Seaweed functional diversity revisited: Confronting traditional groups with quantitative traits

Macroalgal (seaweed) beds and forests fuel coastal ecosystems and are rapidly reorganizing under global change, but quantifying their functional structure still relies on binning species into coarse groups on the assumption that they adequately capture relevant underlying traits. To interrogate this 'group gambit', we measured 12 traits relating to competitive dominance and resource economics across 95 macroalgal species collected from the UK and widespread on North-East Atlantic rocky shores. We assessed the amount of trait variation explained by commonly used traditional groups-(a) two schemes based on gross morphology and anatomy and (b) two categorizations of vertical space use-and examined species reclassification into post hoc, so-called emergent groups arising from the functional trait dataset. We then offer an alternative, emergent grouping scheme of macroalgal functional diversity. (a) Morphology and anatomy-based groups explained slightly more than a third of multivariate trait expression with considerable group overlap (i.e. low precision) and extensive mismatch with underlying trait expression (i.e. low accuracy). (b) Categorizations of vertical space use accounted for about a quarter of multivariate trait expression with considerable group overlap. Nonetheless, turf species tended to display attributes of opportunistic forms. (c) A nine-group emergent scheme provided a highly explanatory and parsimonious alternative to traditional functional groupings. Synthesis. Our analysis using a comprehensive dataset of directly measured functional traits revealed a general mismatch between traditional groups and underlying traits, highlighting the deficiencies of the group gambit in macroalgae. While existing grouping schemes may allow first order approximations, they risk considerable loss of information at the trait and, potentially, ecosystem levels. Instead, we call for further development of a trait-based approach to macroalgal functional ecology to capture unfolding community and ecosystem changes with greater accuracy and generality.