1
Supporting information
2
Table S1. Squalius valentinus sampling locations, number of specimens examined and
3
GenBank accesion numbers.
4
Table S2. Squalius valentinus localities used in MaxEnt analysis.
5
Table S3. Genetic diversity of populations in each river, major basins, and the complete
6
dataset based on mitochondrial cytochrome b and RAG1 genes. n = number of samples
7
analysed; h = number of haplotypes;  = nucleotide diversity; HD = haplotype or allele
8
diversity; S = number of polymorphic sites; k = number of pairwise differences. (SD)
9
Standard deviations.
10
Table S4. Pairwise ST comparisons (above diagonal) among Squalius valentinus
11
populations based on cytochrome b gene. Bold numbers represent significant values at
12
P  0.05 after Bonferroni’s correction.
13
Table S5. Demographic characteristics of each population of rivers and major basins
14
and the complete dataset for mitochondrial cytchrome b gene. R2 (Ramos-Osins and
15
Rozas test); Fs (Fu’s Fs test); D (Tajima’s D test); r = raggedness index. P-values in
16
brackets, bold numbers correspond to significant values in neutrality tests. Final column
17
shows time since population expansion at evolutionary rate of 0.8% per lineage per
18
million years (Perea et al., 2010).
19
Table S6. Long-term (historical) migration analysis of Squalius valentinus populations
20
based on cytochrome b gene. M (mutation-scaled immigration rate from migrating to
21
receiving population), m (effective immigration rate, m=M/μ), Θ (mutation-scaled
22
effective population size, Θ=2Neμ), n = number of individuals. μ= 1.74 x 10-5 per
23
nucleotide per generation.
1
24
Table S7. Sum of Bayesian posterior probabilities of regression models that include a
25
given factor. The highest probability value is represented in bold.
26
Table S8. Sum of Bayesian posterior probabilities of regression models that include a
27
given factor when only the five variables with the highest Bayesian probability values
28
of temperature and precipitation are analysed. Bold value indicates factor with the
29
highest probability.
30
Fig. S1. Historical demography of Squalius valentinus populations based on Mismatch
31
distributions (A) and Extended Bayesian Skyline plot (EBSP) of effective population
32
size (Ne) over time (B). Mismatch analysis shows distributions observed (dotted lines)
33
and expected (continuous line) under an expansion model. EBSP: solid line indicates
34
the mean posterior Ne estimate, grey lines represent the 95% HPD credibility intervals.
35
X-axis shows units of time in thousands of years ago (kya). Y-axis shows estimated Ne
36
in hundreds of thousands x generation time.
37
Fig. S2. Mismatch distributions observed (dotted lines) and expected under an
38
expansion model (continuous line) for individual Squalius valentinus populations (A)
39
and for the Júcar basin (B).
40
Figure S3. Historical demography of individual Squalius valentinus populations based
41
on Extended Bayesian Skyline plot (EBSP) of effective population size (Ne) over time.
42
The solid line indicates the mean posterior Ne estimate, grey lines represent the 95%
43
HPD credibility intervals. X-axis shows units of time in thousands of years ago (kya).
44
Y-axis shows estimated Ne in hundreds of thousands x generation time.
45
2
46
Table S1. Squalius valentinus sampling locations, number of specimens examined and GenBank Accesion numbers. In Genbank Accesion
47
numbers column all haplotypes and alleles found in each locality, with its frequency within brackets, are included.
Population
Locality
Utm coordinates
(Basin)
Mijares 1
Mijares R, Los Cantos, Teruel
30T 703784 4443460
Number of
Population
GenBank Accesion numbers (Haplotype or
individuals
label
Allele Frequency)
5 (MT-CYTB)
MIJ
MT-CYTB: KR871722 (1), KR871738 (4)
(Mijares)
5 (RAG1)
Mijares 2
Mijares R, Espadilla, Teruel
30T 725684 4434231
1 (MT-CYTB)
RAG1: KR871769 (8), KR871773 (2)
MIJ
(Mijares)
MT-CYTB: KR871722 (1) RAG1: KR871769
(1), KR871773 (1)
1 (RAG1)
Mijares 3
Mijares R, Olba, Teruel
30T 701228 4445227
19 (MT-CYTB)
MIJ
MT-CYTB: KR871722 (7), KR871738 (12)
(Mijares)
14 (RAG1)
RAG1: KR871769 (18), KR871773 (9),
KR871775 (1)
Albentosa
Albentosa R, Albentosa, Teruel
30 T 689749 4441736
26 (MT-CYTB)
ALBE
MT-CYTB: KR871722 (5), KR871738 (21)
(Mijares)
24 (RAG1)
3
RAG1: KR871769 (35), KR871773 (4),
KR871779 (1), KR871780 (1), KR871781 (1),
KR871782 (2), KR871783 (4)
Carbo (Mijares)
Carbo R, Tributary of
30T 719791 4453224
5 (MT-CYTB)
CAR
Villahermosa R. Villahermosa del
MT-CYTB: KR871722 (1), KR871738 (3),
KR871739 (1)
5 (RAG1)
Río, Castellón
RAG1: KR871769 (7), KR871773 (1),
KR871775 (2)
Villahermosa
Villahermosa R, Cedramán,
(Mijares)
Castellón
30T 0722795 4449102
36 (MT-CYTB)
VIL
31 (RAG1)
MT-CYTB: KR871722 (33), KR871738 (3)
RAG1: KR871769 (48), KR871773 (11),
KR871794 (1), KR871795 (2)
Turia 1 (Turia)
Turia R, Villamarchante, Valencia
30S 704229 4382466
5 (MT-CYTB)
TUR
MT-CYTB: KR871721 (1), KR871722 (3),
KR871745 (1)
5 (RAG1)
RAG1: KR871769 (6), KR871773 (4)
Turia 2 (Turia)
Turia R, Gestalgar, Valencia
30S 686112 4385961
5 (MT-CYTB)
TUR
MT-CYTB: KR871722 (1), KR871731 (1),
KR871748 (3)
5 (RAG1)
4
RAG1: KR871769 (9), KR871773 (1)
Turia 3 (Turia)
Turia R, Calles, Valencia
30S 673683 4399244
5 (MT-CYTB)
TUR
MT-CYTB: KR871738 (2), KR871745 (2),
KR871746 (1)
5 (RAG1)
RAG1: KR871769 (5), KR871773 (3),
KR871778 (2)
Turia 4 (Turia)
Turia R, Chulilla, Valencia
30S 680900 4391748
20 (MT-CYTB)
TUR
13 (RAG1)
Tuéjar 1 (Turia)
Tuéjar R (Source), Valencia,
30S 667961 4403291
5 (MT-CYTB)
RAG1: KR871769 (20), KR871773 (6)
TUE
5 (RAG1)
Tuéjar 2 (Turia)
Tuéjar R, Calles,Valencia
30 S 673625 4399753
23 (MT-CYTB)
MT-CYTB: KR871722 (13), KR871748 (7)
MT-CYTB: KR871745 (3), KR871746 (2)
RAG1: KR871769 (10)
TUE
MT-CYTB: KR871722 (15), KR871738 (1),
KR871748 (5), KR871758 (1), KR871759 (1),
22 (RAG1)
KR871760 (2)
RAG1: KR871769 (32), KR871773 (12)
5
Júcar 1 (Júcar)
Júcar R (Rambla Pampanera),
30S 677574 4345586
5 (MT-CYTB)
JUC
Cortés de Pallás, Valencia
MT-CYTB: KR871722 (3), KR871729 (1),
KR871730 (1)
7 (RAG1)
RAG1: KR871769 (13), KR871773 (1)
Júcar 2 (Júcar)
Júcar R, Millares, Valencia
30S 692227 4345386
5 (MT-CYTB)
JUC
MT-CYTB: KR871722 (3), KR871732 (1),
KR871733 (1)
4 (RAG1)
RAG1: KR871769 (8)
Júcar 3 (Júcar)
Júcar R (irrigation pond), Antela,
30S 708209 4328302
5 (MT-CYTB)
JUC
Valencia,
MT-CYTB: KR871740 (2), KR871741 (2),
KR871742 (1)
5 (RAG1)
RAG1: KR871769 (10)
Albaida (Júcar)
Albaida R, Genovés, Valencia
30S 717852 4316704
4 (MT-CYTB)
ALBA
4 (RAG1)
Barranco del
Barranco del agua, Tributary of
agua (Júcar)
Júcar basin, Jarafuel, Valencia
30S 664334 4334018
16 (MT-CYTB)
7 (RAG1)
6
MT-CYTB: KR871722 (2), KR871748 (2)
RAG1: KR871769 (7), KR871773 (1)
AGUA
MT-CYTB: KR871722 (16)
RAG1: KR871769 (12), KR871785 (2)
Cabriel 1
Cabriel R, Requena, Valencia
30S 663373 4372481
7 (MT-CYTB)
CAB
MT-CYTB: KR871722 (7)
(Júcar)
7 (RAG1)
Cabriel 2
Cabriel R (Rambla Albosa),
(Júcar)
Venta del Moro, Valencia
Cabriel 3
RAG1: KR871769 (14)
30S 640897 4371913
2 (MT-CYTB)
CAB
MT-CYTB: KR871720 (1), KR871722 (1)
Cabriel R Vadicañas, Cuenca.
30S 627085 4367169
3 (MT-CYTB)
CAB
MT-CYTB: KR871719 (2), KR871722 (1)
Grande R, Quesa, Valencia
30S 0695064 4333065
40 (MT-CYTB)
GRA
MT-CYTB: KR871722 (16), KR871735 (3),
(Júcar)
Grande (Júcar)
KR871736 (1), KR871437 (1), KR871748 (2),
25 (RAG1)
KR871757 (17)
RAG1: KR871769 (45), KR871773 (1),
KR871775 (2), KR871793 (2)
Magro 1 (Júcar)
Magro R, Montroy, Valencia
30S 705579 4357255
7 (MT-CYTB)
MAG
MT-CYTB: KR871722 (5), KR871734 (1),
KR871735 (1)
7 (RAG1)
RAG1: KR871768 (2), KR871769 (10)
7
Magro 2 (Júcar)
Magro R, Turis, Valencia.
30S 697216 4362401
6 (MT-CYTB)
MAG
7 (RAG1)
Micena (Júcar)
Micena R, Otos, Valencia
30S 721722 4303673
32 (MT-CYTB)
MT-CYTB: KR871722 (5), KR871747 (1)
RAG1: KR871769 (11), KR871773 (3)
MIC
27 (RAG1)
MT-CYTB: KR871748 (32)
RAG1: KR871769 (51), KR871773 (1),
KR871784 (2)
Sellent (Júcar)
Sellent R, Sellent, Valencia
30S 708812 4322980
5 (MT-CYTB)
SEL
MT-CYTB: KR871722 (2), KR871734 (1),
KR871735 (2)
5 (RAG1)
RAG1: KR871769 (6), KR871773 (4)
Verde (Júcar)
Verde R, Massalavés, Valencia
30S 714225 4335320
1 (MT-CYTB)
VER
1 (RAG1)
Albufera 1
Font del Forner spring, Sollana,
(Albufera)
Valencia
30S 725883 4350876
5 (MT-CYTB)
RAG1: KR871769 (2)
ALBU
5 (RAG1)
Albufera 2
Font del Barret spring, Sollana,
30S 725883 4350876
33 (MT-CYTB)
8
MT-CYTB: KR871722 (1)
MT-CYTB: KR871723 (4), KR871724 (1)
RAG1: KR871769 (8), KR871771 (2)
ALBU
MT-CYTB: KR871722 (14), KR871723 (1),
(Albufera)
Valencia
18 (RAG1)
KR871733 (1), KR871741 (5), KR871748 (6),
KR871761 (1), KR871762 (1), KR871763 (1),
KR871764 (1), KR871765 (1), KR871766 (1)
RAG1: KR871769 (28), KR871770 (2),
KR871773 (6)
Bullent (Serpis)
Bullent R, Pego, Alicante
30S 752960 4307746
11 (MT-CYTB)
BUL
MT-CYTB: KR871722 (4), KR871725 (1),
KR871726 (1), KR871727 (3), KR871728 (1),
8 (RAG1)
KR871767 (1)
RAG1: KR871769 (14), KR871772 (2)
Serpis (Serpis)
Serpis R, Barranco de la
30S 0730211 4300889
31 (MT-CYTB)
SER
Encantada. Beniarrés, Alicante
MT-CYTB: KR871722 (9), KR871727 (19),
KR871743 (1), KR871744 (1), KR871750 (1)
27 (RAG1)
RAG1: KR871769 (42), KR871772 (3),
KR871776 (2), KR871777 (3), KR871791 (2),
KR871792 (2)
Vinalopó
Vinalopó R, Banyeres de Mariola,
30S 703293 4287051
30 (MT-CYTB)
9
VIN
MT-CYTB: KR871722 (29), KR871756 (1)
(Vinalopó)
Alicante
Monnegre
Monnegre R, Tibi, Alicante
27 (RAG1)
30S 709792 4268351
(Monnegre)
37 (MT-CYTB)
RAG1: KR871769 (52), KR871790 (2)
MON
MT-CYTB: KR871722 (36), KR871749 (1)
35 (RAG1)
RAG1: KR871769 (62), KR871774 (2),
KR871786 (2), KR871787 (2), KR871788 (2)
Algar (Algar)
Algar R, Callosa D’en Sarriá,
Alicante
30S 752598 4282925
34 (MT-CYTB)
21 (RAG1)
ALG
MT-CYTB: KR871722 (20), KR 871738 (2),
KR871751 (3), KR871752 (1), KR871753 (6),
KR871754 (1), R871755 (1)
RAG1: KR871769 (39), KR871773 (1),
KR871789 (2)
48
10
49
Table S2. Localities of presence of Squalius valentinus used in MaxEnt analysis.
Locality
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
Longitude
0.24
-0.00
-0.04
-0.06
-0.07
-0.07
-0.07
-0.13
-0.17
-0.17
-0.18
-0.18
-0.26
-0.28
-0.28
-0.29
-0.36
-0.36
-0.36
-0.39
-0.39
-0.40
-0.41
-0.42
-0.47
-0.49
-0.49
-0.50
-0.51
-0.52
-0.52
-0.53
-0.53
-0.59
-0.60
-0.61
-0.62
-0.62
-0.62
-0.63
-0.63
-0.64
11
Latitude
40.21
38.85
40.03
38.86
38.59
38.66
38.68
39.94
38.95
39.04
38.68
38.86
39.58
39.04
39.22
38.86
40.03
40.12
40.21
39.22
39.31
39.04
38.86
38.50
40.22
39.77
39.86
39.50
39.14
38.87
39.05
38.51
38.69
40.13
39.86
39.59
39.32
39.41
39.50
39.05
39.14
38.69
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
-0.64
-0.64
-0.72
-0.73
-0.74
-0.74
-0.74
-0.74
-0.75
-0.75
-0.82
-0.85
-0.85
-0.85
-0.86
-0.96
-0.96
-0.96
-0.97
-0.97
-0.97
-1.07
-1.08
-1.08
-1.09
-1.09
-1.09
-1.20
-1.20
-1.20
-1.31
-1.32
-1.43
-1.43
-1.55
-1.55
-0.66
-1.10
-1.14
-1.21
-1.36
-1.52
-0.61
-0.73
-1.21
-0.71
12
38.78
38.87
39.86
39.59
39.14
39.23
39.32
39.41
38.87
39.05
39.32
39.32
39.41
39.59
39.23
39.51
39.60
39.69
39.24
39.33
39.42
39.78
39.51
39.69
39.06
39.15
39.33
39.33
39.42
39.51
39.60
39.33
39.33
39.42
39.42
39.51
38.71
39.49
39.29
39.43
39.49
39.44
39.34
39.60
39.43
39.39
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
-0.94
-0.77
-0.59
-0.59
-0.74
-0.39
-0.38
-0.62
-0.83
-0.97
-0.89
-1.04
-0.97
-0.38
-0.08
-0.52
-0.35
-0.61
-0.64
-0.10
-0.44
-0.59
-0.49
-0.42
-1.10
-0.77
50
13
39.24
39.24
39.08
39.03
39.12
40.16
40.16
39.57
39.60
39.73
39.66
39.76
39.73
39.28
38.88
39.14
38.83
40.12
40.13
38.66
38.85
38.54
38.97
40.20
39.14
40.10
51
Table S3. Genetic diversity of populations in each river, major basins, and the complete
52
dataset based on mitochondrial cytochrome b (normal) and RAG1 (bold). n = number of
53
samples analysed; h = number of haplotypes;  = nucleotide diversity; HD = haplotype
54
or allele diversity; S = number of polymorphic sites; k = number of pairwise
55
differences. (SD) Standard deviations.
Population
Complete dataset
Mijares basin
Mijares R.
Villahermosa R.
Carbo R.
Albentosa R.
Turia basin
n
h
 (SD)
HD (SD)
S
k
478
49
0.0011 (0.000007)
0.689 (0.022)
63
1.242
762
28
0.00033 (0.00003)
0.301 (0.021)
29
0.342
92
3
0.00054 (0.00008)
0.515 (0.014)
5
0.591
160
10
0.00046 (0.00006)
0.436 (0.043)
9
0.11
25
3
0.00044 (0.00005)
0.480 (0.060)
1
0.480
40
3
0.00043 (0.00007)
0.465 (0.062)
2
0.481
36
2
0.00022 (0.00007)
0.157 (0.077)
1
0.157
62
4
0.00038 (0.00059)
0.374 (0.068)
4
0.413
5
3
0.00182 (0.0000007)
0.700 (0.218)
5
2
10
3
0.00051 (0.00019)
0.511 (0.164)
2
0.556
26
2
0.00029 (0.00009)
0.323 (0.097)
1
0.323
48
7
0.00055 (0.00013)
0.461 (0.086)
6
0.593
65
10
0.00087 (0.00010)
0.700 (0.048)
10
0.952
14
Turia R.
Tuejar R.
Albufera L.
Júcar basin
Júcar R.
Júcar R. (Irrigation
pond in Antela)
Cabriel R.
Magro R.
Grande R.
110
3
0.00037 (0.00004)
0.392 (0.044)
2
0.4
35
7
0.00084 (0.00013)
0.689 (0.060)
7
0.921
56
3
0.00042 (0.00006)
0.434 (0.061)
2
0.452
30
8
0.00090 (0.00016)
0.724 (0.076)
7
0.993
54
2
0.00032 (0.00006)
0.352 (0.064)
1
0.352
38
12
0.00147 (0.00021)
0.821 (0.046)
14
1.616
46
4
0.00051 (0.00014)
0.375 (0.084)
4
0.549
131
13
0.00152 (0.00014)
0.693 (0.029)
16
1.664
210
7
0.00018 (0.0004)
0.188 (0.036)
6
0.195
10
5
0.00091 (0.00032)
0.667 (0.163)
5
1
22
2
0.00008 (0.00007)
0.091 (0.091)
1
0.091
5
3
0.0046 (0.00036)
0.800 (0.164)
3
1.6
-
-
-
-
-
-
12
3
0.00052 (0.00021)
0.439 (0.158)
2
0,356
14
1
-
-
-
-
13
4
0.00098 (0.00050)
0.423 (0.164)
7
1.077
26
3
0.00033 (0.00011)
0.342 (0.110)
2
0.110
40
6
0.00177 (0.00014)
0.667 (0.046)
7
1.945
15
Sellent R.
Albaida R.
Micena R.
Barranco del Agua
Bullent basin
Serpis basin
Vinalopó basin
Monnegre basin
Algar basin
50
4
0.00018 (0.00007)
0.190 (0.0073)
3
0.197
5
3
0.00291 (0.00073)
0.800 (0.164)
6
3.2
10
2
0.00049 (0.00009)
0.533 (0.095)
1
0.533
4
2
0.00061 (0.00019)
0.667 (0.204)
1
0.667
8
2
0.00023 (0.00017)
0.250 (0.180)
1
0.25
32
1
-
-
-
-
54
3
0.00010 (0.00005)
0.108 (0.057)
2
0.11
16
1
-
-
-
-
-
-
-
-
-
-
11
6
0.00149 (0.00036)
0.836 (0.089)
7
1.636
16
2
0.00021 (0.00012)
0.233 (0.126)
1
0.233
31
5
0.00068 (0.00018)
0.555 (0.075)
6
0.748
54
6
0.0004 (0.00009)
0.392 (0.083)
5
0.432
30
2
0.00012 (0.00011)
0.067 (0.061)
2
0.133
54
2
0.00007 (0.00004)
0.073 (0.048)
1
0.115
37
2
0.00005 (0.00005)
0.054 (0.050)
1
0.054
70
5
0.00026 (0.00096)
0.215 (0.065)
4
0.282
36
7
0.00074 (0.00014)
0.660 (0.076)
6
0.813
16
42
3
0.00013 (0.00007)
56
17
0.138 (0.071)
2
0.141
57
Table S4. Pairwise ST comparisons (above diagonal) among Squalius valentinus populations based on cytochrome b gene. Bold numbers
58
represent significant values at P  0.001 after Bonferroni’s correction.
Mijares
Mijares
Turia
Albufera
Júcar
Serpis
Bullent
Algar
Monnegre
-
Turia
0.27366
-
Albufera
0.26543
0.06278
-
Júcar
0.23739
0.06633
0.08777
-
Serpis
0.38597
0.38597
0.32124
0.29709
-
Bullent
0.20572
0.20572
0.14250
0.16790
0.05834
-
Algar
0.19651
0.08703
0.09207
0.11289
0.40579
0.19843
-
Monnegre
0.33539
0.08342
0.09251
0.09845
0.57426
0.36969
0.08230
18
-
Vinalopo
Vinalopó
0.31472
0.07326
0.07791
0.09174
0.52672
59
60
61
19
0.30365
0.06687
0.00283
-
62
Table S5. Demographic characteristics of each population of rivers and major basins and the complete dataset for mitochondrial cytchrome b
63
gene. R2 (Ramos-Osins and Rozas test); Fs (Fu’s Fs test); D (Tajima’s D test); r = raggedness index. P-values in brackets, bold numbers
64
correspond to significant values in neutrality tests. Final column shows time since population expansion at evolutionary rate of 0.8% per lineage
65
per million years (Perea et al., 2010).
Populations
R2 (P)
Fs (P)
D (P)
r (P)
Time since
expansion (kya)
Entire dataset
0.010 (0.007)
-60.410
-2.448 (<0.001)
-7.345 (>0.10)
37 200-70 600
(0.0001)
Mijares
0.097 (0.521)
1.109 (0.764)
-0.861 (>0.10)
-2.836 (<0.05)
-
Turia
0.0511 (0.063)
-5.097 (0.003)
-1.498 (>0.10)
-2.052 (>0.05)
57 700-109 500
Albufera
0.0523 (0.016)
-7.759 (0.001)
-1.704 (>0.05)
-3.032 (>0.05)
101 000-191 900
Júcar
0.0510 (0.143)
-3.445 (0.089)
-1.172 (>0.10)
-2.587 (<0.05)
-
20
Bullent
0.121 (0.014)
-2.183 (0.032)
-1.230 (>0.10)
-1.723 (>0.10)
99 200-188 000
Serpis
0.102 (0.228)
-1.359 (0.141)
-1.420 (>0.10)
-2.692 (<0.05)
31 400-59 700
Vinalopó
0.179 (0.548)
-0.396 (0.371)
-1.507 (>0.10)
-2.381 (>0.10)
-
Monnegre
0.162 (0.523)
-1.385 (0.238)
-1.130 (>0.10)
-1.817 (>0.10)
-
Algar
0.087 (0.082)
-2.422 (0.466)
-1.033 (>0.10)
-0.776 (>0.10)
-
66
67
21
68
Table S6. Long-term (historical) migration analysis of Squalius valentinus populations based on cytochrome b gene. M (mutation-scaled
69
immigration rate from migrating to receiving population), m (effective immigration rate, m=M/μ), Θ (mutation-scaled effective population size,
70
Θ=2Neμ), n = number of individuals. μ= 1.74 x 10-5 per nucleotide per generation1. MIJ (Mijares); TUR (Turia); JUC (Júcar); ALBU (Albufera);
71
SER (Serpis); BUL (Bullent); ALG (Algar); MON (Monnegre); VIN (Vinalopó).
MIGRATING
RECEIVING POPULATION
POPULATION
MIJ
TUR
n
Θ
MIJ
TUR
JUC
ALBU
SER
BUL
ALG
MON
VIN
92
0.0006
-
M=17491
M=4892
M=2775
M=13341
M=8908
M=15675
M=10408
M=11035
m=0.30
m=0.08
m=0.04
m=0.24
m=0.16
m=0.26
m=0.18
m=0.18
-
M=10842
M=5108
M=13208
M=12142
M=10142
M=11825
M=11141
m=0.19
m=0.09
m=0.23
m=0.21
m=0.18
m=0.20
m=0.19
-
M=6092
M=11408
M=9175
M=6441
M=6525
M=7869
m=0.11
m=0.20
m=0.16
m=0.11
m=0.11
m=0.14
-
M=15091
M=18025
M=16575
M=17375
M=15479
65
0.0010
M=11842
m=0.21
JUC
ALBU
131
38
0.0010
0.0625
M=9525
M=12408
m=0.16
m=0.21
M=12958
M=17158
M=16742
22
SER
BUL
ALG
MON
VIN
72
1
31
11
36
37
30
0.0006
0.056
0.0567
0.0067
0.0571
m=0.22
m=0.30
m=0.29
m=0.26
m=0.31
m=0.29
m=0.30
m=0.27
M=9225
M=5175
M=3358
M=2608
-
M=17941
M=5441
M=5342
M=5892
m=0.16
m=0.09
m=0.06
m=0.04
m=0.31
m=0.09
m=0.09
m=0.10
M=13108
M=15241
M=12642
M=10058
M=15341
-
M=15258
M=15975
M=15582
m=0.23
m=0.26
m=0.22
m=0.17
m=0.27
m=0.26
m=0.28
m=0.27
M=13075
M=15875
M=13475
M=12241
M=14425
M=16925
-
M=17608
M=15708
m=0.23
m=0.28
m=0.23
m=0.21
m=0.25
m=0.29
m=0.31
m=0.27
M=13208
M=16841
M=14375
M=13325
M=15558
M=16608
M=16208
-
M=17241
m=0.23
m=0.29
m=0.25
m=0.23
m=0.27
m=0.29
m=0.28
M=13591
M=16541
M=14358
M=11625
M=15591
M=17941
M=16375
M=16792
m=0.24
m=0.29
m=0.25
m=0.20
m=0.27
m=0.31
m=0.28
m=0.29
μ= 1.74 x 10-5 estimated on the basis of μ= 8.68 x 10-6 and a generation time of 2 years (see main text).
23
m=0.30
-
73
Table S7. Sum of Bayesian posterior probabilities of regression models performed in GESTE software that include a given factor. The highest
74
probability value is represented in bold.
VARIABLE
LIG
LGM
CURRENT PERIOD
FUTURE (2080)
TEMPERATURE (ALL VARIABLES)
BIO1: Annual mean temperature
0.322
0.473
0.484
0.480
BIO2: Mean diurnal range
0.151
0.471
0.502
0.428
BIO3: Isothermality
0.390
0.459
0.511
0.454
BIO4: Temperature seasonality
0.311
0.573
0.494
0.477
BIO5: Maximum temperature of the warmest month
0.423
0.471
0.466
0.489
BIO6: Minimum temperature of the coldest month
0.356
0.481
0.477
0.466
BIO7: Temperature annual range;
0.363
0.702
0.472
0.443
BIO8: Mean temperature of the wettest quarter
0.192
0.624
0.462
0.467
BIO9: Mean temperature of the driest quarter;
0.331
0.485
0.473
0.483
BIO10: Mean temperature of the warmest quarter
0.324
0.482
0.510
0.462
BIO11: Mean temperature of the coldest quarter
0.349
0.492
0.478
0.470
24
PRECIPITATION (ALL VARIABLES)
BIO12: Precipitation of the warmest month
0.422
0.310
0.462
0.446
BIO13: Precipitation of the wettest moth
0.442
0.292
0.610
0.494
BIO14: Precipitation of the driest month
0.492
0.345
0.458
0.501
BIO15: Precipitation seasonality
0.461
0.412
0.541
0.442
BIO16: Precipitation of the wettest quarter
0.447
0.340
0.465
0.439
BIO17: Precipitation of the driest quarter
0.473
0.389
0.465
0.457
BIO18: Precipitation of the warmest quarter
0.475
0.319
0.475
0.491
BIO19: Precipitation of the coldest quarter
0.460
0.310
0.461
0.449
SEASONALITY
BIO4: Temperature seasonality
0.230
0.466
0.420
0.464
BIO7: Temperature annual range
0.581
0.266
0.265
0.590
BIO15: Precipitation seasonality
0.404
0.235
0.251
0.467
“EXTREME” CLIMATIC CONDITIONS
BIO5: Maximum temperature of the warmest month
0.177
0.215
0.285
0.180
BIO6: Minimum temperature of the coldest month
0.864
0.276
0.274
0.669
25
BIO13: Precipitation of the wettest moth
0.124
0.214
0.251
0.114
BIO14: Precipitation of the driest month
0.243
0.202
0.197
0.400
75
26
76
Table S8. Sum of Bayesian posterior probabilities of regression models performed in GESTE software that include a given factor when only the
77
five variables with the highest Bayesian probability values of temperature and precipitation are analysed. Bold value indicates factor with the
78
highest probability.
VARIABLE
LIG
LGM
CURRENT PERIOD
FUTURE (2080)
TEMPERATURE (5 VARIABLES)
BIO1: Annual mean temperature
-
-
0.790
0.474
BIO2: Mean diurnal range
-
-
0.510
-
0.499
-
0.560
-
-
0.452
0.428
-
BIO5: Maximum temperature of the warmest month
0.465
-
-
0.450
BIO6: Minimum temperature of the coldest month
0.694
-
-
0.458
BIO7: Temperature annual range
0.616
0.454
-
-
BIO8: Mean temperature of the wettest quarter
-
0.429
-
-
BIO9: Mean temperature of the driest quarter;
-
0.569
-
0.421
BIO10: Mean temperature of the warmest quarter
-
0.471
0.797
-
BIO3: Isothermality
BIO4: Temperature seasonality
27
BIO11: Mean temperature of the coldest quarter
0.570
0.426
-
0.529
PRECIPITATION (5 VARIABLES)
BIO12: Precipitation of the warmest month
-
-
-
-
BIO13: Precipitation of the wettest moth
-
-
0.542
0.544
BIO14: Precipitation of the driest month
0.467
0.468
-
0.764
BIO15: Precipitation seasonality
0.393
0.425
0.542
-
-
0.431
0.538
-
BIO17: Precipitation of the driest quarter
0.442
0.545
0.459
0.636
BIO18: Precipitation of the warmest quarter
0.449
0.802
0.463
0.430
BIO19: Precipitation of the coldest quarter
0.149
-
-
0.435
BIO16: Precipitation of the wettest quarter
28
79
Fig. S1. Historical demography of Squalius valentinus populations based on Mismatch
80
distributions (A) and Extended Bayesian Skyline plot (EBSP) of effective population
81
size (Ne) over time (B). Mismatch analysis shows distributions observed (dotted lines)
82
and expected (continuous line) under an expansion model. EBSP: solid line indicates
83
the mean posterior Ne estimate, grey lines represent the 95% HPD credibility intervals.
84
X-axis shows units of time in thousands of years ago (kya). Y-axis shows estimated Ne
85
in hundreds of thousands x generation time.
86
87
29
88
Fig. S2. Mismatch distributions observed (dotted lines) and expected under an
89
expansion model (continuous line) for individual Squalius valentinus populations (A)
90
and for the Júcar basin (B).
91
92
30
93
Figure S3. Historical demography of individual Squalius valentinus populations based
94
on Extended Bayesian Skyline plot (EBSP) of effective population size (Ne) over time.
95
The solid line indicates the mean posterior Ne estimate, grey lines represent the 95%
96
HPD credibility intervals. X-axis shows units of time in thousands of years ago (kya).
97
Y-axis shows estimated Ne in hundreds of thousands x generation time.
98
99
100
31