Variation in sediment stability and relation to indicators of microbial abundance in the Okura Estuary, New Zealand

Estuarine, Coastal and Shelf Science 57 (2003) 123–136

Variation in sediment stability and relation to indicators of microbial abundance in the Okura Estuary, New Zealand S.D. Lelievelda, C.A. Pilditchb,*, M.O. Greenc a Department of Earth Sciences, University of Waikato, Private Bag 3105, Hamilton, New Zealand Department of Biological Sciences, University of Waikato, Private Bag 3105, Hamilton, New Zealand c National Institute of Water and Atmospheric Research (NIWA), P.O. Box 11-115, Hamilton, New Zealand b

Received 2 April 2001; received in revised form 14 May 2002; accepted 20 May 2002

Abstract Secretions of mucus by benthic microbes potentially bind estuarine intertidal sediment, thus affecting stability by raising the erosion threshold. Existing models for predicting onset of erosion—such as the Shields diagram—have been built from laboratory studies of abiotic sediments, hence their accuracy when applied to natural sediment may be limited. In this study, variability in critical shear velocity (u*crit) of natural intertidal sediments is correlated with indicators of microbial abundance, and based on those correlations we develop a predictor for the erosion threshold of natural sediments. Sediment cores, collected over 9 months from four sites of contrasting grain size (77–185 lm) in the Okura Estuary (Auckland, New Zealand), were eroded in a laboratory flume to determine u*crit. Critical shear velocity for initiation of motion ranged from a minimum of 0.52 cm s
1 at the coarse-grained site to a maximum of 1.45 cm s
1 at the fine-grained site, values of which are up to three times those measured for equivalent abiotic sediment. The increase in u*crit with decreasing grain size was correlated with indicators of microalgal biomass (pigment) and mucilage content (carbohydrate) in the surface 2 mm of sediment. Abiotic measures of hcrit (non-dimensional u*crit) were adjusted via a stability factor, which was expressed as a function of sediment pheopigment content to estimate the erosion threshold in natural sediments in a better way. This method takes into account the temporal variations in sediment stability that occur irrespective of grain size. Ó 2003 Elsevier Science B.V. All rights reserved. Keywords: sediment entrainment; tidal flats; microbial biomass; stabilization; pigments; polysaccharides; temporal variations; New Zealand

1. Introduction Sediment transport is an issue of central importance to workers from a multitude of disciplines. For example, engineers are concerned with estimating the effects of sediment movement in relation to structure stability, shoreline dynamics, and navigational safety (Krone, 1976). Biologists are interested in sediment transport regimes owing to their controlling effect on benthic community composition (Hall, 1994). This control is achieved via influence on benthic recruitment (e.g. Rhoads & Young, 1970), post-larval dispersal (e.g. Commito, Thrush, Pridmore, Hewitt, & Cummings, 1995; Emerson & Grant, 1991), benthic secondary pro-

* Corresponding author. E-mail address: [email protected] (C.A. Pilditch).

duction (Emerson, 1989), and regulation of material fluxes between benthic and pelagic environments (e.g. Grant, Mills, & Hopper, 1986). An ability to accurately predict sediment transport is clearly desirable. The critical bed shear stress for initiation of sediment motion, scrit, is commonly written in non-dimensional form as the ÔShields parameterÕ, hcrit: hcrit ¼

scrit ðqs
qÞgD

ð1Þ

where q and qs are fluid and sediment density, respectively, g is acceleration due to gravity, D is sediment grain size, and scrit ¼ qUcrit 2 ; where U*crit is the critical friction velocity. The ÔShields diagramÕ, which relates hcrit to flow and sediment variables, is commonly used to predict onset of sediment transport on flat and rippled beds under both steady and unsteady flows,

0272-7714/03/$ - see front matter Ó 2003 Elsevier Science B.V. All rights reserved. doi:10.1016/S0272-7714(02)00336-0

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although strictly speaking, it is applicable only to the ideal case of steady, uniform flow over a plane bed composed of well-sorted particles (Heinzelmann & Wallisch, 1991; Larsen, Sternberg, Shi, Marsden, & Thomas, 1981). Furthermore, laboratory experiments that underpin the Shields diagram have used artificially settled abiotic sediment, even though natural sediments are inhabited by organisms that influence erodibility by modifying grain characteristics (Nowell, Jumars, & Eckman, 1981), and indeed the bulk of theory on initial sediment motion has been derived in this manner (Hall, 1994). Divergence between actual and abiotic-based prediction of onset of sediment transport can therefore be expected. The aim of this study is to quantify the divergence, and to prepare modifications of abiotic approximations of u*crit to improve predictions. Organisms can increase or decrease sediment erosion threshold relative to abiotic sediment, leading to stabilization and destabilization, respectively (Jumars & Nowell, 1984). One mechanism of stabilization is sediment binding by microbial mucous secretions (extracellular polymeric substances or EPS). All ÔmicrobesÕ—the collective term for microalgae (including diatoms), cyanobacteria and bacteria—contribute to the pool of EPS in the sediment (Taylor & Paterson, 1998). Benthic diatoms are ubiquitous in intertidal sediments (Grant, 1988) and secrete carbohydrate-rich EPS for the purposes of attachment and locomotion (Edgar & PickettHeaps, 1984). Non-motile epipsammic diatoms remain attached to sediment grains, while epipelic diatoms actively move through the sediment, and this latter mode of life is associated with higher mucilage-production rates (Vos, de Boer, & Misdorp, 1988). Fine sediments tend to favor the growth of epipelic diatoms, whilst both forms can be present in sandy sediments (Yallop, de Winder, Paterson, & Stal, 1994). Accumulations of microbial cells and mucilage (ÔbiofilmsÕ) at the sediment surface lower sediment susceptibility to erosion by reducing bottom roughness, and hence surface frictional drag, as well as by increasing intergrain-adhesion (Grant, 1988). Increased erosion resistance as a result of microbial mediation has been demonstrated in both laboratory (e.g. Holland, Zingmark, & Dean, 1974; Madsen, Nilsson, & Sundba¨ck, 1993; Sutherland, Grant, & Amos, 1998; Vos et al., 1988) and field (e.g. Austen, Andersen, & Edelvang, 1999; Daborn et al., 1993; De Boer, 1981; Grant, Bathmann, & Mills, 1986; Grant & Gust, 1987; Grant, Mills et al.,1986; Neumann, Gebelein, & Scoffin, 1970; Sutherland, Amos, & Grant, 1998a; Underwood & Paterson, 1993a,b; Yallop et al., 1994) studies. The range of increase relative to abiotictype control sediments is 200–500%, casting doubt on the predictive capacity of erosion threshold models based on abiotic sediments. While diatom films have a stabilizing effect on sediments, they may be eroded and lost during storms

(Colijn & Dijkema, 1981; Grant, 1988). In shallow environments, wave-orbital motions and associated turbulence erode sediments and influence development of microbial populations (Delgado, de Jonge, & Peletier, 1991). The persistence of microbes therefore depends upon the physical stability of the substrate (Vos et al., 1988). Additionally, feeding by benthic macrofauna can decrease microbial biomass (e.g. Pace, Shimmel, & Darley, 1979) and sediment EPS content due to ingestion, as organisms consume particles and attached microbes (Decho, 1990). Bioturbation of surficial sediment as a result of activities, such as tracking and burrowing also has the potential to decrease microbial biomass (Hall, 1994). This illustrates the interplay of microbially induced sediment stabilization with destabilization by both physical and biological reworking. Of the studies that have concurrently measured in situ sediment erodibility and biological parameters associated with microbial abundance, several have identified potential indicators of erodibility, including microalgal biomass (chlorophyll a) and EPS (carbohydrate) content (e.g. Dade et al., 1990; Grant & Gust, 1987; Sutherland et al., 1998a; Underwood & Paterson, 1993b). Previous studies have typically focused on sediments with visible microbial accumulations (e.g. Grant, Bathmann et al., 1986; Grant & Gust, 1987; Yallop et al., 1994) that have an inferred binding effect. However, visible accumulations are not ubiquitous features of intertidal environments. In this study, we address the link between sediment erosion threshold and microbial abundance on a New Zealand sandflat devoid of visible microbial accumulations, which is more representative of intertidal areas, at least in New Zealand. Few studies on sediment erodibility and relation to indicators of microbial abundance comprise both spatial and temporal (i.e. >1 season) dimensions, and those that do (e.g. De Brouwer, Bjelic, de Deckere, & Stal, 2000) have typically been carried out at northern-hemisphere mudflats exposed to seasonal climatic extremes. In this study, we quantify spatial and temporal erosion thresholds of intertidal sediment in a setting where milder seasonal variation in light and temperature is presumed to weaken the seasonal influence on primary production and, hence, the potential for annual variation in microbially induced sediment stabilization. Finally, we correlate variability in u*crit with indicators of microbial abundance, and based on those correlations, we develop a predictor for erosion threshold of natural sediments. The new predictor is more accurate than standard abiotic threshold models when applied to biotic sediments. 2. Study area Okura Estuary lies on the east coast of New Zealand approximately 25 km north of Auckland (Fig. 1). At the mouth, the estuary is 600 m wide, narrowing to less than 30 m at the head, some 3.5 km inland. The estuary

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3. Materials and methods 3.1. Sampling protocol

Fig. 1. Map of study area showing sampling sites. Shaded areas indicate intertidal flats.

comprises a single main channel bordered by intertidal flats that are submerged to an average depth of 1 m at high water. Tides are semi-diurnal with a mean range of 1.5 m, and the predominant winds are from the southwest. Due to limited fetch, wave-generated resuspension of sediment on the intertidal flats is forced primarily by northeasterly winds, which blow parallel to the long axis of the estuary from the adjacent coastal ocean. Sediment grain size and sediment transport (specifically, predicted frequency of resuspension) are both greatest near the entrance and both decrease toward the head of the estuary (Green & Oldman, 1999). Four mid-intertidal sampling sites were selected along a transect aligned with this grain size/resuspension gradient (Fig. 1). The outermost site 1 (S1) lay near the estuary mouth and was characterized by sandy sediments with wave-generated ripples. Site 4 (S4) was 1 km further inland on the seaward side of a sand ridge, where silt has accumulated on the sediment surface, and was devoid of bedforms. Sites 1–3 were non-cohesive, while S4 was cohesive (see Section 4). All sites had similar inundation periods (5.4–6.0 h per tidal cycle), thereby standardizing the time available for diatoms to migrate to the sediment surface (e.g. Pinckney & Zingmark, 1991). Macrofaunal community composition in the vicinity of S1 and S2 was dominated by the suspension-feeding bivalves Paphies australis (pipi) and Austrovenus stutchburyi (cockle) and the deposit-feeding bivalve Macomona liliana (wedge shell) (Hewitt, Cummings, & Norkko, 1998). Feeding tracks of M. liliana were abundant on the sediment surface at S3 throughout the study period. Polychaete worms increased in number with distance from the estuary mouth (Hewitt et al., 1998), and outnumbered bivalves at S4 (personal observation).

S1–S3 were sampled six times on a 4–6 weeks basis between March and November, 1999. S4 was added in June to increase the range of sediment types studied. At low tide, three to four 10-cm-deep sediment cores (13 cm diameter) were collected within a 5-m radius of a permanent marker at each site for laboratory flume estimates of u*crit. Cores were positioned to avoid obvious disruptions in the sediment surface (e.g. shells) that would add variability to u*crit estimates, and where appropriate, cores were consistently centered over a ripple crest to standardize for bedform effects. Sediment collected with syringe cores (2.5 cm diameter), positioned randomly around the perimeter of the larger cores, was used to determine pigment, carbohydrate, water, and particulate organic matter (POM) content. Initially (March and April), two syringe cores (one for pigment/ carbohydrate, the other for water content/POM) per large core were collected and sectioned into ten 1-mm slices. Analysis of these slices demonstrated that u*crit was correlated only with surface (0–2 mm) pigment/carbohydrate contents. For the remaining trips, the number of pigment/carbohydrate syringe cores was increased to three and only the surface sediment was analysed. The large cores and the pigment/carbohydrate syringe cores were stored in the dark until analysis at 4 and
18  C, respectively, while water content/POM samples were sectioned immediately after collection. Maximum storage times for the large cores and pigment/carbohydrate syringe cores were 48 h and 14 days, respectively. Surface sediment (0–2 mm) was sampled at each site for grain size analysis. In July 1999, an additional three large cores per site were collected and ashed to provide an abiotic control against which the microbial effects on initiation of sediment motion could be compared. The surface 5 cm of each core was raised into a stainless steel ring, sliced, and dried at 75  C for 48 h, before ashing at 450  C for 7 h. Following cooling, sediment was replaced into core barrels and carefully rehydrated by placing a polystyrene disk on the sediment to diffuse the force of the water stream and prevent disruption of the sediment surface. Cores were left to settle for several hours prior to determination of u*crit. Surface sediment (0–2 mm) was also ashed ðn ¼ 3Þ to compare mean grain sizes and silt/clay contents with the site averages for the natural sediments. 3.2. Determination of critical shear velocity The u*crit for the sediment held in each large core (ÔnaturalÕ and abiotic control) was determined from visual observations of the intact cores in a recirculating flume within 24 h of collection. The flume is similar to

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that described by Roegner, Andre´, Lindegrath, Eckman, and Grant (1995). Briefly, the flume consists of a 7.23-mlong, 50-cm-wide, and 50-cm-deep acrylic channel with a 40-cm diameter return pipe that runs beneath the flume. An impeller in the descending arm of the return pipe regulates flow speed via a variable speed AC motor. Turbulence at the channel entrance is rectified by two plastic light diffusers (1.5 cm2 mesh) placed 40 and 52 cm downstream of the entrance. The flume was filled to 15 cm water depth with seawater filtered to 5 lm at ambient temperature. In the test section of the flume (550 cm downstream of the entrance), a hole cut in the floor allowed the insertion of the large cores from the underside of the flume. The core insert occupied the central 25% of the flume floor and was free of flow artifacts developed at the channel walls (see Nowell & Jumars, 1987). The cores were inserted flush with the flume floor and held in place with nylon collars fitted with internal O-rings that formed a watertight seal. Sediment in the cores was raised, until it leveled with the flume bottom, using a hydraulic jack, and an empty core barrel was placed over the sediment surface to ensure minimal disturbance during channel filling. The sediment surface was continuously observed while the flow speed (recorded as flume motor output (Hz)) was gradually increased. The underlying criterion was to determine the force required to initiate onset of erosion, regardless of the mechanism of failure. Two stages of initial motion similar to the incipient motion and incipient transport definitions of Mantz (1977) were watched for. For the non-cohesive sediments (S1–S3), the first stage of erosion (u*crit,1) was defined as the initiation of grain rolling, where grains would roll a short distance and then stop. To minimize scatter resulting from bursting (e.g. Miller, McCave, & Komar, 1977; Sutherland, Amos, & Grant, 1998b) approximately 20 grains had to move simultaneously before u*crit,1 was recorded. Stage 2 (u*crit,2) involved semi-continuous grain rolling from most of the core surface area. Transport in cohesive sediments typically involves several modes of erosion (e.g. Mehta, 1991; Otsubo & Muraoka, 1988). Thus, for the cohesive sediments (S4), u*crit,1 was defined as the point where mud particles began to be dislodged, which was generally manifested as the development of pitting on the sediment surface. u*crit,2 involved larger-scale surface failure where lumps of mud were swept away. Flume motor outputs for both stages of erosion were converted into u*crit values using the formula u ðcm s
1 Þ ¼ 0:048 Hz
0:067 (r2 ¼ 0:96; n ¼ 10). Estimates of u* derived from vertical velocity profiles taken in the center of the core insert were regressed against motor output (5–50 Hz at 5-Hz intervals) to provide the calibration equation. Velocity measurements were made with a 10 MHz Sontek ADV (acoustic Doppler velocimeter) sampling at 2 Hz, then u* estimated from

the regression of velocity vs. the natural log height above the bed (z ¼ 0:4–8:0 cm; n ¼ 11; r2 > 0:95; Muschenheim, Grant, & Mills, 1986). 3.3. Analytical methods The surface 2 mm of each syringe core was sectioned at 1-mm intervals and quarter-slices were taken for pigment and carbohydrate analysis. Chlorophyll a and pheopigment contents were determined fluorometrically (Parsons, Marita, & Lalli, 1984), and the colloidal (soluble) and bulk (bound) carbohydrate fractions were estimated using the phenol-sulphuric acid assay (Dubois, Gilles, Hamilton, Rebers, & Smith, 1956; Underwood, Paterson, & Parkes, 1995). Pigment and carbohydrate (expressed as glucose equivalents) contents were all standardized to sediment dry weight (lg g
1). Sediment-water content (%) was calculated from the difference between wet and dry weight (24 h at 55  C), while POM (%) was determined by weight loss on ignition (450  C for 7 h). Sediment grain size was determined from volumetric particle distributions measured with a Malvern Mastersizer-S (Malvern Instruments Ltd). 3.4. Data analysis Sediment pigment and carbohydrate contents in the 0–1 and 1–2 mm slices were averaged, then contents in the three syringe cores were pooled to provide a single value per large core. Where site content was required, pooled values for replicate large cores were combined. A two-way analysis of variance (ANOVA) with site and time as fixed factors identified a significant site  time interaction ðp ¼ 0:0001Þ, necessitating the use of one-way ANOVA ða ¼ 0:05Þ (Minitab, version 12.22, 1998) to test differences in u*crit between sites for each sampling trip (Ôspatial differencesÕ) and between sampling dates for each site (Ôtemporal differencesÕ). Prior to analysis, log-transformations were made, where necessary, to satisfy the assumptions of normality and homoscedasticity. Tukey’s multiple comparison test was used to locate any differences identified by ANOVA ða ¼ 0:05Þ.

4. Results 4.1. Site characteristics Wave-generated ripples (Table 1) and subsurface shell hash were prominent at S1 and absent at S4 on all sampling dates, illustrating a decrease in wave exposure and physical sediment reworking along the transect. In keeping with this exposure gradient, mean particle diameter also decreased from S1 to S4 (Table 2), and values were similar at the start and end of the study.

S.D. Lelieveld et al. / Estuarine, Coastal and Shelf Science 57 (2003) 123–136 Table 1 Mean ripple wavelength (cm; 1 SD, n ¼ 12) throughout the study Site March 1 2 3 4

April

May

June

September November

10.4  2.0 11.1  1.9 14.2  2.4 10.1  1.7 3.8  0.7 5.6  0.9 9.1  0.9 8.8  1.3 3.8  0.5 Absent Absent Absent Absent

10.0  1.9 10.6  1.1 Absent Absent

9.3  1.9 6.2  0.6 3.9  0.4 Absent

Sites 1–3 were classified as fine sand and S4 very fine sand. S4 had a clay content (<2 lm) of 7%, which is within the range (5–10%; Dyer, 1986) that can impart cohesive properties on sediment. Despite the fine-sand classification, S4 was observed as a sticky and saturated substrate, and displayed characteristics of a cohesive sediment by eroding as a series of flocs. Unfortunately, temporal variations in grain size could not be determined because of instrument failure. Water content and POM were not determined on all trips, hence available data for the surface 2 mm were averaged over the study period (Table 2). S1 and S2 were the most variable in terms of water content; however, this was mostly due to surface pools of water in ripple troughs. Excluding samples taken in the troughs, S4 had the highest water content (34%) owing to reduced drainage. There was little difference in POM content in the sites S1–S3, but S4 contained double the content of other sites (2.4%), reflecting the higher content of silt and clay. 4.2. Critical shear velocity A general increase in u*crit,1 (0.6–1.7 cm s
1) and u*crit,2 (1.0–2.2 cm s
1), going from S1 to S4, was evident on each sampling trip (Fig. 2), although thresholds for S1 and S2 were comparable. Spatial differences (between sites) in u*crit,1 were significant in June, September and November, while differences in u*crit,2 were significant in May through to November (Table 3a). No differences between S1 and S2 were detected on any sampling trip,

Table 2 Physical site characteristics (mean water content and POM as a % of sediment dry weight (1 SD, n ¼ 9–21) and grain size at the start and end of the study) Grain size analysis Water content Site (%) 1

36.8  5.8

2

35.5  4.2

3

27.2  1.8

4

33.6  1.9

POM (%)

Date

1.09  0.32 March November 0.98  0.10 March November 1.02  0.11 March November 2.38  0.15 November

supporting the observation that erosion thresholds at these sites were similar. Values of u*crit,1 and u*crit,2 at S1 varied little over the study period (from 0.52 to 0.68 and from 0.86 to 1.04 cm s
1, respectively; Fig. 2, Table 3b). Only one temporal difference was found in u*crit,2 between June and November ðp ¼ 0:023Þ. Changes in erosion threshold at S2 were also minor, with the small decrease in September/November and the small May peak causing the respective temporal differences found in u*crit,1 and u*crit,2 (Table 3b). In contrast, erosion resistance at S3 displayed temporal dynamics, particularly for u*crit,2 which ranged from 0.92 to 1.77 cm s
1. At S3, u*crit,1 was higher during the last three sampling trips. u*crit,2 increased from March to June, and also tended to be higher in the latter half of the study. Additionally, both u*crit,1 and u*crit,2 at S3 showed marked increases relative to S1 and S2 from June through to November (Fig. 2, Table 3a). Changes in the coefficient of variation (v; temporal standard deviation/temporal mean) for u*crit,1 and u*crit,2 support trends of increased temporal variability at S3 (v ¼ 0:17 and 0.23, respectively) compared with S1 (v ¼ 0:087 and 0.073, respectively) and S2 (v ¼ 0:10 and 0.15, respectively). Temporal differences at S4 were not significant; however, without data for the first 3 months of the study, it is difficult to compare temporal trends at this site with those at S1–S3. The decrease in grain size moving from S1 to S4 was not reflected in estimates of abiotic u*crit (Fig. 3), there were no significant spatial differences for either u*crit,1 (one-way ANOVA; p ¼ 0:344) or u*crit,2ðp ¼ 0:07Þ. The mean grain size of the abiotic cores was similar (5%) to the natural sediment (Table 2); however, the ashing procedure did lower the silt/clay content at S3 and S4 by 40 and 22%, respectively. In contrast, u*crit for natural sediments, when pooled across sampling trips, showed a strong site effect (p ¼ 0:000 for both stages) increasing from S1 to S4, although values for S1 and S2 were similar (Fig. 3). At S1 and S2, u*crit for natural sediment was, respectively, 1.1 and 1.2 times greater than that of the abiotic sediment. At S3, this difference increased by 1.5 times and at S4, the difference was twoto three-fold. Inter-site variation in u*crit for natural sediments was, therefore, much greater than could be explained by changes in mean grain size. 4.3. Sediment pigment content

Mean % Silt/ size clay % Clay (lm) (<63 lm) (<2 lm) 185 183 176 178 147 142 77

127

2.8 4.4 2.5 4.9 5.6 10.6 43.9

1.1 1.0 1.1 0.6 1.6 2.1 6.7

Maximum chlorophyll a contents (surface 2 mm) at all sites were measured in June, and the increase over average values ranged from 15% at S1 to 23% at S4. Individual site minima occurred in different sampling months (Table 4). When pooled across the study period, chlorophyll a contents at S1–S3 were similar (9.4– 10.2 lg g
1). As with u*crit, however, average S4 chlorophyll a content (21 lg g
1) was double that of other sites.

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Fig. 2. Variations in (a) u*crit,1 and (b) u*crit,2. Data represent means + 1 SD (n ¼ 3–4) ( site 1,

No consistent spatial trend across S1 to S3 was evident on individual sampling trips. As was the case for u*crit, chlorophyll a content at S1 showed little variability over the study period, contrasting to marked temporal differences at S2–S4 (Table 4). Pheopigment and total pigment (chlorophyll a + pheopigment) contents pooled across the study period increased from S1 to S4 (2.5–17.6 and 11.9–38.2 lg g
1, respectively), although values at S1 and S2 were similar. As was the case for u*crit, temporal variability in pheopigment content was greatest at S3 ðv ¼ 0:51Þ; Chlorophyll a to pheopigment ratios generally decreased from S1 through to S4 (Table 4), indicating that the proportion of living microalgae was smallest at the least exposed S4.

site 2,

site 3,

n site 4).

4.4. Carbohydrate Most of the carbohydrate measured (77–93%) was present in the bulk phase. When pooled across the study period, both colloidal and bulk carbohydrate fractions showed an increase in content from S1 to S4 (75–149 and 517–1628 lg g
1, respectively), although as for u*crit, values at S1 and S2 were similar. On individual sampling trips, colloidal carbohydrate showed an increase in content from S1 to S3/S4 during all months except March and May. Bulk carbohydrate displayed the same spatial trends every sampling trip. There was no consistency in the occurrence of minimum and maximum values for either carbohydrate fraction across the sites. Only bulk carbohydrate displayed temporal variation during the

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Table 3 Results of a one-way ANOVA carried out to detect differences in u*crit,1 and u*crit,2 with site (a) or time (b) as the factor u*crit (Stage) Month

F-value

p-value Tukey’s test

(a) Spatial analysis 1 March 1 April 1 May 1 June 1 September 1 November 2 March 2 April 2 May

0.53 2.64 0.51 24.17* 241.08* 18.30* 3.50 2.23 11.48*

0.605 0.125 0.616 0.000 0.000 0.001 0.075 0.163 0.003

S1 S1 S1

S2 S3 S2 S3 S2 S3

S1

S2 S3

2 2 2

16.75 162.62 35.28

0.001 0.000 0.000

S1 S1 S1

S2 S3 S2 S3 S2 S3

(b) Temporal analysis 1 S1 S2

2.30 5.58

0.097 0.004

S3

3.81

0.022

Difference between Ma and S; A and N; A and S M A Ma S N J

S4 S1

4.01 3.64

0.091 0.023

N

S2

5.44

0.005

Ma M A

S3

13.22*

0.000

M

S4

2.48

0.164

2

June September November

M A A

S4 S4 S4

S4 S4 S4

Ma S

J

J

S

N

Ma S

J

N

Note: S4 was not sampled in March, April, and May. Sites/months not directly connected by a line are significantly different in a Tukey’s multiple comparison test at a ¼ 0:05. Significant p-values are in bold. M, March; A, April; Ma, May; J, June; S, September; N, November. * Data log-transformed prior to analysis.

study period, with ranges from 442 to 594, 515 to 693 and 782 to 1115 lg g
1 for S1–S3, respectively.

5. Discussion 5.1. Variation in microbial indicators Chlorophyll a contents at the non-cohesive S1–S3 and the cohesive S4 were similar to those found in previous studies of intertidal sands (e.g. Grant, 1988; Grant, Bathmann et al., 1986; Grant, Mills et al.,1986; Yallop et al., 1994) and intertidal muds (e.g. Underwood & Paterson, 1993b; Yallop et al., 1994), respectively. Chlorophyll a displayed no consistent spatial trend, other than S4 contents being up to two times those of other sites. Colijn and Dijkema (1981) and De Jong and de Jonge (1995) also found higher chlorophyll a contents in muddy sediments and attributed the relationship to reduced hydrodynamic energy. Organic compounds associated with silt and clay particles can stimulate diatom growth (De Jonge, 1985), and by inference, the growth of other microautotrophs, which

Fig. 3. Average values of (a) u*crit,1 and (b) u*crit,2 for each sampling trip pooled across the study period and compared with the erosion threshold of abiotic sediments ( abiotic, n natural). Error bars represent +1 SD (n ¼ 3–6).

may explain the greater chlorophyll a content at S4. De Jonge (1985) found that diatom cells in sandy sediments preferred mud coatings to bare sand grain surfaces, stressing the importance of mud particles (defined as particles <55 lm) as a substratum. Silt/clay content varied little from S1 to S3 (Table 2) and could explain the parallel lack of variation in chlorophyll a content averaged over the study period. Chlorophyll a minima at each site occurred at different times during the year, suggesting that they were not a function of season. While other studies have identified clear seasonal variations in chlorophyll a contents (e.g. Colijn & Dijkema, 1981; De Jong & de Jonge, 1995), these studies were conducted in environments of greater seasonal change where winter primary production was significantly reduced. Chlorophyll a maxima in the present study occurred in June (winter) at all the sites and may have been related to low densities of primary consumers, but without data from macrofaunal surveys, this hypothesis cannot be substantiated. The lack of

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Table 4 Mean (1 SD, n ¼ 3–4) surficial (0–2 mm) sediment contents of chlorophyll a (chl. a), pheopigment (pheo.), total pigment (TP), colloidal carbohydrate (CC), bulk carbohydrate (BC), and total carbohydrate (TC) throughout the study period Sample date

Site

Chl. a (lg g
1)

Pheo. (lg g
1)

TP (lg g
1)

Chl. a/Pheo.

CC (lg g
1)

BC (lg g
1)

TC (lg g
1)

March March March April April April May May May June June June June Sept September September September November November November November

S1 S2 S3 S1 S2 S3 S1 S2 S3 S1 S2 S3 S4 S1 S2 S3 S4 S1 S2 S3 S4

9.7  2.2 6.1  1.2 9.4  0.8 10.5  1.2 9.2  1.2 10.7  1.4 10.9  0.3 10.6  1.0 9.0  0.5 11.7  0.8 11.3  0.3 12.5  1.4 25.6  1.9 9.7  0.4 9.7  0.6 8.7  0.9 18.7  3.3 8.5  1.5 10.6  0.8 10.5  0.8 17.6  2.2

2.2  0.5 1.4  0.3 2.6  0.7 2.0  0.4 2.1  0.3 3.3  1.4 2.7  0.3 3.7  0.8 4.2  0.9 2.1  0.3 2.2  0.0 9.1  1.6 15.2  2.2 1.7  0.3 1.7  0.3 4.1  1.3 23.6  6.4 4.2  1.5 2.8  0.7 3.9  0.2 14.0  2.2

11.9  2.0 7.5  1.4 12.0  0.9 12.5  1.4 11.3  1.5 14.0  1.2 13.6  0.3 14.3  1.4 13.2  1.2 13.8  1.0 13.5  0.3 21.6  3.0 40.7  3.5 11.4  0.3 11.5  0.9 12.8  1.1 42.3  9.1 12.6  0.8 13.4  1.0 14.5  0.7 31.6  4.4

4.7  1.9 4.5  1.1 3.9  1.0 5.4  0.8 4.4  0.2 3.6  1.4 4.0  0.4 2.9  0.7 2.2  0.5 5.7  0.5 5.2  0.1 1.4  0.1 1.7  0.2 5.7  1.0 5.7  0.8 2.3  0.9 0.8  0.2 2.3  1.3 3.9  0.9 2.7  0.3 1.3  0.1

98.8  68.8 108  71 102  46 71.0  32.5 73.8  24.7 133  22 101  9 77.8  6.9 91.9  14.1 50.1  9.1 57.0  18.6 83.1  27.8 157  51 65.3  23.0 82.1  17.0 122  50 213  48.2 52.0  11.2 77.1  11.4 86.9  15.8 102  26

552  91 557  28 804  158 474  68 515  46 906  165 594  38 693  27 781  94 441  41 529  22 1114  78 1589  439 516  22 582  20 908  45 2031  972 497  14 598  4 841  51 1303  217

651  133 666  96 907  192 545  95 588  40 1040  180 695  45 771  31 873  80 491  37 586  29 1197  67 1747  458 581  45 664  37 1030  48 2244  987 549  25 675  7 927  38 1405  236

temporal variations in chlorophyll a content at S1 appears to be correlated with the higher sediment transport of this more exposed site, which could act to suppress growth in microautotrophic populations (Delgado et al., 1991). Pheopigment is the breakdown product of chlorophyll a, which is produced when microalgal cells die or pass through digestive tracts of consumers (Head, Hargrave, & Subba Rao, 1994; Shuman & Lorenzen, 1975; Spooner, Keely, & Maxwell, 1994). As pheopigment is a breakdown product, it is able to accumulate in sediments. With respect to cell death, the increase in pheopigment content from S1 to S4 was likely a function of the increase in chlorophyll a content. An increase in abundance of microalgal consumers from S1 to S4 could also cause a corresponding increase in pheopigment, which requires investigation of benthic community structure to confirm. In addition, the higher reworking of S1 sediments could cause resuspension of pheopigment (Le Rouzic, Bertru, & Brient, 1995), precluding accumulation and keeping levels low. In contrast, minimal sediment disturbance at S4 is presumed to facilitate pheopigment build-up, and this could explain the lower chlorophyll-to-pheopigment ratio in S4 sediments. Comparing carbohydrate contents determined by the phenol-sulphuric acid assay between studies may not be feasible owing to variations in analytical protocol (Underwood et al., 1995). The method used in this study was identical to that used by Underwood et al. (1995), and values obtained at the cohesive S4 compared well with the values measured by these workers at an intertidal mudflat. As found by Yallop et al. (1994), carbohydrate contents at the cohesive site were significantly

higher than contents at the non-cohesive sites. Yallop et al. (1994) attributed higher contents of colloidal carbohydrate at their mudflat site to greater epipelic diatom biomass; however, the site of these workers had a mean grain size of only 7 lm and a small proportion of fine sand (favorable to epipelic diatoms) compared with S4 in the current study. Average colloidal contents at S4 were an order of magnitude less than those found at the cohesive site by Yallop et al. (1994), suggesting that EPS production was considerably less and probably attributable mostly to a greater abundance of epipsammic diatoms, rather than increased migratory activities of epipelic diatoms. 5.2. Variation in u*crit Erosion of abiotic cores indicated that initiation of sediment motion was similar across the sites (Fig. 3), despite differences in grain size. In contrast, u*crit for natural sediments progressively increased over abiotic values from S1 to S4, suggesting that biological influences on sediment stability became increasingly more important as grain size decreased. Accurately quantifying the effect of mucous exudates on sediment stability was difficult owing to an immeasurable loss in physical–chemical grain bonding resulting from the ashing procedure used to generate the abiotic cores. While ashing did not alter the mean grain sizes of the four sites, there was a reduction in clay content (<2 lm) of 40 and 22% for S3 and S4 sediments, respectively. This change affected S4 abiotic sediments, which eroded in a non-cohesive manner at similar critical shear velocities to other sites despite in situ cohesive properties. Together,

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biological and physical–chemical modification of S4 sediments increased abiotic u*crit,1 by 210% and u*crit,2 by 133%. We suggest that losses in physical–chemical bonding were minimal for the non-cohesive sediments (S1–S3), hence biological modification increased abiotic u*crit,1 by 13–46% and u*crit,2 by 10–41%. These increases are generally lower than those reported in the literature for non-cohesive sediments. For example, Grant and Gust (1987) reported an increase of 300% and Vos et al. (1988) reported a 100% increase. However, these workers investigated sediments with visible microbial accumulations, which were absent at Okura sites. In addition, differences in abiotic sediment preparation may make comparisons among the studies difficult. Grant and Gust (1987) also measured erosion threshold in visually clean sediments and found an increase over their abiotic control of 23%, this being similar to the increase measured at S2 in the current study. Visible microbial accumulations are therefore not a prerequisite for measurable sediment stabilization. The increase in u*crit with decreasing grain size may be attributed to both physical and biological causes. Frequency of sediment resuspension graded from high at S1 to low at S4, hence the potential for physical disturbance of surficial sediment structure decreased to a minimum at S4. Infrequent disturbance would minimize susceptibility to particle entrainment by facilitating consolidation of the sediment surface (Kornman & De Deckere, 1998), which in turn would increase u*crit. Additionally, minimal disturbance would facilitate the accumulation of mucilage in surface sediments, which is consistent with the high carbohydrate contents observed at S4. Finally, the increased clay content at S4 may also be important for increasing sediment stability. From a physical perspective, the clay content of S4 sediments (7%) was within the range (5–10%; Dyer, 1986) that can impart cohesive properties on sediment. From a biological perspective, a larger microbial population is expected in finer sediments, which would correlate with increased erosion resistance owing to increased EPS secretions. Chlorophyll a and carbohydrate contents at S4 were up to two and four times those of other sites, respectively, suggesting that populations of at least the microautotrophs were indeed higher at S4. Spatial differences (i.e. between sites) in u*crit,1 were significant only in June, September, and November. No significant differences were found between S1 and S2, however, suggesting that the higher exposure of these sites situated closest to the estuary mouth limited biological stabilization. Minimal temporal variation in erosion threshold at S1 and S2 further supports this hypothesis. From the temporal trends in u*crit,1 (Fig. 2a), S3 appeared to have reduced erosion resistance in March, April, and May, causing homogeneity in u*crit,1 between sites. Increased macrofaunal activity at S3 may have caused decreased stability from March to

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May (e.g. Kornman & De Deckere, 1998; Widdows, Brinsley, Salkeld, & Lucas, 2000), but data on macrofaunal abundance and activity are required to test this hypothesis. Although S3 chlorophyll and carbohydrate contents did not show marked temporal variation, the strength of sediment–mucous binding may have been weaker during the first 3 months as EPS quality varies with species (Decho, 1990). Sediment resuspension is another potential cause of decreased erosion resistance at S3 in March, April, and May, and this idea was tested by determining frequency of resuspension at each site prior to sampling. Because Okura estuary is shallow, tidal currents on the sand flats are weak, and resuspension is driven primarily by wave-orbital motions at the bed (Green & Oldman, 1999). Local wind measurements were input into the wave model, WGEN (Black, 1997), which generated a corresponding time series of bed wave-orbital velocities at each site. The threshold orbital speed applicable to the fine sand and wave periods of 3–4 s at Okura was 20 cm s
1. The duration of sediment resuspension on the intertidal flat during 7 days prior to each sampling trip was at least three times higher in March (10 h) and April (13 h) than in other months, which corresponds to the periods when u*crit was lower at S3. If the mechanism for this reduction in stability was removal of microbial inhabitants and their mucous exudates via abrasion, this would presumably be reflected in chlorophyll a and carbohydrate measures; however, no decreases were observed in March or April. Miller (1989) found no decrease in microbial abundance following a period of storm-induced transport events at a sandbar crest, nor was a decrease found following an 8-h rippled-bed transport event in a laboratory flume. Although not measured at Okura, transport rates during resuspension events would not have come close to those found to produce significant reductions in microbial biomass in Miller’s (1989) laboratory experiments. It, therefore, appears that resuspension events in March and April disturbed surficial sediment structure at S3 by loosening grains, thereby promoting early entrainment and lowering u*crit,1 to values similar to that of S1 and S2. Increased frequency of resuspension does not explain spatial homogeneity of u*crit,1 for May, as wave modeling identified no resuspension events prior to this sampling trip. Interestingly, a large number of cockle tracks were noted on all the S3 core surfaces taken in May. Bioturbation is likely to loosen surficial sediment, again increasing susceptibility to early entrainment and lowering u*crit,1 (initiation of grain rolling). Also, chlorophyll a contents were comparatively low at S3 in May, while pheopigment contents were high, suggesting increased macrofaunal feeding on microautotrophs. Spatial differences in u*crit,2 were significant on all the trips except March and April. Homogeneity of u*crit,2

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from S1 to S3 for these two sampling trips may also be attributed to disruption of surficial sediment structure during resuspension events. Although increased bioturbation may have reduced u*crit,1 at S3 in May, u*crit,2 (semi-continuous grain rolling from most of the core surface area) would not be affected unless tracks covered the majority of the core surface areas. Changes in silt content may have also contributed to the temporal variations observed in u*crit. Due to instrument failure, we can only present grain size data for the beginning and end of the study. Given that the percentage of clay (<2 lm) at S1–S3 was well below the content of 5–10% suggested by Dyer (1986) to impart cohesive properties on sediment, we think that small fluctuations in the percentage clay content would not greatly alter the erosion threshold at these sites. Furthermore, analysis of sediment samples collected monthly for 12 months from sites close to S1 and S2 demonstrated minimal variation in mean grain size and clay content (S. D. Lelieveld, unpublished data). 5.3. Predicting u*crit To allow direct comparison of threshold between sites, u*crit was converted to hcrit using Eq. 1 and plotted as a function of dimensionless grain size, S* (Madsen & Grant in Larsen et al., 1981): 1=2

Dð½s
1gDÞ ð2Þ 4m where m is the kinematic viscosity of seawater, s ¼ qS =q; and D is the mean sediment grain size (Table 2). For this purpose, qs was taken as 2.65 g cm
3, q as 1.0 g cm
3, and m as 0.01 cm2 s
1 for all sites. Since all other values are equal, S* decreases with decreasing grain size and so data plot from low to high S* in the order S4, S3, S2, S1. Both hcrit,1 (non-dimensional u*crit,1) and hcrit,2 (non-dimensional u*crit,2) are plotted in Fig. 4 together with a standard Shields curve for abiotic sediment extended for values of S* smaller than 0.85 using the method of Mantz (1977). Abiotic hcrit,1 and hcrit,2 plotted below the Shields curve, but followed the same general trend (Fig. 4), indicating that erosion resistance increased with decreasing grain size. Compared with abiotic sediments, hcrit,1 and hcrit,2 for natural sediments both increased more rapidly with decreasing S*. Although the observed increase in abiotic hcrit with decreasing S* was consistent with the Shields curve, the abiotic values plotted below Shields predicted values (Fig. 4). The most probable reason is a difference in threshold criteria. The Shields diagram is considered to represent incipient transport (Mantz, 1977), which is close in definition to erosion of our stage 2. In fact, hcrit,2 most closely matched the Shields curve, and residual differences could be due to dissimilarity in flow conditions, bed topography, and/or the composite grain size S ¼

Fig. 4. Average values of (a) hcrit,1 and (b) hcrit,2 for each sampling trip pooled for the study period and compared to Shields values (— Shields, –n– abiotic,   m   natural). Error bars represent 1 SD (n ¼ 6).

of sediments at each site. These differences will also contribute to the offset between our natural hcrit values and the Shields curve. Differences in hcrit between natural and abiotic sediments were expressed in terms of a stability factor (SF): hcrit;1 or 2 ðnaturalÞ SF1 or 2 ¼ : ð3Þ hcrit;1 or 2 ðabioticÞ SF, therefore, represents the discrepancy in erosion threshold relative to the abiotic value and provides a platform for analysing the biological effects. SF1 and SF2 were plotted against S* (Fig. 5), and for both stages of erosion, SF was close to one for higher values of S* (i.e. in coarser sediments), but increased with decreasing S* (particularly for stage 1). Although sediment stability was greater in finer sediments, the correlation between SF and S* is in fact a spurious one, which happens because the microbial content of the sediment also correlated with S* (it need not necessarily). Data from all sampling trips were combined, and bulk, colloidal and total carbohydrate, chlorophyll a, pheopigment, and total pigment contents were regressed

S.D. Lelieveld et al. / Estuarine, Coastal and Shelf Science 57 (2003) 123–136

Fig. 5. SF plotted as a function of dimensionless grain size (S*) for stages 1 and 2 erosion (–m– SF1,   n   SF2). Error bars represent 1 SE (n ¼ 3–6).

(individually) on SF values to identify potential predictors of sediment stability. Pearson’s correlation coefficients between the indicators were also calculated to determine the strength of correlation. All six indicators produced significant regressions with both stages of SF. Pheopigment described the most variability (r2 ¼ 0:84 (SF1) and 0.70 (SF2)), followed by total pigment (r2 ¼ 0:79 (SF1) and 0.68 (SF2)) (Table 5). Total carbohydrate also produced strong relationships with both stages of SF (r2 ¼ 0:60 and 0.52), while colloidal carbohydrate produced comparatively weak relationships (r2 ¼ 0:38 and 0.20). To test whether the high sediment contents of the indicators at S4 were driving the significant relationships obtained with SF, S4 was excluded in the analysis and the equality of regression slopes between data sets with and without S4 were tested (following Zar, 1984). With the exception of chlorophyll a and colloidal carbohydrate, pairs of slopes were not found to be significantly different, indicating that S4 values

Table 5 Linear regression statistics for the two stages of SF vs. potential predictors of erodability (all relationships are significant ð p ¼ 0:000Þ) Predictor

Slope

Intercept

r2

SF stage 1 Pheopigment Total pigment Total carbohydrate Bulk Chlorophyll a Colloidal

0.1093 0.0626 0.0012 0.0013 0.1145 0.0086

0.95 0.46 0.45 0.51 0.18 0.65

0.84 0.79 0.60 0.58 0.56 0.38

SF stage 2 Pheopigment Total pigment Total carbohydrate Bulk Chlorophyll a Colloidal

0.0656 0.0385 0.0007 0.0008 0.0720 0.0041

1.05 0.75 0.74 0.77 0.56 0.98

0.70 0.68 0.52 0.52 0.51 0.20

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were not driving the top four relationships obtained with SF. The relationship between SF and pheopigment was not appreciably improved by including any combination of the remaining indicators of microbial abundance (multiple linear regression r2 increased <2%). This was due to the significant correlations that existed amongst the indicators (Table 6). In most instances, however, there was not a good strength of dependency between indicators. The exception to this was high dependency between bulk carbohydrate and pheopigment (r ¼ 0:90); suggesting that either of these indicators could be estimated from measurements of the other. If the relationship between colloidal carbohydrate and sediment erodibility is strong in muddy substrates (e.g. Sutherland et al., 1998a; Underwood & Paterson, 1993b) where motile epipelic diatoms dominate, then a strong relationship between the bulk fraction and SF could be expected in sandy sediments where epipsammic species dominate. This may occur because epipsammic diatoms are semi-permanently attached to sediment grains, hence their EPS secretions are more closely associated with grains (i.e. quantified by bulk carbohydrate) than with void spaces (colloidal fraction). Our results, which show that bulk carbohydrate was a better predictor of SF, support this idea. Mucus concentrations in the colloidal fraction would still contribute to sediment stability, hence the reason why total carbohydrate explained more variability in SF than did carbohydrate fractions individually. It is interesting to note that the best predictor of SF was pheopigment content, which is a measure of senescing microalgal cells (Spooner et al., 1994). To date, no comparable study has been conducted where this is the case. General trends in chlorophyll a indicated that contents at S1 to S3 were similar, while trends in pheopigment content matched those of u*crit, increasing from S1 to S4. This explains the improved predictability of SF with pheopigment from a statistical viewpoint, but not from an ecological one. Measurements of u*crit are likely to integrate across time and reflect some sediment

Table 6 Pearson’s correlation coefficients (r) amongst sediment carbohydrate and pigment contents

Bulk carbohydrate Chlorophyll a Pheopigment

Colloidal carbohydrate

Bulk carbohydrate

Chlorophyll a

0.647 0.000 0.348 0.003 0.476 0.000

0.664 0.000 0.902 0.000

0.785 0.000

r, top number in each cell; p, bottom number in each cell. Significant relationships are indicated by bold p-values. Data have been combined across all sampling trips.

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history (such as disturbance by resuspension), but chlorophyll a contents represent near-instantaneous measures of living biomass, which lacks a temporal dimension. Owing to the capacity of pheopigment to accumulate, this indicator may reflect recent history of microalgal biomass levels, and hence indirectly indicate sedimentbinding history. Sutherland et al. (1998a) found a strong relationship between chlorophyll a and u*critðr2 ¼ 0:95Þ in sediments inhabited by biofilms. The same relationship in the current study (expressed here as the relationship between chlorophyll a and SF) was much weaker (r2 ¼ 0:56 (SF1) and 0.51 (SF2)). Paterson (1997) noted that it is the mechanism of stabilization that is important, hence a relationship between biomass (chlorophyll a) and sediment erosion threshold will be produced only if biomass is directly related to the binding influence. Biofilms lower sediment susceptibility to erosion by reducing bottom roughness as well as by increasing intergrain-adhesion (Grant, 1988) so microbial biomass in this instance is likely to be directly related to reduced erosion threshold. An absence of visible biofilms at Okura meant that biomass would only be strongly related to sediment erosion threshold if constituent organisms were the dominant controlling factor. As discussed, macrofaunal activities and physical disturbance were likely to have influenced erosion threshold, thereby reducing the strength of any relationship between chlorophyll a and u*crit, a conclusion reached also by Riethmu¨ller, Heineke, Ku¨hl, and Keuker-Ru¨diger (2000). The strength of the relationships observed between SF and the microbial indicators may have been influenced by the alterations in physical–chemical properties of the ashed sediment. SF at S4 for instance may have been amplified by comparisons of hcrit between the abiotic sediment, which was non-cohesive following ashing and the cohesive natural sediment. This possibility was tested by regressing the microbial indicators with u*crit for natural sediments directly. The maximum difference in r2 values between regressions against u*crit vs. regressions against SF was 0.14, while the average difference was only 0.05. As such, the issue of ashing artifacts introduced into the SF equation is not of significant concern. A model of erosion threshold based on raw u*crit values is site-specific, whereas SF is non-dimensional, and, therefore, has potentially universal application. Given an abiotic value of u*crit, one can easily derive the ÔnaturalÕ value from a non-dimensional model, hence we see the development of this type of model as a step forwards on the path to more accurately predicting initiation of sediment motion. As a final point of discussion, the issue of separating the effects of physical cohesion and biological adhesion needs addressing. This is a fundamental problem faced by all researchers in the field of biostabilization of sediments, and calls for the utilization of experimental work

to control for physical cohesion, thereby furthering our understanding of the mechanisms underlying biologically induced sediment stabilization. In this study, however, only S4 showed any evidence of physical cohesion (as defined by clay content and mechanism of erosion). The fact that the relationship between SF and microbial indicators did not change following the removal of S4 from the data set satisfies us that we can relate sediment stability to indicators of microbial abundance. In summary, the following equation is the best predictor of SF at the Okura site: SF1 ¼ 0:109  Pheopigment þ 0:95 ðr 2 ¼ 0:84Þ SF2 ¼ 0:066  Pheopigment þ 1:05 ðr 2 ¼ 0:70Þ

ð4Þ

where pheopigment content ranges from 1.1 to 30.4 lg g
1. Eqs. 3 and 4 can be used to give more accurate predictions of erosion threshold than would be derived from the abiotic Shields curve. The next step in improving prediction is to increase the range of conditions—both environmental (e.g. exposed vs. sheltered intertidal flats) and biological (e.g. varying microbial community composition)—where SF is measured to test the generality of Eq. 4. Although this study has defined two stages of erosion threshold, reference to the Shields diagram can only produce one value for abiotic hcrit. Given that abiotic hcrit,2 most closely matched the Shields curve, it is likely that SF2 values would produce the most accurate estimate of natural hcrit. In order to progress from an empirically derived Eq. 4 to the (semi)theoretical-type, it is necessary to further understand the mechanics of microbial sediment binding. Finally, it is necessary to incorporate physical disturbance factors (such as resuspension) into the prediction equation to allow for the consideration of the contrasting effects of stabilization and destabilization.

6. Conclusions The increase in sediment erosion threshold with decreasing grain size in the Okura Estuary was correlated with increased microalgal biomass and sediment EPS content. Changes in macrofaunal abundance and frequency of sediment resuspension are possible controls on spatial patterns of sediment characteristics, influencing both susceptibility of particle entrainment and mucilage production/accumulation in surface sediments. The non-dimensional erosion threshold of natural sediments was up to 14 times that of equivalent abiotic sediment, illustrating the necessity of adjusting abioticbased predictions of hcrit to estimate erosion threshold in natural sediments in a better way. In the Okura Estuary, this can be done via multiplication with SF (Eq. 3) that can be predicted from sediment pheopigment content (Eq. 4). This microbial indicator requires testing in a variety of estuarine environments to confirm its generality.

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Acknowledgements We thank S. Stephens, B. Lynch-Blosse, and G. Lelieveld for field assistance, and J. Oldman and I. MacDonald for determining frequencies of sediment resuspension at the sampling sites. The manuscript benefited from the constructive criticism of two anonymous reviewers and T. Sutherland. Funding for the flume and ADV was provided via a grant from the School of Science and Technology, University of Waikato, which is gratefully acknowledged. This research was also supported by the ÔEffects of Sediments on Estuarine and Coastal EcosystemsÕ program, funded by the (New Zealand) Foundation for Research, Science and Technology (Contract CO1X0024).

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