3.5 Population genetic structure
Analysis of molecular variance (AMOVA) based on the AFLP dataset from 43
populations of P. villosa showed that the proportion of genetic
variation among populations was 35.84%, while that within populations
was 64.16%, and the value of average pairwiseF ST was 0.35841 (P < 0.001)
(Table 4), which showed that the genetic variation of P. villosamainly occurred within populations. More interestingly, when aggregating
populations into Groups 1 and 2, we found that 22.38% of the genetic
variation occurred among populations within groups
(F CT = 0.28501, P < 0.001),
while most of genetic variation (56.14%) existed within populations
(F ST = 0.43865, P < 0.001)
(Table 4). Overall, the genetic variation at the population- and local
geographic-scale was much higher than regionally in P. villosa .
Additionally, the result of neutrality test suggested the value of
Tajima’s D and Fu’s Fs was positive, but non-significant
for all populations of P. villosa (Table 3).
The Mantel test revealed that there was a significant positive
correlation between geographic distance and F STfor 43 populations (r = 0.282, P < 0.05) (Figure
1). Similarly, we detected a strong, significant, positive correlation
between geographic distance and F ST for Group 1
(r = 0.622, P < 0.05) and a weak but significant
positive correlation for Group 2 (r = 0.372, P <
0.05). Simultaneously, results from UPGMA tree, SplitsTree network,
PCoA, and STRUCTURE suggested that 43 populations of P. villosawere divided into two groups, which were largely consistent with our
assessment using SAMOVA (Figure 2 - 5).
3.6Distributional
change of P. villosa
ENMs for P. villosa yielded relatively high AUC, demonstrating
reliable model performance (AUC = 0.969, Figure S3). For the eleven
non-biological variables used for modeling, the most significant factor
for the spatial distribution pattern of P. villosa was altitude
(Alt), followed by temperature annual range (bio 7) and precipitation of
warmest quarter (bio 18), whose
contribution
rates were 40.0%, 17.2% and 16.7%, respectively (Table 5). In
comparison with the LIG, we observed a contraction in highly suitable
habitat during
the
LGM based on the MaxEnt models (Table 6 & Figure 6). Similarly, the
spatial distribution of the present was continuously shrinking compared
to the potential range during the LGM (Figure 7). The simulated
distribution based on present climate data was mostly congruent with the
actual distribution range of P. villosa , which was mainly
distributed in the Inner Mongolia Plateau with an area of approximately
111.2450 × 104 km2 (Table 6 &
Figure7). Simultaneously, we estimated the future changes in the
potential spatial distribution under the RCP 2.6 and RCP 8.5 scenarios
for the 2050 s and 2070 s. According to the future model predictions,
the areas of suitable habitat is likely to remain stable under the
climatic scenario of RCP 2.6 for the 2050s and 2070s, whereas there was
an increase of highly suitable areas based on RCP 8.5 (Table 7 & Figure
8).
When we compared the niches of hypothesis of niche identity was rejected
when the empirically observed value for D and/or I was
significantly lower than the values expected from the pseudo-replicated
data sets. Therefore, identity tests between two groups indicated that
there was distinct niche differentiation (P < 0.01)
(Figure 9).
The
niche of two groups differs mainly in that it was characterized by
high
elevation and temperature.