2.5 Influence of landscape on gene flow and genetic distance
To preliminarily predict the influence of the different landscape types
on the genetic differentiation of M. alternatus , the distance
models IBD and LCP were used at the fine scale (with Shunchang as an
example). In the LCP model, four groups of resistance values were set
for the ten landscape types base on the opinion method (Supporting
information Table S5): i ) host landscapes and roads were given
the lowest resistance value (the resistance value of P.
massoniana , P. elliottii , mixed-forest with hosts, and roads was
1), and nonhost landscapes (except roads) were divided into medium and
high resistance (the medium resistance value of broad-leaved forest,C. lanceolata , water, and nudation was 8; the high resistance
value of farmland was 64 and for urban was 512); ii ) host
landscapes and roads were given the medium resistance value of 8,
whereas nonhost landscapes (except roads) were given the lowest
resistance value of 1; iii ) host landscapes and roads were given
the medium resistance value of 64, whereas nonhost landscapes (except
roads) were given the lowest resistance value of 1; iiii ) host
landscapes and roads were given the high resistance value of 512,
whereas nonhost landscapes (except roads) were given the lowest
resistance value of 1. The LCP on the four resistance surfaces was
calculated using the R package gdistance (Vanetten, 2014).
Finally, Mantel tests between the two distances and genetic distances
(FST(1-FST)) were performed in the R
package ecodist (Goslee & Urban, 2007).
In addition, to maintain objectivity, the least-cost transect analysis
(LCTA) developed by Strien et al. (2012) was used to study the influence
of landscape types on dispersal behavior and gene flow of M.
alternatus at the fine scale. This analysis provided an effective
solution to objectively define the resistance values of different
landscape types based on transects. In the analysis, each landscape type
in a linear path, with different transect widths, between two sampling
points is quantified, and the correlation between those values and the
genetic distance is analyzed. The analysis can also determine the
probable migration habitats and the landscape types that either inhibit
or facilitate the gene flow. However, it is unlikely that species will
migrate directly between two points following a straight line.
Therefore, the LCTA uses the least-cost path to replace the straight
line between two points and then calculates the percentage of each
landscape type in the transect. For each transect, the proportions of
each of the landscape types and the transect length were used as
explanatory variables.
In this study, the first step in the LCTA was to create a series of
resistance surfaces, in which each landscape type (P. massoniana ,C. lanceolata , P. elliottii , mixed forest (including some
hosts), broad-leaved forest, urban, farmland, road, water, and nudation)
was successively considered as the optimal dispersal habitat. The
optimal dispersal habitat was given the lowest resistance value of 1,
whereas the other landscape types were given gradually increasing
resistance values (23, 26,
29) (Supporting information Table S6). A variety of
transect widths (200 m, 300 m, 600 m) were evaluated. In each transect,
Geospatial Modelling Environment v0.7.4.0 (Beyer, 2015) was used to
calculate the proportions of each of the landscape types and the
transect length. A total of 90 LCP data sets were obtained (10 land
cover types × 3 resistance values × 3 transect widths). The correlations
between those variables (proportions of each of the landscape types and
the transect length) and the FST were tested through
maximum-likelihood population effects (MLPE) (Clarke et al., 2002). In
each MLPE, the model was estimated with restricted estimation maximum
likelihood (REML), and the individual deme was used as a random effect.
To explain the relationships between the different landscape types and
the gene flow, Akaike’s Information Criteria (AIC) (Gurka, 2006), which
is considered to be effective in choosing an REML hybrid model, was used
to select the optimal model from the 90 data sets.