Seascape Genomics
Neutral
population structure in N. lapillus (N=3,820 loci) was summarized
into 11 PCs that explained 100% of the variation in the dataset. The
best model describing neutral population structure included two
environmental predictor variables (env_PC1: p=0.0104; env_PC2:
p=0.0129, Table 2). This model was significant, but only explained a
small proportion of variation in the genetic dataset (p=0.0005,
R2adj=0.1862; Table 4; Fig. 5). The outlier dataset
(N=24 loci) was summarized into five PCs, explaining 87.35% of the
variation in the dataset. Here, the best model contained three
environmental predictor variables (env_PC1: p=0.0100; env_PC2:
p=0.0003; env_PC3: p=0.0019; Table 2) and one geographic spatial
variable (dbMEM_2: p=0.0016; Table 2; Fig. 6). This model was also
significant, but in contrast to the neutral model, explained a larger
proportion of the variation in the genetic dataset (p=0.0001,
R2adj=0.5756; Table 4; Fig. 5). Partial RDAs
attributed most of this variation to the environmental predictors
(R2adj=0.2975, p=0.0022), with a smaller and
non-significant proportion attributed to the geographic vector
(R2adj=0.0004, p=0.4369).
Neutral population structure in S. umbilicalis (N=11,457 loci),
was summarized into 11 PCs that explained 100% of the variation in the
dataset. The best model describing neutral population structure included
two environmental predictor variables (env_PC2: p=0.0091; env_PC3:
p=0.0299; Table 2), two geographic spatial variables (dbMEM_2:
p=0.0366; dbMEM_3: p=0.0037; Table 2; Fig. 6), and one larval
connectivity vector (AEM5: p=0.0268; Table 2; Fig. 6). This model was
significant, but only explained a small proportion of variation in the
genetic dataset (p=0.0013, R2adj=0.1739; Table 4; Fig.
7). The largest proportion of explainable variation was attributed to
the geographic vectors (R2adj=0.0422, p=0.2020), with
smaller proportions attributed to the larval connectivity vector
(R2adj=0.0281, p=0.2874) and environmental variables
(R2adj=0.0207, p=0.2969), however, none of these
partitioned models were significant. The outlier dataset (N=143 loci),
was also summarized into 11 PCs, explaining 100% of the variation in
the dataset. The best model contained two geographic spatial vectors
(dbMEM_2: p=0.0142; dbMEM_3: p=0.0016; Table 2; Fig. 6) and one larval
connectivity vector (AEM5: p=0.0356; Table 2). This model was also
significant and explained a relatively large proportion of the variation
in the genetic dataset (p=0.0009, R2adj= 0.4559; Table
4; Fig. 7). Most of the explainable variation in this model was
attributed to geographic vectors (R2adj=0.3940,
p=0.0008), with a much smaller and non-significant proportion attributed
to the larval dispersal vector (R2adj=0.0459,
p=0.1353).