Abstract
Seaweeds are major contributors to coastal primary production and are increasingly targeted for aquaculture and blue carbon strategies. However, general and low-cost models to estimate their productivity and carbon capture remain lacking. We tested whether the surface area to volume ratio (AV) predicts primary productivity across 11 tropical macroalgal species from the Brazilian coast, using ex situ light–dark bottle oxygen incubations. Net primary productivity (NPP), gross primary productivity (GPP), and respiration (R) were quantified and related to species-specific AV values obtained from morphometric analysis. AV was strongly correlated with NPP (r = 0.72) and GPP (r = 0.68), but weakly and negatively with R (r = –0.22). Regression models with AV as the predictor explained 57–65% of the variance in NPP and GPP, enabling derivation of predictive equations for primary productivity and carbon capture. Based on these relationships, we developed C eaweed , a trait-based framework that scales from species to communities by combining AV distributions with species frequencies. This model provides a reproducible and low-cost tool for estimating seaweed carbon capture and supports applications in aquaculture, ecosystem monitoring, and blue carbon accounting. C eaweed represents the first cross-taxa quantitative model linking algal morphology to carbon capture and establishes AV as a predictive functional trait for macroalgal productivity.