Grazing livestock move by Lévy walks: Implications for soil health and environment.

Grazing livestock plays an important role in the context of food security, agricultural sustainability and climate change. Understanding how livestock move and interact with their environment may offer new insights on how grazing practices impact soil and ecosystem functions at spatial and temporal scales where knowledge is currently limited. We characterized daily and seasonal grazing patterns using Global Positioning System (GPS) data from two grazing strategies: conventionally- and rotationally-grazed pastures. Livestock movement was consistent with the so-called Lévy walks, and could thus be simulated with Lévy-walk based probability density functions. Our newly introduced "Moovement model" links grazing patterns with soil structure and related functions by coupling animal movement and soil structure dynamics models, allowing to predict spatially-explicit changes in key soil properties. Predicted post-grazing management-specific bulk densities were consistent with field measurements and confirmed that rotational grazing produced similar disturbance as conventional grazing despite hosting higher stock densities. Harnessing information on livestock movement and its impacts in soil structure within a modelling framework can help testing and optimizing grazing strategies for ameliorating their impact on soil health and environment.

Sediment source fingerprinting: benchmarking recent outputs, remaining challenges and emerging themes.

PURPOSE: This review of sediment source fingerprinting assesses the current state-of-the-art, remaining challenges and emerging themes. It combines inputs from international scientists either with track records in the approach or with expertise relevant to progressing the science. METHODS: Web of Science and Google Scholar were used to review published papers spanning the period 2013-2019, inclusive, to confirm publication trends in quantities of papers by study area country and the types of tracers used. The most recent (2018-2019, inclusive) papers were also benchmarked using a methodological decision-tree published in 2017. SCOPE: Areas requiring further research and international consensus on methodological detail are reviewed, and these comprise spatial variability in tracers and corresponding sampling implications for end-members, temporal variability in tracers and sampling implications for end-members and target sediment, tracer conservation and knowledge-based pre-selection, the physico-chemical basis for source discrimination and dissemination of fingerprinting results to stakeholders. Emerging themes are also discussed: novel tracers, concentration-dependence for biomarkers, combining sediment fingerprinting and age-dating, applications to sediment-bound pollutants, incorporation of supportive spatial information to augment discrimination and modelling, aeolian sediment source fingerprinting, integration with process-based models and development of open-access software tools for data processing. CONCLUSIONS: The popularity of sediment source fingerprinting continues on an upward trend globally, but with this growth comes issues surrounding lack of standardisation and procedural diversity. Nonetheless, the last 2 years have also evidenced growing uptake of critical requirements for robust applications and this review is intended to signpost investigators, both old and new, towards these benchmarks and remaining research challenges for, and emerging options for different applications of, the fingerprinting approach.

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 0-100% 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.

Magnetic susceptibility as a simple tracer for fluvial sediment source ascription during storm events.

Sediment tracing using a single tracer, low frequency magnetic susceptibility (Xlf), was used to apportion suspended sediment to geologically defined source areas and to interpret sediment source changes during flood events in the degraded catchment of the Vuvu River, a headwater tributary of the Mzimbubu River, South Africa. The method was tested as a simple tool for use by catchment managers concerned with controlling erosion. The geology of the 58 km2 catchment comprises two distinct formations: basalt in the upper catchment with a characteristically high magnetic susceptibility and shales with a low magnetic susceptibility in the lower catchment. Application of an unmixing model incorporating a Monte Carlo uncertainty analysis showed that Xlf provided a means to assign the proportion of each geological province contributing to the river's sediment load. Grab water samples were collected at ten-minute intervals during flood events for subsequent analysis of suspended sediment concentration and the magnetic susceptibility of the filtered sediment. Two floods are presented in detail, the first represents a significant event at the start of the wet season (max. discharge 32 m3 s-1); the second was a smaller flood (max discharge 14 m3 s-1) that occurred a month later. Suspended sediment concentrations during the twelve monitored events showed a characteristic decline over the wet season. The main source of suspended sediment was shown to be from the mudstones in the lower catchment, which contributed 86% of the total measured load. The sediment dynamics during the two floods monitored in detail were quite different from each other. In the first the sediment concentration was high (11 g L-1), peaking after the flood peak. The Xlf value increased during the event, indicating that contribution to the sediment load from basalt in the upper catchment increased during the recession limb. In the second, smaller flood the sediment peak (6 g L-1) coincided with the flood peak. Low Xlf values indicated that mudstones in the lower catchment soils dominated the sediment load throughout the entire event. Sediment tracing using a single property (Xlf) was thus used effectively to study changing sediment sources both between and during a flood event in a catchment with strongly contrasting magnetic signatures in different areas. The results support the use of magnetic susceptibility as a simple and cheap tool to determine sediment provenance that can be used to guide catchment restoration in similar environments.

The use of an ordinary colour scanner to fingerprint sediment sources in the South African Karoo.

The widespread adoption of sediment fingerprinting methodologies for the purpose of catchment management has been restricted by the high cost of tracer analysis as well as the potential for significant uncertainties to be present in results. Sediment colour has shown potential to be an inexpensive tracer able to discriminate between sediment sources. However, at present colour has not been demonstrated to be conservative during sediment erosion and transport. Sediment particle size and organic matter have been shown to strongly affect sediment colour, introducing significant uncertainties associated with its use. This study aimed to assess the suitability of colour as a tracer when it is measured using a commercially available colour scanner. The use of hydrogen peroxide (H2O2) to decompose sediment-associated organic matter was assessed as a means of minimising uncertainty. The impact of particle size on the accurate use of colour signatures as a tracer was also assessed. It was concluded that colour performed comparably to mineral magnetic signatures and showed good potential for use as a tracer. The use of H2O2 pre-treatment and limitation of the analysis to either the <32 µm or the >32 µm fraction of the samples were indicated to be important methods to limit uncertainties associated with organic matter and particle size. The methods used were considerably more time and cost effective than the measurement of most conventional tracers.

A reconnaissance survey of channel bank particulate phosphorus concentrations, controls and estimated contributions to riverine loads across England.

Channel banks can contribute a significant proportion of fine-grained (<63 µm) sediment to rivers, thereby also contributing to riverine total particulate phosphorus loads. Improving water quality through better agricultural practices alone can be difficult since the contributions from non-agricultural sources, including channel banks, can generate a 'spatial mismatch' between the efficacy of best management applied on farms and the likelihood of meeting environmental objectives. Our study undertook a reconnaissance survey (n = 76 sites each with 3 profiles sampled) to determine the total phosphorus (TP) concentrations of channel banks across England and to determine if TP content can be predicted using readily accessible secondary data. TP concentrations in adjacent field topsoils, local soil soil type/texture and geological parent material were examined as potential predictors of bank TP. Carbon and nitrogen content were also analysed to explore the impacts of organic matter content on measured TP concentrations. The results suggest that channel bank TP concentrations are primarily controlled by parent material rather than P additions to adjacent topsoils through fertilizer and organic matter inputs, but significant local variability in concentrations prevents the prediction of bank TP content using mapped soil type or geology. A median TP concentration of 873 mg kg-1 was calculated for the middle section of the sampled channel bank profiles, with a 25th percentile of 675 mg kg-1, and 75th percentile of 1159 mg kg-1. Using these concentrations and, in comparison with previously published estimates, the estimated number of inland WFD waterbodies in England for which channel bank erosion contributes >20% of the riverine total PP load increased from 15 to 25 (corresponding range of 17-35 using the 25th and 75th percentiles of measured TP concentrations). Collectively, these 25 waterbodies account for 0.2% of the total inland WFD waterbody area comprising England.

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.

Storm dust source fingerprinting for different particle size fractions using colour and magnetic susceptibility and a Bayesian un-mixing model.

In the context of the continued increased global uptake of fingerprinting procedures to explore fluvial sediment sources, far less attention has been paid to dust source tracing and especially using different particle size fractions and low-cost tracers such as colour and magnetic susceptibility. The objective of this study, therefore, was to apportion local dust storm source contributions for the < 63-µm and 63-125-µm fractions of dust samples in a case study in central Iran. Colour and magnetic susceptibility properties were measured on 62 source samples and six dust storm samples. Statistical methods were used to select four different composite fingerprints for discriminating the dust sediment sources. These statistical approaches comprised (1) the Kruskal-Wallis H test (KW-H), (2) a combination of KW-H and discriminant function analysis (DFA), (3) a combination of KW-H and principal components and classification analysis (PCCA), and (4) a combination of KW-H and a general classification and regression tree model (GCRTM). Local dust source contributions were ascribed using a Bayesian un-mixing model using the final composite fingerprints. For both the < 63- and 63-125-µm fractions, the different composite signatures consistently suggested that alluvial fan material was the dominant source of the dust samples. The root mean square differences between the apportionment results using the different fingerprints ranged from 0.5 to 1.6% for the < 63-µm fraction and from 1.8 to 5.8% for the 63-125-µm fraction. The Wald-Wolfowitz runs test was used to compare the posterior distributions of the predicted source proportions created using the alternative final composite fingerprints and the results indicated that most of the pairwise comparisons were significantly different (p ≤ 0.05). For the < 63-µm fraction, the RMSE and MAE estimates of divergence between the modelled and known virtual source mixtures using the different final composite signatures ranged between 1.5 and 23.4% (with a corresponding mean value of 9.4%). The equivalent estimates for the 63-125-µm fraction were 1.2-20.1% (8.3%). The findings clearly demonstrate that colour and magnetic susceptibility tracers offer low-cost options for apportioning dust sources.