1 Appendix S1: Supporting Methods 2 3 Extent of flood impacts on water quality 4 The extent of flood impacts on water quality was evaluated using permutational 5 multivariate analysis of variance (PERMANOVA) (Anderson, 2001). Two-way 6 PERMANOVA examined whether water quality in January 2011 differed from water 7 quality in other years (January 2002 - 2012), on impacted reefs (that changed in 8 composition) and those that were seemingly unimpacted (did not change 9 composition). The factors were: impact (fixed orthogonal factor) and year (fixed 10 orthogonal factor). To interrogate variation in the magnitude of flood impacts between 11 impacted and unimpacted reefs, one-way PERMANOVAs were then conducted for 12 reefs in each category (with year as a fixed orthogonal factor). A posteriori pair-wise 13 tests were used to interrogate significant results variation following PERMANOVA. 14 15 Relationship between coral assemblages, processes and flood impacts 16 Relationships between temporal changes in coral assemblages and variation in 17 ecological indicators (for algal production, herbivory and calcification) (McClanahan 18 et al., 2012), and flood impact variables were examined using RELATE and BEST 19 analyses. RELATE tests for correlation is the structure of different multivariate 20 resemblance matrices (in this case between coral assemblage composition and both 21 ecological process and physical impact variables) on the basis of spearman correlation 22 tests. BEST is essentially a permutational non-parametric multivariate alternative to 23 multiple regression, which identifies the environmental variables that best explain 24 patterns of similarity in biotic assemblages. BEST analyses were used to identify the 1 1 process or impact variables with strongest correlation to temporal changes in coral 2 assemblages (Clarke et al., 2008). To determine if ecological indicators were stronger 3 on reserve than fished reefs, analyses were conducted separately. The coral 4 assemblage similarity matrix was identical to that used in earlier analyses. Variables 5 included in the ecological indicator matrix were: algae cover, macroalgae : hard coral, 6 and turf algae : calcifying substrate. Variables included in the flood impact matrix 7 were: pH, salinity, secchi depth, turbidity, total nitrogen and phosphorus, and cover 8 sediment deposited on coral. Water quality data was obtained from the Ecosystem 9 Health Monitoring Program for each location and monitoring event (EHMP, 2010). 10 Similarity matrices of ecological indicator and flood impact variables were calculated 11 using Euclidean distances. Variation in ecological indicator variables between reserve 12 and fished reefs was examined using two-way ANOVA, conducted separately for 13 both reef categories. The factors were: year (fixed orthogonal factor) and site (random 14 factor). Data were ln(x + 1) transformed to improve heterogeneity of variance. Post 15 hoc Tukey’s tests were used to differentiate significant means. 16 17 Testing potential alternative explanations for flood responses 18 The observed pattern of flood impact could also potentially be explained by co- 19 variation in coral assemblage sensitivity or flood severity between marine reserve and 20 fished locations. To determine whether differences in assemblage sensitivity could 21 account for the greater impact at fished reefs, we compared coral composition prior to 22 the flood (i.e. in January 2010) between impacted marine reserve and fished reefs 23 using two-way PERMANOVA, applied to Euclidean (log x+1) similarity matrices. 24 The factors were: protection (fixed orthogonal factor) and site (random factor). To 25 investigate whether fished reefs were subjected to poorer flood conditions than 2 1 reserve reefs, we examined variation between reserve and fished sites in: (1) their 2 proximity to the source of impact (i.e. the distance to river mouth), (2) the probable 3 duration of depressed salinity (based on residency times), and (3) the actual 4 magnitude of flood impacts (quantified as degraded water quality and sedimentation). 5 Variation in proximity to impact, flood duration and flood magnitude were assessed 6 using one-way PERMANOVA (applied to Euclidean similarity matrices), with 7 protection set as a fixed orthogonal factor (there was no site-level replication for these 8 variables so site could not be included as a factor in the analysis). The proximity of 9 each location to the Brisbane River mouth was quantified as the minimum distance 10 over water using ArcGIS (ESRI, Redlands, USA). Residency times for each location 11 were obtained from existing circulation models for Moreton Bay (Dennison & Abal, 12 1999). The actual magnitude of flood impacts experienced at each location was 13 determined from water quality (i.e pH, salinity, secchi depth, turbidity, total nitrogen 14 and phosphorus) and sedimentation (i.e. cover of soft sediment deposited over coral). 15 Water quality data collected immediately after the flood (January 2010) was obtained 16 for each location from the Ecosystem Health Monitoring Program (EHMP, 2010). 17 Given that we interpret non-significant reserve effects as evidence that these 18 factors cannot explain the ecological patterns we detected, it is worth calculating the 19 statistical power of each PERMANOVA analysis. Calculating power of multivariate 20 tests, including PERMANOVA, is not straightforward. We applied the concept of 21 simulating multivariate datasets and using Monte Carlo routines to estimate power 22 (Irvine et al., 2011). For a particular PERMANOVA test, we first calculated the mean 23 and standard deviation of each dependent variable. We then generated random 24 numbers with these same means and standard deviations, except that we shifted the 25 mean values inside reserves such that they were 30% greater than mean values in non- 3 1 reserves. That is, we used an effect size of 30%, which is a typical value expected by 2 ecologists in field experiments. We ran the PERMANOVA test on these created data 3 and recorded whether the main reserve effect was significant. We repeated this 4 procedure 10 times, and our power result was the proportion of times the test was 5 significant. 4 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 exploratory community analyses: similarity profiles and biota-environment linkage. Journal of Experimental Marine Biology and Ecology, 366, 56-69. 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 indicators to support coral reef management in a changing climate. PLoS ONE, 7, e42884. 42810.41371/journal.pone.0042884. 5