Supporting Information Appendix S1. Detailed statistical methods. Analyzing abundance metrics with unrestricted randomization/permutation procedure To confirm the results of the GLMM analysis presented in the text, we used an unrestricted randomization/permutation procedure to analyze for differences in parasite prevalence, intensity, and ecological abundance between protected and open-access areas for all parasite species detected. Data were randomized (with replacement) over all cells in the experimental design 10,000 times. For each of these 10,000 runs, a test statistic was calculated for the main effect of protection status (i.e., protected or open-access). These statistics were aggregated to form null distributions to which the actual test statistics (the statistics derived from the actual data) were compared. In this way, randomization allowed us to see how extreme our test statistic was relative to many possible test statistics, in a manner that was independent of sample size and unaffected by unequal variances or other assumptions of parametric tests (Manly 1997). For analyses of prevalence, this randomization procedure was run on a logistic ANOVA framework where the main effects were protection status and site (Algarrobo, El Quisco, Las Cruces); for all other analyses, the procedure was run on a two-way ANOVA framework with the same main effects. These procedures were modified from Howell (2009) and implemented in R statistical computing software (code available by request to the corresponding author). Error propagation Because our ecological abundance metric is a composite of two variables (# of parasites per host * host density), its variance should account for the variance associated with both component variables (Taylor 1997). We performed a simple bootstrapping routine to accomplish this error propagation. We multiplied 500 values (randomly selected with replacement) from the distribution of actual values of # parasites per host by 500 values (randomly selected with replacement) from the distribution of actual values of host density to create theoretical probability distributions for each site–status combination (e.g., Algarrobo open-access area). This procedure was implemented in R statistical computing software (code available by request to the corresponding author). We then performed both the GLMM procedure outlined in the text and the randomization procedure outlined in the previous paragraph on the resulting bootstrapped values of parasite ecological abundance. References Colwell, R. K. 2009. EstimateS: Statistical estimation of species richness and shared species from samples. Version 8.2. User’s Guide and application published at: http://purl.oclc.org/estimates. Gotelli, N. J. and R. K. Colwell. 2001. Quantifying biodiversity: Procedures and pitfalls in the measurement and comparison of species richness. Ecology Letters 4:379–91. Howell, D. C. 2009. Permutation tests for factorial ANOVA designs. http://www.uvm.edu/~dhowell/StatPages/More_Stuff/Permutation%20Anova/PermTests Anova.html. Accessed 15 July 2011. Taylor, J. R. 1997. Propagation of uncertainties. Pages 45–91. An Introduction to Error Analysis: The Study of Uncertainties in Physical Measurements. University Science Books, Sausalito, CA. Table S1. Side-by-side comparison of results from two approaches to analyzing differences in parasite epidemiological abundance between protected and open-access areas: (in black text) generalized linear mixed model with negative binomial error and response variable # of parasite individuals per host versus (in grey text) permutation analysis approach with response variables prevalence (% of hosts infected) and intensity (# of parasite individuals per infected host). Permutation analysis compares the F statistic from empirical data to a null F distribution created by resampling, which results in a p-value, reported below as pprevalence and pintensity. Results for prevalence and intensity of parasitic infection for fish hosts is shown in Figure S3, and for invertebrate hosts in Figure S4. For Fissurella latimarginata and Loxechinus albus, body size was included as a covariate in both the negative binomial GLMM and the permutation analysis, because hosts differed substantially in body size between protected and open access areas. Results for the effects of fishing status and body size are therefore shown for parasites in both hosts. Host Parasite taxon log-lik df Estimate SE z praw pcorrected pprevalence pintensity Cheilodactylus variegatus Lepeophtheirus sp. Clavellotis dilatata Gnathiid sp. Cymothoid sp. Encotyllabe sp. Microcotyle nemadactylus –17.68 –69.54 –4.83 –4.83 –115.37 –4.83 70 70 70 70 70 70 0.928 0.219 12.200 10.000 0.840 12.200 1.237 0.607 569.3 190.1 0.344 570.3 0.75 0.36 0.02 0.05 2.45 0.02 0.45 0.72 0.98 0.96 0.014 0.98 0.75 0.96 0.98 0.98 0.047 0.98 0.23 0.56 0.14 0.34 0.0045 0.19 0.55 0.82 na na 0.0136 na Aplodactylus punctatus Lepeophtheirus frecuens Clavellotis dilatata Gnathiid sp. –105.67 –46.73 –5.14 116 116 116 –0.737 –1.027 12.600 0.350 0.664 566.9 –2.11 –1.55 0.02 0.035 0.122 0.98 0.10 0.27 0.98 0.60 0.74 0.14 0.15 0.29 na Fissurella latimarginata Proctoeces lintoni (status) Proctoeces lintoni (size) –37.21 –37.21 128 128 –5.360 1.590 3.850 1.240 –1.39 1.28 0.16 0.20 0.32 0.36 0.46 0.75 na na Loxechinus albus Pinnaxodes chilensis (status) Pinnaxodes chilensis (size) –148.86 –148.86 146 146 –0.020 0.021 0.183 0.037 –0.11 0.56 0.91 0.58 0.98 0.88 0.60 0.93 na na Table S2. Side-by-side comparison of results from two approaches to analyzing differences in parasite ecological abundance between protected and open-access areas: (in black text) generalized linear mixed model with negative binomial error versus (in grey text) permutation analysis. The response variable in both analyses is ecological abundance of parasites (density of parasites, or # of parasite individuals per host * density of hosts). Host Parasite taxon log-lik df Estimate SE z praw pcorrected ppermutation Cheilodactylus variegatus Lepeophtheirus sp. Clavellotis dilatata Encotyllabe sp. –138.11 –563.81 –1069 998 998 998 5.870 1.297 1.791 3.020 0.155 0.100 1.94 8.38 17.90 0.0518 < 0.0001 < 0.0001 0.13 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 Aplodactylus punctatus Lepeophtheirus frecuens Clavellotis dilatata –606.11 –286.68 998 998 0.648 –0.105 0.122 0.214 5.32 –0.49 < 0.0001 0.6200 < 0.0001 0.89 0.0086 0.5328 Fissurella latimarginata Proctoeces lintoni –386.89 998 4.564 0.765 5.97 < 0.0001 < 0.0001 < 0.0001 Loxechinus albus Pinnaxodes chilensis –299.76 998 4.166 0.812 5.13 < 0.0001 < 0.0001 < 0.0001 (a) (b) Figure S1. Observed parasite species accumulation curves. The cumulative number of parasite species (as estimated by Colwell et al.’s Mao Tau value) is plotted against number of parasite individuals encountered for two fish hosts, (a) bilagay (Cheilodactylus variegatus) and (b) jerguilla (Aplodactylus punctatus), collected from protected (black line) and open-access (grey line) areas. Each curve represents the average of 5,000 replicate randomizations (without replacement) of host order. Because there was systematic variation in the number of parasites carried by fish hosts from protected versus open-access areas, we plotted all rarefied values against number of parasite individuals, not number of fish, as recommended by Gotelli and Colwell (2001) in cases where sampling intensity varies between sampling areas. (a) (b) Figure S2. (a) Mean jackknife estimates of parasite species richness and 95% confidence intervals for parasites of bilagay (Cheilodactylus variegatus) and jerguilla (Aplodactylus punctatus) in reserves and open-access areas. Replicates are jackknife parasite species richness estimates for each host within each site–protection status combination (i.e., Algarrobo open-access, Algarrobo protected, El Quisco open-access, El Quisco protected, Las Cruces open-access, Las Cruces protected). Jackknife parasite species richness does not differ significantly between protected and open-access areas (mixed-effects linear model, t7 = 1.3189, p = 0.2287). (b) However, the non-significantly greater parasite species richness in protected relative to open-access areas (below and in Figure S1) is driven by the detection of several parasite species in protected areas that were not observed in open-access areas. Overall community species richness (all taxa observed) is given in the table above. Cells highlighted in grey indicate where a particular species that was detected in one fishing status group (e.g., protected) was not detected in the other (e.g., open-access). Figure S3. Prevalence (% of hosts infected) of gill parasites of (a) bilagay (Cheilodactylus variegatus) and (b) jerguilla (Aplodactylus punctatus). Intensity (# parasite individuals per infected host) of gill parasites of (c) bilagay (Cheilodactylus variegatus) and (d) jerguilla (Aplodactylus punctatus). Protected areas are shown as shaded columns and open-access areas as white columns. Open-Access (a) (c) Protected (b) (d) Figure S4. Prevalence (% of hosts infected) for Proctoeces lintoni in keyhole limpet (Fissurella latimarginata) in (a) open-access and (b) protected areas. Prevalence (% of hosts infected) for Pinnaxodes chilensis in red sea urchin (Loxechinus albus) in (c) open-access and (d) protected areas. Uninfected hosts are shown as unfilled columns, and infected hosts as black columns.