Spatial trends in the wind abrasion resistance of cultivated machair soil, South Uist, Scottish Outer Hebrides

Catena 135 (2015) 1–10

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Spatial trends in the wind abrasion resistance of cultivated machair soil, South Uist, Scottish Outer Hebrides E.J. Young a,c,⁎, B.M. McKenzie a, J.W. McNicol d, A.H.J. Robertson b, R. Wendler b, S. Dawson c a

The James Hutton Institute, Invergowrie, Dundee, DD2 5DA, UK The James Hutton Institute, Craigiebuckler, Aberdeen, AB15 8QH, UK Geography, School of the Environment, University of Dundee, DD1 4HN, UK d Biomathematics and Statistics Scotland, Invergowrie, Dundee, DD2 5DA, UK b c

a r t i c l e

i n f o

Article history: Received 16 December 2014 Received in revised form 17 June 2015 Accepted 26 June 2015 Available online xxxx Keywords: Machair Fourier transform infrared (FTIR) spectroscopy Soil erosion Wind erosion Wind abrasion resistance

a b s t r a c t The machair is a low-lying coastal grassland system with internationally recognised conservation importance, found only in parts of northern and western Scotland and Ireland. Machair soils are exposed to some of the highest mean wind-speeds in the UK and are susceptible to aeolian erosion. Samples of machair soils were collected from three transects perpendicular to the coast. A simple rotary sieve was used to quantify soil wind abrasion resistance (WAR) of the collected samples. WAR is directly linked to the threshold velocity at which sediment removal is initiated on exposed soil. Sieving subdivided the samples into erodible, potentially erodible, and non-erodible fractions and permitted testing of differences between the fractions. Physical and chemical tests were employed to characterise soil samples and fractions, and in a novel approach, FTIR spectroscopy allowed visualisation of the changes in soil composition along the transects. Principal component analysis and pruned regression trees were employed to model wind abrasion resistance from soil properties, including the FTIR spectra. Results indicated intra- and inter-site variability in wind abrasion covering the full range of possible wind abrasion resistance values. Strong correlations were found between wind abrasion resistance and soil organic matter concentrations, persistence of water repellence, mean particle size, arsenic concentration, water content at time of sampling, and sand mineralogy. For wind abrasion resistance greater than 10 regression tree analysis was capable of predicting whether actual wind abrasion resistance values were low (b 15), medium (N 15 and b35), or high (N 35). © 2015 Published by Elsevier B.V.

1. Introduction Wind erosion of agricultural soil adversely affects soil quality through removal of organic matter, nutrients, and soil particles. Additionally, offsite adverse consequences may include damage to crops and infrastructure. Soil erosion in Scotland is most frequently connected with high rainfall events, but episodes of wind erosion may be locally severe, particularly where the structural stability of sandy coastal soil is low. Machair is a low-lying coastal grassland system, found only in parts of northern and western Scotland and Ireland. Despite its limited geographical extent, the ecological and geomorphological importance of the machair landscape is recognised by a range of national and

Abbreviations: CP, Cille Pheadair; E, erodible; M, Milton; NE, non-erodible; PE, potentially erodible; TB, Tobha Mor; WAR, wind abrasion resistance. ⁎ Corresponding author at: The James Hutton Institute, Invergowrie, Dundee, DD2 5DA, UK. E-mail addresses: [email protected] (E.J. Young), [email protected] (B.M. McKenzie), [email protected] (S. Dawson).

http://dx.doi.org/10.1016/j.catena.2015.06.015 0341-8162/© 2015 Published by Elsevier B.V.

international conservation designations and legislation, including: Special Area of Conservation, Special Protection Area (EC Birds Directive), Site of Special Scientific Interest, National Scenic Area, Geological Conservation Review site, National Nature Reserve, and Environmentally Sensitive Area (Angus and Hansom, 2004). The idealised machair landscape has a characteristic sequence of landforms that changes inland from the coast, which includes the beach, dunes, low-lying grasslands (which have traditionally been used for low intensity cropping), and the ‘blacklands’, a transition zone between the coastal plains and inland peats. Most of the grasslands are cultivated in narrow (typically less than 30 m wide) strips running inland from the coast, and may extend from within metres of the dune crest to the ‘blacklands’ transition zone. Sediment transfer from the beach and dunes to the machair grasslands by wind erosion is part of the natural development of the machair system. However, excessive wind erosion may be caused by poor agricultural management, vegetation denudation and subsoil exposure by rabbit burrowing, and sediment extraction (Angus, 2001; MacDonald, 2011; Ritchie, 1971; SAIR, 2004; Seaton, 1968). The effects can be severe locally, and may include crop burial (Fig. 1), damage to archaeological sites, and blockage of drainage channels (Mather and Ritchie, 1977; Moore et al., 2005; SAIR, 2004; Seaton, 1968).

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Fig. 2. Location of field sites. TB. Tobha Mor. M. Milton. CP. Cille Pheadair. Grey shaded area indicates South Uist Machair National Scenic Area. Adapted from Thorsen et al. (2010). Fig. 1. Windblown sand deposited on agricultural land in close proximity to the coastal dune crest at Cille Pheadair (CP), June 2012.

Several descriptive studies of machair soil have identified trends in soil properties along transects moving inland from the coast, which may influence the susceptibility of the soil to wind erosion (Table 1). However, despite the susceptibility of machair soil to damaging wind erosion, little empirical information is available about soil quality. This may be due in part to difficulties associated with the remoteness of machair locations (particularly in terms of distance from research facilities), and the fragility of the soil (making transportation of large quantities of undisturbed material impractical). For these reasons, sensitivity to wind erosion was investigated using a rotary sieve after Tisdall et al. (2012). This approach facilitated the determination of soil wind abrasion resistance (WAR) which is directly linked to the threshold velocity at which wind erosion is initiated on exposed soil (Hagen et al., 1999). Furthermore, rotary sieving facilitated the separation of machair soils into readily erodible, potentially erodible material, and non-erodible fractions (see Section 3.2.1). The aims of this study were i) to investigate the nature of variations in a range of soil properties on moving across the machair catena; ii) to quantify spatial variations in WAR of machair soils; and iii) to establish which machair soil properties are linked to lower WAR, with implications for management.

2. Investigation area Three field sites in the South Uist Machair National Scenic Area located between 7.38° and 7.46° west, and 57.15° and 57.31° north, and at altitudes between 3 and 16 m above OD (Fig. 2) were selected for study. The coastal machair dune and grassland systems overlie a geomorphologically mature Pliocene strandflat surface of Lewisian gneiss (Dawson et al., 2013). Machair soil is a calcareous regosol and the soils in the ‘blacklands’ transition zone are eutrophic peats and peaty gleys (Soil Survey of Scotland, 1985). The parent materials of machair soils are a combination of silicate sand derived from the gneiss bedrock and windblown calcareous shell sand from the offshore continental shelf.

Land-use is dominated by crofting, a low-intensity form of agriculture that involves strip cultivation, the use of seaweed as fertiliser, and cereal growth (mainly rye, Secale cereale and oats, Avena sativa) on a 2–4 year rotation. During fallow (uncultivated) periods, machair vegetation is predominantly fixed dune grassland communities (Angus, 2001). Soil is generally exposed from April to June after ploughing in late Spring (Wilson, 1978). Trees are extremely rare on the machair grasslands, and there is little scrub-type vegetation. 2.1. Meteorological information The climate is oceanic, with dry summers, and mild, wet winters. Data from the South Uist range meteorological station indicates a mean annual temperature of 8–9 °C, and mean total annual rainfall of 900–1200 mm (UK Meteorological Office data, 2005–2010). The area is characterised by high wind speeds, with an annual mean wind speed of 7–8 ms−1 with prevailing winds from the west and southwest. This wind pattern has contributed to the formation of the machair throughout the Holocene; sand is blown inland from the coastal dunes and is deposited on the grasslands, forming the calcareous machair soils (Angus, 2001). Wind speed distribution by wind direction for wet and dry conditions between April and June is shown in Fig. 3. April to June is the period when machair soil is typically bare, and therefore most susceptible to wind erosion. Hourly wind speed data are from the Benbecula Airport MIDAS station (station ID = 18909), covering the period 1999–2003 (hourly precipitation information is not available for more recent years for this site) (UK Meteorological Office, 2015). 3. Methods 3.1. Field sampling Bulk soil at 0–10 cm depth and soil cores at 0–5 cm depth (Rowell, 1994) were collected at the same time and location in June 2012 from one strip at each site in the 1st year of fallow following cultivation. At each site, sampling was along a transect perpendicular to the coastline with samples collected at 10–13 locations over 300–450 m. Sampling interval varied from 15–55 m, with smaller intervals over profile

Table 1 Trends in machair soil properties from the coast inland towards the blacklands transition zone, and their predicted effect on sensitivity to wind erosion. Soil property

Reference

Expected effect on wind abrasion resistance (WAR) Mechanism

– Decrease in shell derived CaCO3 content – Increase in Corg – Decrease in pH – Decrease in particle size

Angus (2006) Randall (2004) Randall (2004) Randall (2004)

Reduced WAR Increased WAR N/A Reduced or increased WAR

– Increase in water content – Decrease in electrical conductivity

Randall (2004) Increased WAR Randall (2004) N/A

Low density and plate-like shape → preferential entrainment Improved aggregation N/A Smaller particles → preferential entrainment or clay minerals → improved aggregate stabilisation Improved cohesion and abrasion resistance N/A

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The first method used involved dividing the sample into 3 fractions as it passed through the rotary sieve: sediment passing through the sieve within the first 40 s was collected and classified as ‘erodible’ (E); sediment passing through the sieve between 41 s and 300 s was classified as ‘potentially erodible’ (PE); sediment remaining on the rotary sieve after 300 s was classified as ‘non-erodible’ (NE). These soil fractions were retained for further analysis to establish if any chemical or physical differences between E, PE, and NE fractions were present. Mass–time curves in Nelson (1998) and Tisdall et al. (2012) indicate that the rate of material passing through the rotary sieve decreases after ~ 40 s of sieving, hence 40 s was selected as the cut-off between readily erodible (E) and PE material. The second method uses the mass of sediment remaining on the sieve at 14 s (recorded while the first method was conducted) as an indication of the WAR of the soil, as this was found to be sufficient time for N98% of non-aggregated particles b 850 μm in diameter to pass through the sieve (Nelson, 1998), and samples with N 40% of their original mass remaining on the sieve after 14 s are considered to be non-erodible by wind (after Leys et al., 1996). This method of quantifying wind abrasion resistance allows comparison with published investigations of this parameter (e.g. Tisdall et al., 2012).

Fig. 3. Wind speed distribution for A. dry conditions (n = 2427) and B. wet conditions (n = 502) between April and June. Benbecula Airport MIDAS station (station ID = 18909), hourly weather data, 1999–2003. Wind roses produced using Enviroware wind rose generator.

sections with greater apparent variability in soil character. At each of the locations on each transect, four further samples were taken perpendicular to the transect (i.e. parallel to the coast) to equally cover the width of the cultivated strips. Soil was sealed immediately on collection, and stored at 5 °C on return to the laboratory. 3.2. Laboratory methods 3.2.1. Measurement of wind abrasion resistance Two approaches were used to quantitatively investigate wind abrasion: i) dividing soil into fractions based on the mass–time curves obtained from rotary sieving by Nelson (1998), and ii) calculating the wind abrasion resistance (WAR) value (e.g. Leys et al., 1996; Tisdall et al., 2012). This investigation of the sensitivity of machair soil to wind erosion used a modified rotary sieve capable of handling much smaller samples that standard rotary sieves (e.g. Chepil, 1962) to simulate wind abrasion, after the methods of Nelson (1998) and Tisdall et al. (2012). Consistent with Nelson (1998) and Tisdall et al. (2012), a mesh size of 850 μm was used. Air-dry soil samples 40 g (± 0.1 g) were placed in the barrel of the sieve, which then rotated for 300 s at 7 rpm. Material passing through the sieve was collected on a digital electronic balance (± 0.01 g), with the mass recorded every 10 s for the first 40 s, and every 20 s from 41 to 300 s. Four sub-samples of each main transect soil sample were tested, and the mean values used.

3.2.2. Analysis of soil properties Soil properties analysed were generally selected due to their potential to influence soil erodibility by wind (e.g. particle size, organic matter content, water repellency). However, some additional soil properties were also measured as a ‘by product’ of other analyses. For example, it has been suggested that the ratio of carbonate to silicate sands (Randall, 2004), and the use of seaweed fertiliser (Angus, 2001) (with high arsenic concentration) might influence machair soil susceptibility to wind erosion. For resource reasons the full set of properties was measured for 10 locations at each of the three transects. The techniques used to quantify these properties (Fourier transform infrared spectroscopy (FTIR) and X-ray fluorescence (XRF), respectively) generated additional data related to different minerals and elements which were also assessed due to the ready availability of this information, e.g. XRF analysis to characterise arsenic concentrations also provided concentrations for 10 other elements (see Section 3.2.2.2). 3.2.2.1. Analysis of physical soil properties. A Beckman Coulter LS 13 320 Laser Diffraction Particle Size Analyser (e.g. Rowan et al., 2012) and associated software (Beckman Coulter LS 3 Series Version 6.01) were used to determine mean dispersed particle size, skewness, kurtosis, the percentage of each sample consisting of particles with a diameter N850 μm, and the percentage of suspension size particles (b 0.10 mm). Water repellence was assessed by comparison of infiltration rates for water and ethanol after the method of Hallett and Young (1999). The persistence, or stability, of water repellence was characterised by the water drop penetration time (WDPT) method and classification scheme (Doerr et al., 2006; Krammes and Debano, 1965). Bulk density and water content were established on field-moist samples when returned to the laboratory. 3.2.2.2. Analysis of chemical soil properties. The mineralogy and organic matter content of the samples were investigated using Fourier transform infra-red (FTIR) analysis (conducted on bulk soil samples and E, PE, and NE soil fractions). Samples for FTIR analysis were finely milled using a McCrone mill to ensure representative and reproducible spectra were obtained (Robertson et al., 2013). FTIR spectra were recorded on a Bruker Vertex 70 FTIR spectrometer (Bruker, Ettlingen, Germany) fitted with a potassium bromide beam splitter and a deutroglycine sulphate detector. A Diamond Attenuated Total Reflectance (DATR) sampling accessory, with a single reflectance system, was used to produce “transmission-like” spectra. Samples were placed directly on a DATR/KRS-5 crystal, and a flat tip powder press was used to achieve even distribution and contact. Spectra were acquired by averaging 200 scans at 4 cm−1

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Table 2 R values for linear regressions of soil variables against WAR for samples from all sites. Statistical significance (2-tailed T-test) is indicated by asterisks (p b 0.1 = *, p b 0.05 = **, p b 0.01 = ***). Variable

R2

Range

Mean

Bulk (B)/core (C) sample

Elevation Distance from the coast WDPT (class) WDPT (time) Water repellence Water content (field-moist) Bulk density Particle size Particle size (skewness) Particle size (kurtosis) Suspension-size material pH LOI (550 °C) K Ca Ti Cr Mn Fe Co Cu Zn As Sr

−0.30 0.36* −0.41** 0.02 0.29 0.63*** 0.42** 0.56*** 0.42** 0.25 0.02 −0.27 0.66*** −0.28 −0.71*** −0.24 −0.46** −0.16 −0.16 0.44** −0.37** 0.57*** 0.62*** −0.79***

3–16 m 0–440 m 0–9 (after Doerr et al., 2006) 1–6180 s 1.1–172.22 (after Hallett and Young, 1999) 0.35–87.89% (by mass) 0.17–1.19 g/cm3 271.3–506.6 μm 0.55–2.03 −0.32–6.45 0–32.81% 7.86–8.75 1.48–66.59% (by mass) 912–5530 ppm 90,100–334,667 ppm 129–2560 ppm 0–179 ppm 120–558 ppm 5070–20,133 ppm 0–29 ppm 13–24 ppm 40–91 ppm 0–5.7 ppm 438–1657 ppm

5.88 m 180 m – 515 s 19.75 9% 0.88 g/cm3 383.15 μm 1.01 0.90 3.90% 8.42 8.47% 3861 ppm 237,568 ppm 939 ppm 41 ppm 245 ppm 10,809 ppm 8 ppm 19 ppm 58 ppm 1.2 ppm 1297 ppm

N/A N/A C C C C C B B B B C C B B B B B B B B B B B

resolution over the range 370–4000 cm−1. A correction was made to the ATR spectra to allow for differences in depth of beam penetration at different wavelengths, using OPUS software (Bruker, Ettlingen, Germany, version 6.0). The spectra were also baseline corrected, using the OPUS software Automatic Baseline Correction and a rubber band correction method on 64 baseline points. No correction was required for water vapour and CO2 as the spectrometer is continuously flushed with dry air. X-ray fluorescence (XRF) analysis was also used to characterise soil properties. A Bruker S-1 Turbo SD instrument (Bruker Nano GmbH, Berlin) with an Rh tube and a Si drift detector was used to determine the concentration of selected elements (K, Ca, Ti, Cr, Mn, Fe, Co, Cu, Zn, As, and Sr) (Shand and Wendler, 2014). The finely milled soil sample was held in a 25 mm diameter cup with a thin polypropylene window and the acquisition time was 120 s. In the absence of suitable certified reference calcareous soils, quantification of element concentrations in the samples was achieved with the manufactures' soil programme. The performance of the instrument was regularly assessed using a certified reference soil (NIM-GBW07402, from LGC Standards, UK). Soil pH was measured using a Hanna H1 99121 pH metre on field moist soils. As the soil cores were relatively dry they were moistened using HI 7051 aqueous preparation solution prior to measurement. 3.3. Statistical analysis The individual effect of each of the measured soil variables (Table 2), apart from the FTIR responses, on wind abrasion resistance (WAR) was assessed in turn by simple linear regression. These regressions included the concentrations of selected elements obtained using XRF analysis. The non-FTIR responses were also combined in a multiple regression to estimate WAR. This was unsuccessful and as an alternative, a pruned regression tree was fitted. A regression tree is a hierarchical collection of binary nodes and branches. At each node the binary split of the variable or factor which best predicts the response (WAR) is selected. Thus a variable (or a factor) and a dividing point is selected for every node. From each node two branches, each representing one of the subsets of the samples generated by the split of the selected variable or factor, are drawn. The entire process is repeated for each branch until a predetermined degree of predictive accuracy is achieved. At each terminal node the response (WAR) is estimated as the mean response of the

samples belonging to that node. This procedure is vulnerable to overfitting. To prevent over-fitting the original regression tree was pruned. Starting with all the non-FTIR responses, the final tree contained four predictors: arsenic (As) concentrations, WDPT class, WC, and mean PS. To validate the tree two further sample sets were processed, one containing 13 samples which had low WAR values (b10), and one containing 11 samples which covered the full range of measured WAR values (0–40). These sample sets were from the soil samples not initially selected for FTIR and XRF analysis. Principal component analysis (PCA) of the FTIR spectra was used to measure inter- and intra-site variability and to build a multiple regression model to estimate WAR from the spectral data. All statistical analyses were carried out in Genstat for Windows, 15th edition. 4. Results 4.1. Wind abrasion resistance A very strong correlation was found between WAR, and E (R2 = 0.98, p b 0.01, n = 37) and NE (R2 = 0.93, p b 0.01, n = 37) fractions. A weaker, but still significant correlation was found between WAR and the PE fraction (R2 = 0.44, p b 0.01, n = 37). From the four proxies for wind erosion investigated (WAR, E, PE, NE), WAR was selected for comparison with other properties to permit comparison with other work (e.g. Leys et al., 1996; Tisdall et al., 2012). Wind abrasion resistance (WAR) assessed using the modified rotary sieve showed considerable intra- (site CV values of 0.94 (Cille Pheadair (CP)), 1.05 (Tobha Mor (TB)), and 1.67 (Milton (M))) and inter-site variation (CV for all WAR values = 1.57). WAR moving inland along each transect showed a significant increase at both CP and M, but the trajectory differed between the two sites (Fig. 3). At CP, WAR increased linearly along the transect, a distance of ~ 300 m, and the soil was estimated to become non-erodible by wind at distances N60 m inland. At M, WAR was constant along the first 100 m, the increase in WAR occurred from 100 to 250 m, and beyond 250 m the soil was deemed non-erodible. At TB WAR did not increase with distance from the coast. All samples at TB were erodible by wind. Sites CP and M had similar ranges of WAR, with samples from both transects showing close to minimum (0) and maximum (40) potential

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values for WAR (Fig. 4). Samples from TB had both a much smaller range of WAR values, and a much lower mean value. However, mean WAR at CP was approximately twice that at M. As with CP and M, the minimum WAR at TB was close to the minimum possible value, but the maximum was also close to the minimum possible value, at 2.5. Despite the generally low WAR values, and the high percentage of E material in many of the soil samples, there were significant inland sections of CP and M that had considerable WAR and thus were less erodible. Soil is considered non-erodible by wind if the mass of air-dry aggregates N850 μm exceeds 40% of the mass (Leys et al., 1996). On this basis, at CP soil N130 m and at M soil N340 m inland from the coast would be considered non-erodible by wind. While at TB the majority of samples had very low WAR, at CP and M many of the samples had intermediate to high values of WAR, indicating that over large parts of the machair, soil had some resistance to wind abrasion. It should be noted that the results were for topsoil only; where the subsoil is exposed (e.g. by rabbit burrowing, ploughing), WAR is likely to be lower than measured values due to lower organic matter concentration with depth.

4.1.1. Erodible (E), potentially-erodible (PE), and non-erodible (NE) soil fractions The relative amounts of E, PE, and NE fractions confirmed the trends of decreasing susceptibility to wind erosion with distance from the coast identified from WAR. At CP and M, there was a switch from samples which consist of N 90% E material at the coast, to N90% NE material at the inland end of the transect (Fig. 5A and B). At CP, this transition occurred gradually, with an approximately linear decrease in the E fraction (and a concurrent increase in the NE fraction) over the full transect length (~300 m). At M, the switch from E to NE occurred more abruptly, ~100 m from the inland end of the transect. At TB, with all samples containing N90% E material, the pattern was different from CP and M (Fig. 5C). Samples from all three sites generally had very low amounts of PE material. However, at CP the greatest mass of PE material was recorded, peaking at ~30% of the sample mass at the middle of the transect where the switch from soil which is predominantly E material to soil which is predominantly NE material occurred. At M, the greatest proportion of PE material was also found at the distance where the switch from E dominated to NE dominated soil occurred, although this peak was considerably less than at CP, ~8%. All other samples from M, and all samples from TB, included b5% PE material. Loss-on-ignition analysis of E and NE fractions revealed that NE material had higher Corg concentrations (mean = 27%; st. dev. = 20%) than E material (mean = 5%; st. dev. = 2%) (Fig. 6). However, it should be noted that the sample size is small (n = 14) as no NE material was found at TB, and some samples at M and CP did not contain both NE and E fractions.

Fig. 4. Regressions of mean WAR values for each sample against distance from the coast for Cille Pheadair (CP), Milton (M), and Tobha Mor (TB). Note that the maximum possible value for WAR is 40.

Fig. 5. Graphs showing the percentage of each 40 g sample that was erodible, potentially erodible, and non-erodible, with distance from the coast at A) Cille Pheadair CP, B) Milton M, C) Tobha Mor TB.

4.2. Correlations between wind abrasion resistance and soil properties Table 2 shows correlation values (R2) for WAR against a range of soil properties. Correlations with an R2 value N0.5 are shown in Fig. 7. Very strong positive correlations existed between WAR and Zn, LOI, water content at time of sampling, and As. Weaker, but statistically significant, positive correlations existed between WAR and Co, distance from the coast, and PS (skewness). Very strong negative correlations existed between WAR and PS (mean), Sr, and Ca, with weaker, but statistically significant negative correlations, between WAR and WDPT (class), bulk density, Cr, and Cu. No significant correlation with elevation, PS (kurtosis), WDPT (mean time), water repellence, pH, K, Ti, Mn, and Fe were found.

Fig. 6. Box-plot comparing Corg concentrations for E and NE soil fractions.

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Fig. 7. Scatterplots of soil properties with an R2 correlation N0.5 with WAR: A. water content; B. mean particle size; C. LOI; D. calcium; E. zinc; F. arsenic; G. strontium.

4.3. FTIR FTIR analysis showed that the mineralogy for samples from all sites was similar (Fig. 8). The composition of all samples was dominated by carbonate and siliceous sands, with relatively low organic matter content, and very low clay mineral content (generally b1%). For samples from M and CP coastal material (calcareous sands) decreased with distance from the coast, with the mineralogy of inland samples being

progressively more influenced by peat and the silicate bedrock (Fig. 8A and B). Organic matter concentration also increased on moving inland at CP and M, with greater organic matter concentrations at the inland end of the latter site. This was not the case at Tobha Mor, where coastal sediments dominated all samples. A subtle but consistent difference between the spectra of E and NE soil fractions was that the E fractions had lower silicate to carbonate ratio, and concurrently, the NE fractions had lower carbonate to silicate

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0–1250 cm−1, representing predominantly silicate material. PC2 showed the largest peaks for the silicate stretch of the spectra, with smaller peaks at the organic carbon and carbonate parts of the spectra. PC3 showed the largest peaks for the organic carbon stretch, with smaller peaks at the carbonate and silicate stretches. PC4 and PC5 (accounting for 1.1% and 0.4% of variance, respectively) showed variation in the size of peaks across the spectra, with larger peaks focused across the mineral bands for PC4, and across the organic bands for PC5. Multiple regression analysis using the principal components indicated that best predictions of WAR were made using principal components 1–3, and 5 (adj R2 = 65.98%, residual mean square = 90.25) (Fig. 9). Principal component 4 was not used as it was found to have a lower correlation with WAR, and therefore did not contribute to the regression in terms of accounting for WAR variance. 4.5. Regression tree analysis The pruned regression tree had 5 terminal nodes and consisted of responses WDPT class, mean particle size, As content, and water content at the time of sampling, and had a residual mean square of 5.4. The ability of the regression tree to predict WAR was then tested using two further ‘validation’ sample sets, one containing 13 samples which had low WAR values (b 10), and one containing 11 samples which covered the full range of measured WAR values (0–40). These sample sets were from the soil samples not initially selected for FTIR and XRF analysis. Validation using the pruned regression tree was unsuccessful for samples with WAR less than or equal to 10. For samples with WAR greater than 10 the tree predicted accurately whether a sample had a low WAR value (10 b WAR b 15), a medium WAR value (15 b WAR b 35) or a high WAR value (WAR N 35). However, the pruned tree predictions were insensitive within these bands (Fig. 10). The majority of samples measured had low or high WAR values, with very few samples falling into the ‘medium’ category of WAR. 5. Discussion and interpretation Trends in WAR at the three sites investigated indicate that machair soils are at risk of wind erosion, with evidence for gradual or sudden decreases in erodibility with increasing distance from the coast, which are likely to be related to the width of the transition zone between Fig. 8. A) Fourier transform infrared (FTIR) spectra of the extreme coastal sample from each site; B) Fourier transform infrared (FTIR) spectra of the extreme inland sample from each site; C) example spectra of erodible (E) and non-erodible (NE) fractions of the same soil sample (CP5). Note the difference in the ratio of carbonate (1000–1100 cm−1) to silicate (1400–1500 cm−1) bands between the two fractions.

ratio (Fig. 8C). The difference was approximately 5–10% of the absorption band in the spectra. The lower silicate to carbonate ratio in E relative to NE fractions was consistent in samples with low Corg content from the coastal ends of the transects (b5%), but was less obvious as organic carbon content increased (N10%) towards the inland end of the transect. 4.4. Principal component analysis Principal component analysis (PCA), based on the covariance matrix of the FTIR spectra, was used to investigate which parts of the FTIR spectra i) explained inter-site variance between samples, and ii) were correlated with WAR. The first, second, and third principal components accounted for 77.7%, 14.2%, and 6.3% of the variance among spectra, respectively. The greater a wavelength's loading the more that wavelength contributes to a principal component. PC1 showed greatest peaks at wavelengths ~ 1400–1550 cm−1, equivalent to the main carbonate stretch, and smaller peaks at wavelengths ~2500–3750 cm−1, part of the spectra representing organic carbon and clay minerals, and

Fig. 9. Comparison of measured WAR and WAR predicted by principal components 1–3 and 5.

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Fig. 10. Comparison of measured WAR and WAR predicted by the regression tree analysis. V1 = initial samples used for validation; V2 = additional samples used for validation.

calcareous regosols and ‘blacklands’ soils. Evidently there is significant variation between machair systems within one island. During rotary sieving, samples were categorised into erodible (E), potentially erodible (PE), and non-erodible (NE) categories. All soil samples from all sites contained relatively little material classified as PE. Little PE material at TB, most of M, and either end of CP suggest that most machair soil is almost entirely composed of either nonerodible material or erodible material, and that wind abrasion has limited potential to break down non-erodible material to form erodible material, i.e. the change in the rate of material passing through the sieve occurs abruptly (typically in less than 10 s) rather than gradually for the majority of samples. It is only at CP that the evidence is stronger for breakdown of aggregates by wind abrasion. The findings related to soil fractions suggest that the potentially erodible fraction is not a particularly useful concept when investigating machair soils at the three sites studied. However, the high correlation between PE material and the WAR value (as was found with E and NE fractions) indicates that this approach is suitable for assessing wind abrasion risk and comparable with results obtained using other methods (e.g. Leys et al., 1996; Nelson, 1998; Tisdall et al., 2012). The advantage of the E-PE-NE approach to quantifying WAR is that the sub fractions can be independently characterised, and the chemical and/or physical differences between fractions investigated. There are clear implications for management, e.g. identifying key soil properties linked to NE material. A strong positive correlation between WAR and organic matter concentration was expected as improved aggregate formation is observed with greater organic matter concentrations (Chepil, 1954, 1955). The relative concentrations of carbonate and silicate based sand were also expected to affect sensitivity to wind erosion due to the different shapes, sizes, and densities of particles of carbonate and silicate sand, and the effects of these variations on entrainment and transport (Ritchie, 1974). However, results suggest that the ratio of carbonate to silicate based sand only influences erodibility at organic matter concentrations less than 5%. The results from FTIR analysis suggest that higher ratios of shell-derived carbonate to silicate material are correlated with lower WAR. However, the method used to assess sensitivity to wind

erosion by assessing WAR does not test for preferential entrainment or duration of transport. This suggests that the carbonate/silicate ratio affects aggregate strength and/or formation, as well as potentially influencing transport dynamics. Previous studies have found mixed relationships between carbonate and WAR. Black and Chanasyk (1989) found that adding carbonate to soil at concentrations b 10% decreased WAR, while Chepil (1954) found that increasing calcium carbonate from 3% to 10% led to an increased WAR in a highly erodible soil. Qualitative consideration of the FTIR results (Fig. 6, Section 4.3) indicates tentative support for this relationship, however further quantitative analysis of the spectra would be required to establish whether there is any significant correlation. However, it is recognised that most studies of the impact of carbonate material on soil erodibility are conducted using synthetic carbonates which are unlikely to react with soils in the same way as the calcareous shell fragments present in machair soils. The predictions of WAR using PCA were generally more accurate at lower actual values of WAR (b 15). This may be due to the significance of carbonate (PC1) and silicate (PC2) wavelengths in the predictions; which appear, from the FTIR data to influence WAR where organic matter concentrations are low (b 10%). Furthermore, FTIR analysis showed that non-erodible soil fractions generally contained slightly less carbonate material than their erodible counterparts (typically 5–10% less). That mean particle size was negatively correlated with WAR may be linked to the results for carbonate by differences in the shape, size, and density of carbonate particles relative to silicate particles. However, the relationship between coarser soil texture and low WAR is widely acknowledged (e.g. Black and Chanasyk, 1989; Chepil, 1958), and is valid for a range of soil mineralogies. The results for water content and related soil properties agree with previous findings. However, as samples were air dry when sieved, the strong positive correlation found between water content (measured on field moist samples) and WAR (measured on air dried samples) may be due to the greater proximity of poorly drained soil samples to the ‘blacklands’ transition zone, where soils would be expected to have a higher organic matter concentration, and a greater silicate to carbonate ratio. Regression tree analysis identified WDPT class as a key property for predicting WAR. WDPT is a measure of the persistence of water repellence. For the machair soil, more persistent water repellence was correlated with lower WAR. The effect of water repellence on erosion is usually considered with relation to erosion by water with increased run-off generated by low infiltration (e.g. Wessel, 2006), but water repellent soils are usually poorly aggregated, as is the machair soil. Some of the significant correlations between WAR and elements (investigated using XRF analysis) are unexpected. While the strong negative correlation between calcium and WAR is to be expected as a consequence of the presence of calcium in calcium carbonate, the strong positive correlations between WAR and zinc and arsenic are less easy to explain. The relationship with arsenic may be due to the use of seaweed fertiliser on the machair, which has a high arsenic content (Castlehouse et al., 2003), or due to association with organic matter. This would imply that the use of seaweed fertiliser may improve soil wind abrasion resistance. While Thorsen et al. (2010) showed that the application of kelp to machair soil was not required annually to improve soil aggregation, a gradual increase over time is in agreement with local perceptions that using seaweed as fertiliser reduces soil erosion by wind (Angus, 2001). It should be noted that plotting arsenic against WAR shows no relationship between arsenic and WAR at Tobha Mor, and the relationship appears to be strongest at Cille Pheadair. This suggests that the relationship between arsenic and wind abrasion resistance may be complicated, and possibly dependent on other soil properties, e.g. mineralogy or differences in frequency or form of seaweed application. The strong negative correlation between WAR and Sr may be a consequence of the relationship between Sr and Ca (R2 = 0.84 for the soil samples investigated), e.g. substitution of Sr for Ca during dynamic sedimentary processes (Salminen et al., 2005).

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The two approaches used to predict WAR (PCA, regression tree) indicated that the concentrations of carbonate, silicate, and organic matter (PCA prediction of WAR), and WDPT class, mean particle size, water content, and arsenic (potentially related to organic matter) (regression tree prediction of WAR) were key variables to estimate wind abrasion resistance. For each of these a mechanism of action can be proposed. However, the absence of organic matter from the latter set of properties, and its relatively low significance in the former (PC3, explaining 6.3% of variance) is surprising given that the relationship between organic matter and WAR is widely recognised and generally strong. However, it is possible that the absence of organic matter in the models may be attributable to correlations with other variables. A moderate correlation was found between organic matter and water content (R2 = 0.44), but correlations with other properties included in the model were low (R2 b 0.2). It should be noted that the concentrations of organic matter here are very low (b 5%) for many of the soil samples and hence may not be present in sufficient concentrations to significantly improve WAR, with the results that other soil properties may have more influence. The quantification of trends in WAR and other soil properties across the cultivated machair from the coast to the blacklands support the general observations of e.g. Ritchie (1974), Randall (2004), Angus (2001) (e.g. increased organic matter and silicate sand, decreased carbonate material on moving inland). However, they also indicate considerable inter-site variation in the trends observed, the range of WAR values, and the distances over which changes in WAR occur. This suggests that a ‘blanket’ approach for improving soil stability on the sandy machair soils may not be appropriate or useful; instead, management may need to be modified for each location. For example, soil properties and WAR 250 m from the coast vary dramatically between sites; at this distance from the coast at CP, WAR is high, and there may be no need for intervention to improve WAR, while at TB and M, WAR is very low at this distance, and could usefully be improved. The high variation in WAR at any given distance from the coast is likely to be a combination of several pedagogical and geomorphological factors, e.g. the relative proximity to organic-rich peat soils, water table levels, and the nature of sediment transport dynamics from the coastal dunes (if present). The nature (gradual or sudden) and width of the ‘blacklands’ transition zone may also play a role. Some of the properties identified as corresponding to high WAR can be estimated relatively-easily in the field, for example, the amount of soil organic matter, water content, and the relative abundance of silicate and carbonate sand and this will increase with methodological advances. Although the methods used to model WAR were unable to accurately predict WAR values from soil properties, they provided reasonable estimations of whether WAR was high, intermediate, or low which may be more useful in terms of management than an exact value given the large range of WAR values found. Furthermore, the results also indicate the potential of FTIR as a predictive method with the advent of portable FTIR devices; based on the results presented in this paper from machair soils, it is evident that FTIR spectra encompass the majority of properties correlated with WAR.

6. Conclusions We investigated variations in wind abrasion resistance on moving across cultivated plots within the machair landscape of South Uist, by separating soils into E, PE, and NE fractions; in particular, the E and NE fractions were found to be useful concepts for investigating machair soils. Many of the soil properties correlated with WAR are relatively easy to measure, or can even be estimated by eye in the field (e.g. carbonate and silicate dominated sands are visibly different). Attempts to predict WAR from machair soil properties have merit; while it was not possible to accurately estimate actual WAR values, a pruned regression tree was capable of estimating whether WAR was high (N 35), moderate

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(N15 and b 35), or low (b 15), which may be sufficient for management purposes. The results indicate that WAR is very low for the samples taken closest to the coast at all three sites. This is of concern as much of the machair coast is experiencing coastal retreat, with slumping and undercutting of the machair grasslands where they meet the beach or dune crest (Mather and Ritchie, 1977). This is of particular note at Cille Pheadair, where cultivated land stretches to within a few metres of the eroding dune crest. During several site visits sand entrainment within 3 m of the dune crest was visible. While the transfer of sand is a natural part of machair sediment dynamics, the lack of dune vegetation at Cille Pheadair and the proximity of cultivation to the dune crest make the replacement of lost sand unlikely. In contrast to Cille Pheadair, an area of dune vegetation (~20–30 m wide) is left uncultivated at Milton and Tobha Mor. It may be beneficial to leave such a margin at the coastal end of all cultivated areas where coastal erosion is a risk, and/or to maximise measures to prevent soil erosion over areas of low WAR close to the coast. Without further investigation of other machair systems within and without South Uist, it is not possible to determine which relationship between WAR and distance from the coast is more typical of the machair, or whether high variability between systems would be found throughout other machair areas. Establishing this information has important implications for management. However, it should be noted that this study has not investigated whether the zone of erodible/nonerodible soil varies seasonally or annually. This would be a useful avenue for further work, particularly in light of climate predictions for this area which indicate that drier summers and windier conditions can be expected (UKCIP, 2010); factors which may seasonally further exacerbate wind erosion. Further research might also usefully establish the most appropriate methods for raising soil WAR while maintaining the traditional, low-intensity management practices identified as important for maintaining the unique machair fauna and flora. Acknowledgements This work was partly funded by the Rural & Environment Science & Analytical Services Division of the Scottish Government (R033003). EJY acknowledges financial support from a University of Dundee, James Hutton Institute CECHR scholarship. FTIR analysis was carried out by Angela Main, and we thank Allan Lilly for comments on an earlier version of the manuscript. References Angus, S., 2001. The Outer Hebrides: Moor and Machair. The White Horse Press, Cambridge. Angus, S., 2006. De tha machair? Towards a machair definition. Sand Dune Machair. 4, 7–22. Angus, S., Hansom, J., 2004. Tir a'Mhachair, Tir nan Loch? Climate change scenarios for Scottish Machair systems: a wetter future? In: Green, D.R. (Ed.), Littoral 2004: 7th International Symposium: Delivering Sustainable Coasts: Connecting Science and Policy. Aberdeen, Scotland, 20th–22nd September 2004. Cambridge Publications, pp. 565–569 Black, J.M.W., Chanasyk, D.S., 1989. The wind erodibility of some Alberta soils after seeding: aggregation in relation to field parameters. Can. J. Soil Sci. 69, 835–847. Castlehouse, H., Smith, C., Raab, A., Deacon, C., Meharg, A.A., Feldmann, J., 2003. Biotransformation and accumulation of arsenic in soil amended with seaweed. Environ. Sci. Technol. 37, 951–957. Chepil, W.S., 1954. Factors that influence clod structure and erodibility of soil by wind: III. Calcium carbonate and decomposed organic matter. Soil Sci. 77, 473–480. Chepil, W.S., 1955. Factors that influence clod structure and erodibility of soil by wind: V. Organic matter at various stages of decomposition. Soil Sci. 80, 413–421. Chepil, W.S., 1958. Soil conditions that influence wind erosion. USDA-ARS. Technical Bulletin No. 1158. U.S. Government Print Office, Washington D.C. Chepil, W.S., 1962. A compact rotary sieve and the importance of dry sieving in physical analysis. Soil Sci. Soc. Am. J. 26, 4–6. Dawson, A.G., Dawson, S., Cooper, J.A.G., Gemmell, A., Bates, R., 2013. A Pliocene age and origin for the strandflat of the Western Isles of Scotland: a speculative hypothesis. Geol. Mag. 150 (02), 360–366. Doerr, S.H., Shakesby, R.A., Blake, W.H., Humphreys, G.S., Chafer, C., Wallbrink, P.J., 2006. Occurrence, prediction and hydrological effects of water repellency amongst major soil and land use types in a humid temperate climate. Eur. J. Soil Sci. 57, 741–754.

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