Anna Katrina Cresswell
email: anna.cresswell@csiro.au

T J J Langlois

S K Wilson

Joachim Claudet

R Fulton

M A Fisher

R C Vanderklift

D P Thomson

M Renton

C J Fulton

R Fisher

M A Vanderklift

R C Babcock

R D 11

M.D E Haywood

10

M Depczynski

M Westera

Ayling 12

A M 13

B Fitzpatrick

Halford A R 15

D L Mclean

Pillans R D 10

A J Cheal

Tinkler P 16

Edgar G J 11

N A J Graham

17

T H Holmes

Disentangling the

Keywords: Marine protected area, MPA, fisheries, coral reef, Ningaloo, adaptive management, recreational fishing, Lethrinus

whether they are published or not. The documents may come

localised variation in fishing activity at the scale of marine parks and typical reserve networks are rarely available (Lewin et al., 2006). Here, we synthesise a unique 30 year dataset from within a multiple-use marine park at Ningaloo Reef, Western Australia. The type of fishing activity at Ningaloo Reef (almost exclusively recreational) in combination with a highly protected and regulated network of reserves that have undergone significant expansion during the study period, offers the opportunity to advance on previous studies and inform on the potential impacts of recreational fisheries. We integrate data from numerous agencies with varied survey designs and methods, and therefore use a meta-analytical approach to compare the abundance and biomass of select targeted and non-targeted tropical reef fish inside reserves with adjacent fished areas. We tested two hypotheses: (1) the relative abundance/biomass of targeted fish taxa will be greater inside reserves than outside due to recreational fishing activity; and (2) the observed relative abundance and biomass will vary with survey method, age and size of reserve, spatial variability in fishing activity (including shore-based fishing) and/or habitat. Our study offers four main novelties. First, the effect of recreational fishing on targeted species is assessed in isolation from commercial fishing. Second, we explicitly consider potential interactions between variables. Third, the influence of changes in the reserve network are considered in the context of the increasingly common adaptive management. Fourth, we consider the influence of shore-based fishing, which has rarely been investigated. We therefore provide advances on previous work that are of importance for future planning and assessment of protected areas.

Material and methods

Study region

Data for this study are from the Ningaloo Marine Park (NMP) on the western Australian coastline (22°S, 113°E; Fig. 1). The Park covers the majority of Ningaloo Reef (a World Heritage site) which is a fringing coral reef almost 300 km in length. The reef encompasses a sheltered lagoon that is highly accessible by shore-based fishers and those operating recreational vessels (Smallwood & Beckley, 2012). Despite a relatively small permanent human population, this area is a popular tourism destination for recreational fishers (Sumner et al., 2002;Smallwood & Beckley, 2012;Mitchell et al., 2018). There have not been any major commercial fishing activities within the marine park since the 1970s, (for summary see pg. 78, CALM (2005) and pg. 70, DPIRD (2017)). Recreational spearfishing has additional restrictions of varying degrees outside of the reserves, with spearfishing prohibited along a 70km stretch of coast between Tantabiddi Well and Winderabandi Point, and spearfishing for Labridae and Serranidae prohibited throughout the Park (DPIRD, 2018) (Fig. 1). A network of eight no-take marine reserves was established as part of the Park in April 1987 to cover 10% of the total marine park area ~22,400 ha, (Fig. 1a) (CALM, 1989). In 2005, the majority of the existing eight reserves were expanded in size and 10 new reserves were added (Fig. 1b), increasing the reserve coverage to 88,365 ha (34% of the NMP). At the same time, three reserves, covering 1,929 ha, were established as a part of the 28,616 ha Muiron Islands Marine Management Area (MIMMA), immediately adjacent to the northern boundary of the NMP (CALM, 2005). Together, the NMP and MIMMA form a continuous network (CALM, 2005). There is some variation in the regulations along the boundaries of the 21 current reserves, complicating terminology and analysis, with eight reserves allowing shore-based fishing from their coastal boundaries (Appendix A, CALM, 2005). According to recent classifications of marine reserves, the two forms of reserves in the present study, those with shore-based fishing prohibited and those where it is allowed, would classify as Fully Protected Areas and Highly Protected Areas, respectively (Horta e Costa et al., 2016), both of which would be expected to provide protection for fished species (Zupan et al., 2018). We explicitly include consideration of the effect of shore-based fishing in our analyses.

Survey data

Data from all major research and monitoring programs surveying fish in the NMP over the last 30 years (1987 -2017) were collated (Appendix B) to create a very large synthesis of information. Locations of individual samples are given as Appendix C. Three different survey methods were used to census fish: Baited Remote Underwater stereo-Video (BRUV), Diver Operated stereo-Video (DOV) and Underwater Visual Census (UVC) (Langlois et al., 2010;Murphy & Jenkins, 2010). The majority (90%) of surveys also estimated the length of fish (an in situ estimate of total length for UVC, and fork length measured from stereo-video for DOV and BRUV), which allowed estimates of biomass using formulae from FishBase (Froese, 2018) (Appendix B). Data were organised hierarchically with a sample (individual UVC or DOV transect or a BRUV deployment) being the lowest level of replication. Samples were classified to the next hierarchical level and termed a 'comparison pair', based on the criteria: (i) that there were at least two samples inside and two samples outside a given reserve, (ii) these samples were collected within 2 weeks of each other, (iii) samples were collected more than 200 m from within or outside of the reserve boundaries (excluding one reserve, the small size of which meant this was not a logical rule), (iv) samples were collected using the same survey method within one of four habitat categories (see Table 1). Data satisfying these conditions consisted of 4,444 samples classified into 305 relative abundance comparison pairs and 3,892 samples classified into 268 relative biomass comparison pairs. These data covered seven of the initial eight reserves and 16 of the 21 current reserves (Appendix B).

Fish groups

We consider three main fish groups common at Ningaloo Reef, at family or subfamily and species level (Appendix D) which differ in terms of their behaviour and representation in fisheries catch reports. This included: parrotfishes (Scarinae), which are not typically targeted by fishers in Australia, and two groups which are highly targeted by recreational fishers in the region (Ryan et al., 2013;Ryan et al., 2015) that have different behaviours; emperors (Lethrinidae; mobile roving predators) and groupers (Epinephelinae; mostly site-attached ambush predators). Previous work has indicated both Epinephelinae and Lethrinidae are vulnerable to fishing and many species in both subfamilies are targeted across the Indo-Pacific [START_REF] Abesamis | The intrinsic vulnerability to fishing of coral reef fishes and their differential recovery in fishery closures[END_REF]. Species level analyses included two species from Lethrinidae: the spangled emperor, Lethrinus nebulosus, which is recognised as the most highly targeted species in the region, consistently featuring at the top of the estimated catch for the bioregion over the 30-year study period, and the yellow-tailed emperor, L. atkinsoni, a species that is anecdotally retained by fishers and featured as the 6 th most common species recorded in the 1998/9 catch survey, but was a minor component in subsequent surveys (Sumner et al., 2002;Ryan et al., 2013;Ryan et al., 2015). The Chinaman Rockcod, Epinephelus rivulatus (Epinephelinae) was also considered, with catches comparable to those of L. nebulosus across the catch reports (Ryan et al., 2013;Ryan et al., 2015). Individual species were not considered from the Scarinae subfamily due to inconsistencies in the accuracy of identification of species from this family.

Meta-analysis

We used a mixed-effects meta-analytical approach to assess the effect of the reserves on fish abundance and biomass. We calculated effect sizes as log-ratios for each of the comparison pairs inside to outside the reserves (Claudet et al., 2008) (see Appendix E for formulas). A constant was added to the mean abundance (c= 0.5) and mean biomass (c = 100 g) to allow calculation of the log ratio in cases where fish were absent either inside or outside (i.e. zero values). We ran a sensitivity analysis on the value of the constant (Appendix F) to determine an appropriate value. The size of the constant impacted the magnitude of the effect size, but in general did not influence the significance. Nonetheless, the exact magnitude of the overall effect size should be interpreted with caution. In cases where both the inside and outside mean count were zero, the samples were excluded from the analysis. Effect sizes were weighted by the inverse of the sum of the within-and among-study variances (Appendix E). Weighted effect sizes and variances were calculated using the metafor package (Viechtbauer, 2010) in the statistical program R (R Core Team, 2017) with the variance estimator set to "REML," restricted maximum likelihood estimator. Overall effect sizes were comparable for both abundance and biomass and for simplicity we presented the abundance results as these were available for a larger dataset, providing biomass results in Appendix H.

Sources of variability

We considered seven variables that might mediate the response of fish abundance and biomass to the presence of the reserves (Table 1): (i) the number of years between when a sample was collected and when the zoning went into place; (ii) initial or current zoning scheme (see Figure A1); (iii) survey method; (iv) four coarse habitats with distinct coral and algae assemblages: 'exposed reef slope', 'reef flat', 'back reef & lagoon coral', and 'lagoon algae'; (v) spatial area of a reserve; (vi) an estimate of fishing pressure outside of individual reserves; (vii) on the presence of shore-based fishing zones adjacent to some reserves. Data were explored following the protocol of Zuur et al. (2010) and transformed to normalise their distribution where appropriate (see Table 1). As all effect sizes were heterogeneous (Appendix G), we first explored the influence of the seven variables using weighted mixed-effects categorical meta-analyses and meta-regression, considering each variable as a moderator in isolation to determine which variables explained significant heterogeneity in the overall effect size (see Appendix E for formulas). We also investigated reserve identity to allow comparison between individual reserves. Given there were correlations among the variables and potential interactions and nonlinear effects, we used weighted full-subsets generalised additive mixed modelling (FSSgam) (Fisher et al. 2018 A simple function for full-subsets multiple regression in ecology with R. Ecology and Evolution) to investigate the relative importance of each variable in explaining variability in the overall effect size for each fish group. The response variable, effect size e, was modelled with a Gaussian distribution using gam() in the mgcv package in R (Wood, 2011). Years protection and boat fishing were included as continuous smoothers in the FSSgam to allow for non-linear relationships. The distribution of reserve size was not much improved by transformation and sqrt(reserve size) was therefore included in the model set as a linear predictor. Reserve identity was highly collinear with other variables (in particular reserve size), and therefore, rather than including this as a random effect, a smoother of the mean latitude of comparison pairs was included in all models (and as part of the null model). This yielded comparable results to including reserve identity as a random effect. Interactions between factor variables habitat and shore fishing and the continuous variables reserve size and years protection were tested. In all models the smoothing parameter was limited to a simple spline, allowing only monotonic relationships (k=3) for all continuous variables except for latitude, which was unlimited. Summed AICc weights were used as a metric of variable importance to investigate the relative importance of each predictor variable across the full set of models (Anderson & Burnham, 2002). Variables included in the most parsimonious model (fewest variables and lowest estimated degrees of freedom within two units of the AICc) were plotted to visualise the shape and direction of relationships between the variables and the effect size. We interpret results of variable importance and the top models with caution and consider the results of the mixed-effects meta-analyses and meta-regression alongside the results of the FSSgam. Lastly, given the importance of temporal patterns in investigations of protected areas, we explicitly investigate data from the Osprey reserve (see Fig. 1), the best temporally replicated reserve in the dataset. Using available and relatively consistently collected UVC and DOV data we estimated mean fish density as count per transect area. We tested for significant linear and quadratic relationships and fitted generalised additive mixed models between the density of L. nebulosus and survey year.

Results

When compared to areas open to fishing, Lethrinidae were on average 57% more abundant (78% more biomass) inside the reserves (e = 0.45±0.12, 95%CI, Fig. 2a), however the effect was heterogeneous (Q T = 2002.6, df = 301, p<0.001, Table G1). The most parsimonious model for Lethrinidae abundance consisted of an interaction between habitat and reserve size (Table 2), with the same true for biomass (Appendix H). The categorical meta-analysis supported the importance of habitat for relative abundance; showing it explained significant heterogeneity among effect sizes (Q M = 39.5, df = 3, p<0.001, Table G2) with the most positive effect identified in back reef & lagoon coral sites with an average of 93% more Lethrinidae inside the reserves (e = 0.66±0.14, 95%CI) (Fig. 2a, Fig. G1) in this habitat. On the reef flat Lethrinidae were 53% more abundant inside the reserves (e = 0.42±0.32, 95%CI) while there was no significant effect on the exposed reef slope and a negative effect in the lagoon algae habitat (Fig. G1). The interaction of reserve size and habitat was evident as an increase in effect size with increasing reserve size in the back reef & lagoon coral habitat versus no clear trends in the other habitats. Lethrinus nebulosus were on average 42% more abundant (86% more biomass) inside reserves than outside (e = 0.35 ±0.15,95%CI,Fig 2a). The effect was heterogeneous (Q T = 1971.1, df = 256, p<0.001, Table G1). The most parsimonious model included the interaction between habitat and reserve size with these two variables also having the highest variable importance across the full-subsets model set (Table G3, Fig. 2b). The same was true in the biomass analysis (Appendix H). Habitat explained significant heterogeneity for relative fish abundance (Q M = 32.5, df = 3, p<0.001, Table G2) and L. nebulosus were on average 84% more abundant within back reef & lagoon coral sites inside the reserves (e = 0.61±0.17, 95%CI), whereas no differences were observed for the reef flat or exposed reef slope sites and a negative effect was observed for lagoon algae sites (Fig. G1). As for Lethrinidae, the interaction of reserve size and habitat was evident by an increase in the effect size with increasing reserve size in the back reef & lagoon coral habitat and no clear effects in the other habitats. On average, the abundance of L. atkinsoni was 40% more abundant (60% more biomass) inside reserves than outside (e = 0.34±0.09, 95%CI). The effect was heterogeneous (Q T = 1739.7, df = 279, p<0.001, Table G1). The most parsimonious model included zoning scheme and method, which also had the highest importance according to weighted AICc. These two variables explained significant heterogeneity according to the categorical mixed-effects meta-analyses. Predictions indicated that the BRUV method contributed the most to the positive effect size of L. atkinsoni (Fig. 3c), though this was not significant, nor were the differences between initial and current zoning, showing a slightly higher effect size from the older zoning scheme. Multiple variables explained significant heterogeneity for L. atkinsoni according to the categorical meta-analysis and the meta-regression (Table G2), including habitat (Q M = 14.6, df = 3, p<0.001, Table G2). Reef flat sites had 94% higher abundance, (e = 0.66±0.26, 95%CI) and back reef & lagoon coral sites 43% higher abundance (e = 0.36±0.12, 95%CI) inside the reserves. There were no significant effects for the other habitats (Fig. G1). The biomass analysis for L. atkinsoni indicated that years protection may interact with habitat, and that on the reef flat the effect size was higher and showed a parabolic pattern with years protection (Fig. H2). The effect size for Epinephelinae abundance was significantly negative with 9% fewer fishes inside than outside the reserves (e = -0.09±0.08, 95%CI), although this result was heterogeneous (Q T = 1125.7, df = 276, p<0.001, Table G1). Variable importance scores showed no variables with high importance relative to the Lethrinidae and L. nebulosus model sets. Reserve size and years protection were present in the most parsimonious model, while for the biomass it was method and boat fishing (Appendix H). There were weak increasing trends for both reserve size and years protection, however the lack of strongly important or consistent variables in these model sets means the results should be interpreted cautiously. On average there was no significant difference inside to outside the reserves for E. rivulatus abundance (e = -0.06±0.09, 95%CI), though the effect was heterogeneous (Q T = 477.3, df = 166, p<0.001, Table G1). Zoning scheme and boat fishing had the highest variable importance across the model set and featured in the most parsimonious model. The effect size transitioned from no effect for low boat fishing activity, to a positive effect when there was high boat fishing activity, but the confidence intervals did not show this trend to be significant. The initial reserve network (in place longer) had a more positive effect than the newer reserves, but again this was not significant (Fig. 3e). The control fish group, Scarinae, showed no significant difference inside to outside the reserves (e = -0.01±0.11, 95%CI) and this effect was heterogeneous (Q T = 1701.1, df = 260, p<0.001, Table G1). All variables had low importance according to AICc (Fig 2b ,Table 2) and while boat fishing and shore fishing appear in the most parsimonious model we interpret this with caution. In the biomass analysis habitat made up the most parsimonious model (Appendix H). In the full-subsets analysis reserve size and habitat appeared with the highest variable importance (for Lethrinidae and L. nebulosus) while other variables -survey method, years protection, zoning scheme and shore fishing -had low importance across all six fish groups. In many cases the heterogeneity statistics from the mixed-effect meta-analysis models supported the findings of the full-subsets analysis, but for some variables such as shore fishing, the meta-analysis indicated this variable explained significant heterogeneity for all fish groups, except for L. nebulosus, while the full-subsets gave this variable low relative importance. The temporal investigation of effect sizes for the most highly targeted fish, L. nebulosus, at Osprey reserve gave results that generally confirmed what was found in the full meta-analysis for L. nebulosus, showing effect sizes that are mostly positive though time, with higher abundance and biomass inside than outside (Fig 4). There were not show strong or significant patterns with time, except for the abundance density outside of the reserve, which had a significantly negative linear trend (P=0.032). Generalised additive model fits indicate that, particularly in the latter half of the study period, both abundance and biomass may have declined both inside and outside the reserve, while there is some indication that abundance initially increased inside of the reserve following establishment. However, confidence in these trends is low and the gam fits were not statistically different from null models (except for abundance density outside the reserve, P = 0.048).

4. Discussion Across the 30 year synthesis higher abundance and biomass of certain targeted fish taxa inside the reserves suggests that recreational fishing can have significant effects in isolation from commercial harvest, as also shown in some previous studies (Denny et al., 2004;Shears et al., 2006;Babcock et al., 2007). We found the extent of this effect was variable among targeted taxa and influenced by a range of other factors. While our analyses revealed higher relative abundance and biomass of lethrinids (Lethrinus nebulosus and L. atkinsoni) inside reserves, no significant effect was found for the abundance of Epinephelus rivulatus, and a small negative effect was detected for the epinephelids as a group. All effects were heterogeneous, which was not surprising given the size and complexity of the synthesised dataset (including differences in size and age of reserves) and given that fish responses to reserves are known to vary with taxon-specific, ecological and zoning factors (Barrett et al., 2007;Claudet et al., 2010;Edgar et al., 2014). Here we advance previous findings with the largest meta-analysis on recreational fishing in isolation from commercial fishing, illustrating the new information that can be gained from synthesising existing data, though we do not discount the advantages of strategic and consistent monitoring data. We show that it is important for assessments of reserves to take into account habitat effects, and potential interactions with factors such as reserve size or age, as well as variability in fishing activity, or differences in survey method in order to avoid oversimplified conclusions on how fish abundance and biomass respond to management. Recreational fishing, specifically angling, is the only major fishing activity within the marine park. Some previous studies in the Park have linked higher abundance and biomass of targeted species inside reserves to protection from fishing (Westera, 2003;Babcock et al., 2008;Fitzpatrick et al., 2015); though results of other studies are more equivocal (Wilson et al., 2012;Wilson et al., 2018a). The reasons behind the disparate conclusions are unclear, but may be due to limited and/or varied spatial and temporal scales of the individual studies, different survey methodologies, the confounding influence of habitat, or high variability in target species abundance distributions. We also investigated the regulations on shore-based fishing on the coastal boundaries of reserves, with the hypothesis that this may influence the ability of the reserves to maintain higher abundance and biomass of fishes. There were mixed results with the full-subsets analysis indicating this variable had low importance, while the meta-analysis showed it did explain significant heterogeneity, and indicated effect sizes were larger (though not significantly) when shore fishing was prohibited. However, this factor was likely correlated with other variables not available in the present study, such as accessibility to reserves, which would influence our ability to test this hypothesis. High correlation between fish recruitment and larger natural cycles (El Niño Southern Oscillation) has also been suggested as a reason for inconsistencies in fishes response to reserves (Wilson et al., 2018b). In the present study we found high variability in the relative fish abundances of lethrinids among the different reserves, which can at least partly account for the varied conclusions of previous studies at smaller spatial scales (Fig. G2). Nonetheless, when all data were pooled the average effect was clearly positive for abundance and biomass of the three lethrinid groups. The magnitudes of the positive effects were small (max 57% higher inside) relative to studies in other parts of the world (Watson & Ormond, 1994;Russ et al., 2015). A significant positive response for L. atkinsoni (40% higher), similar to that of L. nebulosus (42%) was not expected, given L. atkinsoni does not feature highly in catch reports (Ryan et al., 2017), suggesting it may be more susceptible to recreational angling that previously recognised. Known differences in behaviour between lethrinid and serranid taxa did not correlate with their response to reserves as expected. Lethrinids are known to have large home ranges relative to many epinephelids, including E. rivulatus, and are therefore more likely to move across reserve boundaries (Mackie & Black, 1999;Pillans et al., 2014;Babcock et al., 2017), with the expectation that they may experience lower levels of protection than epinephelids. However, we only observed positive responses for the lethrinids. It is possible that higher counts of lethrinids than epinephelids in the dataset may have reduced the power to detect an effect in the latter group, or there are other factors that have not been captured in our analyses. The age of no-take reserves has been shown to be a significant positive correlate of relative fish abundance for targeted species (Claudet et al., 2008;Edgar et al., 2014;Zupan et al., 2018) and demonstrated increases in effect size with time help attribute positive effect sizes to the presence of a protected area, rather than other factors (Russ et al., 2015). In the present study there was negligible evidence of changes in effect sizes with age of reserve. Where relationships were present, the shape of the trend was generally parabolic, showing an increase initially, before subsequent decrease around 2005, though no relationships were significant. This was supported by examining data for L. nebulosus, from the best temporally replicated reserve, Osprey, where again no clear temporal patterns were found. Potentially of concern for managers was the significantly negative decline in L. nebulosus density outside of the Osprey reserve, and a slight increase followed by a decrease inside this reserve. However the confidence intervals on all temporal patterns were large. These findings are in contrast with previous studies, for example Russ et al. (2015) showed lethrinids continued to increase in density inside reserves in the Philippines on time scales of 8-30 years. In the present study rezoning in 2005 made temporal analyses more complex, though by including zoning scheme as a variable we partly addressed this. Effect sizes were not strongly influenced by this variable, implying that the effect sizes were broadly consistent across the initial and current reserve networks. Where zoning scheme did feature for L. atkinsoni, the older reserves had a more positive effect, as expected. The absence of a strong temporal link with effect size must be considered when interpreting the positive effect sizes, however there are various factors which may have contributed to the absence of a strong relationship. First, while there is limited evidence of a reduction in fishing activity within the Park (Ryan et al., 2015(Ryan et al., , 2017) ) a shift in fishing activity to areas offshore (>100m depth) (West et al., 2015;Mitchell et al., 2018), which are not part of the current survey data, is likely. Second, the mobile behaviour of lethrinid fishes may be capping the levels of the observed effect size, if a proportion of their population is travelling further than the reserve boundaries. Pillans et al. (2014) found that approximately 60% of lethrinid individuals move at scales greater than the average reserve size over a year period. Third, illegal fishing within the reserves may also limit a temporal increase in effect size, as Smallwood and Beckley (2012) found 8-12% of observed vessels were fishing inside reserves in the Park in 2007. Fourth, we do not discount that the unevenness of sampling though time, with some years being more highly sampled than others (Fig. B2) potentially influenced our capacity to detect a trend if it were present. The analysis of L. nebulosus density at Osprey showed that the temporal patterns inside and outside reserves can be complex and not always captured by the overall effect size. Parallel declines or increases in density occurring both inside and outside are masked from the effect size, and such declines have been observed in other fisheries closures on the western Australian coast (Bornt et al., 2015).

Though our study only had a very coarse level of habitat classification available, our results support previous studies (Miller & Russ, 2014;Rees et al., 2018a;Rees et al., 2018b), showing the importance of habitat when assessing the ability of reserves to support target species abundance. We further demonstrate interactions between habitat and reserve size, showing that conclusions on both the magnitude and direction (positive or negative) of observed effects for the relative abundance of Lethrinidae and L. nebulosus are influenced by this interaction. In the case of L. atkinsoni biomass we also found an interaction between habitat and reserve age, though the models were not as strong. Previous studies have demonstrated the positive influence of larger and older reserves (Halpern & Warner, 2002;Claudet et al., 2008;Edgar et al., 2014;Zupan et al., 2018), however the interaction with habitat has not previously been explored. Furthermore, it is noteworthy that effect sizes were greatest in the back reef & lagoon coral habitat for L. nebulosus, while for L. atkinsoni, the effect was greatest on the reef flat, a result that may be attributed to these habitats being preferred by the adults of each species respectively (Babcock et al., 2008;Wilson et al., 2017). This is important when considering potential changes to habitat inside or outside of reserves, as Russ et al. (2015), showed that changes in benthic habitat due to disturbance could markedly influence the effect of reserves for lethrinids. We advise that reserves must incorporate adequate amounts of the essential habitats of the species or communities they are designed to protect, and assessment of reserve effectiveness must account for possible interactions between habitat and reserve size and age. While habitat was particularly important for the lethrinid groups, it was not found to be an important predictor for Epinephelinae or E. rivulatus. Again, this was contrary to expectations given the often high site fidelity of Epinephelinae (Mackie & Black, 1999). However, the relatively coarse habitat classification available for our analyses likely did not adequately capture the habitat requirements for this group. Previous work has shown E. rivulatus is strongly associated with macroalgal habitats at Ningaloo Reef (Wilson et al., 2012) but that variability in the quality of macroalgal habitats can be substantial and have major implications for fish abundance (Fulton et al., 2014;Wilson et al., 2014;Lim et al., 2016). Furthermore, Beckley and Lombard (2012) found that deeper habitats seaward of the reef have relatively lower spatial protection from recreational fishing, despite these habitats potentially supporting a high biomass of epinephelids (Babcock et al., 2008). It is thus plausible that habitats outside of the reserves were more appropriate for Epinephelinae, particularly prior to re-zoning in 2005, which could explain the overall negative and null effects for these groups. A much better understanding of the habitat requirements, electivity and movement across seascapes by targeted taxa and appropriate 'micro-habitat' classifications are needed to more fully understand these results.

Where the boat fishing variable appeared in models for E. rivulatus, there were subtle positive trends in effect size as fishing activity increased, i.e. where boat fishing was most prevalent the effect size was greater. Our metric for fishing activity is unlikely to be representative across the 30 years of data, as it was an estimate from 2007 (Smallwood & Beckley, 2012), yet still showed some importance. We think this is a particularly important factor when assessing reserves, as variability in fishing activity (spatially and temporally) makes it very difficult to disentangle the true effect of the reserves if this variability is not quantified. We suggest that finer-scale spatiotemporal data on the pressures outside, and indeed inside, of reserves would clarify reserve assessments, both in the case of the present study but also more generally in any assessment of spatial protection. In the case of marine reserves, quantitative standardised data on fishing activity at the scale of individual reserves should be prioritised alongside the collection of ecological data. Synthesizing data from multiple survey methods leads to larger datasets, and the advent of video-based methods in the last decades (e.g. BRUV and DOV) has increased the diversity of methods used to monitor fish. Contrary to expectations, in general, survey method did not strongly influence the effect size. The strongest effect sizes (Lethrinidae and L. nebulosus) were consistently detected regardless of the survey method. L. atkinsoni exhibited a more positive effect when surveyed by remote video as compared to diverbased methods, which may be partly explained by fish behaviours associated with both the attraction to bait and avoidance of divers (Watson et al., 2005;Goetze et al., 2015), particularly on SCUBA (Radford et al., 2005). On balance, we did not distinguish a single survey method as optimal, and in most cases it was appropriate to compare data from the three methods for the effect size calculation. This is likely possible because of the nature of our effect size, which, as a ratio, is more robust to different units of measurement. However, this cannot provide the same level of information as standardised temporal data on fish density inside to outside, as shown by density patterns inside and outside at Osprey, underlying the overall effect size for this reserve. We therefore suggest that monitoring programs should prioritise resurveying existing monitoring sites with comparable methodology to build more robust time-series data, else adopt the method(s) that are best suited to surveying the taxa of interest.

5. Conclusions There were two major challenges in addressing the aims of this study. The first stemmed from the nature of the available data, as while we showed that new information can be gained from collaboration and the synthesis of disparate data, a lack of consistent temporal data meant it was not possible to understand the temporal changes to the fish populations. This was demonstrated by some complex trends in the estimated fish density inside and outside the Osprey reserve that underlay the overall effect size. Therefore, the value of consistent monitoring across time and space in unequivocal, particularly given a likely increase in adaptive management complicating temporal assessments. Indeed at Ningaloo, a new Australian Marine Park, in commonwealth waters directly seaward of the Ningaloo Marine Park of the present study has recently been implemented (1/1/18). Our findings suggest that consistent monitoring, producing data that can be compared to that of the present study should be implemented for this new Park. The second challenge was explored by Underwood (1995), who argued that ecological research can better aid management if management interventions are treated as testable hypotheses. No-take marine reserves can provide experiments with which to test hypotheses regarding the effects of fishing (Ballantine & Langlois, 2008). However, our study has highlighted that variability in 'experimental design,' resulting from a range of complexities including spatial and temporal variability in fishing activity, shore fishing zones adjacent to notakes areas and modifications to reserve design over time, make determining the long-term outcomes of these experiments. We suggest that in order to best analyse across such complicated experimental designs it is necessary to account for (i) habitat; (ii) potential interactions between habitat and reserve size and age; and (iv) variability in fishing activity outside of reserves and compliance inside reserves. Regarding the last point, integration of the collection of fishing activity data with the collection of ecological data is likely to help interpret the true effects of reserves. The two are clearly intertwined and having data on both the pressure and the response is essential for holistic assessments of the efficacy of spatial management interventions. on the coastal boundaries of the reserves where shore-based fishing is allowed. Figure 2. a) Relative fish abundance inside to outside the reserves (back-transformed weighted mean effect sizes) with 95% confidence intervals), for the six fish groups: Lethrinidae, Lethrinus nebulosus, L. atkinsoni, Epinephelinae, Epinephelus rivulatus and Scarinae. Effect sizes are significant when the confidence intervals do not overlap 1.0. Open dots correspond to non-significant effects (i.e. no effect). Sample sizes are given in Table F1. Triangular points show the predicted effect size when habitat was included as a moderator variable in the meta-analysis, for the habitat with the largest mean effect (orange represents the back reef & lagoon coral, and blue represents the reef flat). b) Importance scores (based on summed Akaike weights corrected for finite samples (AICc)) from full-subsets analyses exploring the influence of seven variables on the overall effect size for each fish taxa: 1 is highly important while 0 is not important. Red X symbols mark the variables that were included in the most parsimonious models for each fish taxa (also see Table 2 and Fig. 3). Figure 3. Predicted relative fish abundance inside to outside reserves (back-transformed predicted weighted effect sizes) with 95% confidence intervals) for the six fish groupsa) Lethrinidae; b) Lethrinus nebulosus c) Lethrinus atkinsoni; d) Epinephelinae; e) Epinephelus rivulatus; f) Scarinae for abundance-as a function of variables present in the most parsimonious models (Table 2) from full-subsets GAMM analysis. Ribbons represent 95% confidence intervals Figure 4. Effect sizes for a) abundance and b) biomass from comparison pairs for the Osprey reserve through time and estimated density of c) abundance and d) biomass inside and outside the reserve through time. Ribbons indicate 95% confidence intervals on generalised additive models.

Figure and Tables

Description

Description of variable levels Source

Years protection

Years between zoning and survey data collection

A survey is classified to a single reserve based on its location. In cases where rezoning means that a reserves size was increased, a survey falling inside the old area is classified as the pre-zoning reserve, while if the survey falls in the extended area it is classified as the post-zoning reserve.

Years since protection is calculated on the same principal relating to the initial zoning or the rezoning dependent on survey location. 0 -30 years from time of survey to reserve implementation.

Boat fishing * (logtransformation)

A mean estimate of the number of vessels recreationally fishing at the outside reserve survey sites Mean density of vessels observed fishing during aerial surveys in peak season in 2007. Each survey sample latitude and longitude was assigned the value of the underlying spatial data in Fig. 4 of Smallwood and Beckley (2012). For surveys inside the reserves is was assumed that fishing activity was 0. 0 -0.625 vessels fishing per 9 km 2 Mean: 0.12; Median: 0.11 * Not available for the Muiron Islands Other estimates of fishing activity exist (Sumner et al. 2002) In most surveys biomass had been calculated by the respective data custodians from the estimated or measured fish length. In these cases the provided values were used (with the reasoning that different survey methods may warrant slightly different biomass calculations). In cases where length data was available but biomass had not been calculated the fish counts and length estimates were converted to biomass (kg) using constants and formulas from Fishbase (www.fishbase.org).

where L is the estimated total length of the fish and a and b are constants for the species or family in question. In cases where data provided fork length measurements, these were converted to total length using the formula from FishBase: where TL is estimated total fish length, FL is the measured fork length from BRUV or DOV video and c and d are parameters specific to the fish species in question.

More data was available for some NTMRs and years, with increased sampling after 2005 (Appendix B). Double-zero-events were removed from analysis. For the remaining data a constant of 0.5 fish (half the smallest unit in the abundance analysis) was added to both the inside and outside mean counts, while for biomass a constant of 100 grams was added to both the inside and outside mean biomass.

Given there is no clear protocol for the size of the constant used to deal with zeros in a lnR meta-analysis, we conducted a sensitivity analysis on the value of the constant. We ran the sensitivity analysis for the abundance data for the six taxa examined. We tested constant values, c, ranging from c = 0.01 to c = 1 in 0.01 increments. It is clear that the size of the constant used influenced the mean overall effect size. For example, the magnitude of for L. nebulosus total abundance varied from = 0.73 (0.54) when c = 0.01 to = 0.28 (0.08) when c = 1. However, while the constant size impacted the magnitude of the effect size, it did not influence the significance, except for E. rivulatus, which had the highest count of zero-events, and transitioned from marginally negative to not significantly different from one. From this analysis we decided a constant of 0.5 would be an adequate, and conservative addition for the calculation of lnR in this analysis. Given the high levels of inherent variability expected in fish count data [START_REF] Samoilys | Application of underwater visual census to assessing coral reef fish stocks in the tropical Pacific[END_REF][START_REF] Cappo | Evaluation of sampling methods for reef fish populations of commercial, recreational interest[END_REF] and additional variation from uncontrolled variables, even with addition of a constant, overall differences in abundance would have to be consistent in order to observe statistical significance. Nonetheless, we urge caution in the interpretation of the magnitude of the overall effect.

Figure 1

 1 Figure 1 The Ningaloo Marine Park (NMP) and Muiron Islands Marine Management Area boundaries (dotted lines) with the location of sanctuary zones (referred to as reserves in the present study) shown in green along the Ningaloo coast of Western Australia under the a) initial (1987 -2005) and b) current (2005 -2017) zoning schemes. Tantabiddi Well and Winderabandi Point are indicated with red markers spearfishing is prohibited between these locations. The Osprey reserve is also indicated. In b) blue regions indicate zones on the coastal boundaries of the reserves where shore-based fishing is allowed.

Figure B2 .

 B2 Figure B2. Temporal and spatial distribution of samples: a) number of samples by year, with shade indicating the type of survey method (BRUV, Baited Remote Underwater stereo-Video, DOV, Diver Operated stereo-Video and UVC, Underwater Visual Census); b) distribution of samples inside and outside reserves for each of the four major habitats; c) distribution of samples inside and outside for each reserve under both the initial and current zoning. References

Figure F1 .

 F1 Figure F1. Sensitivity analysis for the size of the constant added to inside/outside mean abundance showing the resulting transformed mean weighted effect size, exponential of , and 95% confidence interval (CI) for values of constant c ranging from 0.01 to 1 in 0.01 increments. a) Lethrinidae, b) L. nebulosus, c) L. atkinsoni, d) Epinephelinae, e) E. rivulatus, f) Scarinae. The mean effect sizes are considered significant when the confidence intervals do not include one.

Table 1 .

 1 Description and summary of the seven variables used in analysis

	Variable
	(transformation
	used in
	analyses)

Table 2 .

 2 Top Generalised Additive Mixed Models (GAMMs) for predicting the response ratio inside to outside reserves, , for abundance from full subset analyses for the abundance of the six fish groups. Difference between the lowest reported corrected Akaike Information Criterion (ΔAICc), AICc weights (ωAICc), variance explained (R 2 ) and estimated degrees of freedom (EDF) are reported for model comparison. Model selection was based on the most parsimonious model (fewest variables and lowest EDF) within two units of the lowest AICc. This model is shown in bold text.

	Fish group	Model

Table F1 . Summary of total number of comparisons and zero events

 F1 References family/subfamily level allowed the inclusion of data from targeted but rare species. Note that there is current discussion of whether Epinephelinae may be better classified as its own family, 998 Epinephelidae(Ma & Craig 2018), however we have chosen to name it as its subfamily here.

		• Epinephelus fuscoguttatus		986	• Epinephelus quoyanus
		• Epinephelus hexagonatus			987	• Epinephelus rivulatus
		• Epinephelus lanceolatus			988	• Epinephelus tauvina
		• Epinephelus macrospilos			989	• Epinephelus tukula
		• Epinephelus maculatus • Epinephelus malabaricus			990 991	• Plectropomus leopardus 925 • Plectropomus maculatus
		• Epinephelus melanostigma Table D1. Total count of fish groups across all data • Epinephelus merra 927 • Epinephelus polyphekadion	992 993 994	• Plectropomus spp. 926 • Variola albimarginata • Variola louti
		Taxa •	996		Total count across synthesised data
		LETHRINIDAE	997		10307
		L. nebulosus * 999 4183
		L. atkinsoni			4765	000
		EPINEPHELINAE			4012	001
		E. rivulatus Scarinae 002			1119
		SCARINAE  Calotomus carolinus			44931
		 Calotomus spinidens  Cetoscarus ocellatus			928
		 Chlorurus bleekeri			929
		 Chlorurus microrhinos  Chlorurus sordidus			930
		Genera included in the family/ sub-family analysis that were sampled in the synthesised data are 931  Hipposcarus longiceps
		summarised below.  Leptoscarus vaigiensis 932 933 Lethrinidae 934  Scarus chameleon  Scarus dimidiatus  Scarus flavipectoralis Fish Group		Total number of possible comparisons	Double-zero events	Zero-events	Final sample size (n) in calculation of mean weighted effect size
	935 936 937 938 939 940 941 942 943 944 945	 Gnathodentex aureolineatus  Gymnocranius euanus  Gymnocranius grandoculis  Gymnocranius griseus  Gymnocranius spp.  Lethrinus amboinensis  Lethrinus atkinsoni  Lethrinus genivittatus  Lethrinus harak  Lethrinus laticaudis  Lethrinus lentjan  Scarus frenatus  Scarus ghobban  Scarus globiceps  Scarus niger LETHRINIDAE L. nebulosus L. atkinsoni EPINEPHELINAE E. rivulatus ABUNDANCE SCARINAE  Scarus spp.  Scarus schlegeli  Scarus rubroviolaceus LETHRINIDAE  Scarus oviceps  Scarus prasiognathos  Scarus psittacus L. nebulosus L. atkinsoni EPINEPHELINAE BIOMASS E. rivulatus  Scarus rivulatus SCARINAE	305 305 305 305 305 305 268 268 268 268 268 268	946 947 948 949 950 951 952 956 955 954 953	 Lethrinus microdon  Lethrinus miniatus  Lethrinus nebulosus  Lethrinus obsoletus 4 151 48 162 25 101 28 110 138 210 44 46  Lethrinus olivaceus  Lethrinus ravus  Lethrinus rubrioperculatus 301 257 280 277 167 261  Lethrinus semicinctus  Lethrinus variegatus  Lethrinus spp.  Monotaxis grandoculis 4 49 264 45 143 223 21 86 247 15 87 253 112 180 156 45 48 223
					957
		Epinephelinae * 958
	959	• Aethaloperca rogaa			968	• Cephalopholis sonnerati
	960	• Anyperodon leucogrammicus		969	• Cromileptes altivelis
	961	• Cephalopholis spp.			970	• Epinephelus spp.
	962	• Cephalopholis argus			971	• Epinephelus areolatus
	963	• Cephalopholis boenak			972	• Epinephelus bilobatus
	964	• Cephalopholis cyanostigma		973	• Epinephelus coeruleopunctatus
	965	• Cephalopholis formosa			974	• Epinephelus coioides
	966	• Cephalopholis miniata			975	• Epinephelus corallicola
	967	• Cephalopholis sexmaculata		976	• Epinephelus fasciatus

We thank the Department of Biodiversity, Conservation and Attractions and the Department of Primary Industries and Regional Development staff in Exmouth, in particular we are grateful for comments from Claire Smallwood with expertise on recreational fishing in the study region and Peter Barnes for expertise on the management regimes of the Ningaloo Marine Park. We would like to thank: Claire Butler for

Funding

The study was possible through funding provided by numerous organisations and programs: CSIRO, the Western Australian Marine Science Institution, WA Department of Biodiversity, Conservation and Attractions, The Australian National University, the Australian Institute of Marine Science, Caring for our Country, and the Gorgon Barrow Island Net Conservation Benefits Fund, BHP-CSIRO Industry-Science Ningaloo Outlook Marine Research Partnership, AIMS-Woodside Energy, WA State NRM program and Royalties for Regions program, Coastwest, the University of Western Australia, Edith Cowan University School of Natural Sciences, the Jean Rogerson Postgraduate Scholarship Appendices

assistance and advice handling spatial data in QGIS; Emma Lawrence for discussion on the sensitivity analysis; Glenn Moore (WA Museum) for advising on the current taxonomy of Serranidae and Epinephelinae; George Cresswell, Susan Blackburn and Brett Molony for comments and editing. Field data are extremely time intensive to collect and we appreciatively acknowledge the many people who collected the data and provided logistical support for the numerous studies included in this data synthesis: UWA; Todd Bodd, Matt Birt, Brigit Vaughan, Isabella Lindgren; CSIRO; Geordie Clapin, Nicole Murphy, David Kozak, Julia Phillips, Ryan Downie, Fiona Graham, Kylie Cook, Catherine Seytre, Auriane Jones, Monique Grol, Andrea Zabala Peres, Helene Boulloche-Sabine, Lydiane Mattio, Cindy Bessey, Melanie Trapon, Doug Bearham, James McLaughlin, Ryan Crossing, Mark Wilson, Margaret Miller, Darren Dennis, David Milton, Rodrigio Bustamante, Tim 835 The rezoning of the eight reserves in 2005, with the addition of new reserves and the expansion of the 836 existing reserves required that data be clearly classified to account for this. Figure A1 shows an example of 837 how samples were referenced based on their spatial location to the initial or current reserves and the time 838 period of sampling. 839 Figure A1. Example classification of 'initial' and 'current' zoning. The boundaries of the initial 1987 -2005 reserves, Cloates and Dugong, are indicated by a green outline, while the current reserve (2005 -2017) is shown in black which combined the two initial reserves into one larger reserve, 'Cloates/Dugong rezoned' (also see Table 1). Pre 2005, surveys were classified as inside/outside based on their spatial relation to initial reserves (green outline). Post 2005, surveys were classified as inside/outside based on their spatial relation to the current reserves (black). If an inside reserve is within the initial boundaries, but surveyed post 2005, it was referenced as initial as shown in c). If a sample was collected at a site within the new area of the rezoned reserve post the rezoning, it was referenced as current as seen in d).

Appendix B-Data summary

Suitable data were identified through searches on Google Scholar and research databases compiled by the Department of Biodiversity, Conservation and Attractions. In addition, relevant researchers from universities, research institutions, industry and citizen science programs operating in Western Australia were contacted to source unpublished data. Nine major custodians contributed data to this study. Table B1 summarises the survey methods used by each custodian, the number of samples collected, the temporal span of data, the reserves surveyed and directs to further reading for more information. Figure B2 graphically illustrates the important data constraints.

Appendix E: Formulas used for calculating effect sizes

For each inside/ outside comparison pair a log response ratio (e) (Hedges et al. 1999) was calculated as the ratio of the mean abundance/ biomass inside to outside a reserve for comparison pair i and fish group j as where and and are the mean abundance or biomass inside (I) and outside (O) a reserve. Therefore, a positive implies a greater fish abundance/ biomass inside the reserves than outside. A log response ratio was appropriate because it is independent of the actual unit of measurement across the different survey methods.

The variance of was also quantified (i.e. the within-study variance, where a study is a comparison pair), given that sampling error plays an important role in introducing variability in the overall outcome of a meta-analysis:

Here, is calculated from the standard deviation, , sample size, , and mean for inside (I) and outside (O) for comparison pair i and fish group j.

Effect sizes were weighted by the inverse of the sum of the within-and among-study variances, ,. The within-study variance (where a study is a comparison pair) was the sum of the variances associated with each mean in the log-ratio. The among-study variance was calculated using the metafor package (Viechtbauer 2010) in the statistical program R (R Core Team 2017).

This weighting minimized the influence of studies with low statistical power, and increased the influence of studies with high statistical power, meaning that each inside/ outside replicate did not contribute equally to the final pooled outcome. Weighted meta-analyses of this sort are considered to increase the precision and power of meta-analyses (Osenberg et al. 1999) and was an appropriate approach in the present study as there was a large distribution of sample sizes and variance associated with the inside/ outside comparison pairs.

The weighted cumulative effect size for fish group j, , was obtained as with associated variance, .

where and are defined above. The overall heterogeneity ( ) for fish group j was calculated as and its significance was tested against the distribution with degrees of freedom.

We used a random-effects model to calculate and using the metafor package in R through the call rma( , , method = "REML", data = ) following the suggestions in [START_REF] Viechtbauer | Bias and efficiency of meta-analytic variance estimators in the random-effects model[END_REF] that the REML, restricted maximum likelihood estimator for variance, strikes a good balance between unbiasedness and efficiency.

We ran mixed-effects categorical analyses and meta-regression to examine how the seven additional variables (Table 1, main text) mediated the overall effect size . For a given level (L) of the factor, a weighted cumulative effect size was calculated as

Where is the number of comparison pairs in level, L, of the factor, and and are defined above. The heterogeneity of the model explained by the factor ( ) was calculated as

The significance of was tested against the distribution with degrees of freedom Appendix F -Sensitivity analysis for case of one-armed zero events [START_REF] Cheng | Impact of including or excluding both-armed zero-event studies on using standard meta-analysis methods for rare event outcome: a simulation study[END_REF] define two scenarios relating to zero events: zero-event and double-zeroevent. In the present study there were both double-zero-events (a zero mean abundance/biomass for both inside and outside) and zero-events (zero mean abundance/biomass for either the inside or the outside), summarised in Table 1. In both cases, lnR cannot be computed due to division by zero and/or logarithm of zero. The former case are often removed from meta-analyses, while the latter is often handled via the addition of a small constant [START_REF] Bradburn | Much ado about nothing: a comparison of the performance of meta-analytical methods with rare events[END_REF][START_REF] Spittal | Meta-analysis of incidence rate data in the presence of zero events[END_REF]. The total number of comparisons (n) used in the final calculated of the weighted average effect size is given.

In the present study the species examined can be encountered infrequently on a survey. As well, one of the key species, Lethrinus nebulosus, is a schooling species which, when it occurs, can be present in larger numbers. Consequently there are many zero counts in the data which needed to be explicitly considered. G2 and G3 summarise the results of weighted mixed-effects meta-analyses for all 1394 seven variables and for reserve identity modelled individually. Figure 1 shows the predicted 1395 effect size for the cases where the moderator reserve identity explained a significant amount 1396 of heterogeneity for habitat so that it can be directly compared to the overall effect sizes in 1397

Appendix G -Meta-analysis statistics

Figure 2 * Total heterogeneity is provided in Table G1 Figure G1 shows the transformed predicted effect sizes as relative fish abundance by habitat while Figure G2 shows this for reserve identity. Figure G1. Relative fish abundance inside to outside reserves (back-transformed weighted mean effect sizes) with 95% confidence intervals) for the Lethrinidae, Lethrinus nebulosus, and L. atkinsoni, the fish groups for which habitat explained a significant amount of variation. Effect sizes are considered significant when the confidence intervals do not include one. Black dots correspond to significant effects while grey dots correspond to nonsignificant effects. Figure G2 is included to show that there is heterogeneity between individual reserves and because it may be useful to managers of the Ningaloo Marine Park.

Figure G2. Relative fish abundance (Transformed weighted mean effect sizes ( with 95% confidence intervals (CI)), for the Lethrinidae, Lethrinus nebulosus, Lethrinus atkinsoni, Epinephelinae and Epinephelus rivulatus, the fish groups for which reserve identity explained a significant amount of variation. Results are categorised into panels based on the initial or current zoning. Point shape indicates whether shore fishing is allowed or prohibited along the coastal edge of the reserve (circle = prohibited; triangle = allowed). Effect sizes are considered significant when the confidence intervals do not include one. Black dots correspond to significant effects while grey dots correspond to non-significant effects.

Appendix H -Biomass results

Table H1. Top Generalised Additive Mixed Models (GAMMs) for predicting the response ratio inside to outside reserves, , for biomass from full subset analyses for the abundance of the six fish groups. Difference between the lowest reported corrected Akaike Information Criterion (ΔAICc), AICc weights (ωAICc), variance explained (R 2 ) and estimated degrees of freedom (EDF) are reported for model comparison. Model selection was based on the most parsimonious model (fewest variables and lowest EDF) within two units of the lowest AICc. This model is shown in bold text. )) with 95% confidence intervals (CI)), for the relative abundance of the six fish taxa: Lethrinidae, Lethrinus nebulosus, L. atkinsoni, Epinephelinae, Epinephelus rivulatus and Scarinae. Effect sizes are considered significant when the confidence intervals do not include one. Open dots correspond to non-significant effects. Sample sizes are given in Appendix G. b) Importance scores (based on summed Akaike weights corrected for finite samples (AICc)) from fullsubsets analyses exploring the influence of seven variables on the overall effect size for each fish taxa. Reserve size was square-root transformed and boat fishing was log-transformed in all models. Red X symbols mark the variables that were included in the most parsimonious models for each fish taxa (also see Table H1 and Fig. H2).

Figure H2. Predicted relative fish biomass inside to outside reserves (back-transformed predicted weighted effect sizes) with 95% confidence intervals) for the six fish groupsa) Lethrinidae; b) Lethrinus nebulosus c) Lethrinus atkinsoni; d) Epinephelinae; e) Epinephelus rivulatus; f) Scarinae for abundance-as a function of variables present in the most parsimonious models (Table G4) from full-subsets GAMM analysis. Ribbons represent 95% confidence intervals