrez-nucleotide","attrs":<"text":"GQ464902","term_id":"300498451","term_text":"GQ464902">> GQ464902 Swain (2010)
PsPSH1P. sp. sakurajimensisNA <"type":"entrez-nucleotide","attrs":<"text":"DQ997842","term_id":"119887821","term_text":"DQ997842">> DQ997842 <"type":"entrez-nucleotide","attrs":<"text":"DQ997886","term_id":"119887923","term_text":"DQ997886">> DQ997886 Reimer et al. (2007a)
PsPWS1P. sp. sakurajimensisNA <"type":"entrez-nucleotide","attrs":<"text":"DQ997863","term_id":"119887842","term_text":"DQ997863">> DQ997863 <"type":"entrez-nucleotide","attrs":<"text":"DQ997887","term_id":"119887924","term_text":"DQ997887">> DQ997887 Reimer et al. (2007a)
PsPEWn1P. sp. sakurajimensisNA <"type":"entrez-nucleotide","attrs":<"text":"DQ997862","term_id":"119887841","term_text":"DQ997862">> DQ997862 NAReimer et al. (2007a)
PsGYiP. sp. sakurajimensis <"type":"entrez-nucleotide","attrs":<"text":"KF499720","term_id":"556699376","term_text":"KF499720">> KF499720 NANAReimer et al. (2013)
Ps1595P. sp. sakurajimensis <"type":"entrez-nucleotide","attrs":<"text":"KF499697","term_id":"556699330","term_text":"KF499697">> KF499697 NA <"type":"entrez-nucleotide","attrs":<"text":"KX389490","term_id":"1159852698","term_text":"KX389490">> KX389490 Reimer et al. (2013)
Ps1597P. sp. sakurajimensis <"type":"entrez-nucleotide","attrs":<"text":"KF499696","term_id":"556699328","term_text":"KF499696">> KF499696 NA <"type":"entrez-nucleotide","attrs":<"text":"KF499778","term_id":"556699478","term_text":"KF499778">> KF499778 Reimer et al. (2013)
Ps1635P. sp. sakurajimensis <"type":"entrez-nucleotide","attrs":<"text":"KF499735","term_id":"556699406","term_text":"KF499735">> KF499735 NA <"type":"entrez-nucleotide","attrs":<"text":"KF499776","term_id":"556699476","term_text":"KF499776">> KF499776 Reimer et al. (2013)
PhIsK2P. heliodiscusNANA <"type":"entrez-nucleotide","attrs":<"text":"DQ997885","term_id":"119887922","term_text":"DQ997885">> DQ997885 Reimer et al. (2007a)
PhIsK11P. heliodiscusNANA <"type":"entrez-nucleotide","attrs":<"text":"DQ997880","term_id":"119887917","term_text":"DQ997880">> DQ997880 Reimer et al. (2007a)
PhEK1P. heliodiscusNANA <"type":"entrez-nucleotide","attrs":<"text":"DQ997882","term_id":"119887919","term_text":"DQ997882">> DQ997882 Reimer et al. (2007a)
PhSaiLL1P. heliodiscus <"type":"entrez-nucleotide","attrs":<"text":"AB219214","term_id":"82175288","term_text":"AB219214">> AB219214 <"type":"entrez-nucleotide","attrs":<"text":"AB219223","term_id":"82175306","term_text":"AB219223">> AB219223 NAReimer, Takishita & Maruyama (2006)
PhEK1P. heliodiscusNA <"type":"entrez-nucleotide","attrs":<"text":"AB219224","term_id":"82175308","term_text":"AB219224">> AB219224 NAReimer, Takishita & Maruyama (2006)
PhPpM1P. heliodiscus <"type":"entrez-nucleotide","attrs":<"text":"AB219216","term_id":"82175292","term_text":"AB219216">> AB219216 NANAReimer, Takishita & Maruyama (2006)

As numerous indels (inserts and deletions) were confirmed in ITS-rDNA sequences, alignment was performed using ClustalW (Thompson, Higgins & Gibson, 1994) with gap penalties of 10 for open and 1 for extended, followed by manual fixing for obviously misaligned areas such as gap position. Sequences of the 5.8S rDNA region located between internal transcribed spacer 1 (ITS1) and internal transcribed spacer 2 (ITS2) were removed from analyses because the substitution rate is apparently lower than ITS1 and ITS2, and an admixture of different substitution rates could lead to a misleading choice of the appropriate substitution model. Additionally, in order to not overestimate for genetic distance in following phylogenetic analyses, sites were removed if they had a percentage of gaps and/or ambiguous sites higher than 95% (partial-deletion option).

Fifty-six out of sixty-five specimens had one or more degenerate codes in sequences of the ALG11 region. All degenerate codes were divided into two standard bases using PHASE v2.1.1, which implements a Bayesian statistical method for reconstructing haplotypes from population genotype data (Stephens, Smith & Donnelly, 2001 Stephens & Scheet, 2005). Furthermore, first and second codon positions were removed from the dataset by checking amino acid sequences after translation.

Thus, each dataset was modified as needed, with additional previously reported sequences added from GenBank, and we generated four alignments (1) mtCOI 451਋p of 47 sequences (2) mt 16S-rDNA 697 bp of 54 sequences (3) ITS-rDNA 317 bp of 60 sequences and (4) ALG11 578 bp of 121 sequences. These were used for subsequent phylogenetic analyses.

Substitution model selection

Substitution models for each gene were estimated by jModelTest v2.1.3 (Darriba et al., 2012) through the following steps. Initially, likelihood calculations were carried out for all substitution models with configurations of seven substitution schemes, equal or unequal base frequencies (+F), rate variation among sites with a number of rate categories (+G, nCat 5) and base tree topology (ML optimized). Subsequently, the most appropriate model for each marker was selected under (i) the corrected Akaike information criterion (AICc) for Maximum-Likelihood and neighbor-joining phylogenetic estimation, or (ii) Bayesian information criterion (BIC) for Bayes estimation. Thus, the (i)TrN/(ii)TrNef for mt 16S-rDNA, (i)F81/(ii)JC for mtCOI, (i,ii)K80+Γ for ITS-rDNA, and (i)K80+Γ/(ii)TPM1uf+Γ models for ALG11 were employed, respectively.

Gene tree estimations

For four distinct datasets (mt 16S-rDNA, mtCOI, ITS-rDNA, ALG11), phylogenetic analyses were applied independently with the optimal substitution model under AICc estimated by jModelTest. Maximum-Likelihood (ML) analyses were performed using PhyML (Guindon & Gascuel, 2003) and neighbor-joining (NJ) methods were performed using MEGA5.2.2 (Tamura et al., 2011). All other parameters besides substitution model and the discrete gamma distribution were implemented with the default value. Bootstrap analyses (Felsenstein, 1985) of 1,000 replicates were tested to evaluate the support of every branch.

Bayesian inference for gene trees was performed using BEAST v.1.7.0 (Drummond et al., 2010) with the optimal substitution model under BIC. All parameters were used as default values except for the molecular clock, in which the rate was changed to the log-normal relaxed model, while only the substitution model for ALG11 was modified to TPM1uf after generating the initial setting file. Four Markov chain Monte Carlo (MCMC) simulations were run for 10 million generations with sampling intervals of 1,000. Convergence of analyses and adequacy of the sample sizes, with ESS values above 200 (ESS = the number of effectively independent draws from the posterior distribution that the Markov chain is equivalent to) were confirmed in Tracer v.1.5. (Rambaut et al., 2013). Analyses were combined using LogCombiner v.1.8.0, which is included within BEAST, after excluding the first 10% as burn-in. Obtained trees were summarized in a maximum clade credibility tree using TreeAnotator v.1.8.0 and visualized in FigTree v.1.4.0.

Species tree estimations

*BEAST estimates the species tree directly from the sequence data, nucleotide substitution model parameters and the coalescent process (Heled & Drummond, 2010). The species trees were built by grouping all 235 sequences by putative species groups and simultaneously estimating each of three individual gene trees (mt 16S-rDNA, ITS-rDNA and ALG11), and the summary species trees using BEAST were drawn for two different species model (1) a six species model including P. tuberculosa, P. sp. yoron, P. mutuki, P. aff. mutuki, P. sp. sakurajimensis sensu Reimer et al. (2007a) and Reimer et al. (2007b) and P. heliodiscus, and (2) a four species model combining P. sp. yoron with P. tuberculosa, and P. aff. mutuki with P. mutuki, along with P. sp. sakurajimensis and P. heliodiscus.

All parameters were used as default except for (1) the molecular clock rate, which was changed to the log-normal relaxed model (Drummond et al., 2006), (2) the substitution rate for mt 16S-rDNA, for which the range was calibrated to between 0.001-0.002/Mya based on the reported substitution rate for mtCOI (Shearer et al., 2002), and (3) the substitution model for ALG11 was modified to TPM1uf after generating the setting file. MCMC analyses were run for 100 million generations with sampling intervals of 10,000 and excluding the first 10% as burn-in. All the parameters in the output file were confirmed in Tracer v1.5. Obtained trees were summarized in a maximum clade credibility tree using TreeAnotator v.1.8.0.

Mechanisms of Speciation - A Population Genetic Approach

In his review of White's book on Modes ofSpeciation, Sewall Wright (249) stated, "The least adequate aspect of 'Modes of Speciation' is the absence of systematic discussion of speciation as a process by which reproductive isolation of a whole population traces to genetic phenomena at the level of the individual. The investigation of such processes is the subject of popula­ tion genetics. Serious consideration requires mathematical analysis and experiments as well as consideration of the results of speciation to which most attention is paid The purpose of this review is to initiate the discussion absent in White's (234) book and much of the speciation literature. I begin with a brief survey of what descriptive population genetics has revealed about the results of speciation. Next, I present a population-genetic classification of modes of speciation followed by a discussion of how theoretical, experimental, and ." descriptive population-genetic studies can be related to each of the modes. Finally, I discuss the possible relative importance of these modes and out­ line why it is important for population genetics and speciation theory to become integrated. DESCRIPTIVE POPULATION GENETICS OF SPECIATION Comparisons Not Involving Hybridization It is possible to study interspecific differences in the


Annual Review of Ecology, Evolution, and Systematics &ndash Annual Reviews

Dispersal and the transition to sympatry in vertebrates

Under allopatric speciation models, a key step in the build-up of species richness is population dispersal leading to the co-occurrence of previously geographically isolated forms. Despite its central importance for community assembly, the extent to which the transition from spatial segregation (allopatry or parapatry) to coexistence (sympatry) is a predictable process, or alternatively one governed by chance and the vagaries of biogeographic history, remains poorly understood. Here, we use estimated divergence times and current patterns of geographical range overlap among sister species to explore the evolution of sympatry in vertebrates. We show that rates of transition to sympatry vary predictably according to ecology, being faster in marine or strongly dispersive terrestrial clades. This association with organism vagility is robust to the relative frequency of geographical speciation modes and consistent across taxonomic scales and metrics of dispersal ability. These findings reject neutral models of dispersal assembly based simply on evolutionary age and are not predicted by the main alternative view that range overlap is primarily constrained by biotic interactions. We conclude that species differences in dispersal limitation are fundamental in organizing the assembly of ecological communities and shaping broad-scale patterns of biodiversity over space and time.

1. Introduction

Most new species arise in geographical isolation (allopatry) and so a key step in the assembly of communities is the geographical expansion and overlap (sympatry) of these previously spatially isolated forms [1–4]. Niche assembly models predict that this build-up of sympatric diversity is a highly deterministic process [2,5]. In particular, by preventing the coexistence of ecologically similar species, competition for ecological resources is expected to generate non-random patterns in community structure, including the regular spacing of functional traits [5], the mutually exclusive occurrence of related species across geographical space [2] and slowdowns in the rate of community assembly over time [3]. It is sometimes postulated that dispersal is also a powerful force driving the predictable assembly of communities if the odds of range expansion, and thus the rate of species arrival, are heavily weighted by differences in organism vagility [6–8]. More often, however, the process of dispersal into a community has been viewed as a largely stochastic event with respect to species identity [9], with classic ‘dispersal assembly’ models assuming that the combination of lineages and traits assembling into communities is inherently unpredictable [10,11]. This ‘neutral-dispersal’ viewpoint has strikingly different implications for how local or regional biota assemble over time and yet, although extrinsic variation in the opportunity for dispersal may drive consistent patterns in range size or sympatric diversity [10,12–14], the role of intrinsic dispersal ability in regulating the geographical expansion of populations and their transition to sympatry is unclear, particularly over long macroevolutionary timescales [9,15].

The key problem is that the historical sequence of range expansion events leading to current patterns of sympatry cannot be directly followed through time. Thus, while many studies have examined how dispersal ability is related to current macroecological patterns (e.g. range size [7,14,15], range occupancy [16] and β-diversity [17]), most of these are based on a correlative approach that does not explicitly address the underlying mechanisms. For instance, while a positive correlation between range size and vagility has been taken as evidence that dispersal ability drives rates of range expansion, the same statistical relationship is expected under a purely stochastic model if weak dispersers are subject to more rapid allopatric speciation [18–20], giving them less time to expand their distributions [15,21]. Testing the role of dispersal in limiting geographical range expansions, and the build-up of species within communities, thus requires a more mechanistic approach that explicitly deals with the dynamics of how sympatry between species arises over evolutionary time.

Here, we address the need for a historical framework by extending a recently developed phylogenetic model [22] to explore the transition to sympatry following speciation events in vertebrates (figure 1a). Using data on the geographical overlap and evolutionary divergence times between pairs of sister species (each other's closest relatives figure 1a), we first compare the incidence of sympatry across major vertebrate clades encompassing a broad spectrum of dispersal potential: amphibians, primates, New World birds, reef fish and cetaceans (figure 1b). We then test the extent to which variation in the incidence of sympatry over time can be explained by a Deterministic Dispersal (DD) model in which transition rates to sympatry (σ) vary predictably according to intrinsic differences in organism vagility. We compare the fit of this deterministic model to a Stochastic Dispersal (SD) model in which differences in the probability of sympatry arise purely due to variation across species in the evolutionary time for dispersal (i.e. species age). Finally, we examine how the relative importance of these stochastic and deterministic processes may vary with scale, conducting our analysis across major vertebrate clades and separately within New World birds, the group for which phylogenetic data and robust indices of relative dispersal ability are most widely available (figure 1b).

Figure 1. Evolutionary age and the incidence of sympatry (black in (a), colours in (b)) and allopatry (white) across vertebrate sister species. (a) Horizontal bars represent sister lineages (n = 533) ranked from bottom to top by increasing age (Ma). Inset provides a schematic summary of the evolutionary transition model and estimated parameters (γ = relative frequency of allopatric speciation, σ = rate of transition from allopatry to sympatry, ɛ = rate of transition from sympatry to allopatry). (b) The same data showing the incidence of sympatry and allopatry plotted separately for five focal groups.

Vertebrates provide the best template for these analyses because speciation generally involves a period of spatial separation [1,23,24]. Reproductive isolation may evolve in geographical isolation (allopatry) or in the presence of gene flow (parapatry), but in either case reproductively isolated vertebrate lineages arising from a single parent species tend to occur in sympatry only after undergoing a transition from spatial segregation to overlap [4,25,26]. However, robust assessments of these spatial dynamics must account for the possibility that some species have diverged in sympatry [1,23] and that, in regions with unstable environmental conditions (e.g. high latitudes [6]), sister species may have passed through multiple phases of sympatry and allopatry, such that currently allopatric species may in the past have had overlapping ranges [23,24,27]. To address this possibility, our likelihood framework allows us to robustly model and account for both potential variation in the relative frequency of allopatric (γ) and sympatric (1 − γ) speciation and for reverse transitions from sympatry back to allopatry (ɛ) (figure 1a).

2. Material and methods

(a) Phylogenetic data and sister species

We obtained published molecular phylogenies for large well-defined clades of vertebrates (tree frogs [28], salamanders [29], cetaceans [30], primates [31] and reef fish [32,33]), favouring trees constructed using a relaxed-clock Bayesian method. For birds, we generated trees for nine predominantly New World families (Passeriformes (Emberizidae, Icteridae, Parulidae, Thamnophilidae, Thraupidae, Troglodytidae and Turdidae), Piciformes (Ramphastidae) and Trogoniformes (Trogonidae)), with the addition of a recently published phylogeny for the ovenbirds (Furnariidae [34]). Trees were constructed in BEAST v. 1.5.4 [35] and dated using the well-established avian molecular clock [36] (see the electronic supplementary material, Appendix S1, for detailed phylogenetic procedures, and database S3 for sequence data and their sources).

From phylogenetic trees, we identified sister species pairs and their estimated divergence times, restricting our analysis to sister pairs from well-sampled genera (≥70% species sampling, median = 97% species sampling electronic supplementary material, Appendix S1). Divergence times estimated from gene trees are expected to predate the time of population splitting because of ancestral polymorphisms. However, the magnitude of this effect is relatively small (0.2–0.3 Ma versus median pair age of 2.89 Ma [36]) and is not expected to vary with respect to dispersal. We excluded terrestrial sisters separated by marine barriers (i.e. species occurring on different landmasses or islands) because the factors limiting dispersal are likely to differ from those on land (e.g. salt water immersion for non-volant organisms). Our final dataset included 533 sister pairs from 33 families and eight taxonomic orders (electronic supplementary material, database S1).

(b) Geographical ranges and sympatry

Sister species were assigned as sympatric or allopatric based on current spatial overlap of species breeding distributions [37]. We quantified overlap using polygon range maps downloaded from the IUCN Red List assessment ( or kindly provided by C.D.L. Orme and I.P.F. Owens (birds) (electronic supplementary material, database S1) [38]. Marginal range overlap along narrow contact zones or owing to mapping error may not reflect true sympatry. For example, partially reproductively isolated species meeting in clinal hybrid zones are often mapped with narrowly overlapping ranges, but it would be incorrect to assume that they co-occur as independent populations because the hybrid zone is generally formed by a single interbreeding population [39]. Thus, here we only defined sympatry as non-trivial range overlap (more than 10% of the smaller species range). We do not distinguish between allopatric (geographically isolated) or parapatric (abutting) sisters, because for the purposes of our analysis these are equivalent in having non-overlapping distributions. Similarly, in montane regions, we treated species occurring on the same mountains but occupying distinct altitudinal ranges as non-overlapping, whether they were reported to be in narrow contact (parapatry) or disjunct (allopatry). Following previous studies (e.g. [25]), we used additional literature searches to corroborate geographical assignments, including evidence confirming that populations are sympatric during the breeding season. To test the robustness of our results to geographical assignments, we repeated our analysis using a more conservative threshold of 20% range overlap to define sympatry. Finally, in our analysis focused on birds, we accounted for potential latitudinal variation in rates of sympatry and dispersal by calculating the mean absolute latitude of species range centroids for each sister pair (i.e. (|Latitude Sister 1|+|Latitude Sister 2|)/2).

(c) Indices of dispersal ability

To quantify relative organism vagility across vertebrates, we searched the literature for estimates of species FST, an index of genetic differentiation among populations (0 = no differentiation, 1 = complete differentiation). Note that our measures of FST are calculated between populations of the same species, rather than between sister species. While FST does not directly measure gene flow [40], it is strongly correlated with broad-scale differences in dispersal ability [41] and provides a reliable index of relative vagility that is comparable across taxonomic groups [15,20,42]. We only used estimates based on comparable markers (microsatellites and/or allozymes) and measured over large spatial scales (i.e. more than 100 km see the electronic supplementary material, Appendix S1). In total, we obtained FST values for 79 species (electronic supplementary material, database S2). To ensure that our results were robust to differences in study extent, we repeated all analyses for different subsets of the data (250–5000 km (main analysis), 400–2000 km and 100–5000 km see the electronic supplementary material, Appendix S1 and table S2).

For analyses focused specifically on birds, we quantified relative vagility using wing shape as a biometric index of long-distance flight efficiency and dispersal ability. Previous empirical studies have shown that species with a higher wing aspect-ratio exhibit greater natal and migratory dispersal distances [43,44], less spatial population genetic differentiation [45] and are less prone to geographical isolation [18]. Thus, following Claramunt et al. [18], we used the hand–wing index (HWI) to quantify wing aspect-ratio, as

As an alternative measure of dispersal, we used information in the literature to assign sister pairs to one of three dispersal syndromes (1 = weak, 2 = medium or 3 = strong) on the basis of three ecological and life-history traits: migratory tendency (1 = sedentary, 2 = short-distance migrants, 3 = long-distance migrants), degree of territoriality (1 = permanent year-round territoriality, 2 = seasonal or weak territoriality, 3 = non-territorial) and diet (1 = insectivore, 2 = omnivore, 3 = herbivore (i.e. fruit, seeds and nectar)). While these life-history traits are strongly correlated, each has been strongly linked to dispersal propensity, rates of gene flow and the likelihood of geographical isolation [19,45] (see the electronic supplementary material, Appendix S1, for further justification of traits, and database S1 for scores and data sources). In the case of diet, analyses were restricted to tropical birds (i.e. |Lat| < 23.5°, n = 225) because some categories (e.g. insectivores) have dramatically different relationships with dispersal or migratory tendency in the temperate zone. Where sister species differed in dispersal syndrome (this occurred in less than 10% of pairs), we assigned the pair the higher of the sister species' scores, thus maintaining the ordinal scoring system.

(d) Modelling the transition to sympatry

We modelled the dynamics of sympatry across species as a constant rate Markov process [22]. This model allowed us to calculate the probability that a pair of species exists in its current geographical state (i.e. allopatry or sympatry) given the estimated time since divergence (t Ma) and the parameters governing the rate of transition from allopatry to sympatry (σ) and from sympatry back to allopatry (ɛ figure 1a). We used maximum likelihood (ML) to fit this model to our empirical data and estimate these parameters, implemented in the R package msm [46].

We accounted for the possibility that some species may diverge in sympatry by including a parameter, γ, describing the probability that speciation occurred in allopatry (or sympatry, i.e. 1 − γ figure 1a) and evaluated a series of biogeographic scenarios of varying complexity. First, we assumed that all species pairs arise in allopatry (γ = 1, Allo-one-way), and then undergo an irreversible transition to sympatry with rate σ (i.e. we fix ɛ = 0). Second, we considered a two-way model (Allo-two-way) in which ɛ is also estimated as a free parameter, thus allowing reverse transitions back to allopatry (when ɛ = 0, this reduces to the Allo-one-way scenario). For completeness, we also fitted a Symp-two-way model in which all speciation occurs in sympatry (i.e. γ = 0). Finally, we estimated the relative frequency of speciation modes by fitting a Mixed-two-way model in which γ was allowed to vary from 0 to 1 in increments of 0.01. We used this likelihood profile to obtain estimates of σ under both the ML speciation scenario and for models across the 95% confidence interval set (i.e. within 1.92 log-likelihood units of the best model).

Our modelling framework assumes that the instantaneous probability of a sister pair transitioning between states is constant with species age and allows us to test whether these transition probabilities are equivalent, or instead vary, across sister species pairs. For each speciation scenario, we fitted a DD model in which σ was allowed to vary independently across vertebrate clades, comparing this to an SD model in which all species are governed by the same underlying σ. Relative model fit was assessed using Akaike information criterion (AIC) and posterior predictive simulations (electronic supplementary material, Appendix S1). We tested whether clade-specific estimates of σ (ln-transformed) were predicted by mean clade FST (arcsine transformed) using phylogenetic generalized least squares (PGLS) in the package caper [47]. Phylogenetic distances between clades were taken from published estimates of divergence times in vertebrates (electronic supplementary material, Appendix S1 and Box S1).

To test the effects of dispersal in birds, we took the best-fitting speciation scenario for this clade (Allo-one-way and Allo-two-way model) and compared the fit of an SD model to a DD model, in which each dispersal trait was included as a continuous covariate on σ. We used the DD model to quantify the hazard ratio (i.e. the ratio of transition rates per unit change in the dispersal index). A significant relationship between dispersal and σ could arise if σ happens to be high in a few strongly dispersive clades. To control for this phylogenetic non-independence, we re-fitted all models including ‘avian family’ as an additional covariate on σ. Finally, to ensure that our results are not driven by covariation between dispersal ability and latitude, we repeated our analysis including the absolute latitude of each sister pair as a covariate on σ.

(e) Simulation tests

Estimates of divergence times, and the relative frequency of geographical states across vertebrate groups, could be influenced by taxonomic uncertainty. However, this seems unlikely to influence our conclusions because taxonomic revisions in vertebrates generally involve the elevation of allopatric subspecies to distinct species [24,39] and would thus tend to accentuate the existing pattern of young allopatric lineages in our data. In any case, we explicitly account for divergence times when modelling the temporal dynamics of sympatry, and simulations assuming different levels of cryptic diversity show that such dynamics are only weakly affected by differences in the sampling of recently diverged allopatric lineages (electronic supplementary material, Appendix S1 and figure S1).

To assess the robustness of our modelling framework and ability to distinguish different biogeographic scenarios (e.g. Allo-one-way versus Allo-two-way), we also performed extensive simulations under different combinations of σ (σ = 0.01–10), ɛ (ɛ = 0.01–0.10) and γ (γ = 0 – 1) and tested how this influenced the relationship between sympatry and species age (electronic supplementary material, Appendix S1). We then fitted our likelihood models to these simulated datasets to test whether estimates of σ were robust to the occurrence of sympatric speciation (i.e. γ > 0) and reversals to allopatry (i.e. ɛ > 0), both of which could provide alternative explanations for differences in observed levels of sympatry (electronic supplementary material, Appendix S1).

3. Results

(a) Taxonomic patterns of sympatry in vertebrates

Across our dataset, we found that 31% of vertebrate sister pairs are sympatric, with the remaining species occupying allopatric (or parapatric) distributions (figure 1a). As expected, the frequency of sympatry increased with time since divergence (figure 1a), the median age of sympatric species being almost twice as old (4.1 Ma) as allopatric species (2.2 Ma). However, we also found that different vertebrate clades are characterized by substantial differences in the incidence of sympatry in sister species, ranging from only 5% in primates to 61% in cetaceans (figure 1b).

(b) Reliability of estimated transition rates and biogeographic scenarios

Simulations showed that different biogeographic scenarios (Allo-one-way, Allo-two-way, Symp-two-way and Mixed-two-way models) each leave distinct signatures in how the probability of sympatry varies with species age (electronic supplementary material, figures S2). In particular, while γ controls the initial probability of sympatry, the shape of the age–sympatry relationship is independently determined by both σ and ɛ. Specifically, while σ determines how rapidly sympatry initially increases with species age, the relative value of ɛ determines the level at which sympatry eventually aysmptotes (i.e. σ/(σ + ɛ) electronic supplementary material, figure S2). Because of these independent effects, our simulations confirm that σ can be reliably estimated under a broad range of conditions and that these estimates are robust to the occurrence of both sympatric speciation and reverse transitions to allopatry (electronic supplementary material, figures S3).

(c) Dispersal and the transition to sympatry in vertebrates

When we applied our modelling framework across all vertebrates, we found that a model in which each vertebrate clade is characterized by a distinct transition rate to sympatry (DD) fits significantly better (ΔAIC = 38.5) than one assuming equal rates (SD, table 1 electronic supplementary material, table S1). A scenario whereby allopatric speciation is the universal route through which species diverge fits best, specifically when allowing for reverse transitions to allopatry (Allo-two-way), and with sympatry arising at a rate that varies markedly across groups (table 1). Simulations using the ML parameter estimates showed that this DD Allo-two-way model predicts the present incidence of sympatry observed across vertebrate groups with a high degree of accuracy (electronic supplementary material, Appendix S1 and figure S4). Differences in the incidence of sympatry across clades would be expected due to variation in species age but our results show that this alone is unable to predict the observed patterns (electronic supplementary material, figure S4).

Table 1. Stochastic and deterministic models of the transition to sympatry in vertebrates. Biogeographic scenario indicates the geographical speciation mode (allopatric: Allo-two-way and Allo-one-way sympatric: Symp-two-way or estimated: Mixed-two-way) and whether transitions to sympatry were irreversible (Allo-one-way) or not (Allo-two-way, Symp-two-way, Mixed-two-way). npar indicates the number of estimated parameters AIC, Akaike information criterion.

According to the DD Allo-two-way model, we estimate that σ is slowest in primates (σ = 0.05) and amphibians (σ = 0.06), intermediate in birds (σ = 0.14) and fastest in in reef fish and cetaceans (σ = 1.61) (electronic supplementary material, table S1). These differences in rates translate into dramatic differences in how the probability of sympatry accumulates with time since speciation in each group (figure 2a). For instance, our model predicts that after 5 Ma of divergence, only 21–23% of primate and amphibian sister pairs will have attained sympatry compared with 46% of birds, closely matching the incidence of sympatry observed across age quantiles (figure 2a). By contrast, in reef fish and cetaceans, we estimate rapid transitions between geographical states (i.e. high σ and ɛ), so that the probability of sympatry is largely independent of age and is instead simply determined by the relative rates of σ and ɛ (figure 2a).

Figure 2. (a) The percentage of sympatric sister pairs as a function of time since divergence predicted under the Allo-two-way (ML estimates, dark shading) and Mixed-two-way (light shading, 95% confidence set) models. Grey circles (for illustration only) denote observed values within age quantiles. (b) The relationship between mean group FST (higher values indicate reduced dispersal) and the transition rate to sympatry (σ) (ln-transformed). Coloured bars indicate the ML value of σ and spread of FST estimates for each vertebrate group (95% quantile). Coloured circles correspond to the range of estimates of σ shown in (a). n = number of sister pairs in (a), and numbers of species with FST data in (b).

When we modelled each vertebrate clade separately, we found that among terrestrial clades the Allo-one-way and Allo-two-way models have an almost equal fit and that estimates of σ are similar regardless of whether we account for reverse transitions to allopatry or not (electronic supplementary material, table S1). Thus, while there is evidence that some currently allopatric pairs may formerly have had overlapping ranges (under the Allo-two-way model, we estimate ɛ > 0), this appears to occur relatively infrequently and the inclusion of this additional parameter does not significantly increase the likelihood of the data (electronic supplementary material, table S1). Our models therefore show that the low levels of sympatry observed among terrestrial sister species can only be explained by a slow transition rate to sympatry (electronic supplementary material, table S1). By contrast, for marine groups (reef fish and cetaceans), we found that even opposing speciation scenarios (i.e. γ = 1 or 0) have an almost equal likelihood, leading to greater uncertainty in estimates of σ (figure 2b electronic supplementary material, table S1 and figure S5). To account for this uncertainty when testing the relationship between σ and FST, we fitted our PGLS model using the values of σ from across the 95% confidence set of Mixed-two-way models fit to each clade (figure 2b see the electronic supplementary material, Appendix S1). Our phylogenetic comparative analysis shows a significant negative association between σ and FST, indicating that sympatry is attained more rapidly in groups characterized by low levels of within-species genetic differentiation, indicative of large dispersal distances (figure 2b slope = –8.61, p = 0.034, r 2 = 0.82 (results are the median estimates from across 1000 models sampled from the 95% CI set)). We find that regardless of the geographical context of speciation, patterns of sympatry in the ocean require a model with rapid transitions between geographical states (figure 2 electronic supplementary material, table S1). As a result, the negative relationship between σ and FST is robust to variation in speciation scenarios (electronic supplementary material, table S2). Furthermore, all these results remained qualitatively unchanged regardless of the spatial scale over which FST was measured and when we repeated our analysis defining sympatry as more than 20% range overlap (electronic supplementary material, tables S1 and S2).

(d) Dispersal and the transition to sympatry in birds

We found substantial variation in the frequency of sympatry across avian clades, with sympatry among sisters being rare in some families (e.g. Furnariidae 22.4%), intermediate in others (e.g. Icteridae 41.2%) or even widespread (e.g. Thraupidae 50%). Our results support a DD model in which σ is strongly accelerated in bird species with a high HWI, indicative of greater flight performance (hazard ratio = 5.45 (95% CI 1.41 : 21.06), p < 0.05, ΔAIC = 4.09 electronic supplementary material, table S3 figure 3a). According to this model, the mean waiting time to sympatry (i.e. 1/σ) is more than four times shorter among the strongest (4.1 Ma) compared with the weakest (19 Ma) fliers. This association between vagility and σ was also present when using ecological or life-history traits (figure 3b electronic supplementary material, table S3): σ was faster in long-distance migrants than in sedentary species (ΔAIC = 8.86 figure 3b electronic supplementary material, table S3), for species in which territoriality is weak or absent compared with those defending fixed territories year-round (ΔAIC = 17.23 figure 3b electronic supplementary material, table S3), and among tropical species adapted to tracking patchy resources (e.g. fruit, seeds and nectar) compared with those specialized on more stable, uniformly distributed resources (e.g. arthropods) (ΔAIC = 10.12 figure 3b electronic supplementary material, table S3). With the exception of diet, all effects remained significant even after accounting for potential differences in σ across avian families (electronic supplementary material, table S3). Our data confirm a positive relationship between σ and latitude (hazard ratio = 1.02 (95% CI 1.002 : 1.04), p < 0.05, ΔAIC = 2.35), and we estimate that the average waiting time to sympatry is almost twice as long in the tropics (9 Ma, |Lat| < 23.5°) compared with the temperate zone (4.9 Ma). Importantly, the associations we detected between dispersal and σ remained significant when we included latitude as an additional covariate in our models (electronic supplementary material, table S3). Finally, these results remained qualitatively unchanged when using different definitions of sympatry (more than 10% or more than 20% range overlap) and when accounting for reverse transitions to allopatry (i.e. ɛ > 0 electronic supplementary material, table S4).

Figure 3. The relationship between dispersal and the transition rate to sympatry (σ) in birds (n = 275) according to (a) the HWI and (b) ecological dispersal syndromes. Black lines show ML rate estimates and grey shading the 95% CI.

4. Discussion

Our analyses reveal that the dramatic differences across vertebrates in the dynamics of sympatry following speciation cannot be explained simply by differences in species age and thus evolutionary time for dispersal, but instead are predictable on the basis of intrinsic differences in dispersal ability. Transition rates to sympatry are fastest in highly mobile marine organisms (reef fish and cetaceans) and slowest in non-volant terrestrial taxa (amphibians and primates). Birds, with their power of flight, are intermediate between these extremes of dispersal limitation and accordingly transition to sympatry at an intermediate rate. It is possible that focusing on such dramatically different clades overemphasizes the importance of intrinsic dispersal constraints, and that such effects are likely to decline at increasingly fine taxonomic scales due to reduced contrast, or greater ecological equivalency, among species [11]. However, when we focused exclusively on birds, we found that transition rates to sympatry remained highly predictable, varying deterministically in accordance with differences in dispersal potential. Our analyses show that rates of sympatry were consistently higher in species with greater vagility or flight performance, as indicated by three separate traits: migratory behaviour, non-territoriality and the HWI. Together, these results suggest that dispersal limitation is a key deterministic mechanism regulating geographical range expansion and thus the tempo and sequence of how sympatry between species arises over time.

The vertebrate groups we studied span the full range of dispersal potential, from marine organisms that can travel vast distances during transoceanic migrations (cetaceans) or as larvae carried on the current (reef fish) [15,42], to weakly dispersing amphibians and primates where sister species are often separated by extremely narrow geographical barriers (e.g. rivers [48]). In marine groups, we find that range dynamics are rapid relative to the time scale of speciation, implying that dispersal is unlikely to limit species distributions. This finding may help to explain why previous studies have shown mixed or weak evidence for a relationship between geographical range size, evolutionary age and dispersal potential in the oceans [15,49]. By contrast, we found that the probability of sympatry in terrestrial vertebrates increases only slowly with time since divergence and that clade-wide differences in species age are therefore an important contributor to variation in the incidence of sympatry. Thus, our results not only demonstrate that dispersal is a highly deterministic force driving predictable patterns of sympatry across clades, but also highlight the critical importance of evolutionary time in explaining the build-up of sympatric diversity in terrestrial systems.

Across birds and most other terrestrial organisms, traits associated with low dispersal are concentrated in the tropics, where more stable environmental conditions and stronger geographical or environmental barriers are expected to slow the pace of range dynamics compared with high latitudes [19]. Overall, our results are consistent with those of previous studies suggesting that rates of sympatry increase with latitude [25,26]. However, even after accounting for this geographical variation, we find that species with contrasting ecologies are characterized by substantial differences in the transition rate to sympatry. These transitions are slowest in highly sedentary bird species, feeding on relatively stable resources (i.e. tropical insectivores) that remain on fixed territories year-round and that lack biomechanical adaptations for sustained flight (i.e. have rounded wings) [18,19,45], with the opposite set of traits accelerating the transition to sympatry. Thus, dispersal limitation appears to mediate range dynamics regardless of taxonomic scale or geographical context.

The pervasive association we find between vagility and the transition to sympatry is not predicted by conventional explanations for limits to coexistence, including niche assembly models focused on interspecific competition [3,22,50], or those highlighting the role of reproductive interference [25], and shared natural enemies (e.g. pathogens [51]). Rather, these hypotheses are not specific regarding the association between rates of sympatry and differences in dispersal ability, and instead predict that rates of sympatry increase with time since speciation as constraints on range overlap weaken [22,50]—the opposite patterns to those we detect in vertebrates. We do not conclude that biotic interactions play no role in limiting the geographical overlap of closely related species. On the contrary, widespread evidence of this effect has been reported in previous studies, including apparent delays in sympatry caused by interspecific competition across ecological gradients [3,25,51] and within a single avian radiation (Furnariidae) [22]. However, our results across a much broader taxonomic sample of birds and other vertebrates suggest that the signature of biotic constraints on coexistence is swamped by other factors over longer timescales and that the overall tempo of range expansions leading to sympatry are primarily dictated by differences in dispersal limitation.

The spread of populations observed following recent environmental change [6,8] or the introduction of species to novel regions [52] is often rapid relative to the rates of species diversification, perhaps implying that dispersal constraints would be unlikely to limit range expansion over the macroevolutionary timescales studied here [15,49]. The patterns revealed in our sample of marine taxa are potentially consistent with this view, with transitions between sympatry and allopatry occurring so rapidly as to erase any signal of the geographical context of speciation [27]. By contrast, we show that differences in intrinsic dispersal potential provide the best explanation for the failure of many terrestrial vertebrate species to attain sympatry even millions of years after speciation. This makes sense because barriers to dispersal in terrestrial systems have extremely protracted effects that vary according to dispersal limitation, for example when rivers, mountain ranges or narrow regions of unsuitable climate and vegetation cause longstanding disjunctions in the geographical ranges of sister species in lineages with low dispersal ability, but not those with high dispersal ability [19,45,48].

The idea that dispersal simply accelerates geographical range expansion, and hence range overlap, seems plausible enough, yet the underlying process may be more nuanced. For example, if increased dispersal ability leads to greater propagule pressure—in this case, a larger number of individuals invading the geographical range of their sister lineage—then this may increase the opportunity for evolutionary divergence in ecological or reproductive traits by processes such as character displacement [53]. Theoretically, this could accelerate divergence in ecological niches or mating signals, thus relaxing ecological competition, strengthening reproductive isolation and ultimately facilitating early range invasions in dispersive sister species. From this perspective, the roles of biotic interaction and dispersal limitation are not independent, as evolutionary processes may reduce the extent to which competition and interbreeding place constraints on range overlap in dispersive taxa.

Dispersal has long been viewed as an integral component of community assembly, but the concept commonly adopted is akin to Simpson's classic ‘sweepstakes' model [54], in which invasion is a lottery and species have an equal chance of holding a winning ticket [9–12,55]. Thus, ‘dispersal assembly’ models generally assume that rates of biogeographic dispersal are equivalent across species (i.e. neutral) [10–12], and any evidence of predictable structure in communities is automatically attributed to biotic interactions [2,56,57]. Our results are contrary to these assumptions and suggest instead that dispersal limitation is the key deterministic mechanism regulating the tempo and sequence of how sympatry between species arises over time. A corollary of this conclusion is that contemporary patterns of species co-occurrence may be consistent with stochasticity and yet mask high levels of non-neutrality in the history of assembly. We propose that biases in dispersal may drive many of the predictable differences in structure observed across ecological communities.

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