1
FIGURES
2
3
4
5
6
7
8
9
10
11
12
Fig. B1. Bi-variate plots of three principal components (PC1-PC3) based on the distribution of biomass across habitats (Appendix A4). Clusters of
data points represent Sakhalin taimen populations with similar life-history strategies. Open circles: semi-anadromous (SA), filled circles: lagoon (LG),
open triangles: lake (LK), and filled triangle: freshwater resident (FR) migratory phenotypes. Population codes are presented in Fig.1.
Explanation. To see if we could logically group populations by the spatial habitat distribution we carried out a Principal Components Analysis
(PCA) on the data matrix of relative biomass values (Table A3) with the SPSS software package (SPSS for Windows (2002) Release 11.5. Standard
version. SPSS Inc.). The premise of this analysis is that a larger relative biomass values for a particular habitat type reflects the main foraging location
of the sampled population. Namely, a given population was assigned to one of the following four life-history variants, freshwater resident (FR), lake
1
13
14
15
16
17
18
19
20
(LK), lagoon (LG), or semi-anadromous (SA), depending on weather large values of relative biomasses were reached in the river, lake, lagoon, or
seashore, respectively. Additional insight was also gained if relative biomass was high in coastal waters, suggesting a higher likelihood of migration
with other populations.
While the sample size for this analysis was very low, other statistical aspects of the PCA analysis revealed typically satisfactory values (i.e., very
high communalities, and a highly significant Bartlett’s test of sphericity). Moreover, the analysis only attempts to characterize the populations in terms
of the distribution of biomass across populations, and not to project any predictive value – a hierarchical cluster analysis (data not shown) revealed the
same four groups of populations.
21
2
TS
SES
All samples
NES
22
23
24
25
26
27
28
Fig. B2. Geographic-genetic correlation between Sakhalin taimen populations.
A Mantel test revealed a significant correlation for all samples (R2 = 0.27, p<0.001); a positive trend, but insignificant correlation for NES
(northeastern Sakhalin) (R2 = 0.37, p=0.080); a significant correlation for TS (Tatar Strait, northeastern Sakhalin and continental samples)(R2 = 0.63,
p<0.001); and a non-significant negative trend for SES (southeastern Sakhalin)(R2 = 0.092, p=0.35); genetic distance d was linearized as d/(1-d).
3
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
Fig. B3. Plot of imbalance (white), M (grey), and expansion (black) statistics. Negative values indicate decrease in population sizes.
To test historical trends in declining/expanding effective population sizes, we applied the imbalance index -ln ˆ (Kimmel et al. 1998), statistic
M (Garza and Williamson (2001), and the expansion index Sk (Zhivotovsky et al. 2000) to ten loci with dinucleotide repeats. The tests are based on the
ratios of various statistics of allelic variation: -ln ˆ : (variance in repeat score)/(expected heterozygosity); M: (number of alleles)/(allele range in repeat
score); Sk: (variance in repeat score)/(the fourth central moment in repeat score) as different statistics are changing in time with different rates when the
population size is changing. For the plot convenience, the Garza-Williamson’s original M-value was modified as (M-0.68)∙10, where 0.68 is the
method threshold.
REFERENCES
Garza JC, Williamson EG (2001) Detection in population size using data from microsatellite loci. Mol Ecol 10, 305–318.
Kimmel M, Chakraborty R, King JP, Bamshad M, Watkins WS, Jorde LB (1998) Signatures of population expansion in microsatellite repeat
data. Genetics, 148, 1921-1930.
Zhivotovsky LA, Bennett L, Bowcock AM, Feldman MW (2000) Human population expansion and microsatellite variation. Mol Biol Evol 17,
757-767.
4