Browsing by Author "Addy, S."

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    Assessing n-alkane and neutral lipid biomarkers as tracers for land-use specific sediment sources
    (Elsevier, 2023-03-28) Wiltshire, C.; Waine, Toby; Grabowski, Robert C.; Meersmans, Jeroen; Thornton, B.; Addy, S.; Glendell, M.
    Sediment fingerprinting (SF) methods using taxonomic-specific biomarkers such as n-alkanes have been successfully used to distinguish sediment sources originating from different land uses at a catchment scale. In this study, we hypothesise that using a combination of soil biomarkers of plant, fungal and bacterial origin may allow greater discrimination between land uses in SF studies. Furthermore, we assess if the inclusion of short chain (shorter than C22) neutral lipid fatty acids (SC-NLFA) improves land use discrimination, considering the Loch Davan catchment (34 km2) in Scotland as a case study. Fatty acids are commonly used to measure abundance and diversity of soil microbial and fungal communities. The spatial distribution of these soil communities has been shown to depend mainly on soil properties and, therefore, soil types and land management practices. The n-alkane and SC-NLFA concentrations and their compound specific stable isotope signatures (CSSI) in four land cover classes (crop land, pasture, forest, and moorland) were determined and their contribution to six virtual sediment mixture samples was modelled. Using a Bayesian un-mixing model, the performance of the combined n-alkane and SC-NLFA biomarkers in distinguishing sediment sources was assessed. The collection of new empirical data and novel combinations of biomarkers in this study found that land use can be distinguished more accurately in organic sediment fingerprinting when combining n-alkanes and SC-NLFA or using SC-NLFA and their CSSI alone. These results suggest that fingerprinting methods using the output of unmixing models could be improved by the use of multiple tracer sets if there is a commensurate way to determine which tracer set provides the “best” capacity for land use source discrimination. This new contribution to the organic sediment fingerprinting field highlights that different combinations of biomarkers may be required to optimise discrimination between soils from certain land use sources (e.g., arable-pasture). The use of virtual mixtures, as presented in this study, provides a method to determine if addition or removal of tracers can improve relative error in source discrimination. Combining biomarkers from different soil communities could have a significant impact on the identification of recent sources of sediment within catchments and therefore on the development of effective management strategies.
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    Evaluating erosion risk models in a Scottish catchment using organic carbon fingerprinting
    (Springer, 2024-07-10) Wiltshire, Katy; Meersmans, Jeroen; Waine, Toby William; Grabowski, Robert C.; Thornton, Barry; Addy, S.; Glendell, Miriam
    Purpose: Identification of hotspots of accelerated erosion of soil and organic carbon (OC) is critical to the targeting of soil conservation and sediment management measures. The erosion risk map (ERM) developed by Lilly and Baggaley (Soil erosion risk map of Scotland, 2018) for Scotland estimates erosion risk for the specific soil conditions in the region. However, the ERM provides no soil erosion rates. Erosion rates can be estimated by empirical models such as the Revised Universal Soil Loss Equation (RUSLE). Yet, RUSLE was not developed specifically for the soil conditions in Scotland. Therefore, we evaluated the performance of these two erosion models to determine whether RUSLE erosion rate estimates could be used to quantify the amount of soil eroded from high-risk areas identified in the ERM. Methods: The study was conducted in the catchment of Loch Davan, Aberdeenshire, Scotland. Organic carbon loss models were constructed to compare land use specific OC yields based on RUSLE and ERM using OC fingerprinting as a benchmark. The estimated soil erosion rates in this study were also compared with recently published estimates in Scotland (Rickson et al. in Developing a method to estimate the costs of soil erosion in high-risk Scottish catchments, 2019). Results: The region-specific ERM most closely approximated the relative land use OC yields in streambed sediment however, the results of RUSLE were very similar, suggesting that, in this catchment, RUSLE erosion rate estimates could be used to quantify the amount of soil eroded from the high-risk areas identified by ERM. The RUSLE estimates of soil erosion for this catchment were comparable to the soil erosion rates per land use estimated by Rickson et al. (Developing a method to estimate the costs of soil erosion in high-risk Scottish catchments, 2019) in Scottish soils except in the case of pasture/ grassland likely due to the pastures in this catchment being grass ley where periods of surface vegetation cover/root network absence are likely to have generated higher rates of erosion. Conclusion: Selection of suitable erosion risk models can be improved by the combined use of two sediment origin techniques— erosion risk modelling and OC sediment fingerprinting. These methods could, ultimately, support the development of targeted sediment management strategies to maintain healthy soils within the EU and beyond.