Abstract
The biennial plant Gentianella bohemica is a subendemic of the Bohemian Massif, where it occurs in seminatural grasslands. It has become rare in recent decades as a result of profound changes in land use. Using amplified fragment length polymorphisms (AFLP) fingerprint data, we investigated the genetic structure within and among populations of G. bohemica in Bavaria, the Czech Republic, and the Austrian border region. The aim of our study was (1) to analyze the genetic structure among populations and to discuss these findings in the context of present and historical patterns of connectivity and isolation of populations, (2) to analyze genetic structure among consecutive generations (cohorts of two consecutive years), and (3) to investigate relationships between intrapopulational diversity and effective population size (N(e)) as well as plant traits. (1) The German populations were strongly isolated from each other (pairwise F(ST)= 0.29-0.60) and from all other populations (F(ST)= 0.24-0.49). We found a pattern of near panmixis among the latter (F(ST)= 0.15-0.35) with geographical distance explaining only 8% of the genetic variance. These results were congruent with a principal coordinate analysis (PCoA) and analysis using STRUCTURE to identify genetically coherent groups. These findings are in line with the strong physical barrier and historical constraints, resulting in separation of the German populations from the others. (2) We found pronounced genetic differences between consecutive cohorts of the German populations (pairwise F(ST)= 0.23 and 0.31), which can be explained by local population history (land use, disturbance). (3) Genetic diversity within populations (Shannon index, H(Sh)) was significantly correlated with N(e) (R(S)= 0.733) and reflected a loss of diversity due to several demographic bottlenecks. Overall, we found that the genetic structure in G. bohemica is strongly influenced by historical periods of high connectivity and isolation as well as by marked demographic fluctuations in declining populations.