Arctic-Atlantic gradient shaped PFAS exposure variability in sympatric guillemot species off Iceland

Per- and polyfluoroalkyl substances (PFAS) are persistent organic pollutants of growing environmental concern in marine ecosystems. While previous approaches have focused on mean concentrations, we here propose treating PFAS exposure variability as an ecological signal rather than statistical noise. To examine this variability-as-signal hypothesis, we analysed PFAS concentrations in plasma and stable isotopes in plasma and red blood cells from 112 individuals of two sympatric guillemot species ( Uria lomvia , n = 45; U. aalge , n = 67) across five Icelandic colonies during the 2018 breeding season. The dual-tissue isotopic approach allowed us to assess foraging consistency across different temporal scales, providing context for interpreting PFAS exposure patterns. PFAS variability was dominated by two compound groups: Long-chain perfluoroalkyl carboxylic acids (PFCAs, 79% of variance) and perfluorooctane sulfonate (PFOS, 13% of variance). We standardised individual variability into Z-scores to quantify individual expression of these exposure patterns, revealing three distinct variability clusters corresponded with oceanographic transitions between Arctic and Atlantic waters. Segmented regression analysis showed that significant threshold effects at the Arctic-Atlantic habitats (distinguished by isotopic breakpoints δ ¹³ C _consist = 0.19, δ ¹⁵ N _consist = 0.00) with contrasting ecological drivers: PFOS variability responded to habitat-driven shifts (δ ¹³ C, p < 0.01) and PFCA variability to trophic indicators (δ ¹⁵ N). Both species exhibited similar PFAS patterns when foraging in the same water masses, with notable exceptions where niche partitioning occurred at oceanographic boundaries. Our findings demonstrate that water mass characteristics and foraging strategies create structured PFAS variability patterns that reflect local ecological adaptations within broader geographical gradients. This variability-focused framework reveals ecological dimensions of contamination that complement traditional mean-based approaches and may improve understanding of contaminant risks in rapidly changing marine ecosystems.