Cryptic population structuring in Scandinavian lynx: reply to Pamilo.

In a recent Commentary in this journal, Pamilo (2004) criticized our analysis of the spatial genetic structure of the Eurasian lynx in Scandinavia (Rueness et al. 2003). The analyses uncovered a marked geographical differentiation along the Scandinavian peninsula with an apparent linear gradient in the north-south direction. We used computer simulations to check on the proposition that the observed geographical structure could have arisen by genetic drift and isolation by distance in the approximate 25 generations that have passed since the last bottleneck. Pamilo disapproved of our choice of population model and also how we compared the outcome of the simulations with data. As these issues should be of interest to a wider audience we discuss them in some detail.

Cryptic population structure in a large, mobile mammalian predator: the Scandinavian lynx.

The Eurasian lynx (Lynx lynx) is an example of a species that has gone through a severe bottleneck, leading to near extinction in Scandinavia around 1930-- a pattern shared with several other large carnivorous mammals. Here we extend previous genetic analyses of northern European lynx, confirming that lynx from the Scandinavian Peninsula represent a distinct clade differing clearly from European conspecifics. Furthermore, and despite a recent bottleneck and subsequent range expansion, we detect marked genetic differentiation within Scandinavia. This differentiation is largely manifested as a north-south gradient, with a linear increase in the quantity FST/(1 - FST). Aided by computer simulations we find that this pattern is unlikely to have arisen by random genetic drift in the short time since lynx started to expand in the 1950s, suggesting that the spatial structure may predate the bottleneck. Individual-based analyses indicate that, instead of a continuous gradient, Scandinavian lynx may be structured into three more or less distinct groups, possibly corresponding to northern, central and southern subpopulations. The presence of such structuring was unknown previously and was unexpected from general considerations on the mobility of the species, historical data and the absence of geographical barriers. Our study demonstrates how molecular markers may be used to detect cryptic population structure, invisible using traditional methods.

Phylogeography and population history of Atlantic mackerel (Scomber scombrus L.): a genealogical approach reveals genetic structuring among the eastern Atlantic stocks.

Despite the resolving power of DNA markers, pelagic and migratory marine fish species generally show very little geographical population structuring. In mackerel (Scomber scombrus L.) population differentiation has been detected only at a transatlantic scale. By applying two regions in mitochondrial DNA (mtDNA) (D-loop and cytochrome b (cytb)) in combination with genealogical and frequency-based statistical approaches, our data suggest population differentiation among eastern Atlantic spawning stocks. In contrast, and indicative of homing behaviour, no genetic structuring was observed among shoals of individuals outside the spawning season. Among spawning stocks, mtDNA D-loop sequences detected differentiation within the eastern Atlantic, while the cytb gene detected transatlantic differentiation. The impact of recurrent events (e.g. gene flow restricted by isolation by distance) and historic events (e.g. population range expansions) among spawning stocks was investigated applying a nested cladistic analysis of geographical distribution of cytb haplotype lineages. In the eastern Atlantic, historical population range expansion, presumably in connection with recolonization of northern areas after the last glaciation, is suggested to be the main factor determining mtDNA lineage distribution. This was supported by estimates of mtDNA nucleotide diversity, where the highest diversity was observed for the stock spawning in the Bay of Biscay, for which the size estimate is only 15% of the largest stock (Celtic Sea). In addition to revealing population differentiation, our data demonstrate the importance of sampling strategy and the power of applying statistical methods addressing both ongoing and historical population processes.