Population Structure
Both DAPC and STRUCTURE indicated that the most likely number of
population clusters was two and the second most likely was three, as
determined by BIC and the ΔK method, respectively. The ΔKmethod is biased toward K = 2 (Janes et al., 2017; Campana, Hunt,
Jones, & White, 2011) and simulation studies have shown that the mean
probability (MeanLnP(K)) output from STRUCTURE performs better in
scenarios with high gene flow and low F ST (Latch,
Dharmarajan, Glaubitz, & Rhodes, 2006). Because K = 3 produced
the highest MeanLnP(K) in the STRUCTURE analysis (Table S4a), we
consider this a relevant model and show the proportion of individual
membership in each cluster as defined by each of the two analyses for
both K = 2 and K = 3 (Figure 5). We also ran STRUCTURE
separately for individuals caught on MK and MT, with settings described
above except we ran simulations for K = 1–5. We found no
evidence of structure among MK individuals. MT individuals were divided
into two clusters - one with individuals ≥ 2000 masl and one with
individuals < 2000 masl, with individuals of mixed ancestry at
2000 masl (Table S4b).
Cluster membership is mostly concordant between the DAPC and STRUCTURE
analyses, except STRUCTURE assigned mixed ancestry to many individuals
while DAPC did not. This is not unexpected as previous studies have
shown that DAPC may underestimate admixture (Frosch et al., 2014) while
STRUCTURE is more accurate at assigning mixed ancestry (Bohling, Adams
& Waits, 2012). When K = 2, individuals at 2000 and 2400 masl on
MT form a separate cluster from low elevation MT + MK (Figures 4 & 5),
with mixed ancestry individuals at 2000 masl MT. For K = 3, the
divisions are between high elevation MT, low elevation MT, and MK, with
individuals assigned mixed ancestry at low elevation MK (900 masl) and
2000 masl MT (Figure 5).
The PCA shows a similar pattern. Eigenvector 1 (7% variation explained)
separates the two mountains, with overlap among individuals at 900 masl.
Eigenvector 2 (4% variation explained) partially separates individuals
by elevation, with lower elevation individuals clustered together
(Figure 6). With K = 2, after removing individuals that could not
be assigned to a STRUCTURE cluster (cutoff q kvalue < 0.6), F ST is 0.05 (p =
0.0001). The AMOVA showed 95% of variation is partitioned within
clusters and 5% between. With K = 3, removing individuals withq k values < 0.6,F ST between MK and low elevation MT was 0.035
(p = 0.001), between MK and high elevation MT 0.092 (p =
0.0001), and between low elevation MT and high elevation MTF ST = 0.065 (p = 0.0005) (Tables 2a &
2b). The AMOVA showed that 94% of variation is distributed within
clusters, and 6% among.
Including data for all 80 individuals, the Mantel test revealed a
significant, positive correlation between genotypic distance and
geographic distance (r = 0.287, p = 0.0001). Including the
58 unrelated individuals, the correlation was weaker but statistically
significant (r = 0.05, p < 0.0001). The
correlogram showed significant positive autocorrelation between
individuals at distances of 200 m and less (r = 0.091, pr -rand ≥ p r -data = 0.0001) and 200 m–1 km
(r = 0.036, p r -rand ≥ p r -data =
0.0001); autocorrelation was no longer significant at 2 km (r =
-0.001, p r -rand ≥ p r -data =0.598). At
subsequent distance classes (5, 10, 15, and 18 km), individuals have
greater genetic distance than expected at random; i.e., there is a
significant negative correlation between genetic and geographic distance
(p r -rand ≤ r- data = 0.009, 0.0001, 0.0001, 0.0001,
respectively) (Figure S4). The average geographic distance between pairs
of first-order relatives was 162.5 m, second order was 1.2 km, third
order was 4.8 km, and between distant or ‘unrelated’ individuals was 12
km (Table 1). Differences between first and third, first and distant,
second and distant, and third and distant relatives were significant
(p < 0.05).