Shifts in biomass and structure of habitat‐formers across a latitudinal gradient

Abstract

Abstract Global patterns of plant biomass drive the distribution of much of the marine and terrestrial life on Earth. This is because their biomass and physical structure have important consequences for the communities they support by providing food and habitat. In terrestrial ecosystems, temperature is one of the major determinants of plant biomass and can influence plant and leaf morphology. In temperate marine systems, macroalgae are major habitat‐formers and commonly display highly variable morphology in response to local environmental conditions. Variation in their morphology, and thus habitat structure on temperate reefs, however, is poorly understood across large scales. In this study, we used a trait‐based approach to quantify morphological variability in subtidal rocky reefs dominated by the algal genus Sargassum along a latitudinal gradient, in southeastern Australia (~900 km). We tested whether large‐scale variation in sea surface temperature (SST), site exposure, and nutrient availability can predict algal biomass and individual morphology. We found Sargassum biomass declined with increasing maximum SST. We also found that individual morphology varied with abiotic ocean variables. Frond size and intraindividual variability in frond size decreased with increasing with distance from the equator, as SST decreased and nitrate concentration increased. The shape of fronds displayed no clear relationship with any of the abiotic variables measured. These results suggest climate change will cause significant changes to the structure of Sargassum habitats along the southeastern coast of Australia, resulting in an overall reduction in biomass and increase in the prevalence of thalli with large, highly variable fronds. Using a space‐for‐time approach means shifts in morphological trait values can be used as early warning signs of impending species declines and regime shifts. Consequently, by studying traits and how they change across large scales we can potentially predict and anticipate the impacts of environmental change on these communities.