1
A test of
psbK-psbI and atpF-atpH
as potential plant DNA barcodes using
the flora of the Kruger National Park as a model system (South Africa)
Renaud Lahaye
1,*
, Vincent Savolainen
2,3
, Sylvie Duthoit
1
, Olivier Maurin
1
and Michelle
van der Bank
1
Author affiliation:
1
Department of Botany and Plant Biotechnology, APK Campus,
University of Johannesburg, P. O. Box 524, Auckland Park 2006, Johannesburg, South
Africa;
2
Royal Botanic Gardens, Kew, Richmond TW9 3DS, UK;
3
Imperial College
London, Silwood Park Campus, Buckhurst Road, Ascot SL5 7PY, UK.
We thank the South African National Research Foundation, the University of
Johannesburg, SASOL, the UK Darwin Initiative, and The Royal Society (UK) for
funding. We also thank the Kruger National Park, South African National Parks, H.
Eckhardt, I. Smit, G. Zambatis, T. Khosa, for granting access to the park and sharing
data; Stephen Boatwright for proofreading the manuscript; and T. Rikombe, R. Bryden,
T. Mhlongo, H. van der Bank for fieldwork.
*
To whom correspondence should be addressed: lahaye@cict.fr
Nature Precedings : hdl:10101/npre.2008.1896.1 : Posted 16 May 2008
2
Introduction
DNA barcoding is a new technique that uses short, standardized DNA sequences
(400-800 bp) of an organism to determine its identity. Because this sequence has to be
variable enough to identify individual species, but not too variable within the same
species so that a clear threshold can be defined between intra- and inter-specific
diversities, it is very challenging to apply this technique to all species on the planet
1
. A
DNA barcode has been identified for animals, i.e. the mitochondrial gene cox1
2-5
, which
shows strong abilities in identifying cryptic species, accelerating biodiversity inventories
and helping to identify species from degraded material (e.g. to control trade of
threatened)
3, 6-9
. For plants, the identification of a suitable DNA barcode is more
problematic. Cho et al.
10
showed that mitochondrial DNA evolves too slowly in plants to
provide a region variable enough to discriminate between species. Then the quest for the
best suitable barcode started
11
and is still ongoing
12
.
Kress et al.
13
opened the debates and suggested the use of multiple genes to
identify plant species quickly and accurately. At the Second International Barcode of Life
Conference in Tapei (September 2007), at least five different plant DNA barcodes were
proposed
12
, but no consensus reached. Among those, both atpF-atpH and psbK-psbI
suggested by Kim et al.
12
have not yet been tested. Here, we evaluate the use of these
loci as DNA barcodes for plants by applying them to a wide range of plant species. The
two new intergenic loci atpF-atpH and psbK-psbL are both localized in the large single
copy (LSC) of the plastid genome. The genes atpF and atpH encode ATP synthase
subunits CFO I and CFO III, respectively
14
. Both genes psbK and psbI encode two low
molecular mass polypeptides, K and I, respectively, of the photo-system II
15
. These two
new loci are conservative from algae to land plants and even in parasitic plants
16-18
. In
this study, we focus on the trees and shrubs from the Kruger National Park (hereafter
KNP), part of the Maputaland-Pondoland-Albany hotspot
19
in southern Africa. On a
selected sampling from the 2,700 taxa surveyed in the area, we applied several metrics
following Lahaye et al.
20
to evaluate the efficiency of combining matK either to trnH-
psbA and/or atpF-atpH and/or psbK-psbI
12
for DNA barcoding purposes.
Nature Precedings : hdl:10101/npre.2008.1896.1 : Posted 16 May 2008
3
Material and Methods
Sampling. In total 101 taxa from the KNP were sampled, covering 18 families from the
monocotyledons to the euasterids II. This dataset included 31 species of trees and shrubs
in which we had more than one representative per species, 3 species of Orchids, one of
which with 2 representatives, and 3 parasitic plants, one of which is achlorophyllous.
Parasitic plants have been sampled to test the universality of the potential DNA barcodes.
We used Amborella trichopoda Baill. (complete genome GenBank accession AJ506156)
as outgroup for the phylogenetic analyses. All specimens were collected in different
ecosystems when possible (Figure 1) and voucher specimens are available as detailed in
Table 1.
Collection and preservation. Collection of plant material was done in the KNP with the
assistance of the park's rangers. Plants were sampled and pressed for herbarium voucher
specimens in triplicate, one for the herbarium of the KNP, one for Kew Herbarium (K;
United Kingdom), and one for the herbarium at Pretoria (PRE; South Africa).
Information about the locality and habit of collected plants were entered on a palmtop-
GPS to facilitate their further treatment, and also noted on hard copy for security. For
each plant collected, leaf material was stored in silica for molecular studies, and flowers
and fruit stored in ethanol when available.
DNA sequencing. Total DNA was extracted from dried leaf material using the standard
method of Doyle and Doyle
21
and cleaned with QIAquick silica columns (Qiagen,
Helden, Germany). Sequences of matK and trnH-psbA for each taxa were published in
Lahaye et al.
20
and their accession numbers are available from GenBank (Table 1). We
amplified atpF-atpH and psbK-psbI using PCR as follows: 35 cycles, 30 sec denaturation
at 94ºC, 40 sec annealing at 51ºC, and 40 sec extension at 72ºC. Primers were kindly
provided by Kim Ki-Joong: atpF-atpH- atpF 5'-ACTCGCACACACTCCCTTTCC-3',
atpH 5'-GCTTTTATGGAAGCTTTAACAAT-3'; and psbK-psbI: psbK- 5'-
TTAGCCTTTGTTTGGCAAG-3', psbI- 5'-AGAGTTTGAGAGTAAGCAT-3'. After
cycle sequencing using Big Dye terminator v3.1 and sequencing on a 3130xl genetic
analyzer (Applied Biosystems, UK), electropherograms were edited using SEQUENCER
4.6 software (Genes Codes Corporation, USA) and DNA sequences aligned by eye in
Nature Precedings : hdl:10101/npre.2008.1896.1 : Posted 16 May 2008
4
PAUP4b10*
22
(incomplete sequences at both ends were excluded from the analyses).
Taxa with missing data (amplification or sequencing failed) were removed from the
combined matrix in order to analyze complete matrices.
Figure 1. Map of the KNP with landsystems following Venter (1990) and collecting points from
this study
.
Nature Precedings : hdl:10101/npre.2008.1896.1 : Posted 16 May 2008
5
Plant Family
name Checked on IPNI
Voucher Location GPS
Altitude matK trnH-psbA
atpF-atpH
psbK-psbI
Fabaceae
Acacia exuvialis Verdoorn
OM260
KNP
S24 58 54.3 E31 34 26.3
284 m
EU214205 EU213781
-
EU626889
Fabaceae
Acacia exuvialis Verdoorn
RL1204
KNP
S25 29 35.4 E31 28 12.3
319 m
EU214206 EU213782
EU626796
EU626890
Fabaceae
Acacia exuvialis Verdoorn
RL1412
KNP
S25 21 41.5 E31 30 56.5
320 m
EU214207 EU213783
-
EU626891
Fabaceae
Acacia nigrescens Oliver
RL1111
KNP
S25 06 26.4 E31 30 24.5
452 m
EU214208 -
EU626797
EU626892
Fabaceae
Acacia nigrescens Oliver
RL1205
KNP
S25 29 35.4 E31 28 12.3
319 m
EU214209 EU213784
EU626798
EU626893
Fabaceae
Acacia nigrescens Oliver
RL1656
KNP
S22 41 29.6 E31 01 37.2
439 m
EU214210 EU213785
EU626799
EU626894
Fabaceae
Acacia tortilis Hayne
OM261
KNP
S24 59 20.9 E31 34 34.5
266 m
EU214213 EU213788
EU626800
EU626895
Fabaceae
Acacia tortilis Hayne
RL1483
KNP
S24 36 53.6 E31 40 51.4
333 m
EU214211 EU213786
EU626801
EU626896
Fabaceae
Acacia tortilis Hayne
RL1608
KNP
S22 57 38.1 E31 14 50.5
302 m
EU214212 EU213787
EU626802
EU626897
Orchidaceae
Acampe praemorsa ( Roxb. ) Blatt. & McCann
RBN203 KNP
S22 42 06.1 E30 58 14.4
504 m
EU214214 EU213789
EU626803
EU626898
Amborellaceae
Amborella trichopoda Baill. -
-
-
-
AJ506156
AJ506156 AJ506156 AJ506156
Orchidaceae
Ansellia africana Lindl.
OM1163 KNP
S25 12 54.8 E31 35 36.0
280 m
EU214215 -
EU626804
EU626899
Orchidaceae
Ansellia africana Lindl.
OM531
KNP
S25 19 54.3 E31 44 28.5
225 m
EU214216 -
EU626805
EU626900
Orchidaceae
Bonatea speciosa Willd.
RL1158
KNP
S25 13 11.4 E31 23 41.8
472 m
EU214217 EU213790
EU626806
EU626901
Asteraceae
Brachylaena huillensis O.Hoffm.
OM1281 KNP
S23 28 54.6 E31 33 27.0
421 m
EU214218 EU213791
EU626807
EU626902
Asteraceae
Brachylaena huillensis O.Hoffm.
OM247
KNP
S25 06 12.7 E31 35 44.2
276 m
EU214219 EU213792
EU626808
EU626903
Asteraceae
Brachylaena huillensis O.Hoffm.
RBN360 KNP
S22 42 51.4 E31 23 46.3
507 m
EU214220 EU213793
EU626809
EU626904
Combretaceae
Combretum apiculatum Sond.
RL1100
KNP
S25 06 24.7 E31 30 41.4
389 m
EU214221 EU213794
EU626810
EU626905
Combretaceae
Combretum apiculatum Sond.
RL1185
KNP
S25 23 11.2 E31 30 42.1
391 m
EU214222 EU213795
EU626811
EU626906
Combretaceae
Combretum apiculatum Sond.
RL1355
KNP
S25 20 11.4 E31 49 48.0
213 m
EU214223 EU213796
EU626812
EU626907
Combretaceae
Combretum collinum Fresen.
OM722
KNP
S25 00 07.4 E31 21 07.0
378 m
EU214224 EU213797
EU626813
EU626908
Combretaceae
Combretum collinum Fresen.
RL1164
KNP
S25 14 44.5 E31 26 39.8
419 m
EU214225 EU213798
EU626814
EU626909
Combretaceae
Combretum collinum Fresen.
RL1392
KNP
S25 25 45.2 E31 26 26.4
334 m
EU214226 EU213799
EU626815
EU626910
Combretaceae
Combretum hereroense Schinz
RL1120
KNP
S25 06 28.6 E31 29 58.5
383 m
EU214227 EU213800
EU626816
EU626911
Combretaceae
Combretum hereroense Schinz
RL1183
KNP
S25 23 11.2 E31 30 42.1
391 m
EU214228 EU213801
EU626817
EU626912
Combretaceae
Combretum hereroense Schinz
RL1364
KNP
S25 17 18.5 E31 46 34.6
235 m
EU214229 EU213802
EU626818
EU626913
Euphorbiaceae
Croton gratissimus Burch
OM785
KNP
S23 48 24.9 E31 38 27.2
285 m
EU214230 EU213803
EU626819
EU626914
Euphorbiaceae
Croton gratissimus Burch
RL1619
KNP
S22 45 43.6 E31 10 50.8
379 m
EU214231 EU213804
EU626820
EU626915
Euphorbiaceae
Croton gratissimus Burch
RL1621
KNP
S22 45 52.1 E31 10 29.1
414 m
EU214232 EU213805
EU626821
EU626916
Nature Precedings : hdl:10101/npre.2008.1896.1 : Posted 16 May 2008
6
Plant Family
name Checked on IPNI
Voucher Location GPS
Altitude matK
trnH-psbA
atpF-atpH
psbK-psbI
Euphorbiaceae
Croton megalobotrys Müll.Arg.
OM774
KNP
S24 03 13.4 E31 43 50.0
211 m
EU214233 EU213806
EU626822
EU626917
Euphorbiaceae
Croton megalobotrys Müll.Arg.
RL1540
KNP
S23 54 53.6 E31 40 18.7
201 m
EU214234 EU213807
EU626823
EU626918
Euphorbiaceae
Croton megalobotrys Müll.Arg.
RL1574
KNP
S23 11 37.5 E31 32 16.5
246 m
EU214235 EU213808
EU626824
EU626919
Euphorbiaceae
Croton pseudopulchellus Pax
RBN186 KNP
S22 39 57.7 E30 59 57.6
468 m
EU214236 EU213809
EU626825
EU626920
Euphorbiaceae
Croton pseudopulchellus Pax
RBN262 KNP
S22 26 00.7 E31 10 57.6
291 m
EU214237 EU213810
EU626826
EU626921
Euphorbiaceae
Croton pseudopulchellus Pax
RL1650
KNP
S22 40 09.2 E30 57 39.6
451 m
EU214238 EU213811
-
EU626922
Orchidaceae
Eulophia R.Br.
OM473
KNP
S25 03 40.0 E31 23 11.2
351 m
EU214239 EU213812
EU626827
EU626923
Proteaceae
Faurea rochetiana Chiov. ex Pic.Serm.
OM1413 KNP
S25 08 43.0 E31 14 33.4
726 m
EU214240 EU213813
EU626828
EU626924
Proteaceae
Faurea rochetiana Chiov. ex Pic.Serm.
OM1461 KNP
S25 08 43.6 E31 14 32.6
722 m
EU214241 EU213814
EU626829
EU626925
Proteaceae
Faurea rochetiana Chiov. ex Pic.Serm.
OM1463 KNP
S25 08 43.1 E31 14 33.1
727 m
EU214242 EU213815
EU626830
EU626926
Proteaceae
Faurea saligna Harv.
OM1438 KNP
S25 19 31.7 E31 21 42.3
486 m
EU214243 EU213816
EU626831
EU626927
Proteaceae
Faurea saligna Harv.
OM1443 KNP
S25 19 16.9 E31 20 59.5
523 m
EU214244 EU213817
EU626832
EU626928
Proteaceae
Faurea saligna Harv.
OM1445 KNP
S25 19 39.5 E31 22 08.8
473 m
EU214245 EU213818
EU626833
EU626929
Moraceae
Ficus abutilifolia Miq.
OM557
KNP
S25 04 41.4 E31 24 54.5
414 m
EU214248 EU213821
EU626834
EU626930
Moraceae
Ficus abutilifolia Miq.
RL1471
KNP
S24 52 32.9 E31 45 21.9
256 m
EU214246 EU213819
EU626835
EU626931
Moraceae
Ficus abutilifolia Miq.
RL1501
KNP
S24 22 39.3 E31 35 51.8
369 m
EU214247 EU213820
EU626836
EU626932
Moraceae
Ficus glumosa Delile
OM564
KNP
S25 04 36.8 E31 25 03.7
473 m
EU214249 EU213822
EU626837
EU626933
Moraceae
Ficus glumosa Delile
RL1407
KNP
S25 23 41.1 E31 30 02.4
466 m
EU214250 EU213823
EU626838
EU626934
Moraceae
Ficus glumosa Delile
RL1429
KNP
S25 08 29.6 E31 14 42.6
665 m
EU214251 EU213824
EU626839
-
Moraceae
Ficus sycomorus L.
RBN197 KNP
S22 40 53.4 E30 57 43.2
445 m
EU214252 EU213825
EU626840
EU626935
Moraceae
Ficus sycomorus L.
RL1598
KNP
S23 06 46.1 E31 27 16.5
264 m
EU214253 EU213826
EU626841
EU626936
Moraceae
Ficus sycomorus L.
RL1614
KNP
S22 45 43.1 E31 11 18.4
356 m
EU214254 EU213827
EU626842
EU626937
Malvaceae
Grewia bicolor Juss.
OM329
KNP
S25 04 18.8 E31 36 29.5
363 m
EU214255 EU213828
EU626843
EU626938
Malvaceae
Grewia bicolor Juss.
RL1545
KNP
S23 36 52.2 E31 27 36.5
290 m
EU214256 EU213829
EU626844
EU626939
Malvaceae
Grewia bicolor Juss.
RL1658
KNP
S22 41 29.6 E31 01 37.2
439 m
EU214257 EU213830
EU626845
EU626940
Malvaceae
Grewia flavescens Juss.
OM323
KNP
S25 04 18.8 E31 36 29.5
363 m
EU214258 EU213831
-
EU626941
Malvaceae
Grewia flavescens Juss.
RL1472
KNP
S24 52 32.9 E31 45 21.9
256 m
EU214259 EU213832
EU626846
EU626942
Malvaceae
Grewia flavescens Juss.
RL1604
KNP
S22 58 18.8 E31 15 13.5
305 m
EU214260 -
-
-
Malvaceae
Grewia villosa Willd.
RL1342
KNP
S24 58 56.5 E31 46 02.3
208 m
EU214261 EU213833
-
EU626943
Malvaceae
Grewia villosa Willd.
RL1523
KNP
S24 10 31.8 E31 38 53.8
255 m
EU214262 EU213834
EU626847
EU626944
Nature Precedings : hdl:10101/npre.2008.1896.1 : Posted 16 May 2008
7
Plant Family
name Checked on IPNI
Voucher Location GPS
Altitude matK
trnH-psbA
atpF-atpH
psbK-psbI
Malvaceae
Grewia villosa Willd.
RL1569
KNP
S23 24 48.9 E31 32 21.8
363 m
EU214263 EU213835
-
EU626945
Apiaceae
Heteromorpha arborescens Cham. & Schltdl.
OM1430 KNP
S25 13 27.0 E31 20 34.3
655 m
EU214264 EU213836
EU626848
EU626946
Apiaceae
Heteromorpha arborescens Cham. & Schltdl.
OM1488 KNP
S24 59 58.3 E31 21 04.3
359 m
EU214265 EU213837
EU626849
EU626947
Apiaceae
Heteromorpha arborescens Cham. & Schltdl.
OM1516 KNP
S25 20 29.0 E31 31 25.8
426 m
EU214266 EU213838
EU626850
EU626948
Hydnoraceae
Hydnora johannis Becc.
OM534
KNP
S25 21 37.5 E31 43 11.1
241 m
EU214267 -
-
EU626949
Arecaceae
Hyphaene coriacea Gaertn.
OM1184 KNP
S25 08 03.4 E31 56 37.7
167 m
EU214268 EU213775
EU626851
EU626950
Arecaceae
Hyphaene coriacea Gaertn.
OM1187 KNP
S25 17 45.4 E31 51 44.5
185 m
EU214269 EU213776
EU626852
EU626951
Arecaceae
Hyphaene coriacea Gaertn.
OM236
KNP
S25 03 08.3 E31 48 38.6
201 m
EU214271 EU213778
EU626853
EU626952
Arecaceae
Hyphaene coriacea Gaertn.
OM755
KNP
S24 29 10.7 E31 48 29.4
259 m
EU214270 EU213777
EU626854
EU626953
Arecaceae
Hyphaene petersiana Klotzsch ex Mart
OM1296 KNP
S22 38 18.4 E31 08 25.1
382 m
EU214272 EU213779
EU626855
EU626954
Arecaceae
Hyphaene petersiana Klotzsch ex Mart
OM908
KNP
S22 32 55.9 E31 04 25.5
347 m
EU214273 EU213780
EU626856
EU626955
Myrothamnaceae
Myrothamnus flabellifolia Welw.
OM1137 KNP
S25 06 15.4 E31 24 58.6
452 m
EU214275 EU213840
EU626857
EU626956
Myrothamnaceae
Myrothamnus flabellifolia Welw.
OM1209 KNP
S25 04 03.5 E31 33 04.7
485 m
EU214276 EU213841
EU626858
EU626957
Myrothamnaceae
Myrothamnus flabellifolia Welw.
OM285
KNP
S25 04 01.2 E31 33 04.8
577 m
EU214274 EU213839
EU626859
EU626958
Anacardiaceae
Rhus gueinzii Sond.
OM265
KNP
S24 59 25.4 E31 27 19.6
268 m
EU214277 EU213842
EU626860
EU626959
Anacardiaceae
Rhus gueinzii Sond.
RL1366
KNP
S25 17 23.1 E31 46 06.3
208 m
EU214278 EU213843
EU626861
EU626960
Anacardiaceae
Rhus gueinzii Sond.
RL1474
KNP
S24 52 08.3 E31 45 22.4
283 m
EU214279 EU213844
EU626862
EU626961
Anacardiaceae
Rhus leptodictya Diels
RBN205 KNP
S22 42 13.5 E30 57 56.4
487 m
EU214280 EU213845
EU626863
EU626962
Anacardiaceae
Rhus leptodictya Diels
RL1645
KNP
S22 42 06.5 E30 58 10.5
499 m
EU214281 EU213846
EU626864
EU626963
Anacardiaceae
Rhus leptodictya Diels
RL1655
KNP
S22 41 29.1 E31 01 38.4
448 m
EU214282 EU213847
EU626865
EU626964
Anacardiaceae
Rhus transvaalensis Engl.
OM282
KNP
S25 08 53.2 E31 14 38.3
664 m
EU214283 EU213848
EU626866
EU626965
Anacardiaceae
Rhus transvaalensis Engl.
OM943
KNP
S25 08 30.6 E31 14 07.8
610 m
-
EU213849
EU626867
EU626966
Anacardiaceae
Rhus transvaalensis Engl.
RL1427
KNP
S25 08 59.4 E31 14 35.0
630 m
EU214284 EU213850
EU626868
EU626967
Solanaceae
Solanum panduriforme Drège ex Dunal
OM1115 KNP
S25 00 44.2 E31 27 13.7
341 m
EU214285 EU213851
EU626869
EU626968
Solanaceae
Solanum panduriforme Drège ex Dunal
OM326
KNP
S25 04 18.8 E31 36 29.5
363 m
EU214286 EU213852
EU626870
EU626969
Solanaceae
Solanum panduriforme Drège ex Dunal
OM350
KNP
S25 04 17.5 E31 36 29.2
354 m
EU214287 EU213853
EU626871
EU626970
Apiaceae
Steganotaenia araliacea Hochst.
OM1350 KNP
S23 52 55.8 E31 15 00.9
422 m
EU214288 EU213854
EU626872
EU626971
Apiaceae
Steganotaenia araliacea Hochst.
OM1517 KNP
S23 52 56.3 E31 15 06.4
420 m
EU214289 EU213855
EU626873
EU626972
Apiaceae
Steganotaenia araliacea Hochst.
OM566
KNP
S25 04 36.8 E31 25 03.7
473 m
EU214290 EU213856
EU626874
EU626973
Orobanchaceae
Striga elegans Benth.
OM683
KNP
S25 04 02.4 E31 33 06.1
383 m
EU214291 -
EU626875
EU626974
Nature Precedings : hdl:10101/npre.2008.1896.1 : Posted 16 May 2008
8
Plant Family
name Checked on IPNI
Voucher Location GPS
Altitude matK
trnH-psbA
atpF-atpH
psbK-psbI
Loganiaceae
Strychnos decussata ( Pappe ) Gilg
OM900
KNP
S22 35 35.0 E31 06 37.5
329 m
EU214292 EU213857
EU626876
EU626975
Loganiaceae
Strychnos decussata ( Pappe ) Gilg
RL1560
KNP
S23 24 53.0 E31 32 29.7
379 m
EU214293 EU213858
EU626877
EU626976
Loganiaceae
Strychnos decussata ( Pappe ) Gilg
RL1561
KNP
S23 24 53.0 E31 32 29.7
379 m
EU214294 EU213859
EU626878
EU626977
Loganiaceae
Strychnos madagascariensis Spreng. ex Baker
RL1433
KNP
S25 08 24.1 E31 14 51.5
641 m
EU214295 EU213860
EU626879
EU626978
Loganiaceae
Strychnos madagascariensis Spreng. ex Baker
RL1460
KNP
S24 58 21.4 E31 23 21.8
342 m
EU214296 EU213861
EU626880
EU626979
Loganiaceae
Strychnos madagascariensis Spreng. ex Baker
RL1559
KNP
S23 24 53.0 E31 32 29.7
379 m
EU214297 EU213862
EU626881
EU626980
Loganiaceae
Strychnos spinosa Lam.
OM220
KNP
S24 59 49.9 E31 46 10.3
208 m
EU214298 EU213863
EU626882
EU626981
Loganiaceae
Strychnos spinosa Lam.
RL1346
KNP
S25 04 51.2 E31 51 53.2
185 m
EU214299 EU213864
EU626883
EU626982
Loganiaceae
Strychnos spinosa Lam.
RL1652
KNP
S22 39 39.3 E30 58 17.4
430 m
EU214300 EU213865
EU626884
EU626983
Loranthaceae
Tapinanthus Blume
OM825
KNP
S22 59 46.4 E31 17 32.6
312 m
EU214301 -
EU626885
EU626984
Velloziaceae
Xerophyta retinervis Baker
OM1213 KNP
S25 08 32.4 E31 14 23.7
678 m
EU214302 EU213866
EU626886
EU626985
Velloziaceae
Xerophyta retinervis Baker
OM516
KNP
S25 16 03.6 E31 47 53.3
267 m
EU214303 EU213867
EU626887
EU626986
Velloziaceae
Xerophyta retinervis Baker
OM562
KNP
S25 04 36.8 E31 25 03.7
473 m
EU214304 EU213868
EU626888
EU626987
Table 1. Material sampled for this study, species checked in IPNI, voucher, GPS and altitude information, GenBank accession numbers. All
vouchers have been collected in triplicate, one for Kew Herbarium, one for the herbarium of the KNP at Skukuza (South Africa), and one for the
National Herbarium at Pretoria (South Africa).
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9
Genetic analyses. Inter- and intra-specific genetic divergences were calculated using
each potential DNA barcode following Meyer and Paulay
23
. Three different metrics were
used to characterize intra-specific divergence: (i) average pairwise distances between all
individuals sampled within those species that had at least two representatives, (ii) `mean
theta', with theta being the average pairwise distances calculated for each species that had
more than one representative, thereby eliminating biases associated with uneven
sampling among taxa and (iii) average coalescent depth, i.e. the depth of a node linking
all sampled extant members of a species, `book-ending' intra-specific variability. Genetic
distances between con-generic species were used to characterize inter-specific
divergence. For each barcode, pairwise distances were calculated with the simplest K2P
model following Lahaye et al.
20
in which this model showed the best results. This model
also utilizes the CBOL advises for distance calculations (barcoding.si.edu/). Wilcoxon
Signed Rank Tests were performed to compare intra- and inter-specific variability for
every pair of barcodes following Kress and Erickson
24
. We evaluated `DNA barcoding
gaps'
23
by comparing the distribution of intra- versus inter-specific divergences. Median
and Wilcoxon Two-Sample Tests were used to evaluate whether these distributions
overlapped.
Phylogenetic analyzes. To evaluate whether species were recovered as monophyletic
with each barcode, we used standard phylogenetic techniques. Note that this is not to say
that barcodes can be used to reconstruct phylogenies, because in this case we are
disregarding the recovered inter-specific relationships. Trees were built with PAUP4b10*
22
using Maximum Parsimony (MP) and UPGMA, the two best algorithms in terms of
percentages of species correctly identified
20
. UPGMA trees were inferred with
PAUP4b10* from K2P distances. MP analyses were performed using tree bisection-
reconnection (TBR), branch swapping and 1,000 random addition sequence replicates
keeping 10 trees at each step. MP analyses have been performed with and without coding
indels as a 5
th
state in order to assess the impact of keeping this information for barcoding
purposes.
Coalescence analyses. For each barcode, we identified those clusters that were derived
from an independent coalescence process and asked whether they matched previously
recognized taxonomic species, using methods developed by Pons et al.
25
and Fontaneto
Nature Precedings : hdl:10101/npre.2008.1896.1 : Posted 16 May 2008
10
et al.
26
. The likelihood of waiting times between successive branching events on a DNA
barcode-based tree was calculated under the null model that all terminals were derived
from a single coalescence process, and under the alternative model that all taxa derived
from a set of two independently evolving populations. With the alternative model, a
threshold age T was calculated, at which point the older nodes represented inter-specific
diversification events whereas the younger nodes represented separate coalescent
processes typical of intra-specific clusters. We used DNA barcode-based trees from MP
and transformed branch lengths with nonparametric rate smoothing
27
to produce
ultrametric trees, i.e. branch lengths reflecting time only. We also used the ultrametric
UPGMA trees. Likelihood models were determined using an R script available from
TGB.
Results & Discussion
Molecular characteristics and PCR success. Amplification was generally successful for
each potential barcode tested with more than 92% of taxa successfully amplified and
sequenced (Table 2). The best percentage was given by matK with 99% of taxa
sequenced and the lowest percentage was obtained for trnH-psbA with 92%. The
potential DNA barcode psbK-psbI showed PCR and sequencing performances very close
to those of matK with 98% of taxa successfully amplified. Both atpF-atpH and trnH-
psbA failed to amplify the parasitic/non-chlorophytic plant Hydnora johanis. Alignment
of sequences was unproblematic for matK and psbK-psbI, but trnH-psbA and atpF-atpH
presented significant difficulties due to a high level of length variation (225 to 758 bp and
218 to 847 bp, respectively). Because its alignment was not reliable by Clustal X, we
performed a first visual alignment between congeneric species and then aligned all taxa
by adding as many gaps as necessary to keep the homology between congeneric species
for inter- and intraspecific calculations. The alignment of trnH-psbA revealed a highly
conservative intron only for the Orchidaceae and Amaryllidaceae which has been
identified previously
20, 28
. Combining matK to one of the other potential barcodes
allowed building a matrix including sequences for all taxa (Table 2).
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11
matK
99%
psbK-psbI
98%
trnH-psbA
92.1%
atpF-atpH
93.1%
matK+trnH-psbA
100%
matK+trnH-psbA+atpF-atpH
100%
matK+trnH-psbA+psbK-psbI
100%
matK+atpF-atpH
100%
matK+psbK-psbI
100%
matK+atpF-atpH+psbK-psbI
100%
4 loci
100%
Table 2. Percentages of taxa represented in each matrix by at least one sequence.
Intra- and Inter-specific diversities. Performances of each DNA barcode was assessed
by means of inter- and intra-specific diversity calculated from K2P (Kimura's two
parameters) pairwise distance matrices (barcoding.si.edu/; Table 3). The highest inter-
specific diversity was reached by atpF-atpH (3.45%) followed by trnH-psbA (2.55%) and
the lowest was given by psbK-psbI (1.06%) with matK between these (1.34%). Regarding
the different metrics to infer the intra-specific differences, the mean theta was in most
cases similar to the average of overall intra-specific distances because there is no bias
associated with species over-sampled in our study with the majority of the species
represented by three specimens. The mean coalescent depth was slightly superior to the
average of overall interspecific distances because it takes into consideration only the
highest distance between specimens sampled for a species. Results showed the highest
mean of intraspecific differences for trnH-psbA regardless of the metric used (Table 3).
The lowest values were obtained for both atpF-atpH and psbK-psbI. Wilcoxon rank tests
performed on the different distance matrices showed with very high significance that
trnH-psbA had by far the highest inter-specific variability, followed by matK and atpF-
atpH, which had a similar divergence (Table 4). The highest intra-specific distances were
also significantly reached by trnH-psbA whereas the three other loci presented almost
similar values (Table 5).
Nature Precedings : hdl:10101/npre.2008.1896.1 : Posted 16 May 2008
12
matK trnH- atpF- psbK- 4
loci matK+ matK+atpF- matK+psbK- matK+ matK+
matK+psbK-
psbI+
psbA
atpH
psbI
trnH-
psbA
atpH+trnH-
psbA
psbI+trnH-
psbA
atpF-
atpH
psbK-
psbI atpF-atpH
Mean of all interspecific
distances
0.0134 0.0255 0.0345 0.0106 0.0172 0.0175
0.0189
0.0157
0.0168
0.0118
0.0150
St.
deviation
+/-
0.0127 0.0227 0.0665 0.0096 0.0151 0.0154
0.0180
0.0121
0.0201
0.0092
0.0159
Mean of all intraspecific
distances
0.0012 0.0017 0.0004 0.0005 0.0009 0.0012
0.0009
0.0011
0.0007
0.0009
0.0007
St.
deviation
+/-
0.0040 0.0041 0.0015 0.0012 0.0015 0.0026
0.0017
0.0021
0.0020
0.0026
0.0016
Mean
Theta
0.0012 0.0015 0.0007 0.0005 0.0008 0.0012
0.0009
0.0010
0.0007
0.0009
0.0007
St.
deviation
+/-
0.0037 0.0032 0.0023 0.0010 0.0013 0.0023
0.0015
0.0018
0.0018
0.0024
0.0015
Mean coalescent depth
0.0017 0.0023 0.0008 0.0008 0.0013
0.0017
0.0014
0.0016
0.0012
0.0013
0.0011
St.
deviation
+/-
0.0050 0.0047 0.0023 0.0016 0.0018 0.0032
0.0021
0.0026
0.0026
0.0033
0.0021
Number of measurements for
93
90
84
91
95
95
95
95
95
95
95
all intraspecific distances
Number of measurements for
200
194
168
194
206
206
206
206
206
206
206
all interspecific distances
Table 3. Measures of inter- and intra-specific K2P distances for four potential barcodes and different combinations applied to a selective sampling
from the KNP.
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13
Wilcoxon Signed-Ranks Test
Interspecific pair-distances
matK vs trnH-psbA
W+ = 1462, W- = 14648, N = 179, p <= 2.216e-21
matK<<trnH-psbA
matK vs atpF-atpH
W+ = 4977, W- = 5608, N = 145, p <= 0.5341
matK = atpF-atpH
matK vs psbK-psbI
W+ = 8655, W- = 6051, N = 171, p <= 0.0447
matK > psbK-psbI
trnH-psbA vs atpF-atpH
W+ = 8482, W- = 3608, N = 155, p <= 1.345e-05
trnH-psbA > atpF-atpH
trnH-psbA vs psbK-psbI
W+ = 13538, W- = 2572, N = 179, p <= 2.88e-15
trnH-psbA >> psbK-psbI
atpF-atpH vs psbK-psbI
W+ = 7663, W- = 2922, N = 145, p <= 2.902e-06
atpF-atpH > psbK-psbI
4 loci vs matK+trnH-psbA
W+ = 7286, W- = 12217, N = 197, p <= 0.002095
4 loci < matK+trnH-psbA
4 loci vs matK+trnH-psbA+atpF-atpH
W+ = 5244, W- = 14259, N = 197, p <= 1.859e-08
4 loci < matK+trnH-psbA+atpF-atpH
4 loci vs matK+trnH-psbA+psbK-psbI
W+ = 6661, W- = 12060, N = 193, p <= 0.0005137
4 loci < matK+trnH-psbA+psbK-psbI
4 loci vs matK+atpF-atpH
W+ = 14310, W- = 5193, N = 197, p <= 1.284e-08
4 loci > matK+atpF-atpH
4 loci vs matK+psbK-psbI
W+ = 15830, W- = 3673, N = 197, p <= 3.333e-14
4 loci > matK+psbK-psbI
4 loci vs matK+psbK-psbI+atpF-atpH
W+ = 15351, W- = 4152, N = 197, p <= 2.807e-12
4 loci < matK+atpF-atpH+psbK-psbI
matK+trnH-psbA vs matK+trnH-psbA+atpF-atpH
W+ = 12287, W- = 6434, N = 193, p <= 0.0001661
matK+trnH-psbA > matK+trnH-psbA+atpHF
matK+trnH-psbA vs matK+trnH-psbA+psbK-psbI
W+ = 13374, W- = 6129, N = 197, p <= 6.174e-06
matK+trnH-psbA > matK+trnH-psbA+psbK-
psbI
matK+trnH-psbA vs matK+atpF-atpH
W+ = 13379, W- = 6124, N = 197, p <= 5.995e-06
matK+trnH-psbA > matK+atpF-atpH
matK+trnH-psbA vs matK+psbK-psbI
W+ = 16218, W- = 3285, N = 197, p <= 7.1e-16
matK+trnH-psbA >> matK+psbK-psbI
matK+trnH-psbA vs matK+atpF-atpH+psbK-psbI
W+ = 13179, W- = 6324, N = 197, p <= 1.894e-05
matK+trnH-psbA > matK+atpF-atpH+psbK-
psbI
Table 4. Wilcoxon signed rank tests of inter-specific divergence among loci.
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14
Wilcoxon Signed-Ranks Test -
Intraspecific pair-distances
matK vs trnH-psbA
W+ = 298, W- = 605, N = 42, p <= 0.05574
matK < trnH-psbA
matK vs atpF-atpH
W+ = 334, W- = 162, N = 31, p <= 0.09384
matK = atpF-atpH
matK vs psbK-psbI
W+ = 299, W- = 229, N = 32, p <= 0.5189
matK = psbK-psbI
trnH-psbA vs atpF-atpH
W+ = 340, W- = 95, N = 29, p <= 0.008339
trnH-psbA > atpF-atpH
trnH-psbA vs psbK-psbI
W+ = 375, W- = 121, N = 31, p <= 0.01318
trnH-psbA > psbK-psbI
atpF-atpH vs psbK-psbI
W+ = 89, W- = 142, N = 21, p <= 0.3662
atpF-atpH = psbK-psbI
4 loci vs matK+trnH-psbA
W+ = 450, W- = 981, N = 53, p <= 0.01898
4 loci < matK+trnH-psbA
4 loci vs matK+trnH-psbA+atpF-atpH
W+ = 486, W- = 945, N = 53, p <= 0.04263
4 loci < matK+trnH-psbA+atpF-atpH
4 loci vs matK+trnH-psbA+psbK-psbI
W+ = 319, W- = 1007, N = 51, p <=
0.001283
4 loci < matK+trnH-psbA+psbK-psbI
4 loci vs matK+atpF-atpH
W+ = 923, W- = 508, N = 53, p <= 0.06687
4 loci = matK+atpF-atpH
4 loci vs matK+psbK-psbI
W+ = 901, W- = 530, N = 53, p <= 0.1015
4 loci = matK+psbK-psbI
4 loci vs matK+psbK-psbI+atpF-atpH
W+ = 906, W- = 525, N = 53, p <= 0.09256
4 loci = matK+atpF-atpH+psbK-psbI
matK+trnH-psbA vs matK+trnH-psbA+atpF-atpH
W+ = 810, W- = 271, N = 46, p <= 0.003294
matK+trnH-psbA > matK+trnH-psbA+atpHF
matK+trnH-psbA vs matK+trnH-psbA+psbK-psbI
W+ = 833, W- = 392, N = 49, p <= 0.02864
matK+trnH-psbA > matK+trnH-psbA+psbK-
psbI
matK+trnH-psbA vs matK+atpF-atpH
W+ = 924, W- = 252, N = 48, p <=
0.0005795
matK+trnH-psbA > matK+atpF-atpH
matK+trnH-psbA vs matK+psbK-psbI
W+ = 854, W- = 371, N = 49, p <= 0.01652
matK+trnH-psbA > matK+psbK-psbI
matK+trnH-psbA vs matK+atpF-atpH+psbK-psbI
W+ = 1068, W- = 363, N = 53, p <=
0.001832
matK+trnH-psbA > matK+atpF-atpH+psbK-
psbI
Table 5. Wilcoxon signed rank tests of intra-specific difference among loci.
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15
In a multi loci approach for DNA barcoding purposes, the highest mean of inter-specific
variability was achieved by matK combined with trnH-psbA and atpF-atpH whereas the
highest mean of intra-specific distances were given by combining matK with trnH-psbA
(Table 3). Wilcoxon statistical rank tests showed the combination matK + trnH-psbA
having the highest inter-specific pair-distances (Table 4). They revealed also that all the
combinations including trnH-psbA had a higher intra-specific variability than
combinations without it (Table 5).
Distribution of distances. Accuracy of each DNA barcode was assessed by looking at the
distribution of inter- and intraspecific K2P distances to infer the barcoding gap
23
.
Distributions were similar for each single potential barcode with two peaks of inter- and
intraspecific variability that could be distinguished (Figure 2).
Figure 2. Relative distributions of inter-specific divergence between con-generic species (yellow)
and intra-specific K2P distances (red) for four single loci: matK, trnH-psbA, psbK-psbI and atpF-
atpH. Barcoding gaps were assessed with Median tests and Wilcoxon Two-Sample tests, and all
were highly significant (p<0.0001).
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16
Each distribution also showed a slight overlap between intra- and inter-specific distances,
but to a lesser extent for matK and trnH-psbA. Combining the different loci showed
distributions with a slight decrease of this overlap (Figure 3).
Figure 3. Relative distributions of inter-specific divergence between con-generic species (yellow)
and intra-specific K2P distances (red) for 7 different combinations keeping matK for each.
Barcoding gaps were assessed with Median tests and Wilcoxon Two-Sample tests, and all were
highly significant (p<0.0001).
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17
Two clear peaks were still distinguishable and a slight overlap still occurred between low
classes of intra- and inter-specific distances, but the overlap observed was less than that
for the single locus approach. These observations were confirmed by median and
Wilcoxon two samples statistical tests differentiating the medians for the former and the
medians plus the distributions between the inter- and intra-specific distances for the
latter. For each distribution, Median and Wilcoxon two sample tests were significant
(Table 6). In a single locus approach, the highest significances were given by matK and
psbK-psbI. Combining the loci made the significance increasing with the highest
significance given by the combination matK+trnH-psbA+psbK-psbI.
K2P distributions
median test
Wilcoxon Two Sample Test
matK
#A = 199 #B = 93, Median = 0.00524, p <= 1.11e-26
#A = 200 #B = 93, W = 6020.5, p <= 9.314e-30
trnH-psbA
#A = 194 #B = 90, Median = 0.00799, p <= 1.11e-22
#A = 194 #B = 90, W = 5634, p <= 6.125e-29
atpF-atpH
#A = 168 #B = 84, Median = 0.00216, p <= 1.52e-23
#A = 168 #B = 84, W = 5526, p <= 8.996e-21
psbK-psbI
#A = 194 #B = 91, Median = 0.00509, p <= 1.44e-29
#A = 194 #B = 91, W = 5333, p <= 2.524e-32
4 loci
#A = 206 #B = 95, Median = 0.00608, p <= 1.23e-28
#A = 206 #B = 95, W = 5507, p <= 2.394e-36
matK+trnH-psbA
#A = 206 #B = 95, Median = 0.00648, p <= 8.07e-28
#A = 206 #B = 95, W = 5675, p <= 4.825e-35
matK+trnH-psbA+atpF-
atpH
#A = 206 #B = 95, Median = 0.00574, p <= 5.11e-29
#A = 206 #B = 95, W = 5642.5, p <= 2.711e-35
matK+trnH-psbA+psbK-
psbI
#A = 206 #B = 95, Median = 0.00676, p <= 5.11e-29
#A = 206 #B = 95, W = 5540, p <= 4.338e-36
matK+atpF-atpH
#A = 206 #B = 95, Median = 0.00401, p <= 1.2e-26
#A = 206 #B = 95, W = 6318, p <= 2.802e-30
matK+psbK-psbI
#A = 206 #B = 95, Median = 0.00607, p <= 8.07e-28
#A = 206 #B = 95, W = 6064, p <= 4.064e-32
matK+atpF-atpH+psbK-
psbI
#A = 206 #B = 95, Median = 0.00493, p <= 2.92e-28
#A = 206 #B = 95, W = 6026.5, p <= 2.151e-32
Table 6. Median and Wilcoxon two sample statistical tests applied to the distributions of intra-
and inter-specific K2P distances for each potential DNA barcode.
Species identification. The performance of each DNA barcode in identifying and
delineating species was assessed by the percentage of monophyletic species recovered by
MP and UPGMA analyses (Table 7). Because trnH-psbA and atpF-atpH were highly
variable and their alignment showed many indels, MP analyses were performed with and
without coding the gaps as 5
th
state to infer whether this information could be useful for
barcoding purposes. The highest values of species monophyly were obtained from
UPGMA reconstruction. The UPGMA analysis of trnH-psbA gave 90.3% of species
monophyletic but only 77.4% supported by BS>50%. Although matK and psbK-psbI
grouped 87.5% of the species under UPGMA reconstruction, they gave 78.1% of
monophyletic species with a BS>50%, a value higher than trnH-psbA. MatK showed the
Nature Precedings : hdl:10101/npre.2008.1896.1 : Posted 16 May 2008
18
best percentage of species correctly identified using MP reconstruction. Coding the gaps
as 5
th
state in the MP analysis did not significantly affect the results obtained for matK
and psbK-psbI, but it increased the percentages of species correctly identified by 6% and
7% given by the more variable atpF-atpH and trnH-psbA, respectively. In a multi-loci
approach, it is noteworthy that combining all potential barcodes did not result in 100%
monophyly for species whatever the reconstruction method. Each barcode failed in
grouping the two different species of Faurea. That can be done by using the intergenic
locus atpF-atpH and by coding the gaps in the matrix as 5
th
state of character, but this
decreases the total percentage of monophyletic species. In a multi-loci approach,
combining matK and psbK-psbI gave the highest percentage of monophyletic species
(Table 7).
UPGMA MP
MP+5th state
character
trnH-psbA
90.3 (77.4)
71 (71)
77.4 (74.2)
matK
87.5 (78.1)
75 (75)
75 (75)
psbK-psbI
87.5 (78.1) 62.5 (68.8)
53.1 (53.1)
atpF-atpH
82.8 (69)
65.5 (65.5)
72.4 (69)
matK+psbK-psbI
93.8 (87.5) 81.3 (81.3)
59.4 (56.3)
matK+trnH-psbA+psbK-psbI
93.5 (90.3) 87.1 (87.1)
80.6 (80.6)
matK+atpF-atpH+psbK-psbI
93.1 (86.2) 86.2 (86.2)
82.8 (82.8)
matK+trnH-psbA+atpF-
atpH+psbK-psbI
92.9 (89.3) 85.7 (85.7)
82.1 (82.1)
matK+trnH-psbA
90.3 (87.1) 83.9 (83.9)
77.4 (77.4)
matK+atpF-atpH
89.7 (82.8) 79.3 (79.3)
79.3 (79.3)
matK+trnH-psbA+atpF-atpH
89.3 (85.7) 82.1 (82.1)
82.1 (82.1)
Table 7. Proportion (%) of monophyletic species (with BS > 50% in brackets) recovered with
UPGMA and MP analyses with gaps not coded and coded as a fifth character state.
Coalescence. The accuracy of the DNA barcode can be assessed by evaluating the ability
of each candidate to give genetic clusters that are derived from an independent
coalescence process and that corresponds to a recognized taxonomic species
25, 26
. The
highest number of genetic clusters corresponding to taxonomic species was given using
the UPGMA trees. Transforming MP trees by NPRS for coalescence analysis gave half
the genetic clusters corresponding to taxonomic species compared to the UPGMA trees
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19
(Table 7). In a single barcode approach, matK gave the highest numbers of genetic
clusters corresponding to taxonomic species (Table 8). When matK was combined with
psbK-psbI the value increased from 22 to 23 genetic clusters corresponding to recognized
species. Molecular evolutionary rates of both matK and psbK-psbI showed higher
abilities to differentiate independently evolving entities corresponding to taxonomic
species than the high variable trnH-psbA and atpF-atpH.
UPGMA
MP
Nos. of potential genetic
clusters
matK
22 11
32
psbK-psbI
20 15
32
atpF-atpH
18 12
29
trnH-psbA
16 12
31
matK+psbK-psbI
23 8
32
matK+atpF-atpH+psbK-psbI
20 4
29
matK+atpF-atpH
20 6
29
matK+trnH-psbA+psbK-psbI
3 7
31
matK+trnH-psbA+atpF-atpH+psbK-
psbI
3 1
28
matK+trnH-psbA
3 8
31
matK+trnH-psbA+atpF-atpH
3 5
28
Table 8. Coalescence analyses indicating the number of independent genetic clusters
corresponding to taxonomically recognized species.
Our results showed that combining matK to trnH-psbA and psb-psbI can slightly
increase its performance in identifying species. However we still support the conclusion
of Lahaye et al.
20
, i.e. that matK should be used for DNA barcoding of plants in a single
locus approach and that case-by-case additional barcodes are developed for problematic
groups.
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