Estuarine, Coastal and Shelf Science 65 (2005) 30e42 www.elsevier.com/locate/ECSS
Spatial dynamics of microphytobenthos determined by PAM fluorescence B. Jesus a,b,*, V. Brotas a, M. Marani c, D.M. Paterson b a
Instituto de Oceanografia, Faculdade de Cieˆncias da Universidade de Lisboa, Lisbon, Portugal b Sediment Ecology Research Group, University of St Andrews, St Andrews, Fife, UK c Centro Internazionale di Idrologia ‘D. Tonini’ and Dept. IMAGE, University of Padova, Padova, Italy Received 25 October 2004; accepted 2 May 2005 Available online 23 June 2005
Abstract The distribution of microphytobenthos on intertidal flats is heterogeneous and described as ‘‘patchy’’ over spatial scales of centimetres to kilometres. The spatial structure and the dynamics of microphytobenthic biomass during tidal emersion were investigated by creating high resolution fluorescence distribution maps of biomass (Fo) at the sediment surface (0.5 cm pixels, 20 cm!20 cm). The PSII maximum quantum efficiency was also calculated for correlation with biomass structure. Intertidal areas of the Biezelingsche Ham (Westerschelde) and Zandkreek (Oosterschelde), The Netherlands, were sampled during June 2000. The spaceetime variability of the discrete fields obtained was analysed by studying their spatial variogram and their time autocorrelation. Sites showed significant differences in absolute variation of microphytobenthic biomass and in the dynamics of the development of spatial structure throughout the emersion period. The variograms indicated little spatial correlations in the biomass fields above the 2 cm scale, while the time autocorrelation showed a strong persistence of biomass structure at time scales between 1 h and 5 h. The Biezelingse Ham biofilm was shown to be migratory in nature while the Zandkreek system was more stable. At the former site, biomass distribution became more homogenous with time, as upward migration of cells attenuated horizontal patchiness. At the Biezelingsche Ham site, maximum PSII quantum efficiencies varied inversely with biomass areas; reasons for this may include nutrient depletion and CO2 limitation within dense biofilms. Ó 2005 Elsevier Ltd. All rights reserved. Keywords: microphytobenthos; PAM fluorescence; patchiness; spatial distribution
1. Introduction Microphytobenthos (MPB) assemblages are important elements of estuarine ecosystems. They are composed of benthic microalgae that live in and on sediments of intertidal and shallow subtidal areas where light reaches the sediment surface. These assemblages provide ecological services in the form of carbon flux * Corresponding author. Instituto de Oceanografia, Faculdade de Cieˆncias da Universidade de Lisboa, Campo Grande 1749-016, Lisboa, Portugal. E-mail address:
[email protected] (B. Jesus). 0272-7714/$ - see front matter Ó 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.ecss.2005.05.005
(Middelburg et al., 2000), primary productivity (Brotas et al., 1995; MacIntyre et al., 1996; Underwood and Kromkamp, 1999), nutrient flux (Sunba¨ck and Grane´li, 1988) and in the stabilization of the sediments (Paterson, 1989). They exhibit high levels of spatial (Guarini et al., 1998; Sandulli and Pinckney, 1999; Seuront and Spilmont, 2002) and temporal variability (Pinckney et al., 1994; Paterson et al., 1998), at different scales ranging from centimetres to kilometres and from minutes to years, respectively. The importance of describing and interpreting spatial patterns at all scales is well recognized as a starting point in ecological studies (Underwood et al., 2000). Studies of microphytobenthic
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between the minimum fluorescence yield (Fo) and the chlorophyll a present at the sediment surface, furthermore, these authors showed that migratory rhythms were the main cause for short-term variability in intertidal benthic primary productivity (Seroˆdio et al., 2001). Using F15 o (minimum fluorescence yield after 15 min of dark adaptation) Honeywill et al. (2002) showed that it was possible to estimate microphytobenthic biomass using in situ PAM fluorometry. The primary aim of this work was to investigate the spatial structure and the dynamics of MPB by producing fluorescence distribution maps of the sediment surface during an emersion period. Biomass comparisons per se are restricted by the limitation of the technique (Honeywill et al., 2002). A secondary aim was to test how PSII maximum quantum efficiency was affected by the heterogeneity of surface biomass.
spatial structure have always been conducted over time scales longer than the tidal emersion period and using destructive sampling methods (Guarini et al., 1998; Sandulli and Pinckney, 1999). Fluorescence techniques now provide a novel solution to this problem. Variability of photosynthetic active biomass within an emersion period has been recognized as an important factor in the calculation of productivity models (Seroˆdio et al., 2001; Perkins et al., 2001, 2002). Microphytobenthic assemblages often display vertical migration patterns synchronized with diurnal emersion periods, these movements can cause considerable variation of biomass at the surface during a single emersion period (Pinckney et al., 1994; Seroˆdio et al., 1997). The cells accumulating at the sediment surface form a visible layer that has been described as a ‘‘transient biofilm’’ (Paterson et al., 2003). The common understanding of biofilm refers to a more permanent structure but these self-organising transient films have an important role in the ecology of depositional systems. Until recently, studies on microphytobenthic spatial distribution used destructive sampling techniques and therefore only permitted a single spatial measurement at a single point during an emersion period. Studies concerning changes in the spatial structure have used repeated sampling of different sediment areas, thus increasing the variability of the results. The recent introduction of pulse amplitude modulated (PAM) fluorescence techniques in the study of microphytobenthic biofilms allows changes in the surface biomass to be monitored without destroying biofilm structure, and therefore the ability to follow biomass changes throughout a single emersion period. Seroˆdio et al. (1997) showed a relationship
2. Materials and methods 2.1. Study sites Sampling was carried out in June 2000 on two intertidal mudflats in the Netherlands, Biezelingsche Ham (51 26#N, 3 55#E) and Zandkreek (51 32#N, 3 54#E) (Fig. 1). The Biezelingsche Ham site was sampled during one emersion period when low tide coincided with midday, and Zandkreek was sampled during two consecutive emersion periods that coincided with the beginning and end of the day. Zandkreek is a low nutrient site with sandy-mud sediments and Biezelingsche Ham is a high nutrient muddy site.
3º 6´ E
4º 1´ E
10 km
Netherlands 52º 0´ N
52º 0´ N
N
Belgium 51º 5´ N
51º 5´ N
51º 0´ N
3º 6´ E
4º 1´ E
Fig. 1. Study sites. (-) Biezelingsche Ham; ( ) Zandkreek.
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Biofilms of both sites were diatom-dominated (Hagerthey et al., 2002). Light was measured throughout the day with a Li-192 cosine-corrected quantum sensor and logged every minute with a Licor 1000 datalogger. 2.2. Fluorescence measurements Fluorescence measurements were made using a waterproof pulse amplitude modulated (PAM) fluorometer (Waltz Diving Pam). Fo measurements were taken after 15e20 min of dark adaptation interspaced by 1-h time intervals. A closed chamber with a lid containing 100 measuring points (holes) was used as a template to sample an area of 400 cm2 (Fig. 2). The purpose of the lid was to dark adapt the sediment surface as required to measure the Fo parameter, and to ensure that the exact position of each measurement was known, the probe distance to the sediment was 4 mm (G0.3 mm), the total time required to complete the 100 measurements was approximately 6 min. The distance of the probe from the sediment is important (Honeywill et al., 2002) so a flat area of intertidal flat was selected where variation in probe height would not confound the results. The resultant measurement resolution is a circle of 0.4 cm diameter (approx 12.6 mm2 in area). Between each set of measurements the lid was taken off and the surface exposed to ambient light for 45 min. The measurements began at the beginning of the emersion period and finished as close as possible to the end of the emersion period. Fluorescence terminology followed Genty et al. (1989) where PSII maximum efficiency is calculated as the ratio: Fv ðFm Fo Þ Z Fm Fm
ð1Þ
where Fm is the maximum fluorescence yield measured after a saturating pulse and Fo the minimum fluorescence yield immediately before the saturating pulse. In this study values were approximated due to the limited period of dark adaptation. Absolute comparisons of biomass are not valid but the examination of relative variation in biomass is legitimate. The potential of low biomass to influence efficiency (Fv/Fm) measurements
Top view
Lateral view
25 cm
has been noted and a threshold level (50 relative fluorescence units) set before data was treated as reliable (Honeywill et al., 2002). The reliability of the biomass/ F15 o relationship for the selected sites has been confirmed in a number of previous studies (Forster, personal communication, EU BIOPTIS programme). For a more detailed explanation of fluorescence see Consalvey et al. (2005). 2.3. GIS manipulation Data was imported into IDRISIÔ GIS software and a surface map was generated by interpolation of the 100 measured points for each time of sampling. The interpolation method was inverse distance weight (IDW) and data were normalized between 0 and 1 before interpolation to make relative changes in the fluorescence signal comparable between times. The definition of a ‘‘patch’’ is not straightforward when monitoring a continuous surface that is changing rapidly. The criteria used to define a patch frontier at one time might not be appropriate at another time. Therefore it was important to define a criterion that allowed valid comparisons between different times. Since the fluorescence surface could be represented as a 3D surface, a criterion was established based on the surface slope, i.e., rate of change in biomass (fluorescence) concentration over distance. Thus a patch frontier was established where the slope between two adjacent areas was greater than a set value. From initial analysis of the data, a slope value of 6% was used as an initial threshold to delineate the patches in the remaining maps at both sites. 2.4. Low-temperature scanning electron microscopy (LTSEM) Sediment samples from Biezelingsche Ham were taken after the last fluorescence measurement, selected from areas of the sediment that were visually distinct and then paired with the fluorescence points. Samples were taken using aluminium foil, and frozen in situ with liquid nitrogen to preserve the sediment surface. Samples were kept frozen and then mounted and viewed cryogenically on the stage of a low-temperature scanning electron microscope (LTSEM) (Paterson, 1995). 2.5. Statistical analysis
20 cm lid base
25 cm Fig. 2. PVC template chamber used to construct the fluorescence maps, each point in the lid corresponded to a measured point (nZ100). Distance between the sediment and the PAM probe was 4 mm.
PSII efficiency data was compared between areas of high biomass (HB) and low biomass (LB) at Biezelingsche Ham. Data were arcsine transformed and a repeated measurements ANOVA performed to test for significant differences between times and biomass. Repeated measures ANOVA is designed to take into account the effect of measuring the same sample over time, i.e. lack
B. Jesus et al. / Estuarine, Coastal and Shelf Science 65 (2005) 30e42
of independence. The Scheffe´ test was used for post-hoc analysis. Twenty-two points were randomly selected from within the 78 low biomass points to balance the analysis with the initial 22 high biomass points (Fig. 3). Normalized data was examined using spatial and temporal statistics. The spatial variogram of the biomass field B(x,t) (for fixed t) may be defined, assuming homogeneity and isotropy (quite reasonable in the present case as no variability of the controlling physical factors within the grid appeared to be relevant), as g(d )Z1/2E{[B(xCd,t)B(x,t)]2} (e.g., Goovaerts, 1997), where E{ } is the (spatial) expectation operator and d is the modulus of the displacement vector d. The variogram characterizes the degree of spatial correlation of biomass values measured at different locations within the grid. Experimental variograms were determined for each biomass discrete field at each time, using an optimised estimator (Li and Lake, 1994) and were used to study the possible signature of diatom movements in biomass space correlations. The study of the temporal correlation of biomass fields was performed by computing, for each site x within the grid, the time autocorrelation function r(x,t)ZE[(B(x,tCt)mt(x))(B(x,t)mt(x))]/s 2t(x), where mt(x) and s 2t(x) are the temporal mean and variance of biomass at site x and E[ ] is the (temporal) expectation operator (e.g. Goovaerts, 1997). The space-dependent autocorrelation function r(x,t) was then averaged (under the homogeneity assumption introduced above) over all sites, to obtain an ‘ensemble’ temporal autocorrelation r(t), representative of the overall correlation between biomass fields observed at different times separated by a lag t. Inferences were limited by the size of the area measured (4 mm diameter), the consequent pixel resolution (0.126 cm2) and by the spacing (2 cm) between measurement, nevertheless provided detailed information on the spaceetime structure of the biomass field.
Fig. 3. Biezelingsche Ham Fo surface map of the first measurement series, the vertical axis was standardized between 0 and 1. The overlaid points (dark circles) represent areas of high biomass where PAM relative units were O140.
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3. Results 3.1. Fo surface variation The variation in time interval for the dark adaptation period introduced due to the measurement procedure was between 15 and 20 min. There was no evidence in the data for a temporal change in Fo related to this variation (not shown) and this was therefore discounted as a factor. The surface distribution of biomass on sediments at Biezelingsche Ham changed considerably during the emersion period (Fig. 4). The region started with a few distinct areas of high biomass (1000 h) and converged to a more homogeneous surface by the end of the emersion period (1500 h). Although the Fo signal from the entire surface significantly increased (ANOVA, p!0.001) (Fig. 5) throughout the emersion period, the low biomass areas increased more than the high biomass areas, thus producing a more homogeneous surface. At Biezelingsche Ham, Fo significantly declined (Scheffe´ test, p!0.001) in the last hour before the immersion period (Fig. 5). The Zandkreek surface was less dynamic throughout both emersion periods and did not show a visible pattern of change in the fluorescence topography (Fig. 6). Fo increased slightly throughout the two emersion periods and showed no sign of decrease before each immersion period with only a small decrease registered between the last measurement of the first emersion period (0930 h) and the first measurement of the second emersion period (1540 h; Fig. 7). 3.2. Patch dynamics The Biezelingsche Ham surface showed a decrease in the number of patches, paralleled by an increase on the average patch area during the first 3 h (Table1). After 3 h, there was an increase in the number of patches and a decrease in the average surface area of each patch. The variogram values computed from data for each time interval are all very close to the sill value (i.e. the variance, Fig. 8), even though variograms for hours 3e6 show a modest increase at smaller lags. All experimental variograms show an evident ‘nugget effect’ (e.g. Goovaerts, 1997), i.e. a non-negligible value of g(d ) for values of the lag d close to zero, which is likely caused by the inadequate density of the data with respect to the typical scale of variability of the biomass field. The spatial statistics thus suggest that the biomass distribution is not strongly spatially correlated within the 2e 20 cm scale range. The overall range of variability of the variance was relatively small among fluorescence fields at all times, varying from 0.041 relative fluorescence units2 to 0.017 relative biomass units2. The maximum value of the variance varied with time such that there was a slight increase (0.035 fluorescence units2 to 0.041 fluorescence units2) from Time 1 to Time 2 and then
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B. Jesus et al. / Estuarine, Coastal and Shelf Science 65 (2005) 30e42 700
1000h
1100h
Fluorescence (rel. units)
600
Fm15 Fo15
500 400 300 200 100 0 0900
1100
1300
1500
Time (hours)
1200h
1300h
1400h
1500h
Fig. 4. Fo changes at Biezelingsche Ham surface over a 6 h emersion period. The vertical axis was standardized between 0 and 1.
a fairly steady decline in variance from Time 2 to Time 6 (Fig. 8). Since the maximum values of the fields are fixed to one by normalization, the decrease in the variance is due to increased homogeneity of the spatial distributions over time. The Zandkreek surface exhibited more patches per area than Biezelingsche Ham but showed little variation
Fig. 5. Changes in the fluorescence yield from the surface of the Biezelingsche Ham site over a 6 h emersion period (nZ100, meanGSE).
in their number or average area, even when considering both emersion periods (Table 1). Spatial analysis was separated into the tidal periods before and after immersion. There was no clear trend in the variance of the patch dynamics before the flood tide (Fig. 9, T1eT4). After the tidal ebb the variance increased, showing lower spatial correlation (Fig. 9, T5eT9) between regions as the exposure period lengthened. Variograms showed a somewhat larger variability than in the case of Belingse Ham, possibly indicating a slightly stronger spatial correlation, particularly for measurements after flooding (hours 5e9). Time autocorrelation functions were computed for fluorescence values measured at each point at different time steps and then averaged over all measurement points to obtain an ensemble estimate as described above. The overall time autocorrelation functions determined through this procedure demonstrate relatively high correlation values. With a 1 h lag, correlation values were all O0.95. These values did diminish consistently with time, but even after 6 h of exposure the lowest correlation value was O0.8 (Table 2). This indicates a strong persistence in the relative spatial distribution of biomass over time, for both Biezelingsche Ham and Zandkreek. The maximum time correlation of biomass was observed within emersion periods (e.g., Zandkreek data, Table 2) and was minimum when interrupted by a tidal immersion period. The correlation between the last time period before immersion and the same area immediately after immersion was 0.56 (Table 2), much smaller than autocorrelation values computed within each emersion period, indicating an effect of flooding on biomass values, which might be linked to increased diatom mobility. 3.3. PSII maximum efficiency patterns The Biezelingsche Ham biofilms showed a clear pattern of high biomass areas (high Fo) surrounded by
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A
B 0630h
1540h
0730h 1640h
1740h
0830h
1840h
0930h
1940h
Fig. 6. Fo changes at the Zandkreek surface over two consecutive emersion periods. The vertical axis was standardized between 0 and 1. (A) First emersion period, (B) second emersion period.
low biomass areas (low Fo) at the beginning of the emersion period (Fig. 3). Low biomass areas showed a significant increase (Scheffe´ test, p!0.05) in PSII efficiency from T1 (1000 h) to T4 (1300 h) (Table 3), while high biomass areas showed no significant change during this period (Table 3). High biomass and low biomass areas showed a significant decrease (Scheffe´ test, p!0.05) of PSII efficiencies in the last 2 h (Fig. 10). Throughout the emersion period, the low biomass areas always had higher efficiencies (Scheffe´ test, p!0.05) with the exception of the first measurement, which showed no
difference between areas (Table 3). The efficiencies of the Zandkreek site decreased in the first emersion period and increased in the second emersion period, showing a negative relationship with ambient light (Fig. 11). 3.4. Biezelingsche Ham: LTSEM The high biomass areas (Fig. 12A,C,E,F) were dominated by epipelic diatoms strongly packed at the surface and covered with large quantities of extracellular polymeric substances (EPS) (Fig. 12A,F). The EPS
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B. Jesus et al. / Estuarine, Coastal and Shelf Science 65 (2005) 30e42 700 0.04
Fluorescence rel. units2
Fluorescence (rel. units)
600 500 400 300 200
0.03
0.02 Time 1 Time 2 Time 3 Time 4 Time 5 Time 6
0.01
Fo15 100
Fm15 0.00
0 0600
0800
1000
1200
1400
1600
1800
0
2
4
6
2000
Time (hours) Fig. 7. Changes in the minimum and maximum fluorescence yields (Fo and Fm respectively) of the Zandkreek biofilms over two consecutive emersion periods (nZ100, meanGSE). The grey bar represents the emersion period.
matrix frequently exhibited trapped material from the water column such as planktonic diatoms and debris (Fig. 12E). When fractures of the surface were examined a deeper layer of diatoms was visible underneath the superficial layer (Fig. 12A,C). Diatoms cannot be identified using LTSEM and so it was impossible to use this technique to differentiate the species composition of the surface and sub-surface assemblages. The low biomass areas showed a community dominated by epipelic diatoms but not as densely packed and with less EPS (Fig. 12B) than the high biomass areas. Table 1 Descriptive statistics of Biezelingsche Ham and Zandkreek fluorescence surfaces examined throughout the emersion periods. Number of patches and respective areas were calculated using IDRISIÔ descriptive tools No. of patches
10
12
14
16
18
20
Fig. 8. Variogram analysis of the patch dynamics by normalized fluorescence units for the spatial analysis of Fo conducted at each time period during tidal emersion at Biezelingsche Ham over a 6 h emersion period.
They also showed evidence of grazing with the obvious presence of faecal pellets (Fig. 12D).
4. Discussion Non-invasive fluorescence methods were successfully used to illustrate variability in the biomass distribution and patchiness of photosynthetic biofilms on a centimetre scale and also the variation in the efficiencies of photophysiology between regions of differing biomass. Each of these areas will be discussed. 4.1. Biomass distribution and patch dynamics At the onset of the emersion period, both sites were relatively patchy with the Zandkreek site showing
Average areaGstd. dev. (cm2) 0.05
8 7 5 9 7 15
11.3G9.6 27.8G31.5 40.2G36.4 22.6G51.8 23.7G35.5 7.3G12.1
Zandkreek 1st emersion period T1 e 0630 T2 e 0730 T3 e 0830 T4 e 0930
14 13 14 12
1.8G1.3 2.1G2.5 2.0G2.4 2.3G2.5
2nd emersion period T5 e 1540 T6 e 1640 T7 e 1740 T8 e 1840 T9 e 1940
12 15 13 14 14
3.8G4.6 2.9G5.9 3.8G6.5 3.4G6.1 2.9G6.2
0.04
Fluorescence rel. units2
Biezelingsche Ham T1 e 1000 T2 e 1100 T3 e 1200 T4 e 1300 T5 e 1400 T6 e 1500
8
Distance (cm)
0.03
Time 1 Time 2 Time 3 Time 4 Time 5 Time 6 Time 6 Time 8 Time 9
0.02
0.01
0.00
0
2
4
6
8
10
12
14
16
18
20
Distance (cm) Fig. 9. Variogram analysis of the patch dynamics by normalized fluorescence units for the spatial analysis of Fo conducted at each time period leading towards the flood tide (T 1e4) and then after tidal ebb (T 5e9) at Zandkreek.
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B. Jesus et al. / Estuarine, Coastal and Shelf Science 65 (2005) 30e42 Table 2 Time correlation of biomass values determined by Fo within the same spatial area measured hourly for the sediment surface at Biezelingsche Ham (one emersion period) and Zandkreek (two consecutive emersion periods)
Time lagZ1 h Time lagZ2 h Time lagZ3 h Time lagZ4 h Time lagZ5 h Zandkreek pre-flood vs Zandkreek post-flood
Biezelingsche Ham
Zandkreek: pre-flood
Zandkreek: post-flood
0.96 0.92 0.88 0.83 0.81
0.98 0.96 0.95
0.95 0.94 0.91 0.80 0.56
a higher number of smaller patches while Biezelingsche Ham showed greater levels of absolute variation between fewer patches (Table 4). The Zandkreek site remained relatively stable with time but at the Biezelingsche Ham site biofilm material accumulated in areas of low biomass. This resulted in the Biezelingsche Ham surface becoming more homogeneous with time (Fig. 4). Variogram analysis (Figs. 8 and 9) indicates that there was little spatial relationship between biomass levels at different points within the limits of resolution of the current technique (2 cm). The variogram by definition (under the postulated hypotheses) tends to the variance for large spatial lags, indicating a complete asymptotic loss of correlation (biomass levels at very distant locations must be, for physical reasons, completely uncorrelated). The presence of a nugget effect and the values of g(d ) quite close to the variance (sill) for dZ2 cm, indicates that very little correlation persists between points located at such a distance, suggesting that 2 cm is too large a spatial separation to accurately determine the shape of the variogram and that biomass spatial structure of microphytobenthos should be examined at an even smaller scale, where most of the spatial correlation must be located. This poor correlation between biomass distributions on a spatial scale O2 cm contrasts with the temporal analysis (Table 2). Relative biomass between time intervals showed high correlation values, despite the relative increase of cells in the low biomass areas over the emersion period. The
implications of these results are that relative spatial patterns of microphytobenthos, at the 2 cm scale, are persistent over the emersion period. This was supported by both the Biezelingsche Ham and Zandkreek data. However, the correlation between biomass distributions for the time period immediately before and after the Zandkreek tidal inundation was much lower (0.56) suggesting some re-orientation of the patch dynamics during immersion. This is interesting new information. However, the data suggests that the lateral mobility of microphytobenthos during the emersion period is hardly significant, at a horizontal scale of O2 cm, since significant movement over this scale would likely be evident in changes in the shape of the spatial variograms. Little is known about the lateral movement of diatoms once at the surface of intertidal sediments. This study suggests that lateral movement at a scale O2 cm is not a strong vector for cell re-disruption over the surface when the sediment was exposed. How microphytobenthos patchiness is established and maintained on a micrometre to centimetre scale over the emersion period is not well investigated in the literature. There are three possible mechanisms that may lead to an increase in biomass in low-biomass areas (Fig. 13). 1. Diatoms move horizontally from HB to LB areas. 2. Sub-surface diatoms migrate preferentially to areas of LB. 3. Sub-surface diatoms migrate homogeneously to the surface and then horizontally from areas of HB to LB. The first hypothesis (Fig. 13) suggests diatoms migrate laterally from the HB areas to LB areas. This implies that biofilm biomass at the surface would not rise throughout emersion and a decrease in HB areas should parallel an increase in LB, which was not observed. This hypothesis is therefore rejected. The second hypothesis (Fig. 13) suggests that sub-surface diatoms migrate preferentially to areas of low biomass. This implies that cells can discriminate between surface areas of different biomass and that biomass overall should increase as the emersion period continues. Many
Table 3 Comparison of the PSII maximum efficiency in three different situations: between high and low biomass areas at each sampling time; between different times at low biomass areas; between different times at high biomass areas. Repeated measurements ANOVA, *p!0.01, nsnon-significant High vs low biomass Time Time Time Time Time Time
1 2 3 4 5 6
F value 1.05 6.83 19.93 35.05 24.17 13.84
p value ns
0.65 0.01** 0.00** 0.00** 0.00** 0.00**
Low biomass Time Time Time Time Time Time
1 2 3 4 5 1
vs vs vs vs vs vs
Time Time Time Time Time Time
F value 2 3 4 5 6 6
8.65 8.42 17.02 71.35 66.93 15.95
p value ns
0.08 0.00** 0.00** 0.00** 0.00** 0.00**
High biomass Time Time Time Time Time Time
1 1 1 1 4 5
vs vs vs vs vs vs
Time Time Time Time Time Time
2 3 4 6 6 6
F value
p value
0.22 0.07 0.07 26.64 16.39 13.01
0.65ns 0.93ns 0.93ns 0.00** 0.00** 0.00**
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0.60 1000 0.58 500
0 0800
PPFD High biomass Low biomass All 1000
0.56
1200
1400
1600
0.54
PPFD Fv /Fm
2000
0.65 0.60
1500
PPFD
PPFD
0.62 1500
Maximum PSII efficiency
0.64
2000
0.70
2500
0.55 1000 0.50 500
0.45
0
0600
0.40 0800
1000
1200
1400
1600
1800
2000
Maximum PSII Efficiency
0.66
2500
0.35
Time (hours)
Time (hours)
Fig. 10. Changes in the PSII maximum quantum efficiency yield of the Biezelingsche Ham site over one emersion period. High biomass, nZ22; low biomass, nZ78; all, nZ100, meanGSE. The grey bars represent the emersion periods.
Fig. 11. Changes in the maximum PSII maximum quantum efficiency of the Zandkreek site over two consecutive emersion periods (nZ100, meanGSE). The grey bar represents the emersion period.
organisms, including diatoms, respond to chemical cues. High biomass at the sediment surface creates sharp chemical gradients which might act as a signal to sub-surface cells. This would require further research and this hypothesis cannot be excluded. The final hypothesis (Fig. 13) is a combination of model 1 and model 2 where sub-surface diatoms migrate preferentially or more quickly to LB areas in combination with lateral migration from HB to LB areas. This again would entail a loss of biomass from HB areas and a gradual increase in overall biomass. Biomass loss from peak areas only occurred near the end of the emersion period and therefore substantial lateral migration seems a weak hypothesis and that any increase in low biomass areas occurred by the accumulation from below. The spatial variogram analyses performed at different times are consistent with low diatom lateral mobility at the 2 cm spatial scale over time intervals of a few hours, as evidenced by the high variogram values observed (nearly equal to the variance). At a larger scale, Guarini et al. (1998) has suggested that microphytobenthos biofilms may reach critical biomass at the surface and enter a cycle of spreading/shrinking around high density regions. One indication that the HB areas might have been present from the beginning of the emersion was the existence of a dense EPS matrix (Fig. 10E) on the sediment surface which was observed from the beginning of the emersion period. The presence of such a welldeveloped matrix may also influence diatom distribution, since its presence may impair diatom locomotion and therefore lateral and vertical migration. We conclude that lateral migration of diatoms cannot account for the increase of biomass despite the fact that diatoms have the capability of movement at speeds over the surface that would allow a distance of O2.1 mm to be travelled in 1 h (Hay et al., 1993).
The synchronized increase and decrease of Fo and Fm at the Biezelingsche Ham surface (Fig. 5) indicated vertical migration as the major mechanism behind the variations in the surface fluorescence. This assemblage showed an endogenous rhythm anticipating tidal immersion by migrating downwards, as evidenced by the significant decrease (Scheffe´ test, p!0.01) in both parameters. The stability in the number and areal coverage of the Zandkreek patches (Table 1), and the high temporal correlation values observed, suggest a stable fluorescence surface where, if vertical migration was occurring, relative levels of biomass were maintained and the overall migratory exchange of cells was less than for Biezelingsche Ham. The fact that the overall value of Fo remained stable throughout two successive emersion periods suggests that this biofilm might have reached a state of biomass equilibrium. In theory, the biofilm may reach a biomass at which the fluorescence response is saturated (maximal) given the depth of light penetration. However, Fo values did not peak in an asymptotic manner providing evidence that biofilms had not saturated the fluorescence response. The small increase in Fo and the inverse relationship of Fm with ambient light exhibited by Zandkreek is likely to be a result of the non-migratory nature of this biofilm at this time (Fig. 7). Fm was shown to decrease with increasing light probably due to non-photochemical quenching (NPQ, used here as a generic term including both PSII down-regulation and/or chronic photo-inhibition), a reaction to potentially damaging levels of light intensity (Mu¨ller et al., 2001; Ge´vaert et al., 2003). Although this is the first study where spatial variations were registered on the hourly scale of emersion periods the average patch size was consistent with ranges estimated by other authors (4e191 cm2) for single time measurements for both sites (Table 1) (Blanchard, 1990; Sandulli and Pinckney, 1999).
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Fig. 12. Low-temperature scanning electron micrographs of the surface intertidal sediments. (A,C,E,F) High biomass (HB) regions. (B,D) Low biomass (LB) regions. (A) HB surface showing high densities of cells embedded in a matrix of EPS; (B) LB surface showing low densities of cells and less EPS; (C) detail of BH surface showing an underlying layer of diatom cells visible through a surface fracture; (D) detail of LB region showing evidence of grazing by the presence of faecal pellets; (E) HB surface showing evidence of material from the water column; (F) HB detail of diatoms forming a bridge of EPS over a deeper layer of diatoms. Scale bar is 10 mm for (B,C,E,F) and 100 mm for (A,D).
Hypotheses for structuring factors at larger spatial scales include morphological structures, like sand waves or ripples (Plante-Cuny and Reys, 1986) and sediment characteristic (Paterson et al., 2003). At smaller spatial scales the most frequently structuring factors are interspecific competition (Sandulli and Pinckney, 1999), spatial distribution of nutrient efflux (Ho¨pner and Wonneberger, 1985) and control by grazing pressure (Hagerthey et al., 2002). A general consensus
is that no single factor is responsible for structuring the biofilm but rather a complex interaction of biotic and abiotic parameters (Stolz, 2003). Although both assemblages were diatom-dominated, the relative composition of the assemblages was different (personal observation) and information from the literature suggests that this variation may be controlled by nutrient and grazing pressure at these sites (see Hagerthey et al., 2002).
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Table 4 Fo coefficient of variation: Biezelingsche Ham (one emersion period) and Zandkreek (two consecutive emersion periods) Coefficient of variation
Biezelingsche Ham (%)
Zandkreek (%)
Time Time Time Time Time Time Time Time Time
104 105 93 69 51 50
20 21 21 21 26 24 24 25 25
1 2 3 4 5 6 7 8 9
1
HB
LB
HB
LB
HB
HB
LB
HB
2
HB
LB
4.2. Diel variation in PSII maximum efficiencies: Biezelingsche Ham vs Zandkreek The overall maximum PSII efficiency pattern was almost opposite in the two sites. The fact that Biezelingsche Ham efficiencies increased during the first 4 h, even at increasing PPFD (Fig. 10), supports the hypothesis advanced by some authors (Kromkamp et al., 1998) that diatoms sub-cycle in the top biofilm layer as a protective response to prevent photoinhibition. The decrease in the last 2 h of exposure may be an indication that this behaviour is not sufficient to protect cells and NPQ begins or that nutrient depletion may become important in a highly metabolic biofilm (Geider et al., 1993). The downward migration during the last hour (Fig. 5) could potentially amplify the absolute decrease in the efficiencies by leaving behind less healthy cells. Zandkreek assemblages exhibited a pattern described that is normally associated with algae that do not have the mobility to move away from the light source, and therefore rely more on down-regulation by NPQ processes (Mu¨ller et al., 2001; Perkins et al., 2002; Ge´vaert et al., 2003). In this case cells showed high efficiencies in the morning, decreased during the day and increased again towards the end of the day and showed a strong negative correlation of efficiency with ambient light. This further supports the proposal that the Zandkreek assemblages were non-migrational at the time of measurement and remained exposed to ambient light through the water column. As a result of this underwater exposure to light the maximum PSII efficiency in the beginning of the second emersion period was lower than in the end of the first emersion period (Fig. 11). The fact that Fv/Fm showed diurnal variation even after 15 min dark adaptation indicates that this assemblage was either photo-inhibited throughout the day or that 15 min was not enough to fully dissipate the electron transport chain proton gradient, diatoms have been reported to maintain NPQ in darkness by maintaining the proton gradient through poorly known mechanisms (Ting and Owens, 1993).
(a)
(a) (b)
3
HB
LB
HB
LB
HB
(a) (b)
Fig. 13. Theoretical models of how diatoms might move or migrate so that the surface distribution of cells becomes more homogeneous during the emersion period. (a) Sediment surface; (b) diatoms; (HB) high biomass; (LB) low biomass. (1) Diatoms move preferentially and horizontally from HB to LB areas; (2) sub-surface diatoms migrate preferentially to areas of LB; (3) sub-surface diatoms migrate homogeneously throughout the surface and then horizontally from areas of HB to LB.
4.3. PSII maximum efficiency patterns of Biezelingsche Ham surface: high biomass vs low biomass The thickness of the biofilm has been described as one of the factors that might affect the overall pattern of the PSII efficiency of microphytobenthos assemblages. Light attenuation through the biofilm results in cells at the base of the biofilm receiving lower levels of photon flux density and achieving higher levels of PSII maximum efficiency (Kromkamp et al., 1998; Perkins et al., 2002; Forster and Kromkamp, 2004; Seroˆdio, 2004). This was not the case in the present study where regions of high biomass also showed lower levels of PSII maximum efficiency (Table 3). The present study is not sufficiently detailed to provide an explanation for this behaviour but some observations can be made. If the regions of high biomass were maintained between tides (personal observation) these areas formed a more permanent biofilm structure. Under these conditions, the surface of the biofilm was exposed to light during the emersion period which may result in a reduced PSII maximum efficiency. In addition, nutrient from the
B. Jesus et al. / Estuarine, Coastal and Shelf Science 65 (2005) 30e42
sediment may become limiting to the lower reaches of the biofilm where cells are not migrating down into the sediment. Therefore nutrient depletion or CO2 limitation at the base of the biofilm acts as a stress which limits the photosynthetic efficiency of cells at the base of the biofilm: the result of this combination of factors leading to a reduced efficiency for less transient areas of high biomass (Geider et al., 1993; Kromkamp et al., 1998). The non-intrusive analysis of the spatial changes over an emersion period, both vertical and horizontal, provides insights into the short-term dynamics of microphytobenthos assemblages. The results confirmed the value of using the PAM fluorescence technique to study microphytobenthic communities in situ, particularly by facilitating the discrimination of biomass changes at small spatial scales, thus allowing for in situ testing difference in PSII efficiencies associated with biofilms of different thickness.
Acknowledgments B. Jesus was funded by a Ph.D. grant from FCT (Praxis XXI BD21634/99). This work was also funded by HIMOM project (Contract no. EVK3-2001-00043 I) and TIDE project (EVK3-CT-2001-00064). Thanks to I. Davidson for the LTSEM assistance, to R. Forster for the light data. We are grateful to M. Consalvey and R. Perkins for their comments on the manuscript.
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