Stress ecology in fucus: abiotic, biotic and genetic interactions

Stress regimes defined as the synchronous or sequential action of abiotic and biotic stresses determine the performance and distribution of species. The natural patterns of stress to which species are more or less well adapted have recently started to shift and alter under the influence of global change. This was the motivation to review our knowledge on the stress ecology of a benthic key player, the macroalgal genus Fucus. We first provide a comprehensive review of the genus as an ecological model including what is currently known about the major lineages of Fucus species with respect to hybridization, ecotypic differentiation and speciation; as well as life history, population structure and geographic distribution. We then review our current understanding of both extrinsic (abiotic/biotic) and intrinsic (genetic) stress(es) on Fucus species and how they interact with each other. It is concluded that (i) interactive stress effects appear to be equally distributed over additive, antagonistic and synergistic categories at the level of single experiments, but are predominantly additive when averaged over all studies in a meta-analysis of 41 experiments; (ii) juvenile and adult responses to stress frequently differ and (iii) several species or particular populations of Fucus may be relatively unaffected by climate change as a consequence of pre-adapted ecotypes that collectively express wide physiological tolerences. Future research on Fucus should (i) include additional species, (ii) include marginal populations as models for responses to environmental stress; (iii) assess a wider range of stress combinations, including their temporal fluctuations; (iv) better differentiate between stress sensitivity of juvenile versus adult stages; (v) include a functional genomic component in order to better integrate Fucus' ecological and evolutionary responses to stress regimes and (vi) utilize a multivariate modelling approach in order to develop and understand interaction networks.