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.