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. 2015 Oct 27;7:plv124. doi: 10.1093/aobpla/plv124

DNA barcodes reveal microevolutionary signals in fire response trait in two legume genera

Abubakar Bello 1,*, Barnabas H Daru 2, Charles H Stirton 1, Samson B M Chimphango 1, Michelle van der Bank 3, Olivier Maurin 3, A Muthama Muasya 1
PMCID: PMC4670488  PMID: 26507570

This article is one of the series of publications carried out as part of the ongoing revision of the genus Psoralea. The genus was last revised in 1930 by Miss Helena Forbes. Since then, no thorough revision has been carried out on the genus beside the 1981 generic changes by Prof C.H. Stirton where a new genus Otholobium is described. The two genera represent a recent and rapid diversification of a lineage with a center of diversity and endemism in the Cape Floristic Region of South Africa.

Keywords: Fabaceae, Otholobium, Psoralea, reseeders, resprouters, South Africa

Abstract

Large-scale DNA barcoding provides a new technique for species identification and evaluation of relationships across various levels (populations and species) and may reveal fundamental processes in recently diverged species. Here, we analysed DNA sequence variation in the recently diverged legumes from the Psoraleeae (Fabaceae) occurring in the Cape Floristic Region (CFR) of southern Africa to test the utility of DNA barcodes in species identification and discrimination. We further explored the phylogenetic signal on fire response trait (reseeding and resprouting) at species and generic levels. We showed that Psoraleoid legumes of the CFR exhibit a barcoding gap yielding the combination of matK and rbcLa (matK + rbcLa) data set as a better barcode than single regions. We found a high score (100 %) of correct identification of individuals to their respective genera but a very low score (<50 %) in identifying them to species. We found a considerable match (54 %) between genetic species and morphologically delimited species. We also found that different lineages showed a weak but significant phylogenetic conservatism in their response to fire as reseeders or resprouters, with more clustering of resprouters than would be expected by chance. These novel microevolutionary patterns might be acting continuously over time to produce multi-scale regularities of biodiversity. This study provides the first insight into the DNA barcoding campaign of land plants in species identification and detection of the phylogenetic signal in recently diverged lineages of the CFR.

Introduction

The primary goal of DNA barcoding is the identification of an unknown sample by correctly matching a specific genetic marker to a reference sequence library. However, DNA barcoding can also be used as a tool for addressing fundamental questions in ecology, evolution and conservation biology (Kress et al. 2015). For evolutionary biologists and ecologists, one of the goals of DNA barcoding is to understand the origin of species and the factors causing the difference in species richness in different biomes across the globe. Generally, the full diversity of species in most diverse habitats is still poorly known (Kress et al. 2015). The primary focus of this article is to explore the application of DNA barcoding in some recently diverged lineages of an exceptionally unique fire derived biodiversity hotspot to determine its efficacy in species identification and detection of microevolutionary signals.

The Greater Cape Floristic Region (GCFR) is a home to Fynbos and the Succulent Karoo biomes—two major biodiversity hotspots located in the winter rainfall area of southern Africa (Myers et al. 2000) (Fig. 1). The Fynbos biome (also called the CFR) is famed for its high species diversity consisting of ∼9000 species of vascular plants packed into an area of 90 760 km2 of which ∼69 % are endemic (Manning and Goldblatt 2012). The family Fabaceae consists of ∼764 species in 43 genera. It is the second largest family in the CFR flora after Asteraceae. Three of the major clades of Fabaceae include the Crotalarieae (300 species), Podalyrieae (125 species) and African Psoraleeae (120 species). These legume lineages differ in their patterns of diversification, with Crotalarieae and Podalyrieae originating in the Eocene ca. 40 Ma (Edwards and Hawkins 2007; Schnitzler et al. 2011) and the African Psoraleeae originating during the Pliocene ca. 5 Ma (Egan and Crandall 2008). This suggests that the African Psoraleeae is a young lineage, which has undergone rapid recent radiation giving rise to ∼75 species of Psoralea L. and ∼53 species of Otholobium C.H.Stirt. (Stirton 2005; Manning and Goldblatt 2012). Majority of species in Otholobium and Psoralea have a narrow distribution and are frequently restricted to a single mountain stream or slope or a single soil type. In addition, several species are listed in the IUCN Red List under different levels of conservation categories ranging from extinct in the wild (e.g. Psoralea gueinzii and P. cataracta) to endangered (e.g. Otholobium bowieanum, O. incanum, P. fascicularis and P. filifolia) and vulnerable (O. hamatum, O. venustum, P. abbottii and P. alata) (Raimondo et al. 2009).

Figure 1.

Figure 1.

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Map of the GCFR showing the Fynbos and the Succulent Karoo Biomes constructed based on Mucina and Rutherford (2006).

Fynbos is a fire prone vegetation that requires regular burning for its persistence. The high species richness in the Fynbos biome has been ascribed to fire (Cowling et al. 1996; Linder 2003; Power et al. 2011). Plants adapt to fires in two major ways: as resprouters or reseeders (Bell 2001). Resprouting plants survive fire as individuals and then replace the lost structures by resprouting from surviving tissues. Conversely, reseeding individuals are often killed by fire (Fig. 2) and the population is re-established by a new generation growing from seeds (Bell 2001). Fire-survival and regeneration strategies of plants have been the subject of numerous studies (e.g. Keeley 1977; Bond 1985; Le Maitre and Midgley 1992; Schutte et al. 1995; Pausas and Keeley 2014; Scott et al. 2014). Cowling (1987) postulated that the high species diversity in the Gondwanan floras (Australian kwongan and Cape fynbos) may be ascribed to recurrent fires, edaphic specialization and short dispersal distance. There are noticeable differences in the allocation of resources to reserve storage, vegetative growth and reproductive effort linked with these fire-survival strategies (Bond and van Wilgen 1996; Bell 2001; Bond and Midgley 2001; Scott et al. 2014). For example, while reseeders are generally characterized by a shorter lifespan, they tend to grow rapidly and taller with much allocation of resources predominantly above ground. Resprouters, on the other hand, have longer lifespans, slower growth, produce fewer seeds and have a below ground resource allocation in starch-rich lignotubers (Hansen et al. 1991; Bell and Ojeda 1999). Reseeders produce larger numbers of viable seeds than do resprouters due to their greater reliance on seed for survival (Hansen et al. 1991; Bell 2001), resulting in elevated post-fire recruitment. There are also reported differences in seed yield and quality with reseeders having higher N and P concentrations in the seeds than those of congeneric resprouters (Hansen et al. 1991). Other differences include nutritional requirements with reseeders requiring more nutrients than the resprouters due to the high nutritional costs of seed production and growth (Hansen et al. 1991; Bell 2001). These strategies influence speciation rates in woody genera in the fynbos (Wells 1969; Litsios et al. 2014), with reseeders shown to have higher diversification rates than resprouters (Litsios et al. 2014). Other studies have shown that fire-survival and regeneration strategy (reseeding/resprouting) is a character of taxonomic, ecological and evolutionary importance in Fynbos legumes (Schutte et al. 1995; Litsios et al. 2014; Scott et al. 2014).

Figure 2.

Figure 2.

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A recent fire burn in the Cape Fynbos, Table Mountain on 5 March 2015. Photograph: A.B.

Traditionally, species identification depends primarily on morphological features (morphospecies). As molecular data became increasingly available and new techniques such as DNA barcoding emerged, species identification is becoming fast, reliable and more accurate. Here, we use matK and rbcLa and the combination of the two regions (matK + rbcLa), based on their recognition as core plant barcode markers by the Consortium for the Barcode of Life Plant Working Group (CBOL 2009) to (i) test their efficacy in identifying species of two southern African Psoraleoid genera (Otholobium and Psoralea); (ii) explore the potential of the DNA barcode markers in grouping Psoraleoid legume sequences into molecular operational taxonomic units (MOTUs) or genetic species units and (iii) test the power of DNA barcodes in revealing microevolutionary patterns including fire-survival and regeneration strategies. The genera Otholobium and Psoralea were chosen for this study because they both have species with reseeding and resprouting modes of regeneration (Fig. 3). Furthermore, although the two genera are closely related (Dludlu et al. 2013), they differ markedly in terms of their morphology and ecology. For example, Otholobium species differ from Psoralea by the absence of a cupulum on the flower pedicel (unique structure in Psoralea, Tucker and Stirton 1991); possession of entire recurved mucronate-obovate to oblanceolate leaflets and inflorescences characterized by bracteate triplets of flowers, with each triplet subtended by a single variously shaped bract (Stirton 1981). Leaves of Psoralea range from 1- to 19-foliolate compound structures or reduced to small-scale-like structures with only P. aculeata having a recurved mucro (Stirton 1989; Manning and Goldblatt 2012), and each flower is subtended by a pair of free minute bracts. The two genera also differ in terms of habitat preferences. Eighty per cent of Psoralea species inhabit seeps, marshes, riverbanks and/or moist, mist laden high-altitude habitats, while Otholobium species occur predominantly in drier habitats, with only 11 % of species occupying the moist habitats favoured by Psoralea (Stirton 1989; Manning and Goldblatt 2012).

Figure 3.

Figure 3.

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Habit in Otholobium and Psoralea species: (A) reseeding, O. spicatum; (B) resprouting, O. rotundifolium; (C) reseeding, P. pinnata; (D) resprouting, Psoralea sp. nov. Photographs: C.H.S. (A–C) and A.B. (D).

Methods

Taxon sampling

We collected 172 samples representing 26 species of Otholobium and 43 species of Psoralea across their distribution range in the CFR. Where possible, each species was represented by two or more different samples. In all, we collected 72 samples of Otholobium and 100 samples of Psoralea (voucher specimens are deposited at the Bolus Herbarium (BOL) and listed in Table 1). Of these samples, 23 out of the 26 species of Otholobium and 26 out of 43 species of Psoralea are represented by more than one sample. Only samples for which sequences for both genes (matK and rbcLa) are available were included in the analyses. The final data set used in the analyses included 4 reseeding (27 samples) and 22 resprouting (35 samples) species of Otholobium, and 35 (43 samples) reseeding and 8 (56 samples) resprouting species of Psoralea. Information on fire response strategy was extracted from Stirton (1989), Manning and Goldblatt (2012) and Snijman (2013). To our knowledge, no species included in our analysis show both fire response strategies in wild populations. Collection details including GPS coordinates, altitude and photographs of specimens are available online in the Barcode of Life Data Systems (BOLD; www.boldsystems.org) together with DNA sequences.

Table 1.

List of voucher specimens and the DNA sequence BOLD ID reference number.

Taxon name Collector Number BOLD ID Herbarium Distribution
Otholobium acuminatum Muasya & Stirton AMM3850 FAUCT199-11 BOL Africa
Otholobium acuminatum Muasya & Stirton AMM3603 FAUCT144-11 BOL Africa
Otholobium arborescens Muasya & Stirton AMM3279 FAUCT051-11 BOL Africa
Otholobium beanianum sp. nov. Muasya & Stirton AMM3350 FAUCT067-11 BOL Africa
Otholobium bracteolatum Muasya & Stirton AMM3963 FAUCT229-11 BOL Africa
Otholobium bracteolatum Muasya & Stirton AMM3164 FAUCT002-11 BOL Africa
Otholobium bracteolatum Muasya & Stirton AMM3879 FAUCT208-11 BOL Africa
Otholobium bracteolatum ssp. limnophilum ssp. nov. Muasya & Stirton AMM & Stirton 13155 FAUCT367-11 BOL Africa
Otholobium bracteolatum ssp. limnophilum ssp. nov. Muasya & Stirton AMM3204 FAUCT030-11 BOL Africa
Otholobium candicans Muasya & Stirton AMM3911 FAUCT223-11 BOL Africa
Otholobium candicans Muasya & Stirton AMM3369 FAUCT072-11 BOL Africa
Otholobium candicans Muasya & Stirton AMM3563 FAUCT130-11 BOL Africa
Otholobium crewii sp. nov. Muasya & Stirton AMM3264 FAUCT041-11 BOL Africa
Otholobium flexuosum Muasya & Stirton AMM3276 FAUCT049-11 BOL Africa
Otholobium flexuosum Muasya & Stirton AMM3280 FAUCT052-11 BOL Africa
Otholobium fruticans Muasya & Stirton AMM3480 FAUCT106-11 BOL Africa
Otholobium fruticans Muasya & Stirton AMM3397 FAUCT081-11 BOL Africa
Otholobium hamatum Muasya & Stirton AMM3310 FAUCT060-11 BOL Africa
Otholobium hamatum Muasya & Stirton AMM3306 FAUCT059-11 BOL Africa
Otholobium hirtum Muasya & Stirton AMM3326 FAUCT063-11 BOL Africa
Otholobium hirtum Muasya & Stirton AMM3991 FAUCT232-11 BOL Africa
Otholobium hirtum Muasya & Stirton AMM3190 FAUCT018-11 BOL Africa
Otholobium hirtum Muasya & Stirton AMM3373 FAUCT074-11 BOL Africa
Otholobium hirtum Muasya & Stirton AMM3372 FAUCT073-11 BOL Africa
Otholobium hirtum Muasya & Stirton AMM3499 FAUCT112-11 BOL Africa
Otholobium hirtum Muasya & Stirton AMM3878 FAUCT207-11 BOL Africa
Otholobium lucens sp. nov. Muasya & Stirton AMM3570 FAUCT133-11 BOL Africa
Otholobium mundianum Muasya & Stirton AMM3885 FAUCT211-11 BOL Africa
Otholobium obliquum Muasya & Stirton AMM3198.1 FAUCT023-11 BOL Africa
Otholobium parviflorum Muasya & Stirton AMM3199 FAUCT024-11 BOL Africa
Otholobium parviflorum Muasya & Stirton AMM3542 FAUCT119-11 BOL Africa
Otholobium prodiens Muasya & Stirton AMM3845 FAUCT196-11 BOL Africa
Otholobium prodiens Muasya & Stirton AMM3854 FAUCT201-11 BOL Africa
Otholobium pustulatum Muasya & Stirton AMM3286 FAUCT054-11 BOL Africa
Otholobium rotundifolium Muasya & Stirton AMM3929 FAUCT227-11 BOL Africa
Otholobium rotundifolium Muasya & Stirton AMM3173 FAUCT009-11 BOL Africa
Otholobium rubicundum Muasya & Stirton AMM5982 FAUCT359-11 BOL Africa
Otholobium schutteae sp. nov. Muasya & Stirton AMM3575 FAUCT134-11 BOL Africa
Otholobium spicatum Muasya & Stirton AMM3445 FAUCT097-11 BOL Africa
Otholobium spicatum Muasya & Stirton AMM3498 FAUCT111-11 BOL Africa
Otholobium spicatum Muasya & Stirton AMM3906 FAUCT220-11 BOL Africa
Otholobium spicatum Muasya & Stirton AMM3568 FAUCT132-11 BOL Africa
Otholobium stachyerum Muasya & Stirton AMM3837 FAUCT194-11 BOL Africa
Otholobium stachyerum Muasya & Stirton AMM3872 FAUCT206-11 BOL Africa
Otholobium stachyerum Muasya & Stirton AMM3791 FAUCT183-11 BOL Africa
Otholobium stachyerum Muasya & Stirton AMM3604 FAUCT145-11 BOL Africa
Otholobium stachyerum Muasya & Stirton AMM3851 FAUCT200-11 BOL Africa
Otholobium striatum Muasya & Stirton AMM3339 FAUCT064-11 BOL Africa
Otholobium striatum Muasya & Stirton AMM3363 FAUCT071-11 BOL Africa
Otholobium striatum Muasya & Stirton AMM3561 FAUCT129-11 BOL Africa
Otholobium striatum Muasya & Stirton AMM4106 FAUCT247-11 BOL Africa
Otholobium striatum Muasya & Stirton AMM3351 FAUCT068-11 BOL Africa
Otholobium striatum Muasya & Stirton AMM3318 FAUCT062-11 BOL Africa
Otholobium thomii Muasya & Stirton AMM3187 FAUCT016-11 BOL Africa
Otholobium uncinatum Muasya & Stirton AMM3175 FAUCT010-11 BOL Africa
Otholobium uncinatum Muasya & Stirton AMM3263 FAUCT040-11 BOL Africa
Otholobium uncinatum Muasya & Stirton AMM3261 FAUCT038-11 BOL Africa
Otholobium velutinum Muasya & Stirton AMM & Stirton 13106 FAUCT362-11 BOL Africa
Otholobium virgatum Muasya & Stirton AMM3908 FAUCT222-11 BOL Africa
Otholobium virgatum Muasya & Stirton AMM3395 FAUCT079-11 BOL Africa
Otholobium virgatum Muasya & Stirton AMM3163 FAUCT001-11 BOL Africa
Otholobium virgatum Muasya & Stirton AMM3191 FAUCT019-11 BOL Africa
Psoralea aculeata Muasya & Stirton AMM3183 FAUCT012-11 BOL Africa
Psoralea aculeata Muasya & Stirton AMM3405 FAUCT088-11 BOL Africa
Psoralea aculeata Muasya & Stirton AMM3550 FAUCT124-11 BOL Africa
Psoralea aculeata Muasya & Stirton AMM3170 FAUCT006-11 BOL Africa
Psoralea affinis Muasya & Stirton AMM3903.2 FAUCT215-11 BOL Africa
Psoralea affinis Muasya & Stirton AMM3868 FAUCT203-11 BOL Africa
Psoralea alata Muasya & Stirton AMM3262 FAUCT039-11 BOL Africa
Psoralea alata Muasya & Stirton AMM3398 FAUCT082-11 BOL Africa
Psoralea alata Muasya & Stirton AMM3880 FAUCT209-11 BOL Africa
Psoralea alata Muasya & Stirton AMM3901 FAUCT213-11 BOL Africa
Psoralea aphylla Muasya & Stirton AMM3400 FAUCT084-11 BOL Africa
Psoralea arborea Muasya & Stirton AMM3212 FAUCT032-11 BOL Africa
Psoralea arborea Muasya & Stirton AMM3248 FAUCT037-11 BOL Africa
Psoralea arida sp. nov. Muasya & Stirton AMM3526 FAUCT113-11 BOL Africa
Psoralea arida sp. nov. Muasya & Stirton AMM4098 FAUCT246-11 BOL Africa
Psoralea asarina Muasya & Stirton AMM3907 FAUCT221-11 BOL Africa
Psoralea asarina Muasya & Stirton AMM3476 FAUCT105-11 BOL Africa
Psoralea asarina Muasya & Stirton AMM3552 FAUCT126-11 BOL Africa
Psoralea axillaris Muasya & Stirton AMM3834 FAUCT192-11 BOL Africa
Psoralea axillaris Muasya & Stirton AMM3848 FAUCT198-11 BOL Africa
Psoralea axillaris Muasya & Stirton AMM3827 FAUCT191-11 BOL Africa
Psoralea axillaris Muasya & Stirton AMM5874 FAUCT356-12 BOL Africa
Psoralea brilliantissima sp. nov. Muasya & Stirton AMM3621 FAUCT152-11 BOL Africa
Psoralea cf. latifolia Muasya & Stirton AMM4028 FAUCT234-11 BOL Africa
Psoralea congesta Muasya & Stirton AMM5462 FAUCT328-11 BOL Africa
Psoralea elegans sp. nov. Muasya & Stirton AMM3591 FAUCT139-11 BOL Africa
Psoralea filifolia Muasya & Stirton AMM4321 FAUCT278-11 BOL Africa
Psoralea fleta Muasya & Stirton AMM3273 FAUCT047-11 BOL Africa
Psoralea fleta Muasya & Stirton AMM3341 FAUCT065-11 BOL Africa
Psoralea fleta Muasya & Stirton AMM3342 FAUCT066-11 BOL Africa
Psoralea forbesii sp. nov. Muasya & Stirton AMM3578 FAUCT135-11 BOL Africa
Psoralea forbesii sp. nov. Muasya & Stirton AMM3592 FAUCT140-11 BOL Africa
Psoralea gigantea Muasya & Stirton AMM3203 FAUCT029-11 BOL Africa
Psoralea glaucescens Muasya & Stirton AMM3289 FAUCT056-11 BOL Africa
Psoralea glaucescens Muasya & Stirton AMM3312 FAUCT061-11 BOL Africa
Psoralea imbricata Muasya & Stirton AMM4030 FAUCT235-11 BOL Africa
Psoralea imbricata Muasya & Stirton AMM3439 FAUCT094-11 BOL Africa
Psoralea imbricata Muasya & Stirton AMM3544 FAUCT120-11 BOL Africa
Psoralea imbricata Muasya & Stirton AMM3904 FAUCT218-11 BOL Africa
Psoralea imbricata Muasya & Stirton AMM3396 FAUCT080-11 BOL Africa
Psoralea imbricata Muasya & Stirton AMM3399 FAUCT083-11 BOL Africa
Psoralea imminens sp. nov. Muasya & Stirton AMM3596 FAUCT141-11 BOL Africa
Psoralea ivumba sp. nov. Muasya & Stirton AMM3374 FAUCT075-11 BOL Africa
Psoralea ivumba sp. nov. Muasya & Stirton AMM3165 FAUCT003-11 BOL Africa
Psoralea keetii Muasya & Stirton AMM3599 FAUCT143-11 BOL Africa
Psoralea laevigata Muasya & Stirton AMM3457 FAUCT099-11 BOL Africa
Psoralea laxa Muasya & Stirton AMM3646 FAUCT156-11 BOL Africa
Psoralea laxa Muasya & Stirton AMM4325 FAUCT279-11 BOL Africa
Psoralea laxa Muasya & Stirton AMM3548 FAUCT122-11 BOL Africa
Psoralea laxa Muasya & Stirton AMM3870 FAUCT205-11 BOL Africa
Psoralea muirii sp. nov. Muasya & Stirton AMM4181 FAUCT257-11 BOL Africa
Psoralea odoratissima Muasya & Stirton AMM3532 FAUCT116-11 BOL Africa
Psoralea odoratissima Muasya & Stirton AMM3557 FAUCT127-11 BOL Africa
Psoralea oligophylla Muasya & Stirton AMM3798 FAUCT185-11 BOL Africa
Psoralea oreophila Muasya & Stirton AMM3463 FAUCT102-11 BOL Africa
Psoralea oreophila Muasya & Stirton AMM3464 FAUCT103-11 BOL Africa
Psoralea oreopola sp. nov. Muasya & Stirton AMM4370 FAUCT283-11 BOL Africa
Psoralea oreopola sp. nov. Muasya & Stirton AMM4376 FAUCT285-11 BOL Africa
Psoralea oreopola sp. nov. Muasya & Stirton AMM3271 FAUCT044-11 BOL Africa
Psoralea pinnata Muasya & Stirton AMM3169 FAUCT005-11 BOL Africa
Psoralea pinnata Muasya & Stirton AMM3403 FAUCT086-11 BOL Africa
Psoralea pinnata Muasya & Stirton AMM3186 FAUCT015-11 BOL Africa
Psoralea pinnata Muasya & Stirton AMM3547 FAUCT121-11 BOL Africa
Psoralea pinnata Muasya & Stirton AMM3172 FAUCT008-11 BOL Africa
Psoralea pinnata Muasya & Stirton AMM3171 FAUCT007-11 BOL Africa
Psoralea pinnata Muasya & Stirton AMM3189 FAUCT017-11 BOL Africa
Psoralea plauta Muasya & Stirton AMM3611 FAUCT149-11 BOL Africa
Psoralea pullata Muasya & Stirton AMM3178 FAUCT011-11 BOL Africa
Psoralea pullata Muasya & Stirton AMM3903.1 FAUCT214-11 BOL Africa
Psoralea repens Muasya & Stirton AMM3809 FAUCT186-11 BOL Africa
Psoralea repens Muasya & Stirton AMM3168 FAUCT004-11 BOL Africa
Psoralea restioides Muasya & Stirton AMM3216 FAUCT033-11 BOL Africa
Psoralea rhizotoma sp. nov. Muasya & Stirton AMM3659 FAUCT158-11 BOL Africa
Psoralea rigidula Muasya & Stirton AMM3390 FAUCT077-11 BOL Africa
Psoralea sordida sp. nov. Muasya & Stirton AMM3579 FAUCT136-11 BOL Africa
Psoralea sordida sp. nov. Muasya & Stirton AMM3580 FAUCT137-11 BOL Africa
Psoralea sparsa sp. nov. Muasya & Stirton AMM3567 FAUCT131-11 BOL Africa
Psoralea speciosa Muasya & Stirton AMM3458 FAUCT100-11 BOL Africa
Psoralea speciosa Muasya & Stirton AMM3610 FAUCT148-11 BOL Africa
Psoralea speciosa Muasya & Stirton AMM3456 FAUCT098-11 BOL Africa
Psoralea speciosa Muasya & Stirton AMM3607 FAUCT146-11 BOL Africa
Psoralea suaveolens sp. nov. Muasya & Stirton AMM4396 FAUCT286-11 BOL Africa
Psoralea suaveolens sp. nov. Muasya & Stirton AMM4975 FAUCT303-11 BOL Africa
Psoralea triflora sp. nov. Muasya & Stirton AMM3862 FAUCT202-11 BOL Africa
Psoralea usitata Muasya & Stirton AMM4344 FAUCT281-11 BOL Africa
Psoralea usitata Muasya & Stirton AMM4071 FAUCT244-11 BOL Africa
Psoralea usitata Muasya & Stirton AMM3440 FAUCT095-11 BOL Africa
Psoralea usitata Muasya & Stirton AMM3528 FAUCT114-11 BOL Africa
Psoralea usitata Muasya & Stirton AMM3541 FAUCT118-11 BOL Africa
Psoralea usitata Muasya & Stirton AMM3194 FAUCT020-11 BOL Africa
Psoralea usitata Muasya & Stirton AMM3414 FAUCT092-11 BOL Africa
Psoralea usitata vigilans sp. nov. Muasya & Stirton AMM3415 FAUCT093-11 BOL Africa
Psoralea usitata vigilans sp. nov. Muasya & Stirton AMM4340 FAUCT280-11 BOL Africa
Psoralea verrucosa Muasya & Stirton AMM3357 FAUCT070-11 BOL Africa
Psoralea verrucosa Muasya & Stirton AMM3905 FAUCT219-11 BOL Africa
Psoralea verrucosa Muasya & Stirton AMM3353 FAUCT069-11 BOL Africa
Psoralea verrucosa Muasya & Stirton AMM3269 FAUCT042-11 BOL Africa
Psoralea verrucosa Muasya & Stirton AMM4371 FAUCT284-11 BOL Africa
Psoralea verrucosa Muasya & Stirton AMM3270 FAUCT043-11 BOL Africa
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DNA extraction, sequencing and alignment

All the samples were sent to the Canadian Centre for DNA Barcoding (CCDB) in Canada, where total DNA was extracted and the two core DNA barcodes (matK and rbcLa) were sequenced according to standard CCDB protocols (Ivanova et al. 2006). Sequence alignment was performed using Multiple Sequence Comparison by Log Expectation (MUSCLE v. 3.8.31, Edgar 2004) plugin in Geneious v.8.0.4 (Kearse et al. 2012) and manually adjusted using MESQUITE v.2.5 (Maddison and Maddison 2008). The two regions were aligned separately and then combined.

Evaluation of DNA barcodes

First, we evaluated the performance of the DNA markers (matK, rbcLa and matK + rbcLa) in species identification and delimitation of African Psoraleoid legumes at species and generic levels by applying two criteria commonly used to evaluate the utility of the DNA barcodes in species discrimination: the barcode gap of Meyer and Paulay (2005) and discriminatory power (Hebert et al. 2004b). Barcode gap was assessed by comparing intraspecific variation (i.e. the amount of genetic variation within species) to interspecific variation (between species). A good barcode should exhibit a significant gap, meaning that sequence variation within species should be significantly lower than between species. Statistical significance between intra- and interspecific variation was assessed using Wilcoxon test in R (R Core Team 2013).

The discriminatory power of DNA barcoding was tested by evaluating the proportion of correct species identification at different taxonomic level (species and generic) using matK, rbcLa and matK + rbcLa regions. All sequences were labelled according to the names of the species from which the sequences were generated. The test of discriminatory power was carried out using two methods: the ‘best close match’ (Meier et al. 2006) and the ‘near neighbour’ using the functions bestCloseMatch and nearNeighbour implemented in the R package Spider (Brown et al. 2012). Before the test, we determined the optimized genetic distance suitable as threshold for taxon identification using the function localMinima also implemented in Spider (Brown et al. 2012).

The function bestCloseMatch conducts the ‘best close match’ analysis (Meier et al. 2006) by searching for the closest individual in the data set. If the closest individual is within a given threshold, the outcome is scored as ‘correct’, and if it is further, then the result is ‘no ID’ (no identification). If more than one species is tied for closest match, the outcome of the test is an ‘ambiguous’ identification. When all matches within the threshold are different species to the query, the result is scored as ‘incorrect’. The nearNeighbour function finds the closest individual and returns the score ‘true’ (similar to ‘correct’ in the bestCloseMatch method) if their names are the same, but if the names are different, the outcome is scored as ‘false’ (similar to ‘incorrect’ in the bestCloseMatch method).

Barcode test of species delimitation

Apart from investigating the potential of DNA markers in identifying species, we explored their ability in assigning morphologically delimited species into genetic units, i.e. ‘MOTUs’ or ‘genetic species’ (sensu Saunders and McDevit 2013). We considered MOTUs as groupings or clusters of specimens that fall around a medoid. The goal is to verify the optimal number of clusters (species) that may be inferred from the pairwise genetic distance matrices of Psoraleoid legumes. A match between our genetic species and morphologically delimited species would indicate that one could serve as a surrogate for the other (see Stahlhut et al. 2013), and thus lend support to the discriminatory power of DNA barcoding. We used partition around medoids (PAM) approach using the R package Cluster (Mächler et al. 2015; R Core Team 2015). Our decision in choosing PAM was made after testing the performance of several clustering algorithms including ‘Agglomerative Nesting (agnes)’, ‘Divisive Analysis Clustering (diana)’ and ‘Fuzzy Analysis Clustering (fanny)’. Results from these other approaches were not reported for at least one of the two main reasons. Firstly, they yielded identical results to PAM and are less straight forward to explain. For example, fanny does not produce unique clusters. Instead, it groups each species (probabilistically) to multiple clusters. The second reason was that the methodologies employed by some of the algorithms do not easily accommodate the restriction of cluster sizes.

The PAM algorithm works as follows: given a specific number of clusters (k), desired from a distance matrix, PAM searches for species (here referred to as medoids) that are representative of the data. The number of medoids sought is usually the same as the number of desired clusters k. Each cluster is then constructed such that the distance of any other sample, in the cluster, from its medoid is minimal. Cluster sizes between 2 and 69 were first investigated for each distance matrix. An optimal cluster size was then chosen as the one that yielded the maximum silhouette coefficient (Kaufman and Rousseeuw 1990). A silhouette coefficient measures the quality of clustering, derived as an average of the silhouette widths over all species. We used the silhouette width as an aggregate of a measure of the suitability of a cluster for each observation it contains relative to the next best cluster for the observations. Silhouette coefficients range between 0 and 1.

Barcode test for phylogenetic signal

We explored the potential of the DNA barcode data to reveal microevolutionary patterns by testing for phylogenetic signal in the affinity of lineages to fire-survival and regeneration strategies. We used a phylogeny of the southern African Psoraleoid species and a binary matrix of reseeders versus resprouters. The phylogeny was reconstructed using a combination of matK and rbcLa data sets, based on a maximum-likelihood (ML) approach (Stamatakis et al. 2008), enforcing topological constraints from a consensus tree of the Bayesian analysis of the data set. We used the GTR + G + I substitution model based on the result of Akaike information criterion from Modeltest v.2.3 (Nylander 2004), and ran 1000 ML searches. Phylogenetic signal was tested on the ML best tree and binary matrix of reseeders versus resprouters using the D statistics of Fritz and Purvis (2010) in the R package Caper (Orme et al. 2012). The D statistics calculates the sum of changes of a binary trait along the branches of a phylogeny, and compares it with a random model and clumping expected under a Brownian evolution. Significance was assessed by shuffling the trait values 999 times at the tips of the phylogeny. D = 1 corresponds to a random distribution of traits at the tip of the phylogeny; D = 0 corresponds to a Brownian motion model (Fritz and Purvis 2010).

Results

For the core barcode loci, we obtained 332 sequences (165 and 167 for matK and rbcLa, respectively) from 172 specimens representing 72 Otholobium and 100 Psoralea. Sequence recoverability was higher for rbcLa than for matK (98.1 and 97.1 % of specimens, respectively, Fig. 4). The combined matK + rbcLa sequence data were obtained from 98.1 % of the specimens sampled (Fig. 4). For rbcLa, we recovered 95.7 % of the 69 species sequenced, and 93.6 % for matK and when combined with rbcLa, i.e. matK + rbcLa. Both barcodes combined yielded a total of 1326 bp (770 bp for matK and 549 bp for rbcLa).

Figure 4.

Figure 4.

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Percentage of specimens and species of Otholobium and Psoralea from which rbcL and matK barcodes were recovered. Numbers in parentheses are the total number of individuals (specimens, species).

The mean interspecific distances for the single and combined regions are lower than 1 %, ranging from 0.002013 in rbcLa to 0.008612 in matK. The mean intraspecific variation for each and combined DNA regions was also low, ranging from 0.000108 in rbcLa to 0.001251 in the combined data set, matK + rbcLa. The mean intraspecific distances in all cases are significantly lower than interspecific distances (Wilcoxon test, P < 0.0001). The minimum interspecific genetic distance is greater than the maximum intraspecific genetic distance in matK + rbcLa data set (Fig. 5A), indicating the existence of a barcode gap in the data set. The comparison between the lowest interspecific distances (red lines) versus the maximum intraspecific distances (black lines) is shown in Fig. 5B. Further, we found 72 % (116) of the individuals with barcode gap and 28 % (45) without a barcode gap in matK + rbcLa data set. We also found 12 % (19) of the individuals with barcode gap and 88 % (152) without a barcode gap in matK data set. Lastly, we found only 3 % (2) of the individuals with barcode gap in rbcLa data set and 97 % (168) without a barcode gap.

Figure 5.

Figure 5.

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(A) Evaluation of barcode gap in the data set. Boxplot of the interspecific (inter) and intraspecific (intra) genetic distances for matK + rbcLa, matK and rbcLa data sets, indicating the existence of a barcode gap, i.e. minimum interspecific distance is greater than the maximum intraspecific distance. The bottom and top of the boxes show the first and third quartiles, respectively, the median is indicated by the horizontal line, the range of the data by the vertical line and outliers by dots. (B) Line plot of the barcode gap for the 171 Psoraleiod individuals. The black lines indicate where the minimum interspecific distance is greater than the maximum intraspecific distance (an indication of a barcode gap); the red lines show where this pattern is reversed, i.e. the situation where there is no barcoding gap.

Testing the efficacy of DNA barcoding based on discriminatory potential shows that the calculated thresholds ranged from 0.045 in matK to an optimized value of 0.36 for the full data set (matK + rbcLa). Using these cut-offs, we found 100 % true and correct identification in all the data sets for the near-neighbour and best close match analyses, respectively, in identifying the individuals to their respective genera (Psoralea or Otholobium). In terms of identifying the individuals at the species level, we found 25 % success rate for matK compared with 4 % in rbcLa for the near-neighbour method, which did not improve when the two barcodes were combined (matK + rbcLa) (Table 2). Similarly, for the best close match analysis, matK + rbcLa and matK exhibited 11 % correct identification rate as opposed to failure in rbcLa (0 %) data set (Table 2).

Table 2.

Performance of the DNA barcodes in identification of individuals to species or genera of Psoraleoid legumes evaluated based on discriminatory potential. Values in parenthesis represent identification of individuals to genera. ‘True’ indicates instances when the near-neighbour method finds the closest individual in the data set and their names are the same or ‘False’ if different. ‘Correct’, ‘Incorrect’, ‘Ambiguous’ and ‘No id’ are used in the best close match method, when the name of the closest match is the same, different, more than one species is the closest match and no species are within the threshold distance, respectively.

DNA barcoding regions Number of genetic species (MOTUs) Near neighbour
 Best close match
True (%) False (%) Ambiguous (%) Correct (%) Incorrect (%) No ID (%)
matK + rbcLa 36 25 (100) 75 (0) 51 (0) 11 (100) 38 (0) 0
matK 33 25 (100) 75 (0) 53 (0) 11 (100) 36 (0) 0
rbcLa 7 4 (100) 96 (0) 79 (0) 0 (100) 21 (0) 0
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Of the 69 morphologically delimited species included in the analyses, varying discriminatory power in the performance of the DNA markers in grouping specimens into genetic species (MOTUs) was found. rbcLa grouped all the specimens into 7 genetic species only (silhouette coefficient = 0.98), followed by matK (33 genetic species; silhouette coefficient = 0.84; Table 3). The combination of matK + rbcLa grouped specimens into 37 genetic species unit (silhouette coefficient = 0.84). We, therefore, discussed our results based on the core barcode, i.e. matK + rbcLa data set.

Table 3.

Genetic species delimited using the best DNA barcode region (matK + rbcLa) identified in this study.

No. Composition of genetic species or MOTUs
1 [1] O. acuminatum Muasya & Stirton3603 [15] O. spicatum Muasya & Stirton3906
[2] O. acuminatum Muasya & Stirton3850 [16] O. stachyerum Muasya & Stirton3604
[3] O. arborescens Muasya & Stirton3279 [17] O. stachyerum Muasya & Stirton3851
[4] O. candicans Muasya & Stirton3369 [18] O. stachyerum Muasya & Stirton3872
[5] O. flexuosum Muasya & Stirton3276 [19] O. striatum Muasya & Stirton3318
[6] O. flexuosum Muasya & Stirton3280.1 [20] O. striatum Muasya & Stirton3339
[7] O. hirtum Muasya & Stirton3499 [21] O. striatum Muasya & Stirton3351
[8] O. obliquum Muasya & Stirton3198.1 [22] O. striatum Muasya & Stirton3363
[9] O. parviflorum Muasya & Stirton3199 [23] O. striatum Muasya & Stirton3561
[10] O. pustulatum Muasya & Stirton3286 [24] O. striatum Muasya & Stirton4106
[11] O. rotundifolium Muasya & Stirton3173 [25] O. thomii Muasya & Stirton3187
[12] O. rotundifolium Muasya & Stirton3929 [26] O. uncinatum Muasya & Stirton3261
[13] O. spicatum Muasya & Stirton3498 [27] O. uncinatum Muasya & Stirton3263
[14] O. spicatum Muasya & Stirton3568
2 [1] O. beanianum sp. nov. Muasya & Stirton3350
3 [1] O. bracteolatum limnophilum sp. nov. Muasya & Stirton3204
4 [1] O. bracteolatum limnophilum sp. nov. Stirton13155 [4] O. hirtum Muasya & Stirton3373
[2] O. fruticans Muasya & Stirton3397 [5] O. mundianum Muasya & Stirton3885
[3] O. fruticans Muasya & Stirton3480 [6] O. parviflorum Muasya & Stirton3542
5 [1] O. bracteolatum Muasya & Stirton3164
[2] O. bracteolatum Muasya & Stirton3879
[3] O. bracteolatum Muasya & Stirton3963
6 [1] O. candicans Muasya & Stirton3563
[2] O. schutteae Muasya & Stirton3575
7 [1] O. candicans Muasya & Stirton3911
8 [1] O. crewii Muasya & Stirton3264 [4] O. virgatum Muasya & Stirton3395
[2] O. virgatum Muasya & Stirton3163 [5] O. virgatum Muasya & Stirton3908
[3] O. virgatum Muasya & Stirton3191
9 [1] O. hamatum Muasya & Stirton3306
[2] O. hamatum Muasya & Stirton3310
10 [1] O. hirtum Muasya & Stirton3190 [4] O. hirtum Muasya & Stirton3878
[2] O. hirtum Muasya & Stirton3326 [5] O. hirtum Muasya & Stirton3991
[3] O. hirtum Muasya & Stirton3372
11 [1] O. lucens Muasya & Stirton3570
12 [1] O. prodiens Muasya & Stirton3845
[2] O. prodiens Muasya & Stirton3854
13 [1] O. rubicundum Muasya & Stirton5982
14 [1] O. spicatum Muasya & Stirton3445
15 [1] O. stachyerum Muasya & Stirton3791
16 [1] O. stachyerum Muasya & Stirton3837
17 [1] O. uncinatum Muasya & Stirton3175
18 [1] O. velutinum Stirton13106
19 [1] P. aculeata Muasya & Stirton3170 [4] P. verrucosa Muasya & Stirton3269
[2] P. oreopola Muasya & Stirton4370 [5] P. verrucosa Muasya & Stirton3905
[3] P. plauta Muasya & Stirton3611
20 [1] P. aculeata Muasya & Stirton3183 [23] P. oreophila Muasya & Stirton3464
[2] P. aculeata Muasya & Stirton3405 [24] P. oreopola Muasya & Stirton3271
[3] P. aculeata Muasya & Stirton3550 [25] P. oreopola Muasya & Stirton4376
[4] P. affinis Muasya & Stirton3868 [26] P. pinnata Muasya & Stirton3403
[5] P. affinis Muasya & Stirton3903 2 [27] P. pinnata Muasya & Stirton3407
[6] P. aphylla Muasya & Stirton3400 [28] P. pinnata Muasya & Stirton3547
[7] P. arida Muasya & Stirton4098 [29] P. pullata Muasya & Stirton3903 1
[8] P. asarina Muasya & Stirton3907 [30] P. rhizotoma Muasya & Stirton3659
[9] P. axillaris Muasya & Stirton3848 [31] P. rigidula Muasya & Stirton3390
[10] P. axillaris Muasya & Stirton5874 [32] P. sordida Muasya & Stirton3579
[11] P. cf. latifolia Muasya & Stirton4028 [33] P. sordida Muasya & Stirton3580
[12] P. elegans Muasya & Stirton3591 [34] P. speciosa Muasya & Stirton3458
[13] P. fleta Muasya & Stirton3341 [35] P. speciosa Muasya & Stirton3607
[14] P. forbesii Muasya & Stirton3578 [36] P. speciosa Muasya & Stirton3610
[15] P. forbesii Muasya & Stirton3592 [37] P. suaveolens Muasya & Stirton4975
[16] P. gigantea Muasya & Stirton3203 [38] P. triflora Muasya & Stirton3862
[17] P. imminens Muasya & Stirton3596 [39] P. usitata Muasya & Stirton3194
[18] P. ivumba Muasya & Stirton3374 [40] P. usitata Muasya & Stirton3440
[19] P. keetii Muasya & Stirton3599 [41] P. usitata Muasya & Stirton3528
[20] P. laevigata Muasya & Stirton3457 [42] P. usitata Muasya & Stirton3541
[21] P. latifolia Muasya & Stirton3646 [43] P. usitata Muasya & Stirton4071
[22] P. odoratissima Muasya & Stirton3557 [44] P. verrucosa Muasya & Stirton4371
21 [1] P. alata Muasya & Stirton3262
[2] P. alata Muasya & Stirton3398
[3] P. alata Muasya & Stirton3901
22 [1] P. alata Muasya & Stirton3880
[2] P. laxa Muasya & Stirton3548
[3] P. laxa Muasya & Stirton3870
23 [1] P. arborea Muasya & Stirton3212 [7] P. glaucescens Muasya & Stirton3289
[2] P. axillaris Muasya & Stirton3827 [8] P. ivumba Muasya & Stirton3165
[3] P. axillaris Muasya & Stirton3834 [9] P. pinnata Muasya & Stirton3169
[4] P. brilliantissima Muasya & Stirton3621 [10] P. pinnata Muasya & Stirton3172
[5] P. congesta Muasya & Stirton5462 [11] P. repens Muasya & Stirton3168
[6] P. filifolia Muasya & Stirton4321 [12] P. repens Muasya & Stirton3809
24 [1] P. arborea Muasya & Stirton3248 [5] P. odoratissima Muasya & Stirton3532
[2] P. arida Muasya & Stirton3526 [6] P. pinnata Muasya & Stirton3171
[3] P. asarina Muasya & Stirton3476 [7] P. usitata Muasya & Stirton4344
[4] P. asarina Muasya & Stirton3552 [8] P. usitata vigilans sp. nov. Muasya & Stirton4340
25 [1] P. fleta Muasya & Stirton3273
26 [1] P. fleta Muasya & Stirton3342 [6] P. imbricata Muasya & Stirton3904
[2] P. imbricata Muasya & Stirton3396 [7] P. imbricata Muasya & Stirton4030
[3] P. imbricata Muasya & Stirton3399 [8] P. verrucosa Muasya & Stirton3353
[4] P. imbricata Muasya & Stirton3439 [9] P. verrucosa Muasya & Stirton3357
[5] P. imbricata Muasya & Stirton3544
27 [1] P. glaucescens Muasya & Stirton3312
28 [1] P. laxa Muasya & Stirton4325
29 [1] P. muirii Muasya & Stirton4181
30 [1] P. oligophylla Muasya & Stirton3798
31 [1] P. oreophila Muasya & Stirton3463
32 [1] P. pinnata Muasya & Stirton3186
[2] P. pinnata Muasya & Stirton3189
33 [1] P. pullata Muasya & Stirton3178
34 [1] P. restioides Muasya & Stirton3216
[2] P. sparsa Muasya & Stirton3567
[3] P. speciosa Muasya & Stirton3456
35 [1] P. usitata ssp. nov. usitata Muasya & Stirton3414
36 [1] P. usitata ssp. vigilans sp. nov Muasya & Stirton3415
37 [1] P. verrucosa Muasya & Stirton3270
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Lastly, we found a weak but significant phylogenetic signal in the affinity of lineages to fire-survival and regeneration strategies. This was significant under the Brownian motion model (Dresprouters = 0.797, P = 0.003 and Dreseeders = 0.798, P = 0.002, where D = 0 corresponds to a Brownian motion model, and D = 1 indicates no phylogenetic signal) (Fig. 6). Multiple origin of reseeder habit is observed in both genera, but it is predominant in Psoralea (Fig. 6).

Figure 6.

Figure 6.

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Maximum-likelihood tree of Psoraleoid legumes derived from a combination of the core DNA barcodes matK and rbcLa showing the distribution of fire-survival and regeneration strategies as reseeders (red) versus resprouters (blue).

Discussion

A key criterion for a standard plant barcode is universality, meaning that the DNA barcode should be easily recovered from all plants, ideally with a single primer pair (CBOL 2009). Our amplification and sequencing success was higher for rbcLa than for matK, consistent with the results of several other studies that sampled broadly across land plants (e.g. Lahaye et al. 2008; CBOL 2009; Xiang et al. 2011a; Saarela et al. 2013). Recovery of rbcLa was higher (98.1 %) than matK in this study. This corresponds to the results of other studies on plants in which rbcLa recovery ranged from 90 to 100 % (Fazekas et al. 2008; Lahaye et al. 2008; CBOL 2009; Jeanson et al. 2011; Pang et al. 2011; Xiang et al. 2011a; Kuzmina et al. 2012; Saarela et al. 2013).

Several other criteria have also been defined for the identification of the best DNA barcode marker (Hebert et al. 2004a; Kress and Erickson 2007; Lahaye et al. 2008; CBOL 2009). Firstly, it should exhibit a barcode gap, i.e. higher genetic variation between species than within species (Meyer and Paulay 2005). Secondly, it must provide a maximal discrimination among species. We measured the efficacy of the core plant DNA barcode regions (matK and rbcLa) (CBOL 2009) to identify African Psoraleoid legumes using the two approaches: ‘barcode gap’ and discriminatory potential (Meyer and Paulay 2005). We found that interspecific distance is significantly greater than intraspecific distance. Our mean distances correspond to the results obtained in other plant groups such as Myristicaceae (Newmaster et al. 2008), Rosaceae (Pang et al. 2011), Taxus L. (Taxaceae) (Liu et al. 2011) and in regional Canadian Arctic Flora (Saarela et al. 2013). The second approach was that of Meier et al. (2006), i.e. comparing the smallest interspecific versus the greatest intraspecific distances, instead of comparing the mean distances alone. This approach also reveals the existence of a barcode gap, thus confirming the barcode potential of all the candidates. However, the combination of matK and rbcla data sets (matK + rbcla) in all the cases showed greater intraspecific variation than the individual regions alone. This supports the recommendation of the CBOL (2009) that a combination of the two regions (matK and rbcLa) is the preferred standard barcode region for plants.

In addition, we found that all the three data sets have a strong discriminatory power (100 %) in identifying individuals to their respective genera within the Psoraleoid legumes using the near-neighbour and the best close match methods. This supports the utility of DNA barcoding as a means to identify and allocate species between the two genera. Multiple other studies have demonstrated that the core barcode loci routinely provide high discrimination at the genus level, usually >90 % (e.g. Kress et al. 2009; Saarela et al. 2013). Accordingly, we found that rbcLa and matK loci singly distinguish 100 % of genera in our data set. However, their application within species yielded a poor discrimination success, i.e. <50 % with more proportion of ambiguity (51 % matK + rbcLa data set to 79 % in rbcLa data set; Table 2). This result is not surprising, given that several other plant studies have reported poor utility of the core DNA barcodes at lower taxonomic level especially among closely related species and in taxa characterized by recent rapid radiation and hybridization. For example, Clement and Donoghue (2012) reported low levels of discrimination and genetic variation among closely related species of Viburnum. Similarly, Xiang et al. (2011b) reported that rbcLa alone was unable to distinguish genera within Juglandaceae, and neither rbcLa nor matK could discriminate species of Berberis, Ficus or Gossypium (Piredda et al. 2011). In taxa with hybridization issues, for example, Quercus, matK and rbcLa were unable to distinguish any of the 12 sympatric species examined (Roy et al. 2010). The possible causes of the poor discrimination of the species in Psoraleoid legumes observed in this study can be attributed to their recent rapid radiation (Egan and Crandall 2008) and multiple instances of strong hybridization (A. Bello, C.H. Stirton, S.B.M. Chimphango, A.M. Muasya, in preparation; see examples in paragraph below) among the species. Given these caveats, it is clear that additional variable loci are necessary to improve the within-species discrimination success as recommended by the CBOL (2009).

Another feature of interest is the low congruence in assigning morphologically delimited species to genetic species. Several reasons could account for this. Firstly, it could suggest that species are generally not monophyletic (Rieseberg and Brouillet 1994). Secondly, the mismatch could be due to poor performance of the DNA barcodes resulting in over-splitting of taxa. Thirdly, it could be that speciation events do not always match morphological differences, indicating that rapid changes in morphology can occur with minimal evolutionary change (Adams et al. 2002). Fourthly, it could indicate that taxa whose multiple accessions are appearing in diverse clades represent cryptic species, where broad morphological concepts on species are masking genetic patterns. This may be true in Otholobium where widespread species (O. candicans, O. striatum and O. hirtum) may be treated too broadly. Hybridization may account for some of the patterns in Psoralea as some of the taxa have been observed forming hybrids in the field, e.g. P. pinnata × P. aculeata, P. sordida × P. forbesii and P. intonsa × P. oreopola.

In general, there was a weak but significant phylogenetic signal in fire-survival and regeneration strategies of lineages as reseeders or resprouters in Psoraleoid legumes than would be expected by chance. Lineages show significant phylogenetic conservatism in their affinity to fire-survival and regeneration strategies with more clustering of resprouters at the tip of the phylogeny than might be expected by chance. Our phylogeny suggests a multiple origin of these traits implying that the species inherited the resprouting trait from their most recent common ancestor. We hypothesize that the scattering of the reseeding trait across the phylogenetic tree was the result of independent evolutionary events (convergent evolution), probably as a response to fire. It could also mean that the character was inherited from a more ‘basal’ ancestor of the group and then ‘switched off’ in some species but not in others again, in response to fire. However, this remains hypothetical at this stage, pending the availability of more data.

Legumes are regarded as one of the most successful families of flowering plants on Earth both from evolutionary and ecological perspectives, owing to their flexible adaptation to different environments (Rundel 1989). This is evident in the way resprouters and reseeders have evolved to survive in their respective microhabitats in the CFR (Schutte et al. 1995), and frequently dominant in after-fire landscapes. Previous comparative studies on these functional groups have focussed on aspects of taxonomy and physiology (Schutte et al. 1995; Power et al. 2011). Here, we provide evidence of a weak but significant phylogenetic signal in fire response trait of lineages as reseeders or resprouters in Psoraleoid legumes than expected by chance. Schutte et al. (1995) suggested that there is a substantial difference between resprouters and reseeders, adding that gene flow between resprouting parents and their offspring may occur over time, since the parents are not killed by fire. Seed set does occur in resprouters but is generally very poor and may not occur over a number of fire episodes. The seeds of resprouters are generally larger than those produced copiously by all reseeders (C. H. Stirton, pers. obs.).

In contrast, temporal isolation in gene flow might occur in reseeding taxa, as there is less chance of interbreeding between parents and offspring, and thus, each new generation may be a cohort of its own. It is not known how much seed remains in the seed bank and it is possible that some seeds may germinate in a later fire episode. It should be borne in mind, however, that parents and offspring could coexist if fires are patchy, if fire temperature affects the proportion of the seed bank that can be stimulated to germinate, if fires are too hot and if the seed bank comprises different genetic cohorts. The consequence of these is that speciation would more readily occur in reseeders, as interbreeding between parents and their progeny is unlikely. Given these caveats, our results provide some extrinsic support for the idea that reseeders speciate faster than resprouters as the number of reseeding species in our study outnumbered that of the resprouters. Schutte et al. (1995) reported that there is a faster rate of speciation and differentiation within reseeders, than in resprouters, but did not provide any genetic evidence for this. Most reseeding species of legumes in the CFR are short lived (ca. 8−15 years), with few exceptions, e.g. in Podalyria calyptrata and in some forest margin species of Virgilia with relatively long lifespans (>40 years). In the younger genus Psoralea, there are more reseeders than resprouters, whereas in the older genus Otholobium, there are more resprouters than reseeders and fewer species overall. Among the Psoraleoid legumes, reseeders are frequently observed on wet valleys near mountain streams, while resprouters are common in drier habitats, a phenomenon also observed in African Restionaceae, which shares increased diversification in reseeders (Litsios et al. 2014).

Conclusions

This study showed that DNA barcoding may be useful in species identification and in inferring the impacts of recurrent fires on gene flow in resprouting and reseeding taxa in the CFR. In general, we showed that Psoraleoid legumes of the CFR exhibit a barcoding gap with high scores for correct identification of individuals to their respective genera. We found a considerable match between genetic and morphologically delimited species supporting the discriminatory power of DNA barcoding. We also found that lineages in Psoraleeae showed a weak but significant phylogenetic conservatism in their affinity for different fire response trait with more clustering of resprouters in Psoralea at the tip of the phylogeny than expected by chance. Our phylogeny suggests a convergent origin of the reseeding trait in African Psoraleoid genera. We conclude that these novel microevolutionary patterns might be acting continuously over time to produce multi-scale regularities of biodiversity especially in a biodiversity hotspot as the CFR.

Accession Numbers

All data for the project were managed in the BOLD database in a project called ‘Fabaceae@UCT’ (project code FAUCT). Detailed voucher information, including the scientific names of taxa sampled, BOLD ID numbers, collectors and collection numbers, for all sequences are given in Table 1.

Sources of Funding

This study was supported by grants from the South African National Research Foundation (NRF; A.M.M.); Nigeria Tertiary Education Trust Fund (NTETF)/Umaru Musa Yar'adua University Katsina, Nigeria (Fellowship Grant; A.B.) and University of Cape Town, J. W. Jagger Centenary Gift Scholarship (to A.B.).

Contributions by the Authors

A.B. and B.H.D. performed the data analyses and were involved in writing and editing; C.H.S., A.M.M., S.B.M.C. and A.B. performed the fieldwork and were involved in writing and editing; M.v.d.B. and O.M. provided contribution to the concept and the design of the work and also handled the sequencing activities. All the authors read and approved the final manuscript.

Conflict of Interest Statement

None declared.

Acknowledgements

We thank the University of Cape Town Research Council. We also thank the Canadian Center for DNA barcoding for sequencing support and Mr Hassan Saddiq of UCT Statistics Department for some of the statistical analyses support. We thank the Eastern Cape Government for permit NO. CRO 153/13CR (21.11.2013: A. Bello) and Cape Nature for permit AAA008-000350028 (02.09.2011: C.H. Stirton). Two anonymous reviewers who made some valuable comments and suggestions on an earlier version of the manuscript are gratefully acknowledged.

Literature Cited

  1. Adams RP, Pandey N, Rezzi S, Casanova J. 2002. Geographic variation in the Random Amplified Polymorphic DNAs (RAPDs) of Juniperus phoenicea, J.p. var. canariensis, J.p. subsp. eu-mediterranea, and J.p. var. turbinata. Biochemical Systematics and Ecology 30:223–229. 10.1016/S0305-1978(01)00083-7 [DOI] [Google Scholar]
  2. Bell DT. 2001. Ecological response syndromes in the flora of Southwestern Western Australia: fire resprouters versus reseeders. The Botanical Review 67:417–440. 10.1007/BF02857891 [DOI] [Google Scholar]
  3. Bell TL, Ojeda F. 1999. Underground starch storage in Erica species of the Cape Floristic Region—differences between seeders and resprouters. New Phytologist 144:143–152. 10.1046/j.1469-8137.1999.00489.x [DOI] [Google Scholar]
  4. Bond WJ. 1985. Canopy stored seed reserves (serotiny) in Cape Proteaceae. South African Journal of Botany 51:181–186. [Google Scholar]
  5. Bond WJ, Midgley JJ. 2001. Ecology of sprouting in woody plants: the persistence niche. Trends in Ecology and Evolution 16:45–51. 10.1016/S0169-5347(00)02033-4 [DOI] [PubMed] [Google Scholar]
  6. Bond WJ, Van Wilgen BW. 1996. Fire and the evolutionary ecology of plants. In: Bond WJ, Van Wilgen BW, eds. Fire and plants. London: Chapman and Hall, 123–147. [Google Scholar]
  7. Brown SDJ, Collins RA, Boyer S, Lefort M-C, Malumbres-Olarte J, Vink CJ, Cruickshank RH. 2012. Spider: an R package for the analysis of species identity and evolution, with particular reference to DNA barcoding. Molecular Ecology Resources 12:562–565. 10.1111/j.1755-0998.2011.03108.x [DOI] [PubMed] [Google Scholar]
  8. Consortium for the Barcode of Life Plant Working Group (CBOL). 2009. A DNA barcode for land plants. Proceedings of the National Academy of Sciences of the USA 106:12794–12797. 10.1073/pnas.0905845106 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Clement WL, Donoghue MJ. 2012. Barcoding success as a function of phylogenetic relatedness in Viburnum, a clade of woody angiosperms. BMC Evolutionary Biology 12:73 10.1186/1471-2148-12-73 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Cowling RM. 1987. Fire and its role in coexistence and speciation in Gondwanan shrublands. South African Journal of Science 83:106–112. [Google Scholar]
  11. Cowling RM, Rundel PW, Lamont BB, Arroyo MK, Arianoutsou M. 1996. Plant diversity in Mediterranean-climate regions. Trends in Ecology and Evolution 11:362–366. 10.1016/0169-5347(96)10044-6 [DOI] [PubMed] [Google Scholar]
  12. Dludlu MN, Stirton CH, Chimphango SBM, Bello A, Muasya AM. 2013. Phylogenetic position of the southern African members of the tribe Psoraleeae based on molecular and morphological data. South African Journal of Botany 89:150–155. 10.1016/j.sajb.2013.06.019 [DOI] [Google Scholar]
  13. Edgar RC. 2004. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Research 32:1792–1797. 10.1093/nar/gkh340 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Edwards D, Hawkins JA. 2007. Are Cape floral clades the same age? Contemporaneous origins of two lineages in the genistoids s.l. (Fabaceae). Molecular Phylogenetics and Evolution 45:952–970. 10.1016/j.ympev.2007.09.014 [DOI] [PubMed] [Google Scholar]
  15. Egan AN, Crandall KA. 2008. Incorporating gaps as phylogenetic characters across eight DNA regions: ramifications for North American Psoraleeae (Leguminosae). Molecular Phylogenetics and Evolution 46:532–546. 10.1016/j.ympev.2007.10.006 [DOI] [PubMed] [Google Scholar]
  16. Fazekas AJ, Burgess KS, Kesanakurti PR, Graham SW, Newmaster SG, Husband BC, Percy DM, Hajibabaei M, Barrett SCH. 2008. Multiple multilocus DNA barcodes from the plastid genome discriminate plant species equally well. PLoS ONE 3:e2802 10.1371/journal.pone.0002802 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Fritz SA, Purvis A. 2010. Selectivity in mammalian extinction risk and threat types: a new measure of phylogenetic signal strength in binary traits. Conservation Biology 24:1042–1051. 10.1111/j.1523-1739.2010.01455.x [DOI] [PubMed] [Google Scholar]
  18. Hansen A, Pate JS, Hansen AP. 1991. Growth and reproductive performance of a seeder and a resprouter species of Bossiaea as a function of plant age after fire. Annals of Botany 67:497–509. [Google Scholar]
  19. Hebert PDN, Penton EH, Burns JM, Janzen DH, Hallwachs W. 2004a. Ten species in one: DNA barcoding reveals cryptic species in the neotropical skipper butterfly Astraptes fulgerator. Proceedings of the National Academy of Sciences of the USA 101:14812–14817. 10.1073/pnas.0406166101 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Hebert PDN, Stoeckle MY, Zemlak TS, Francis CM. 2004b. Identification of birds through DNA barcodes. PLoS Biology 2:e312 10.1371/journal.pbio.0020312 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Ivanova NV, De Waard JR, Hebert PDN. 2006. An inexpensive, automation-friendly protocol for recovering high-quality DNA. Molecular Ecology Notes 6:998–1002. 10.1111/j.1471-8286.2006.01428.x [DOI] [Google Scholar]
  22. Jeanson ML, Labat JN, Little DP. 2011. DNA barcoding: a new tool for palm taxonomists? Annals of Botany 108:1445–1451. 10.1093/aob/mcr158 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Kaufman L, Rousseeuw PJ. 1990. Finding groups in data: an introduction to cluster analysis. New York: Wiley. [Google Scholar]
  24. Kearse M, Moir R, Wilson A, Stones-Havas S, Cheung M, Sturrock S, Buxton S, Cooper A, Markowitz S, Duran C, Thierer T, Ashton B, Mentjies P, Drummond A. 2012. Geneious basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28:1647–1649. 10.1093/bioinformatics/bts199 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Keeley JE. 1977. Seed production, seed populations in soil, and seedling production after fire for two congeneric pairs of sprouting and nonsprouting chaparal shrubs. Ecology 58:820–829. 10.2307/1936217 [DOI] [Google Scholar]
  26. Kress WJ, Erickson DL. 2007. A two-locus global DNA barcode for land plants: the coding rbcL gene complements the non-coding trnH-psbA spacer region. PLoS ONE 2:e508 10.1371/journal.pone.0000508 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Kress WJ, Erickson DL, Jones FA, Swenson NG, Perez R, Sanjur O, Bermingham E. 2009. Plant DNA barcodes and a community phylogeny of a tropical forest dynamics plot in Panama. Proceedings of the National Academy of Sciences of the USA 106:18621–18626. 10.1073/pnas.0909820106 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Kress WJ, García-Robledo C, Uriarte M, Erickson DL. 2015. DNA barcodes for ecology, evolution, and conservation. Trends in Ecology and Evolution 30:25–35. [DOI] [PubMed] [Google Scholar]
  29. Kuzmina ML, Johnson KL, Barron HR, Hebert PD. 2012. Identification of the vascular plants of Churchill, Manitoba, using a DNA barcode library. BMC Ecology 12:25 10.1186/1472-6785-12-25 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Lahaye R, Van der Bank M, Bogarin D, Warner J, Pupulin F, Gigot G, Maurin O, Duthoit S, Barraclough TG, Savolainen V. 2008. DNA barcoding the floras of biodiversity hotspots. Proceedings of the National Academy of Sciences of the USA 105:2923–2928. 10.1073/pnas.0709936105 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Le Maitre DC, Midgley JJ. 1992. Plant reproductive ecology. In: Cowling RM, ed. The ecology of fynbos, nutrients, fire and diversity. Cape Town: Oxford University Press, 135–175. [Google Scholar]
  32. Linder HP. 2003. The radiation of the Cape flora, southern Africa. Biological Reviews of the Cambridge Philosophical Society 78:597–638. 10.1017/S1464793103006171 [DOI] [PubMed] [Google Scholar]
  33. Litsios G, Wüest RO, Kostikova A, Forest F, Lexer C, Linder HP, Pearman PB, Zimmermann NE, Salamin N. 2014. Effects of a fire response trait on diversification in replicated radiations. Evolution 68:453–465. 10.1111/evo.12273 [DOI] [PubMed] [Google Scholar]
  34. Liu J, Möller M, Gao LM, Zhang DQ, Li DZ. 2011. DNA barcoding for the discrimination of Eurasian yews (Taxus L., Taxaceae) and the discovery of cryptic species. Molecular Ecology Resources 11:89–100. 10.1111/j.1755-0998.2010.02907.x [DOI] [PubMed] [Google Scholar]
  35. Mächler M, Rousseeuw P, Struyf A, Hubert M, Hornik K. 2015. Cluster:cluster analysis basics and extensions. R package version 2.0.11. https://cran.r-project.org/web/packages/cluster/citation.html (12 August 2015). [Google Scholar]
  36. Maddison WP, Maddison DR. 2008. Mesquite: a modular system for evolutionaryanalysis. Version 2.5 http://mesquiteproject (15 May 2015). [Google Scholar]
  37. Manning J, Goldblatt P. 2012. Plant of the Greater Cape Floristic Region: 1. The Core Cape Flora. Pretoria: South Africa National Biodiversity Institute, p 870. [Google Scholar]
  38. Meier R, Shiyang K, Vaidya G, Ng PK. 2006. DNA barcoding and taxonomy in Diptera: a tale of high intraspecific variability and low identification success. Systematic Biology 55:715–728. 10.1080/10635150600969864 [DOI] [PubMed] [Google Scholar]
  39. Meyer CP, Paulay G. 2005. DNA barcoding: error rates based on comprehensive sampling. PLoS Biology 3:e422 10.1371/journal.pbio.0030422 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Mucina L, Rutherford MC (eds). 2006. Vegetation map of South Africa, Lesotho and Swaziland. Pretoria: South African National Biodiversity Institute. [Google Scholar]
  41. Myers N, Mittermeier RA, Mittermeier CG, Da Fonseca GA, Kent J. 2000. Biodiversity hotspots for conservation priorities. Nature 403:853–858. 10.1038/35002501 [DOI] [PubMed] [Google Scholar]
  42. Newmaster SG, Fazekas AJ, Steeves RA, Janovec J. 2008. Testing candidate plant barcode regions in the Myristicaceae. Molecular Ecology Resources 8:480–490. 10.1111/j.1471-8286.2007.02002.x [DOI] [PubMed] [Google Scholar]
  43. Nylander JAA. 2004. MrModeltest v2. Programdistributed by the author. Sweden: Evolutionary Biology Centre, Uppsala University. [Google Scholar]
  44. Orme D, Freckleton R, Thomas G, Petzoldt T, Fritz S, Isaac N, Pearse W. 2012. Caper: comparative analyses of phylogenetics and evolution in R. R package version 0.5. https://cran.r-project.org/web/packages/caper/caper.pdf (12 August 2015). [Google Scholar]
  45. Pang X, Song J, Zhu Y, Xu H, Huang L, Chen S. 2011. Applying plant DNA barcodes for Rosaceae species identification. Cladistics 27:165–170. 10.1111/j.1096-0031.2010.00328.x [DOI] [PubMed] [Google Scholar]
  46. Pausas JG, Keeley JE. 2014. Evolutionary ecology of resprouting and seeding in fire-prone ecosystems. New Phytologist 204:55–65. 10.1111/nph.12921 [DOI] [PubMed] [Google Scholar]
  47. Piredda R, Simeone MC, Attimonelli M, Bellarosa R, Schirone B. 2011. Prospects of barcoding the Italian wild dendroflora: oaks reveal severe limitations to tracking species identity. Molecular Ecology Resources 11:72–83. 10.1111/j.1755-0998.2010.02900.x [DOI] [PubMed] [Google Scholar]
  48. Power SC, Cramer MD, Verboom GA, Chimphango SBM. 2011. Legume seeders of the Cape Floristic Region inhabit more fertile soils than congeneric resprouters—sometimes. Plant Ecology 212:1979–1989. 10.1007/s11258-011-9958-3 [DOI] [Google Scholar]
  49. Raimondo D, Von Staden L, Foden W, Victor JE, Helme NA, Turner RC, Kamundi DA, Manyama PA. 2009. Red list of South African plants. Pretoria: South African National Biodiversity Institute. [Google Scholar]
  50. R Core Team. 2013. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; http://www.R-project.org/ (22 June 2015). [Google Scholar]
  51. R Core Team. 2015. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; http://www.R-project.org/ (22 June 2015). [Google Scholar]
  52. Rieseberg LH, Brouillet L. 1994. Are many plant species paraphyletic? Taxon 43:21–32. 10.2307/1223457 [DOI] [Google Scholar]
  53. Roy S, Tyagi A, Shukla V, Kumar A, Singh UM, Chaudhary LB, Datt B, Bag SK, Singh PK, Nair NK, Husain T, Tuli R. 2010. Universal plant DNA barcode loci may not work in complex groups: a case study with Indian Berberis species. PLoS ONE 5:e13674 10.1371/journal.pone.0013674 [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Rundel PW. 1989. Ecological success in relation to plant form and function in the woody legumes. In: Stirton CH, Zarucchi JL, eds. Advances in legume biology, 377–398. Monograph of Systematic Botany from the Missouri Botanical Garden. 29. MO, USA: Missouri Botanical Garden. [Google Scholar]
  55. Saarela JM, Sokoloff PC, Gillespie LJ, Consaul LL, Bull RD. 2013. DNA barcoding the Canadian Arctic flora: core plastid barcodes (rbcL + matK) for 490 vascular plant species. PLoS ONE 8:e77982 10.1371/journal.pone.0077982 [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Saunders GW, Mcdevit DC. 2013. DNA barcoding unmasks overlooked diversity improving knowledge on the composition and origins of the Churchill algal flora. BMC Ecology 13:9 10.1186/1472-6785-13-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Schnitzler J, Barraclough TG, Boatwright JS, Goldblatt P, Manning JC, Powell MP, Rebelo T, Savolainen V. 2011. Causes of plant diversification in the cape biodiversity hotspot of South Africa. Systematic Biology 10:1093/sysbio/syr006. [DOI] [PubMed] [Google Scholar]
  58. Schutte AL, Vlok JHJ, Van Wyk BE. 1995. Fire-survival strategy—a character of taxonomic, ecological and evolutionary importance in fynbos legumes. Plant Systematics and Evolution 195:243–259. 10.1007/BF00989299 [DOI] [Google Scholar]
  59. Scott AC, Bowman DMJS, Bond WJ, Pyne SJ, Alexander ME. 2014. Fire on earth: an introduction. Hoboken, NJ: Wiley Blackwell, p 434. [Google Scholar]
  60. Snijman DA. 2013. Plants of the Greater Cape Floristic Region: The Extra Cape Subregion. Pretoria: SANBI, p 557. [Google Scholar]
  61. Stahlhut JK, Fernández-Triana J, Adamowicz SJ, Buck M, Goulet H, Hebert PDN, Huber JT, Merilo MT, Sheffield CS, Woodcock T, Smith MA. 2013. DNA barcoding reveals diversity of Hymenoptera and the dominance of parasitoids in a sub-arctic environment. BMC Ecology 13:2: 10.1186/1472-6785-13-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Stamatakis A, Hoover P, Rougemont J. 2008. A rapid bootstrap algorithm for the RaxML web servers. Systematic Biology 57:758–771. 10.1080/10635150802429642 [DOI] [PubMed] [Google Scholar]
  63. Stirton CH. 1981. Studies in the Leguminosae: Papilionoideae of southern Africa. Bothalia 13:317–326. [Google Scholar]
  64. Stirton CH. 1989. A revision of Otholobium (Papilionoideae, Leguminosae. PhD Dissertation, University of Cape Town, South Africa. [Google Scholar]
  65. Stirton CH. 2005. Tribe Psoraleeae. In: Lewis G, Schrire B, Mackinder B, Lock M, eds Legumes of the world. Kew: Royal Botanic Gardens, pp 447–451. [Google Scholar]
  66. Tucker SC, Stirton CH. 1991. Development of the cymose inflorescence, cupulum and flower of Psoralea pinnata (Leguminosae: Papilionoideae: Psoraleeae). Botanical Journal of the Linnean Society 106:209–227. 10.1111/j.1095-8339.1991.tb02292.x [DOI] [Google Scholar]
  67. Wells PV. 1969. The relation between mode of reproduction and extent of speciation in woody genera of the California chaparral. Evolution 23:264–267. 10.2307/2406790 [DOI] [PubMed] [Google Scholar]
  68. Xiang X-G, Hu H, Wang W, Jin X-H. 2011a. DNA barcoding of the recently evolved genus Holcoglossum (Orchidaceae: Aeridinae): a test of DNA barcode candidates. Molecular Ecology Resources 11:1012–1021. 10.1111/j.1755-0998.2011.03044.x [DOI] [PubMed] [Google Scholar]
  69. Xiang X-G, Zhang J-B, Lu A-M, Li R-Q. 2011b. Molecular identification of species in Juglandaceae: a tiered method. Journal of Systematics and Evolution 49:252–260. 10.1111/j.1759-6831.2011.00116.x [DOI] [Google Scholar]

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