2.3 Population genetic analysis
Individual fish with genotype call rates below 0.7 (n =10) were
excluded, resulting in a total of 2,572 individuals used in further
analyses. We quantified genetic diversity in several ways described here
and these measures were used for the indicators (section 2.4). We
assessed the most likely number of populations (K ) using
structure (v.2.3.4; Pritchard et al., 2000; Falush et al.,
2003). For this, we pooled the material from time points and localities
within metapopulation, in order to investigate whether the same genetic
populations appear in multiple lakes and/or are stable over time. In
total, seven metapopulations and three separate lakes were analyzed
(Table S1) like this with structure. We used the default model
allowing population admixture and correlated allele frequencies,
applying the alternative (population-specific) ancestry prior, with
ALPHA=1/number of samples (i.e. the number of lake and time point
combinations; Wang, 2019). No á priori information was used. The burn-in
length was 250,000 and the number of Markov chains (MCMC) 500,000.
Estimations of Q (assignment probability; the mean individual
probability of belonging to a certain genetic cluster) and the most
likely value of K (simulated K =1-15) was repeated over 20
runs, with the output analysed using kfinder (v.1.0; Wang,
2019) and structure
harvester (v.0.6.94; Earl & vonHoldt, 2012). Mean individual Qover the 20 runs was derived from the clumpp software (v.1.1.2;
Jakobsson & Rosenberg, 2007). The most likely number of K was
based on the parsimony index (PI ) recommended by Wang (2019).
Individuals were assigned to the cluster for which they had the highestQ .
We defined the clusters identified by structure as populations
and all further analyses are based on these populations. We use
“population” and “cluster” synonymously from here on, and if such
populations occur within the same metapopulation we also use term
“subpopulation” for such populations/clusters). We measured genetic
diversity at two points in time for each population by estimating
observed and expected heterozygosity (H O;H E), the average number of alleles per locus
(N A) using genalex v.6.5 (Peakall &
Smouse, 2006, 2012), allelic richness (A R) using
fstat (v.2.9.4; Goudet, 2003), and the proportion of
polymorphic loci (P L). Confidence intervals for
diversity measures, as well as tests for normality of data were
calculated in statistica (v.7.1; StatSoft, Inc., 2005). To test
for changes in the genetic diversity measures over time we performed
non-parametric Wilcoxon matched pairs test as well as Student’s t test
for paired samples (heterozygosity, average number of alleles per locus,
allelic richness), and χ 2 tests (proportion of
polymorphic loci) using statistica and Microsoft
excel. Statistical analyses investigating relationships between
genetic diversity and the physical parameters of the localities were
carried out using statistica.
We estimated effective population size (N e) for
individual populations (cluster) with the temporal method using
tempofs (sampling plan II; Jorde & Ryman, 2007), as well as
with the linkage disequilibrium method (Hill, 1981; Waples, 2006; Waples
& Do, 2010) as implemented in neestimator v.2.1 (Do et al.,
2014). Confidence intervals for N e were obtained
from the respective software. N e for
metapopulations were estimated using the temporal method only.N e estimation in substructured (non-isolated)
populations is complex and we follow suggestions from Ryman et al.
(2014, 2019) when interpreting the estimations (below).
F ST (Weir & Cockerham, 1984) quantifying
temporal genetic heterogeneity within populations and genetic
heterogeneity among subpopulations within metapopulations were obtained
using genepop (v.4.3; Raymond & Rousset, 1995; Rousset, 2008).
chifish (v.5.0; Ryman, 2006) was used for significance testing
of allele frequency change over time, while testing for spatial genetic
differentiation was performed in genepop and
statistica. The relationship among genetic clusters and
metapopulations was illustrated with a phylogenetic tree from
Poptree2 (Takezaki et al., 2010).