Tracing pollution in Brown algae: Compositional profiles of potentially toxic elements

Brown macroalgae are widely used as biomonitors of marine pollution, but their Potentially Toxic Element (PTE) composition has never been assessed through compositional data analysis (CoDA). This study presents the first comprehensive application of CoDA to characterize PTE patterns in brown algae worldwide. Across more than 1320 records, Zn consistently dominated tissue compositions (73.8 %), followed by Cu and Ni, while Pb and Cd contributed modestly and showed higher variability. Multivariate analyses revealed structured gradients in elemental composition, with taxonomic identity, particularly species, emerging as the strongest predictor of PTE profiles (R² = 33.9 %). Nevertheless, substantial heterogeneity remained across studies, underscoring the influence of local environmental conditions and methodological differences. This highlights the need for standardized protocols to improve comparability in long-term biomonitoring. Extended profiling of Fucus spp. revealed a pattern dominated by Fe (44.9 %) and Al (36.0 %), with Zn (8.78 %) and As (7.23 %) as secondary contributors, and trace levels of Cu, Ni, Cr, Cd, Pb, and Hg. Significant shifts in Fucus spp. compositional profiles were detected over three decades (1990-2021), with recent samples showing a marked increase in Al, and declines in Cu, Ni, and Cr, suggesting changes in particulate deposition and PTE availability. Compositional analysis proved effective in identifying outlier samples from contaminated sites. Altogether, our results demonstrate that PTE profiles in brown algae are not random but taxonomically structured, reflecting species-specific physiological capacities for PTE regulation as well as differences in habitat and ecological niche. Combining absolute and compositional approaches improves the sensitivity, robustness, and ecological relevance of biomonitoring and bioremediation assessments.