We present a novel application for quantitatively apportioning sources of organic matter in streambed sediments via a coupled molecular and compound-specific isotope analysis (CSIA) of long-chain leaf wax n-alkane biomarkers using a Bayesian mixing model. Leaf wax extracts of 13 plant species were collected from across two environments (aquatic and terrestrial) and four plant functional types (trees, herbaceous perennials, and C3 and C4 graminoids) from the agricultural River Wensum catchment, UK. Seven isotopic (δ13C27, δ13C29, δ13C31, δ13C27-31, δ2H27, δ2H29, and δ2H27-29) and two n-alkane ratio (average chain length (ACL), carbon preference index (CPI)) fingerprints were derived, which successfully differentiated 93% of individual plant specimens by plant functional type. The δ2H values were the strongest discriminators of plants originating from different functional groups, with trees (δ2H27-29=-208‰ to -164‰) and C3 graminoids (δ2H27-29=-259‰ to -221‰) providing the largest contrasts. The δ13C values provided strong discrimination between C3 (δ13C27-31=-37.5‰ to -33.8‰) and C4 (δ13C27-31=-23.5‰ to -23.1‰) plants, but neither δ13C nor δ2H values could uniquely differentiate aquatic and terrestrial species, emphasizing a stronger plant physiological/biochemical rather than environmental control over isotopic differences. ACL and CPI complemented isotopic discrimination, with significantly longer chain lengths recorded for trees and terrestrial plants compared with herbaceous perennials and aquatic species, respectively. Application of a comprehensive Bayesian mixing model for 18 streambed sediments collected between September 2013 and March 2014 revealed considerable temporal variability in the apportionment of organic matter sources. Median organic matter contributions ranged from 22% to 52% for trees, 29% to 50% for herbaceous perennials, 17% to 34% for C3 graminoids and 3% to 7% for C4 graminoids. The results presented here clearly demonstrate the effectiveness of an integrated molecular and stable isotope analysis for quantitatively apportioning, with uncertainty, plant-specific organic matter contributions to streambed sediments via a Bayesian mixing model approach.
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