New Results

The Genealogical Sorting Index and species delimitations

David J. Winter, Steven A. Trewick, Jon M. Waters, Hamish G. Spencer
doi: https://doi.org/10.1101/036525

Abstract

The Genealogical Sorting Index (gsi) has been widely used in species-delimitation studies, where it is usually interpreted as a measure of the degree to which each of several predefined groups of specimens display a pattern of divergent evolution in a phylogenetic tree. Here we show that the gsi value obtained for a given group is highly dependent on the structure of the tree outside of the group of interest. By calculating the gsi from simulated datasets we demonstrate this dependence undermines some of desirable properties of the statistic. We also review the use of the gsi delimitation studies, and show that the gsi has typically been used under scenarios in which it is expected to produce large and statistically significant results for samples that are not divergent from all other populations and thus should not be considered species. Our proposed solution to this problem performs better than the gsi in under these conditions. Nevertheless, we show that our modified approach can produce positive results for populations that are connected by substantial levels of gene flow, and are thus unlikely to represent distinct species. We stress that the properties of gsi made clear in this manuscript must be taken into account if the statistic is used in species-delimitation studies. More generally, we argue that the results of genetic species-delimitation methods need to be interpreted in the light the biological and ecological setting of a study, and not treated as the final test applied to hypotheses generated by other data.

Introduction

Genetic sequence data and phylogenetic methods are increasingly being used to aid in the discovery and delimitation of species (reviewed in Fujita et al. 2012). The widespread application of such data and analyses to alpha taxonomy has confirmed that evolutionarily distinct species will not necessarily fall into reciprocally monophyletic groups in phylogenies estimated from DNA sequences. Indeed, species can remain paraphyletic with respect to their close relatives in gene trees even millions of years after they begin to diverge (Tajima 1983; Hudson & Coyne 2002).

A number of methods have been developed with the objective of delimiting species using such unsorted gene trees (Knowles & Carstens 2007; O’Meara 2010; Yang & Rannala 2010, 2014; Ence & Carstens 2011; Zhang et al. 2011). The increasingly popular use of these methods in empirical species-delimitation studies has inspired a number of methodological papers exploring their statistical properties. These theoretical investigations have shown the methods to be powerful and accurate when their underlying assumptions are met, but it has become clear that violations of these assumptions can generate misleading results (Reid & Carstens 2012; Carstens et al. 2013; Edwards & Knowles 2014; Olave et al. 2014). Thus, species delimitation methods are most useful when their statistical properties are understood, and studies can be designed and interpreted in the light of these properties.

Although not exclusively designed for species-delimitation studies, the gsi of Cummings et al. (2008) has been widely used in this context (see references in Table 1). This statistic is a measure of the degree to which a pre-defined group of leaves in a phylogenetic tree falls into an exclusive region in that tree. The value of the statistic ranges from 0 to 1, with higher values corresponding to more phylogenetic exclusivity. In this way, the gsi aims to bridge the gap between monophyly and paraphyly as categorical terms, and in so doing, quantify the degree to which a lineage has become distinct as a result of evolutionary divergence. The calculation of the gsi is usually accompanied by an hypothesis test, in which the gsi for each group is compared to values calculated from trees in which the tip labels have been permuted.

View this table:
Table 1.

Summary of papers in which the gsi is principally used for species delimitation. “n.groups” refers to the greatest number of putative species considered in a single analysis

When compared to other widely used species delimitation methods (Table 2) the gsi has many desirable properties. As well as having power to detect recently diverged lineages, the gsi differs from many comparable methods in not needing, as input parameters, the values of often difficult-to-estimate quantities such as the effective population size or mutation rate of the genetic sequences under consideration. The gsi value obtained for a given group is also purported to be comparable to those obtained for different groups within the same tree and between those arising from different studies (Cummings et al. 2008).

View this table:
Table 2.

“θ” refers to the population mutation rate for each locus, “τ” to the over-all height of a species tree in coalescent units. For DISCUSS, the ω parameter specifies a prior on the on the number of species present in a dataset, and λ and μ represent the speciation and extinction rates of a birth-death processes generating the underlying species tree. The description of the popABC approach to species delimitation refers specifically to the method employed by Camargo et al (Camargo et al. 2012)

In applying the gsi to empirical data, however, we have found the value of this statistic to be highly dependent on the structure of the tree outside of the group of interest. This dependence is not reflected in the way the statistic is typically applied and interpreted in species-delimitation studies, and this mismatch between the gsi as it is used and goals of species delimitation undermines the many advantages of the statistic.

The gsi measures exclusivity relative to the entire tree

An example serves to illustrate the dependence of the gsi obtained for a particular group on the over-all structure of the phylogeny from which it is calculated. Take the tree presented in Figure 1. To calculate gsi for the group “a” in this tree we first need to calculate the intermediate statistic gs, which is defined as Embedded Image where dμis the degree (i.e. the number of connections) of the node u, which is one of U total nodes in the smallest sub-tree uniting all members of a group and n is the minimum number of nodes that could be used to unite this group, which is one less than the number of leaves. In the case of Figure 1, n is 3, but all 7 nodes in the tree are needed to create a sub-tree uniting group “a”. As the tree is fully dichotomous, the degree of each node is 3. Thus, gs is 3 / (7 × (3-2)) = 3/7. To obtain gsi for group “a” the observed value of gs is normalised using the maximum and minimum obtainable value for the statistic given the size of the group and the number of nodes in the tree:

Figure 1.
Figure 1.

Hypothetical phylogenetic tree, with tips assigned to two groups “a” and “b”

Embedded Image

Here max(gs) is 1 (the case in which a group is united by the minimum number of nodes, i.e. monophyly) and min(gs) is given by the equation Embedded Image where diis the degree of node i, one of I nodes in the entire tree. Because the smallest sub-tree uniting the group “a” in Figure 1 is the entire tree, min(gs) for this group is equal to the observed gs value. Thus, the numerator in equation (2) is 3/7 - 3/7 = 0 and so the value of gsi is also 0. This result is desirable, as the tree presented in Figure 1 has each group arranged in the least exclusive fashion possible. Nevertheless, defining min(gs) in this way means the value of gsi is partially dependent on the degree to which other groups in the tree fall into exclusive regions.

Consider now the tree presented in Figure 2, which could be obtained from genetic data underlying the topology illustrated in Figure 1 by simply adding further data from two distantly related taxa. Because the clade containing the “a” and “b” groups is unchanged the observed value of gs for “a” is still 7. The addition of the two groups “c” and “d” to the tree, however, has added 8 new dichotomous nodes. Thus the value of min(gs) is now 3/(15×(3-2)) = 3/15 and, following equation (2), gsi is equal to [3/7 - 3/15-3/15] ≈ 0.29. This difference arises from the inclusion of min(gs) in the calculation of gsi, which makes a gsi value obtained for a group a reflection of that group's exclusivity relative to the entire tree. This property is not desirable for a species-delimitation statistic, as it means large gsi values can be obtained for groups that do not represent a population that is divergent from all other samples in a given analysis. Moreover, it also compromises comparisons between groups within one study, or between values obtained from different studies.

Figure 2.
Figure 2.

Hypothetical phylogenetic tree, obtained by adding two additional groups (“c” and “d”) to the tree presented in Figure 2.

A similar issue affects the hypothesis test that is often performed alongside the gsi. Statistical significance, or P-values, arising from this test are usually reported for each of several putative species under consideration in a single analysis. In practice, these P-values are interpreted as the results of independent tests that each group being considered is divergent from all other groups. In fact, as Cummings et al. (2008) make clear, because the test is performed by permuting group assignments across the entire tree, the null hypothesis being tested is that all individuals included in the tree come from a single panmictic population. It is seldom the case that all individuals considered in a species-delimitation study could plausibly have come from a randomly mating population. Thus, statistically significant results may simply represent the rejection of an implausible null hypothesis.

The gsi and species delimitation

The problems discussed above are most likely to affect interpretation of the gsi when the statistic is calculated for a large number of groups, especially when those groups are likely to be divergent from at least some others under consideration. To determine how often the gsi has been used in such contexts, we performed a literature review (Table 1). We identified papers recorded as citing Cummings et al. (2008) in Web Of Science and Google Scholar. For each study we recorded the context in which the gsi was used, the largest number of groups considered in a single analysis and the criteria by which those groups were determined. The results of this analysis show the gsi has mainly been used in the context of species-delimitation (54 of 78 empirical studies) and that these studies have often applied the statistic to several groups (mean = 9.9, median = 6) for which there is a priori evidence for evolutionary divergence. The basis of the group assignment is frequently an existing taxonomic distinction, or a preliminary phylogenetic or clustering analysis performed on data from which the gsi was calculated. Worryingly, these circumstances are exactly those in which (as we show above) use of the gsi can be misleading. We did not find any papers in which the plausibility of the null hypothesis was considered in discussing the statistical significance of results.

To investigate how large the effect of including multiple divergent groups in the calculation of gsi is likely to be in practice, we calculated the statistic from simulated datasets. We used the program ms (Hudson 2002) to simulate gene trees arising from neutral evolution under the demographic history depicted in Figure 3, that is, four divergent populations with two (“a” and “b”) diverging at a time point t which was varied among simulations. We performed 500 simulations for each value of t between 0 and 1 Ne generations in 0.05 Ne increments, sampling 10 individuals per population in each simulation. For each simulation, we calculated the gsi for group “a” twice, first considering all populations in the simulation (the “four-population tree”) then after discarding individuals from the divergent populations “c” and “d” (the “two-population tree”).

Figure 3.
Figure 3.

Demographic history under which simulations were performed. In each simulation gsi was calculating from a gene tree containing 10 individuals from each population (the four-population tree) and from a gene tree from which tips corresponding to individuals from populations “c” and “d” had been dropped (the two-population tree).

As expected, the mean gsi value obtained for the group “a” tracks the divergence of this population from population “b” in all simulations (Fig 4). However, the gsi value calculated from the four-population tree is substantially larger than the value obtained from the two-population tree, even though the same individuals make up the “a” population in each case. The difference is especially pronounced early in the divergence process, indeed, the expected value of gsi in the four-population case is high (0.40) even when t = 0 Ne (i.e. when populations “a” and “b” are panmictic with respect to each other). For every simulation, including those in which the “a” and “b” populations were panmictic, the calculation based on the four population-tree produced a significant result. By contrast, calculations based on the two-population tree produced a near-uniform distribution of P-values under panmixia and became increasingly likely to return significant results as the populations diverged.

Figure 4.
Figure 4.

Above: The gsi value obtained for a group depends on the overall structure of the tree from which it is calculated. The red line represents the mean gsi value calculated for group “a” in the simulation depicted in Figure 3 when the all four populations are included in the calculation. The blue line is the mean gsi calculated for the same population when the divergent populations “c” and “d” are first discarded.

Below: The hypothesis test usually performed alongside the calculation of gsi readily produces significant results for populations that are not divergent from all other populations. The red line represents the proportion of simulations in which population “a” was found to have a significant pattern of exclusivity in the four-population tree. The blue line represents the same proportion calculated from the two-population tree.

These simulations confirm that both the value and the significance obtained for the gsi of a given group is partially dependent on the degree to which other groups fall into exclusive regions of the phylogeny being considered. As we note above, this characteristic is not desirable in a statistic purported to relate only to the group under consideration, as it makes comparison of gsi values obtained from different trees problematic. Significant results can readily be obtained from populations that are not genealogically divergent from all others groups in an analysis.

Aligning the gsi with species delimitation as it is practiced

As noted above, the gsi has many desirable properties (Table 2). Unlike many species delimitation methods, the gsi can be calculated without the need for often difficult-to-estimate population-genetic parameters as input. Additionally, the relative simplicity of the gsi means the statistic can be applied to large datasets. Unlike the GMYC (Pons et al., 2006), the gsi can be applied to unsorted gene trees and the gsi can be used to test the validity of proposed species suggested by morphological or other data. Given these unique properties of the gsi, we do no propose that empiricists discarding the statistic entirely. Rather, the properties described here should be carefully considered before the statistic before is applied to datasets.

There are likely to be multiple ways to reasonably incorporate the gsi in particular species delimitation studies; here we propose a general solution that retains the gsi's simplicity but removes its dependence on the overall structure of the tree from which it is calculated. Our proposed statistic, the “mean pairwise gsi” or pwgsi is calculated for a pair of groups, after all tips other than those in the groups of interest have been dropped from the phylogenetic tree under consideration. This approach can be applied to all putative species under consideration in a given study, or only to a subset that are of particular interest.

For example, to analyse the tree and group-assignments depicted in Figure 2 we first produce trees representing the possible pairwise comparisons of groups (Fig 5). For each tree, the pwgsi is simply the mean of the gsi values obtained for these two groups. Thus, in the case of Figure 5 the pwgsi for the “a:b” comparison is 0 and all other comparisons have pwgsi of 1. This approach requires at most Embedded Imagevalues to be calculated, where η is the number of groups being considered. Thus, the pairwise approach is not subject to the computation limitations of methods that consider all possible partitions of a group-assignment (O’Meara 2010; Ence & Carstens 2011), and can be applied to datasets in which a relatively large number of groups are being considered. It also relatively easy to calculate the pwgsi with the existing genealogicalSorting software, as we demonstrate in Supplementary Text 1.

Figure 5.
Figure 5.

Trees used for pairwise comparison of all groups in Figure 2.

The pairwise approach aligns well with the goals of species delimitation studies, as the pwgsi quantifies each population's exclusivity relative to all other populations. Moreover, the pattern of pwgsi values resulting from a single analysis can identify groups that are not divergent with respect to each other, but are divergent from all other groups and thus might be considered part of a single divergent population in subsequent analyses (as is the case with “a” and “b” in example discussed above). This approach uses the same procedure as in the calculation of the two-population scores in Fig 4. We can infer from Fig 4, therefore, that the pwgsi tracks lineage divergence and a permutation test applied to a particular between-population comparison has a strong power to detect divergent groups.

pwgsi and population structure

The high power of the pwgsi to detect an exclusive distribution of tips on a phylogeny may seem to make it an ideal statistic for species delimitation in the presence of incomplete lineage sorting. Care needs to be taken, however, in the interpretation of these results. The short period of divergence required to obtain significant results means that such results can be obtained even for what may turn out to be transient isolation between populations. Moreover, speciation is not the only one way in which a non-random distribution of groups might occur on a phylogeny. Specifically, sub-populations within a population with some degree of genetic structuring may be expected to fall into partially exclusive regions of a gene tree. To investigate the degree to which population structure affects the value of the pw-gsi, we simulated neutral gene trees under the scenario depicted in Figure 6. In this case, three populations diverged instantaneously at a time point that was held constant at 5 Ne generations. Two of the descendent populations (“a” and “b”) were united by ongoing and constant gene flow due to migration at a rate 4Nem with values {1, 2, 5, 25, 100}*. We performed 1500 simulations for each migration-rate value and sampled.

Figure 6.
Figure 6.

Demographic history under which population structure simulations were performed. Arrows represent ongoing gene flow between populations “a” and “b” after their divergence at 5 Ne generations.

The pwgsi value increased for the “a:b” comparison as the number of migrants exchanged between these populations decreased (and thus the populations became more structured) (Fig 7). We also investigated the power of the pwgsi to detect population structure in these simulations by performing 104 group-label permutations per simulation. Significant results were obtained even with relatively limited population structure (Fig. 7). For example, when 4Nem = 100, a value giving a negligible expected FST of < 0.01 (Wright 1949), more than 10% of simulations produced a result with a P-value < 0.05.

Figure 7.
Figure 7.

The pwgsi measures population structure as well as lineage divergence.

Above: In the top panel, boxes represent distributions of pwgsi for the groups “a” and “b”, which were sampled from populations experiencing gene flow at a constant rate which varies along the x-axis (large values of Nem correspond to more migration and thus less population-structure).

Below:. Boxes represent distributions of P-values obtained for the “a:b” comparison; red line the drawn at P = 0.05.Note, note x-axis is on a log-10 scale.

Although speciation with gene flow is certainly possible (Emelianov et al. 2004; Davison et al. 2005; Niemiller et al. 2008; de León et al. 2010) and perhaps even common (Nosil 2008), it is generally accepted even a small number of successful migrants are enough to prevent speciation in the absence of selection (Slatkin 1995; Gavrilets 2000). Speciation is only possible with greater rates of migration when very strong divergent selection is acting (Felsenstein 1981; Kirkpatrick & Ravigné 2002). Clearly then, the results of the pwgsi cannot be treated as unambiguous evidence that the groups being considered are different species. Rather, researchers need to consider it in the design of their studies and the interpretation of results. In particular, the pwgsi may be a poor choice of statistic if a putative species is known to have a distinct geographic distribution with respect to others to which it is being compared (or if the population samples being analysed come from different regions).

By contrast, our finding that the pwgsi measures population structure may make it a useful statistic for within-species phylogeographic studies, complementing the AMOVA approach (Excoffier et al. 1992) that is currently widely used for sequence data in this context. Indeed, the gsi has already been used in this context (Chen & Hare 2011; Gustafsson & Olsson 2012).

Conclusions

Genetic sequences are a potentially powerful source of data for the discovery and delimitation of species. The results reported above, however, emphasise the care that needs to be taken in interpreting the results of DNA-based species-delimitation methods. We have shown that a naïve interpretation of gsi, a statistic that has been widely used in species-delimitation studies, can lead to erroneous conclusions. Although the gsi remains powerful approach to species when applied carefully (as in the pwgsi described here) it is still possible to obtain large gsi values and statistically significant results from populations connected by substantial gene flow.

Thus, we suggest that this statistic and other species-delimitation methods should be used as part of a genuinely integrative approach to taxonomy. In particular, the phylogenetic and population-genetic signals measured by species-delimitations methods should considered within the biological and ecological setting of a study, rather than as final arbiters of species’ status applied to hypotheses generated by other data.

Acknowledgments

We thank three anonymous reviewers, Steffen Klaere, Giulio Dalla Riva, and Michael Cummings, whose comments on this manuscript greatly improved it. SK in particular should be credited with the insight described in the footnote on pgX.

Footnotes

  • ↵* Note, the inclusion of population “c” in this design illustrates the importance of the pw-gsi approach to quantifying lineage divergence. As this simulation proceeds population “c” is expected to become increasingly exclusive in gene trees arising from this history; thus the gsi values of “a” and “b” will increase over the course

References

  1. Almendra AL, Rogers DS, González-Cózatl FX (2014) Molecular phylogenetics of the Handleyomys chapmani complex in Mesoamerica. Journal of Mammalogy, 95, 26–40.
  2. Ardila NE, Giribet G, Sánchez JA (2012) A time-calibrated molecular phylogeny of the precious corals: reconciling discrepancies in the taxonomic classification and insights into their evolutionary history. BMC Evolutionary Biology, 12, 246.
    OpenUrlPubMed
  3. Ashalakshmi NC, Nag KSC, Karanth KP (2014) Molecules support morphology: species status of South Indian populations of the widely distributed Hanuman langur. Conservation Genetics, 16, 4358.
    OpenUrl
  4. Bagley JC, Alda F, Breitman MF et al. (2015) Assessing species boundaries using multilocus species delimitation in a morphologically conserved group of neotropical freshwater fishes, the Poecilia sphenops species complex (Poeciliidae) PLoS ONE, 10, e0121139.
    OpenUrl
  5. Bon M-C, Hoelmer KA, Pickett CH et al. (2015) Populations of Bactrocera oleae (Diptera: Tephritidae) and Its Parasitoids in Himalayan Asia. Annals of the Entomological Society of America, sav 114.
  6. Boykin LM, Schutze MK, Krosch MN et al. (2014) Multi-gene phylogenetic analysis of south-east Asian pest members of the Bactrocera dorsalis species complex (Diptera: Tephritidae) does not support current taxonomy. Journal of Applied Entomology, 138, 235–253.
    OpenUrl
  7. ↵
    Camargo A, Morando M, Avila LJ, Sites JW Jr. (2012) Species delimitation with ABC and other coalescent-based methods: a test of accuracy with simulations and an empirical example with lizards of the Liolaemus darwinii complex (Squamata: Liolaemidae). Evolution, 66, 2834–2849.
    OpenUrlCrossRefPubMedWeb of Science
  8. ↵
    Carstens BC, Pelletier TA, Reid NM, Satler JD (2013) How to fail at species delimitation. Molecular Ecology, 22, 4369–4383.
    OpenUrlCrossRefPubMedWeb of Science
  9. Cesar S. Herrera CL (2013) Revision of the genus Corallomycetella with Corallonectria gen. nov. for C. jatrophae (Nectriaceae, Hypocreales). Mycosystema, 32, 518–544.
    OpenUrl
  10. ↵
    Chen G, Hare MP (2011) Cryptic diversity and comparative phylogeography of the estuarine copepod Acartia tonsa on the US Atlantic coast. Molecular Ecology, 20, 2425–2441.
    OpenUrlCrossRefPubMedWeb of Science
  11. Corcoran P, Dettman JR, Sun Y et al. (2014) A global multilocus analysis of the model fungus Neurospora reveals a single recent origin of a novel genetic system. Molecular Phylogenetics and Evolution, 78, 136–147.
    OpenUrlCrossRefPubMed
  12. Costanzo KS, Taylor DJ (2010) Rapid ecological isolation and intermediate genetic divergence in lacustrine cyclic parthenogens. BMC Evolutionary Biology, 10, 166.
    OpenUrl
  13. ↵
    Cummings MP, Neel MC, Shaw KL (2008) A genealogical approach to quantifying lineage divergence. Evolution, 62, 2411–2422.
    OpenUrlCrossRefPubMedWeb of Science
  14. ↵
    Davison A, Chiba S, Barton NH, Clarke B (2005) Speciation and Gene Flow between Snails of Opposite Chirality. PLoS Bioogyl, 3, e282.
    OpenUrl
  15. Derkarabetian S, Hedin M (2014) Integrative taxonomy and species delimitation in harvestmen: a revision of the western North American genus Sclerobunus (Opilionesaa: Laniatores: Travunioidea) ( W Arthofer, Ed,). PLoS ONE, 9, e10982.
    OpenUrl
  16. Donald KM, Preston J, Williams ST et al. (2012) Phylogenetic relationships elucidate colonization patterns in the intertidal grazers Osilinus Philippi, 1847 and Phorcus Risso, 1826 (Gastropoda: Trochidae) in the northeastern Atlantic Ocean and Mediterranean Sea. Molecular Phylogenetics and Evolution, 62, 35–45.
    OpenUrlCrossRefPubMed
  17. Doyle VP, Oudemans PV, Rehner SA, Litt A (2013) Habitat and host indicate lineage identity in Colletotrichum gloeosporioides s.l. from wild and agricultural landscapes in North America. PLoS ONE, 8, e62394.
    OpenUrl
  18. ↵
    Edwards DL, Knowles LL (2014) Species detection and individual assignment in species delimitation: can integrative data increase efficacy? Proceedings of the Royal Society of London B: Biological Sciences, 281, 2013–2765.
    OpenUrlCrossRef
  19. Egea E, David B, Choné T et al. (2016) Morphological and genetic analyses reveal a cryptic species complex in the echinoid Echinocardium cordatum and rule out a stabilizing selection explanation. Molecular Phylogenetics and Evolution, 94, 207–220.
    OpenUrlCrossRefPubMed
  20. ↵
    Emelianov I, Marec F, Mallet J (2004) Genomic evidence for divergence with gene flow in host races of the larch budmoth. Proceedings of the Royal Society B: Biological Sciences, 271, 97–105.
    OpenUrlCrossRefPubMedWeb of Science
  21. ↵
    Ence DD, Carstens BC (2011) SpedeSTEM: a rapid and accurate method for species delimitation. Molecular Ecology Resources, 11, 473–480.
    OpenUrl
  22. Escobar D, Zea S, Sánchez JA (2012) Phylogenetic relationships among the Caribbean members of the Cliona viridis complex (Porifera, Demospongiae, Hadromerida) using nuclear and mitochondrial DNA sequences. Molecular Phylogenetics and Evolution, 64, 271–284.
    OpenUrl
  23. Esposito LA, Bloom T, Caicedo-Quiroga L et al. (2015) Islands within islands: diversification of tailless whip spiders (Amblypygi, Phrynus) in Caribbean caves. Molecular Phylogenetics and Evolution, 93, 107–117.
    OpenUrl
  24. Faustová M, Sacherová V, Sheets HD, Svensson J-E, Taylor DJ (2010) Coexisting Cyclic Parthenogens Comprise a Holocene Species Flock in Eubosmina. PLoS ONE, 5, e11623.
    OpenUrlPubMed
  25. ↵
    Felsenstein J (1981) Skepticism towards Santa Rosalia, or why are there so few kinds of animals? Evolution, 124–138.
  26. Fernández-Mazuecos M, Vargas P (2014) Quaternary radiation of bifid toadflaxes (Linaria sect. Versicolores) in the Iberian Peninsula: low taxonomic signal but high geographic structure of plastid DNA lineages. Plant Systematics and Evolution, 301, 1411–1423.
    OpenUrl
  27. Fourie A, Wingfield MJ, Wingfield BD, Barnes I (2014) Molecular markers delimit cryptic species in Ceratocystis sensu stricto. Mycological Progress, 14, 1020.
    OpenUrl
  28. ↵
    Fujita MK, Leaché AD, Burbrink FT, McGuire JA, Moritz C (2012) Coalescent-based species delimitation in an integrative taxonomy. Trends in ecology & evolution, 27, 480–488.
    OpenUrlCrossRefPubMedWeb of Science
  29. Fusinatto LA, Alexandrino J, Haddad CFB et al. (2013) Cryptic Genetic diversity is paramount in small-bodied amphibians of the genus Euparkerella (Anura: Craugastoridae) endemic to the Brazilian Atlantic forest ( D Fontaneto, Ed,). PLoS ONE, 8, e79504.
    OpenUrl
  30. ↵
    Gavrilets S (2000) Waiting time to parapatric speciation. Proceedings of the Royal Society B: Biological Sciences, 267, 2483–2492.
    OpenUrlCrossRefPubMedWeb of Science
  31. Gazis R, Rehner S, Chaverri P (2011) Species delimitation in fungal endophyte diversity studies and its implications in ecological and biogeographic inferences. Molecular Ecology, 20, 3001–3013.
    OpenUrlCrossRefPubMedWeb of Science
  32. Gehesquière B, Crouch JA, Marra RE et al. (In Press) Characterization and taxonomic reassessment of the box blight pathogen Calonectria pseudonaviculata, introducing Calonectria henricotiae sp. nov. Plant Pathology.
  33. Groeneveld LF, Blanco MB, Raharison J-L et al. (2010) MtDNA and nDNA corroborate existence of sympatric dwarf lemur species at Tsinjoarivo, eastern Madagascar. Molecular Phylogenetics and Evolution, 55, 833–845.
    OpenUrlCrossRefPubMed
  34. ↵
    Gustafsson DR, Olsson U (2012) Flyway homogenisation or differentiation? Insights from the phylogeny of the sandpiper (Charadriiformes: Scolopacidae: Calidrinae) wing louse genus Lunaceps (Phthiraptera: Ischnocera). International Journal for Parasitology, 42, 93–102.
    OpenUrlPubMed
  35. Hendrixson BE, DeRussy BM, Hamilton CA, Bond JE (2013) An exploration of species boundaries in turret-building tarantulas of the Mojave Desert (Araneae, Mygalomorphae, Theraphosidae, Aphonopelma). Molecular Phylogenetics and Evolution, 66, 327–340.
    OpenUrlCrossRefPubMed
  36. ↵
    Hudson RR (2002) Generating samples under a Wright-Fisher neutral model of genetic variation. Bioinformatics, 18, 337.
    OpenUrlCrossRefPubMedWeb of Science
  37. ↵
    Hudson RR, Coyne JA (2002) Mathematical consequences of the genealogical species concept. Evolution; international journal of organic evolution, 56, 1557–1565.
    OpenUrlCrossRefPubMedWeb of Science
  38. Hu H, Al-Shehbaz IA, Sun Y et al. (2015) Species delimitation in Orychophragmus (Brassicaceae) based on chloroplast and nuclear DNA barcodes. Taxon, 64, 714–726.
    OpenUrl
  39. Jones G, Aydin Z, Oxelman B (2015) DISSECT: an assignment-free Bayesian discovery method for species delimitation under the multispecies coalescent. Bioinformatics, 31, 991–998.
    OpenUrlCrossRefPubMed
  40. Keith R, Hedin M (2012) Extreme mitochondrial population subdivision in southern Appalachian paleoendemic spiders (Araneae: Hypochilidae: Hypochilus), with implications for species delimitation. Journal of Arachnology, 40, 167–181.
    OpenUrl
  41. ↵
    Kirkpatrick M, Ravigné V (2002) Speciation by natural and sexual selection: models and experiments. The American Naturalist, 159, S22–S35.
    OpenUrlCrossRefPubMedWeb of Science
  42. ↵
    Knowles LL, Carstens BC (2007) Delimiting species without monophyletic gene trees. Systematic Biology, 56, 887–895.
    OpenUrlCrossRefPubMedWeb of Science
  43. Koopman MM, Baum DA (2010) Isolating nuclear genes and identifying lineages without monophyly: an example of closely related species from southern Mmadagascar. International Journal of Plant Sciences, 171, 761–771.
    OpenUrlCrossRefWeb of Science
  44. ↵
    De León LF, Bermingham E, Podos J, Hendry AP (2010) Divergence with gene flow as facilitated by ecological differences: within-island variation in Darwin's finches. Philosophical Transactions of the Royal Society B: Biological Sciences, 365, 1041–1052.
    OpenUrlCrossRefPubMed
  45. Levsen ND, Tiffin P, Olson MS (2012) Pleistocene Speciation in the Genus Populus (Salicaceae). Systematic Biology, 61, 401–412.
    OpenUrlCrossRefPubMed
  46. Lopes JS, Balding D, Beaumont MA (2009) PopABC: a program to infer historical demographic parameters. Bioinformatics, 25, 2747–2749.
    OpenUrlCrossRefPubMedWeb of Science
  47. Lu B, Bi K, Fu J (2014) A phylogeographic evaluation of the Amolops mantzorum species group: Cryptic species and plateau uplift. Molecular Phylogenetics and Evolution, 73, 40–52.
    OpenUrl
  48. Martinsson S, Rhodén C, Erséus C (In press) Barcoding gap, but no support for cryptic speciation in the earthworm Aporrectodea longa (Clitellata: Lumbricidae). Mitochondrial DNA, 1–9.
  49. Medina R, Lara F, Goffinet B, Garilleti R, Mazimpaka V (2012) Integrative taxonomy successfully resolves the pseudo-cryptic complex of the disjunct epiphytic moss Orthotrichum consimile s.l. (Orthotrichaceae). Taxon, 61, 1180–1198.
    OpenUrl
  50. ↵
    Niemiller ML, Fitzpatrick BM, Miller BT (2008) Recent divergence with gene flow in Tennessee cave salamanders (Plethodontidae: Gyrinophilus) inferred from gene genealogies. Molecular Ecology, 17, 2258–2275.
    OpenUrlCrossRefPubMedWeb of Science
  51. Niemiller ML, McCandless JR, Reynolds RG et al. (2013) Effects of climatic and geological processes during the pleistocene on the evolutionary history of the northern cavefish, Amblyopsis spelaea (Teleostei: Amblyopsidae). Evolution, 67, 1011–1025.
    OpenUrl
  52. Niemiller ML, Near TJ, Fitzpatrick BM (2012) Delimiting species using multilocus data: diagnosing cryptic diversity in the southern cavefish, Typhlichthys subterraneus (Teleostei: Amblyopsidae). Evolution, 66, 846–866.
    OpenUrlCrossRefPubMedWeb of Science
  53. ↵
    Nosil P (2008) Speciation with gene flow could be common. Molecular Ecology, 17, 2103–2106.
    OpenUrlCrossRefPubMedWeb of Science
  54. ↵
    Olave M, Solà E, Knowles LL (2014) Upstream analyses create problems with dna-based species delimitation. Systematic Biology, 63, 263–271.
    OpenUrlCrossRefPubMed
  55. ↵
    O’Meara BC (2010) New heuristic methods for joint species delimitation and species tree inference. Systematic Biology, 59, 59–73.
    OpenUrlCrossRefPubMedWeb of Science
  56. Parmakelis A, Kotsakiozi P, Stathi I, Poulikarakou S, Fet V (2013) Hidden diversity of Euscorpius (Scorpiones: Euscorpiidae) in Greece revealed by multilocus species-delimitation approaches. Biological journal of the Linnean Society, 110, 728–748.
    OpenUrl
  57. Pazoutová S, Pešicová K, Chudíčková M, utka PŠ, Kolařík M (2015) Delimitation of cryptic species inside Claviceps purpurea. Fungal Biology, 119, 7–26.
    OpenUrl
  58. Pérez G, Burgess TI, Slippers B et al. (2013) Teratosphaeria pseudonubilosa sp. nov., a serious Eucalyptus leaf pathogen in the Teratosphaeria nubilosa species complex. Australasian Plant Pathology, 43, 67–77.
    OpenUrl
  59. Pettengill JB, Moeller DA (2012) Tempo and mode of mating system evolution between incipient Clarkia species: temporal dynamics of mating system evolution. Evolution, 66, 1210–1225.
    OpenUrlCrossRefPubMedWeb of Science
  60. Pino-Bodas R, Ahti T, Stenroos S, Martin MP, Burgaz AR (2013) Multilocus approach to species recognition in the Cladonia humilis complex (Cladoniaceae, Ascomycota). American Journal of Botany, 100, 664–678.
    OpenUrlAbstract/FREE Full Text
  61. ↵
    Pons J, Barraclough TG, Gomez-Zurita J et al. (2006) Sequence-based species delimitation for the DNA taxonomy of undescribed insects. Systematic biology, 55, 595–609.
    OpenUrlCrossRefPubMedWeb of Science
  62. Prévot V, Jordaens K, Sonet G, Backeljau T (2013) Exploring species level taxonomy and species delimitation methods in the facultatively self-fertilizing land snail genus Rumina (Gastropoda: Pulmonata). PLoS ONE, 8, e60736.
    OpenUrlCrossRefPubMed
  63. Ramirez JL, Miyaki CY, Frederick PC, Lama SND (2014) Species delimitation in the genus Eudocimus (Threskiornithidae: Pelecaniformes): first genetic approach. Waterbirds, 37, 419–425.
    OpenUrl
  64. ↵
    Reid NM, Carstens BC (2012) Phylogenetic estimation error can decrease the accuracy of species delimitation: a Bayesian implementation of the general mixed Yule-coalescent model. BMC Evolutionary Biology, 12, 196.
    OpenUrlCrossRefPubMed
  65. Sakalidis ML, Hardy GESJ, Burgess TI (2011) Use of the Genealogical Sorting Index (GSI) to delineate species boundaries in the Neofusicoccum parvum-Neofusicoccum ribis species complex. Molecular Phylogenetics and Evolution, 60, 333–344.
    OpenUrlCrossRefPubMed
  66. Salariato DL, Zuloaga FO, Al-Shehbaz IA (2012) Morphometric studies and taxonomic delimitation in Menonvillea scapigera and related species (Cremolobeae: Brassicaceae). Plant Systematics and Evolution, 298, 1961–1976.
    OpenUrl
  67. Sánchez-Ramírez S, Tulloss RE, Guzmán-Dávalos L et al. (In Press) In and out of refugia: historical patterns of diversity and demography in the North American Caesar's mushroom species complex. Molecular Ecology.
  68. Schmidt-Lebuhn AN, de Vos JM, Keller B, Conti E (2012) Phylogenetic analysis of Primula section Primula reveals rampant non-monophyly among morphologically distinct species. Molecular Phylogenetics and Evolution, 65, 23–34.
    OpenUrlCrossRefPubMed
  69. ↵
    Slatkin M (1995) A measure of population subdivision based on microsatellite allele frequencies. Genetics, 139, 457.
    OpenUrlAbstract/FREE Full Text
  70. Su X, Wu G, Li L, Liu J (2015) Species delimitation in plants using the Qinghai-Tibet Plateau endemic Orinus (Poaceae: Tridentinae) as an example. Ann Bot, 116, 35–48.
    OpenUrlCrossRefPubMed
  71. ↵
    Tajima F (1983) Evolutionary relationship of DNA sequences in finite populations. Genetics, 105, 437–460.
    OpenUrlAbstract/FREE Full Text
  72. Taole MM, Burgess TI, Gryzenhout M, Wingfield BD, Wingfield MJ (2011) DNA sequence incongruence and inconsistent morphology obscure species boundaries in the Teratosphaeria suttonii species complex. Mycoscience, 53, 270–283.
    OpenUrl
  73. Udayanga D, Castlebury LA, Rossman AY, Hyde KD (2014) Species limits in Diaporthe: molecular reassessment of D. citri, D. cytosporella, D. foeniculina and D. rudis. Persoonia - Molecular Phylogeny and Evolution of Fungi, 32, 83–101.
    OpenUrl
  74. Valcárcel V, Vargas P (2010) Quantitative morphology and species delimitation under the general lineage concept: Optimization for Hedera (Araliaceae). American Journal of Botany, 97, 1555–1573.
    OpenUrlAbstract/FREE Full Text
  75. Vigalondo B, Fernández-Mazuecos M, Vargas P, Sáez L (2015) Unmasking cryptic species: morphometric and phylogenetic analyses of the Ibero-North African Linaria incarnata complex. Botanical Journal of the Linnean Society, 177, 395–417.
    OpenUrlCrossRef
  76. Viricel A, Rosel PE (2014) Hierarchical population structure and habitat differences in a highly mobile marine species: the Atlantic spotted dolphin. Molecular Ecology, 23, 5018–5035.
    OpenUrlCrossRef
  77. Walker DM, Castlebury LA, Rossman AY, White Jr. JF (2012) New molecular markers for fungal phylogenetics: Two genes for species-level systematics in the Sordariomycetes (Ascomycota). Molecular Phylogenetics and Evolution, 64, 500–512.
    OpenUrlCrossRefPubMed
  78. Walstrom VW, Klicka J, Spellman GM (2012) Speciation in the White-breasted Nuthatch (Sitta carolinensis): a multilocus perspective. Molecular Ecology, 21, 907–920.
    OpenUrlCrossRefPubMedWeb of Science
  79. Wang X, Zang R, Yin Z, Kang Z, Huang L (2014) Delimiting cryptic pathogen species causing apple Valsa canker with multilocus data. Ecology and Evolution, 4, 1369–1380.
    OpenUrl
  80. Weisrock DW, Rasoloarison RM, Fiorentino I et al. (2010) Delimiting species without nuclear monophyly in Madagascar's mouse lemurs. PLoS ONE, 5, e9883.
    OpenUrlCrossRefPubMed
  81. Willyard A, Wallace LE, Wagner WL et al. (2011) Estimating the species tree for Hawaiian Schiedea (Caryophyllaceae) from multiple loci in the presence of reticulate evolution. Molecular Phylogenetics and Evolution, 60, 29–48.
    OpenUrlCrossRefPubMed
  82. ↵
    Yang Z, Rannala B (2010) Bayesian species delimitation using multilocus sequence data. Proceedings of the National Academy of Sciences, 107, 9264–9269.
    OpenUrlAbstract/FREE Full Text
  83. ↵
    Yang Z, Rannala B (2014) Unguided Species Delimitation Using DNA Sequence Data from Multiple Loci. Molecular Biology and Evolution, 31, 3125–3135.
    OpenUrlCrossRefPubMedWeb of Science
  84. ↵
    Zhang C, Zhang D-X, Zhu T, Yang Z (2011) Evaluation of a Bayesian Coalescent Method of Species Delimitation. Systematic Biology, 60, 747–761.
    OpenUrlCrossRefPubMed
  85. Zhao Y, Qi Z, Ma W et al. (2013) Comparative phylogeography of the Smilax hispida group (Smilacaceae) in eastern Asia and North America - Implications for allopatric speciation, causes of diversity disparity, and origins of temperate elements in Mexico. Molecular Phylogenetics and Evolution, 68, 300–311.
    OpenUrlCrossRefPubMed
Back to top
Posted January 12, 2016.
Download PDF
Email
The Genealogical Sorting Index and species delimitations
David J. Winter, Steven A. Trewick, Jon M. Waters, Hamish G. Spencer
bioRxiv 036525; doi: https://doi.org/10.1101/036525
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools

Subject Area