1.0
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
-0.8
r
y = -0.0009x + 0.0727
R² = 0.080
0
50
100
150
200
250
300
Geographic distance (km)
Fig. 1 Isolation by distance relationship between pairwise relatedness and
geographical distance of rock wren (Xenicus gilviventris) across the lower
South Island. The trend, represented as a solid line, was significant (p <
0.001) as determined through Mantel tests with 9,999 permutations.
a)
K
b)
0
1000000
Number of MCMC
1 2 3 4 5 6 7
K
Fig. 2 Number of rock wren (Xenicus gilviventris) genetic populations simulated from
the posterior distribution generated in GENELAND visualised as a) trace of number
of populations (K) along one million MCMC iterations showing good mixing around
K=2 and b) histogram showing simulated number of populations with a clear mode at
K=2.
∆K
250
-3000
150
-3100
100
-3150
50
-3200
∆K
-3050
0
Ln P(X|K)
Ln P(X|K)
200
-3250
1
2
3
4
5
6
7
K
Fig. 3 Determination of K, the number of genetic populations of rock wren (Xenicus
gilviventris) in the lower South Island from STRUCTURE analysis. The mean loglikelihood of the data was lowest for the model K = 1 and then increased sharply for
K = 2, after which there was an overall plateau through to K = 7. When ln P(X|K)
plateaus or increases only slightly, then the model with the lowest value of K that
captures the major structure of the data is usually the best choice (Pritchard et al.
2010). Delta-K (∆K) cannot be calculated for K = 1; however given that ln P(X|K) was
lowest at K = 1 this indicates that this was not the best model for our data. The
highest estimate for ∆K was at K = 2, confirming this model was a better choice than
any of the models with larger values of K (Evanno et al. 2005).