Delimiting species by GCPSR

Ana Marcela Florez Rueda

Introduction

The most important question regarding the species problem is species delimitation, species are fundamental units of systematic, ecological and evolutionary studies and the accurate documentation and delimitation of species is increasingly important as the species diversity of the world's biota is increasingly reduced and threatened. (1)

Using ESC, one can reinterpret other species concepts as operational criteria useful for delimitation of independent lineages ie. species. This work focuses on Phylogenetic Species Recognition, having as a criterion monophyly as in the PSC (sensu Mishler and Theriot)(2), the drawback of this type of PSR is that individuals are grouped very well, but the decision about where to place the limit of the species is subjective. PSR can avoid the subjectivity of determining the limits of a species by relying on the concordance of more than one gene genealogy (3,4). Genealogical Concordance Phylogenetic Species Recognition (GCPSR) has become feasible for most groups of organisms due to the increased ease of obtaining large amounts of nucleic acid sequence data. The strength GCPSR lies in its comparison of more than one gene genealogy, where the different gene trees are concordant they have the same tree topology due to fixation of formerly polymorphic loci following genetic isolation; these concordant branches connect species. Fig. 1 Conflict among gene trees is likely to be due to recombination among individuals within a species, and the transition from concordance to conflict determines the limits of species (5)
Fig. 1. Simultaneous analysis of three gene genealogies shows how the transition from concordance among branches to incongruity among branches can be used to diagnose species.(5)


A prevailing theme that emerges from studies that use concordance of multiple gene genealogies to recognize species boundaries is that recognizes additional genetically isolated species that had not been recognized previously, due to the lack of taxonomically informative morphological characters (phenotypic simplicity or plasticity) or incomplete reproductive isolation among species (5). This is the case of some Neurospora species, in which the reproductive success of crosses with species-specific tester strains has been used to assign most Neurospora individuals to one of five outbreeding biological species: N. crassa, N. intermedia, N. sitophila, N. tetrasperma, and N. discreta but N. crassa and N.intermedia are assumed to be closely related sibling species between which reproductive isolation may not be complete(6), the purpose of this analyses is to test if these are independent lineages through GCPSR, the same aproach is going to be used to delimite species of the mygalomorph spider Antrodiaetus riversi(7). It is expected that the combined data set due to presence of all the evidence of the loci shows a better resolution in the species delimitation.


Methods

Data sets

Neurospora data set : four microsatellite loci were analysed (DMG, TMI, TML, QMA) with 15 sequences per loci representing the five outbreeding biological species: N. crassa, N. intermedia, N. sitophila, N. tetrasperma, and outgroup Neurospora species (N. dodgei, N. galapagosensis, N. africana, and N. lineolata), these sequences were retrieved from the GenBank website under accessions AY225899– AY225949, from Dettman 2003 work(6).

Antrodiaetus riversi data set: two mitocondrial and one nuclear loci were retrieved from the genebank website, number of accesions, gene specification and accesions numbers respectively for each locus follows: 37 accesions for 28S ribosomal RNA gene (DQ898772 - DQ898808), 36 accesions for 12S ribosomal RNA gene (DQ898809 - DQ898844) and 36 accesions for cytochrome oxidase subunit I (COI) gene (DQ898845- DQ898880). In the combined data set a total of 10 terminals were used as outgroup including species from genus Aliatypus, Atypoides and
Antrodiaetus.

Tree searches

The reconstruction of the genealogy between terminals was assesed using direct optimization method as implemented in POY 4.0 (8), for each single locus data set as well as for the combined data set, two different matrix cost were used one of equal costs 111 and one that favours transitions and penalizes transversions and gaps 421. The search strategy was a initial build of 100 Wagner trees posterior selection of the unique trees with minimal cost that then were summited to branch permutation with SPR followed by TBR, 50 bootstraps replicates were run to determine support values on each of the single locus data sets and in combined analyses of all the loci for each case.

GCPSR

A clade was recognized as an independent evolutionary lineage if it satisfied either of two criteria: 1. Genealogical concordance: the clade was present in the majority (3/4) of the single-locus genealogies. This criterion revealed the genealogical patterns shared among loci, regardless of levels of support.(3,4,5,6) 2. Genealogical nondiscordance: the clade appeared with bootstrap support values SV(1.0) in at least one single-locus genealogy, and was not contradicted in any other single-locus genealogy at the same level of support. This criterion prohibited poorly supported nonmonophyly at one locus from undermining well-supported monophyly at another locus.(6)



Results and discusion

Table 1. Sumarized results for all the searches under the two set cost used 111 and 421.

Based on the genealogical concordance criterion 4 species were recognized in the Antrodiaetus riversi complex. figs 2-5.

• Sp1(purple) appears with 1.00 support value (SV) in the 28S ribosomal RNA gene, and in the 12S ribosomal RNA gene SV(1.0) , and was not contradicted in any other single-locus genealogy.
• Sp2 (orange/red) appears in the in the 28S ribosomal RNA gene, and in the 12S ribosomal RNA gene and combined data set with SV(1.0) The orange clade is also present in the cytochrome oxidase subunit I gene tree found using equal cost SV(1.0).
• Sp3(green): is consistently present in all the topologies found.
• Sp4(blue): was found in in the 28S ribosomal RNA gene, and in the 12S ribosomal RNA gene with SV(0.8) .

The results of the combined data set did not behave as expected, none of the species delimited based on the single locus gene trees, was supported in the combined data set.

Fig2. Results of single gene 12S ribosomal RNA, four species identified, identical results for both matrix cost used.

Fig3. Results of single gene cytochrome oxidase subunit I gene tree found with equal cost. No species delimited by SV(1.0)The topology found with the differential matrix cost is different but also no species were found delimited by SV(1.0)

Fig4. Results of single 28S ribosomal RNA gene, four species identified, identical results for both matrix cost used.

Fig5. Results combined data set with differential matrix costs, pecies 2 and 3 delimited with no support. Results with equal cost yield different topology with less resolution ie. no species delimited.

Neurospora species

The results obtained from the analyses supports the independence of the lineages and thus the species status of Neurospora crassa, Neurospora intermedia and Neurospora tetrasperma. figs 6-10

• The two Neurospora intermedia accesions (in blue) were present as an independent lineage in the DGM and in the TML topologies as well as in the combined analysis with SV(1.0)
• Neurospora tetrasperma and accesion FGSC8815 (in green) clustered together as a species in the TMI, TML and combined analysis with SV(1.0).
• The three accesions of N. crassa plus accesion FGSC8834 (in red) , were found to be a species with SV(1.0) in the TML and combined analysis. Also in the QMA topology the highest SV(0.9) was for two different clades belonging to this species.
• Although the TMI and DGM topologies show clades containing N. crassa and N. intermedia SV(1.0, 0.80) respectively, the well supported clades SV(1.0) present in the TML topology for each species were not contradicted at the same level of support.fig 6. Results for TML microsatellite sequence data set with equal cost matrix. Identical results were obtained with differential matrix cost.fig 7. Results for DGM microsatellite sequence data set with equal cost matrix. Identical results were obtained with differential matrix cost.

fig8. Results for QMA microsatellite sequence data set with equal cost matrix.

fig 9. Results for TMI microsatellite sequence data set with equal cost matrix. Identical results were obtained with differential matrix cost.

Fig10. Results combined data set with equal costs species delimited with no support except N. intermedia, results with equal cost yield different topology with less resolution ie. no species delimited.

None of the combined data set showed better resolution expected delimiting species,this possibly due to the the effect of non congruent loci in the combined data set that consequently diminished its resolution, thus for GCPSR the analysis should be made only examining single locus data sets, and looking for clades with SV(1.0).


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