Network analysis identifies weak and strong links in a metapopulation system

6 pages, 6 figures.-- Printed version published Dec 2, 2008.-- Supporting information available at: http://www.pnas.org/content/early/2008/11/20/0805571105/suppl/DCSupplemental
An additional movie (22 s) can be found at: http://ifisc.uib-csic.es/publications/downfile.php?fid=3615
ArXiv pre-print version available at: http://arxiv.org/abs/0810.3488
Full-text paper available Open Access at the journal site.
The identification of key populations shaping the structure and connectivity of metapopulation systems is a major challenge in population ecology. The use of molecular markers in the theoretical framework of population genetics has allowed great advances in this field, but the prime question of quantifying the role of each population in the system remains unresolved. Furthermore, the use and interpretation of classical methods are still bounded by the need for a priori information and underlying assumptions that are seldom respected in natural systems. Network theory was applied to map the genetic structure in a metapopulation system using microsatellite data from populations of a threatened seagrass, Posidonia oceanica, across its whole geographical range. The network approach, free from a priori assumptions and of usual underlying hypothesis required for the interpretation of classical analysis, allows both the straightforward characterization of hierarchical population structure and the detection of populations acting as hubs critical for relaying gene flow or sustaining the metapopulation system. This development opens major perspectives in ecology and evolution in general, particularly in areas such as conservation biology and epidemiology, where targeting specific populations is crucial.
We acknowledge financial support from the Spanish MEC (Spain) and FEDER through project FISICOS (FIS2007-60327), the Portuguese FCT and FEDER through project NETWORK(POCI/MAR/57342/2004) a postdoctoral fellowship (SAH), the BBVA Foundation (Spain), and the European Commission through the NEST-Complexity project EDEN (043251).