small-scale spatial variability in intertidal and subtidal turfing algal assemblages and the...

Small-scale spatial variability in intertidal and

subtidal turfing algal assemblages and the temporal

generality of these patterns

M.A. Coleman *

Marine Ecology Laboratories (A11), Centre for Research on Ecological Impacts of Coastal Cities,

Science Road, University of Sydney, Sydney NSW 2006, Australia

Received 15 June 2001; received in revised form 20 September 2001; accepted 5 October 2001

Abstract

Spatial and temporal variation in patterns of distribution and abundance of algal assemblages is

large and often occurs at extremely small spatial and temporal scales. Despite this, few studies

investigate interactions between these scales, that is, how patterns of spatial variation change through

time. This study investigated a number of scales of spatial variation (from tens of centimetres to

kilometres) in assemblages of intertidal and subtidal turfing algae. Significant differences were found

in the composition and abundances of species in assemblages of turf at all spatial scales tested. Much

of the variation among assemblages could, however, be explained at the scale of quadrats (tens of

centimetres apart) (27F 1.4 (SE)% of dissimilarity) with an additional 7F 1.2% explained at the

scale of sites (tens of metres apart) and 10F 1.5% at the scale of locations (kilometres apart).

Although the greatest dissimilarity in assemblages occurred at the scale of habitats, this accounted

for a relatively small proportion of the overall variation in assemblages. These patterns were

consistent through time, that is, at each sampling time the spatial scale explaining the greatest

proportion of variation in assemblages was replicate quadrats separated by tens of centimetres. These

patterns appear to be due to small-scale variation in patterns of distribution and abundances of the

individual species that comprise turfing algal assemblages. The results of this experiment suggest

that large scale processes have less effect on patterns of variability of algal assemblages than those

occurring on relatively smaller spatial scales and that small-scale spatial variation should not be

considered as simply ‘‘noise’’. D 2002 Elsevier Science B.V. All rights reserved.

Keywords: Algal assemblages; Algal turf; Scale; Spatial variation; Temporal generality

0022-0981/02/$ - see front matter D 2002 Elsevier Science B.V. All rights reserved.

PII: S0022-0981 (01 )00358 -6

* Corresponding author. Tel.: +61-02-9351-4282; fax: +61-02-9351-6713.

E-mail address: [email protected] (M.A. Coleman).

www.elsevier.com/locate/jembe

Journal of Experimental Marine Biology and Ecology

267 (2002) 53–74

1. Introduction

Variation in distributions, abundances and composition of species is an intrinsic and

important component of all habitats and has been shown to occur at a variety of spatial and

temporal scales (see reviews by Foster et al., 1988; Levin, 1992). Some patterns of

distribution and abundance are general and show little variation at small scales (e.g.

geographic patterns—Foster et al., 1988), while other patterns are specific to particular

places or times (e.g. Foster et al., 1988; Morrisey et al., 1992; Levin, 1994; Archambault

and Bourget, 1996; Underwood and Chapman, 1998a,c). Understanding these patterns of

variation and how they change in time and space is, therefore, important and required to

fully understand the ecology of the organism or assemblage being studied.

Marine algae, in particular, show great spatial variation in patterns of distribution and

abundance (e.g. among quadrats of low-shore algal turfs in NSW; Underwood and

Chapman, 1998a). This variation may result from spatial differences in pre-recruitment

processes such as the dispersal and availability of propagules (Deysher and Norton,

1982; Hoffmann and Ugarte, 1985; Andrew and Veijo, 1998), recruitment itself

(Santelices, 1990), or post-recruitment processes such as grazing (Foster, 1975; Neushul

et al., 1976; Fletcher, 1987), competition (Lubchenco, 1980; Steneck et al., 1991) and

various other disturbances (e.g. Santelices and Ojeda, 1984; Kennelly, 1987a,b; Ken-

drick, 1991).

Patterns of distribution and abundances of algal assemblages are also known to vary

temporally. Temporal variation in assemblages may be due to differences in pre-recruit-

ment processes such as the availability of propagules (Hoffmann and Ugarte, 1985) and

other factors influencing patterns of recruitment over time (e.g. Foster, 1975; Gunnill,

1980; Kennelly and Larkum, 1983). These differences occur because reproduction is often

seasonal and because many species survive during only short, specific periods of time.

Alternatively, temporal variability in the occurrence or intensity of post-recruitment

processes that influence patterns of distribution and abundance have also been shown to

structure assemblages of algae (e.g. physical disturbance through storms; Kennelly,

1987a).

Despite the great spatial and temporal variation in algal assemblages, there are

relatively few studies that investigate interactions between these scales, that is, how

patterns of spatial variation change through time. Patterns of distribution of organisms

can change with season (Dethier, 1982; Cubit, 1984), temperature (Branch, 1975;

Emerson and Zedler, 1978), tides (Moulton, 1962) and other factors that are temporally

variable. Hence, this may result in changes in the spatial scales at which these organisms

vary.

Turfing algal assemblages (short, erect species of macroalgae that cover substantial

areas of substratum) are a common feature of rocky intertidal shores and subtidal reefs all

over the world (e.g. Grahame and Hanna, 1989; Stewart, 1989; Dye, 1993; Benedetti-

Cecchi and Cenelli, 1994; Akioka et al., 1999). Moreover, these assemblages have been

shown to be highly dynamic; the abundances, distributions and diversity of species within

them change over a variety of spatial and temporal scales (e.g. Ballesteros, 1988;

Underwood and Chapman, 1998a). Before the processes causing this variation can be

understood, the scales at which these patterns of variation occur must first be identified.

M.A. Coleman / J. Exp. Mar. Biol. Ecol. 267 (2002) 53–7454

This study was designed, therefore, to determine the scales of spatial variation of the

species composition, distributions and abundances of algae in intertidal and subtidal turfs

and how general these patterns were through time. Specifically, it was predicted that:

(a) There would be differences in the composition, distributions and abundances of

species in turfs between intertidal and subtidal habitats, among locations separated by

kilometres, among sites separated by tens of metres and among quadrats separated by tens

of centimetres.

(b) Since conditions between habitats are often assumed to be more different than those

within habitats, variation in patterns of distribution and abundance of turfing algal assem-

blages would be greatest between distinct habitats (low-intertidal shores and shallow-

subtidal reefs).

(c) Given the observation that turfs in other parts of the world can vary on small spatial

scales (as discussed above), it was also predicted that within habitats, the proportion of

variation explained at small spatial scales (tens of centimetres) would be greater than at the

larger spatial scales tested (tens of metres to kilometres).

(d) Based on the great temporal variability exhibited by many algal assemblages (as

discussed above), it was predicted that the spatial scales on which assemblages of turfing

algae vary will change through time.

Fig. 1. Map of Sydney Harbour and Botany Bay showing the positions of locations (Cape Banks, Bare Island and

Camp Cove) separated by kilometres. There were three randomly chosen intertidal and three randomly chosen

subtidal sites (separated by tens of metres) within each of these locations.

M.A. Coleman / J. Exp. Mar. Biol. Ecol. 267 (2002) 53–74 55

2. Materials and methods

2.1. Spatial variability

Sampling was done during September 1999 in three randomly chosen intertidal and

three randomly chosen subtidal sites (tens of metres apart) within each of three randomly

chosen locations (kilometres apart; Fig. 1). The locations were Cape Banks (CB) and

Bare Island (BI) in Botany Bay and Camp Cove (CC) in Sydney Harbour (Fig. 1). Sites

were areas of turfing algae of approximately 5� 5 m and were chosen to be at least 10

m apart. They also had similar levels of wave exposure, substratum type and top-

ography. Subtidal sites were areas of turf between 1- and 3-m depth at low tide, while

intertidal sites were areas of turf exposed when low tides were less than 0.4 m above

mean low water springs.

At each site, the percentage cover of all species of algae (i.e. estimates of abundance

and number of species of canopy, epiphytes and primary cover) was estimated in

haphazardly placed 20� 20-cm quadrats (tens centimetres apart) with 16 grid points

(n = 20). A pilot study was done prior to these experiments to determine what the ‘optimal’

size of quadrat and intensity of sampling was for sampling intertidal and subtidal turfs.

Estimates of precision (standard error as a percentage of the mean) associated with

Table 1

Analysis of variance of Bray–Curtis dissimilarity values calculated between pairs of centroids for each of the

spatial scales under investigation for (a) raw and (b) presence/absence data (n= 5)

Source df (a) Raw data (b) Presence/absence data

MS F MS F

Habitat = ha 1 983.58 0.32 238.44 0.08

Location = lo 2 301.85 0.32 742.55 1.19

Site (ha� lo) 12 935.62 9.73 626.00 10.65

Scale = sc 3 6410.17 37.45** * 13840.45 54.87** *

ha� lo 2 3097.36 3.31 3104.30 4.96

ha� sc 3 66.81 0.73 173.18 1.05

lo� sc 6 171.18 0.65 252.26 0.96

sc� Site (ha� lo) 36 263.55 2.74** * 262.68 4.47** *

ha� lo� sc 6 91.77 0.35 164.69 0.63

Residual 288 96.11 58.78

Only the terms involving ‘Scale’ are relevant to analyses, hence, significant P values for other terms are not

indicated here. Abbreviations for SNK results refer to variation among quadrats (Q), quadrats and sites (QS),

quadrats, sites and locations (QSL) and quadrats, sites, locations and habitats (QSLH). Factors were habitat

(intertidal and subtidal, fixed), location (three randomly chosen, orthogonal), sites (three randomly chosen, nested

in location and habitat) and scale (Q, QS, QSL, QSLH, fixed and orthogonal). Variances were heterogeneous for

raw and presence/absence data (Cochran’s C= 0.10 and 0.09, respectively); P< 0.01 was therefore used.

SNK results for the term ‘Scale’:

(a) Raw data: Q <QS<QSL=QSLH.

(b) Presence/absence data: Q <QS<QSL<QSLH.

*** =P < 0.001.


sampling turfs were compared for three sizes of quadrats (20� 20, 35� 35 and 50� 50

cm) and three intensities of sampling (16, 49 and 100 points per quadrat). It was generally

found that precision increased with decreasing size of quadrat over the range of sizes

Fig. 2. nMDS ordinations for (a) raw data and (b) presence/absence data showing relationships in turfing algal

assemblages among sites within locations, locations within habitats and between intertidal and subtidal habitats.

Symbols represent sites (n= 3 per location); Cape Banks (diamonds), Bare Island (squares) and Camp Cove

(triangles). Open symbols are subtidal sites and closed symbols are intertidal sites.


tested. Within each size of quadrat, there were no clear patterns associated with increasing

intensity of sampling. It was concluded that in terms of estimates of precision, it was better

to increase replication for a small size of quadrat rather than sample fewer quadrats of a

larger size. Thus, over the range of sizes of quadrats and intensities tested, quadrats of

20� 20 cm with an intensity of 16 points were considered ‘optimal’.

To test the hypothesis that there would be differences in assemblages of turf at all scales

(between habitats, among locations within habitats and among sites within locations and

habitats), assemblage data (the total percentage cover of all species) were analysed using

Analysis of Similarities (ANOSIM) (Clarke, 1993). Since there were only three sites per

location (thus not enough permutations to get P values less than 10% if locations were

analysed separately), all sites were analysed together and pairwise tests between sites

within locations compared. Data were represented graphically using nonmetric multi-

dimensional scaling (nMDS) plots. Raw data were analysed to test for differences in the

composition, distributions and abundances of species in assemblages. Presence/absence

data was also analysed to test for differences only due to the composition and distributions

Fig. 3. Percentage Bray–Curtis dissimilarity values (F SE) for (a) raw and (b) presence/absence data at each

spatial scale analysed. Abbreviations are variation among quadrats (Q), quadrats and sites (QS), quadrats, sites

and locations (QSL) and quadrats, sites, locations and habitats (QSLH). n= 6 replicate Bray–Curtis values per

scale. * Significantly different at P < 0.001.


of species in assemblages, thus eliminating the influence of abundance and giving equal

weighting to rare species.

To test the hypotheses about the spatial scales at which most variation in assemblages

was explained (among quadrats, among sites, among locations or between habitats), a

four-factor analysis of variance (ANOVA) was done on between group Bray–Curtis

dissimilarity values calculated using centroids from each of the spatial scales under

investigation (see Underwood and Chapman, 1998b for method). This method of

analysing variability was chosen as it is not as limited by problems of nonindependence

as many multivariate methods (Underwood and Chapman, 1998b). Centroids were

calculated using randomly chosen sets of replicates from each of the appropriate spatial

scales. Where possible, this was done without replacement so that each centroid was

independent. In addition, each centroid was calculated from equal numbers of replicates

from the appropriate spatial scales.

For example, analyses of the amount of spatial variation were done using two replicates

from each site to calculate the overall centroid (n = 36), four from each intertidal site for

the intertidal centroid (n = 36), four from each subtidal site for the subtidal centroid

(n= 36) and 12 replicates from each site for each of the location centroids (n = 36). These

were chosen at random and without replacement. All 20 replicates from each site were

then used to calculate the centroid from that site (n = 20). Finally, four sets of five

randomly chosen replicates from each site were used to compare with each of the scales;

overall, habitat, location and site. This was done for raw and for presence/absence data for

the same reasons as above.

Differences between and among assemblages may be explained by differences in the

distributions and abundances of the individual species that comprise these assemblages.

Percentage covers of individual species were also analysed, therefore, using a three-factor

analysis of variance to test for differences between habitats, among locations within

habitats and among sites nested within locations and habitats (see Table 1 legend for

factors). Since ANOVA is not greatly affected by heterogeneous variances when the

Table 2

Analyses of variance of abundances of Colpomenia sinuosa and Herposiphonia calva

Source df Colpomenia sinuosa Herposiphonia calva

MS F MS F

Habitat =Ha 1 0.84 5.64 * 0.25 11.63

Location = Lo 2 0.12 0.82 0.51 1.69

Site (Ha�Lo) 12 0.15 13.58** * 0.30 29.13** *

Ha�Lo 2 0.12 0.80 0.02 0.07

Residual 342 0.01 0.01

Factors were habitat (intertidal and subtidal, fixed), locations (Cape Banks, Bare Island and Camp Cove, random,

orthogonal) and sites (n= 3, random) nested within location and habitat. n= 20 replicate quadrats per site.

Variances were heterogeneous (Cochran’s C= 0.23 and 0.34 for C. sinuosa and H. calva, respectively). Data for

Ha�Lo were pooled for C. sinuosa and the terms Ha and Lo were therefore tested over the pooled data.

* =P< 0.05.

*** =P< 0.001.


number of replicates is large (Underwood, 1997), the assumption of homogeneity of

variances can be waived. Heterogeneous variances were, therefore, not transformed and

P < 0.05 was used.

2.2. Temporal variability

To test the hypotheses about the temporal generality of patterns of spatial variation of

turfs, sampling was done at Cape Banks (Fig. 1) in three randomly chosen intertidal and

three randomly chosen subtidal sites. Turfs were sampled at eight times, between Winter

(June) 1999 to Winter (August) 2000. At each site, the percentage cover of all species of

algae (i.e. estimates of abundance and number of species of canopy, epiphytes and primary

cover) was sampled in the same way as above.

To test the hypotheses about which spatial scales most variation in assemblages was

explained (among quadrats, among sites or between habitats), three-factor analyses of

Fig. 4. Percentage covers (F SE) of (a) C. sinuosa and (b) H. calva at each spatial scale within intertidal and

within subtidal habitats. The three locations were Cape Banks (CB, spotted bars), Bare Island (BI, striped bars)

and Camp Cove (CC, plain bars). n= 3 randomly chosen sites per location and n= 20 replicate quadrats per

site.


variance (ANOVA) were done on between group Bray–Curtis dissimilarity values as

above (Underwood and Chapman, 1998b). This was done for each time separately and

using raw and presence/absence data for the same reasons as above. In addition, at each

Fig. 5. nMDS ordinations for (a) raw data and (b) presence/absence data showing relationships in turfing algal

assemblages among sites within habitats and between intertidal and subtidal habitats. Times were pooled so each

symbol represents a site centroid (n= 3 sites per habitat). Open symbols are subtidal sites and closed symbols are

intertidal sites.


time, assemblage data were analysed using ANOSIM and percentage covers of individual

species in turfs using ANOVA to test for differences.

3. Results

3.1. Spatial variability

For raw and presence/absence data there were significant differences in assemblages of

turf between intertidal and subtidal habitats (ANOSIM—R = 0.15 for raw data; R = 0.53 for

presence/absence data; P < 0.05 both, Fig. 2a and b), although this pattern was clearer for

presence/absence data, suggesting that the composition of species contributed much to

these differences (Fig. 2b). Assemblages of turf among locations (within habitats) were

also significantly different (ANOSIM—R = 0.43 for relative abundance data; R = 0.59 for

presence/absence data; P < 0.01 both, Fig. 2a and b). There were always highly significant

differences between pairs of sites within locations in both intertidal and subtidal habitats

(P= 0% for all pairwise comparisons).

The greatest variability in assemblages was at the scales of habitats (69%—raw data)

and locations (approximately 49%—presence/absence data). Nevertheless, much of this

variation in turf could be explained at the scale of quadrats (27% dissimilarity for raw data

and 40% for presence/absence data; Fig. 3a and b, Table 1). Variation among quadrats (Q)

was, therefore, roughly 58% (raw data) and 65% (presence/absence data) of the total

variation in assemblages (i.e. among quadrats, sites, locations and habitats, QSLH; Fig. 3).

For raw data, an additional 7% dissimilarity was explained at the scale of sites (tens of

metres apart) and 10% at the scale of locations and habitats (kilometres apart; Figs. 3a,

Table 1). For presence/absence data, an additional 11% dissimilarity among assemblages

was explained at the scale of sites, 9% at the scale of locations and only 7% at the scale of

habitats (Fig. 3b, Table 1). Significant Scale� Site (ha� lo) interaction terms for Bray–

Curtis measures of dissimilarity for raw data (P < 0.001) and for presence/absence data

Table 3

Summary of results from ANOSIM tests for differences among sites within intertidal and sites within subtidal

habitats at each of the eight sampling times for raw and presence/absence data

Raw data Presence/absence data

R P R P

June 0.416 P < 0.001 0.538 P < 0.001

September 0.677 P < 0.001 0.478 P < 0.001

November 0.464 P < 0.001 0.473 P < 0.001

December 0.559 P < 0.001 0.504 P < 0.001

January 0.691 P < 0.001 0.608 P < 0.001

April 0.389 P < 0.001 0.720 P < 0.001

March 0.688 P < 0.001 0.583 P < 0.001

August 0.616 P < 0.001 0.593 P < 0.001

n= 20 replicates per site, three sites per habitat.


(P < 0.001) suggest that these patterns were variable among sites within locations and

habitats (Table 1). SNK results indicated, however, that almost always the rank orders of

variation were the same, i.e. increased successively from Q through to QSLH despite some

of these increases not being statistically significant.

Variation in abundances of individual species was also great. There were generally

significant differences in abundances between habitats, among locations within habitats

and among sites nested within location and habitats. Colpomenia sinuosa and Herposi-

phonia calva were two of many species that are representative of this variation (Table 2,

Fig. 4a and b). Standard errors associated with estimates of abundance at each site were

Fig. 6. Percentage covers (F SE) of Colpomenia sp. and Ectocarpus sp. at each site within intertidal and subtidal

habitats at each time. n= 3 sites per habitat and 20 quadrats per site. Clear symbols are intertidal sites and shaded

symbols are subtidal sites.


usually great, indicating that there was much variation in species abundances among

replicate quadrats (see Fig. 4). See Appendix A for full species list.

3.2. Temporal variability

There were differences in entire assemblages of turf among sites within intertidal and

sites within subtidal habitats at all eight times (raw and presence/absence data—Fig. 5,

Table 3). Due to these differences it could not be determined whether there were

differences between habitats within times because sites could not be pooled to give

ANOSIM enough replicates for more than 10 permutations and, therefore, P values less

than 10%. Nevertheless, abundances of individual species were generally the same

between intertidal and subtidal habitats (Fig. 6, Table 4) with the exception of Petalonia

fascia and Ulva lactuca, which were more abundant in intertidal than in subtidal habitats at

all eight times (Fig. 6, Table 4). There was always great variation in abundances of

Table 4

Analysis of variance of the variability (dissimilarity) in assemblages of algae at each of the eight sampling times

for raw data

Source df June September November December

MS F MS F MS F MS F

Habitat (Ha) 1 2.26 0.04 812.31 2.26 58.69 0.68 220.51 3.39

Site (Ha) 4 56.43 1.07 358.87 13.21* * 86.07 8.43* * 65.08 2.97 *

Scale (Sc) 2 32.34 0.45 306.65 3.53 30.69 0.85 27.91 0.29

Ha� Sc 2 18.25 0.26 42.32 0.49 110.23 3.05 14.86 0.15

Sc� Si(Ha) 8 71.13 1.34 86.94 3.20 * 36.12 3.54 * 96.92 4.42* *

Residual 18 52.91 27.16 10.20 21.93

Source df January April March August

MS F MS F MS F MS F

Habitat (Ha) 1 267.13 2.33 1.99 0.01 1507.45 1.64 290.20 2.19

Site (Ha) 4 114.79 1.95 161.09 6.09* * 920.19 42.70* * 132.35 3.86 *

Scale (Sc) 2 261.41 2.82 452.48 8.87* * 187.53 1.68 24.78 0.31

Ha� Sc 2 46.22 0.50 123.27 2.42 143.85 1.29 65.04 0.82

Sc� Si(Ha) 8 92.78 1.57 51.02 1.93 111.60 5.18* * 79.77 2.33

Residual 18 58.91 26.44 21.55 34.27

Factors were habitat (intertidal and subtidal, orthogonal, fixed), sites (three random, nested in habitat) and scale

(among quadrats, among quadrats and sites and among quadrats, sites and habitats, orthogonal and fixed). n= 2.

SNK tests are only shown for relevent terms (Sc and Sc� Si(Ha)).

SNK:

September: Site 2 (intertidal): Q <QS=QSH, site 5 (subtidal): Q =QS<QSH.

November: Site 1 (intertidal): Q =QSH<QS, Site 6 (subtidal): Q <QS=QSH.

December: Site 2 (intertidal): Q <QS=QSH.

April: Q <QS=QSH.

March: Site 1 (intertidal): Q >QS=QSH, Site 2 (intertidal): Q>QS=QSH, Site 5 (subtidal): Q <QS=QSH.

* =P< 0.05.

** =P< 0.01.


Fig. 7. Percentage Bray–Curtis dissimilarity values (F SE) for raw data at each spatial scale analysed at each of

the eight sampling times. Abbreviations are variation among quadrats (Q), quadrats and sites (QS) and quadrats,

sites and habitats (QSH). n= 2 replicate Bray–Curtis values per scale.


individual species among sites within times within habitats (e.g. Colpomenia sp. and

Ectocarpus sp., Fig. 6, Table 4).

For raw data, there were no significant differences in the amount of spatial variation

(dissimilarity) in assemblages of turf among quadrats, among sites within habitats or

between habitats at three of the eight sampling times (Fig. 7, Table 5), indicating that

variation among quadrats that were centimetres apart was as great (and often greater than)

variation at other spatial scales (between 64% and 137% of the total dissimilarity). At most

other times, patterns of variation among scales varied within sites, but were mostly

nonsignificant (Fig. 7, Table 5). At time 6 (April 2000) there was significantly more

variation among sites and between habitats than among quadrats (Fig. 7, Table 5). At this

time, however, variation at the scales of quadrats still accounted for a huge proportion

(64%) of the total variation.

For presence/absence data, patterns were clearer and consistent among sites and

between habitats. At seven of the eight sampling times there was significantly less

variation among quadrats than among sites or between habitats (Fig. 8, Table 6),

although at one of these times (November 1999) this pattern varied within intertidal

Table 5

Analysis of variance of the variability (% dissimilarity) in assemblages of algae at each of the eight sampling

times for presence/absence data

Source df June September November December

MS F MS F MS F MS F

Habitat (Ha) 1 103.20 1.13 49.72 0.09 6.54 0.02 138.27 0.46

Site (Ha) 4 91.09 1.04 556.78 11.17* * 285.94 6.45* * 298.20 5.68* *

Scale (Sc) 2 836.63 18.83* * 1236.22 10.64* * 1456.12 47.73* * 1765.29 37.38* *

Ha� Sc 2 120.42 2.71 135.86 1.17 284.99 9.34* * 79.68 1.69

Sc� Si(Ha) 8 44.43 0.51 116.17 2.33 30.51 0.69 47.22 0.90

Residual 18 87.59 49.85 44.34 52.46

Source df January April March August

MS F MS F MS F MS F

Habitat (Ha) 1 4.80 0.01 349.23 0.68 1 0 2.31 0.01

Site (Ha) 4 400.83 10.27* * 515 9.24* * 289.93 7.01* * 178.32 4.80* *

Scale (Sc) 2 759.96 10.39* * 1128.92 9.52* * 960.21 18.40* * 1611.87 22.30* *

Ha� Sc 2 315.22 4.31 49.28 0.42 27.88 0.53 62.35 0.86

Sc� Si(Ha) 8 73.13 1.87 118.59 2.13 52.19 1.26 72.30 1.95

Residual 18 39.01 55.72 41.37 37.12

Factors were as in Table 4. n= 2. * =P < 0.05. SNK tests are only shown for relevent terms (Sc and

Sc� Si(Ha)).

SNK:

June: Q <QS=QSH; September: Q <QS=QSH.

December: Q <QS=QSH; January: Q <QS=QSH.

April: Q <QS=QSH; March: Q<QSH<QS.

August: Q <QS=QSH.

November: Q <QS=QSH, Intertidal: QS>Q=QSH, subtidal: Q <QS=QSH.

** =P < 0.01.


Fig. 8. Percentage Bray–Curtis dissimilarity values (F SE) for presence/absence data at each spatial scale

analysed at each of the eight sampling times. Abbreviations are as in Fig. 7. n= 2 replicate Bray–Curtis values

per scale.


Table 6

Analyses of variance for the percentage covers of some of the species in intertidal and subtidal turfs

Source df Ralfsia verucosa Bare space Zonaria crenata Ectocarpus sp.

MS F MS F MS F MS F

Habitat (ha) 1 12.46 0.09 3748.06 4.52 75.24 0.66 3986.37 0.99

Time (ti) 7 87.81 1.77 1925.11 5.01** * 10.57 0.66 4146.51 3.09 *

Site (si(ha)) 4 140.71 8.55** * 829.14 14.20** * 114.63 16.82** * 4031.41 40.54** *

ha� si 7 80.61 1.63 534.42 1.39 34.76 2.17 2054.38 1.53

ti� si(ha) 28 49.54 6.01** * 383.90 6.57** * 15.99 2.35** * 1343.39 13.51** *

Residual 912 16.46 58.41 6.81 99.44

Total 959

Source df Petalonia fascia Ulva lactuca Corallina officinalis Amphiroa anceps

MS F MS F MS F MS F

Habitat (ha) 1 28.57 8.97 * 12556.16 * 11919.22 3.75 11473.02 12.59* *

Time (ti) 7 2.46 0.64 574.66 1.13 3977.03 1.26 68.91 0.25

Site (si(ha)) 4 3.18 2.94 * 1618.05 48.64** * 3172.34 14.92** * 910.97 22.28** *

ha� si 7 2.46 0.64 519.89 1.18 3328.75 1.05 70.03 0.25

ti� si(ha) 28 3.86 3.56** * 439.16 13.20** * 3159.88 14.85** * 279.83 6.84** *

Residual 912 1.08 33.27 212.59 40.88

Total 959

Source df Colpomenia sp. Herposiphonia secunda Herposiphonia calva Laurencia sp.

MS F MS F MS F MS F

Habitat (ha) 1 4745.37 2.70 1182.87 0.61 4024.67 1.87 27.51 0.42

Time (ti) 7 3388.24 2.84 * 2201.12 1.01 3532.16 3.53* * 20.04 1.25

Site (si(ha)) 4 1760.40 29.20** * 1938.52 37.11** * 2149.37 42.70** * 65.76 13.56** *

ha� si 7 723.52 0.61 1799.52 0.83 409.92 0.41 19 1.18

ti� si(ha) 28 1192.03 19.77** * 2176.28 41.66** * 1000.37 19.88** * 16.03 3.31** *

Residual 912 60.29 52.24 50.33 4.85

Total 959

Source df Martensia fragilis Sargassum spp. Champia viridis

MS F MS F MS F

Habitat (ha) 1 2373.14 60.50 3032.59 1.26 34.22 0.40

Time (ti) 7 520.74 3.10 * 765.83 1.22 52.46 2.60 *

Site (si(ha)) 4 392.35 33.81** * 2407.06 44.64** * 73.92 9.66** *

ha� si 7 348.10 2.07 582.26 0.93 47.22 2.30

ti� si(ha) 28 168.01 14.48** * 629.08 11.67** * 20.14 2.63** *

Residual 912 11.61 53.92 7.65

Total 959

The factors were habitat (ha) (intertidal and subtidal, fixed), time (ti) (June, September, November and December

1999 and January, April, May and August 2000, random and orthogonal) and sites (si) (three randomly chosen

and nested within habitat). n= 20 quadrats per site per time.

* =P< 0.05.

** =P < 0.01.

*** =P < 0.001.


habitats (Table 6). At the one remaining time (May 2000) variation increased from

quadrats to sites to habitats (Fig. 8, Table 6). Despite variation among replicate quadrats

being less than at other scales at all times, it nevertheless accounted for between 59%

and 80% of the total variation (dissimilarity) in the composition of species in assem-

blages.

4. Discussion

The results of this study reflect the great spatial variability often exhibited by algal

assemblages. Although the greatest variability in the composition, distributions and

abundances of species was usually found at the scale of habitats or locations, the greatest

proportion of this variation could be explained at the smallest spatial scale sampled—that

of replicate quadrats separated by tens of centimetres. Very little additional variation was

explained at spatial scales greater than this (tens of metres to kilometres). Moreover, these

patterns were general through time with variation in the composition, distributions and

abundances of species among quadrats usually accounting for a huge proportion of the

total variation in assemblages at each time of sampling.

Small-scale spatial variation appears to be a general pattern of algal assemblages on

temperate rocky shores around the world (e.g. Scheil and Foster, 1986; Foster, 1990;

Underwood and Chapman, 1998a) and this study shows that, for turfing algal assemb-

lages at least, these patterns are temporally consistent. The great variation found in

assemblages of turf at a small scale suggests that small-scale processes have more effect

on the composition, distributions and abundances of species than do large-scale

processes. For example, sedimentation is a process that can vary on small spatial scales

(Kendrick, 1991; Airoldi and Cinelli, 1997). Airoldi and Cinelli (1997) found the

diversity of subtidal algal assemblages to be influenced by small-scale (within habitat)

changes in depositional environment. Small-scale patchiness in the dispersal of prop-

agules is also known to influence the distribution and abundance of many algae

(Anderson and North, 1966; Reed et al., 1988; Kendrick and Walker, 1995; Kendrick,

1994). For example, Sargassum muticum propagules settle within metres from their

source (Kendrick and Walker, 1995), resulting in small-scale patchiness in the distribu-

tion and abundance of this species.

The temporal consistency in small-scale spatial variation suggests that processes that

operate over large temporal scales (i.e. annual variation in recruitment) may have little

effect on patterns of variability of assemblages compared to those operating at relatively

smaller spatial and temporal scales. This is surprising since reproduction of many species

of algae is seasonal, hence, one would expect patterns of abundance and diversity of

species to exhibit large-scale temporal variation. Although this variation may exist, it is

likely to be overshadowed by variation arising from processes occurring on smaller spatial

and temporal scales. For example, although the dispersal and settlement of propagules

across a shore may be relatively uniform in time and/or space, post settlement processes,

which act on relatively smaller scales such as competition, grazing and desiccation, may

act on patterns of distribution and abundance creating much smaller-scale variation. A

similar pattern was documented by Jenkins et al. (2000) who found settlement of the


barnacle Semibalanus balanoides to vary only at the scale of locations separated by

hundreds of kilometres. Recruitment, however, showed significant variation on spatial

scales smaller than this (sites separated by tens of metres), suggesting that processes

occurring post-settlement but pre-recruitment were influencing patterns of distribution and

abundance of the barnacle.

As predicted, the greatest amount of variability in the composition, distributions and

abundances of species in assemblages was usually found between intertidal and subtidal

habitats or among locations. This variability, however, accounted for a relatively small

proportion of the overall variation in turfing algal assemblages. This is surprising since

one would assume that processes that influence patterns of distribution and abundance

between intertidal and subtidal habitats are more different than processes occurring

within the habitats. Processes occurring at small spatial scales within habitats may

increase variation over and above those causing differences between habitats. For

example, although differences between intertidal and subtidal habitats may be due to

variation in levels of light or the availability of algal propagules, these factors are likely

to act on relatively large spatial scales. Processes which are known to structure algal

assemblages within habitats (such as grazing, inter-and intra-specific competition and

various physical disturbances) may then influence patterns of distribution and abundan-

ces of algae, adding a great deal more variability to the existing spatial variation

between intertidal and subtidal habitats. Until there are studies that test hypotheses about

patterns of distribution and abundance of algae between intertidal and subtidal habitats

and the processes that are responsible for these patterns, the validity of this model

cannot be tested.

It is extremely important to identify the scales of variation in any assemblage,

particularly in the detection of environmental impacts. If small-scale variation goes

undetected, differences due to impacts may be confused with differences due to natural

spatial variability (Morrisey et al., 1992; Underwood, 1993; Chapman et al., 1995). That

is, if the spatial scale sampled is greater than the scales of natural spatial variation then

impacts may be detected that do not really exist, the perceived impact simply being a result

of small-scale spatial variation. The converse may also be true (Caswell and Cohen, 1991;

Underwood, 1993, 1994; Chapman et al., 1995). Environmental impacts may influence

assemblages by causing increased variation on small spatial scales (as documented by

Warwick and Clarke, 1993; Kaiser and Spencer, 1996). Consequently, without a sampling

design that incorporates these scales, real impacts cannot be detected. To overcome these

potential problems, experiments need to be replicated at a number of spatial scales smaller

than the scale on which the perceived impact is thought to occur (Morrisey et al., 1992;

Underwood, 1993).

When abundances of individual species were analysed, there was, at all times, great

variation among quadrats (as indicated by large standard errors), among sites and

between habitats. This supports the idea that, over the range of scales tested here,

processes acting on small scales are more important in structuring patterns of

distribution and abundances of species that comprise assemblages than processes

occurring at larger spatial and/or temporal scales. For example, survival of embryos

of Pelvetia fastigiata has been shown to vary on small spatial scales (microhabitats;

Brawley and Johnson, 1991). Similarly, Benedetti-Cecchi (2000) found individual


species of turf-forming algae such as Corallina elongata, Dictyota dichotoma and

Haliptilon virgatum to vary on small spatial and temporal scales. Thus in this study,

since there were no discernable trends in species abundances through time or space, it is

likely that variation in entire assemblages is largely due to a complex mix of different

processes acting on specific species at various spatial and temporal scales. This

highlights the problem of dealing with entire assemblages when the species that

comprise these assemblages show great small-scale spatial and temporal variation in

patterns of distribution and abundance. If we are to understand this variation it appears

necessary to take a species specific approach, that is, examine each individual species

comprising turfing algal assemblages separately. This seems logical considering the

vastly different modes of life histories of algae and the way this is manifested in

patterns of algal distribution and abundance.

In conclusion, small-scale (within habitat) variation is an important and consistent

component of turfing algal assemblages and should be explained before any differences

between habitats can be understood. Small-scale variation within any habitat should not,

therefore, automatically be considered ‘‘noise’’, which masks larger-scale (and what are

usually considered more important) patterns. Although all spatial scales warrant inves-

tigation, those which explain the greatest proportion of variation should, perhaps, be the

focus of our attention.

Acknowledgements

Thank you to Tony Underwood, Phillipe Archambault and Mike Holloway for advice

and discussion. Funds were provided by the Australian Research Council through the

Centre for Research for Ecological Impacts of Coastal Cities and an Australian

Postgraduate Award. [RW]

Appendix A

Combined species list for spatial and temporal sampling experiments. Species are those

that could be identified in the field or are abundant enough to warrant microscopic

examination in the laboratory.

Chlorophyta Phaeophyta Rhodophyta

Bryopsis spp. Colpomenia sinuosa Amphiroa anceps

Chaetomorpha spp. Dictyopteris muelleri Anotrichium tenue

Cladophora spp. Dictyota dichotoma Aspargopsis armata

Codium fragile Dilophus marginatus Botrocladia sp.

Codium lucasi Ectocarpus spp. Ceramium spp.

Ulva lactuca Ecklonia radiata Champia viridis


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