REVIEW
Standard Percent DNA Sequence Difference for Insects Does Not
Predict Species Boundaries
ANTHONY I. COGNATO1
Department of Entomology, Michigan State University, East Lansing, MI 48824
J. Econ. Entomol. 99(4): 1037Ð1045 (2006)
ABSTRACT Diagnosis and assessment of species boundaries of economically important insects are
often problematic because of limited morphological and/or biological characters. DNA data can help
to identify and revise species. Nonoverlapping intra- and interspeciÞc sequence divergences are often
used as evidence for species. Thus, the establishment of a standardized percent nucleotide divergence
to predict species boundaries would aid in cases where species status is suspect. However, given
variation in nucleotide mutation rates and species concepts, association between a standard percent
sequence divergence and species is questionable. This review surveys the percent DNA sequence
difference found between sister-species of economically important insects, to assess whether a
standard divergence associates with all taxa. Sixty-two comparisons of intra- and interspeciÞc pairwise
DNA differences were made for mitochondrial and nuclear loci spanning families of Isoptera,
Phthiraptera, Hemiptera, Coleoptera, Lepidoptera, Diptera, and Hymenoptera. Intra- and interspeciÞc sequence divergences varied widely among insects, 0.04 Ð26.0 and 1.0 Ð30.7%, respectively. The
ranges of intra- and interspeciÞc sequence divergences overlapped in 28 of 62 comparisons. This
implies that a standardized percent sequence divergence would fail to correctly diagnose species for
45% of the cases. Common occurrence of nonmonophyly among closely related species probably
explains this observation. Nonmonophyly and overlap of intra- and interspeciÞc divergences were
signiÞcantly associated. The reviewed studies suggest that a standard percent sequence divergence
does not predict species boundaries among economically important insects. DNA data can help best
to predict species boundaries via its inclusion in nonphenetic phylogenetic analysis and subsequent
systematic expert scrutiny.
Molecular data are vast and varied resources for the
discovery of characters for species diagnosis. Enzymatic genetic variation was the Þrst character system
developed for the diagnosis of morphologically indistinguishable species (Ayala and Powell 1972). Species
were identiÞed by the frequency of alleles with direct
comparison to known specimens and the probability
of misidentiÞcation was assessed based on the amount
of shared alleles between species (Ayala and Powell
1972, Avise 1974, Berlocher 1980). Frequency of alleles allowed for the quantiÞcation and comparison of
intra- and interspecies variability. Patterns of intraand interspecies genic similarities among animal species suggested that there were boundaries for intraand interspeciÞc variation (Avise 1974). For example,
conspeciÞcs exhibited an average of 10% difference of
alleles, whereas congenerics exhibited an average of
40% difference. These boundaries have been used as
guidelines for decisions concerning species validity
(Berlocher 1984). However, examples that demonstrated no variation among subspecies and too much
variation among species were noted (Avise 1974, Lewontin 1974).
1
E-mail: [email protected].
Contemporaneously, variation among DNA restriction enzyme cutting sites and sequences for populations and species was investigated (Avise et al. 1979,
Hale and Singh 1987). Using the convention developed with protein analysis, DNA differences were
summarized by percent similarity, and again intra- and
interspeciÞc variation patterns were observed (Avise
et al. 1983, Satta et al. 1987, Brower and Boyce 1991).
Based on a small sample, intra- and interspeciÞc variation did not overlap for most species; however, the
range of intra- and interspeciÞc variation varied
widely (Vogler et al. 1993). The difference between
intra- and interspeciÞc percent DNA variation has
been used as a “genetic yardstick” to recognize new
species for some taxa (Hung et al. 1999). It was reasoned that intraspeciÞc variation corroborated with
morphologically diagnosable species could be used to
identify new species with similar sequence difference
(Nadler 2002). Application of percent DNA similarity
for the identiÞcation of species and other taxa is currently popularized by the scheme to DNA “barcode”
all life (Hebert et al. 2003a, Blaxter 2004). This system
would identify species as groups of individuals that
exhibit 10 times the average intraspeciÞc pairwise sequence similarity (Hebert et al. 2004). It is debatable
0022-0493/06/1037Ð1045$04.00/0 䉷 2006 Entomological Society of America
1038
JOURNAL OF ECONOMIC ENTOMOLOGY
whether percent pairwise sequence divergence can
be used solely to delimitate (group) or recognize
(rank) species taxa (Nadler 2002, Will and Rubinoff
2004). Given the phenetic basis of percent similarity,
these intra- and interspeciÞc variation patterns are not
phylogenetic hypotheses and provide no recognition
of between homologous and homoplastic characters.
Conversely, DNA data are excellent and sometimes
the sole characters for the taxonomy of many organisms with few morphological or ecological characters
(e.g., nematodes, Nadler 2002). The majority of these
studies use DNA data for phylogenetic reconstruction
and subsequent delimitation of groups of individuals
based on monophyly (those that share a recent common ancestor) (Mishler and Brandon 1987, Brower
1996, Kruse and Sperling 2001). Thus, secondary homologies diagnose monophyletic groups (de Pinna
1991). Recognition of species among the monophyletic groups can be subjective (Wiens and Penkrot
2002, Sites and Marshall 2003). Geographic location,
ecology, behavior, and morphology are used as ranking criteria and the amount and extent of difference is
often particular to each taxon. DNA sequence divergence is also implemented (Cognato et al. 2005a), but
in these cases the average percent sequence divergence calculated for well corroborated sister-species
taxa is used to rank clades of suspected species status.
Thus, the percent sequence difference is associated
with the number of nucleotide changes since the divergence of sister-species from their common ancestor. This method provides an objective means to use
DNA characters to rank species (Nadler 2002) and
embodies the philosophy that species taxa are hypotheses of evolutionary entities (Hey et al. 2003).
The above-mentioned issues are pertinent in the
application of molecular diagnoses of economically
important insects. Economic problems caused by insects often associate with a particular behavioral or
ecological variation found in a subgroup of a species.
These races, biotypes, and subspecies may represent
“real” species in the sense that they are evolutionary
entities (monophyletic groups) (Hey et al. 2003). Alternatively, these groups may harbor a collection of
convergent characteristics that deÞnes the group as an
economic pest (such as defoliators). Association of the
group name (the species taxon) with an evolutionary
entity (the species) is an issue of much debate (Brown
et al. 1995, Rubinoff and Sperling 2004). Given that
few morphological characters often associate with
these groups, molecular data are used to help delimit
the groupÕs evolutionary boundaries and to help diagnose the group. However, the amount of molecular
difference necessary to delimit a species as well as to
equate a standard DNA difference with insect species
remains largely unexplored. SpeciÞcally, the extent of
intra- and interspeciÞc DNA differences overlap is
unknown and the application of a standard DNA sequence difference for delimitation of all species in a
genus, family, or order is unexplored.
This study surveys the percent DNA sequence difference found between sister-species of economically
important insects to assess the potential for a standard
Vol. 99, no. 4
DNA difference to delimit and diagnose species.
Comparison of sister-species better determines the
percent DNA sequence difference than does nonphylogenetic comparisons. Sister-species share a recent common ancestor thus DNA difference reßects
the amount of change that has occurred between two
species since speciation. Other comparisons inßate the
amount of DNA difference because the amount includes change that has occurred before the speciation
of a particular group. For example, the percent DNA
difference between Drosophilia melanogaster Meigen
and Drosophila yakuba Burla would be smaller than
the percentage of DNA difference between D. melanogaster and Anopheles maculipennis Meigen.
Methods
Literature published between 1990 and 2004 concerning DNA variation among economically important insect species and populations was considered.
Publications that included DNA data and a phylogeny
for sister-species or subspecies taxa were selected
for analysis. Furthermore, only studies, which included focal species with at least three individuals that
represented unique DNA sequences were chosen
(Table 1). Monophyly of the focal species was based
on the phylogeny presented in the respective publication. For some taxa, the focal species and/or
sister-species were not monophyletic. In these cases,
all haplotypes for the focal species and related species
that stemmed from a recent common ancestor were
considered. For other taxa, the authors suspected the
occurrence of cryptic species within the focal species
and these cases were recorded (Table 1). Sequence
data for the focal species and sister-groups were obtained from GenBank (http://www.ncbi.nlm.nih.gov)
and arranged in a NEXUS Þle for use with the computer program PAUP* (Swofford 2002). In PAUP*,
intra- and interspeciÞc pairwise JukesÐCantor DNA
differences (Jukes and Cantor 1969) were calculated.
Mean, standard deviation, and range were calculated
for the pairwise differences.
Results and Discussion
The literature search yielded 41 studies of economically important insects that met the criteria necessary
for inclusion in this study. From these studies, 62
comparisons of intra- and interspeciÞc pairwise DNA
differences were made for mitochondrial and nuclear
loci spanning families of Isoptera, Phthiraptera,
Hemiptera, Coleoptera, Lepidoptera, Diptera, and
Hymenoptera.
Use of mitochondrial loci was most common (84%);
cytochrome oxidase I (mitochondrial DNA [mtDNA]
COI) was used in half of these studies. Similar ranges
of intraspeciÞc and interspeciÞc sequence divergence
were found for mitochondrial (0.04 Ð26.0 and 0.15Ð
25.7) and nuclear (0.09 Ð15.9 and 1.0 Ð30.7) DNA, respectively. Mean nuclear intra- and interspeciÞc sequence divergences (4.0 and 13.0, respectively) were
approximately twice the values for mtDNA (2.0 and
Coleoptera
Blepharida flavocostata
B. flavocostata
Ophraella communa
Trirhabda lewisii
Trirhabda sericotrachyla
Aramigus tessellatus
Curculio caryae
Dendroctonus valens
Ips confusus
Ips pini
Liparthrum inarmatum
Liparthrum pilosum
Tomicus piniperda
Aedes flavopictus
Aedes flavopictus
Aedes flavopictus
Anopheles nili
Anopheles nili
Anopheles maculipennis
Anopheles minimus A
Anopheles minimus C
Bactocera scutellata
Bactocera scutellata
Chysomya albiceps
Contarinia asclepiadis
Contarinia vincetoxici
Drosophila grimshawi
Drosophila subobscura
Lucilia cuprina
Lucilia sericata
Phytomyza verticillatae
Phytomyza opacae
Phytomyza n. sp1
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
U
V
W
X
Y
Z
AA
AB
AC
AD
AE
AF
AG
Species
Yes
Yes
No
Yes
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
No
No
N/A
No
Yes
N/A
No
No
No
No
No
No
No
No
N/A
N/A
No
No
No
No
No
No
No
No
No
Yes
No
No
No
No
N/A
No
No
Species
Monophyletic monophyly
suspect
COI⫹tLue⫹COII
COI⫹tLue⫹COII
COI⫹tLue⫹COII
COI⫹tLue⫹COII
COI⫹tLue⫹COII
Yp1
AT-rich
CytB
COI
CytB
12S
18S through 28S
ITS1
ITS2
ITS2
28s, D3
COI
COII
COII
16S
COI⫹tLue⫹COII
COI
COI
COI
COI
COI
COI
COI
COI
COI
3
3
7
6
4
6
33
4
3
4
3
39
34
22
5
5
35
8
7
5
4
3
4
34
15
121
3
25
5
4
7
8
COI
5
COI
No.
haplotypes
or alleles
5.8S⫹ITS2 ⫹ 28S
Gene locus
954
954
2,327
2,327
954
914
990
398
1,167
398
369
1,247
479
317
587
397
524
692
692
1,163
658
675
675
354
399
535
819
762
420
420
420
644
1,307
No.
base pairs
including
gaps
0.28, 0.12 (0.21Ð0.42)
0.4, 0.1 (0.31Ð0.42)
1.3, 1.1 (0.086Ð2.6)
0.16, 0.06 (0.04Ð0.26)
2.2, 1.5 (0.52Ð3.5)
1.1, 0.4 (0.44Ð1.8)
0.95, 042 (0.1Ð2.2)
0.67, 0.41 (0.25Ð1.3)
0.11, 0.05 (0.086Ð0.11)
1.9, 0.69 (1.0Ð2.8)
0.54, 0.27 (0.27Ð0.82)
4.7, 2.4 (0.09Ð10.0)
8.1, 4.1 (0.24Ð15.9)
3.6, 1.9 (0.33Ð8.6)
6.0, 7.4 (0.0Ð14.6)
0.76, 0.01 (0.0Ð1.9)
0.76, 0.53 (0.19Ð2.9)
1.4, 0.94 (0.15Ð3.0)
0.58, 0.28 (0.6Ð0.88)
0.45, 0.2 (0.09Ð0.78)
0.8, 0.4 (0.3Ð1.5)
3.6, 1.5 (2.0Ð4.7)
0.84, 0.61 (0.15Ð1.5)
1.9, 1.0 (0.3Ð3.4)
0.80, 0.35 (0.25Ð1.6)
3.7, 3.8 (0.6Ð16.0)
1.2, 0.86 (0.24Ð1.7)
6.0, 2.2 (0.1Ð9.0)
0.57, 0.3 (0.24Ð0.96)
1.0, 0.61 (0.24Ð1.8)
2.1, 1.7 (0.3Ð3.8)
9.1, 3.4 (1.0Ð15.1)
1.1, 1.0 (0.077Ð2.4)
IntraspeciÞc
difference
mean, SD
2.7, 1.7 (2.4Ð3.0)
2.7, 1.7 (2.4Ð3.0)
1.8, 0.68 (0.7Ð2.4)
1.8, 0.68 (0.7Ð2.4)
5.1, 0.4 (4.6Ð5.9)
1.9, 0.26 (1.5Ð2.2)
13.6, 3.9 (9.1Ð18.2)
16.4, 1.0 (14.8Ð17.5)
3.6, 0.07 (3.5Ð3.7)
10.0, 4.9 (4.4Ð16.8)
0.54, 0.27 (0.27Ð0.82)
18.0, 0.77 (16.7Ð19.6)
27.6, 1.2 (24.1Ð30.7)
17.9, 1.3 (15.6Ð20.0)
15.9, 2.8 (14.3Ð21.3)
1.9, 0.5 (1.0Ð2.4)
3.3, 0.27 (2.7Ð3.7)
3.2, 0.27 (2.6Ð3.8)
3.2, 0.27 (2.6Ð3.8)
0.63, 0.23 (0.26Ð1.0)
11.6, 0.6 (11.0Ð12.7)
10.8, 0.47 (10.1Ð11.5)
10.8, 0.47 (10.1Ð11.5)
11.1, 0.9 (10.0Ð12.6)
3.8, 0.27 (3.2Ð4.3)
8.0, 0.6 (7.5Ð10.0)
11.0, 0.26 (10.8Ð11.3)
12.0, 1.0 (9.8Ð14.3)
3.1, 0.84 (1.7Ð3.9)
4.3, 0.77 (3.6Ð5.6)
6.0, 0.76 (4.9Ð7.4)
16.1, 1.9 (13.5, 18.2)
4.7, 0.49 (4.4Ð5.6)
Divergence compare
with sister species or
clade mean, SD
(range)
No
No
Yes
Yes
No
Yes
No
No
No
No
Yes
No
No
No
Yes
Yes
Yes
Yes
No
Yes
No
No
No
No
No
Yes
No
No
No
No
No
Yes
No
Intra- and
interspeciÞc%
overlap?
P. opacae
Phytomyza n. sp1
Chrysomya rufifacies
C. loti, C. lysimachiae,
C.tritici,
C. loti, C. lysimachiae,
C.tritici,
D. disjuncta
D. guanche, D.
madeirensis
Lucilia sericata
Lucilia cuprina
P. vomitoriae
B. ishigakiensis
Ae. albopictus
Ae. albopictus
Ae. albopictus
A. carnevalei, A. somalicus
A. carnevalei, A. somalicus
A. messeae
Anopheles minimus C
Anopheles minimus A
B. ishigakiensis
T. destruens
Liparthrum inarmatum
Liparthrum pilosum
I. integer, I. plastographus
I. hoppingi
D. rhizophagus
A. intermedius,
A. planioculus,
Curculio nasicus
T. manisi, T. flavolimbata
T. pilosa
O.artiemisiae, O. nuda
B. verdea
B. verdea
Sister species
or clade
Stevens et al. (2002)
Stevens et al. (2002)
Scheffer and
Wiegmann (2000)
Scheffer and
Wiegmann (2000)
Scheffer and
Wiegmann (2000)
Kerdelhué et al.
(2002)
Toma et al. (2002)
Toma et al. (2002)
Toma et al. (2002)
Kengne et al. (2003)
Kengne et al. (2003)
Linton et al. (2003)
Sharpe et al. (2000)
Sharpe et al. (2000)
Muraji and Nakahara
(2001)
Muraji and Nakahara
(2001)
Harvey et al. (2003)
Widenfalk et al.
(2002)
Widenfalk et al.
(2002)
Piano et al. (1997)
Brehm et al. (2004)
Jordal et al. (2004)
Jordal et al. (2004)
Cognato et al. (1999)
Cognato et al. (2003)
Hughes and Vogler
(2004)
Cognato et al. (2005b)
Swigonová and Kjer
(2004)
Normark (1996)
Swigonová and Kjer
(2004)
Funk et al. (1995)
Becerra (2004)
Becerra (2004)
Reference
COGNATO: SPECIES DIAGNOSIS BY USING % DNA DIFFERENCE
Agromyzidae
Agromyzidae
Calliphoridae
Calliphoridae
Agromyzidae
Drosophilidae
Drosophilidae
Cecidomyiidae
Calliphoridae
Cecidomyiidae
Tepheridae
Curculionidae,
Scolytinae
Curculionidae,
Scolytinae
Curculionidae,
Scolytinae
Curculionidae,
Scolytinae
Curculionidae,
Scolytinae
Curculionidae,
Scolytinae
Culicidae
Culicidae
Culicidae
Culicidae
Culicidae
Culicidae
Culicidae
Culicidae
Tepheridae
Curculionidae
Curculionidae
Chrysomelidae
Chrysomelidae
Chrysomelidae
Chrysomelidae
Chrysomelidae
Family
Intra- and interspecific percent nucleotide difference for pairwise comparisons of insect sister-species
A
Symbol
Table 1.
August 2006
1039
Simulium damnosum
Simulium damnosum
Hemiptera
Cinara terminalis
Rhinotermitidae
Rhinotermitidae
Isoptera
Reticulitermes flavipes
Reticulitermes lucifugus
AV
AW
Tortricidae
Ctenopseustis obliquana
Hemiluca electra
Greya politella
AY
AZ
BA
BB
Pediculus humanus humanus
BJ
N/A, not applicable.
Ischnocera
Ischnocera
Amblycera
Amblycera
Ischnocera
Ischnocera
BD
BE
BF
BG
BH
BI
Pediculidae
Prodoxidae
Tegeticula yuccasella
Phthiraptera
Columbicola macrourae
C. macrourae
Dennyus carljonesi
Dennyus distinctus
Docophoroides brevis
Naubates prioni
BC
Prodoxidae
Saturniidae
Tortricidae
Rhinotermitidae
R. lucifugus
Lepidoptera
Archips argyrospila
AX
AN
AO
AP
AQ
AR
AS
AT
AU
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
No
No
No
Yes
Yes
Yes
Yes
No
No
Yes
N/A
N/A
N/A
N/A
No
No
No
No
No
No
Yes
Yes
No
No
No
N/A
No
No
No
N/A
N/A
N/A
No
N/A
N/A
No
N/A
N/A
No
Species
Monophyletic monophyly
suspect
Cynipidae
Cynipidae
Apidae
Apidae
Apidae
Formicidae
Formicidae
Trichogrammatidae
Membracidae
Membracidae
Aphididae
Simuliidae
Simuliidae
Agromyzidae
Family
Enchenopa binota
E. binota
Hymenoptera
Andricus coriarius
A. coronatus
Apis cerana
A. cerana
A. cerana
Myrmecocystus mimicus
M. mimicus
Trichogramma deion
AL
AM
AK
Phytomyza ditmani
AI
AJ
Species
Cont’d
AH
Symbol
Table 1.
COI
EF-1a
COI
CytB
CytB
COI
COI
COI
COI
COI
COI
COI
COII
AT-rich region
16S
CytB
CytB
COI
16S
COII
COI⫹tLue⫹COII
COI
ITS2
COI
12S
COI
16S
ITS1
COI⫹tLue⫹COII
Gene locus
6
4
13
3
3
3
4
51
15
17
3
20
8
29
4
3
3
5
3
7
6
7
13
9
8
9
6
10
3
524
350
383
426
426
379
379
844
234
624
472
475
684
337
518
433
433
1,041
475
681
656
372
502
1,218
335
677
474
280
898
No.
No.
pairs
haplotypes base
including
or alleles
gaps
0.55, 0.26 (0.19Ð0.57)
0.77, 0.46 (0.29Ð1.3)
17.0, 10.0 (0.26Ð26.0)
11.4, 5.7 (4.8Ð15.0)
1.7, 0.7 (0.9Ð2.4)
0.53, 0.26 (0.26Ð0.79)
2.0, 0.5 (1.1Ð2.4)
0.5, 0.2 (0.12Ð1.1)
2.2, 1.0 (0.4Ð4.8)
2.8, 1.6 (0.16Ð5.0)
1.4, 0.05 (0.85Ð1.7)
1.0, 0.5 (0.2Ð2.1)
1.8, 1.5 (0.7Ð3.7)
3.4, 2.5 (0.3Ð8.8)
1.3, 0.76 (2.0Ð2.2)
3.5, 1.0 (2.1Ð5.2)
2.5, 0.5 (2.1Ð3.1)
2.9, 2.2 (0.1Ð5.5)
0.57, 0.33 (0.21Ð0.87)
4.0, 2.3 (0.15Ð7.4)
1.9, 1.8 (0.45Ð4.1)
2.2, 0.84 (0.27Ð3.8)
2.8, 2.6 (0.25Ð10.0)
2.6, 0.77 (0.49Ð3.6)
1.5, 0.5 (0.3Ð2.2)
1.5, 1.2 (0.15Ð3.3)
0.8, 0.6 (0.2Ð1.9)
2.2, 1.0 (0.4Ð4.8)
0.3, 0.06 (0.22Ð0.34)
IntraspeciÞc
difference
mean, SD
0.54, 0.25 (0.19Ð1.0)
8.1, 3.3 (7.5Ð9.1)
24.0, 0.8 (23.3Ð25.6)
15.0, 1.7 (12.2Ð17.7)
12.7, 0.4 (12.2Ð13.1)
21.3, 0.43 (20.8Ð22.0)
1.7, 0.8 (1.6Ð1.8)
5.6, 0.2 (5.2Ð6.0)
4.3, 1.0 (2.6Ð6.2)
11.0, 0.1 (9.4Ð13.0)
3.3, 0.58 (2.6Ð4.1)
2.0, 0.3 (1.5Ð2.6)
5.3, 0.7 (3.9Ð5.9)
17.7, 2.2 (13.7Ð25.7)
6.9, 2.3 (2.0Ð7.1)
4.9, 0.6 (3.8Ð6.0)
5.7, 0.14 (5.5Ð5.7)
5.3, 0.5 (4.7Ð5.9)
1.0, 0.46 (0.64Ð1.5)
4.5, 3.0 (0.15Ð7.6)
2.5, 0.88 (2.0Ð4.1)
3.1, 0.44 (2.6Ð3.8)
9.6, 4.3 (3.9Ð20.0)
12.9, 4.2 (8.3Ð17.6)
12.2, 4.9 (7.0Ð17.3)
4.1, 0.67 (2.9Ð5.5)
1.7, 0.7 (0.4Ð2.3)
13.9, 1.0 (11.7Ð15.2)
7.0, 0.38 (6.7Ð7.6)
Divergence compare
with sister species or
clade mean, SD
(range)
Yes
No
Yes
Yes
No
No
Yes
No
Yes
No
No
Yes
No
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
No
No
Yes
Yes
No
No
Intra- and
interspeciÞc%
overlap?
C. theresae
C. theresae
D. distinctus, D. singhi
D. singhi
D. niethamerri, D. simplex
N. heteroprotus,
N. pterodrorni,
P. humanus capitis
T. synthetica
C. fraterna, C. herana,
C. filicis
H. grotei, H. juno,
H. stonei
G. enchrysa
A. goyerana
R. lucifugus grassei
R. virginicus, R. hagani
R. lucifugus grassei
A. aries, A. kollari
A. askewi
A. nuluensis
A. nuluensis
A. nuluensis
M. depilis
M. depilis
T.kaykai, T.sathon
Enchenopa spp.
Enchenopa spp.
C. atlantica, C. ponderosa
S. pandanophilum
S. pandanophilum
Phytomyza n. sp3,
Sister species or clade
Leo et al. (2002)
Johnson et al. (2003)
Johnson et al. (2003)
Page et al. (1998)
Page et al. (1998)
Page et al. (2004)
Page et al. (2004)
Kruse and Sperling
(2001)
Newcomb and
Gleeson (1998)
Rubinoff and Sperling
(2002)
Brown et al. (1994,
1997)
Pellmyr and LeebensMack (1999)
Foster et al. (2004)
Marini and Mantovani
(2002)
Marini and Mantovani
(2002)
Rokas et al. (2003)
Rokas et al. (2003)
Tanaka et al. (2001)
Tanaka et al. (2001)
Tanaka et al. (2001)
Kronauer et al. (2004)
Kronauer et al. (2004)
Stouthamer et al.
(1999)
Favert and Voegtlin
(2004)
Lin and Wood (2002)
Lin and Wood (2002)
Scheffer and
Wiegmann (2000)
Krüger et al. (2000)
Krüger et al. (2000)
Reference
1040
JOURNAL OF ECONOMIC ENTOMOLOGY
Vol. 99, no. 4
August 2006
COGNATO: SPECIES DIAGNOSIS BY USING % DNA DIFFERENCE
1041
Fig. 1. Range of percent mtDNA COI DNA difference for intra- and interspeciÞc pairwise comparisons. Mean difference
is indicated by perpendicular lines on the horizontal bars. Letters to the right of horizontal bars are refered to in the text.
The gray vertical line indicates 2.0% difference which is often used as a limit to species boundaries.
7.0, respectively). This is counterintuitive to the expectation of greater mtDNA differences because the
mitochondrial genome generally has a faster mutation
rate compared with the nuclear genome (Simon et al.
1994). However, these data are skewed by ribosomal
loci (Table 1), which contain hypervariable regions
(Gerbi 1985) and departure from genomic mutation
rate generalities is expected. The two low copy nuclear genes (Yp1 and Ef-1␣) exhibit similar or lower
mean percent sequence divergences compared with
the mitochondrial data. Given the few studies that use
nuclear loci, there is not enough evidence to assess
the possibility of a standard percent sequence divergence based on nuclear DNA (nDNA). Comparison of
mtDNA COI divergences allows for the most informed
understanding of the patterns of divergence among
species given the large sample size.
Summary of these data showed that intra- and interspeciÞc divergences varied widely among insects
(Fig. 1). Lice exhibited an extreme case of hypervariation (26.0%) (Fig. 1, BE), and hypotheses for this
phenomenon are discussed in Page et al. (1998). Percent intra- and interspeciÞc divergence ranged between 0.10 and 5.5% and 0.15 and 17.6%, respectively,
with the exclusion of data, which occurred 2 SDs from
the means (Fig. 1, F, H, BE, and BH). Other mtDNA
loci exhibited similar patterns of percent divergences
except that mtDNA ribosomal loci often had smaller
divergences (Table 1).
In part, this locus (⬇658 bp, beginning at 5⬘) is
currently promoted to serve as the DNA “barcode” for
animals (Hebert et al. 2003b). A survey of congeneric
comparisons found a mean of 11.3 ⫾5.3% sequence
difference between species pairs, but sister-species
comparisons were not speciÞed (Hebert et al. 2003b).
This study Þnds a mean of 7.4 ⫾5.9% sequence difference among sister-species comparisons. The means
found by each study are similar given the overlap of
standard deviations. An important difference, however, is with the comparison between intra- and interspeciÞc ranges of sequence divergence. Hebert et
al. (2003b) did not calculate intraspeciÞc divergence
but cites that it would likely range between 1.0 and
2.0% (Avise 2000), although Avise (2000) did not explicitly make this claim. This range of divergence was
well below the interspeciÞc range of divergence, and
this gap between intra- and interspecies divergences
was taken as support for the utility of mtDNA COI to
diagnose species (Hebert et al. 2003b). For this study,
intraspeciÞc divergence ranged beyond 2.0% for most
species and often overlapped with the interspeciÞc
divergence. Breaching the 2% boundary is not surprising because this pattern is observed for many other
animals (vertebrates). Avise (2000) (see Þgs. 5.24,
5.26, and 5.27) demonstrated that ⬎2% divergence
between intraspeciÞc phylogroups occurred as frequent or more common than comparisons of ⬍2%.
Monophyletic groups with larger than expected sequence divergence may represent cryptic species;
nonetheless, further investigation is necessary to determine the validity of their species status (DeSalle et
al. 2005).
1042
JOURNAL OF ECONOMIC ENTOMOLOGY
Intra- and interspeciÞc ranges for individual cases
overlapped 10 of 26 times (Fig. 1). This implies that
reliance on a standard percent sequence divergence
would fail to correctly diagnose species for 39% of
the cases. A failure rate of 45% was found considering all 62 cases (Table 1). The overlap of intra- and
interspeciÞc divergences is probably due to the common occurrence of paraphyly and polyphyly among
closely related species (Funk and Omland 2003). Seventeen of the comparisons presented were not monophyletic and of these, 14 exhibited overlap of intraand interspeciÞc divergences. In total, nonmonophyly
and overlap of intra- and interspeciÞc divergences
were signiÞcantly associated (␹2 ⫽ 16.67, P ⫽ 0.001).
Causes of nonmonophyly may be explained by several
biological and evolutionary reasons (e.g., hybridization and lineage sorting), as reviewed in Funk and
Omland (2003). Perhaps for economically important
insects, nonmonophyly can most often be explained
by poor taxonomy, which can result in the recognition
of too many species. Traditionally, small differences
in biology or behavior are used to recognize economically important species before consideration of phylogeny (e.g., Bemesia sp., Bellows et al. 1994, Brown
et al. 1995). Given a phylogeny, these species could
render related species para- or polyphyletic. No matter the reason for nonmonophyly, diagnosis of
species based on a standard percent sequence divergence is compromised because of the likely overlap
intra- and interspeciÞc divergences. Revising species
taxonomy based on monophyly may help decrease
the cases of overlap intra- and interspeciÞc divergences. However, taxonomic revision cannot reconcile “real” paraphyly and polyphyly caused by biological or evolutionary reasons; thus, some economically
important species may only be diagnosed by biological
features.
The reviewed studies suggest that a standard percent sequence divergence does not predict species
boundaries among economically important insects.
Noneconomically important insects, the majority of
insect species, are understudied and undescribed
compared with economically important species. Several studies demonstrate that the inclusion of DNA
data in phylogenetic analysis of individuals allows for
a quicker determination of monophyletic groups and
recognition of potential species (Monaghan et al. 2005,
Smith et al. 2005, Caesar et al. 2006). But application
of a standard divergence to delimit species would be
still be problematic. A standard divergence would
need to be established for each gene and taxon; however, accommodation of the variance would decrease
the predictability of species boundaries. For example,
⬎1.5% intraspeciÞc sequence divergence for mtDNA
COI will likely delimit species of bark beetles (Scolytinae) (Table 1). However, some species have a
boundary near 5.0%, and other exceptions greatly expand the upper boundary, especially for species in
which monophyly is suspect (e.g., Dendroctonus valens
LeConte). Hypothetically, using 16% standard percent mtDNA COI difference for bark beetles based
on the intraspeciÞc difference observed for D. valens
Vol. 99, no. 4
would underestimate species diversity. However, taxonomic revision of suspect species may be justiÞed
and would help to reduce the variance of sequence
divergence.
Clearly, the use of DNA data to diagnose species
and revise taxonomic problems will continue (Savolainen et al. 2005). But the current popularized mode
of phenetic analysis of DNA variation to identify species (Hebert et al. 2004) disregards potential evolutionary information inherent in DNA data. Percent
sequence similarity does not reveal homologies and
homoplasies; thus, evolutionary relationships are obscured. It is necessary that the identiÞcation analysis
incorporates evolutionary information because monophyly of many economically important insect species
taxa is questionable. Therefore, phylogenetic analysis
via parsimony or likelihood optimization of DNA
characters that includes a thorough sampling of individuals from the focal species and potential sisterspecies should be performed (Foster et al. 2004,
DeSalle et al. 2005). The resulting tree provides a phylogenetic hypothesis where monophyly of the focal
species is tested and alternative relationships are suggested. Monophyletic taxa are recognized. Character
states are then mapped on the tree and association of
nucleotide and amino acid replacements, nucleotide
insertions, secondary structure, and/or any other organismal characters could be used as evidence for
species status or diagnosis of monophyletic groups
(Rubinoff and Powell 2004, Cognato et al. 2005a).
Only after phylogenetic analysis and inspection of
associated character states with clades of taxa should
percent sequence divergence be considered within
the context of branch lengths (Nadler 2002). Branch
lengths of a tree, measured as nucleotide change,
could help rank new taxa (species or subspecies)
through comparisons of divergence observed in
sister-species and/or the mean divergence observed
in closely related species which are corroborated by
other morphological and/or biological characters
(Kerdelhué et al. 2002, Jordal et al. 2004, Cognato et
al. 2005a). However, this use relies on a constant nucleotide substitution rate and a uniformed species
concept. These assumptions vary widely among taxa
(Graur and Li 2000, Hey et al. 2003), and exceptions
to a standard expected branch length (divergence)
since speciation are likely. Therefore, reliance on a
standard percent sequence difference alone, for
species diagnosis and assessment of species limits, is
not recommended. When DNA data are applied, species boundaries are best predicted through phylogenetic analysis, consideration of additional biological
evidence, and evaluation with taxonomic expertise
(Lipscomb et al. 2003, Wheeler 2004, DeSalle et al.
2005).
Acknowledgments
I thank John Trumble for the invitation to publish this
review. April D. Harlin-Cognato, Dan Rubinoff, Felix Sperling members of the Holistic Insect Systematic Laboratory
(http://hisl.tamu.edu), and Texas A&M University system-
August 2006
COGNATO: SPECIES DIAGNOSIS BY USING % DNA DIFFERENCE
atic discussion group provided helpful comments on early
manuscript versions. Suggestions from an anonymous reviewer greatly improved this publication. This review was
supported by the National Research Initiative of the USDAÐ
Cooperative State Research, Education and Extension Service, grant 2003-35302Ð13381 and NSF-PEET (DEB0328920).
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Received 2 April 2005; accepted 10 March 2006.