Electronic Supplementary Material for:
Morphological differentiation in the damselfish Abudefduf saxatilis along the Mexican Atlantic
coast is associated with environmental factors and high connectivity.
Evolutionary Biology
Victor J. Piñeros, Oscar Rios-Cardenas, Carla Gutiérrez-Rodríguez, Luis Mendoza-Cuenca.
Corresponding author: Oscar Rios-Cardenas; Red de Biología Evolutiva, Instituto de Ecología
A.C., e-mail address: oscar.rios@inecol.mx.
Materials and Methods
Environmental variables
In accordance with our hypotheses and based on previous studies, we looked for databases that
contained marine environmental variables that are known to affect fish morphology (Booth &
Hixon 1999; Aguilar-Medrano et al. 2013; Fulton et al. 2013). We found data to test our
hypothesis in two main sources: National Oceanic and Atmospheric Administration, NOAA
(http://www.noaa.gov/ocean.html) and Atlas Climático Digital de México
(http://atlasclimatico.unam.mx/atlas/mar/). Although NOAA is the most comprehensive
database, there are no studies that support a direct effect of most of its available variables (i.e.
salinity, dissolved oxygen, silicate, phosphate, nitrate) on the morphological traits included in
our hypotheses. Therefore, we obtained environmental data from the Atlas Climático Digital de
México maintained by the Centro de Ciencias de la Atmósfera from the Universidad Nacional
Autónoma de México (UNAM), which includes data of all our sampling localities (i.e. reefs) at
a fine scale. Environmental variables included: 1) superficial water temperature (ºC); 2) water
chlorophyll-a concentration (mg/m3), as an estimator of primary productivity; and 3)
geostrophic velocity (cm/s), which is a measure of sea-surface flow that takes into account wind
force, earth rotation, tide force and water movement from high pressure to a low-pressure areas
(Lalli & Parson 2006). Sea-surface temperature and water chlorophyll-a concentration have a
spatial resolution of 4 km and are taken by the Aqua MODIS (Moderate-Resolution Imaging
Spectroradiometer) 4-micron satellite sensor of the NASA, for the period between (2003-2012).
Geostrophic velocity data, from the database AVISO (Archiving, Validation, and Interpretation
of Satellite Oceanographic data), has a spatial resolution of 1/3º and includes a period of 18
years (1993-2012).
Primers and PCR conditions for mtDNA amplification
We used primers Cytb-09H (Song 1994) and Cytb-07L (Taberlet et al. 1992) to amplify the cytb; and primers K and G (Lee et al. 1995), as well as internal primers designed for this study
(RC-F: 5'-GTACATATATGTAATATCACCA-3’ and RC-R: 5'GTTTTGTGGATGCATTATTATAAT-3’), to amplify the control region (CR). Cyt-b and CR
PCR reactions consisted of a total volume of 15 µL with 1.5 µL of 50-100 ng DNA template, 3
µL of 5X buffer, 0.3 µL of 5 U/µL GoTaq polymerase (Promega), 0.38 µL of 8 mM dNTPs, 0.9
µL of 25 mM MgCl2, 0.3 µL of 10 mg/mL BSA and 0.3 µL of 10 µM for each oligonucleotide.
The thermal cycler profiles for cyt-b consisted of an initial denaturation step at 95 °C for 60 s,
followed by 35 cycles of 95 °C for 30 s, 58 °C for 45 s and 72 °C for 60 s with a final extension
at 72 °C for 10 min. The CR profile included an initial denaturation step at 93 °C for 5 min,
followed by 35 cycles of 93 °C for 60 s, 55 °C (for K and G primers) and 51 °C (for RC-F and
RC-R primers) for 60 s and 72 °C for 2 min with a final extension at 72 °C for 10 min.
PCR conditions for microsatellites amplification
For the four microsatellites loci previously developed in Abudefduf luridus (Carvalho et al.
2000), the total volume of multiplex reactions was 6 µL, with 3 µL of PCR Multiplex Master
Mix (Qiagen), 0.6 µL of 2 µM for each of the primers and 1 µL of 50-100 ng DNA. Thermal
cycling conditions consisted of an initial denaturation step at 95 °C for 15 min, followed by 30
cycles of 94 °C for 30 s, 52 °C for 90 s and 72 °C for 60 s with a final extension at 60 °C for 30
min.
Statistical power of the microsatellite markers to detect genetic differentiation
We determined the statistical power of the microsatellites markers to detect significant genetic
differentiation between the Gulf of Mexico and the Mexican Caribbean using the computer
software POWSIM 4.1 (Ryman & Palm 2006). We simulated the sampling of 200 individuals
into two populations based on a random drawing of alleles that occurred at the observed overall
frequency in each marine region. We performed the simulations using different FST values and
1000 runs for each value. We determined the statistical power using the proportion of
simulations for which Fisher’s exact and Chi-square tests showed a significant deviation from 0
(significant genetic differentiation; Ryman et al. 2006).
References
Aguilar-Medrano, R., Frédérich, B., Balart, E.F. & De Luna, E. (2013). Diversification of the
pectoral fin shape in damselfishes (Perciformes, Pomacentridae) of the Eastern Pacific.
Zoomorphology, 132, 197-213.
Booth, D.J. & Hixon, M.A. (1999). Food ration and condition affect early survival of the coral
reef damselfish, Stegastes partitus. Oecologia, 121, 364-368.
Carvalho, M.C., Streiff, R., Guillemaud, T., Afonso, P., Santos, R.S. & Cancela, M.L. (2000).
Isolation and characterization of polymorphic microsatellite markers in Abudefduf
luridus (Pisces: Pomacentridae). Molecular Ecology, 9, 993-1011.
Fulton, C.J., Binning, S.A., Wainwright, P.C. & Bellwood, D.R. (2013). Wave-induced abiotic
stress shapes phenotypic diversity in a coral reef fish across a geographical cline. Coral
Reefs, 32, 685-689.
Lalli, C.M. & Parson, T.R. (2006). Biological Oceanography: An introduction. Oxford: Elsevier
Butterworth-Heinemann.20.
Lee, W., Conroy, J., Howell, W.H. & Kocher T.D. (1995). Structure and evolution of teleost
mitochondrial control regions. Journal of Molecular Evolution, 41, 54-66.
Ryman, N. & Palm, S. (2006). POWSIM: a computer program for assessing statistical power
when testing for genetic differentiation. Molecular Ecology Notes, 6, 600-602.
Ryman, N., Palm, S., André, C., Carvalho, G.R., Dahlgren, T.G., Jorde, P.E., Laikre, L.,
Larsson, L.C., Palmé, A. & Ruzzante, D.E. (2006). Power for detecting genetic
divergence: differences between statistical methods and marker loci. Molecular Ecology,
15, 2031-2045.
Song, C. (1994). Molecular evolution of the cytochrome b gene among Percid fishes. PhD
thesis. Champaing. University of Illinois.
Taberlet, P., Meyer, A. & Bouvet, J. (1992). Unusual mitochondrial DNA polymorphism in two
local populations of blue tit (Parus caerulens). Molecular Ecology, 1, 27-36.
Table S1 Morphometric pairwise comparisons using Goodall’s F with 2,500 bootstrap replicates and BGPCA with 1,000 bootstrap replicates of body
and head measurements of Abudefduf saxatilis collected on seven reefs. P values shown in bold indicate significant differences. For site abbreviations
refer to Table 1.
Body Shape
Head Shape
Goodall’s
Reef
LI vs TX
LI vs VE
LI vs AL
LI vs CI
LI vs XC
LI vs CYI
TX vs VE
TX vs AL
TX vs CI
TX vs XC
TX vs CYI
VE vs AL
VE vs CI
VE vs XC
VE vs CYI
AL vs CI
AL vs XC
AL vs CYI
CI vs XC
CI vs CYI
XC vs CYI
F
0.51
1.14
0.44
1.99
1.38
1.61
3.1
1.89
4.69
3.17
4.5
1.08
2.15
1.81
1.12
2.13
1.77
1.76
1.07
0.89
1.19
d.f.
18, 288
18, 216
18, 378
18, 216
18, 162
18, 216
18, 360
18, 522
18, 360
18, 306
18, 360
18, 450
18, 288
18, 234
18, 288
18, 450
18, 396
18, 450
18, 234
18, 288
18, 234
P
0.9523
0.3135
0.9782
0.0112
0.1464
0.0586
< 0.00001
0.0149
< 0.00001
< 0.00001
< 0.00001
0.3645
0.0048
0.0255
0.3272
0.0044
0.0268
0.0279
0.3794
0.5944
0.2741
Bootstrap
Procrustes
F
P
0.51 0.8164
1.14 0.3228
0.44 0.8812
1.99 0.0896
1.38 0.246
1.61 0.1588
3.1 0.0176
1.89 0.0808
4.69 0.0004
3.17 0.0084
4.5 0.0016
1.08 0.372
2.15 0.0556
1.81 0.124
1.12 0.3204
2.13 0.0456
1.77 0.0912
1.76 0.1012
1.07 0.3572
0.89 0.45
1.19 0.2872
BGPCA Goodall’s
P
0.82
0.2
0.53
0.06
0.09
0.07
0.01
0.02
0.01
0.01
0.01
0.39
0.03
0.05
0.4
0.04
0.05
0.15
0.53
0.77
0.3
F
0.35
3.16
2.25
2.17
3.13
2.93
5.2
4.13
3.91
4.39
4.59
2.21
2.78
2.02
0.97
2.11
2.06
1.3
1.33
1.18
1.18
d.f.
26, 364
26, 312
26, 520
26, 286
26, 208
26, 338
26, 520
26, 728
26, 494
26, 416
26, 546
26, 676
26, 442
26, 364
26, 494
26, 650
26, 572
26, 702
26, 338
26, 468
26, 390
P
0.999
< 0.00001
0.000449
0.0012
< 0.00001
< 0.00001
< 0.00001
< 0.00001
< 0.00001
< 0.00001
< 0.00001
0.000554
< 0.00001
0.0026
0.5102
0.001145
0.00169
0.1432
0.13351
0.24613
0.2529
Bootstrap
Procrustes
F
P
0.35 0.928
3.18 0.028
2.29 0.0484
2.15 0.086
3.14 0.0372
2.95 0.0208
5.23 0.0008
4.15 0.0004
3.95 0.004
4.44 0.0032
4.62 0.0012
2.2 0.045
2.74 0.0316
2.04 0.0724
1.01 0.416
2.05 0.0596
2.08 0.0672
1.35 0.2216
1.34 0.244
1.12 0.3452
1.14 0.3416
BGPCA
P
0.97
0.03
0.12
0.05
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.05
0.01
0.07
0.063
0.03
0.11
0.41
0.17
0.25
0.44
Table S2 Summary of microsatellite diversity of Abudefduf saxatilis for each of the 12 loci at each reef and reef system. We show number of analyzed
individuals (N), number of alleles (AN), allelic richness (AR), private allelic richness (AP) observed (HO) and expected (HE) heterozygosity, inbreeding
coefficient (FIS), and frequency of null alleles (FNA) for loci that deviated from HWE due to heterozygote deficits. Abbreviations correspond to those in
Table 1.
Reef/Reef
system
LI
TX
VE
AL
N
AN
AR
AP
HO
HE
FIS
FNA
N
AN
AR
AP
HO
HE
FIS
FNA
N
AN
AR
AP
HO
HE
FIS
N
AN
AR
Absa1
Absa10
Absa17 Absa18
Absa23 Absa30 Absa38 Absa41 2al10 2al13 2al2
2al24
29
19
13.5
2.40
0.79
0.93
0.16
16
12
11.1
0.76
0.94
0.88
-0.03
12
10
10.0
1.00
1
0.85
-0.14
19
14
11.1
29
19
12.9
0.86
0.97
0.92
-0.04
16
13
11.4
0.96
0.81
0.88
0.11
12
13
13.0
1.07
1
0.9
-0.07
19
17
14.0
29
16
11.3
0.21
0.55
0.89
0.4
0.18
16
13
11.6
0.05
0.69
0.9
0.26
12
13
13.0
1.59
0.75
0.91
0.21
19
11
10.1
29
21
14.4
0.53
0.9
0.93
0.06
16
19
15.9
0.12
1
0.93
-0.05
12
18
18.0
0.10
1
0.93
-0.04
19
19
14.8
29
4
2.8
0.00
0.41
0.4
-0.02
16
4
3.5
0.90
0.63
0.53
-0.15
12
3
3.0
0.06
0.42
0.54
0.27
19
2
2.0
29
17
11.7
0.42
0.86
0.91
0.07
16
14
12.3
1.16
0.69
0.89
0.26
0.11
12
11
11.0
0.03
1
0.88
-0.09
19
15
12.7
29
13
10.3
0.20
0.79
0.89
0.12
16
14
12.9
0.63
0.94
0.92
0.01
12
12
12.0
0.24
0.83
0.88
0.1
19
14
11.3
29
13
9.9
0.45
0.9
0.88
0
16
13
11.6
0.90
0.81
0.89
0.12
12
12
12.0
0.00
1
0.9
-0.07
19
14
11.3
29
16
11.6
0.12
0.97
0.9
-0.05
16
13
11.4
0.32
0.94
0.89
-0.02
12
12
12.0
0.08
1
0.89
-0.08
19
14
11.9
29
4
3.7
0.00
0.66
0.64
0
16
5
4.5
0.75
0.38
0.46
0.21
12
4
4.0
0.00
0.67
0.65
0.02
19
5
4.6
29
18
13.4
0.71
0.93
0.93
0.01
16
16
13.5
0.53
0.88
0.9
0.06
12
10
10.0
0.09
0.83
0.86
0.08
19
15
12.4
29
13
10.4
0.42
0.48
0.89
0.47
0.21
16
13
11.4
0.87
0.75
0.89
0.19
12
13
13.0
1.05
0.92
0.89
0.01
19
13
9.9
CYI
CI
XC
TRS
VRS
AP
HO
HE
FIS
FNA
N
AN
AR
AP
HO
HE
FIS
N
AN
AR
AP
HO
HE
FIS
N
AN
AR
AP
HO
HE
FIS
FNA
N
AN
AR
AP
HO
HE
FIS
FNA
N
0.89
0.89
0.86
-0.02
13
13
12.6
0.11
1
0.9
-0.07
18
13
11.1
0.02
0.83
0.89
0.09
15
13
11.4
0.03
0.93
0.88
-0.03
45
21
19.0
4.44
0.84
0.92
0.10
31
0.80
0.95
0.93
0.01
13
11
10.7
0.93
0.92
0.88
-0.01
18
17
14.4
0.15
1
0.92
-0.05
15
14
12.7
0.39
1
0.9
-0.07
45
20
18.4
2.46
0.91
0.92
0.02
31
0.70
0.68
0.89
0.26
13
9
8.8
0.00
0.92
0.87
-0.03
18
10
9.6
0.00
0.83
0.88
0.08
15
10
9.3
0.07
0.53
0.86
0.41
0.17
45
16
15.2
0.32
0.60
0.91
0.35
0.16
31
0.21
0.95
0.91
-0.01
13
12
11.6
0.99
1
0.89
-0.08
18
15
12.7
0.68
0.83
0.9
0.11
15
14
12.6
0.07
0.87
0.89
0.06
45
20
17.6
1.72
0.80
0.92
0.14
31
0.05
0.95
0.93
0
13
18
17.1
1.04
0.85
0.93
0.13
18
19
14.9
0.75
0.94
0.92
0
15
18
15.8
0.06
0.8
0.93
0.17
45
24
21.9
0.89
0.93
0.94
0.02
31
1.42
0.58
0.89
0.37
0.16
13
10
9.7
0.54
0.62
0.87
0.33
18
10
9.6
0.01
0.72
0.89
0.21
15
10
9.0
0.00
0.73
0.83
0.15
45
16
15.1
1.30
0.84
0.91
0.08
31
1.47
0.89
0.87
-0.01
13
9
8.8
0.12
0.92
0.83
-0.07
18
15
12.3
1.14
0.94
0.89
-0.04
15
9
8.6
1.07
1
0.85
-0.14
45
15
14.0
1.67
0.87
0.90
0.04
31
0.11
0.79
0.89
0.14
13
11
10.8
0.92
0.92
0.89
0
18
13
11.6
0.90
0.94
0.9
-0.02
15
15
13.6
1.54
1
0.92
-0.06
45
17
15.5
0.45
0.96
0.91
-0.04
31
0.63
0.68
0.7
0.05
13
3
3.0
0.00
0.46
0.6
0.27
18
4
3.7
0.00
0.67
0.6
-0.08
15
3
3.0
0.00
0.4
0.5
0.23
45
5
4.7
0.69
0.56
0.60
0.08
31
0.80
0.95
0.91
-0.02
13
13
12.5
1.02
0.92
0.89
0
18
16
13.1
0.88
0.94
0.91
-0.01
15
13
11.7
0.16
0.87
0.87
0.03
45
21
19.3
1.23
0.91
0.93
0.03
31
0.24
0.74
0.85
0.16
13
14
13.5
2.11
1
0.91
-0.06
18
12
10.8
0.01
0.78
0.89
0.15
15
9
8.4
0.03
0.73
0.82
0.14
45
17
15.1
1.38
0.58
0.90
0.37
0.17
31
0.00
0.37
0.36
0.01
13
4
3.8
0.96
0.46
0.54
0.19
18
4
3.6
0.01
0.39
0.44
0.14
15
4
3.6
0.21
0.67
0.54
-0.2
45
6
4.8
0.94
0.49
0.45
0.07
31
MRS
AN
AR
AP
HO
HE
FIS
FNA
N
AN
AR
AP
HO
HE
16
16.0
2.01
0.94
0.87
-0.06
46
15
14.1
0.08
0.91
0.90
FIS
0.00
FNA -
19
19.0
2.54
0.97
0.93
-0.03
46
20
18.3
0.74
0.98
0.93
16
16.0
1.72
0.71
0.92
0.24
0.11
46
13
12.4
0.02
0.76
0.89
16
16.0
0.55
0.97
0.91
-0.05
46
19
17.1
1.46
0.89
0.92
22
22.0
0.55
0.97
0.94
-0.02
46
26
23.9
1.74
0.87
0.95
16
16.0
2.73
0.68
0.89
0.26
46
14
12.6
0.23
0.70
0.88
16
16.0
2.38
0.94
0.89
-0.04
46
18
15.9
2.31
0.96
0.88
16
5.0
0.33
0.87
0.91
0.05
46
19
17.0
2.66
0.96
0.92
5
18.0
1.01
0.68
0.68
0.02
46
4
3.7
0.00
0.52
0.57
18
16.0
1.40
0.90
0.91
0.02
46
20
18.3
1.67
0.91
0.92
16
16.0
1.55
0.81
0.89
0.11
46
17
15.4
2.54
0.83
0.89
3
3.0
0.03
0.39
0.44
0.14
46
6
5.2
1.18
0.50
0.51
-0.04
-
0.16
0.07
0.04
-
0.09
-
0.22
0.1
-0.07
-
-0.03
-
0.10
-
0.02
-
0.09
-
0.04
-
FIS values in bold indicate significant deviations from HWE after Bonferroni corrections (α’ = 0.002 for reef; α’ = 0.004 for reef system).
Fig. S1 Anatomical landmarks used in the geometric morphometric analysis of a Body shape:
(1) rostrum, (2) rostral insertion of the dorsal fin, (3) rostral insertion of the pectoral fin, (4)
caudal insertion of the pectoral fin, (5) rostral insertion of the pelvic fin, (6) rostral insertion of
the anal fin, (7) limit between the soft and spiny dorsal fin, (8) caudal insertion of the dorsal fin,
(9) dorsal insertion of the caudal fin, (10) caudal insertion of the anal fin, (11) ventral insertion
of the caudal fin; b Head shape: (1) rostral extremity of the premaxilla, (2) nostril, (3-6) ventral,
rostral, dorsal and caudal margins of the eye, (7) center of the eye, (8) dorsal tip of the
preopercular, (9) dorsal edge of the operculum, (10) ventral edge of the operculum, (11)
insertion of the operculum along the body profile, (12) posteroventral corner of the operculum,
(13-14) rostral and caudal edge of the lower jaw teeth, (15) caudal extremity of the premaxilla;
and c standard length (sl) of Abudefduf saxatilis.
Fig. S2 Statistical power to detect significant population genetic differentiation as a function of
FST. Statistical power was determined as the proportion simulations for which a Chi-square and
b Fisher’s exact tests showed a significant deviation from 0 (significant genetic differentiation).
FST values as low as 0.005 can be detected with both tests at probabilities of 99% with the
number of loci and sample sizes that we used.