Less is more? A novel method for identifying and evaluating non-informative tracers in sediment source mixing models

Purpose Accelerated soil erosion poses a global hazard to soil health. Understanding soil and sediment behaviour through sediment fingerprinting enables the monitoring and identification of areas with high sediment delivery. Land-use specific sediment source apportionment is increasingly determined using the Bayesian mixing model MixSIAR with compound-specific stable isotopes (CSSI). Here, we investigate CSSIs of fatty acid (FA) tracer selection with a novel method to identify and investigate the effect of non-informative tracers on model performance. Methods To evaluate CSSI tracer selection, mathematical mixtures were generated using source soils (n = 28) from the Rhine catchment upstream of Basel (Switzerland). Using the continuous ranked probability (CRP) skill score, MixSIAR’s performance was evaluated for 11 combinations of FAs and 15 combinations of FAs with δ 15 N as a mixing line offset tracer. A novel scaling and discrimination analysis (SDA) was also developed to identify tracers with non-unique mixing spaces. Results FA only tracer combinations overestimated pasture contributions while underestimating arable contributions. When compared to models with only FA tracers, utilizing δ 15 N to offset the mixing line resulted in a 28% improvement in the CRP skill score. δ 15 N + δ 13 C FA 26 was the optimal tracer set resulting in a 62% model improvement relative to δ 15 N + all δ 13 C FAs. The novel SDA method demonstrated how δ 13 C FA tracers have a non-unique mixing space and thus behave as non-informative tracers. Importantly, the inclusion of non-informative tracers decreased model performance. Conclusions These results indicate that MixSIAR did not handle non-informative CSSI tracers effectively. Accordingly, it may be advantageous to remove non-informative tracers, and where feasible, all combinations and permutations of tracers should be assessed to optimize tracer selection. Application of these tracer selection steps can help improve and advance the performance of sediment fingerprinting models and ultimately aid in improving erosion mitigation and management strategies.