Spatial and interannual variations with depth in eelgrass populations

Journal of Experimental Marine Biology and Ecology 291 (2003) 1 – 15 www.elsevier.com/locate/jembe

Spatial and interannual variations with depth in eelgrass populations Anne Lise Middelboe a,b,*, Kaj Sand-Jensen a, Dorte Krause-Jensen b a

Freshwater Biological Laboratory, University of Copenhagen, Helsingørsgade 51, DK-3400 Hillerød, Denmark b National Environmental Research Institute, Department of Marine Ecology, Vejlsøvej 25, DK-8600 Silkeborg, Denmark Received 19 July 2001; received in revised form 12 December 2002; accepted 7 February 2003

Abstract Measurements (1400) of eelgrass (Zostera marina L.) shoot density and biomass along 19 depth transects in Øresund between Denmark and Sweden were analyzed during late summer in 4 years to characterize the spatial and temporal variation in shallow, intermediate and deep waters. The reduced physical harshness and reduced light availability with depth led to the hypotheses that: (1) variability in shoot density and biomass with depth is highest in shallow perturbed waters and in deep light-limited water and lowest at intermediate depths, and (2) spatial and temporal variabilities resemble each other because they are influenced by the same underlying causes. Spatial variances in both shoot density and biomass were significantly positively related to mean values at all depths and variances, therefore had to be compared based on relationships between variances and mean values. The hypotheses were supported by the findings that spatial and temporal variances in shoot density were both significantly highest in shallow water where high physical disturbance and high irradiance are conducive to high variability. However, we observed no significant differences in the variability of shoot density between intermediate and deep waters suggesting that the effects of reduced disturbance and the increased risk of severe light limitation at the depth boundary outweighed each other. Spatial variance in biomass did increase more with means in deep water than in shallow and intermediate waters, while temporal variance showed no differences with depth. Overall, spatio-temporal variability in shoot density and biomass relative to means declined at higher mean values probably because self-shading and space limitation set an upper boundary on eelgrass abundance. Wrong conclusions would have emerged if variances or

* Corresponding author. Present address: Marine Biological Laboratory, University of Copenhagen, Strandpromenaden 5, 3000 Helsingør, Denmark. Tel.: +45-49-21-16-33-261. E-mail address: [email protected] (A.L. Middelboe). 0022-0981/03/$ - see front matter D 2003 Elsevier Science B.V. All rights reserved. doi:10.1016/S0022-0981(03)00098-4

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coefficients of variation of shoot density and biomass with depth had been compared directly without correcting for the influence of different means. D 2003 Elsevier Science B.V. All rights reserved. Keywords: Zostera marina; Spatial variability; Temporal variability; Biomass; Shoot density

1. Introduction Variations in density of benthic animals, algae and plants in shallow coastal waters are large, often unpredictable, and poorly understood. Mean abundance and temporal changes in abundance often differ among places located at different depths along a gradient at a single site or among places located at different sites along the shore (Chesson, 1985). Abiotic –biotic interactions often change with spatial and temporal scales, representing a major challenge in the study of general patterns in marine ecology and in the assessment of environmental impacts. This complexity demands considerable effort in the experimental design and in the subsequent timeconsuming sampling and determination of species abundance (Underwood, 1996). A careful choice of statistical procedures is also required since they have profound importance for the interpretations and conclusions (Underwood and Chapman, 2000). Studies on benthic organisms in shallow coastal waters are useful for the evaluation of patterns and processes, which change along environmental gradients (Connell, 1972; Menge, 1978). Benthic habitats represent vertical gradients of reduced physical harshness from shallow to deep water as well as reduced energy input to photosynthesis. Thus, with increasing depth benthic macroalgae and rooted plants experience the contrasting influence of reduced mechanical disturbance, facilitating size development and long-term survival, and reduced light availability, restricting photosynthesis and plant growth. As a consequence, maximum biomass of macroalgae and plant communities is often found at intermediate water depths while reduced biomass is found in shallow water on wave-swept shores or in deep, calm waters of great shade (Dring, 1982; Thom, 1990; Krause-Jensen et al., in press). Previous studies have not attempted to evaluate whether these depthrelated patterns are paralleled by differences in stability of macrophyte abundance with depth. The substantial replacement of species in macroalgal communities along depth gradients makes it difficult to determine the importance of the environmental gradients for the abundance and life processes of individual species (Lobban and Harrison, 1994). Evaluation of gradients is easier for seagrass species which form monospecific stands extending across the entire depth range from close to the upper level of permanent submergence to the maximum depth of growth where approximately 11% of surface irradiance remains (Duarte, 1991). Eelgrass (Zostera marina L.) is a particularly important representative among the seagrasses because it forms widely distributed monospecific meadows on soft bottoms throughout most of the northern temperate region (Den Hartog, 1970).

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Eelgrass meadows show considerable spatial and temporal variation in abundance. Regions with harsh wave action (e.g. the Danish North Sea coast) are completely devoid of eelgrass, while protected, shallow bays and estuaries can have an almost complete cover of the sea bottom (Ostenfeld, 1908). Shallow eelgrass populations are probably quite variable both spatially and temporally because of physical disturbance by waves, currents and ice. Examples show that shallow eelgrass populations may disappear almost entirely following harsh winters with thick ice-cover or ice-scouring but under favorable conditions they can rapidly recover from survived shoots or dense seed stocks buried in the sediments (Wium-Andersen and Borum, 1984). Such a burst of shoot density following population decline is unlikely to take place in deeper water where light limitation restricts the formation of new turions (Krause-Jensen et al., 2000) and seed densities are small (Phillips et al., 1983). Eelgrass populations can also disappear during profound shading from phytoplankton blooms or during prolonged anoxia (Terrados et al., 1999; Holmer and Bundgaard, 2001; Greve et al., 2003) and deep-growing populations should recover more slowly due to permanent light-limitation of plant growth. Accordingly, eelgrass populations located at intermediate depths between the shallow, physically perturbed and the deep, light-limited ranges of eelgrass distribution will probably show the greatest year-to-year stability in their presence and abundance. Patterns of year-to-year variations in eelgrass stands are likely to be paralleled by similar patterns of spatial variation between neighboring stands and the degree of variability should depend on the selected spatio-temporal scales. Sampling in late summer should dampen both year-to-year and spatial variability as compared to sampling in winter because the stands can then approach the upper bounds of shoot density and biomass during several months of low physical disturbance and high temperature and irradiance suitable for growth (Olesen and Sand-Jensen, 1994a). This effect will be most pronounced in shallow waters where irradiance is high. The deepest-growing stands, in contrast, face permanent light limitation and should take longer time to reach the upper bounds of biomass following episodes of mortality so that local spots of low biomass do not reach the same biomass as unaffected neighboring spots within the growth season. Consequently, eelgrass may also exhibit a high spatial variance in biomass close to the lower depth limit despite the small physical disturbance. Variances measured at different places or times are not directly comparable because they often are related to mean values. Since mean abundance of photosynthetic organisms changes systematically along depth gradients, it is necessary to control for differences in means before comparing spatial and temporal variance at different depths (Underwood and Chapman, 2000). With these statistical considerations in mind, the present study tests the hypotheses that: (1) spatial and temporal variabilities in shoot density and biomass are highest in shallow perturbed waters and in deep light-limited water and lowest at intermediate water depths, and (2) spatial and temporal variabilities of eelgrass abundance show similar patterns with depth because they are influenced by the same underlying causes. In order to test these predictions, we evaluated the smallscale spatial and the interannual variability in shoot density and biomass in 1400 samples within established eelgrass meadows in Øresund between Denmark and Sweden during a 4-year period.

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2. Materials and methods 2.1. Field data The study comprised about 1400 measurements of shoot density and above-ground biomass conducted during late summer of 1996, 1997, 1998 and 2000 at 75 sites situated along 19 depth transects in Øresund. Øresund is a narrow (15 – 20 km wide) strait between Denmark and Sweden connecting the Kattegat and the Baltic Sea. The narrowness of the strait prevents very high physical disturbances by wave action and eelgrass grows to a depth of 6 –7 m along both the Danish and the Swedish coast. A continuous flow of water to and from the Baltic Sea prevents ice from covering Øresund except in very cold winters. Field data originate from the Danish and Swedish Authorities’ Control and Monitoring Program for the new bridge and tunnel across Øresund. The 19 transects were located within a distance of 45 km. Along each transect plants were harvested in six replicate samples of 1/16 m2 collected at each of 3– 4 depths by SCUBA divers. The distance between replicate samples was less than 10 m but the length of the transects ranged from 450 to 6870 m (average 2000 m). In the laboratory, the green shoots of each sample were counted and dried at 105 jC for 24 h. As all eelgrass data originated from the same waterbody and thus were subjected to the same range of light and exposure levels, it was reasonable to analyse all eelgrass data together and thereby obtain information on the overall spatial and temporal variation of shoot density and above-ground biomass. Other details on sites, sampling, laboratory analyses and light conditions are given by Krause-Jensen et al. (2000). 2.2. Data analysis The means and spatial variances of shoot density and biomass were calculated from six replicate measurements at any given depth and transect every summer. In order to perform statistical analyses of changes in variability with depth, the total numbers of 239 mean values and variances (each based on six individual measurements) were analysed in three depth intervals (0– 2.5, 2.5 – 4.0, and >4.0 m). Depth intervals were defined in order to obtain approximately the same number of values (73 –90) in each interval (Table 1). The interannual variation of shoot density and biomass was calculated by means of a nested analysis of variance by which the six replicate samples were nested in time (years). In the calculation of interannual variation, we needed to separate the spatial variance from the temporal variance. In the nested analysis of variance, the mean square estimates among sampling years were calculated as the mean square among replicate samples plus nrT2, where n = number of replicate samples and rT2 = temporal variance (Underwood and Chapman, 2000). The interannual variance was therefore calculated by subtracting mean square among replicates from mean square among years in the nested ANOVA and dividing the resulting values by the number of replicate samples (Underwood, 1996; Underwood and Chapman, 2000). The mean values for each depth and site over 4 years

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Table 1 Mean ( F 95% C.L.), mean spatial variance ( F 95% C.L.) and number of observations of shoot density (m and biomass (g dw m 2) of Z. marina in three depth intervals

2

)

Mean ( F 95% C.L.)

Mean variance ( F 95% C.L.)

Number of observations

Shoot density 0.0 – 2.5 m 2.5 – 4.0 m >4.0 m

851 F 89 556 F 49 278 F 38

80,604 F 27,136 22,995 F 4398 11,356 F 4183

76 90 73

Biomass 0.0 – 2.5 m 2.5 – 4.0 m >4.0 m

127 F 13 131 F 12 92 F 11

1972 F 470 1884 F 447 1227 F 664

76 90 73

were used to calculate the year-to-year variance at every site yielding 19– 22 estimates in each depth interval. Nested analysis of variance was used to test for differences in means and spatial variances between the three depth intervals. Sites were nested in years and years were then nested in depths. The nested design ensured that variability at other scales (among years or sites) were included in the evaluation of differences between depths. One way analyses of variance were used to test for differences in means and temporal variances between the three depth intervals. Because it is more correct to use a balanced ANOVA, random samples corresponding to the group with the lowest number of replicates were chosen from the total number of replicates and used in the analysis. Comparisons of variance are more difficult to perform if variances are related to the means of the distribution and the means differ among places or times being compared (Underwood and Chapman, 2000). In such cases, it is not appropriate to compare variances without controlling for the influence of the mean. Instead, we compared the regression lines of variances to means for each depth interval. Tests of homogeneity of regression were used to test if slopes were significantly different in the three depth intervals (Sokal and Rohlf, 1995). If slopes were homogeneous among the three regression lines, analysis of covariance (ANCOVA) was used to test for homogeneity of the y-intercept for the three regression lines (Sokal and Rohlf, 1995).

3. Results 3.1. Means, spatial and year-to-year variances of shoot density Mean values and spatial variances in shoot density and biomass are shown graphically with depth (Fig. 1). Mean shoot densities were significantly different among all three depth intervals declining from shallow water to intermediate depths and to deep water (Tables 1 and 2). Mean variances in shoot density also changed significantly among depth

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Fig. 1. Means and spatial variances in shoot density and biomass of Z. marina with depth in Øresund.

intervals being significantly higher in shallow water than in intermediate and deep waters (Tables 1 and 2). The spatial variance in shoot density did not differ significantly between intermediate and deep waters (Table 2). There were significantly positive relationships between variances and means of shoot density in all three depth intervals (Fig. 2). The slopes of the lines were significantly different (test of homogeneity of regression, F = 10.1, P < 0.001) as spatial variance in shoot density increased more steeply with the mean in shallow water than in intermediate

A.L. Middelboe et al. / J. Exp. Mar. Biol. Ecol. 291 (2003) 1–15 Table 2 Nested analysis of variance for spatial patterns of shoot density (m

Shoot density Mean Depth Year(Depth) Site(Year + Depth) Variance Depth Year(Depth) Site(Year + Depth) Biomass Mean Depth Year(Depth) Site(Year + Depth) Variance Depth Year(Depth) Site(Year + Depth)

2

) and biomass (g dw m

7

2

)

Sum of squares

Degree of freedom

Mean square

F

Level of significance

1.1  107 3.9  105 1.4  107

2 9 156

5.4  106 4.2  104 8.8  104

120 0.48 9  1015

*** n.s. ***

1.7  1011 4.9  1010 9.1  1011

2 9 156

8.3  1010 5.4  109 5.8  109

14.5 0.93 9  1015

*** n.s. ***

4.9  104 5.5  104 4.2  105

2 9 156

2.4  104 6.1  103 2.7  103

3.8 2.3 9.0  1015

n.s. * ***

1.1  107 9.8  107 9.4  108

2 9 156

5.3  106 1.1  107 6.0  106

0.46 1.8 9.0  1015

n.s. n.s. ***

Sites are nested in years and years are nested in depths. Level of significance: *P < 0.05, **P < 0.01 and ***P < 0.001, n.s. = not significant.

and deep waters (Table 3). The slopes of the regression lines in intermediate and deep waters were not significantly different ( F = 0.03). In the temporal analysis, means of shoot density showed significant differences among depth intervals declining from shallow to deep water (Table 4) as observed in the spatial analysis (Table 1). Shoot density in deep water was significantly lower than in intermediate and shallow waters (ANOVA, P < 0.001). Likewise, the mean variance between years differed significantly between depths (ANOVA, P < 0.001) being higher in shallow water than in intermediate and deep waters which did not differ significantly between each other (Table 4). There were significantly positive relationships between the interannual variance and mean values of shoot density in intermediate and deep waters, while the positive relationship was not significant in shallow water (Table 3). There were no significant differences between slopes of the relationships among depths ( F = 2.0, P = 0.14), but at a given mean value the intercept was significantly higher in shallow water than in intermediate and deep waters (ANCOVA, F = 3.79, P < 0.05). 3.2. Means, spatial and year-to-year variances of biomass Mean biomass was not significantly different between depth intervals. Mean biomass was smaller in deep water than in intermediate and shallow waters but the pattern was not

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Fig. 2. Linear regression relationships between spatial variance and mean of shoot density in (A) shallow water (0 – 2.5 m), (B) intermediate depths (2.5 – 4 m) and (C) deep water (>4 m). Statistics of regression lines are shown in Table 3.

significant (Tables 1 and 2). Spatial variances of biomass did not differ significantly among depths. There were significantly positive relationships between spatial variances and means of biomass in all three depth intervals (Table 3). The three regression lines were significantly different ( F = 4.15, P < 0.05). The spatial variance increased more steeply with the mean in deep water than in intermediate and shallow waters. The relationship between variance and mean was not significantly different between intermediate and shallow waters ( F = 0.005). The mean biomass in the interannual analysis differed significantly among depths (ANOVA, P < 0.001) being significantly higher in shallow and intermediate waters than in deep water (Table 4). The interannual variance of biomass did not differ significantly among depths and there was no significant relationship between interannual variances and mean biomasses (Table 3).

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Table 3 Relationships between spatial (SVD) and temporal variances (TVD) in shoot density and spatial (SVB) and temporal variances (TVB) in biomass and mean values in the three depth intervals

Spatial variances Shoot density 0.0 – 2.5 m 2.5 – 4.0 m >4.0 m Biomass 0.0 – 2.5 m 2.5 – 4.0 m >4.0 m Temporal variances Shoot density 0.0 – 2.5 m 2.5 – 4.0 m >4.0 m Biomass 0.0 – 2.5 m 2.5 – 4.0 m >4.0 m

Relationship between variance and mean

Coefficient of correlation

Level of significance

SVD = SVD = SVD =

85,756 + 195D 7172 + 54D 6153 + 63D

0.64 0.61 0.57

*** *** ***

SVB = SVB = SVB =

203 + 17B 255 + 16B 2187 + 37B

0.46 0.42 0.57

*** *** ***

TVD = 44,276 + 31D TVD = 12,068 + 68D TVD = 102 + 74D

0.18 0.49 0.62

n.s. * **

TVB = 1293 + 5.8B TVB = 1941 + 3.1B TVB = 1574 + 2.6B

0.15 0.05 0.08

n.s. n.s. n.s.

Shoot density (m 2) = D, biomass (g dw m ***P < 0.001, n.s. = not significant.

2

) = B. Level of significance: *P < 0.05, **P < 0.01 and

3.3. Overall spatial and interannual patterns The overall changes in shoot density and biomass with depth were the same in the spatial and interannual analysis (Table 5). The variance patterns were congruent for the spatial and temporal variance of shoot density which both declined with depth along with the reduction in mean shoot density.

Table 4 Means ( F 95% C.L.), mean temporal variances ( F 95% C.L.) and number of observations of shoot density (m 2) and biomass (g dw m 2) of Z. marina in three depth intervals Mean ( F 95% C.L.)

Mean variance ( F 95% C.L.)

Number of observations

Shoot density 0.0 – 2.5 m 2.5 – 4.0 m >4.0 m

744 F 153 613 F 106 220 F 63

67,412 F 25,890 28,849 F 15,368 15,419 F 7398

19 19 22

Biomass 0.0 – 2.5 m 2.5 – 4.0 m >4.0 m

126 F 21 135 F 17 74 F 17

2028 F 805 2351 F 1120 1775 F 636

19 19 22

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Table 5 Summary of patterns in means and variances of shoot density and biomass with depth (h: highest value, m: intermediate value, l: lowest value) Mean pattern 1–2–3

Variance pattern 1–2–3

Differences in variance

Relationship between variance and mean

Differences in slope

Differences in intercept

Shoot density Spatial h–m–l Temporal h–m–l

h–m–l h–m–l

1>2 and 3 1>2 and 3

all 2 and 3

1>2 and 3 –

– 1>2 and 3

Biomass Spatial Temporal

h–m–l m–h–l

– –

all –

3>2 and 3 –

– –

m–h–l m–h–l

The different depth intervals are described as 1: shallow interval, 2: intermediate depths, 3: lower depths. Only significant ( P < 0.05) differences are indicated.

The spatial variances in biomass did not differ significantly among depths. However, when differences in mean biomass were taken into account, the spatial variances increased more with the mean values in deep water than in intermediate and shallow waters. Temporal variability of biomass was not significantly different between depths. All relationships between spatial variance and means of shoot density and biomass were positive for all depths (Fig. 2 and Table 3). The relationships were also positive for the temporal patterns, but only significantly so for shoot density in intermediate and deep waters. The standard deviation increased proportionally less than the means resulting in significant reductions of the coefficients of variation at higher mean values (Table 6). Thus, the shoot density and biomass of eelgrass became relatively less variable (i.e. more stable) at all depths as the mean values increased.

Table 6 Relationships between coefficients of variation and mean values of shoot density and biomass in three depth intervals at spatial (n = 73 – 90) and temporal scales (n = 19 – 20) Corr. coef. (r) Spatial scale

Temporal scale

Shoot density 0.0 – 2.5 m 2.5 – 4.0 m >4.0 m

0.001 n.s. 0.46*** 0.26*

0.48* 0.43 n.s. 0.67***

Biomass 0.0 – 2.5 m 2.5 – 4.0 m >4.0 m

0.25* 0.43*** 0.29*

0.67* 0.30 n.s. 0.73***

r = Coefficients of correlation. Level of significance: *P < 0.05, **P < 0.01 and ***P < 0.001, n.s. = not significant.

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4. Discussion 4.1. Variations of eelgrass abundance with depth Variations in the abundance of organisms among sites are often evaluated based on changes in variance or coefficients of variation. These comparisons of variation are, however, problematic when the variances depend on the means and means differ among places or times being sampled (Underwood and Chapman, 2000). Due to the significantly positive, but variable relationships between spatial variance and mean values of eelgrass shoot density and biomass in all three depth intervals and between temporal variance and mean values of shoot density in the intermediate and lower depth intervals, statistical comparisons of variability had to be based on regressions of variances on means in order to remove the influence of different mean numbers. When this influence of mean values had been accounted for, the significant differences remaining were: (1) higher spatial and temporal variances in shoot density in shallow than in deeper water, and (2) higher spatial variance in biomass in deep water than in intermediate and shallow waters. The results, therefore, only in part supported our hypothesis that spatio-temporal variance in shoot density and biomass should be highest in shallow and deep water and smallest at intermediate depths. The higher spatio-temporal variances in shoot density in shallow water is most likely due to the fact that high disturbance here leads to the most profound decline of shoot density, while high irradiance permits a rapid burst of numerous new shoots from seeds and surviving shoots resulting in densities markedly above that eventually reached when the biomass has recovered and self-thinning has taken place (Wium-Andersen and Borum, 1984; Olesen and SandJensen, 1994a). The observation that shoot density was more variable and biomass less variable in shallow than in deep water may reflect the different modes of regulation for the two population parameters (Olesen and Sand-Jensen, 1994b). Thus, high disturbance and high irradiance should increase the variability of shoot density, while the variability of summer biomass should be enhanced by high disturbance but reduced by high irradiance, allowing the plant stands to rapidly reach an upper boundary constrained by intense self-shading. The deepest-growing eelgrass populations are, however, strongly light-limited and their biomass is low thereby diminishing the self-regulation of biomass through mechanisms of self-shading and self-thinning. Consequently, the biomass in deep water is likely to increase throughout the summer of high incident surface irradiance without reaching an upper boundary (Krause-Jensen et al., 2000) before reduced light in the autumn leads to a decline of the biomass. Deep-water stands subjected to episodes of mortality may not have time to reach the same biomass as unaffected neighboring stands within the growth season, and such a delay would tend to increase both spatial and temporal variability in the biomass of deep-growing eelgrass stands. The spatial and temporal variances in eelgrass shoot density and biomass declined relative to the mean values as the mean values rose. This behavior is probably also due to self-regulation of shoot density and biomass in the stands through self-thinning and self-shading as the biomass increases (Olesen and Sand-Jensen, 1994a,b; Krause-Jensen

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et al., 2000). This self-regulation will dampen spatial and interannual variations particularly in the late summer values reported here, because there has been a long preceding spring –summer period with suitable growth conditions and low physical disturbance during which the eelgrass stands have had the time to approach the maximum density. The observed variability of shoot density and biomass is probably a general pattern among the widespread eelgrass populations. The genus Zostera has a cosmopolitan distribution in temperate and subtropical regions extending to 72jN on the Northern Hemisphere and to 46jS on the Southern Hemisphere (Hemminga and Duarte, 2000). Physical disturbance by wave action and increasing light attenuation are the most important factors for the vertical variability with depth (Fonseca and Bell, 1998; Fonseca et al., 2002; Krause-Jensen et al., in press) and they should be important for the density and biomass regulation of all Zostera and other seagrass populations worldwide (Short and Wyllie-Echeverria, 1996). In the cold parts of the distribution areas (as in this study), damage by winter ice and consumption by waterfowl (Jacobs et al., 1981; Tubbs and Tubbs, 1983; Fox, 1996) can enhance the variability of seagrass abundance in shallow water. Also, physical disturbance by sediment transport and siltation (Fortes, 1988; Talbot et al., 1990; Duarte et al., 1997) can reduce the stability of seagrass abundance in shallow water and are likely to generate a similar pattern of variability with depth as observed here in North temperate eelgrass populations. Shoot density and biomass showed different patterns of variability with depth but both variables were relatively stable at intermediate water depths. Eelgrass stands at intermediate depth should therefore be important for the long-term persistence of eelgrass meadows. Extensive die-backs during unfavorable conditions are most likely to be directed against populations in either shallow water (e.g., damage by ice, wave action and waterfowl grazing) or in deep water (e.g., shading and oxygen depletion) which can subsequently be recolonised from eelgrass populations surviving at intermediate depths. If the vertical extension of eelgrass cover becomes too small due to steeply sloping bottom profiles, untransparent waters or overall harsh physical conditions, there will be no stable populations remaining at intermediate depths to ensure the long-term persistence of eelgrass cover. Reduced depth extension of eelgrass populations is a known consequence of cultural eutrophication in the world’s coastal zones (Duarte, 1995; Borum, 1996; Vidal et al., 1999) and the main cause of seagrass decline worldwide (Hemminga and Duarte, 2000). High nutrient concentrations favor the growth of other photosynthetic organisms such as phytoplankton, epiphytes and free-floating ephemeral algae that reduce the availability of light for eelgrass populations and thereby restrict their growth capacity and cover (Nielsen et al., 2002). As eelgrass growth is constrained in shallow water by physical damage and in deep water by light limitation, there will be a lower threshold in the depth penetration of eelgrass below which eelgrass populations are unable to persist for longer periods. 4.2. Scales of variation The spatial and temporal variances will depend on the scales of analyses. Small spatial scales, as reported here, will reduce the spatial variability because populations sampled

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close to each other are likely to have experienced the same environmental conditions. Temporal variance determined at interannual scales will be smaller than variance at seasonal scales and interannual variance should be smallest based on late summer measurements which are closest to the biomass ceiling (Olesen and Sand-Jensen, 1994a,b). Still, the spatial and temporal variances should be positively related, as observed here, because the environmental conditions leading to profound spatial variations in eelgrass shoot density and biomass should also have an impact on the interannual patterns. For example, times or sites with intense disturbance should show a great spatial heterogeneity among replicate samples, which also reduces the mean values used for determination of interannual variability. If we had examined the seasonal variation in plant shoot density and biomass, we would probably have seen a decline in variability with depth for both parameters. The high absolute levels and seasonal variability in physical disturbance and irradiance in shallow water are likely to generate substantial seasonal variations while the more moderate level and variability of these physical parameters in deeper waters would tend to dampen seasonal changes in eelgrass parameters. Such a pattern would parallel the tendency of higher seasonal variability in seagrass biomass at higher latitudes where seasonal changes in temperature and light are larger than at lower latitudes (Duarte, 1989). The extreme outcome would probably be that shallow waters only support summer annual eelgrass populations with no surviving winter biomass, while perennial populations with a substantial winter biomass grow in deep water (Keddy, 1987). When late summer biomasses are compared most of the seasonal variability will disappear because high biomasses have had the time to become established in most places. 4.3. Conclusions Analyses of variance patterns among sites and times require that relationships are established between variances and means in order to compare variances without the confounding influence of different mean values. When the positive influence of mean values on variances had been properly removed, spatial and temporal variances of shoot density were significantly highest in shallow water where both high physical disturbance and high irradiance are conducive to high variability. Biomass variabilities were more uniform among depths. Variabilities of shoot density and biomass relative to the means declined at high mean values probably because of light and space limitation within dense stands. Spatial as well as temporal patterns of abundance are influenced by the same stochastic, heterogeneous disturbance of eelgrass stands so it was expectable that spatial and temporal variances exhibited similar patterns, though their magnitudes depended on the scales being studied.

Acknowledgements We thank BIOBASE (MAS 3-CT98-0160) for financial support to ALM and M&Ms (EVK3-CT-2000-00044M&Ms) and CHARM (EVK3-CT-2001-00065) for financial support to DKJ. We also thank ‘‘the Danish Ministry of Environment and Energy’’, ‘‘the

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