Intra-population genetic variation
Summary statistics of the population genetic parameters within each sampling site and population are listed in Table 1. The observed levels of heterozygosity were generally high in both populations (Ho = 0.65–0.72), and the lowest level was observed for above-barrier subpopulation in both systems. When we compare the allelic richness (Ar) and the expected level of heterozygosity, however, the level of genetic variation was the lowest in the above-control subpopulation in BU (BU_AC). The reduced Ar in BU_AC was consistently observed among the loci, suggesting a genome-wide reduction in the genetic diversity in this subpopulation (Table 2). Estimated inbreeding coefficients (Fis) ranged around 0 (-0.044–0.038), in-dicating no strong signature of inbreeding within each subpopulation. However, a significant heterozygote deficiency was detected in MU_AB (P = 0.014 by Fisher’s exact test of HWE), indicating a slight increase of inbreeding in this subpopulation. The other cases of significant deviation from HWE showed excess, rather than deficiency, of heterozygotes (in BU_AC and MU_C, Table 1).
Table 2. Allelic richness.
loci BU_AB BU_BB BU_AC BU_BC BU-total MU_AB MU_BB MU_C MU-total grand-total
Str60 2.00 2.00 2.00 2.00 2.00 2.99 3.49 3.20 3.25 2.73
SL438 5.00 5.78 4.64 4.65 5.29 4.84 4.00 4.94 4.86 5.14
Ssa100 4.97 6.78 4.87 7.59 7.53 6.44 7.11 6.80 6.88 7.59
Ssa85 5.67 4.79 5.28 5.60 6.12 6.98 6.31 5.44 6.70 6.44
Strutta12 11.90 14.94 5.59 14.06 14.32 15.54 14.31 14.86 16.73 17.32
Str73 3.00 3.00 3.00 4.00 3.65 3.00 3.86 3.84 3.60 3.59
Ssa197 5.68 6.54 5.00 6.33 6.59 6.52 7.28 6.39 7.40 7.33
Str591 2.00 2.99 3.00 3.64 3.11 3.00 3.25 2.99 3.16 3.11
Str85 5.67 6.93 5.67 7.61 7.88 5.72 6.59 7.44 6.74 7.66
SsoSL4 6.89 6.79 6.52 9.17 8.41 11.63 11.37 10.97 11.39 10.72
T3_13 8.66 12.65 6.39 12.71 13.02 11.88 13.00 13.49 13.88 14.47
Str15 3.90 5.37 3.99 5.00 5.49 3.63 4.68 3.98 4.19 5.05
Str2 11.99 17.14 6.42 17.45 16.54 14.03 17.55 16.90 17.87 19.97
average 5.95 7.36 4.80 7.68 7.69 7.40 7.91 7.79 8.20 8.55
Inter-population genetic variation
Fst values were calculated to illustrate the inter-population genetic variation (Table 3). Although Fst was less than 0.01 in one case (MU_BB vs. MU_C), the level of inter-population genetic differentiation was all significant by Fisher’s exact test (P < 0.001, meaning that these populations are all genetically distinct).
The pairwise Fst between the two river systems ranged 0.045–0.089, suggesting the moderate-high level of genetic differentiation between the two systems. Within each river system, the Fst distribution varied:
In BU, the pair-wise Fst ranged 0.030–0.128, whereas the range was much lower in MU (Fst = 0.01–0.02, Table 3). When BU_AC and BU_BC are considered as references, the above-barrier subpopulation was consistently more differentiated than the below-barrier subpopulation. Interestingly, however, the level of genetic differentiation between above- and below-control was twice as high as that between above- and below-barrier in BU (Fst = 0.075 vs. 0.037).
Table 3. Between-population genetic differentiations.
Fst BU_AB BU_BB BU_AC BU_CB MU_AB MU_BB
BU_BB 0.037
BU_AC 0.128 0.083
BU_BC 0.084 0.030 0.075
MU_AB 0.089 0.056 0.078 0.063
MU_BB 0.070 0.055 0.077 0.065 0.021
MU_C 0.070 0.045 0.072 0.053 0.020 0.009
Genetic differentiation between all the pairs above was statistically significant (P<0.0001).
A Bayesian analysis of individual assignments using STRUCTURE provided further insights into the population structure of these river systems (Fig. 3). We first conducted the individual assignments in-cluding all genetic samples, which yielded a clear distinction of the genetic composition of the two river populations (Delta-K value best supported number of genetic groups [K] =2, Evanno et al. 2005). We then analyzed the two river populations separately to maximize the power to identify the genetic structure within each system. In BU, the best supported number of genetic group (K) by Delta-K was two, which generally distinguish BU_AB and BU_BB from BU_AC and BU_BC (Fig. 3a). It suggests that c.a. 2 km of the distance has been efficiently isolating these two pairs of subpopulations. The second supported K was four, in which BU_AC and BU_BC were further genetically distinguished. The genetic differentia-tion of BU_AB and BU_BB was rather moderate in K=2-4 (Fig. 3a). At the individual level, however, the STRUCTURE plots suggest that BU_BB has been receiving many down-migrants from BU_AB, but their inter-cross reproduction is either rare or unsuccessful. BU_AB did not contain almost any individual from BU_BB. In the control region, on the other hand, we found up-migrants than down-migrants more often, indicating that without an artificial barrier, brown trout rather tend to recolonize from the main-stem of the river to its branches.
In MU, the best supported K was four, but the overall low genetic differentiation prevented us to draw a clear conclusion from the STRUCTURE plot (Fig. 3b). Nevertheless, it is obvious that MU_AB is geneti-cally most distinct from the others even at the individual level, and that there is little sign of inter-crosses happening between MU_AB and the other subpopulations in MU.
K=3
K=4
BU_AB BU_BB BU_CA BU_CB
BU_AB BU_BB BU_CA BU_CB
BU_AB BU_BB BU_CA BU_CB
artificial barrier
K=2 (statistically best supported) (overall Fst = 0.078)
Fig. 3a: BU.
K=2
K=3
artificial barrier
MU_C
MU_AB MU_BB
MU_C
MU_AB MU_BB
MU_C
MU_AB MU_BB
K=4 (statistically best supported)
(overall Fst = 0.017)
BU & MU MU
BU & MU
BU MU
K=2 (statistically best supported)
0 10 20 30 40 50 60 70 80 90 100
1 2 3 4 5 6 7 8 9 10
K
Delta-K
Fig. 3b: MU, BU & MU.
Effective and census population size
Estimates of effective population size (Ne) and census population size (Nc) are listed in Table 1. Ne var-ied widely in BU (Ne = 4.8–109.8), whereas Ne in MU was rather similar to each other (Ne = 41.7–59.3).
The observed pattern in Ne was consistent with the genetic variation measure using allelic richness (Ar) in general, with an exception of BU_BC in which Ar was the highest among BU subpopulations yet the Ne estimate was intermediate (Ne = 40.9). Results from the pairwise comparisons for above- and below-barriers in the two systems showed reduced effective population sizes above the below-barriers. But the same pattern was also observed when we compared control-above and -below in BU, and the point estimate of Ne was only 4.8 for BU_AC.
Because the small Ne can be a simple consequence of a small Nc in a subpopulation, we compared Ne and Nc by taking their ratio (Table 1). The general trend was unchanged from the comparisons of Ne among subpopulations. However, in each system the above-barrier and/or the above-control subpopula-tions showed the smallest Ne/Nc ratio (0.04–0.14). Again, the smallest value was observed for BU_AC, but when BU_BC was considered as a control of BU_AC, the reduction in Ne/Nc in BU_AC (74.5 %,
= 1–0.04/0.157) was rather smaller than that in BU_AB for which BU_BB was considered as a control (88.4 %, = 1–0.047/0.405), which may reflect the secondary effect of the artificial barrier between BU_AB and BU_BB. The reduction rate for the above-barrier subpopulation was 50.2 % in MU (MU_AB vs.
MU_BB).
Discussion
Our surveys on brown trout populations in Buechwigger and Muemliswilerbach yielded three major con-clusions: First, the amount of genetic variation in above-barrier populations was consistently lower than below-barrier populations. Second, the above-barrier populations were genetically most distinct from the other populations. Finally, the fine-scale sampling scheme (100–300 m distance between above- and below-subpopulations) enables us to understand the recent events of migration and its consequences:
the STRUCTRE plots revealed that the 100 m-long culvert in BU has been an efficient barrier against the up-migration of brown trout, whereas down-migration is certainly and frequently occurring; nevertheless, we have rather scarce evidence of interbreeding between BU_AB and BU_BB; without an artificial barrier, on the other hand, there was a sign of up-migration activities. Although we admit that our statistical power to draw general conclusions is still limited and we need further surveys on more river systems, our results illustrate the advantages of genetic measures in the field surveys at the fine scale.
One limitation of our survey was that we could not determine the age of the fish samples, which might bias our estimates of the genetic variations and the effective population size. For example, the lowest es-timate of Ne in BU_CA might be caused by a different age structure of the subpopulation, which showed the largest average total length in the system at the sampling dates (Fig. 2). In BU, however, the body size distribution was quite distinct, and we can unambiguously distinguish 0+ fishes from >1+ fishes. And even after eliminating the presumably 0+ fish (< 90 mm in BU), the estimates of the effective population size were very similar to our estimates in Table 1, suggesting that at least the different fraction of 0+ fish in each subpopulation is not the cause of the different Ne among populations.
The result from the two study sites is an interesting contribution how river fragmentation is affecting population genetics of brown trout. We are currently preparing to write a paper for submitting to a peer- reviewed journal like Conservation Genetics”.
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