APPENDIX A
Ocean currents help explain population genetic structure
White et al.
Geneland analysis procedures
We inferred spatial clustering of the geo-referenced, multi-locus individuals using a Bayesian
framework applied in program Geneland (Guillot et al. 2005). In Geneland, we ran 10 replicate
simulations considering K = 1-5 clusters (populations) and using 5*105 MCMC iterations, a
thinning rate of 100, burn in of 2500, and the program’s default settings. When sampling design
of a population genetics study is geographically discrete, as is the case here, and coordinate
uncertainty is assumed in Geneland, the program may over-estimate the number of populations
and delineate population domain(s) that contain zero individuals or a few individuals only on the
edges (G. Guillot, pers. comm.). To account for spatial uncertainty in our sampling, and avoid
spurious convergence by the program, we followed Geneland author guidelines (G. Guillot, pers.
comm.) by drawing coordinate locations for each of the 709 sampled individuals from a uniform
distribution on the interval ±0.5 km about the site coordinates listed in Table 1 (i.e., 1.0 km range
in sampling uncertainty), then set coordinate uncertainty in Geneland to zero. We ran 3 sets of
simulations in Geneland using independent sets of randomized coordinate locations. For each
simulation set, we inferred the number of populations from the modal value of K.
Table S1: Isolation by oceanographic distance linear regression coefficient of determination (R2)
values resulting from leave-one-out jackknife.
Locus removed
Site removed
FST
Dest
K13
None
0.32
0.26
Kk2b
None
0.20
0.20
Kk7a
None
0.29
0.21
Kk28a
None
0.27
0.22
Kk33a
None
0.32
0.23
Kk34a
None
0.39
0.27
Kk41a
None
0.27
0.17
Kk48b
None
0.23
0.08
Kk52a
None
0.31
0.23
None
JL
0.31
0.19
None
AC
0.22
0.15
None
RR
0.34
0.18
None
YB
0.23
0.16
None
AS
0.37
0.34
None
AN
0.36
0.21
None
CO
0.35
0.31
None
EL
0.36
0.26
None
IV
0.43
0.35
None
RI
0.34
0.24
None
None
0.33
0.24
Table S2: Summary statistics of linear regression of genetic distance values, with negative
genetic distances set to zero, with Euclidean and oceanographic distance (based on multigeneration connectivity probability) between pairwise sites.
Euclidean distance
Sites
All sites
Islands
Mainland
Cross-channel
Oceanographic distance
FST
Dest
FST
Dest
R2
0.00036
Neg slope
0.31
0.21
p
0.43
0.36
0.0066
0.0158
R2
0.036
Neg slope
0.42
0.47
p
0.24
0.48
0.022
0.0001
R2
Neg slope
Neg slope
0.04
Neg slope
p
0.37
0.44
0.32
0.52
R2
0.0004
Neg slope
0.49
0.42
p
~1
~1
0.003
0.012
(a)
(b)
Fig S1: a) Domain (gray region) and approximate size and location of 448 coastal grid units
(squares) in oceanographic model used to simulate larval dispersal trajectories. Red squares
indicate grid units representing 10 geo-referenced genetic sampling locations. b) Illustrative,
zoomed-in example of a “snap-shot” of the model of larval dispersal simulation in the Santa
Barbara Channel region. Virtual larvae are amidst dispersal and are represented by black dots;
red lines trace the dispersal path of each larvae over a 5-day period previous to the current time
in the image.
0.025
0.02
D
est
0.015
0.01
0.005
Islands
Mainland
Cross-Channel
0
-0.005
-4
-2
0
2
4
6
FST
8
x 10
-3
Fig. S2: Pairwise Dest in relation to pairwise FST among the 10 study sites. Linear regression
correlations: all site pairs (R2 = 0.68), mainland pairs (R2 = 0.49), island pairs (R2 = 0.83), crosschannel pairs (R2 = 0.69). All correlations were statistically significant.
(a)
(b)
10
x 10
-3
0.025
0.02
5
est
D
D
est
0.015
0
0.01
0.005
Islands
Mainland
Cross-Channel
-5
40
Islands
Mainland
Cross-Channel
60
80
100
120
Oceanographic distance [km]
140
0
-0.005
80
100
120
140
160
Oceanographic distance [km]
180
Figure S3: Genetic differentiation, Dest, in relation to derived oceanographic distance between
sites, based on probability of dispersal between sites over a single generation (M, panel a) and
long-term probability of dispersal between sites over a multiple generations (Mss, panel b). See
text and Table 2 for regression statistics.
Figure S4: Isolation by derived oceanographic distance linear regression coefficient of
determination, R2, in relation to iteration number (n) in the Markov Chain, Mss = Mn. Two
patterns in the figure are discussed below: the isolation by oceanographic distance regression
was strongest in relation to pairwise FST, and the regression in relation to Dest was consistent in
strength (but not significance; see Results) regardless of iteration number in the Markov Chain.
FST is a fixation index maximized in the absence of migration as demes drift to alternate
fixation of alleles. Alternatively, Dest estimates the actual degree of differentiation of allele
frequencies among demes, which is controlled by multiple factors, including migration, mutation
rate, founder effects, genetic bottlenecks, etc. (Jost 2009). Our oceanographic distances are only
meant for interpreting population differentiation due to variable migration rates among sites.
Thus, it is unsurprising that the isolation by oceanographic distance regression was stronger in
relation to pairwise FST than Dest, which accounts for additional factors not represented in our
oceanographic model (L. Jost, pers. comm.).
The Markov chain had a negligible effect on correlation strength when Dest was
considered. Whether the difference in the change in R2-values between Dest and FST is due to
chance or to the nature of Dest is unclear to us and to colleagues with whom we shared our results
(e.g., L. Jost, pers. comm.), but could perhaps be resolved by future analytical or simulationbased investigation (L. Jost, pers. comm.).
(a)
(b)
Fig S5: Mean probability of dispersal (Mss, panel a) and derived oceanographic distance (b) in
relation to Euclidean distance between genetic sampling sites.
References
Guillot, G., Mortier, F. & Estoup, A. 2005 GENELAND: a computer package for landscape
genetics. Molecular Ecology Notes 5, 712-715.
Jost, L. 2009 D vs. G(ST): Response to Heller and Siegismund (2009) and Ryman and Leimar
(2009). Molecular Ecology 18, 2088-2091.