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Appendix S1: Supporting Methods
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Extent of flood impacts on water quality
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The extent of flood impacts on water quality was evaluated using permutational
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multivariate analysis of variance (PERMANOVA) (Anderson, 2001). Two-way
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PERMANOVA examined whether water quality in January 2011 differed from water
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quality in other years (January 2002 - 2012), on impacted reefs (that changed in
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composition) and those that were seemingly unimpacted (did not change
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composition). The factors were: impact (fixed orthogonal factor) and year (fixed
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orthogonal factor). To interrogate variation in the magnitude of flood impacts between
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impacted and unimpacted reefs, one-way PERMANOVAs were then conducted for
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reefs in each category (with year as a fixed orthogonal factor). A posteriori pair-wise
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tests were used to interrogate significant results variation following PERMANOVA.
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Relationship between coral assemblages, processes and flood impacts
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Relationships between temporal changes in coral assemblages and variation in
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ecological indicators (for algal production, herbivory and calcification) (McClanahan
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et al., 2012), and flood impact variables were examined using RELATE and BEST
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analyses. RELATE tests for correlation is the structure of different multivariate
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resemblance matrices (in this case between coral assemblage composition and both
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ecological process and physical impact variables) on the basis of spearman correlation
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tests. BEST is essentially a permutational non-parametric multivariate alternative to
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multiple regression, which identifies the environmental variables that best explain
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patterns of similarity in biotic assemblages. BEST analyses were used to identify the
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process or impact variables with strongest correlation to temporal changes in coral
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assemblages (Clarke et al., 2008). To determine if ecological indicators were stronger
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on reserve than fished reefs, analyses were conducted separately. The coral
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assemblage similarity matrix was identical to that used in earlier analyses. Variables
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included in the ecological indicator matrix were: algae cover, macroalgae : hard coral,
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and turf algae : calcifying substrate. Variables included in the flood impact matrix
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were: pH, salinity, secchi depth, turbidity, total nitrogen and phosphorus, and cover
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sediment deposited on coral. Water quality data was obtained from the Ecosystem
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Health Monitoring Program for each location and monitoring event (EHMP, 2010).
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Similarity matrices of ecological indicator and flood impact variables were calculated
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using Euclidean distances. Variation in ecological indicator variables between reserve
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and fished reefs was examined using two-way ANOVA, conducted separately for
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both reef categories. The factors were: year (fixed orthogonal factor) and site (random
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factor). Data were ln(x + 1) transformed to improve heterogeneity of variance. Post
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hoc Tukey’s tests were used to differentiate significant means.
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Testing potential alternative explanations for flood responses
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The observed pattern of flood impact could also potentially be explained by co-
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variation in coral assemblage sensitivity or flood severity between marine reserve and
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fished locations. To determine whether differences in assemblage sensitivity could
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account for the greater impact at fished reefs, we compared coral composition prior to
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the flood (i.e. in January 2010) between impacted marine reserve and fished reefs
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using two-way PERMANOVA, applied to Euclidean (log x+1) similarity matrices.
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The factors were: protection (fixed orthogonal factor) and site (random factor). To
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investigate whether fished reefs were subjected to poorer flood conditions than
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reserve reefs, we examined variation between reserve and fished sites in: (1) their
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proximity to the source of impact (i.e. the distance to river mouth), (2) the probable
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duration of depressed salinity (based on residency times), and (3) the actual
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magnitude of flood impacts (quantified as degraded water quality and sedimentation).
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Variation in proximity to impact, flood duration and flood magnitude were assessed
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using one-way PERMANOVA (applied to Euclidean similarity matrices), with
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protection set as a fixed orthogonal factor (there was no site-level replication for these
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variables so site could not be included as a factor in the analysis). The proximity of
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each location to the Brisbane River mouth was quantified as the minimum distance
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over water using ArcGIS (ESRI, Redlands, USA). Residency times for each location
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were obtained from existing circulation models for Moreton Bay (Dennison & Abal,
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1999). The actual magnitude of flood impacts experienced at each location was
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determined from water quality (i.e pH, salinity, secchi depth, turbidity, total nitrogen
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and phosphorus) and sedimentation (i.e. cover of soft sediment deposited over coral).
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Water quality data collected immediately after the flood (January 2010) was obtained
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for each location from the Ecosystem Health Monitoring Program (EHMP, 2010).
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Given that we interpret non-significant reserve effects as evidence that these
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factors cannot explain the ecological patterns we detected, it is worth calculating the
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statistical power of each PERMANOVA analysis. Calculating power of multivariate
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tests, including PERMANOVA, is not straightforward. We applied the concept of
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simulating multivariate datasets and using Monte Carlo routines to estimate power
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(Irvine et al., 2011). For a particular PERMANOVA test, we first calculated the mean
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and standard deviation of each dependent variable. We then generated random
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numbers with these same means and standard deviations, except that we shifted the
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mean values inside reserves such that they were 30% greater than mean values in non-
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reserves. That is, we used an effect size of 30%, which is a typical value expected by
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ecologists in field experiments. We ran the PERMANOVA test on these created data
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and recorded whether the main reserve effect was significant. We repeated this
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procedure 10 times, and our power result was the proportion of times the test was
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significant.
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References
Anderson MJ (2001) A new method for non parametric multivariate analysis of
variance. Austal Ecology, 26, 32-46.
Clarke KR, Somerfield PJ, Gorley RN (2008) Testing of null hypotheses in
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Dennison WC, Abal EG (1999) Moreton Bay Study: A scientific basis for the healthy
waterways campaign, South East Queensland Regional Water Quality
Management Strategy, Brisbane.
Ehmp (2010) Ecosystem Health Monitoring Program 2008-2009 Annual Technical
Report, South East Queensland Healthy Waterways Partnership, Brisbane.
Irvine KM, Dinger EC, Sarr D (2011) A power analysis for multivariate tests of
temporal trend in species composition. Ecology, 92, 1879-1886.
Mcclanahan TR, Donner SD, Maynard JA et al. (2012) Prioritizing key resilience
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