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Using patterns of variability to test for multiple community states on rocky intertidal shores
Journal of Experimental Marine Biology and Ecology 338 (2006) 222 – 232 www.elsevier.com/locate/jembe
Using patterns of variability to test for multiple community states 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 patterns of succession. This prediction was tested in the Gulf of Maine using data from 60 intertidal plots of varying size that were experimentally cleared of the rockweed Ascophyllum nodosum and from 14 benchmark sites from throughout the Gulf. Rockweed was removed from the experimental clearings ranging from 1 to 8 m in diameter in 1996 and data collected in 2004 were used to test effects of clearing size and location on divergence and variability in species composition. Benchmark data were collected in 2005, and the 14 sites were from a dataset on 53 sites throughout the Gulf of Maine. The selected sites were randomly chosen from all sites with N 80% 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. CAP and PERMDISP analyses revealed significant differences in multivariate dispersion and variability with both clearing size and location. Variability generally increased with clearing size and location effects were related to the north–south positioning of the sites. Benchmark sites were similar to the experimental control plots but as variable as the largest clearings. Results suggest that succession in larger clearings has been more unpredictable than in small clearings. The pattern of variability in the experimental clearings is consistent with the predictions of multiple stable states. However, the large amount of variation among the benchmark sites was due to mussels and was unexpected. This unexpected variability underscores the importance of sampling benchmark sites as 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-scale experimental manipulations to generalizations about broad scale patterns in nature is one of the most difficult issues in ecology. While experiments can provide an extraordinary 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 more than snapshots of the ecological processes under investigation. In addition, both differences among experiments and natural variation of ecosystems make it difficult to generalize. Comparisons among small-scale experiments even with slightly different designs are difficult to interpret and may be of limited value (Underwood and Petraitis, 1993; Petraitis, 1998). Natural variation in ecosystems raises questions not only about average effects (e.g., are per-capita effects of competition
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greater in the tropics than in the temperate zone?) but also the amount of variation itself (e.g., are such interactions more variable?). One of the hallmarks of Underwood's research has been links between these two issues — the “scaling” from experimental inference to ecological generalization, and the measurement and meaning of variability in ecological processes (e.g., Underwood and Denley, 1984; Underwood, 1991, 2000; Chapman and Underwood, 1998; Underwood and Chapman, 1998; Underwood et al., 2000). His work over the last 30 years has not only defined the problems but also provided concrete suggestions on how to approach the problems experimentally and conceptually. Following Underwood's lead and suggestions (Underwood, personal communication; Underwood et al., 2000), we present an experimental test of an explicit hypothesis about variability in succession and use observations from a large-scale survey to set the experimental results into a broader context. Petraitis and Latham (1999) hypothesized that rockweed (Ascophyllum nodosum (L.) Le Jolis) stands and mussel (Mytilus edulis L.) beds in sheltered bays in the Gulf of Maine represent two different equilibrium points in an ecosystem with multiple stable states, and here we test the prediction that succession should be highly variable if the ecosystem, in fact, has multiple stable states. The deterministic theory of multiple stable states is well understood. Model parameters establish the number and position of the equilibrium points, and initial densities of the species define a single trajectory towards a unique stable equilibrium point (e.g., Lewontin, 1969; May, 1977; Scheffer et al., 2001). Yet whether multiple stable states exist in nature has remained a hotly debated subject (Sutherland, 1974; Peterson, 1984; Sousa and Connell, 1985; Sutherland, 1990; Knowlton, 1992; Bertness et al., 2002; Petraitis and Dudgeon, 2004a,b; Didham et al., 2005). Definitive experimental tests of the theory continue to be elusive (Petraitis and Dudgeon, 2004b; Suding et al., 2004; Didham et al., 2005) because random events and historical accidents blur what stability, equilibrium and habitat mean in nature (Lewontin, 1969; Grimm and Wissel, 1997). Thus even though the underlying dynamics may be completely deterministic, succession in natural ecosystems with multiple stable states can appear to be stochastic. Per-capita rates of ecological processes, which form the parameters of models, may vary on a small scale, and so “identical” perturbations of the system are unlikely to cause the same outcome. In addition, large perturbations are more likely than small perturbations to tip the system from one stable state to
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another (Knowlton, 1992). Large perturbations also leave the system open to chance events, and species that undergo catastrophic changes in densities may take a long time to recover, exposing the successional trajectories to the cumulative effect of many small random events. Large-scale spatial events, such as fires, can also lead to an uncertain outcome by uncoupling succession in the center of a disturbed patch from effects of the surrounding ecosystem. Taken together, we predict that if rockweed stands and mussel beds on sheltered shores were alternative states, then the course of succession should be scaledependent with larger perturbations leading to more unpredictable patterns of succession. The work reported here uses species composition data from experimental clearings in A. nodosum stands that were established by Petraitis and his colleagues in 1996 (Petraitis and Dudgeon, 1999, 2005) and were analyzed using methods that are based on an approach proposed by Underwood and Chapman (1998). We also wanted to place our experimental results in broader context, and so we sampled sites throughout the Gulf of Maine with established A. nodosum stands. This survey was used as a benchmark for our experimental data and builds on the work of others (Foster, 1990; Underwood, 2000; Underwood et al., 2000). 2. Methods Abundance and percentage cover of the most common species were sampled at 60 experimental plots on Swan's Island, Maine, USA, and 53 benchmark sites throughout the Gulf of Maine. The experimental plots were established in A. nodosum stands in 1996 at twelve mid intertidal sites. At each site, four clearings (1, 2, 4, and 8 m in diameter) were made, and an uncleared control plot was set up. The size range of the experimental clearings is within the normal range of major ice scour events, which occur infrequently on Swan's Island (Petraitis and Dudgeon, 2005). Sets of clearings were spread over four bays (Burnt Coat Harbor, Mackerel Cove, Seal Cove and Toothacker Cove) with three sets per bay. Detailed information on creation of the clearings, as well as descriptions and locations of the sites and sampling can be found elsewhere (Dudgeon and Petraitis, 2001; Petraitis and Dudgeon, 2005). Benchmark sites were included to examine if the experimental sites on Swan's Island were a representative sample of similar intertidal sites throughout the Gulf of Maine. The 53 benchmark sites included sites with a range of cover by A. nodosum, M. edulis and Fucus
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vesiculosus L. Twenty-nine sites were on Swan's Island. The straight-line distance across the Gulf of Maine from the most southwestern site (Audubon Sanctuary, Biddeford Pool; 43°26.8′N, 70°20.7′W) to the most northeastern site (Seawall, Acadia National Park; 44°14.6′N, 68°18.0′W) was 186 km. Fourteen sites with N 80% A. nodosum canopy were randomly selected from the complete data set to serve as “typical” sites with undisturbed A. nodosum stands. Experimental plots were sampled in July-August 2004, and benchmark sites in June-September 2005. Abundances of mussels (M. edulis and Modiolus modiolus (L.)) and gastropods (Tectura testudinalis (Müller), Littorina littorea (L.), Littorina obtusata L., Littorina saxatilis (Olivi), and Nucella lapillus (L.)), barnacles (Semibalanus balanoides (L.)), and fucoid algae (A. nodosum and F. vesiculosus) were counted within three 50 × 50 cm quadrats per experimental plot and benchmark site. Fucoids and barnacles were
subsampled because of the large numbers of individuals. Barnacle counts were divided into the current year's recruits and older individuals. Percentage cover data were collected for fucoids, barnacles, mussels and other cover. The category of other cover included bare space and all other species, which were rare. Data on canopy cover and surface cover were collected separately. Variables of interest were the densities and percentage cover per plot, and so average densities and average percentage cover per plot rather than per quadrat were used in the analyses (see Petraitis and Dudgeon, 2005, for justification of quadrat placement and use of averages). Multivariate dispersion and canonical discriminant analyses of the abundance data were done using Anderson's PERMDISP and CAP programs, which are available as freeware (http://www.stat.auckland.ac. nz/%257Emja/Programs.htm). PERMDISP tests for
Fig. 1. Average within group distances and approximate 95% confidence limits from the PERMDISP analyses. Panels A and C are clearing type analyses, and panels B and D are location analyses. Abbreviations in panels B and D identify locations; Bench for benchmark sites, MC for Mackerel Cove (North-facing), SC for Seal Cove (North-facing), BC for Burnt Coat Harbor (South-facing) and TC for Toothacker Cove (South-facing). Confidences limits were calculated as 1.96(MSE/n)1/2 where MSE = the error mean square from the PERMDISP ANOVA and n = sample size, which was 11 for the clearing type analyses and 14 for the location analyses. P levels are from the PERMDISP randomizations; letters above the bars indicate significantly different groups from pairwise comparisons and were corrected using sequential Bonferroni tests with α = 0.05.
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Table 1 Assignment of replicates to groups based on CAP discriminant function for type of clearing Original groups
Classified into groups Bench
Control
One
Two
Four
Eight
Bench Control One Two Four Eight
5 2 2 1 1 1
6 5 3 1 1 1
0 4 4 1 3 2
0 0 1 3 3 1
0 0 0 3 0 0
0 0 1 2 3 6
% Correct
% Correct or adjacent
45% 45% 36% 27% 0% 55%
100% 100% 73% 64% 55% 55%
Column labeled “% Correct” gives percentage assigned correctly. Column labeled “% Correct or adjacent” gives percentage assigned correctly or to most similar clearing type (e.g., benchmark sites to either benchmark sites or uncleared controls).
multivariate dispersion using various distance and dissimilarity measures and is a multivariate analog of Levene's test for heterogeneity of variances. Results are reported as the average within-group distance of each group from its centroid. PERMDISP provides post-hoc pairwise tests, which we adjusted using sequential Bonferroni corrections (Sokal and Rohlf, 1995). CAP does a canonical analysis of principal coordinates (Anderson and Robinson, 2003; Anderson and Willis, 2003). We let the CAP program choose the number of eigenvectors (i.e., the value for m) needed to maximize the proportion of observations correctly classified (Anderson and Robinson, 2003). Positions of experimental plots and benchmark sites were plotted on the first two canonical axes. PERMDISP and CAP include
Fig. 2. Constrained MDS plot of averages and standard deviations of clearing types based on CAP analysis of densities. Averages and standard deviations were calculated from the positions of the individual plots, which are given by the CAP analysis output. Analysis and plot are based on m = 3, which gave the smallest crossvalidation error. The first and second squared canonical correlations are 0.399 and 0.126, respectively; trace statistic = 0.568, P b 0.0001. Scales of axes are identical in both directions but the y-axis is cropped to save space.
permutation tests for significance; 9999 permutations were used in all tests. Several transformations, standardizations, dissimilarity measures and distance measures were tried. Choice of transformation, standardization and measure had little effect on CAP results, and so only the results based on Euclidian distances and using unstandardized data are reported. In contrast, results from PERMDISP were very dependent upon whether the data were standardized or not. Differences among the various analyses were caused largely by M. edulis, which had a range of densities several orders of magnitude larger than any other species. When M. edulis was dropped, all analyses gave nearly the same results. Thus two PERMDISP analyses using Euclidian distances and unstandardized data are reported — one based on the complete dataset and the other with M. edulis dropped from the dataset. Percentage cover data were not included in the multivariate analyses for three reasons. First, clearings were made by removing A. nodosum, and so differences
Fig. 3. Constrained MDS plot of averages and standard deviations of locations based on CAP analysis of densities. Details of calculations, analysis and scales are given in Fig. 2. The first and second squared canonical correlations are 0.618 and 0.323, respectively; trace statistic = 1.254, P b 0.0001; m = 9.
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Table 2 Assignment of replicates to groups based on CAP discriminant function for locations Original groups
Classified into groups Bench
North facing bays MC
SC
BC
TC
Bench MC SC BC TC
3 0 3 0 2
1 9 1 0 0
3 3 6 3 0
5 2 4 10 4
2 0 0 1 8
South facing bays
% Correct
% Correct or similar bay
21% 64% 43% 71% 57%
86% 50% 79% 86%
Column labeled “% Correct” gives percentage assigned correctly. Column labeled “% Correct or similar bay” gives percentage assigned correctly or to the bay facing the same direction (e.g., MC to either MC or SC).
among the treatment levels in cover in 2004 are confounded by the original removal in 1996. Second, the A. nodosum canopy cover was used to select the benchmark sites, and these sites were selected to have 80–100% A. nodosum cover. Thus we expected A. nodosum cover to be greater and less variable in the benchmark sites than in the experimental plots. Third, percentage cover and abundance are measured in different units and require standardization so that they are on an equal footing. We had no a priori justification for the most appropriate standardization. However, canopy cover by A. nodosum and F. vesiculosus, and surface cover by mussels, barnacles and other cover were analyzed using univariate and multivariate tests to provide an overall description of differences. Levene's test was used to examine heterogeneity of variances, and ANOVA or Welch's test was used to examine for differences among groups. Welch's test, which is an ANOVA-like analysis for groups with unequal Table 3 P-values for univariate analyses of percentage cover data Data
Levene's Test ANOVA
Clearing size: Fucus canopy b0.0001 Clearing size: Ascophyllum canopy 0.0014 Clearing size: Mussel surface 0.0196 Clearing size: Barnacle surface 0.0892 Clearing size: Bare space and rare species 0.8310 Location: Fucus canopy b0.0001 Location: Ascophyllum canopy b0.0001 Location: Mussel surface 0.0083 Location: Barnacle surface 0.3661 Location: Bare space and rare species 0.7105
b0.0001 b0.0001 0.6266 0.0589 0.5463 b0.0001 b0.0001 0.0180 0.0009 0.0266
Values in bold are significant. ANOVA column gives results from either ANOVA or Welch's test; Welch's test was used if Levene's test for heterogeneity of variances was significant.
Fig. 4. Percentage cover by rockweeds. Confidence limits are based on sample sizes of 11. Letters above bars show significantly different groups based on Tukey's tests.
variances, was used if Levene's test was significant. Tukey's tests were used for post-hoc comparisons. Data were not transformed because we were primarily interested in testing for differences due to variation among groups. CAP analysis was also done to examine differences among locations because the results of the univariate tests did not show a single consistent pattern. The univariate and multivariate analyses were done as one-way designs because of missing data and limitations of PERMDISP. One experimental 2 m clearing was destroyed in 2002 when ice deposited a granite boulder roughly 0.5 m × 1.0 m × 1.5 m on the plot. The boulder was estimated to weigh 1500–2500 kg and could not be removed from the plot. However, PERMDISP requires equal sample sizes with n N 2 and dropping replicates to create a balanced two-way design of treatment × location would have had 2 replicates per cell. Thus the effects of treatment (benchmark vs. control plots vs. 1, 2, 4 and 8 m clearings) and location (benchmark vs. Mackerel Cove vs. Seal Cove vs. Burnt Coat Harbor vs. Toothacker Cove) were examined separately. Sample sizes were balanced by randomly dropping one replicate; n = 11 per group for the clearing type analyses and 14 for the location analyses). 3. Results PERMDISP analysis showed significant differences in multivariate dispersion among clearing types and
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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 were driven strongly by differences between benchmark sites and the other treatment levels (Fig. 1A and B). M. edulis was several orders of magnitude more variable than any other species with average densities ranging from 0 to 5350 per 0.25 m2, and removing them from the analyses revealed four patterns. First, the average dispersion of the benchmark sites was reduced by 98% in both the clearing type and location analyses. Because Euclidian distances are metric, these reductions represent the contribution of mussels to total dispersion. Second, dispersions of benchmark sites and uncleared controls were not significantly different (Fig. 1C). Third, larger clearings are clearly
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more variable than smaller clearings and uncleared controls. Fourth, north-facing bays (Mackerel Cove and Seal Cove) were more variable than south-facing bays (Burnt Coat Harbor and Toothacker Cove), but the pattern was dampened by removing mussels from the analysis (Fig. 1B and D). Canonical ordination based on clearing type separated the samples into three distinct groups along the first axis (Fig. 2). The first canonical axis explained 99.4% of the variation and was correlated with M. edulis (+ 0.50), F. vesiculosus (+ 0.75), and L. littorea (+ 0.39). The second canonical axis, which explained only 0.5% of the variation, accounted for the spread among the two, four, and eight meter clearings. The second axis was correlated with L. littorea (+ 0.87), F. vesiculosus (− 0.65), and L. obtusata (− 0.40).
Fig. 5. Constrained MDS plot of location averages based on CAP analysis of percentage cover data and histograms of percentage cover of barnacles, mussels and Fucus. MDS plot is rotated to match the orientation of locations shown in Fig. 3, and thus the y-axis is the first canonical axis (i.e. negative to positive values run from top to bottom). Scales of axes are identical in both directions but the x-axis is cropped to save space. Details of calculations and analysis and scales are given in Fig. 2. The first and second squared canonical correlations are 0.453 and 0.307, respectively; trace statistic = 0.858, P b 0.0001; m = 3. For the first canonical axis, the three largest correlations are with A. nodosum (+0.897), S. balanoides (− 0.622) and F. vesiculosus (− 0.500). For the second canonical axis, the three largest correlations are with F. vesiculosus (+0.813), other species (− 0.401) and M. edulis (+0.341). For the histograms, barnacles (S. balanoides), mussels, (M. edulis) and Fucus (F. vesiculosus) are identified by SB, ME, and FV, respectively. Data for histograms are grouped as benchmark sites, north-facing bays and south-facing bays. Confidence limits are based on sample sizes of 14 for benchmark sites and 28 for the rest.
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The rate of correct classification varied with the size and type of the clearing (Table 1). The benchmark sites were consistently assigned to the benchmark or control group. Similarly all control plots were assigned correctly or as either benchmark sites or one meter clearings. Misclassification increased as clearing size increased (Fig. 2). Canonical ordination for location separated orientation of the bays (Fig. 3). The first axis explained 99.4% of the variation, and separation along the axis was due to S. balanoides (− 0.66), L. obtusata (− 0.45), and L. littorea (+ 0.66). The second axis explained 0.4% of the variation and was highly correlated with L. littorea (+ 0.53), M. edulis (− 0.67), and N. lapillus (− 0.45). The discriminant function consistently gave the correct assignment of plots from Mackerel Cove and Burnt Coat Harbor (Table 2). Seal Cove plots, Toothacker Cove plots, and the benchmark sites were misclassified more frequently. Assignment to the correct orientation (e.g., classifying plots in Burnt Coat Harbor, a south-facing bay as from either Burnt Coat Harbor or Toothacker Cove) was ≥ 79% for all bays except Seal Cove. Univariate tests showed significant differences among clearing sizes for percentage cover by canopy species only (Table 3). A. nodosum canopy cover was significantly higher in the benchmark sites and control plots, whereas F. vesiculosus canopy cover was significantly greater in clearings larger than two meters in diameter (Fig. 4). For understory species, average percentage cover and standard errors (n = 66) were 48.3 ± 3.8% for S. balanoides, 9.1 ± 2.2% for M. edulis, and 38.5 ± 3.6% for bare space and rare species. There were significant differences among locations for both canopy and understory species (Table 3). The CAP analysis showed that the benchmark sites were separated from the experimental plots on Swan's Island along the first axis by differences in A. nodosum canopy 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 to averaging across clearings of all sizes and uncleared controls. A. nodosum canopy cover in small clearings was due to the draping of long fronds into small clearings by plants from the surrounding stands rather than recruitment of new plants into the clearings. The spread along the second canonical axis separated the south-facing and north-facing bays with F. vesiculosus and S. balanoides more common in the south and M. edulis slightly more common in the north. Levene's tests were significant only for A. nodosum, F. vesiculosus and M. edulis cover (Table 3). The
within-group variances tended to be smaller for benchmark sites and for the control plots (see confidence limits in Figs. 4 and 5). 4. Discussion In systems with multiple stable states, small differences in initial conditions can cause very different outcomes, and experimental perturbations in these systems may initiate alternative successional pathways in “identical” replicate plots (Petraitis and Latham, 1999). The divergence of species composition during succession should be scale-dependent because areas exposed to small perturbations are likely to be quite resilient while areas with large perturbations are more likely to cross a “breakpoint” (sensu May, 1977) and tip towards an alternative species composition (Knowlton, 1992; Petraitis and Latham, 1999). Thus both the average and the variability of species composition should be scale-dependent. Our results are consistent with these predictions. Average species composition is scale-dependent with M. edulis, F. vesiculosus and L. littorea driving most of the difference among clearing sizes (Fig. 2). M. edulis, L. littorea and F. vesiculosus are more common in the 2, 4 and 8 m clearings. F. vesiculosus and L. littorea were the next most variable species after M. edulis. Ranges were 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 increases with clearing size (Fig. 4), and the pattern is similar to what 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 and 73.8% in 2004. The average overall increase between 2002 and 2004 was 40%. The success of F. vesiculosus in clearings is surprisingly because it is so rare in undisturbed stands of A. nodosum. Others have reported similar dominance by Fucus species after removal of A. nodosum (Bertness et al., 2002; Cervin et al., 2004; Jenkins et al., 2004), and it is possible that F. vesiculosus may either be a fugitive species in the system or represent a third community state on par with mussel beds and A. nodosum stands (S. R. Dudgeon, personal communication). Univariate and multivariate analyses of dispersion clearly show variability in species composition is scaledependent (Fig. 1B, Table 3). The MDS plot and crossclassification based on the CAP analysis also provide some indirect evidence for scale-dependent variability. The plots from the large clearings tend to be misclassified more often than plots from small clearings
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(Table 1) and are spread more widely across the MDS plot (see confidence limits in Fig. 2). Our conclusions about scale-dependent variability are predicated upon the removal of M. edulis from the multivariate analysis of dispersion. M. edulis overwhelms the pattern of dispersion, and the trend of greater variability with larger clearings only appears when M. edulis is removed from the analysis (Fig. 1). The effect of variability in M. edulis was particularly noticeable for the benchmark sites where it accounts for 98% of the variation. Even with the removal of M. edulis, the effects of the next two most variable species – F. vesiculosus and L. littorea – are quite pronounced. Why then are benchmark sites so heterogeneous with respect to these three species? We chose the sites with N 80% A. nodosum cover as typical A. nodosum stands but we did not pre-select sites based on M. edulis, F. vesiculosus or L. littorea. We expected some variability due to the geographic spread of the sites and local differences 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 and are poised to become dominants in the system. Mussels and Fucus may recruit and persist as small individuals below the canopy and only be able to dominant the surface once A. nodosum is removed by ice scour; not unlike trees in a forest waiting for a light gap. The success of M. edulis and F. vesiculosus may also be mediated by the activities of L. littorea, which affects M. edulis and F. vesiculosus recruitment (Petraitis, 1987, 1990). This conjecture needs to be examined more closely and is quite different from Petraitis and Latham's (1999) original explanation in which mussel recruitment can only occur and tip the system after ice scour has removed A. nodosum. Others have hypothesized about causes of and tested for scale-dependent variability, and it has repeatedly been suggested that disturbed communities will be more variable than unaffected control sites because of changes in spatial heterogeneity, species composition, or changes in the mean–variance ratio for particular species (Caswell and Cohen, 1991; Warwick and Clarke, 1993; Chapman et al., 1995; Chapman and Underwood, 1998; Foster et al., 2003). The results, however, have been mixed. For example, Warwick and Clarke (1993) compared several types of stressed communities with nearby control communities and found that variability for several measures increased with increased stress, particularly for meiobenthic communities exposed to organic enrichment and for macrobenthic communities bordering an oil field. On the other hand, Chapman et al. (1995) found that
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benthic macroinvertebrate communities at unstressed controls were more variable than at a site affected by sewage. Chapman et al. (1995) also emphasized the importance of multiple controls or benchmark type sites because their two control sites were measurably different from each other and that the samples taken from the polluted site were within the range of natural variation of the controls. Ice scour on rocky intertidal shores is certainly not the same as organic enrichment or sewage input, but we would argue that organisms in clearings made by ice scour are likely to be under stress because of the removal of the rockweed canopy. Rockweeds act as a buffer against physical stresses and dampen spatial variability in recruitment and consumer pressure. We expect clearings will be more spatially heterogeneous because removal of rockweeds accentuates microhabitat differences. For example, the difference between a small crack and a flat surface in terms of protection against desiccation may be minimal under a protective algal canopy but not so if the canopy is removed. The effect on consumers may also be less spatially heterogeneous under an algal canopy (e.g., Menge, 1976; Fairweather, 1988; Fairweather and Underwood, 1991). Given that we might expect algal canopies to dampen the effects of spatial heterogeneity, what is the evidence from other studies of A. nodosum clearings (Bertness et al., 2002, 2004; Cervin et al., 2004; Jenkins et al., 2004)? Cervin et al. (2004) followed the successional patterns for approximately four years in small (0.8 m × 0.3 m) areas cleared of A. nodosum canopy and in uncleared controls with and without the presence of L. littorea. Community composition of understory and canopy species differed between cleared and uncleared plots. Inspection of the error bars in their figures (Cervin et al., 2004, Fig. 3a–e) suggests that the percentage cover of Fucus spp. and the percentage cover of ephemeral green algae were more variable in cleared plots compared to controls. Cervin et al. (2004) suggested that perturbation of the A. nodosum canopy in combination with the exclusion of L. littorea provided the opportunity for these algal groups to colonize the shore, potentially through facilitation, i.e. ephemeral green algae may have facilitated the successful colonization of Fucus spp. by protecting recruits from desiccation. We suspect that this facilitation also may have been more spatially heterogeneous in larger clearings thus leading to greater variability. Jenkins et al. (2004) examined species composition in slightly larger areas (2 m × 2 m) cleared of A. nodosum and in uncleared controls both with and without grazing limpets. They found that cleared plots diverged from
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uncleared controls, and that after 12 years, the algal canopy in the clearings was a mixed stand of A. nodosum and F. serratus whereas A. nodosum remained 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 algal canopy species F. serratus and F. vesiculosus were slightly more variable in cleared plots. Percentage cover of A. nodosum canopy was very variable in the cleared areas (Jenkins et al., 2004, Fig. 1a). The scale-dependent variability in percentage cover suggests that removal of the canopy altered surface heterogeneity which in turn led to variable recruitment and survival of F. serratus, F. vesiculosus, and A. nodosum. In contrast, the results from Bertness and his colleagues are mixed (Bertness et al., 2002, 2004). They worked at several locations in the Gulf of Maine and used 1 m × 1 m and 3 m × 3 m clearings in A. nodosum stands and M. edulis beds with both open and caged plots. From their figures, we were able to compare differences in the standard errors in uncleared control plots and 3 m × 3 m clearings for 21 pairs of measurements involving open plots (Figs. 5, 6, 7, and 9 in Bertness et al., 2002, and Figs 2, 3, 5, 6, and 7 in Bertness et al., 2004). Standard errors were larger in 3 m × 3 m clearings than in uncleared controls for 11 pairs, smaller for 7 pairs, and appeared to be equal for 3 pairs. There were an additional 13 cases for which the standard errors of both the clearings and the controls were smaller than the symbols and so could not be compared. Comparisons of standard errors based on data taken from inside cages were not included because caging creates an extreme form of competitive release for mussels by completely removing consumers and leads to saturation of the system by mussels (e.g., Menge, 1976). Even with the range of normal variability, consumers are never completely absent for the length of time that cages are normally used (i.e., months to years). As a result, we expected a canalization of the response and a reduction in the variability. Not surprisingly, most of the standard errors from data taken inside cages are very small regardless of clearing size (Bertness et al., 2002, 2004). Taken as a whole, our results and the results of others suggest scale-dependent variability in areas cleared of rockweeds. We suspect the increases in variability seen in clearings versus uncleared controls are likely to be due to increased spatial heterogeneity within plots and variability among plots that have been cleared. The decreases in variability in exclusion cages versus open controls are likely caused by canalization of competitive release. We should also note that our experimental clearings on Swan's Island also show striking north-south
differences regardless of scale-dependence. Northfacing 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 in south-facing bays than in north-facing bays. The pattern for F. vesiculosus cover is similar to what was reported in 2002 (compare Fig. 5 with Fig. 2 in Petraitis and Dudgeon, 2005; 5.6% in 2002 versus 15.0% in 2004 in north-facing bays, and 48.1% in 2002 and versus 54.7% in 2004 in south-facing bays). In contrast, barnacle cover has remained the same in north-facing bays (40.4% in 2002 versus 46.6% in 2004) but has increased in south-facing bays (4.3% in 2002 versus 60.9% in 2004). We do not have a simple single-factor explanation for the north-south pattern in barnacle cover. Densities of neither the predatory snail N. lapillus nor the herbivorous snail L. littorea, which can bulldoze young barnacles from the rock, vary among bays (Petraitis and Dudgeon, 2005). Barnacle recruitment tends to be greater in south-facing bays, but this pattern is very volatile 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 a major ice event that was much more severe in northfacing bays (P.S. Petraitis, unpublished data from pointintercept transects in 1999 and 2003). Ice removed 20 ± 7% of the rockweeds in north-facing bays (per site average ± 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 we suspect most of the barnacles were also removed because much of the shoreline in north-facing bays appeared as if it had been sandblasted. Taken together, the data on recruitment, snails and ice do not seem to provide a clear-cut answer. We think the importance of benchmark data as part of experimental studies cannot be underestimated (e.g., Foster, 1990; Underwood, 2000; Underwood et al., 2000; Foster et al., 2003). For example, the most compelling approach to testing for multiple stable states involves showing by experiment that the same site or habitat can be occupied by different self-replacing communities (Peterson, 1984). Yet “same habitat” and “self-replacing” are difficult to define, and the definitions depend on length of time of observation, number of observations, and prior knowledge of natural variation. The very idea of testing for the “same habitat” can lead to attempts to prove a null hypothesis (e.g., Underwood, 1991). Development of tests of bioequivalence may provide a way to deal with these problems (Mapstone, 1995; MacKenzie and Kendall, 2002; Cole and McBride,
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2004; Robinson et al., 2005; Romano, 2005), but these tests require good benchmarks for on the characteristics of each multiple state. Given the lack of benchmark data, it is not surprisingly that Connell and Sousa (1983) concluded there was very little direct experimental evidence for multiple stable states in natural systems. Recent reviews support this view (e.g., Petraitis and Dudgeon, 2004b; Schröder et al., 2005). The benchmark data from our study also highlights how little we know about the underlying causes of broad-scale patterns in a supposedly well-described and well-understood marine intertidal system. Some researchers have assumed the existence of multiple states on rocky intertidal shores would require the system to be stochastic and stands in opposition to deterministic consumer control (e.g., “stochastic alternative states” vs. “consumer controlled deterministic community types” Bertness et al., 2004). Yet standard models of both multiple stable states and consumer control are deterministic and neither requires a stochastic process (Lewontin, 1969; Knowlton, 1992). In contrast, we would argue that the deterministic models of multiple stable states and consumer control can be used to make very different and testable predictions about the patterns of variability found in both experiments and in natural systems (Petraitis and Latham, 1999). The development of these predictions requires good benchmark data. Acknowledgements This paper is dedicated in memory of Bob Horton of Swan's Island, who provided many wet and cold marine ecologists with much needed coffee, food, wine and hospitality for more than 20 years. We thank Erika Carlson Rhile and Nick Vidargas for their help with data collection and Jon Fisher for his comments on an earlier draft of the manuscript. The CAP and PERMDISP programs were written by Marti Anderson, and we are very grateful for her efforts in providing the programs as freeware. This work could not have been done without the support of the residents of Swan's Island who provided access to the shore across their properties. Nearly all of the benchmark data were collected by Erika Rhile who was supported by an RETsupplement award from NSF. Research was supported by National Science Foundation grants (OCE 95-29564 and DEB LTREB 03-14980) to P.S. Petraitis. [SS] References Anderson, M.J., Robinson, J., 2003. Generalized discriminant analysis based on distances. Australian and New Zealand Journal of Statistics 45, 301–318.
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Using patterns of variability to test for multiple community states 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 patterns of succession. This prediction was tested in the Gulf of Maine using data from 60 intertidal plots of varying size that were experimentally cleared of the rockweed Ascophyllum nodosum and from 14 benchmark sites from throughout the Gulf. Rockweed was removed from the experimental clearings ranging from 1 to 8 m in diameter in 1996 and data collected in 2004 were used to test effects of clearing size and location on divergence and variability in species composition. Benchmark data were collected in 2005, and the 14 sites were from a dataset on 53 sites throughout the Gulf of Maine. The selected sites were randomly chosen from all sites with N 80% 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. CAP and PERMDISP analyses revealed significant differences in multivariate dispersion and variability with both clearing size and location. Variability generally increased with clearing size and location effects were related to the north–south positioning of the sites. Benchmark sites were similar to the experimental control plots but as variable as the largest clearings. Results suggest that succession in larger clearings has been more unpredictable than in small clearings. The pattern of variability in the experimental clearings is consistent with the predictions of multiple stable states. However, the large amount of variation among the benchmark sites was due to mussels and was unexpected. This unexpected variability underscores the importance of sampling benchmark sites as 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-scale experimental manipulations to generalizations about broad scale patterns in nature is one of the most difficult issues in ecology. While experiments can provide an extraordinary 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 more than snapshots of the ecological processes under investigation. In addition, both differences among experiments and natural variation of ecosystems make it difficult to generalize. Comparisons among small-scale experiments even with slightly different designs are difficult to interpret and may be of limited value (Underwood and Petraitis, 1993; Petraitis, 1998). Natural variation in ecosystems raises questions not only about average effects (e.g., are per-capita effects of competition
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greater in the tropics than in the temperate zone?) but also the amount of variation itself (e.g., are such interactions more variable?). One of the hallmarks of Underwood's research has been links between these two issues — the “scaling” from experimental inference to ecological generalization, and the measurement and meaning of variability in ecological processes (e.g., Underwood and Denley, 1984; Underwood, 1991, 2000; Chapman and Underwood, 1998; Underwood and Chapman, 1998; Underwood et al., 2000). His work over the last 30 years has not only defined the problems but also provided concrete suggestions on how to approach the problems experimentally and conceptually. Following Underwood's lead and suggestions (Underwood, personal communication; Underwood et al., 2000), we present an experimental test of an explicit hypothesis about variability in succession and use observations from a large-scale survey to set the experimental results into a broader context. Petraitis and Latham (1999) hypothesized that rockweed (Ascophyllum nodosum (L.) Le Jolis) stands and mussel (Mytilus edulis L.) beds in sheltered bays in the Gulf of Maine represent two different equilibrium points in an ecosystem with multiple stable states, and here we test the prediction that succession should be highly variable if the ecosystem, in fact, has multiple stable states. The deterministic theory of multiple stable states is well understood. Model parameters establish the number and position of the equilibrium points, and initial densities of the species define a single trajectory towards a unique stable equilibrium point (e.g., Lewontin, 1969; May, 1977; Scheffer et al., 2001). Yet whether multiple stable states exist in nature has remained a hotly debated subject (Sutherland, 1974; Peterson, 1984; Sousa and Connell, 1985; Sutherland, 1990; Knowlton, 1992; Bertness et al., 2002; Petraitis and Dudgeon, 2004a,b; Didham et al., 2005). Definitive experimental tests of the theory continue to be elusive (Petraitis and Dudgeon, 2004b; Suding et al., 2004; Didham et al., 2005) because random events and historical accidents blur what stability, equilibrium and habitat mean in nature (Lewontin, 1969; Grimm and Wissel, 1997). Thus even though the underlying dynamics may be completely deterministic, succession in natural ecosystems with multiple stable states can appear to be stochastic. Per-capita rates of ecological processes, which form the parameters of models, may vary on a small scale, and so “identical” perturbations of the system are unlikely to cause the same outcome. In addition, large perturbations are more likely than small perturbations to tip the system from one stable state to
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another (Knowlton, 1992). Large perturbations also leave the system open to chance events, and species that undergo catastrophic changes in densities may take a long time to recover, exposing the successional trajectories to the cumulative effect of many small random events. Large-scale spatial events, such as fires, can also lead to an uncertain outcome by uncoupling succession in the center of a disturbed patch from effects of the surrounding ecosystem. Taken together, we predict that if rockweed stands and mussel beds on sheltered shores were alternative states, then the course of succession should be scaledependent with larger perturbations leading to more unpredictable patterns of succession. The work reported here uses species composition data from experimental clearings in A. nodosum stands that were established by Petraitis and his colleagues in 1996 (Petraitis and Dudgeon, 1999, 2005) and were analyzed using methods that are based on an approach proposed by Underwood and Chapman (1998). We also wanted to place our experimental results in broader context, and so we sampled sites throughout the Gulf of Maine with established A. nodosum stands. This survey was used as a benchmark for our experimental data and builds on the work of others (Foster, 1990; Underwood, 2000; Underwood et al., 2000). 2. Methods Abundance and percentage cover of the most common species were sampled at 60 experimental plots on Swan's Island, Maine, USA, and 53 benchmark sites throughout the Gulf of Maine. The experimental plots were established in A. nodosum stands in 1996 at twelve mid intertidal sites. At each site, four clearings (1, 2, 4, and 8 m in diameter) were made, and an uncleared control plot was set up. The size range of the experimental clearings is within the normal range of major ice scour events, which occur infrequently on Swan's Island (Petraitis and Dudgeon, 2005). Sets of clearings were spread over four bays (Burnt Coat Harbor, Mackerel Cove, Seal Cove and Toothacker Cove) with three sets per bay. Detailed information on creation of the clearings, as well as descriptions and locations of the sites and sampling can be found elsewhere (Dudgeon and Petraitis, 2001; Petraitis and Dudgeon, 2005). Benchmark sites were included to examine if the experimental sites on Swan's Island were a representative sample of similar intertidal sites throughout the Gulf of Maine. The 53 benchmark sites included sites with a range of cover by A. nodosum, M. edulis and Fucus
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vesiculosus L. Twenty-nine sites were on Swan's Island. The straight-line distance across the Gulf of Maine from the most southwestern site (Audubon Sanctuary, Biddeford Pool; 43°26.8′N, 70°20.7′W) to the most northeastern site (Seawall, Acadia National Park; 44°14.6′N, 68°18.0′W) was 186 km. Fourteen sites with N 80% A. nodosum canopy were randomly selected from the complete data set to serve as “typical” sites with undisturbed A. nodosum stands. Experimental plots were sampled in July-August 2004, and benchmark sites in June-September 2005. Abundances of mussels (M. edulis and Modiolus modiolus (L.)) and gastropods (Tectura testudinalis (Müller), Littorina littorea (L.), Littorina obtusata L., Littorina saxatilis (Olivi), and Nucella lapillus (L.)), barnacles (Semibalanus balanoides (L.)), and fucoid algae (A. nodosum and F. vesiculosus) were counted within three 50 × 50 cm quadrats per experimental plot and benchmark site. Fucoids and barnacles were
subsampled because of the large numbers of individuals. Barnacle counts were divided into the current year's recruits and older individuals. Percentage cover data were collected for fucoids, barnacles, mussels and other cover. The category of other cover included bare space and all other species, which were rare. Data on canopy cover and surface cover were collected separately. Variables of interest were the densities and percentage cover per plot, and so average densities and average percentage cover per plot rather than per quadrat were used in the analyses (see Petraitis and Dudgeon, 2005, for justification of quadrat placement and use of averages). Multivariate dispersion and canonical discriminant analyses of the abundance data were done using Anderson's PERMDISP and CAP programs, which are available as freeware (http://www.stat.auckland.ac. nz/%257Emja/Programs.htm). PERMDISP tests for
Fig. 1. Average within group distances and approximate 95% confidence limits from the PERMDISP analyses. Panels A and C are clearing type analyses, and panels B and D are location analyses. Abbreviations in panels B and D identify locations; Bench for benchmark sites, MC for Mackerel Cove (North-facing), SC for Seal Cove (North-facing), BC for Burnt Coat Harbor (South-facing) and TC for Toothacker Cove (South-facing). Confidences limits were calculated as 1.96(MSE/n)1/2 where MSE = the error mean square from the PERMDISP ANOVA and n = sample size, which was 11 for the clearing type analyses and 14 for the location analyses. P levels are from the PERMDISP randomizations; letters above the bars indicate significantly different groups from pairwise comparisons and were corrected using sequential Bonferroni tests with α = 0.05.
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Table 1 Assignment of replicates to groups based on CAP discriminant function for type of clearing Original groups
Classified into groups Bench
Control
One
Two
Four
Eight
Bench Control One Two Four Eight
5 2 2 1 1 1
6 5 3 1 1 1
0 4 4 1 3 2
0 0 1 3 3 1
0 0 0 3 0 0
0 0 1 2 3 6
% Correct
% Correct or adjacent
45% 45% 36% 27% 0% 55%
100% 100% 73% 64% 55% 55%
Column labeled “% Correct” gives percentage assigned correctly. Column labeled “% Correct or adjacent” gives percentage assigned correctly or to most similar clearing type (e.g., benchmark sites to either benchmark sites or uncleared controls).
multivariate dispersion using various distance and dissimilarity measures and is a multivariate analog of Levene's test for heterogeneity of variances. Results are reported as the average within-group distance of each group from its centroid. PERMDISP provides post-hoc pairwise tests, which we adjusted using sequential Bonferroni corrections (Sokal and Rohlf, 1995). CAP does a canonical analysis of principal coordinates (Anderson and Robinson, 2003; Anderson and Willis, 2003). We let the CAP program choose the number of eigenvectors (i.e., the value for m) needed to maximize the proportion of observations correctly classified (Anderson and Robinson, 2003). Positions of experimental plots and benchmark sites were plotted on the first two canonical axes. PERMDISP and CAP include
Fig. 2. Constrained MDS plot of averages and standard deviations of clearing types based on CAP analysis of densities. Averages and standard deviations were calculated from the positions of the individual plots, which are given by the CAP analysis output. Analysis and plot are based on m = 3, which gave the smallest crossvalidation error. The first and second squared canonical correlations are 0.399 and 0.126, respectively; trace statistic = 0.568, P b 0.0001. Scales of axes are identical in both directions but the y-axis is cropped to save space.
permutation tests for significance; 9999 permutations were used in all tests. Several transformations, standardizations, dissimilarity measures and distance measures were tried. Choice of transformation, standardization and measure had little effect on CAP results, and so only the results based on Euclidian distances and using unstandardized data are reported. In contrast, results from PERMDISP were very dependent upon whether the data were standardized or not. Differences among the various analyses were caused largely by M. edulis, which had a range of densities several orders of magnitude larger than any other species. When M. edulis was dropped, all analyses gave nearly the same results. Thus two PERMDISP analyses using Euclidian distances and unstandardized data are reported — one based on the complete dataset and the other with M. edulis dropped from the dataset. Percentage cover data were not included in the multivariate analyses for three reasons. First, clearings were made by removing A. nodosum, and so differences
Fig. 3. Constrained MDS plot of averages and standard deviations of locations based on CAP analysis of densities. Details of calculations, analysis and scales are given in Fig. 2. The first and second squared canonical correlations are 0.618 and 0.323, respectively; trace statistic = 1.254, P b 0.0001; m = 9.
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Table 2 Assignment of replicates to groups based on CAP discriminant function for locations Original groups
Classified into groups Bench
North facing bays MC
SC
BC
TC
Bench MC SC BC TC
3 0 3 0 2
1 9 1 0 0
3 3 6 3 0
5 2 4 10 4
2 0 0 1 8
South facing bays
% Correct
% Correct or similar bay
21% 64% 43% 71% 57%
86% 50% 79% 86%
Column labeled “% Correct” gives percentage assigned correctly. Column labeled “% Correct or similar bay” gives percentage assigned correctly or to the bay facing the same direction (e.g., MC to either MC or SC).
among the treatment levels in cover in 2004 are confounded by the original removal in 1996. Second, the A. nodosum canopy cover was used to select the benchmark sites, and these sites were selected to have 80–100% A. nodosum cover. Thus we expected A. nodosum cover to be greater and less variable in the benchmark sites than in the experimental plots. Third, percentage cover and abundance are measured in different units and require standardization so that they are on an equal footing. We had no a priori justification for the most appropriate standardization. However, canopy cover by A. nodosum and F. vesiculosus, and surface cover by mussels, barnacles and other cover were analyzed using univariate and multivariate tests to provide an overall description of differences. Levene's test was used to examine heterogeneity of variances, and ANOVA or Welch's test was used to examine for differences among groups. Welch's test, which is an ANOVA-like analysis for groups with unequal Table 3 P-values for univariate analyses of percentage cover data Data
Levene's Test ANOVA
Clearing size: Fucus canopy b0.0001 Clearing size: Ascophyllum canopy 0.0014 Clearing size: Mussel surface 0.0196 Clearing size: Barnacle surface 0.0892 Clearing size: Bare space and rare species 0.8310 Location: Fucus canopy b0.0001 Location: Ascophyllum canopy b0.0001 Location: Mussel surface 0.0083 Location: Barnacle surface 0.3661 Location: Bare space and rare species 0.7105
b0.0001 b0.0001 0.6266 0.0589 0.5463 b0.0001 b0.0001 0.0180 0.0009 0.0266
Values in bold are significant. ANOVA column gives results from either ANOVA or Welch's test; Welch's test was used if Levene's test for heterogeneity of variances was significant.
Fig. 4. Percentage cover by rockweeds. Confidence limits are based on sample sizes of 11. Letters above bars show significantly different groups based on Tukey's tests.
variances, was used if Levene's test was significant. Tukey's tests were used for post-hoc comparisons. Data were not transformed because we were primarily interested in testing for differences due to variation among groups. CAP analysis was also done to examine differences among locations because the results of the univariate tests did not show a single consistent pattern. The univariate and multivariate analyses were done as one-way designs because of missing data and limitations of PERMDISP. One experimental 2 m clearing was destroyed in 2002 when ice deposited a granite boulder roughly 0.5 m × 1.0 m × 1.5 m on the plot. The boulder was estimated to weigh 1500–2500 kg and could not be removed from the plot. However, PERMDISP requires equal sample sizes with n N 2 and dropping replicates to create a balanced two-way design of treatment × location would have had 2 replicates per cell. Thus the effects of treatment (benchmark vs. control plots vs. 1, 2, 4 and 8 m clearings) and location (benchmark vs. Mackerel Cove vs. Seal Cove vs. Burnt Coat Harbor vs. Toothacker Cove) were examined separately. Sample sizes were balanced by randomly dropping one replicate; n = 11 per group for the clearing type analyses and 14 for the location analyses). 3. Results PERMDISP analysis showed significant differences in multivariate dispersion among clearing types and
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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 were driven strongly by differences between benchmark sites and the other treatment levels (Fig. 1A and B). M. edulis was several orders of magnitude more variable than any other species with average densities ranging from 0 to 5350 per 0.25 m2, and removing them from the analyses revealed four patterns. First, the average dispersion of the benchmark sites was reduced by 98% in both the clearing type and location analyses. Because Euclidian distances are metric, these reductions represent the contribution of mussels to total dispersion. Second, dispersions of benchmark sites and uncleared controls were not significantly different (Fig. 1C). Third, larger clearings are clearly
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more variable than smaller clearings and uncleared controls. Fourth, north-facing bays (Mackerel Cove and Seal Cove) were more variable than south-facing bays (Burnt Coat Harbor and Toothacker Cove), but the pattern was dampened by removing mussels from the analysis (Fig. 1B and D). Canonical ordination based on clearing type separated the samples into three distinct groups along the first axis (Fig. 2). The first canonical axis explained 99.4% of the variation and was correlated with M. edulis (+ 0.50), F. vesiculosus (+ 0.75), and L. littorea (+ 0.39). The second canonical axis, which explained only 0.5% of the variation, accounted for the spread among the two, four, and eight meter clearings. The second axis was correlated with L. littorea (+ 0.87), F. vesiculosus (− 0.65), and L. obtusata (− 0.40).
Fig. 5. Constrained MDS plot of location averages based on CAP analysis of percentage cover data and histograms of percentage cover of barnacles, mussels and Fucus. MDS plot is rotated to match the orientation of locations shown in Fig. 3, and thus the y-axis is the first canonical axis (i.e. negative to positive values run from top to bottom). Scales of axes are identical in both directions but the x-axis is cropped to save space. Details of calculations and analysis and scales are given in Fig. 2. The first and second squared canonical correlations are 0.453 and 0.307, respectively; trace statistic = 0.858, P b 0.0001; m = 3. For the first canonical axis, the three largest correlations are with A. nodosum (+0.897), S. balanoides (− 0.622) and F. vesiculosus (− 0.500). For the second canonical axis, the three largest correlations are with F. vesiculosus (+0.813), other species (− 0.401) and M. edulis (+0.341). For the histograms, barnacles (S. balanoides), mussels, (M. edulis) and Fucus (F. vesiculosus) are identified by SB, ME, and FV, respectively. Data for histograms are grouped as benchmark sites, north-facing bays and south-facing bays. Confidence limits are based on sample sizes of 14 for benchmark sites and 28 for the rest.
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The rate of correct classification varied with the size and type of the clearing (Table 1). The benchmark sites were consistently assigned to the benchmark or control group. Similarly all control plots were assigned correctly or as either benchmark sites or one meter clearings. Misclassification increased as clearing size increased (Fig. 2). Canonical ordination for location separated orientation of the bays (Fig. 3). The first axis explained 99.4% of the variation, and separation along the axis was due to S. balanoides (− 0.66), L. obtusata (− 0.45), and L. littorea (+ 0.66). The second axis explained 0.4% of the variation and was highly correlated with L. littorea (+ 0.53), M. edulis (− 0.67), and N. lapillus (− 0.45). The discriminant function consistently gave the correct assignment of plots from Mackerel Cove and Burnt Coat Harbor (Table 2). Seal Cove plots, Toothacker Cove plots, and the benchmark sites were misclassified more frequently. Assignment to the correct orientation (e.g., classifying plots in Burnt Coat Harbor, a south-facing bay as from either Burnt Coat Harbor or Toothacker Cove) was ≥ 79% for all bays except Seal Cove. Univariate tests showed significant differences among clearing sizes for percentage cover by canopy species only (Table 3). A. nodosum canopy cover was significantly higher in the benchmark sites and control plots, whereas F. vesiculosus canopy cover was significantly greater in clearings larger than two meters in diameter (Fig. 4). For understory species, average percentage cover and standard errors (n = 66) were 48.3 ± 3.8% for S. balanoides, 9.1 ± 2.2% for M. edulis, and 38.5 ± 3.6% for bare space and rare species. There were significant differences among locations for both canopy and understory species (Table 3). The CAP analysis showed that the benchmark sites were separated from the experimental plots on Swan's Island along the first axis by differences in A. nodosum canopy 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 to averaging across clearings of all sizes and uncleared controls. A. nodosum canopy cover in small clearings was due to the draping of long fronds into small clearings by plants from the surrounding stands rather than recruitment of new plants into the clearings. The spread along the second canonical axis separated the south-facing and north-facing bays with F. vesiculosus and S. balanoides more common in the south and M. edulis slightly more common in the north. Levene's tests were significant only for A. nodosum, F. vesiculosus and M. edulis cover (Table 3). The
within-group variances tended to be smaller for benchmark sites and for the control plots (see confidence limits in Figs. 4 and 5). 4. Discussion In systems with multiple stable states, small differences in initial conditions can cause very different outcomes, and experimental perturbations in these systems may initiate alternative successional pathways in “identical” replicate plots (Petraitis and Latham, 1999). The divergence of species composition during succession should be scale-dependent because areas exposed to small perturbations are likely to be quite resilient while areas with large perturbations are more likely to cross a “breakpoint” (sensu May, 1977) and tip towards an alternative species composition (Knowlton, 1992; Petraitis and Latham, 1999). Thus both the average and the variability of species composition should be scale-dependent. Our results are consistent with these predictions. Average species composition is scale-dependent with M. edulis, F. vesiculosus and L. littorea driving most of the difference among clearing sizes (Fig. 2). M. edulis, L. littorea and F. vesiculosus are more common in the 2, 4 and 8 m clearings. F. vesiculosus and L. littorea were the next most variable species after M. edulis. Ranges were 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 increases with clearing size (Fig. 4), and the pattern is similar to what 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 and 73.8% in 2004. The average overall increase between 2002 and 2004 was 40%. The success of F. vesiculosus in clearings is surprisingly because it is so rare in undisturbed stands of A. nodosum. Others have reported similar dominance by Fucus species after removal of A. nodosum (Bertness et al., 2002; Cervin et al., 2004; Jenkins et al., 2004), and it is possible that F. vesiculosus may either be a fugitive species in the system or represent a third community state on par with mussel beds and A. nodosum stands (S. R. Dudgeon, personal communication). Univariate and multivariate analyses of dispersion clearly show variability in species composition is scaledependent (Fig. 1B, Table 3). The MDS plot and crossclassification based on the CAP analysis also provide some indirect evidence for scale-dependent variability. The plots from the large clearings tend to be misclassified more often than plots from small clearings
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(Table 1) and are spread more widely across the MDS plot (see confidence limits in Fig. 2). Our conclusions about scale-dependent variability are predicated upon the removal of M. edulis from the multivariate analysis of dispersion. M. edulis overwhelms the pattern of dispersion, and the trend of greater variability with larger clearings only appears when M. edulis is removed from the analysis (Fig. 1). The effect of variability in M. edulis was particularly noticeable for the benchmark sites where it accounts for 98% of the variation. Even with the removal of M. edulis, the effects of the next two most variable species – F. vesiculosus and L. littorea – are quite pronounced. Why then are benchmark sites so heterogeneous with respect to these three species? We chose the sites with N 80% A. nodosum cover as typical A. nodosum stands but we did not pre-select sites based on M. edulis, F. vesiculosus or L. littorea. We expected some variability due to the geographic spread of the sites and local differences 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 and are poised to become dominants in the system. Mussels and Fucus may recruit and persist as small individuals below the canopy and only be able to dominant the surface once A. nodosum is removed by ice scour; not unlike trees in a forest waiting for a light gap. The success of M. edulis and F. vesiculosus may also be mediated by the activities of L. littorea, which affects M. edulis and F. vesiculosus recruitment (Petraitis, 1987, 1990). This conjecture needs to be examined more closely and is quite different from Petraitis and Latham's (1999) original explanation in which mussel recruitment can only occur and tip the system after ice scour has removed A. nodosum. Others have hypothesized about causes of and tested for scale-dependent variability, and it has repeatedly been suggested that disturbed communities will be more variable than unaffected control sites because of changes in spatial heterogeneity, species composition, or changes in the mean–variance ratio for particular species (Caswell and Cohen, 1991; Warwick and Clarke, 1993; Chapman et al., 1995; Chapman and Underwood, 1998; Foster et al., 2003). The results, however, have been mixed. For example, Warwick and Clarke (1993) compared several types of stressed communities with nearby control communities and found that variability for several measures increased with increased stress, particularly for meiobenthic communities exposed to organic enrichment and for macrobenthic communities bordering an oil field. On the other hand, Chapman et al. (1995) found that
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benthic macroinvertebrate communities at unstressed controls were more variable than at a site affected by sewage. Chapman et al. (1995) also emphasized the importance of multiple controls or benchmark type sites because their two control sites were measurably different from each other and that the samples taken from the polluted site were within the range of natural variation of the controls. Ice scour on rocky intertidal shores is certainly not the same as organic enrichment or sewage input, but we would argue that organisms in clearings made by ice scour are likely to be under stress because of the removal of the rockweed canopy. Rockweeds act as a buffer against physical stresses and dampen spatial variability in recruitment and consumer pressure. We expect clearings will be more spatially heterogeneous because removal of rockweeds accentuates microhabitat differences. For example, the difference between a small crack and a flat surface in terms of protection against desiccation may be minimal under a protective algal canopy but not so if the canopy is removed. The effect on consumers may also be less spatially heterogeneous under an algal canopy (e.g., Menge, 1976; Fairweather, 1988; Fairweather and Underwood, 1991). Given that we might expect algal canopies to dampen the effects of spatial heterogeneity, what is the evidence from other studies of A. nodosum clearings (Bertness et al., 2002, 2004; Cervin et al., 2004; Jenkins et al., 2004)? Cervin et al. (2004) followed the successional patterns for approximately four years in small (0.8 m × 0.3 m) areas cleared of A. nodosum canopy and in uncleared controls with and without the presence of L. littorea. Community composition of understory and canopy species differed between cleared and uncleared plots. Inspection of the error bars in their figures (Cervin et al., 2004, Fig. 3a–e) suggests that the percentage cover of Fucus spp. and the percentage cover of ephemeral green algae were more variable in cleared plots compared to controls. Cervin et al. (2004) suggested that perturbation of the A. nodosum canopy in combination with the exclusion of L. littorea provided the opportunity for these algal groups to colonize the shore, potentially through facilitation, i.e. ephemeral green algae may have facilitated the successful colonization of Fucus spp. by protecting recruits from desiccation. We suspect that this facilitation also may have been more spatially heterogeneous in larger clearings thus leading to greater variability. Jenkins et al. (2004) examined species composition in slightly larger areas (2 m × 2 m) cleared of A. nodosum and in uncleared controls both with and without grazing limpets. They found that cleared plots diverged from
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uncleared controls, and that after 12 years, the algal canopy in the clearings was a mixed stand of A. nodosum and F. serratus whereas A. nodosum remained 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 algal canopy species F. serratus and F. vesiculosus were slightly more variable in cleared plots. Percentage cover of A. nodosum canopy was very variable in the cleared areas (Jenkins et al., 2004, Fig. 1a). The scale-dependent variability in percentage cover suggests that removal of the canopy altered surface heterogeneity which in turn led to variable recruitment and survival of F. serratus, F. vesiculosus, and A. nodosum. In contrast, the results from Bertness and his colleagues are mixed (Bertness et al., 2002, 2004). They worked at several locations in the Gulf of Maine and used 1 m × 1 m and 3 m × 3 m clearings in A. nodosum stands and M. edulis beds with both open and caged plots. From their figures, we were able to compare differences in the standard errors in uncleared control plots and 3 m × 3 m clearings for 21 pairs of measurements involving open plots (Figs. 5, 6, 7, and 9 in Bertness et al., 2002, and Figs 2, 3, 5, 6, and 7 in Bertness et al., 2004). Standard errors were larger in 3 m × 3 m clearings than in uncleared controls for 11 pairs, smaller for 7 pairs, and appeared to be equal for 3 pairs. There were an additional 13 cases for which the standard errors of both the clearings and the controls were smaller than the symbols and so could not be compared. Comparisons of standard errors based on data taken from inside cages were not included because caging creates an extreme form of competitive release for mussels by completely removing consumers and leads to saturation of the system by mussels (e.g., Menge, 1976). Even with the range of normal variability, consumers are never completely absent for the length of time that cages are normally used (i.e., months to years). As a result, we expected a canalization of the response and a reduction in the variability. Not surprisingly, most of the standard errors from data taken inside cages are very small regardless of clearing size (Bertness et al., 2002, 2004). Taken as a whole, our results and the results of others suggest scale-dependent variability in areas cleared of rockweeds. We suspect the increases in variability seen in clearings versus uncleared controls are likely to be due to increased spatial heterogeneity within plots and variability among plots that have been cleared. The decreases in variability in exclusion cages versus open controls are likely caused by canalization of competitive release. We should also note that our experimental clearings on Swan's Island also show striking north-south
differences regardless of scale-dependence. Northfacing 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 in south-facing bays than in north-facing bays. The pattern for F. vesiculosus cover is similar to what was reported in 2002 (compare Fig. 5 with Fig. 2 in Petraitis and Dudgeon, 2005; 5.6% in 2002 versus 15.0% in 2004 in north-facing bays, and 48.1% in 2002 and versus 54.7% in 2004 in south-facing bays). In contrast, barnacle cover has remained the same in north-facing bays (40.4% in 2002 versus 46.6% in 2004) but has increased in south-facing bays (4.3% in 2002 versus 60.9% in 2004). We do not have a simple single-factor explanation for the north-south pattern in barnacle cover. Densities of neither the predatory snail N. lapillus nor the herbivorous snail L. littorea, which can bulldoze young barnacles from the rock, vary among bays (Petraitis and Dudgeon, 2005). Barnacle recruitment tends to be greater in south-facing bays, but this pattern is very volatile 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 a major ice event that was much more severe in northfacing bays (P.S. Petraitis, unpublished data from pointintercept transects in 1999 and 2003). Ice removed 20 ± 7% of the rockweeds in north-facing bays (per site average ± 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 we suspect most of the barnacles were also removed because much of the shoreline in north-facing bays appeared as if it had been sandblasted. Taken together, the data on recruitment, snails and ice do not seem to provide a clear-cut answer. We think the importance of benchmark data as part of experimental studies cannot be underestimated (e.g., Foster, 1990; Underwood, 2000; Underwood et al., 2000; Foster et al., 2003). For example, the most compelling approach to testing for multiple stable states involves showing by experiment that the same site or habitat can be occupied by different self-replacing communities (Peterson, 1984). Yet “same habitat” and “self-replacing” are difficult to define, and the definitions depend on length of time of observation, number of observations, and prior knowledge of natural variation. The very idea of testing for the “same habitat” can lead to attempts to prove a null hypothesis (e.g., Underwood, 1991). Development of tests of bioequivalence may provide a way to deal with these problems (Mapstone, 1995; MacKenzie and Kendall, 2002; Cole and McBride,
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2004; Robinson et al., 2005; Romano, 2005), but these tests require good benchmarks for on the characteristics of each multiple state. Given the lack of benchmark data, it is not surprisingly that Connell and Sousa (1983) concluded there was very little direct experimental evidence for multiple stable states in natural systems. Recent reviews support this view (e.g., Petraitis and Dudgeon, 2004b; Schröder et al., 2005). The benchmark data from our study also highlights how little we know about the underlying causes of broad-scale patterns in a supposedly well-described and well-understood marine intertidal system. Some researchers have assumed the existence of multiple states on rocky intertidal shores would require the system to be stochastic and stands in opposition to deterministic consumer control (e.g., “stochastic alternative states” vs. “consumer controlled deterministic community types” Bertness et al., 2004). Yet standard models of both multiple stable states and consumer control are deterministic and neither requires a stochastic process (Lewontin, 1969; Knowlton, 1992). In contrast, we would argue that the deterministic models of multiple stable states and consumer control can be used to make very different and testable predictions about the patterns of variability found in both experiments and in natural systems (Petraitis and Latham, 1999). The development of these predictions requires good benchmark data. Acknowledgements This paper is dedicated in memory of Bob Horton of Swan's Island, who provided many wet and cold marine ecologists with much needed coffee, food, wine and hospitality for more than 20 years. We thank Erika Carlson Rhile and Nick Vidargas for their help with data collection and Jon Fisher for his comments on an earlier draft of the manuscript. The CAP and PERMDISP programs were written by Marti Anderson, and we are very grateful for her efforts in providing the programs as freeware. This work could not have been done without the support of the residents of Swan's Island who provided access to the shore across their properties. Nearly all of the benchmark data were collected by Erika Rhile who was supported by an RETsupplement award from NSF. Research was supported by National Science Foundation grants (OCE 95-29564 and DEB LTREB 03-14980) to P.S. Petraitis. [SS] References Anderson, M.J., Robinson, J., 2003. Generalized discriminant analysis based on distances. Australian and New Zealand Journal of Statistics 45, 301–318.
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