The representation of sediment source group tracer distributions in Monte Carlo uncertainty routines for fingerprinting: An analysis of accuracy and precision using data for four contrasting catchments

Previous studies comparing sediment fingerprinting un‐mixing models report large differences in their accuracy. The representation of tracer concentrations in source groups is perhaps the largest difference between published studies. However, the importance of decisions concerning the representation of tracer distributions has not been explored explicitly. Accordingly, potential sediment sources in four contrasting catchments were intensively sampled. Virtual sample mixtures were formed using between 10 and 100% of the retrieved samples to simulate sediment mobilization and delivery from subsections of each catchment. Source apportionment used models with a transformed multivariate normal distribution, normal distribution, 25th–75th percentile distribution and a distribution replicating the retrieved source samples. The accuracy and precision of model results were quantified and the reasons for differences were investigated. The 25th–75th percentile distribution produced the lowest mean inaccuracy (8.8%) and imprecision (8.5%), with the Sample Based distribution being next best (11.5%; 9.3%). The transformed multivariate (16.9%; 17.3%) and untransformed normal distributions (16.3%; 20.8%) performed poorly. When only a small proportion of the source samples formed the virtual mixtures, accuracy decreased with the 25th–75th percentile and Sample Based distributions so that when <20% of source samples were used, the actual mixture composition infrequently fell outside of the range of uncertainty shown in un‐mixing model outputs. Poor performance was due to combined random Monte Carlo numbers generated for all tracers not being viable for the retrieved source samples. Trialling the use of a 25th–75th percentile distribution alongside alternatives may result in significant improvements in both accuracy and precision of fingerprinting estimates, evaluated using virtual mixtures. Caution should be exercised when using a normal type distribution, without exploration of alternatives, as un‐mixing model performance may be unacceptably poor. The representation of source group tracer concentrations is perhaps the largest difference between sediment fingerprinting un‐mixing models. Despite this, the effects of different distributions on model accuracy have not been explored explicitly. ‘This study compared a transformed multivariate normal, a normal and a 25th–75th percentile distribution as well as a distribution replicating the retrieved source samples. The 25th–75th percentile distribution produced the lowest mean inaccuracy (8.8%), with the Sample Based being next best (11.5%). The transformed multivariate (16.9%) and untransformed normal distributions (16.3%) performed poorly

Benchmarking the behaviour and characteristics of wine tourists in an emerging wine region

Exploratory research was undertaken to benchmark the profiles and preferences of regional wine tourists in Nova Scotia. This paper describes the consumer profiles of three wine tourist segments in the region: Wine Lovers; Wine Interested; and Curious Tourists. The data (n = 780) were collected over an eighteen-month period at wineries, winery events, farmers markets, and on the tour bus of a popular wine tour operator. Results provide evidence supporting the wine tourist typology found in the literature and give insights for tourism bodies as well as individual businesses

Apportioning sources of organic matter in streambed sediments: An integrated molecular and compound-specific stable isotope approach

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

Tracing the sources, fate, and recycling of fine sediments across a river-delta interface

Deltaic floodplains are thought to be long-term depositional environments, however there remains a limited understanding regarding timescales of depositional and erosional events, sediment delivery pathways and sediment storage. This study uses sediment concentration and sediment fingerprinting to examine the contribution of surface and subsurface sources to suspended sediment transiting the Lower Roanoke River, North Carolina, United States. The Lower Roanoke is disconnected from its high-gradient uplands in the Piedmont and Appalachian Mountains by a series of dams, which effectively restricts suspended sediment delivery from the headwaters. Accordingly, sediments from the Lower Roanoke River basin are the primary source of suspended sediment downstream of the dams. The fingerprinting method utilized fallout radionuclide tracers (210Pbxs and 137Cs) to examine the spatial variation of sediment-source contributions to suspended-sediment samples (n = 79). Three end-member sources were sampled: 1. surface sources (floodplains and topsoils; n = 60), 2. subsurface sources (channel bed and banks; n = 66), and 3. deltaic sources (delta front and prodelta; n = 11). The results demonstrate that with decreasing river slope and increasing influence of estuarine-driven flow dynamics, the relative contribution of surface sediments to the suspended-sediment load increases from 20% (± 2%) in the upper reach, to 67% (± 1%) in the Roanoke bayhead delta (BHD). At the river mouth, the surface-sediment contribution decreases, and the delta front and prodelta sediments contribute 74% (± 1%) to the suspended load. These results indicate, that during the delta transgression, erosion of the lower delta provides an additional source of sediment to the upper delta. At the same time, the lower deltaic plain, considered a sediment sink and long-term sediment-storage site, becomes erosional. The lower river and distributary network of the delta plain, which were thought to only disperse sediments in a seaward direction, may also have an important landward-directed sediment-dispersal component that provides nourishment and fortification to the upper BHD, at the cost of the eroding lower delta. Recognition of these contrasting sediment pathways in the Roanoke River highlights that these complex bidirectional processes may exist in other eroding deltas. Understanding these bidirectional processes will be necessary for the ongoing management of deltaic environments under increasing anthropogenic stress such as land use change and accelerating sea-level rise

An extended Bayesian sediment fingerprinting mixing model for the full Bayes treatment of geochemical uncertainties

Recent advances in sediment fingerprinting research have seen Bayesian mixing models being increasingly employed as an effective method to coherently translate component uncertainties into source apportionment results. Here, we advance earlier work by presenting an extended Bayesian mixing model capable of providing a full Bayes treatment of geochemical uncertainties. The performance of the extended full Bayes model was assessed against the equivalent empirical Bayes model and traditional frequentist optimisation. The performance of models coded in different Bayesian software (‘JAGS’ and ‘Stan’) was also evaluated, alongside an assessment of model sensitivity to reduced source representativeness and non-conservative fingerprint behaviour. Results revealed comparable accuracy and precision for the full and empirical Bayes models across both synthetic and real sediment geochemistry datasets, demonstrating that the empirical treatment of source data here represents a close approximation of the full Bayes treatment. Contrasts in the performance of models coded in JAGS and Stan revealed that the choice of software employed can impact significantly upon source apportionment results. Bayesian models coded in Stan were the least sensitive to both reduced source representativeness and non-conservative fingerprint behaviour, indicating Stan as the preferred software for future Bayesian sediment fingerprinting studies. Whilst the frequentist optimisation generally yielded comparable accuracy to the Bayesian models, uncertainties around apportionment estimates were substantially greater and the frequentist model was less effective at dealing with non-conservative behaviour. Overall, the effective performance of the extended full Bayes mixing model coded in Stan represents a notable advancement in source apportionment modelling relative to previous approaches. Both the mixing model and the software comparisons presented here should provide useful guidelines for future sediment fingerprinting studies

Sensitivity of fluvial sediment source apportionment to mixing model assumptions: A Bayesian model comparison

Mixing models have become increasingly common tools for apportioning fluvial sediment load to various sediment sources across catchments using a wide variety of Bayesian and frequentist modeling approaches. In this study, we demonstrate how different model setups can impact upon resulting source apportionment estimates in a Bayesian framework via a one-factor-at-a-time (OFAT) sensitivity analysis. We formulate 13 versions of a mixing model, each with different error assumptions and model structural choices, and apply them to sediment geochemistry data from the River Blackwater, Norfolk, UK, to apportion suspended particulate matter (SPM) contributions from three sources (arable topsoils, road verges, and subsurface material) under base flow conditions between August 2012 and August 2013. Whilst all 13 models estimate subsurface sources to be the largest contributor of SPM (median ∼76%), comparison of apportionment estimates reveal varying degrees of sensitivity to changing priors, inclusion of covariance terms, incorporation of time-variant distributions, and methods of proportion characterization. We also demonstrate differences in apportionment results between a full and an empirical Bayesian setup, and between a Bayesian and a frequentist optimization approach. This OFAT sensitivity analysis reveals that mixing model structural choices and error assumptions can significantly impact upon sediment source apportionment results, with estimated median contributions in this study varying by up to 21% between model versions. Users of mixing models are therefore strongly advised to carefully consider and justify their choice of model structure prior to conducting sediment source apportionment investigations

The impact of catchment source group classification on the accuracy of sediment fingerprinting outputs

The objective classification of sediment source groups is at present an under-investigated aspect of source tracing studies, which has the potential to statistically improve discrimination between sediment sources and reduce uncertainty. This paper investigates this potential using three different source group classification schemes. The first classification scheme was simple surface and subsurface groupings (Scheme 1). The tracer signatures were then used in a two-step cluster analysis to identify the sediment source groupings naturally defined by the tracer signatures (Scheme 2). The cluster source groups were then modified by splitting each one into a surface and subsurface component to suit catchment management goals (Scheme 3). The schemes were tested using artificial mixtures of sediment source samples. Controlled corruptions were made to some of the mixtures to mimic the potential causes of tracer non-conservatism present when using tracers in natural fluvial environments. It was determined how accurately the known proportions of sediment sources in the mixtures were identified after unmixing modelling using the three classification schemes. The cluster analysis derived source groups (2) significantly increased tracer variability ratios (inter-/ intra-source group variability) (up to 2122%, median 194%) compared to the surface and subsurface groupings (1). As a result, the composition of the artificial mixtures was identified an average of 9.8% more accurately on the 0e100% contribution scale. It was found that the cluster groups could be reclassified into a surface and subsurface component (3) with no significant increase in composite uncertainty(a 0.1% increase over Scheme 2). The far smaller effects of simulated tracer non-conservatism for the cluster analysis based schemes (2 and 3) was primarily attributed to the increased inter-group variability producing a far larger sediment source signal that the non-conservatism noise (1). Modified cluster analysis based classification methods have the potential to reduce composite uncertainty significantly in future source tracing studies

Sensitivity of source sediment fingerprinting to tracer selection methods

In a context of accelerated soil erosion and sediment supply to water bodies, sediment fingerprinting techniques have received an increasing interest in the last 2 decades. The selection of tracers is a particularly critical step for the subsequent accurate prediction of sediment source contributions. To select tracers, the most conventional approach is the three-step method, although, more recently, the consensus method has also been proposed as an alternative. The outputs of these two approaches were compared in terms of identification of conservative properties, tracer selection, modelled contributions and performance on a single dataset. As for the three-step method, several range test criteria were compared, along with the impact of the discriminant function analysis (DFA). The dataset was composed of tracer properties analysed in soil (three potential sources; n = 56) and sediment core samples (n = 32). Soil and sediment samples were sieved to 63 µm and analysed for organic matter, elemental geochemistry and diffuse visible spectrometry. Virtual mixtures (n = 138) with known source proportions were generated to assess model accuracy of each tracer selection method. The Bayesian un-mixing model MixSIAR was then used to predict source contributions on both virtual mixtures and actual sediments. The different methods tested in the current research can be distributed into three groups according to their sensitivity to the conservative behaviour of properties, which was found to be associated with different predicted source contribution tendencies along the sediment core. The methods selecting the largest number of tracers were associated with a dominant and constant contribution of forests to sediment. In contrast, the methods selecting the lowest number of tracers were associated with a dominant and constant contribution of cropland to sediment. Furthermore, the intermediate selection of tracers led to more balanced contributions of both cropland and forest to sediments. The prediction of the virtual mixtures allowed us to compute several evaluation metrics, which are generally used to support the evaluation of model accuracy for each tracer selection method. However, strong differences or the absence of correspondence were observed between the range of predicted contributions obtained for virtual mixtures and those values obtained for actual sediments. These divergences highlight the fact that evaluation metrics obtained for virtual mixtures may not be directly transferable to models run for actual samples and must be interpreted with caution to avoid over-interpretation or misinterpretation. These divergences may likely be attributed to the occurrence of a not (fully) conservative behaviour of potential tracer properties during erosion, transport and deposition processes, which could not be fully reproduced when generating the virtual mixtures with currently available methods. Future research should develop novel metrics to quantify the conservative behaviour of tracer properties during erosion and transport processes. Furthermore, new methods should be designed to generate virtual mixtures closer to reality and to better evaluate model accuracy. These improvements would contribute to the development of more reliable sediment fingerprinting techniques, which are needed to better support the implementation of effective soil and water conservation measures at the catchment scale.</p

Sediment‐associated organic matter sources and sediment oxygen demand in a Special Area of Conservation (SAC): A case study of the River Axe, UK

Oxygen demand in river substrates providing important habitats for the early life stages of aquatic ecology, including lithophilous fish, can arise due to the oxidation of sediment‐associated organic matter. Oxygen depletion associated with this component of river biogeochemical cycling, will, in part, depend on the sources of such material. A reconnaissance survey was therefore undertaken to assess the relative contributions from bed sediment‐associated organic matter sources potentially impacting on the River Axe Special Area of Conservation (SAC), in SW England. Source fingerprinting, including Monte Carlo uncertainty analysis, suggested that the relative frequency‐weighted average median source contributions ranged between 19% (uncertainty range 0–82%) and 64% (uncertainty range 0–99%) for farmyard manures or slurries, 4% (uncertainty range 0–49%) and 35% (uncertainty range 0–100%) for damaged road verges, 2% (uncertainty range 0–100%) and 68% (uncertainty range 0–100%) for decaying instream vegetation, and 2% (full uncertainty range 0–15%) and 6% (uncertainty range 0–48%) for human septic waste. A reconnaissance survey of sediment oxygen demand (SOD) along the channel designated as a SAC yielded a mean SOD5 of 4 mg O2 g−1 dry sediment and a corresponding SOD20 of 7 mg O2 g−1 dry sediment, compared with respective ranges of 1–15 and 2–30 mg O2 g−1 dry sediment, measured by the authors for a range of river types across the UK. The findings of the reconnaissance survey were used in an agency (SW region) catchment appraisal exercise for informing targeted management to help protect the SAC