Physiological community ecology: variation in metabolic activity of ecologically important rocky intertidal...

Rocky intertidal invertebrates live in heterogeneous habitats characterized by steep gradients in wave activity, tidal flux, temperature, food quality and food availability. These environmental factors impact metabolic activity via changes in energy input and stress-induced alteration of energetic demands. For keystone species, small environmentally induced shifts in metabolic activity may lead to disproportionately large impacts on community structure via changes in growth or survival of these key species. Here we use biochemical indicators to assess how natural differences in wave exposure, temperature and food availability may affect metabolic activity of mussels, barnacles, whelks and sea stars living at rocky intertidal sites with different physical and oceanographic characteristics. We show that oxygen consumption rate is correlated with the activity of key metabolic enzymes (e.g., citrate synthase and malate dehydrogenase) for some intertidal species, and concentrations of these enzymes in certain tissues are lower for starved individuals than for those that are well fed. We also show that the ratio of RNA to DNA (an index of protein synthetic capacity) is highly variable in nature and correlates with short-term changes in food availability. We also observed striking patterns in enzyme activity and RNA/DNA in nature, which are related to differences in rocky intertidal community structure. Differences among species and habitats are most pronounced in summer and are linked to high nearshore productivity at sites favored by suspension feeders and to exposure to stressful low-tide air temperatures in areas of low wave splash. These studies illustrate the great promise of using biochemical indicators to test ecological models, which predict changes in community structure along environmental gradients. Our results also suggest that biochemical indices must be carefully validated with laboratory studies, so that the indicator selected is likely to respond to the environmental variables of interest.