A central tenet of ecology and biogeography is that the broad outlines of

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 by 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 from the usual underlying hypotheses required for the interpretation of classical analyses, 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 perspectives in ecology and evolution in general, particularly in areas such as conservation biology and epidemiology, where targeting specific populations is crucial.conservation biology ͉ gene flow ͉ networks ͉ population genetics U nderstanding the connectivity between components of a metapopulation system and their role as weak or strong links remains a major challenge of population ecology (1-3). Advances in molecular biology fostered the use of indirect approaches to understand metapopulation structure, based on describing the distribution of gene variants (alleles) in space within the theoretical framework of population genetics (4-7). Yet, the premises of the classical Wright-Fisher model (4, 6), such as ''migration-drift'' and ''mutation-drift'' equilibrium (8), ''equal population sizes'' or symmetrical rate migration among populations, are often violated in real metapopulation systems. Threatened or pathogen species, for example, are precisely studied for their state of demographic disequilibrium due to decline and local extinctions in the first case, or to their complex dynamics of local decline and sudden pandemic burst in the second. Furthermore, the underlying hypotheses of equal population size and symmetrical migration rates hamper the identification of putative population ''hubs'' centralizing migration pathways or acting as sources in a metapopulation system, which is a central issue in ecology in general, and in conservation biology or epidemiology in particular. Finally, complementary methods of genetic structure analyses, such as hierarchical AMOVA and coalescent methods rely on a priori information (or priors) as to the clustering or demographic state of populations, requiring either subjective assumptions or the availability of reliable demographic, historical or ecological information that are seldom available.Network theory is emerging as a powerful tool to un...

Viable populations of species occur in a given place if three conditions are met: the environment at the place is suitable; the species is able to colonize it; co‐occurrence is possible despite or because of interactions with other species. Studies investigating the effects of climate change on species have mainly focused on measuring changes in climate suitability. Complex interactions among species have rarely been explored in such studies. We extend network theory to the analysis of complex patterns of co‐occurrence among species. The framework is used to explore the robustness of networks under climate change. With our data, we show that networks describing the geographic pattern of co‐occurrence among species display properties shared by other complex networks, namely that most species are poorly connected to other species in the network and only a few are highly connected. In our example, species more exposed to climate change tended to be poorly connected to other species within the network, while species more connected tended to be less exposed. Such high connectance would make the co‐occurrence networks more robust to climate change. The proposed framework illustrates how network analysis could be used, together with co‐occurrence data, to help addressing the potential consequences of species interactions in studies of climate change and biodiversity. However, more research is needed to test for links between co‐occurrence and network interactions.