using patterns of variability to test for multiple community states on rocky intertidal shores

y and Ecology 338 (2006) 222–232www.elsevier.com/locate/jembe

Journal of Experimental Marine Biolog

Using patterns of variability to test for multiple communitystates on rocky intertidal shores

Peter S. Petraitis ⁎, Elizabeth T. Methratta

Department of Biology, University of Pennsylvania, Philadelphia, PA 19104-6018, USA

Accepted 14 June 2006

Abstract

Predictions based on theory of multiple stable states suggest that larger perturbations should lead to more unpredictable patternsof succession. This prediction was tested in the Gulf of Maine using data from 60 intertidal plots of varying size that wereexperimentally cleared of the rockweed Ascophyllum nodosum and from 14 benchmark sites from throughout the Gulf. Rockweedwas removed from the experimental clearings ranging from 1 to 8 m in diameter in 1996 and data collected in 2004 were used totest effects of clearing size and location on divergence and variability in species composition. Benchmark data were collected in2005, and the 14 sites were from a dataset on 53 sites throughout the Gulf of Maine. The selected sites were randomly chosen fromall sites with N80% canopy cover by A. nodosum and were expected to be similar to uncleared control plots from the experiment.Experimental removal of A. nodosum resulted in clearings at 12 sites within 4 bays. Abundances of gastropods, barnacles, mussels,and fucoid algae and the percentage cover of barnacles, mussels, fucoid algae, bare space, and other species were sampled. CAPand PERMDISP analyses revealed significant differences in multivariate dispersion and variability with both clearing size andlocation. Variability generally increased with clearing size and location effects were related to the north–south positioning of thesites. Benchmark sites were similar to the experimental control plots but as variable as the largest clearings. Results suggest thatsuccession in larger clearings has been more unpredictable than in small clearings. The pattern of variability in the experimentalclearings is consistent with the predictions of multiple stable states. However, the large amount of variation among the benchmarksites was due to mussels and was unexpected. This unexpected variability underscores the importance of sampling benchmark sitesas part of experiments.© 2006 Elsevier B.V. All rights reserved.

Keywords: Alternative states; Community ecology; Multiple stable states; Rocky intertidal shores; Succession

1. Introduction

Moving from inferences drawn from small-scaleexperimental manipulations to generalizations aboutbroad scale patterns in nature is one of the most difficultissues in ecology. While experiments can provide anextraordinary level of insight, they are often of such

⁎ Corresponding author. Tel.: +1 215 898 4207; fax: +1 215 898 8780.E-mail address: [email protected] (P.S. Petraitis).

0022-0981/$ - see front matter © 2006 Elsevier B.V. All rights reserved.doi:10.1016/j.jembe.2006.06.022

limited temporal and spatial scope that they are little morethan snapshots of the ecological processes underinvestigation. In addition, both differences amongexperiments and natural variation of ecosystems make itdifficult to generalize. Comparisons among small-scaleexperiments even with slightly different designs aredifficult to interpret and may be of limited value(Underwood and Petraitis, 1993; Petraitis, 1998). Naturalvariation in ecosystems raises questions not only aboutaverage effects (e.g., are per-capita effects of competition

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223P.S. Petraitis, E.T. Methratta / Journal of Experimental Marine Biology and Ecology 338 (2006) 222–232

greater in the tropics than in the temperate zone?) but alsothe amount of variation itself (e.g., are such interactionsmore variable?).

One of the hallmarks of Underwood's research hasbeen links between these two issues — the “scaling”from experimental inference to ecological generaliza-tion, and the measurement and meaning of variability inecological processes (e.g., Underwood and Denley,1984; Underwood, 1991, 2000; Chapman and Under-wood, 1998; Underwood and Chapman, 1998; Under-wood et al., 2000). His work over the last 30 years hasnot only defined the problems but also providedconcrete suggestions on how to approach the problemsexperimentally and conceptually. Following Under-wood's lead and suggestions (Underwood, personalcommunication; Underwood et al., 2000), we present anexperimental test of an explicit hypothesis aboutvariability in succession and use observations from alarge-scale survey to set the experimental results into abroader context.

Petraitis and Latham (1999) hypothesized thatrockweed (Ascophyllum nodosum (L.) Le Jolis) standsand mussel (Mytilus edulis L.) beds in sheltered bays inthe Gulf of Maine represent two different equilibriumpoints in an ecosystem with multiple stable states, andhere we test the prediction that succession should behighly variable if the ecosystem, in fact, has multiplestable states. The deterministic theory of multiple stablestates is well understood. Model parameters establishthe number and position of the equilibrium points, andinitial densities of the species define a single trajectorytowards a unique stable equilibrium point (e.g.,Lewontin, 1969; May, 1977; Scheffer et al., 2001).

Yet whether multiple stable states exist in nature hasremained a hotly debated subject (Sutherland, 1974;Peterson, 1984; Sousa and Connell, 1985; Sutherland,1990; Knowlton, 1992; Bertness et al., 2002; Petraitisand Dudgeon, 2004a,b; Didham et al., 2005). Definitiveexperimental tests of the theory continue to be elusive(Petraitis and Dudgeon, 2004b; Suding et al., 2004;Didham et al., 2005) because random events and his-torical accidents blur what stability, equilibrium andhabitat mean in nature (Lewontin, 1969; Grimm andWissel, 1997). Thus even though the underlying dy-namics may be completely deterministic, succession innatural ecosystems with multiple stable states can appearto be stochastic. Per-capita rates of ecological processes,which form the parameters of models, may vary on asmall scale, and so “identical” perturbations of thesystem are unlikely to cause the same outcome. Inaddition, large perturbations are more likely than smallperturbations to tip the system from one stable state to

another (Knowlton, 1992). Large perturbations alsoleave the system open to chance events, and species thatundergo catastrophic changes in densities may take along time to recover, exposing the successional trajec-tories to the cumulative effect of many small randomevents. Large-scale spatial events, such as fires, can alsolead to an uncertain outcome by uncoupling successionin the center of a disturbed patch from effects of thesurrounding ecosystem.

Taken together, we predict that if rockweed standsand mussel beds on sheltered shores were alternativestates, then the course of succession should be scale-dependent with larger perturbations leading to moreunpredictable patterns of succession. The work reportedhere uses species composition data from experimentalclearings in A. nodosum stands that were established byPetraitis and his colleagues in 1996 (Petraitis andDudgeon, 1999, 2005) and were analyzed usingmethods that are based on an approach proposed byUnderwood and Chapman (1998). We also wanted toplace our experimental results in broader context, and sowe sampled sites throughout the Gulf of Maine withestablished A. nodosum stands. This survey was used asa benchmark for our experimental data and builds on thework of others (Foster, 1990; Underwood, 2000;Underwood et al., 2000).

2. Methods

Abundance and percentage cover of the mostcommon species were sampled at 60 experimentalplots on Swan's Island, Maine, USA, and 53 benchmarksites throughout the Gulf of Maine. The experimentalplots were established in A. nodosum stands in 1996 attwelve mid intertidal sites. At each site, four clearings(1, 2, 4, and 8 m in diameter) were made, and anuncleared control plot was set up. The size range of theexperimental clearings is within the normal range ofmajor ice scour events, which occur infrequently onSwan's Island (Petraitis and Dudgeon, 2005). Sets ofclearings were spread over four bays (Burnt CoatHarbor, Mackerel Cove, Seal Cove and ToothackerCove) with three sets per bay. Detailed information oncreation of the clearings, as well as descriptions andlocations of the sites and sampling can be foundelsewhere (Dudgeon and Petraitis, 2001; Petraitis andDudgeon, 2005).

Benchmark sites were included to examine if theexperimental sites on Swan's Island were a representa-tive sample of similar intertidal sites throughout the Gulfof Maine. The 53 benchmark sites included sites with arange of cover by A. nodosum, M. edulis and Fucus

224 P.S. Petraitis, E.T. Methratta / Journal of Experimental Marine Biology and Ecology 338 (2006) 222–232

vesiculosus L. Twenty-nine sites were on Swan's Island.The straight-line distance across the Gulf of Maine fromthe most southwestern site (Audubon Sanctuary,Biddeford Pool; 43°26.8′N, 70°20.7′W) to the mostnortheastern site (Seawall, Acadia National Park;44°14.6′N, 68°18.0′W) was 186 km. Fourteen siteswith N80% A. nodosum canopy were randomly selectedfrom the complete data set to serve as “typical” siteswith undisturbed A. nodosum stands.

Experimental plots were sampled in July-August2004, and benchmark sites in June-September 2005.Abundances of mussels (M. edulis and Modiolusmodiolus (L.)) and gastropods (Tectura testudinalis(Müller), Littorina littorea (L.), Littorina obtusata L.,Littorina saxatilis (Olivi), and Nucella lapillus (L.)),barnacles (Semibalanus balanoides (L.)), and fucoidalgae (A. nodosum and F. vesiculosus) were countedwithin three 50×50 cm quadrats per experimental plotand benchmark site. Fucoids and barnacles were

Fig. 1. Average within group distances and approximate 95% confidence limanalyses, and panels B and D are location analyses. Abbreviations in panels BCove (North-facing), SC for Seal Cove (North-facing), BC for Burnt CoatConfidences limits were calculated as 1.96(MSE/n)1/2 where MSE=the errorwas 11 for the clearing type analyses and 14 for the location analyses. P leindicate significantly different groups from pairwise comparisons and were

subsampled because of the large numbers of individuals.Barnacle counts were divided into the current year'srecruits and older individuals.

Percentage cover data were collected for fucoids,barnacles, mussels and other cover. The category of othercover included bare space and all other species, whichwere rare. Data on canopy cover and surface cover werecollected separately.

Variables of interest were the densities and percent-age cover per plot, and so average densities and averagepercentage cover per plot rather than per quadrat wereused in the analyses (see Petraitis and Dudgeon, 2005,for justification of quadrat placement and use ofaverages).

Multivariate dispersion and canonical discriminantanalyses of the abundance data were done usingAnderson's PERMDISP and CAP programs, whichare available as freeware (http://www.stat.auckland.ac.nz/%257Emja/Programs.htm). PERMDISP tests for

its from the PERMDISP analyses. Panels A and C are clearing typeand D identify locations; Bench for benchmark sites, MC for MackerelHarbor (South-facing) and TC for Toothacker Cove (South-facing).mean square from the PERMDISPANOVA and n=sample size, whichvels are from the PERMDISP randomizations; letters above the barscorrected using sequential Bonferroni tests with α=0.05.

http://www.stat.auckland.ac.nz/%257Emja/Programs.htm

http://www.stat.auckland.ac.nz/%257Emja/Programs.htm

Table 1Assignment of replicates to groups based on CAP discriminant function for type of clearing

Originalgroups

Classified into groups %Correct

%Correct oradjacent

Bench Control One Two Four Eight

Bench 5 6 0 0 0 0 45% 100%Control 2 5 4 0 0 0 45% 100%One 2 3 4 1 0 1 36% 73%Two 1 1 1 3 3 2 27% 64%Four 1 1 3 3 0 3 0% 55%Eight 1 1 2 1 0 6 55% 55%

Column labeled “% Correct” gives percentage assigned correctly. Column labeled “% Correct or adjacent” gives percentage assigned correctly or tomost similar clearing type (e.g., benchmark sites to either benchmark sites or uncleared controls).


multivariate dispersion using various distance anddissimilarity measures and is a multivariate analog ofLevene's test for heterogeneity of variances. Results arereported as the average within-group distance of eachgroup from its centroid. PERMDISP provides post-hocpairwise tests, which we adjusted using sequentialBonferroni corrections (Sokal and Rohlf, 1995). CAPdoes a canonical analysis of principal coordinates(Anderson and Robinson, 2003; Anderson and Willis,2003). We let the CAP program choose the number ofeigenvectors (i.e., the value for m) needed to maximizethe proportion of observations correctly classified(Anderson and Robinson, 2003). Positions of experi-mental plots and benchmark sites were plotted on thefirst two canonical axes. PERMDISP and CAP include

Fig. 2. Constrained MDS plot of averages and standard deviations ofclearing types based on CAP analysis of densities. Averages andstandard deviations were calculated from the positions of theindividual plots, which are given by the CAP analysis output.Analysis and plot are based on m=3, which gave the smallest cross-validation error. The first and second squared canonical correlationsare 0.399 and 0.126, respectively; trace statistic=0.568, Pb0.0001.Scales of axes are identical in both directions but the y-axis is croppedto save space.

permutation tests for significance; 9999 permutationswere used in all tests. Several transformations, standar-dizations, dissimilarity measures and distance measureswere tried. Choice of transformation, standardizationand measure had little effect on CAP results, and so onlythe results based on Euclidian distances and usingunstandardized data are reported. In contrast, resultsfrom PERMDISP were very dependent upon whetherthe data were standardized or not. Differences amongthe various analyses were caused largely by M. edulis,which had a range of densities several orders ofmagnitude larger than any other species. When M.edulis was dropped, all analyses gave nearly the sameresults. Thus two PERMDISP analyses using Euclidiandistances and unstandardized data are reported — onebased on the complete dataset and the other with M.edulis dropped from the dataset.

Percentage cover data were not included in the mul-tivariate analyses for three reasons. First, clearings weremade by removing A. nodosum, and so differences

Fig. 3. Constrained MDS plot of averages and standard deviations oflocations based on CAP analysis of densities. Details of calculations,analysis and scales are given in Fig. 2. The first and second squaredcanonical correlations are 0.618 and 0.323, respectively; tracestatistic=1.254, Pb0.0001; m=9.

Table 2Assignment of replicates to groups based on CAP discriminantfunction for locations

Originalgroups

Classified into groups %Correct

% Correct orsimilar bay

Bench Northfacingbays

Southfacingbays

MC SC BC TC

Bench 3 1 3 5 2 21%MC 0 9 3 2 0 64% 86%SC 3 1 6 4 0 43% 50%BC 0 0 3 10 1 71% 79%TC 2 0 0 4 8 57% 86%

Column labeled “% Correct” gives percentage assigned correctly.Column labeled “% Correct or similar bay” gives percentage assignedcorrectly or to the bay facing the same direction (e.g., MC to either MCor SC).

Fig. 4. Percentage cover by rockweeds. Confidence limits are based onsample sizes of 11. Letters above bars show significantly differentgroups based on Tukey's tests.


among the treatment levels in cover in 2004 areconfounded by the original removal in 1996. Second,the A. nodosum canopy cover was used to select thebenchmark sites, and these sites were selected tohave 80–100% A. nodosum cover. Thus we expectedA. nodosum cover to be greater and less variable in thebenchmark sites than in the experimental plots. Third,percentage cover and abundance are measured indifferent units and require standardization so that theyare on an equal footing. We had no a priori justificationfor the most appropriate standardization.

However, canopy cover by A. nodosum andF. vesiculosus, and surface cover by mussels, barnaclesand other cover were analyzed using univariate andmultivariate tests to provide an overall description ofdifferences. Levene's test was used to examine heteroge-neity of variances, and ANOVA or Welch's test was usedto examine for differences among groups. Welch's test,which is anANOVA-like analysis for groups with unequal

Table 3P-values for univariate analyses of percentage cover data

Data Levene's Test ANOVA

Clearing size: Fucus canopy b0.0001 b0.0001Clearing size: Ascophyllum canopy 0.0014 b0.0001Clearing size: Mussel surface 0.0196 0.6266Clearing size: Barnacle surface 0.0892 0.0589Clearing size: Bare space and rare species 0.8310 0.5463Location: Fucus canopy b0.0001 b0.0001Location: Ascophyllum canopy b0.0001 b0.0001Location: Mussel surface 0.0083 0.0180Location: Barnacle surface 0.3661 0.0009Location: Bare space and rare species 0.7105 0.0266

Values in bold are significant. ANOVA column gives results fromeither ANOVA or Welch's test; Welch's test was used if Levene's testfor heterogeneity of variances was significant.

variances, was used if Levene's test was significant.Tukey's tests were used for post-hoc comparisons. Datawere not transformed because we were primarilyinterested in testing for differences due to variationamong groups. CAP analysis was also done to examinedifferences among locations because the results of theunivariate tests did not show a single consistent pattern.

The univariate and multivariate analyses were done asone-way designs because of missing data and limitationsof PERMDISP. One experimental 2 m clearing wasdestroyed in 2002 when ice deposited a granite boulderroughly 0.5 m×1.0 m×1.5 m on the plot. The boulderwas estimated to weigh 1500–2500 kg and could not beremoved from the plot. However, PERMDISP requiresequal sample sizes with nN2 and dropping replicates tocreate a balanced two-way design of treatment× locationwould have had 2 replicates per cell. Thus the effects oftreatment (benchmark vs. control plots vs. 1, 2, 4 and 8 mclearings) and location (benchmark vs.Mackerel Cove vs.Seal Cove vs. Burnt Coat Harbor vs. Toothacker Cove)were examined separately. Sample sizes were balanced byrandomly dropping one replicate; n=11 per group for theclearing type analyses and 14 for the location analyses).

3. Results

PERMDISP analysis showed significant differencesin multivariate dispersion among clearing types and


locations, but the patterns depended on whether M.edulis densities were included in the analyses (Fig. 1).The results from the analyses using the full data set weredriven strongly by differences between benchmark sitesand the other treatment levels (Fig. 1A and B).

M. edulis was several orders of magnitude morevariable than any other species with average densitiesranging from 0 to 5350 per 0.25 m2, and removingthem from the analyses revealed four patterns. First,the average dispersion of the benchmark sites wasreduced by 98% in both the clearing type and locationanalyses. Because Euclidian distances are metric, thesereductions represent the contribution of mussels tototal dispersion. Second, dispersions of benchmarksites and uncleared controls were not significantlydifferent (Fig. 1C). Third, larger clearings are clearly

Fig. 5. Constrained MDS plot of location averages based on CAP analysis ofmussels and Fucus. MDS plot is rotated to match the orientation of locat(i.e. negative to positive values run from top to bottom). Scales of axes are ideof calculations and analysis and scales are given in Fig. 2. The first and secondstatistic=0.858, Pb0.0001; m=3. For the first canonical axis, the three largeand F. vesiculosus (−0.500). For the second canonical axis, the three largest cM. edulis (+0.341). For the histograms, barnacles (S. balanoides), mussels, (Mrespectively. Data for histograms are grouped as benchmark sites, north-facinsizes of 14 for benchmark sites and 28 for the rest.

more variable than smaller clearings and unclearedcontrols. Fourth, north-facing bays (Mackerel Coveand Seal Cove) were more variable than south-facingbays (Burnt Coat Harbor and Toothacker Cove), butthe pattern was dampened by removing mussels fromthe analysis (Fig. 1B and D).

Canonical ordination based on clearing type separat-ed the samples into three distinct groups along the firstaxis (Fig. 2). The first canonical axis explained 99.4% ofthe variation and was correlated with M. edulis (+0.50),F. vesiculosus (+0.75), and L. littorea (+0.39). Thesecond canonical axis, which explained only 0.5% of thevariation, accounted for the spread among the two, four,and eight meter clearings. The second axis was corre-lated with L. littorea (+0.87), F. vesiculosus (−0.65),and L. obtusata (−0.40).

percentage cover data and histograms of percentage cover of barnacles,ions shown in Fig. 3, and thus the y-axis is the first canonical axisntical in both directions but the x-axis is cropped to save space. Detailssquared canonical correlations are 0.453 and 0.307, respectively; tracest correlations are with A. nodosum (+0.897), S. balanoides (−0.622)orrelations are with F. vesiculosus (+0.813), other species (−0.401) and. edulis) and Fucus (F. vesiculosus) are identified by SB, ME, and FV,g bays and south-facing bays. Confidence limits are based on sample


The rate of correct classification varied with the sizeand type of the clearing (Table 1). The benchmark siteswere consistently assigned to the benchmark or controlgroup. Similarly all control plots were assignedcorrectly or as either benchmark sites or one meterclearings. Misclassification increased as clearing sizeincreased (Fig. 2).

Canonical ordination for location separated orienta-tion of the bays (Fig. 3). The first axis explained 99.4%of the variation, and separation along the axis was dueto S. balanoides (−0.66), L. obtusata (−0.45), andL. littorea (+0.66). The second axis explained 0.4% ofthe variation and was highly correlated with L. littorea(+0.53),M. edulis (−0.67), and N. lapillus (−0.45). Thediscriminant function consistently gave the correct as-signment of plots from Mackerel Cove and Burnt CoatHarbor (Table 2). Seal Cove plots, Toothacker Coveplots, and the benchmark sites were misclassified morefrequently. Assignment to the correct orientation (e.g.,classifying plots in Burnt Coat Harbor, a south-facingbay as from either Burnt Coat Harbor or ToothackerCove) was ≥79% for all bays except Seal Cove.

Univariate tests showed significant differencesamong clearing sizes for percentage cover by canopyspecies only (Table 3). A. nodosum canopy cover wassignificantly higher in the benchmark sites and controlplots, whereas F. vesiculosus canopy cover was signif-icantly greater in clearings larger than two meters indiameter (Fig. 4). For understory species, average per-centage cover and standard errors (n=66) were 48.3±3.8% for S. balanoides, 9.1±2.2% for M. edulis, and38.5±3.6% for bare space and rare species.

There were significant differences among locationsfor both canopy and understory species (Table 3). TheCAP analysis showed that the benchmark sites wereseparated from the experimental plots on Swan's Islandalong the first axis by differences in A. nodosumcanopy cover (Fig. 5). A. nodosum cover was 97.0±1.2% (n=14) at the benchmark sites and 32.6±4.9%(n=56) on Swan's Island. The moderate amount of A.nodosum cover in Swan's Island bays was due toaveraging across clearings of all sizes and unclearedcontrols. A. nodosum canopy cover in small clearingswas due to the draping of long fronds into smallclearings by plants from the surrounding stands ratherthan recruitment of new plants into the clearings. Thespread along the second canonical axis separated thesouth-facing and north-facing bays with F. vesiculosusand S. balanoides more common in the south and M.edulis slightly more common in the north.

Levene's tests were significant only for A. nodo-sum, F. vesiculosus and M. edulis cover (Table 3). The

within-group variances tended to be smaller forbenchmark sites and for the control plots (seeconfidence limits in Figs. 4 and 5).

4. Discussion

In systems with multiple stable states, smalldifferences in initial conditions can cause very differentoutcomes, and experimental perturbations in thesesystems may initiate alternative successional pathwaysin “identical” replicate plots (Petraitis and Latham,1999). The divergence of species composition duringsuccession should be scale-dependent because areasexposed to small perturbations are likely to be quiteresilient while areas with large perturbations are morelikely to cross a “breakpoint” (sensu May, 1977) and tiptowards an alternative species composition (Knowlton,1992; Petraitis and Latham, 1999). Thus both theaverage and the variability of species compositionshould be scale-dependent.

Our results are consistent with these predictions.Average species composition is scale-dependent withM. edulis, F. vesiculosus and L. littorea driving most ofthe difference among clearing sizes (Fig. 2). M. edulis,L. littorea and F. vesiculosus are more common in the2, 4 and 8m clearings. F. vesiculosus and L. littoreawerethe next most variable species after M. edulis. Rangeswere 0–310 plants per 0.25 m2 for F. vesiculosus and 0–185 snails per 0.25 m2 for L. littorea.

Percentage cover of F. vesiculosus also increaseswith clearing size (Fig. 4), and the pattern is similar towhat was seen in 2002 (Petraitis and Dudgeon, 2005).Cover in uncleared controls was 0.4% in 2002 and 0.5%in 2004, and in 8 m clearings was 54.1% in 2002 and73.8% in 2004. The average overall increase between2002 and 2004 was 40%. The success of F. vesiculosusin clearings is surprisingly because it is so rare inundisturbed stands of A. nodosum. Others have reportedsimilar dominance by Fucus species after removal ofA. nodosum (Bertness et al., 2002; Cervin et al., 2004;Jenkins et al., 2004), and it is possible that F. vesiculosusmay either be a fugitive species in the system orrepresent a third community state on par with musselbeds and A. nodosum stands (S. R. Dudgeon, personalcommunication).

Univariate and multivariate analyses of dispersionclearly show variability in species composition is scale-dependent (Fig. 1B, Table 3). The MDS plot and cross-classification based on the CAP analysis also providesome indirect evidence for scale-dependent variability.The plots from the large clearings tend to be mis-classified more often than plots from small clearings


(Table 1) and are spread more widely across the MDSplot (see confidence limits in Fig. 2).

Our conclusions about scale-dependent variability arepredicated upon the removal of M. edulis from the mul-tivariate analysis of dispersion. M. edulis overwhelmsthe pattern of dispersion, and the trend of greater vari-ability with larger clearings only appears whenM. edulisis removed from the analysis (Fig. 1). The effect ofvariability in M. edulis was particularly noticeable forthe benchmark sites where it accounts for 98% of thevariation. Even with the removal ofM. edulis, the effectsof the next two most variable species – F. vesiculosusand L. littorea – are quite pronounced.

Why then are benchmark sites so heterogeneous withrespect to these three species? We chose the sites withN80% A. nodosum cover as typical A. nodosum standsbut we did not pre-select sites based on M. edulis,F. vesiculosus or L. littorea. We expected some variabil-ity due to the geographic spread of the sites and localdifferences in biological and physiographic properties,but not as much as we observed.

One interesting possibility is that M. edulis and F.vesiculosus occur in the understory as subdominants andare poised to become dominants in the system. Musselsand Fucus may recruit and persist as small individualsbelow the canopy and only be able to dominant thesurface once A. nodosum is removed by ice scour; notunlike trees in a forest waiting for a light gap. Thesuccess of M. edulis and F. vesiculosus may also bemediated by the activities of L. littorea, which affectsM. edulis and F. vesiculosus recruitment (Petraitis, 1987,1990). This conjecture needs to be examined moreclosely and is quite different from Petraitis and Latham's(1999) original explanation in which mussel recruitmentcan only occur and tip the system after ice scour hasremoved A. nodosum.

Others have hypothesized about causes of and testedfor scale-dependent variability, and it has repeatedly beensuggested that disturbed communities will be morevariable than unaffected control sites because of changesin spatial heterogeneity, species composition, or changesin the mean–variance ratio for particular species (Caswelland Cohen, 1991; Warwick and Clarke, 1993; Chapmanet al., 1995; Chapman andUnderwood, 1998; Foster et al.,2003). The results, however, have been mixed. Forexample, Warwick and Clarke (1993) compared severaltypes of stressed communities with nearby controlcommunities and found that variability for severalmeasures increased with increased stress, particularlyfor meiobenthic communities exposed to organic enrich-ment and for macrobenthic communities bordering an oilfield. On the other hand, Chapman et al. (1995) found that

benthic macroinvertebrate communities at unstressedcontrols were more variable than at a site affected bysewage. Chapman et al. (1995) also emphasized theimportance of multiple controls or benchmark type sitesbecause their two control sites were measurably differentfrom each other and that the samples taken from thepolluted site were within the range of natural variation ofthe controls.

Ice scour on rocky intertidal shores is certainly notthe same as organic enrichment or sewage input, but wewould argue that organisms in clearings made by icescour are likely to be under stress because of the removalof the rockweed canopy. Rockweeds act as a bufferagainst physical stresses and dampen spatial variabilityin recruitment and consumer pressure. We expectclearings will be more spatially heterogeneous becauseremoval of rockweeds accentuates microhabitat differ-ences. For example, the difference between a smallcrack and a flat surface in terms of protection againstdesiccation may be minimal under a protective algalcanopy but not so if the canopy is removed. The effecton consumers may also be less spatially heterogeneousunder an algal canopy (e.g., Menge, 1976; Fairweather,1988; Fairweather and Underwood, 1991).

Given that we might expect algal canopies to dampenthe effects of spatial heterogeneity, what is the evidencefrom other studies of A. nodosum clearings (Bertness etal., 2002, 2004; Cervin et al., 2004; Jenkins et al.,2004)? Cervin et al. (2004) followed the successionalpatterns for approximately four years in small(0.8 m×0.3 m) areas cleared of A. nodosum canopyand in uncleared controls with and without the presenceof L. littorea. Community composition of understoryand canopy species differed between cleared anduncleared plots. Inspection of the error bars in theirfigures (Cervin et al., 2004, Fig. 3a–e) suggests that thepercentage cover of Fucus spp. and the percentagecover of ephemeral green algae were more variable incleared plots compared to controls. Cervin et al. (2004)suggested that perturbation of the A. nodosum canopy incombination with the exclusion of L. littorea providedthe opportunity for these algal groups to colonize theshore, potentially through facilitation, i.e. ephemeralgreen algae may have facilitated the successful coloni-zation of Fucus spp. by protecting recruits fromdesiccation. We suspect that this facilitation also mayhave been more spatially heterogeneous in largerclearings thus leading to greater variability.

Jenkins et al. (2004) examined species composition inslightly larger areas (2 m×2 m) cleared of A. nodosumand in uncleared controls both with and without graz-ing limpets. They found that cleared plots diverged from


uncleared controls, and that after 12 years, the al-gal canopy in the clearings was a mixed stand ofA. nodosum and F. serratus whereas A. nodosum re-mained the dominant canopy species in the controls.Inspection of standard errors (Jenkins et al., 2004, Fig.1b–c) indicated that the percentage cover of the algalcanopy species F. serratus and F. vesiculosus wereslightly more variable in cleared plots. Percentage coverof A. nodosum canopy was very variable in the clearedareas (Jenkins et al., 2004, Fig. 1a). The scale-dependentvariability in percentage cover suggests that removal ofthe canopy altered surface heterogeneity which in turnled to variable recruitment and survival of F. serratus,F. vesiculosus, and A. nodosum.

In contrast, the results from Bertness and hiscolleagues are mixed (Bertness et al., 2002, 2004). Theyworked at several locations in the Gulf of Maine andused 1 m×1 m and 3 m×3 m clearings in A. nodosumstands andM. edulis beds with both open and caged plots.From their figures, we were able to compare differencesin the standard errors in uncleared control plots and3 m×3 m clearings for 21 pairs of measurements involv-ing open plots (Figs. 5, 6, 7, and 9 in Bertness et al., 2002,and Figs 2, 3, 5, 6, and 7 inBertness et al., 2004). Standarderrors were larger in 3 m×3 m clearings than in unclearedcontrols for 11 pairs, smaller for 7 pairs, and appeared tobe equal for 3 pairs. There were an additional 13 cases forwhich the standard errors of both the clearings and thecontrols were smaller than the symbols and so could notbe compared.

Comparisons of standard errors based on data takenfrom inside cages were not included because cagingcreates an extreme form of competitive release formussels by completely removing consumers and leads tosaturation of the system by mussels (e.g., Menge, 1976).Even with the range of normal variability, consumers arenever completely absent for the length of time that cagesare normally used (i.e., months to years). As a result, weexpected a canalization of the response and a reduction inthe variability. Not surprisingly, most of the standarderrors from data taken inside cages are very smallregardless of clearing size (Bertness et al., 2002, 2004).

Taken as a whole, our results and the results of otherssuggest scale-dependent variability in areas cleared ofrockweeds. We suspect the increases in variability seen inclearings versus uncleared controls are likely to be due toincreased spatial heterogeneity within plots and variabil-ity among plots that have been cleared. The decreases invariability in exclusion cages versus open controls arelikely caused by canalization of competitive release.

We should also note that our experimental clearingson Swan's Island also show striking north-south

differences regardless of scale-dependence. North-facing bays tend to have fewer S. balanoides and L.obtusata and more L. littorea than south-facing bays.F. vesiculosus and S. balanoides cover are greater insouth-facing bays than in north-facing bays. Thepattern for F. vesiculosus cover is similar to what wasreported in 2002 (compare Fig. 5 with Fig. 2 inPetraitis and Dudgeon, 2005; 5.6% in 2002 versus15.0% in 2004 in north-facing bays, and 48.1% in2002 and versus 54.7% in 2004 in south-facing bays).In contrast, barnacle cover has remained the same innorth-facing bays (40.4% in 2002 versus 46.6% in 2004)but has increased in south-facing bays (4.3% in 2002versus 60.9% in 2004).

We do not have a simple single-factor explanation forthe north-south pattern in barnacle cover. Densities ofneither the predatory snail N. lapillus nor the herbivo-rous snail L. littorea, which can bulldoze youngbarnacles from the rock, vary among bays (Petraitisand Dudgeon, 2005). Barnacle recruitment tends to begreater in south-facing bays, but this pattern is veryvolatile from year to year (Dudgeon and Petraitis, 2001;P.S. Petraitis, unpublished data from 1998 to 2004).Moreover, during the winter of 2000–2003, there was amajor ice event that was much more severe in north-facing bays (P.S. Petraitis, unpublished data from point-intercept transects in 1999 and 2003). Ice removed 20±7% of the rockweeds in north-facing bays (per siteaverage±S.E.; n=6 sites; range: 2–39% loss) and 6±3% in south-facing bays (n=6 sites; range: 2% gain–16% loss). We have no data on barnacle cover, but wesuspect most of the barnacles were also removed be-cause much of the shoreline in north-facing bays ap-peared as if it had been sandblasted. Taken together, thedata on recruitment, snails and ice do not seem toprovide a clear-cut answer.

We think the importance of benchmark data as part ofexperimental studies cannot be underestimated (e.g.,Foster, 1990; Underwood, 2000; Underwood et al.,2000; Foster et al., 2003). For example, the mostcompelling approach to testing for multiple stable statesinvolves showing by experiment that the same site orhabitat can be occupied by different self-replacingcommunities (Peterson, 1984). Yet “same habitat” and“self-replacing” are difficult to define, and the definitionsdepend on length of time of observation, number ofobservations, and prior knowledge of natural variation.The very idea of testing for the “same habitat” can lead toattempts to prove a null hypothesis (e.g., Underwood,1991). Development of tests of bioequivalence mayprovide a way to deal with these problems (Mapstone,1995; MacKenzie and Kendall, 2002; Cole and McBride,


2004; Robinson et al., 2005; Romano, 2005), but thesetests require good benchmarks for on the characteristics ofeachmultiple state. Given the lack of benchmark data, it isnot surprisingly that Connell and Sousa (1983) concludedthere was very little direct experimental evidence formultiple stable states in natural systems. Recent reviewssupport this view (e.g., Petraitis and Dudgeon, 2004b;Schröder et al., 2005).

The benchmark data from our study also highlightshow little we know about the underlying causes ofbroad-scale patterns in a supposedly well-described andwell-understood marine intertidal system. Someresearchers have assumed the existence of multiplestates on rocky intertidal shores would require thesystem to be stochastic and stands in opposition todeterministic consumer control (e.g., “stochastic alterna-tive states” vs. “consumer controlled deterministiccommunity types” Bertness et al., 2004). Yet standardmodels of both multiple stable states and consumercontrol are deterministic and neither requires a stochasticprocess (Lewontin, 1969; Knowlton, 1992). In contrast,we would argue that the deterministic models of multiplestable states and consumer control can be used to makevery different and testable predictions about the patternsof variability found in both experiments and in naturalsystems (Petraitis and Latham, 1999). The developmentof these predictions requires good benchmark data.

Acknowledgements

This paper is dedicated in memory of Bob Horton ofSwan's Island, who provided many wet and cold marineecologists with much needed coffee, food, wine andhospitality for more than 20 years. We thank Erika CarlsonRhile and Nick Vidargas for their help with data collectionand Jon Fisher for his comments on an earlier draft of themanuscript. The CAP and PERMDISP programs werewritten byMarti Anderson, and we are very grateful for herefforts in providing the programs as freeware. This workcould not have been done without the support of theresidents of Swan's Islandwho provided access to the shoreacross their properties. Nearly all of the benchmark datawere collected by Erika Rhile who was supported by anRETsupplement award fromNSF.Researchwas supportedby National Science Foundation grants (OCE 95-29564and DEB LTREB 03-14980) to P.S. Petraitis. [SS]

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