Spatio-temporal variability of micro- and meiobenthic communities in a White Sea intertidal sandflat

Estuarine, Coastal and Shelf Science 60 (2004) 663e671 www.elsevier.com/locate/ECSS

Spatio-temporal variability of micro- and meiobenthic communities in a White Sea intertidal sandflat A.I. Azovskya,), E.S. Chertoprooda, M.A. Saburovab, I.G. Polikarpovb a

Department of Hydrobiology, Biological Faculty, Moscow State University, Vorob’evy Gory, 119899 Moscow, Russia b A.O. Kovalevsky Institute of Biology of the Southern Seas, Pr. Nakhimova, 2, 99011 Sevastopol, Ukraine Received 20 November 2003; accepted 18 March 2004

Abstract Meiobenthos (harpacticoids), microzoobenthos (ciliates) and microphytobenthos (epipelic diatoms and dinoflagellates) were collected during July 1999 using nested sampling at the scales of decimeters, meters and tens of meters. Similarity between samples decreased rapidly with distance for all groups, but unicellular organisms were distributed more heterogeneously at a large scale than meiobenthos. Microspatial (decimeter scale) variations contributed the main part of total variation of harpacticoid abundance, while the large-scale differences were less important. On the contrary, these differences yielded the main variability for ciliates and especially for microalgae. The relative role of temporal variability decreased steadily for the smaller-sized organisms. Stability of spatial structure at multiple scales was estimated by Mantel correlations, Rm, between the successive similarity matrices. For harpacticoids, the results showed intensive and chaotic turnover of microaggregations. For microbenthos, rather high Rm values were found in smaller scales: tens of metersdfor ciliates, from meters ondfor dinoflagellates, and already from decimeter scale and ondfor diatoms. Thus, the general spatial pattern (that is the arrangement of micropatches) was more stable for microbenthos than for meiobenthos. Temporal variability of species structure, in contrast to the spatial one, was highest for the smallest organisms. Correlations between size groups, using both total abundance and species composition, differed for microspatial, meso-spatial or temporal distribution. At the microscale, there were slight but significant negative correlations between harpacticoids and microalgae. Meso-scale distribution of dinoflagellates and diatoms differed significantly due to their different preferences in sediment properties. Ciliates were strongly positively correlated with dinoflagellates. At the microscale, each group had an individual community pattern. At larger scales, species composition of diatoms, ciliates and harpacticoids varied in space in coordination, while dinoflagellates behaved independently. It is hypothesized that body size determines the spatio-temporal scale of the perception of environmental heterogeneity. Ó 2004 Elsevier Ltd. All rights reserved. Keywords: microphytobenthos; ciliates; harpacticoids; dynamics; distribution; body size

1. Introduction There are several groups of benthos commonly distinguished by the body size of organisms: macro-, meio-, microzoo- and microphytobenthos (McIntyre, 1969; Parsons et al., 1977; Higgins and Thiel, 1992). Each of these size groups includes certain taxa and can be considered as a distinctive ecological unit, which has a peculiar set of adaptations as well as specific scales of spatio-temporal perception (Schwinghamer, 1981, 1983; ) Corresponding author. E-mail address: [email protected] (A.I. Azovsky). 0272-7714/$ - see front matter Ó 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.ecss.2004.03.005

Warwick, 1984; Burkovsky, 1992; Burkovsky et al., 1994). The ecology of the main taxa forming these groups has been studied repeatedly, including their spatial distribution, dynamics or feeding modes. Very few attempts have been done, however, to compare the spatiotemporal variability of different size groups. Attention has mostly been paid to the possible between-block trophic interactions, with the main emphasis on such functional characteristics as total abundance, production etc. (Pinckney and Sandulli, 1990; Montagna et al., 1995; Buffan-Dubau and Carman, 2000). Much more rarely, the community patterns have been compared for the organisms of different sizes inhabiting the same site.

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Pronounced spatio-temporal variability has been demonstrated for all the meio- and microbenthic taxa and can be treated as their common feature (Hicks, 1986; Fleeger and Moser, 1990; Sun and Fleeger, 1991; Burkovsky et al., 1994; Saburova et al., 1995; Sach and Bernem, 1996; Rybnikov and Azovsky, 1997; Nicholas and Hodda, 1999), but direct comparisons of the variability between groups are rare (Blanchard, 1990; Pinckney and Sandulli, 1990). It is not clear whether any correspondence exists between the distribution patterns of micro- and meiobenthos. The relative contribution of different spatial and temporal scales to the total variability of their abundance or composition is not well understood. The objective of the present paper is to describe and compare the various aspects of spatio-temporal variability of meiobenthos (harpacticoids), microzoobenthos (ciliates) and microphytobenthos (diatoms and dinoflagellates) on the White Sea intertidal sandflat.

2. Materials and methods 2.1. Site description A study was carried out in the gently sloping, lowenergy, intertidal sandflat of the Chernaya River Estuary (Kandalaksha Bay, the White Sea, Russia, 66(32#N, 33(50#E) (Fig. 1a) during July 1999. The sediment was fine-grained sand (modal particle size of 0.1 to

a

b

A

B

18

15 m

1.5 m

2.2. Sampling program The study area and the location of sampling sites are shown diagrammatically in Fig. 1b. Sampling was designed to assess the way the benthos varied at different spatial and temporal scales. Five plots were arranged at mid-tide level zone in a triangle, from 1.5 to 27 m apart (Fig. 1b). Plots differed in the sediment composition (Table 1), from the hillock of well-sorted coarse-grained sand (plot D) to the small littoral pool with silty sand (plot E). Five sets of samples were taken with time intervals of 1, 7, 14 and 30 days from the first sampling date. On each occasion, three samples were taken 10 cm apart from each plot. All samples were taken during low tide. 2.3. Sampling procedures and enumeration Samples of the top 5 cm of sand were collected using plastic tube corers (2 cm2 cross-section for harpacticoids and 1 cm2dfor microbenthos). The modified method of Uhlig (1968) with filtered seawater instead of ice was used to extract the microalgae and ciliates from the sediment. Organisms were counted in vivo in culture dishes at a magnification of 32! and 56! with a Lomo MBS-9 stereomicroscope in randomly selected fields of view immediately after the extraction (Saburova and Polikarpov, 1989; Burkovsky et al., 1996). Harpacticoids were fixed in sediments by 4% formaldehyde solution and sieved through the 70 mm size mesh. Nauplii were not counted. To calculate the individual biovolumes of the organisms, the nomograms and formulas were used proposed by Chislenko (1968), Burkovsky (1978) and Hillebrand et al. (1999). For every species, the number of individuals (N ) and biomass (B) were estimated, and then the metabolic intensity rate (M ) was calculated by the equation: M ¼ k ! N ! W b ¼ k ! N 1
b ! Bb ;

m

C

0.25 mm), slightly silty (10e25% of the silt and clay fraction) and mesosaprobic (organic carbon content up to 4% of the sediment dry weight). Water temperatures were between 12 and 17 (C, salinity was 16e20. The emersion time at the sampling site was ca. 4 h during the single semi-diurnal tidal cycle and the tidal range was ca. 1.6 m. A detailed survey examining the environmental characteristics of this sandflat was carried out by Burkovsky (1992).

D

E

27 m 1.5 m

Fig. 1. Area of investigation (a) and location of sampling sites (b).

ð1Þ

where N, W and B were the species abundance, individual weight and biomass per unit area, bdallometric coefficient, and kdspecific metabolism intensity of the given taxon (Peters, 1983; Schmidt-Nielsen, 1984; Hendriks, 1999). The M value is proportional to the potential production or, more generally, to the energy

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A.I. Azovsky et al. / Estuarine, Coastal and Shelf Science 60 (2004) 663e671 Table 1 Sediment properties of the sampling plots Parameters

Mean grain size, mm Percentage of silt fraction (!0.1 mm), % Eh in surface layer, mV mean (minemax)

Plots A

B

C

D

E

0.38 15 137.4 (113e162)

0.32 21 132.8 (92e152)

0.37 18 149.1 (119e173)

0.74 12 160.8 (126e181)

0.25 35 130.2 (89e155)

and nutrient fluxes through the population. Metabolic intensity is more desirable measure for between-species comparisons than density or biomass, since density may underestimate, and biomassdoverestimates the role of rare but large-sized forms in a community metabolism. By this reason, M values were used in further calculations as the measure of relative species abundance. The b value was set equal to 0.75 for harpacticoids and ciliates and 0.5 for microalgae (Alimov, 1979; Gutelmaher et al., 1980; Peters, 1983; Raven and K} ubler, 2002). The particular k values were unimportant here since only the relative M values (as percentages of the given species in total metabolism of the taxa (size group)) were used. 2.4. Statistical analysis Pair-wise similarity between samples was estimated by the percentage similarity (Czekanowski) index (Legendre and Legendre, 1998): X DXY ¼ minðXi ; Yi Þ; ð2Þ i¼1

where Xi,Yi are percentage of i-th species in samples X and Y. For every sampling series, the DXY similarity matrices were calculated separately for each size group (harpacticoids, ciliates, diatoms and dinoflagellates). 2.4.1. Analysis of spatio-temporal variability To estimate temporal variability of the communities’ spatial structure the modified Mantel test of concordance was used between the correspondent similarity matrices (Legendre and Legendre, 1998). The standardized Mantel statistic Rm (simple linear correlation coefficient) was computed between the elements of the two matrices. The statistic takes values between C1 (if general spatial pattern did not change) and
1 (if the pattern changed drastically, so that the points most similar at one series became dissimilar at the another series, and vice versa). The significance of Rm was tested by randomization procedure (Legendre and Legendre, 1998). The procedure was modified so that Rm values were computed separately for similarities between sampling points spaced at distances of decimeters, meters and tens of meters (accounting small-, median- and

large-scale structure variations). For estimation of the most ‘‘coarse’’ spatial variations, the samples within each plot were averaged and Rm values for similarities between plots were computed. Two methods were used to estimate the contribution of different spatial and temporal scales in general variability of the benthos distribution. First, the coefficients of variation (CV) of total abundance were calculated for each group of organisms. There were three ‘‘spatial’’ CV: between samples within each plot (decimeter scale), within adjacent pairs of plots (BCC and DCE, meter scale) and between all the samples (scale of tens of meters). These ‘‘spatial’’ CV values were calculated for every series and then averaged. ‘‘Temporal’’ CV (between series) were calculated separately for each of 15 sampling points and then averaged. Second, the data were analyzed by hierarchical (nested) ANOVA. The statistical model of the following type was used: Dijkl ¼ d C Ti C PjðiÞ C SkðijÞ C elðijkÞ ;

ð3Þ

where D is abundance of the organisms in the sample; d is the general mean abundance; Ti is the effect of the ith survey (temporal component); Pj(i) is the effect of the j-th group of plots in the i-th survey (scale of tens of meters); Sk(ij ) is the effect of the k-th plot within the j-th group (scale of meters); and el(ijk) is the random error (variation between the samples within the plot, scale of decimeters). 2.4.2. Analysis of the correspondence between groups Pearson’s linear correlation coefficient was used to estimate possible spatial or temporal relations between the total abundances of the size groups. The problem was that such relations might be manifested at different scales, so it was desirable to estimate them separately. The ordinary way is to calculate the correlation separately for each data subset, i.e. between single samples for each plot at each series, then between plots for each series, and so on. In this way, however, we might obtain 25 small-scale spatial correlations, each by only three points, and five large-scale correlations (one for each series), each by five points. To avoid the problem of many coefficients with low degrees of freedom, all original abundances, x, were

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transformed to the deviations d from the respective means m:

0,8

d1ðijkÞ ¼ xijk
m1ð jkÞ d2ð jkÞ ¼ m1ð jkÞ
m2ðkÞ

0,7

where x is abundance in the given sample, m(1e3) are the respective means, dddeviations; indices in parenthesis are: idnumber of sample within the plot, jdnumber of the plot, kdnumber of the series. In such a way we could get by with only three generalized correlations: correlation between d1 values showed the within-plot (decimeter scale) relations, between d2 valuesdrelations between the plot means (scale of meters and tens of meters), and between d3 valuesd correlation between temporal changes in abundance. To estimate the correspondence in spatial patterns of community structure, Mantel correlations Rm were computed for the matrices of similarity between samples (‘‘fine’’ spatial patterns) or between plots (‘‘coarse’’ patterns). Positive Rm values (high concordance) show that the sites with similar composition of a certain group are also similar by the composition of another group. Negative values (low concordance) correspond the opposite situation, when the spatial similarity patterns for the two groups are different (sites similar by composition of the one group differ by the composition of another). The computations were implemented using the ECOS 1.3 (Azovsky, MSU, 1995), SYSTAT 7.0 (SPSS Inc., 1996), and NTSYS-pc 1.60 (Applied Biostatistics Inc., NY, 1990) software.

3. Results 3.1. General characteristics of the community 3.1.1. Microalgae Epipelic free-living microalgae were represented by five dinoflagellate and 18 diatom species. Amphidinium britannicum C. E. Herdman and Gymnodinium sp. were the most abundant dinoflagellates (up to 97% of the total flagellate abundance). There were no commonly distinct dominants among diatoms, since species structure varied from plot to plot and between series. 3.1.2. Ciliates Fifty-three species were found, 30 of them were common and amounted over 90% of total ciliate abundance and biomass. Ciliate composition and total abundance at the individual plots differed noticeably, with different species dominated depending on the sediment properties.

Similarity

ð4Þ

d3ð jkÞ ¼ m1ð jkÞ
m3ð jÞ

0,6

0,5

0,4

0,3 0,1

1

10

100

Distance, m Harpacticoids Ciliates Microalgae

Fig. 2. Mean similarity between samples as a function of distance.

3.1.3. Harpacticoida Eleven species were found, with a strong dominance of Paraleptastacus kliei and Huntemannia jadensis.1 These two species added up to 90% of total abundance and mainly determined the differences between samples for the group. 3.2. Spatial distribution For every group, mean similarity in species structure was highest between the nearest-neighbor samples (at decimeter distance), dropped down at the distance of meters and then did not change noticeably (Fig. 2). This drop was relatively small for harpacticoids, while for unicellular groups the differences in the community composition increased sharply with distance. Therefore, at large scale, small and slow-moving microbenthic organisms were distributed more heterogeneously than meiobenthos. 3.3. Comparison of abundance variability at different scales To compare variability of micro- and meiobenthic abundance at different scales, coefficients of variation (CV) and nested ANOVA were used. Both methods yielded similar results. Variations in total abundance increased with increasing spatial scale for all groups, but at different degree (Table 2). Over the range of distances studied, the CV value increased for harpacticoids only 1

In some previous articles (Azovsky and Chertoprood, 2003; Rybnikov et al., 2003), Huntemannia jadensis Poppe 1984 was mistakenly reported as Itunella muelleri Gagern 1922.

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Table 2 Average coefficients of spatio-temporal variation (CV, %) of total meio- and microbenthic abundance Group

Harpacticoids Ciliates Diatoms Dinoflagellates

CV, % Decimeters

Meters

Tens meters

Time

46.2 32.1 35.0 42.7

61.1 41.9 56.0 107.4

67.7 57.8 83.0 137.4

77.5 42.2 48.6 68.3

half as much again, for ciliatesdalmost twice as much, and for microalgae the increment was 2.5e3 times. Temporal variability was about half as much again as the small-scale spatial level. Microspatial variations in decimeter scale contributed the main part of total heterogeneity for harpacticoids (Fig. 3), while large-scale differences between plots were less important. On the contrary, just these differences were the main source of variability for ciliates and especially for microalgae. The role of temporal variability (in comparison with the spatial one) decreased steadily for smaller-sized organisms (Fig. 3). 3.4. Stability of the spatial structure Pair-wise comparison of similarity matrices for different sampling dates was used to estimate the average degree of temporal changes in spatial patterns (Table 3). Microspatial structure of the harpacticoid community strongly and irregularly changed in time. Mantel test for between-sample similarity matrices did not reveal any significantly high Rm values, i.e. the points similar in composition in one survey, most likely will appear different in the next survey without keeping any permanence. Only the comparison of betweenplot similarity matrices (in terms of the plot-average abundances) showed a significantly high level of coincidence (Rm ¼ 0:768). Thus, the lifetime of harpacticoid microaggregations usually did not exceed a day, but the ‘‘coarse’’ pattern (specificity of sites in scale of meters and tens of meters) was more stable, generally remaining over several weeks. For microbenthos, rather high Rm means were found in smaller scales: tens of metersdfor ciliates, from meters ondfor dinoflagellates, and already from decimeter

Components of total abundance variance, %

A.I. Azovsky et al. / Estuarine, Coastal and Shelf Science 60 (2004) 663e671 100 80 60 40 20 0 Harpacticoids Ciliates

Diatoms Dinoflagellates

time

Group

meters–tens meters decimeters Fig. 3. Components of total abundance variance for different size groups (after hierarchical ANOVA).

scale and ondfor diatoms. Thus, the general spatial pattern (that is arrangement of structurally similar or distinct micropatches) was more stable for microbenthos than for meiobenthos. On the other hand, the mean similarity between samples taken at different dates decreased noticeably with prolongation of time intervals (Fig. 4), indicating the systematic changes in community structure. Microalgae demonstrated the most considerable changes, whereas the structure of meiobenthos was more stable. Ciliates occupied the intermediate position. Thus, the temporal variability of community structure, in contrast to the spatial variability, was highest for the smallest organisms. 3.5. Correlations between size groups A few significant correlations between total abundance of the groups were found. These correlations changed depending on what aspect of the data was analyzed: microspatial, meso-spatial or temporal distribution (Table 4). At microscale (within plots), there were slight but significant negative correlations between harpacticoids and both groups of microalgae. In the case of diatoms, this negative correlation remained, and even became stronger, at the meso-scale. The meso-scale distributions of dinoflagellates and diatoms differed

Table 3 Measure of spatial pattern stability (mean Mantel correlations between all pairs of spatial similarity matrices obtained at different dates) Group

Harpacticoids Ciliates Diatoms Dinoflagellates

Scale (spatial resolution) Decimeters (sample)

Meters (sample)

Tens meters (sample)

Tens meters (plot)


0.114 0.329 0.503 0.256

0.109 0.167 0.462 0.699*

0.229 0.426 0.564* 0.684*

0.768* 0.670* 0.689* 0.803*

Significant values: *p ! 0:05.

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0,7

A. Decimeters

B. Meters

Harpacticoids

Harpacticoids

Ciliates

Ciliates

Dinoflagellates

0,6

Similarity

Diatoms

C. Tens meters 0,5

Dinoflagellates

D. Tens meters averaging

Harpacticoids Ciliates

Diatoms

Dinoflagellates

0,4

Diatoms

Harpacticoids Ciliates

Diatoms

Dinoflagellates

positive correlation negative correlation

0,3 0

10

20

30

significant correlation (p < 0.05)

40

Time interval, days Harpacticoids Ciliates Microalgae Fig. 4. Mean similarity between samples as a function of time interval between sampling dates.

significantly. The first group had the permanently highest abundance in well-sorted, medium-grained sands at plot A, and lowestdin sediments with high silt content at plot E, whereas diatoms had the inverse distribution. Similar difference between these groups in sediment preferences was also found earlier (Saburova et al., 1991, 1995). Ciliates were strongly positively correlated with dinoflagellates. In the temporal respect, dynamics of harpacticoids and ciliates was positively correlated: abundance of the both groups regularly increased with time, whereas the dynamics of microalgae was irregular and varied from plot to plot. Spatial correspondence between groups by their species structure was estimated using the modified Mantel correlations (Rm) between similarity matrices. Average Rm values also turned to be scale-dependent. The results are summarized diagrammatically at Fig. 5. In microscale, when only the similarities between adjacent

Fig. 5. Diagrams of average between-group correspondences in the spatial structure (Mantel correlations, Rm): Adscale of decimeters; Bdscale of meters; Cdscale of tens of meters (‘‘fine’’ structure, using matrices of between-sample similarities); Ddscale of tens of meters (‘‘coarse’’ structure, using matrices of between-plot similarities).

samples within a plot were accounted, each group had individual spatial pattern. Rm values varied from series to series, most of them were slightly (insignificantly) negative, i.e. samples similar by some group composition differed by the composition of the others (Fig. 5A). At larger scales (meters and tens of meters, Fig. 5BeD), the significant similarity was revealed between spatial structure of ciliates and diatoms, and, in less degree, between these two groups and harpacticoids. Thus, the species composition of these three groups varied consistently from plot to plot. In contrast to them, dinoflagellates behaved independently, keeping the peculiarity of spatial structure at all scales.

4. Discussion The results obtained provide a general picture of the spatial distribution of the micro- and meiobenthic community and its multi-scale variability. It is known

Table 4 Correlations between normalized abundances of the size groups Groups

HarpacticoidseCiliates HarpacticoidseDinoflagellates HarpacticoidseDiatoms CiliateseDinoflagellates CiliateseDiatoms DinoflagellateseDiatoms Significant values: *p ! 0:05; **p ! 0:01.

Correlation coefficients Within plots (scale of decimeters)

Between plots (scale of metersdtens of meters)

Between sampling sets (time)

0.079
0.391**
0.356** 0.012 0.083 0.015

0.109 0.230
0.514** 0.750** 0.227
0.425*

0.472* 0.040
0.120
0.128 0.014
0.023

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that the smaller the organisms, the more fine environmental heterogeneity they could perceive. Furthermore, the short generation time of small-sized organisms causes high temporal variability of their communities (Burkovsky et al., 1994; Azovsky, 2000, 2002). Microbenthos would thus be expected to be more variable than meiobenthos, both by abundance and by species composition. From this standpoint, some of the results look rather unexpected. Indeed, both spatial and temporal variability of species structure increased from harpacticoids to ciliates to microalgae (Figs. 2 and 4). This difference, however, became obvious in large scales only (meters and tens of metersdin space, weeksdin time). Contributions of various components to the total abundance variability also differed for the size groups (Table 2, Fig. 3): microscale (decimeters) heterogeneity played the main role in harpacticoids distribution, whereas the most part of total variance of microbenthos was determined by meso-scale heterogeneity (over 60% for ciliates and over 80%dfor microalgae). The temporal variability of microbenthos was significantly less than the spatial onedan unexpected fact for such fast-reproducing organisms. Spatial patterns of community structure also turned out to be more stable for the smaller organisms. Average Mantel correlations Rm (Table 3) showed that the ‘‘fine’’ spatial pattern of micro-patches completely changed daily for harpacticoids, but remained much longer for microbenthos. As to larger sites (at scale of tens of meters), they kept their peculiarity over at least a month for all groups. Thus, even the small-scale distribution of harpacticoids represents the changeable mosaics, was highly variable in space and time. For microbenthos, the main variability was observed at spatial meso-scale, at which their community is much more heterogeneous both by structure and by total abundance. These facts could be explained by the differences in the motility of these organisms and in their scale of perception of the habitat heterogeneity that also depends on the body size. For meiobenthos, unlike the microorganisms, the average values and the range of variation of environmental parameters are mainly important, but not their local changes (McIntyre, 1969; Burkovsky et al., 1994). Therefore, a relatively homogeneous part of the intertidal zone constitutes a single whole biotope for the most of meiobenthos, regardless of its area. The short lifetime of single harpacticoids microaggregations is caused by the high mobility of these organisms, which are able to move actively in sediments and leave it into near-bottom water during a rising tide (Hicks and Coull, 1983; Hicks, 1992; Rybnikov et al., 2003). As a result, harpacticoids redistribute and the fine pattern of its micropatches completely changes in one or two days. Similar results were found in other studies of meiobenthic distribution that covered sufficiently wide range

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of scales (Hodda, 1990; Sun and Fleeger, 1991; Li et al., 1997; Azovsky and Chertoprood, 2003). Small organisms, like ciliates or epipelic microalgae, are more sensitive to local variations of the environment and have a low capability for movement (Harper, 1969; Burkovsky, 1984; Saburova and Polikarpov, 2003). Therefore, they perceive the same habitat as more heterogeneous. An area of tens of square meters presents to them a whole spectrum of distinguishable microhabitats occupied by different local communities (Burkovsky, 1992; Burkovsky et al., 1994; Saburova et al., 1995). Existing differences in granulometry, redox potential, organic content and other sedimentary properties are apparently enough to maintain the diversity of local communities for a relatively long time, which causes the above-mentioned stability of spatial structure. The stable difference in microalgae inhabiting the two adjacent plots (D and E) is a good example. Small forms (diatoms of genera Caloneis, Diploneis and Navicula, and dinoflagellates of genera Gymnodinium) predominate in coarse, well-sorted sand, whereas relatively large algaed dinoflagellate Amphidinium britannicum and diatoms Tropidoneis lepidoptera v. lepidoptera (Greg.) Cleve and Petroneis humerosa (Breb. ex W. Smith) Stickle and Manndare the main dominants in fine silty sand at 1e1.5 m apart. Donkinia recta (Donk.) Grun. in Van Heurck takes the place of P. humerosa at medium-grained sands. It is known that biotic factors, like competition or predatorepray interactions, act mainly at small spatial scales, in the forming and dissociation of microaggregations; abiotic factors, on the contrary, play the leading role in determining the larger-scale habitat heterogeneity (Arlt, 1973; Parsons et al., 1977; Burkovsky et al., 1994). Therefore, the observed patterns could also depend on the factors that mainly determine the distribution of auto- and heterotrophic organisms. Patchiness of ciliates and harpacticoids to a large degree depends on the biotic factors, like food or (for harpacticoids) their life history (Arlt, 1973; Montagna et al., 1983, 1995; Burkovsky, 1984; Fleeger and Decho, 1987). The abiotic features of habitat (granulometry, pH, Eh, content of inorganic nutrients or dissolved organic matter etc.) are of the main importance for the microalgae, while predation may often be less significant (Saburova et al., 1995; Mazei et al., 2001). On the one hand, the sensible degree of habitat heterogeneity for microorganisms is from smaller scales, on the other hand, these factors are less changeable in time than the biotic ones. As a result, the spatial structure of small-sized, slow-moving organisms turns out to be more stable at microscale, while the one of larger organisms with long generation timedat mesoscale. As regards the correlations between the abundances of the size groups, the data show rather weak concordance in their microspatial distribution (Table 4). The only significant correlations were found between

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harpacticoids and microalgae because of low microalgal density in the samples with highest numbers of crustaceans. Taking into account the short lifetime of such aggregations, this is presumably the result of intensive grazing of microalgae, the main food for the many benthic harpacticoids (Montagna et al., 1995; BuffanDubau and Carman, 2000). The lack of any pronounced correlations between total algae abundance and another consumers, ciliates, could be explained by either the minor part of the primary production eliminated by ciliates in the Chernaya River Estuary, or by their high trophic specialization (Burkovsky, 1992; Mazei et al., 2001). Food selectivity (selective feeding) can obscure the direct correlations between summarized abundances even in case of intensive trophic interactions. All other between-group correlations, both of abundance or species structure, are found at meso-scale (Fig. 5) and most likely reflect common biotopic preferences of dominating species. Stable correspondence between meso-scale spatial patterns of harpacticoids, ciliates and diatom algae indicates the similar distribution of the factors that determine their species structure at that scale. 5. Conclusions The communities of microphyto-, microzoo- and meiobenthos inhabiting the same biotope were found to be differently sensitive to its spatial heterogeneity and temporal variability. The patterns they form were essentially scale-dependent and related to the organisms’ body size and motility. The design of the present study does not allow definite identification of the extrinsic and intrinsic causes of the observed patterns. It does, however, clearly demonstrate that different size groups of benthos form different and mainly uncorrelated patterns and have different spectra of variability over the spatio-temporal scales. The results support the view that these groups are distinctive and in many respects are structurally independent components of the ecosystem (Schwinghamer, 1981, 1983; Warwick, 1984; Burkovsky et al., 1994; Azovsky 2000). Acknowledgements The authors thank two ECSS reviewers for their comments on the manuscript. This study was partially supported by Russian Fund of Basic Research (grant no. 03-04-48018). References Alimov, A.F., 1979. Metabolic rate of water poikilothermic animals. In: General Principles of Water Ecosystem Studies. Leningrad, Nauka, pp. 5e20 (in Russian).

Arlt, G., 1973. Vertical and horizontal microdistribution of the meiofauna in the Greifswalder Bodden. Oikos Suppl. 15, 105e111. Azovsky, A.I., 2000. Concept of scale in marine ecology: linking the words or the worlds? Web Ecology 1, 28e34. Available from http: //www.oikos.ekol.lu.se/we/we.html. Azovsky, A.I., 2002. Size-dependent speciesdarea relationships in benthos, or whether this world is more diverse for microbes? Ecography 25, 273e282. Azovsky, A.I., Chertoprood, E.S., 2003. Spatio-temporal dynamics of the White Sea littoral harpacticoid community. Oceanology 43, 103e111. Blanchard, G.F., 1990. Overlapping microscale dispersion patterns of meiofauna and microphytobenthos. Marine Ecology Progress Series 68, 101e111. Buffan-Dubau, E., Carman, K.R., 2000. Diel feeding behavior of meiofauna and their relationships with microalgal resources. Limnology and Oceanography 45, 381e395. Burkovsky, I.V., 1978. Structure, dynamics and production of psammophilic ciliates community. Zoologicheskiy Zhurnal 57, 325e337 (in Russian). Burkovsky, I.V., 1984. Ecology of free-living ciliates. Moscow State University Press, Moscow, 208 pp. (in Russian). Burkovsky, I.V., 1992. Structural and functional organization and stability of marine benthic communities. Moscow State University Press, Moscow, 208 pp. (in Russian). Burkovsky, I.V., Azovsky, A.I., Mokievsky, V.O., 1994. Scaling in benthos: from microfauna to macrofauna. Archive fu¨r Hydrobiologie Supplement 99, 517e535. Burkovsky, I.V., Aksenov, D.E., Azovsky, A.I., 1996. Mezoscale structure of marine psammophilic ciliates community. Okeanologia 36, 588e592 (in Russian). Chislenko, L.L., 1968. Nomograms for Weight Estimation of Water Organisms by Their Body Size and Form. Leningrad, Nauka, 105 pp. (in Russian). Fleeger, J.W., Decho, A.W., 1987. Spatial variability of interstitial meiofauna: a review. Stygologia 3, 35e54. Fleeger, J.W., Moser, E.B., 1990. On the scale of aggregation of meiobenthic copepods on a tidal mudflat. Marine Ecology 11, 227e237. Gutelmaher, B.L., Vedernikov, V.I., Sukhanova, I.N., 1980. Photosyntetic activity of the common species of the Black Sea phytoplankton. In: Ecosystems of the Black Sea Pelagic Zone. Nauka, Moscow, pp. 118e121 (in Russian). Harper, M.A., 1969. Movement and migration of diatoms on sand grains. British Phycology Journal 4, 97e103. Hendriks, A.J., 1999. Allometric scaling of rate, age and density parameters in ecological models. Oikos 86, 293e310. Hicks, G.R.F., 1986. Distribution and behaviour of meiofaunal copepods inside and outside seagrass beds. Marine Ecology Progress Series 31, 159e170. Hicks, G.R.F., 1992. Tidal and dial fluctuations in abundance of meiobenthic copepods on an intertidal estuarine sandbank. Marine Ecology Progress Series 87, 15e21. Hicks, G.R.F., Coull, B.C., 1983. The ecology of marine meiobenthic copepods. Oceanography and Marine Biology Annual Review 21, 67e175. Higgins, R.P., Thiel, H., 1992. Prospectus. In: Higgins, R.H., Thiel, H. (Eds.), Introduction to the Study of Meiofauna. Smithsonian Institution Press, Washington D.C., pp. 11e13. Hillebrand, H., Du}rselen, C.-D., Kirschtel, D., Pollingher, U., Zohary, T., 1999. Biovolume calculation for pelagic and benthic microalgae. Journal of Phycology 35, 403e424. Hodda, M., 1990. Variation in estuarine littoral nematode populations over three spatial scales. Estuarine, Coastal and Shelf Science 30, 325e340. Legendre, P., Legendre, L., 1998. Numerical Ecology, second English ed. Elsevier Science BV, Amsterdam, 853 pp.

A.I. Azovsky et al. / Estuarine, Coastal and Shelf Science 60 (2004) 663e671 Li, J., Vincx, M., Herman, P.M.J., Heip, C., 1997. Monitoring meiobenthos using cm-, m-, km- scales in the Southern Bight of the North sea. Marine Environmental Research 43, 265e278. Mazei, Y.A., Burkovsky, I.V., Saburova, M.A., Polikarpov, I.G., Stolyarov, A.P., 2001. Trophic structure of psammophilic ciliate community in Chernaya River Estuary. Zoologichesky Zhurnal 85, 1142e1151 (in Russian). McIntyre, A.D., 1969. Ecology of marine meiobenthos. Biological Reviews 44, 245e290. Montagna, P.A., Blanchard, G.F., Dinet, A., 1995. Effect of production and biomass of intertidal microphytobenthos on meiofaunal grazing rates. Journal of Experimental Marine Biology and Ecology 185, 149e165. Montagna, P.A., Coull, B.C., Herring, T.L., Dudley, B.W., 1983. The relationship between abundances of meiofauna and their suspected microbial food (diatoms and bacteria). Estuarine, Coastal and Shelf Science 17, 381e394. Nicholas, W.L., Hodda, M., 1999. The free-living nematodes of a temperate, high-energy, sandy beach: faunal composition and variation over space and time. Hydrobiologia 394, 113e127. Parsons, T.P., Takahashi, M., Hargrave, B., 1977. Biological Oceanographic Processes, third ed. Pergamon Press, Oxford, 432 pp. Peters, R.H., 1983. The Ecological Implications of Body Size. Cambridge University Press, Cambridge, 329 pp. Pinckney, J., Sandulli, R., 1990. Spatial autocorrelation analysis of meiofaunal and microalgal populations on an intertidal sandflat: scale linkage between consumers and resources. Estuarine, Coastal and Shelf Science 30, 341e353. Raven, J.A., Ku }bler, J.E., 2002. New light on the scaling of metabolic rate with the size of algae. Journal of Phycology 38, 11e16. Rybnikov, P.V., Azovsky, A.I., 1997. On fauna and ecology of Harpacticoida of intertidal sand-mud flat of the White Sea. In: Proceedings of the Second Scientific Conference, White Sea Biological Station of the Moscow State University. Moscow State University Press, Moscow, pp. 48e61 (in Russian).

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Rybnikov, P.V., Kondar’, D.V., Azovsky, A.I., 2003. Properties of the White Sea littoral sediments and their influence on the fauna and distribution of Harpacticoida. Oceanology 43, 91e102. Saburova, M.A., Polikarpov, I.G., 1989. Comparative analysis of some methods of quantitative count of benthic diatom algae. Proceedings of 20th Conference of Young Scientists of the Biological Faculty, Moscow State University Press, Moscow, Part 2, pp. 109e113 (in Russian). Saburova, M.A., Burkovsky, I.V., Polikarpov, I.G., 1991. Spatial distribution of microphytobenthos on the White Sea intertidal sandflat. Uspekhi Sovremennoy Biologii 111, 882e889 (in Russian). Saburova, M.A., Polikarpov, I.G., Burkovsky, I.V., 1995. Spatial structure of an intertidal sandflat microphytobenthic community as related to different spatial scales. Marine Ecology Progress Series 129, 214e232. Saburova, M.A., Polikarpov, I.G., 2003. Diatom activity within soft sediments: behavioral and physiological processes. Marine Ecology Progress Series 251, 115e126. Sach, G., Bernem van, H., 1996. Spatial patterns of Harpacticoida copepods on tidal flats. Senckenbergiana Maritima 26, 97e106. Schmidt-Nielsen, K., 1984. Scaling. Why is Animal Size so Important? Cambridge University Press, Cambridge, 259 pp. Schwinghamer, P., 1981. Characteristic size distribution of integral benthic communities. Canadian Journal of Fisheries and Aquatic Sciences 38, 1235e1263. Schwinghamer, P., 1983. Generating ecological hypotheses from biomass spectra using causal analysisda benthic example. Marine Ecology Progress Series 13, 151e166. Sun, B., Fleeger, J.W., 1991. Spatial and temporal patterns of dispersion in meiobenthic copepods. Marine Ecology Progress Series 71, 1e11. Uhlig, G., 1968. Quantitative methods in the study of interstitial fauna. Transaction American Microscopy Society 87, 226e232. Warwick, R.M., 1984. Species size distribution in marine benthic communities. Oecologia 61, 32e41.