The authors have declared that no competing interests exist.
Conceived and designed the experiments: SW CS. Performed the experiments: SW. Analyzed the data: SW. Contributed reagents/materials/analysis tools: SW. Wrote the paper: SW CS.
One of the major distinctions of riparian habitats is their linearity. In linear habitats, gene flow is predicted to follow a one-dimensional stepping stone model, characterized by bidirectional gene flow between neighboring populations. Here, we studied the genetic structure of
Riparian habitats host a rich assemblage of specialist plant species confined to floodplains
One of the important characteristics distinguishing riparian from other habitats is their linearity. Linear habitats may function as corridors, facilitating rapid movement of individuals and genes across a landscape
In plants, quantifying migration is a notoriously difficult task, because it is often not possible to observe the dispersal of propagules directly
The various ways for migration to occur in plant populations allow us to test an explicit hypothesis on gene flow. Our first hypothesis states that gene flow is mainly directed downstream, as expected if hydrochory is the most important dispersal mode. Our alternative hypothesis is that gene flow should be bidirectional as predicted in a riparian species dispersed mainly by a combination of vectors including water, wind, and animals. We approached testing the null hypothesis by making use of migrate-n, a powerful software that allows the quantification of bidirectional migration rates and population sizes in a coalescent framework using Markov Chain Monte Carlo computing, and by quantifying contemporary migration with assignment tests using the software GeneClass2, which can be used to identify and assign first-generation migrants to source populations. In migrate-n, apart from testing upstream vs. downstream stepping-stone migration models, we tested whether populations in a catchment were consistent with a single panmictic population. Last but not least, we tested whether there was statistical support for an island model (gene flow bidirectional and occurring between all populations).
Our second hypothesis relates to the spatial distribution of gene pools in multiple catchments. We hypothesize that in a plant species dispersed via water, there should be genetic divergence between populations from different catchments because the crossing of catchments would not be feasible with this dispersal vector. Hence, if the populations of a riparian plant in multiple catchments have remained isolated over many generations, each catchment should be populated by its unique gene pool (‘one catchment – one gene pool’ hypothesis). Alternatively, gene flow by other vectors than water would lead to the spatial distribution of gene pools across multiple catchments.
Our third objective was to explore patterns of genetic diversity across space and between genetic clusters. We hypothesized that genetic diversity should be related to elevation, with highest diversity in downstream sites as a consequence of seed dispersal with water. Moreover, in agreement with population genetic theory, larger populations should harbor more genetic diversity than smaller populations and downstream populations could be larger owing to the immigration of individuals from upstream sites. Finally, we analyzed the mating system using population-specific inbreeding coefficients
Our fourth hypothesis concerned isolation by distance
Here, we used population genetic analyses of a large dataset of microsatellite genotypes to understand regional patterns of gene flow and genetic diversity in
Analysis of contemporary migration based on first-generation migrants using the software GeneClass2 revealed a number of first-generation migrants within the Inn and Rhine catchments, with migration being directed both upstream and downstream (
Migration between sampled sites | ||
Nr. migrants | Direction | Catchment |
12 | Downstream | Inn |
7 | Upstream | Inn |
5 | Downstream | Rhine |
10 | Upstream | Rhine |
1 | Downstream | Rhone |
2 | Upstream | Rhone |
5 | Downstream | Ticino |
2 | Upstream | Ticino |
Migration from outside the sampled sites | ||
Nr. migrants | Direction | Catchment |
6 | Unknown | Inn |
13 | Unknown | Rhine |
6 | Unknown | Rhone |
3 | Unknown | Ticino |
*In addition, one event between catchments (from Inn to Ticino).
The table gives the number of migrants and the direction of migration in each catchment, assessing migration between the sampling sites and listing migrants that had a high likelihood to originate from outside of the sampled sites.
Model selection based on natural logarithmic Bayes Factors in analysis of recent migration with Migrate-n gave support for migration being directed downstream in a stepping-stone fashion in the Rhine catchment (
Bayes Factors (LBF) | |||||
Catchment | Full | Step bidir | Downstream | Step downst | Panmixia |
Inn | −5,788,873 | −3,493,117 | −688,498 | −786,023 | 0 |
Maggia | −314,121 | — | NC | — | 0 |
Rhine | −6,003,175 | −735,991 | −11,927 | 0 |
−182,051 |
Rhone | −412,522 | — | NC | — | 0 |
Model parameters | |||||
Catchment | Migration rate | Population size | |||
Inn | — | 1625.3 | |||
Maggia | — | 566.9 | |||
Rhine | M1→2 | 8.2 | Pop1 550.2 | ||
M2→3 | 36.1 | Pop2 150.3 | |||
M3→4 | 30.4 | Pop3 416.9 | |||
Pop4 250.3 | |||||
Rhone | — | 991.9 |
Bayes Factors were constructed in comparison with the model with the largest log likelihood (Bayes Factor zero). Model probability was calculated by dividing the marginal likelihood of a given model by the sum of the marginal likelihoods of all models. Model probabilities:
*0.01<
**0.05<
***0.95<
NC, no convergence of model, thus excluded for calculation of Bayes Factors. Migration rate estimates and population sizes of transformed data are shown in the lower panel. Populations were sorted in downstream order with increasing number, i.e. Pop1 was the most upstream. M1→2 denotes the migration rate from population 1 to population 2.
Our data exhibited a high amount of genetic differentiation between populations. Analysis of molecular variance revealed significant genetic structure due to the grouping of sites by river (31.9% of total variance,
A. Allelic richness. The shading of the map shows biogeographic regions of Switzerland, as used in analysis of molecular variance. B. Results from Bayesian analysis of population structure. Map data: modified from Vector25 © 2011 swisstopo (contract number 5704000000); biogeographic regions: modified following data from BAFU, CH-3003 Bern, Switzerland.
Source | Df | SS | Varcomp | Perc |
Between rivers | 11 | 5484.4 | 2.041 | 31.9 |
Between sites within rivers | 19 | 2668.8 | 1.947 | 30.4 |
Between individuals within sites | 1083 | 3605.5 | 0.918 | 14.4 |
Between individuals | 1114 | 1663.5 | 1.493 | 23.3 |
Total | 2227 | 13422.2 | 6.399 | 100.0 |
Value | ||||
0.319 |
||||
0.447 |
||||
0.381 |
||||
0.767 |
The table gives the source of variability, the degrees of freedom, the sum of squares, the variance component, the percentage of variation, and the value of the
*, p<0.001.
The population graph approach revealed two disconnected subnetworks which represented the same groups of individuals detected with Bayesian analysis of population structure (
In agreement with the results from Bayesian analysis of population structure and population graphs, analysis of pairwise
Properties of the collecting sites including their allelic richness and inbreeding coefficients are given in
Pop | Biogeographic region | Catchment | River | N | X | Y | Elev | Pop | HE | AR | |
BED1 | South side of Alps | Ticino | Ticino | 35 | 8.524550 | 46.505080 | 1335 | 350 | 0.090 | 1.229 | 0.350 |
INN1 | Eastern Central Alps | Inn | Ova da Bernina | 39 | 9.919276 | 46.480341 | 1840 | 150 | 0.137 | 1.388 | 0.841 |
INN2 | Eastern Central Alps | Inn | Beverin | 40 | 9.871962 | 46.552636 | 1750 | 75 | 0.160 | 1.439 | 0.774 |
INN3 | Eastern Central Alps | Inn | Ova da Roseg | 40 | 9.892024 | 46.478693 | 1810 | 250 | 0.123 | 1.316 | 0.939 |
INN4 | Eastern Central Alps | Inn | Ova da Morteratsch | 40 | 9.942740 | 46.446552 | 1915 | 150 | 0.113 | 1.393 | 0.678 |
INN5 | Eastern Central Alps | Inn | Inn | 40 | 9.970937 | 46.601308 | 1840 | 150 | 0.153 | 1.512 | 0.722 |
INN6 | Eastern Central Alps | Inn | Inn | 39 | 10.430319 | 46.858684 | 1060 | 75 | 0.413 | 2.707 | 0.153 |
INN7 | South side of Alps | Inn | Ova da Fedox | 9 | 9.751708 | 46.396107 | 2000 | 37.5 | 0.086 | 1.250 | 0.806 |
INN8 | Eastern Central Alps | Inn | Inn | 34 | 10.424082 | 46.853896 | 1115 | 75 | 0.425 | 2.767 | 0.408 |
INN9 | Eastern Central Alps | Inn | Ova da Varusch | 38 | 10.023618 | 46.610131 | 1720 | 38 | 0.132 | 1.410 | 0.830 |
KAN1 | North side of Alps | Rhine | Kander | 37 | 7.674966 | 46.460681 | 1380 | 150 | 0.230 | 1.812 | 0.219 |
MAG1 | South side of Alps | Ticino | Maggia | 38 | 8.682544 | 46.265588 | 340 | 75 | 0.339 | 2.029 | 0.367 |
MAG2 | South side of Alps | Ticino | Maggia | 39 | 8.632446 | 46.294008 | 374 | 1500 | 0.352 | 2.103 | 0.377 |
MÄI1 | South side of Alps | Ticino | Maira | 43 | 9.684693 | 46.397940 | 1580 | 150 | 0.062 | 1.189 | 0.851 |
MÄI2 | South side of Alps | Ticino | Maira | 56 | 9.612635 | 46.349940 | 1060 | 50 | 0.140 | 1.630 | 0.605 |
MOE1 | South side of Alps | Ticino | Moesa | 36 | 9.156040 | 46.247523 | 302 | 40 | 0.221 | 1.615 | 0.184 |
MRH1 | Eastern Central Alps | Rhine | Mittelrhein | 40 | 8.854504 | 46.674300 | 1250 | 75 | 0.280 | 1.808 | 0.185 |
REU1 | North side of Alps | Rhine | Reuss | 19 | 8.394524 | 47.281451 | 500 | 37.5 | 0.154 | 1.413 | |
RHE1 | North side of Alps | Rhine | Alpenrhein | 33 | 9.546762 | 46.939694 | 540 | 350 | 0.384 | 2.545 | 0.278 |
RHE2 | Eastern Central Alps | Rhine | Hinterrhein | 40 | 9.410571 | 46.786434 | 610 | 350 | 0.338 | 2.440 | 0.320 |
RHE3 | North side of Alps | Rhine | Alpenrhein | 44 | 9.491499 | 47.073971 | 470 | 175 | 0.390 | 2.579 | 0.279 |
RHE4 | North side of Alps | Rhine | Alpenrhein | 40 | 9.547920 | 46.960477 | 570 | 50 | 0.358 | 2.397 | 0.351 |
RHE5 | North side of Alps | Rhine | Alpenrhein | 18 | 9.541920 | 47.277910 | 410 | 19 | 0.075 | 1.504 | 0.409 |
RHE6 | North side of Alps | Rhine | Alpenrhein | 17 | 9.507980 | 47.019300 | 500 | 17 | 0.395 | 2.473 | 0.412 |
RHO1 | Western Central Alps | Rhone | Rhone | 44 | 7.575609 | 46.302413 | 540 | 400 | 0.427 | 2.755 | 0.455 |
RHO2 | Western Central Alps | Rhone | La Navisence | 38 | 7.630048 | 46.126273 | 1674 | 75 | 0.029 | 1.109 | 0.370 |
RHO3 | Western Central Alps | Rhone | Rhone | 36 | 7.585912 | 46.306314 | 560 | 250 | 0.392 | 2.704 | 0.073 |
SEN1 | North side of Alps | Rhine | Sense | 39 | 7.296919 | 46.734403 | 829 | 75 | 0.000 | 1.000 | — |
VRH1 | Eastern Central Alps | Rhine | Vorderrhein | 41 | 9.240862 | 46.785215 | 900 | 350 | 0.259 | 1.743 | 0.482 |
VRH2 | Eastern Central Alps | Rhine | Vorderrhein | 21 | 9.175004 | 46.772721 | 710 | 20 | 0.302 | 2.059 | 0.346 |
VRH4 | Eastern Central Alps | Rhine | Vorderrhein | 41 | 8.957355 | 46.727076 | 900 | 75 | 0.422 | 2.500 | 0.306 |
*Population monomorphic.
The table gives the population name, the number of samples analyzed, the GPS coordinates (map datum WGS84), elevation [m], the midpoint of the estimated interval of population size (Pop), gene diversity HE, allelic richness AR, and the inbreeding coefficient
A clear geographic trend in allelic richness was visible in our data: Populations located in the Engadine, the valley of the river Inn in southeastern Switzerland had lower allelic richness than populations from other regions (
The best linear model as determined by AIC predicting allelic richness included the sites' affiliation to genetic clusters identified by Bayesian analysis of population structure (
Model | Parameters | df | SS | MS | p-value | AIC | |
Cluster | 1 | 3.742 | 3.742 | 17.9 | 0.00021 | 43.3 | |
Residuals | 29 | 6.052 | 0.209 | ||||
Elevation | 1 | 3.361 | 3.361 | 15.2 | 0.00054 | 45.2 | |
Residuals | 29 | 6.433 | 0.222 | ||||
Pop.size | 1 | 0.269 | 0.268 | 0.8 | 0.37330 | 57.4 | |
Residuals | 29 | 9.525 | 0.328 | ||||
Cluster | 1 | 3.742 | 3.742 | 18.3 | 0.00020 | 43.6 | |
Elevation | 1 | 0.330 | 0.330 | 1.6 | 0.21410 | ||
Residuals | 28 | 5.722 | 0.204 | ||||
Elevation | 1 | 3.361 | 3.361 | 16.4 | 0.00036 | 43.6 | |
Cluster | 1 | 0.711 | 0.711 | 3.5 | 0.07261 | ||
Residuals | 28 | 5.722 | 0.204 | ||||
log10.Pop.size | 1 | 0.220 | 0.220 | 1.0 | 0.32200 | 44.0 | |
1 | 2.971 | 2.971 | 13.8 | 0.00095 | |||
Residuals | 27 | 5.830 | 0.216 | ||||
Cluster | 1 | 1.369 | 1.369 | 103.7 | 6.44E-11 | −40.8 | |
Residuals | 28 | 0.370 | 0.013 | ||||
Elevation | 1 | 0.914 | 0.914 | 31.0 | 0.00584 | −16.7 | |
Residuals | 28 | 0.824 | 0.029 | ||||
Pop.size | 1 | 0.000 | 0.000 | 0.000 | 0.98740 | 5.7 | |
Residuals | 28 | 1.738 | 0.062 | ||||
Elevation | 1 | 0.914 | 0.914 | 66.8 | 8,85E-09 | −38.8 | |
Cluster | 1 | 0.455 | 0.455 | 33.3 | 3,89E-06 | ||
Residuals | 27 | 0.369 | 0.014 | ||||
Cluster | 1 | 1.369 | 1.369 | 100.1 | 1,41E-10 | −38.8 | |
Elevation | 1 | 0.000 | 0.000 | 0.0 | 0.87200 | ||
Residuals | 27 | 0.369 | 0.014 |
The table gives the degrees of freedom (df), the sum of squares (SS), mean square (MS), the
The values of the inbreeding coefficient
The Mantel test indicated that there was a significant relation between genetic, i.e.
The linear regression equation used to plot the line was log10(
Our data showed that contemporary gene flow in
Contemporary gene flow, as estimated from analysis of first generation migrants based on assignment tests, took place mainly within catchments, with two exceptions where gene movement was detected between catchments in sites that were spatially proximate (from the Engadine to Bergell valley). Hence, dispersal between catchments is possible in
Moreover, contemporary gene flow occurred both in upstream and downstream direction in
Several studies have found support for either bidirectional gene flow or a source/sink scenario
Contemporary migration was bidirectional. In contrast,historic gene flow was directed downstream in the largest catchment, Rhine. Contemporary and historic directionalities of gene flow may differ for several reasons. Historic gene flow reflects the main directionality of gene flow over a long time, and support for the directionality downstream does not mean that there have never been any events in the other direction. Individuals dispersed by the vectors wind/animals could have lower reproductive success in the populations they are dispersed to, and then their genes may not be traced in historic signal. Moreover, we can not rule out that the importance of individual dispersal vectors may have changed over time. For example, it could well be that some dispersal events represent recent human-aided dispersal in the framework of conservation translocations, which would lead to a discrepancy among contemporary and historic directionalities.
Along three catchments, model selection in Migrate-n based on Bayes Factors provided evidence of panmixia. For the Inn catchment, this result seems plausible as genetic differentiation between sites was generally low. For Rhone and Maggia, however, the result of panmixia is in conflict with the strong population subdivision evident from
Our historic gene flow analysis highlights the importance of water and wind in seed dispersal for the Rhine catchment. Several other studies have emphasized the importance of seed dispersal via hydrochory in riparian and aquatic plants
It is obvious from Bayesian analysis of population structure, analysis of molecular variance, population graphs, and from the contemporary pattern of migration that the sites sampled for
Our data rejected the one-catchment, one gene pool hypothesis, under which we would have expected four genetic clusters to occur, each in one catchment. Instead, there were only two clusters, and the same cluster occurred in multiple catchments. None of the studies we examined for riparian and aquatic plant populations found support for the one-catchment, one gene pool hypothesis. Several studies reported multiple gene pools of riparian and aquatic plants in a single catchment
The spatial distribution of a single gene pool across multiple catchments in
The arguably most striking pattern with respect to genetic diversity found in the data was the vast discrepancy in genetic diversity between Clusters 1 and 2. Cluster 2 sites located in the Engadine valley in southeastern Switzerland exhibited a far lower diversity than all remaining sites, with the notable exception of two (SEN1, RHO2).
One result of interest is the vast discrepancy of inbreeding coefficients across sites, indicating geographic variation in mating system. A high level of inbreeding was inferred for sites belonging to Cluster 2 (Engadine). The only other species of
Contrary to the theoretical expectation
We found significant relationships with elevation in allelic richness and
We found statistical support for isolation by distance in the studied sites; genetic differentiation (pairwise standardized
Based on the regression of log10(
Our sampling included 1114 samples collected from 31 sites situated in all geographic regions where
No specific permits were required for the described field studies. The species we are working with,
DNA was extracted using the DNeasy 96 plant kit (Qiagen). PCR, fragment analyses, and genotyping of 20 nuclear microsatellites were performed as described in
Gene diversity and allelic richness were calculated using FSTAT version 2.93; to map the values, allelic richness was averaged over the 20 nuclear microsatellites. Population-specific inbreeding coefficients
We performed analysis of molecular variance in Arlequin. The
In order to depict the genetic relationships between sites, we calculated population graphs in R using the package ‘gstudio’
To estimate contemporary migration patterns, we used an assignment method allowing to detect first-generation migrants implemented in the software GeneClass2 version 2.0
To test specific models about the directionality of gene flow and to obtain Bayesian estimates of effective population sizes and bidirectional rates of migration in
To identify the factors explaining genetic diversity in
Pairwise estimates of genetic distance are not statistically independent; thus, in this case, significance testing of genetic vs. geographic distance through linear regression is not reliable
If the genetic data follow a stepping stone model, gene flow should decrease with distance. More specifically, the log10 of the gene flow estimate
Logistic support was received from Genetic Diversity Centre (GDC) of ETH Zürich, from WSL, and from University of Iceland. We thank A. Minder and T. Torrossi (GDC) for running fragment analyses on an automated sequencer. We are very grateful to B. Krummenacher who collected tissue samples from many populations included in this study. S. Cheenacharoen and Y. Kophimai helped with extracting DNA; C. Cornejo extracted DNA to establish the microsatellites; T. Karpati and C. Spinelli helped with field work. We acknowledge R. Dyer and P. Beerli who kindly supported us with performing the population graph and Migrate-n analyses, respectively. T. Wüst provided support for running analyses on the Hera computer cluster of WSL.