Similar Regional Effects Among Local Habitats on the Structure of Tropical

Global Ecology and Biogeography, (Global Ecol. Biogeogr.) (2010) 19,363–375

RESEARCH Similar regional effects among local PAPER habitats on the structure of tropical reef

ﬁsh and coral communitiesgeb_516 363..375 Scott C. Burgess*†, Kate Osborne and M. Julian Caley

Australian Institute of Marine Science, PMB ABSTRACT No. 3, Townsville MC, Townsville, Qld 4810, Aim We examined data on corals and reef fishes to determine how particular local Australia habitat types contribute to variation in community structure across regions covering gradients in species richness and how consistent this was over time. Location Great Barrier Reef (GBR), Australia. Methods We compared large-scale (1300 km), long-term (11 years) data on fishes and corals that were collected annually at fixed sites in three habitats (inshore, mid-shelf and outer-shelf reefs) and six regions (latitudinal sectors) along a gradient of regional species richness in both communities. We used canonical approaches to partition variation in community structure (sites ¥ species abundance data matrices) into components associated with habitat, region and time and Procrustes analyses to assess the degree of concordance between coral and fish community structure. Results Remarkably similar patterns emerged for both fish and coral communities occupying the same sites. Reefs that had similar coral communities also had similar fish communities. The fraction of the community data that could be explained by regional effects, independent of pure habitat effects, was similar in both fish (33%) and coral (36.9%) communities. Pure habitat effects were slightly greater in the fish (31.3%) than in the coral (20.1%) community. Time explained relatively little variation (fish = 7.9%, corals = 9.6%) compared with these two spatial factors. Conclusions Our results indicate either that fish and coral communities were structured in similar ways by processes associated with region, habitat and time, or that the variation in fish community structure tracked variation associated with the coral communities at these sites and thereby reflects an indirect link between the environment and the structure of fish communities mediated by corals. Irrespective of the causes of such commonality, we demonstrate that community structure, not *Correspondence: Scott C. Burgess, Australian just species richness, can be related to both habitat differences and regional setting Institute of Marine Science, PMB No. 3, Townsville MC, Townsville, Qld 4810, Australia. simultaneously. E-mail: [email protected] Keywords †Present address: School of Biological Sciences, University of Queensland, Brisbane 4072, Australia, community structure, coral, Great Barrier Reef, habitat, Australia. local–regional, long-term study, reef fish.

tava, 1999). Where local interactions, such as competition, pre- INTRODUCTION dation and parasitism, are mediated by physical and biological Mechanisms structuring biological communities operate at gradients, variation in local diversity among habitat patches is multiple and interacting scales that are often dichotomized as expected to reﬂect such gradients (Hutchinson, 1957; Whittaker, local versus regional effects (Ricklefs, 1987; Ricklefs & Schluter, 1972). In contrast, the imprint of large-scale historical processes, 1993; Cornell & Karlson, 1996; Caley & Schluter, 1997; Srivas- such as speciation, extinction and regional-scale dispersal that

©2010BlackwellPublishingLtd DOI:10.1111/j.1466-8238.2009.00516.x www.blackwellpublishing.com/geb 363 S. C. Burgess et al. causes variation in the number and type of species that accu- effects and their interactions from local processes operating on mulate in a region, may be evident in the diversity of local ecological time-scales, variation in community structure over communities drawn from different regional species pools time can also be partitioned from spatial effects associated with (Ricklefs, 1987; Schluter & Ricklefs, 1993; Leibold et al., 2004). habitat and region (Legendre & Legendre, 1998). Such temporal Overlaid on these processes (which operate at small and large effects may act independently, or in conjunction with, effects of spatial scales and evolutionary time-scales) are smaller-scale habitat and region, but they remain poorly understood because temporal processes (years to decades) such as disturbance and of a general lack of time-series data available for ecological succession that can also influence local demography and the sampling at sufficiently large spatial scales. assembly of communities (Sousa, 1984; Connell et al., 1997). The degree to which habitat, regional diversity and time are The challenge, then, is to understand the relative contributions associated with variation in community structure may also vary of these many spatial and temporal processes and their interac- as a function of the life-history characteristics of the constituent tions to the assembly of biological communities at local and species in communities. Few studies have examined communi- regional scales. ties of very different trophic or taxonomic level from the same Inferences about the assembly of biological communities are environment, though Connolly et al. (2005) reported that the commonly drawn from a range of metrics that vary in complex- frequency distributions of corals and Labrid fish species abun- ity, including species richness, species abundance distributions dances across the Indo-Pacific (covering a gradient in species (SADs) and compositional patterns (McGill et al., 2007). In richness) both approximated a log-normal distribution, which terms of species richness, previous studies of coral communities did not vary across habitats at different depths. A log-normal have demonstrated linear relationships between local and distribution, where few species have very low or very high abun- regional species richness (Cornell & Karlson, 1996; Karlson & dance, can result from stochastic variation in the environment Cornell, 1998; Karlson et al., 2004)–apatternconsistentwith and population growth rates (Connolly et al.,2005).Therefore, many observations of terrestrial systems (Ricklefs, 1987; Ricklefs the similarity between corals and fishes was used in this case to & Schluter, 1993; Caley & Schluter, 1997; Srivastava, 1999). argue against niche-based explanations of community structure. However, what such relationships do not reveal is if two sites Similarly, the most common fish and coral families across the with the same species richness necessarily share SADs or species Indo-Pacific contribute a similar proportion to local species compositions (Connolly et al., 2005). Similarly, comparatively richness across a gradient in regional richness, suggesting that little is known about how regional species richness relates to the similar or linked mechanisms control the composition of both abundance and composition of communities (hereafter com- fish and coral communities over these large scales (Bellwood & munity structure) among habitats and the degree to which such Hughes, 2001). Though large-scale patterns in species richness relationships may vary through time. Gradients in regional are beginning to be revealed, it is still not known if different species richness, environmental differences among habitats and communities show similar abundance and compositional pat- temporal processes may all lead to different patterns of species terns across regions, habitats or time, and if these factors affect abundances and composition at local scales (Stirling & Wilsey, different communities in a comparable manner. 2001; Connolly et al.,2005).Therefore,includingcompositional Here we use canonical methods to partition variation due to information, in addition to abundance, in a multivariate frame- habitat, region and time in fish and coral community structure work should provide further clues as to the processes structuring on Australia’s Great Barrier Reef (GBR). We then assess the communities and identify if, and if so which, particular sites or degree of concordance between these two communities across habitats contain species (or life-history traits) that are consis- these same three factors. The spatial (1300 km of coastline) tently common or rare across different regions, habitats or over and temporal (11 years) scales at which these communities time. have been monitored enabled us to include gradients in species To understand the relative roles of regional diversity, habitat richness, habitats common to all regions and shorter-term eco- and temporal processes in structuring abundances and compo- logical dynamics; a combination of which is rare in most sition patterns within biological communities, variation in com- studies of community structure. By including both fish and munity structure can be partitioned among a common set of coral communities in these analyses, we were able to explore habitats in multiple regions hosting different levels of diversity potential commonality between these patterns of community (Schluter & Ricklefs, 1993; Legendre et al.,2005).Thepropor- structure in groups of species with very different life histories, tion of variation explained by each of these factors should but which occupy the same patches of habitat. While it is provide indications of the relative importance of various pro- known that both fish and coral species richness vary across cesses that could be structuring particular communities. For these habitats, we ask how much variation in community example, the proportion of variation in community structure structure among habitats is consistent across different regions among habitats that is independent of regional setting estimates encompassing gradients in species richness, and conversely the degree to which local processes determine community struc- how much the regional setting mediates the degree to which ture. Similarly, the proportion of variation in community struc- these habitats explain variation in this structure. In addition, ture among regions, after partitioning out pure habitat effects, given the potential for extensive temporal variation in the estimates the degree of regional control of community structure abundances of these species (Caley et al.,1996),wealsoparti- (Borcard et al.,1992;Legendreet al.,2005).Toseparatethese tioned the effect of time.

METHODS Latitudinal Regions 1 Cooktown-Lizard Island

Study system and communities 2 Cairns

3 The habitat types studied vary with respect to local physical Townsville parameters including water clarity, turbidity, wave exposure and 4 Whitsundays nutrient inputs across the continental shelf (Fabricius & De’ath, 5 Swains 2001). The sampling covered gradients in regional species rich- 6 Capricorn-Bunkers ness of c.40%forfishes(Russell,1983)andc.33%forcorals Carter(o) Macgillivray (m) 1 (Veron, 1993) from the southern to the northern GBR. Variation Yonge(o) Lizard Is (m) No Name(o) Martin (i) in the ambient physical environment also varies among latitu- Agincourt (o) Linnet (i) dinal regions, but less systematically than among shelf habitats St. Crispin(o) Opal(o) 2 (Fabricius & De’ath, 2001). Corals are sessile and compete for a Low Isles (i) comparatively small set of limiting resources, mostly space and Green Island (i) Hastings (m) Myrmidon (o) Fitzroy Island (i) Michaelmas (m) Dip (o) light, whereas fishes can use a broader range of trophic and Thetford (m) Chicken (o) Rib (m) Davies (m) habitat resources, which can include corals, and, being mobile, John Brewer (m) 3 19159 (o) have greater control over what they can access and use as juve- Pandora (i) Hyde (o) 4 Havannah Island (i) niles and adults (e.g. Wilson et al.,2008).Inaddition,dispersal 19131 (m) Rebe (o) Middle (i) potential differs markedly between corals (hours to days; 19138 (m) Hayman Island (i) 20104 (m) 5 Babcock & Heyward, 1986) and fishes (weeks to months; Langford and Bird (i) 21529 (m) Border Island (i) Brothers et al.,1983),thelatterbeingpotentiallymoredemo- 22088 (m) graphically open to immigration from local and regional sources Gannet Cay (m) Horseshoe (m) (Caley et al., 1996). East Cay (o) Turner (o) 6 Queensland Chinaman (o) Sampling design and survey methods Broomfield (o) 019038095 Kilometers Wreck (o) Survey data from the long-term monitoring programme One Tree (o) (LTMP) at the Australian Institute of Marine Science (Sweatman Lady Musgrave (o) et al., 2008) were used in these analyses. Data used here were taken from annual surveys (1994–2004) on fixed sites on 46 reefs Figure 1 Map of the 46 reefs surveyed annually from 1994 to of the GBR. Survey reefs were chosen according to their latitude 2004. Solid lines indicate boundaries of each region. Letters in (= region) and position across the continental shelf (= habitat). brackets indicate habitat type: I, inshore; M, mid-shelf; O, Atotalofsixregionsweresampled,coveringadistanceof outer-shelf. approximately 1300 km along the continental shelf (Fig. 1). Three shelf positions were sampled (inner, mid and outer shelf) cases also family. These categories (listed in Table 1) were used in in the four northernmost regions, but the geography of the all subsequent analyses to investigate the structure of these coral southern GBR regions provides only mid- and outer-shelf reefs communities. This pooling into categories was necessary for two in the Swains region, and only outer-shelf reefs in the Capricorn reasons linked to the long-term nature of the monitoring pro- Bunkers region (Fig. 1). We refer to each habitat ¥ region com- gramme. First, the ability to reliably identify all corals within a bination as a locality. In most cases, three reefs were sampled particular species group varies with species, image quality and within each locality, but this number varied slightly between two observer. Second, over the history of the LTMP, the level of and four, particularly in the southern regions. At each reef classification used has changed slightly because observers have sampled, three sites were positioned on the perimeter of its had slightly different levels of taxonomic expertise. The 36 cat- north-eastern flank. At each site, five permanently marked egories used here represent the most reliable classification of transects, 50 m long and running along the 6–9 m depth coral taxa identifiable from these video images. Similarity contour, were sampled. between coral and fish community patterns reported here Hard and soft corals (scleractinia and octocorallia, respec- should be interpreted in terms of coral community structure tively) were sampled using a video recorder in an underwater defined by these categories. Previous studies on the effects of housing. The percentage cover of hard and soft corals was esti- grouping on analyses of community structure, however, indicate mated from the video footage using a point-sampling technique that the patterns of spatial variability in community structure (Adbo et al., 2004). Corals were identified to the highest possible tend to be preserved (Somerfield & Clarke, 1995; Chapman, level of taxonomic resolution. For hard corals, most could be 1998; Anderson et al., 2005), especially at larger spatial scales like identified to genus and some to species. For soft corals, most those used in this study and with grouping at the level of genus could be identified to family or genus. The growth form of the (Somerfield & Clarke, 1995; Anderson et al., 2005). coral was also recorded. Corals were then grouped into 36 cat- Fishes were counted using visual census following the proto- egories mainly based on genus and growth form, but in a few cols of Halford & Thompson (1996). Relatively large and mobile

Table 1 Description of the benthic groups used to deﬁne coral community structure.

Benthic code Family Description

Hard corals 1GB_ACBAcroporidae Acropora spp branching 2GB_ACD Acropora spp digitate 3GB_ACT Acropora spp. tabulate 4GB_ACX Acropora spp. bottlebrush 5GB_ISOE Isopora spp. encrusting 6 GB_ISOSM Isopora spp. submassive 7G_MONT Montipora spp. 8G_PACHYAgariciidae Pachyseris spp. 9 G_PAV Pavona spp. 10 F_DEN Dendrophylliidae Dendrophylliidae 11 GB_ECB Faviidae Echinopora spp. branching 12 GB_ECE Echinopora spp. encrusting 13 GB_ECF Echinopora spp. foliose 14 G_DIPLO Diploastrea heliopora 15 G_LEPT Leptastrea spp. 16 G_PLATETC Platygyra spp. 17 F_FAV Faviidae ‘other’ 18 F_FUNG Fungiidae Fungiidae 19 F_MER Merulinidae Merulinidae 20 F_MUS Mussidae Mussidae 21 F_PECT Pectiniidae Pectiniidae 22 G_POC Pocilloporidae Pocillipora spp. 23 G_SERI Seriatopora spp. 24 G_STYPIST Stylophora pistillata 25 GB_PORB Poritiidae Porites spp. branching 26 GB_PORFES Porities spp. (foliose,encrusting,submassive) 27 GB_PORM Porites spp. massive 28 G_GONALV Goniopora / Alveopora spp. 29 Other_CB ‘Other non-Acropora’Othernon-Acropora branching 30 Other_CE Other non-Acropora encrusting 31 Other_CF Other non-Acropora foliose 32 Other_CM Other non-acropora massive 33 Other_CSM Other non-acropora submassive Soft corals 34 GR_SC Soft coral (Alcyoniidae, Nephtheidae, Xeniidae, Asterospiculariidae) 35 G_SARC Sarcophyton spp. 36 F_GOR Gorgonian

fish species were surveyed along a 50 m ¥ 5 m corridor. Smaller and temporal patterns in the fish and coral community datasets. and more site-attached species from the family Pomacentridae nMDS was performed for each year separately, as well as on (Damselfishes) were counted on a return pass along the same spatially and temporally averaged datasets in both communities. transects but using a 50 m ¥ 1 m corridor. Only fish considered Both fish and coral community datasets (sites ¥ species matri- > 1 year old, based on their size and coloration, were counted. ces) were converted to relative abundances (i.e. row standard- All fish species (249 in total) were identified to and analysed as ized) prior to analyses. Only abundant fish species (n > 10) were individual species. included to minimize zero values, resulting in c.200speciesof fishes (depending on the subset of data). Two dimensions were chosen in the nMDS and in all ordinations stress values were less Data analysis than 0.2. To identify species that were principally responsible for determining the reef dissimilarities, species scores were calcu- Summary of spatial and temporal patterns in fish and coral lated as weighted averages of the reef scores. The weighted aver- community structure ages were ‘expanded’ so that their biased weighted variance was Non-metric multidimensional scaling (nMDS) solutions, using equal to the biased weighted variance of the corresponding reefs Bray–Curtis dissimilarity indices, were used to visualize spatial (Legendre & Legendre, 1998).

Variance partitioning difference in community structure between the factors of interest. The spatially structured, repeated-measures sampling design Partial redundancy analysis (pRDA) was used to estimate the necessitated a multilevel analysis. The error term for the spatial proportion of variance in the fish and coral community datasets effects of habitat and habitat interactions was the among-reef that related to habitat, region, time and their interactions variation. The error term for the effect of year and year by spatial (Borcard et al.,1992;Legendre&Legendre,1998;Legendre interactions was the model residuals. Restrictions of the permu- et al., 2005). pRDA is the extension of partial linear regression to tations for each term in the model were determined by the cases with multivariate responses, where the variation in the exchangeability of observations under the appropriate null community matrix is attributed to one set of factors, once the hypothesis for the term dependent on the multilevel sampling effect of other factors has been taken into account. It is essen- design (Anderson & ter Braak, 2003). Empirical distributions of tially a principal components analysis (PCA) ‘constrained’ by the F-statistic were estimated by permuting all observations on explanatory factors (region, habitat and time in our case). The reefs for tests of spatial effects averaged over sampling years, percentage variation explained by each factor is equal to the such as habitat, region and their interactions. Permutations were (constrained) eigenvalues for the respective canonical axis generated under a reduced model such that residuals were per- divided by the sum of all (unconstrained) eigenvalues in the muted having accounted for the preceding terms in the model community (response) matrix (Legendre & Legendre, 1998). (Legendre & Legendre, 1998). For time-dependent effects, The percentage variation explained by habitat represents the observations within reefs were permuted under the reduced fraction of the community data that could be explained by model. Permutations within reefs were restricted to cycle habitat effects independent of any regional effects. Similarly, the through years to give appropriate tests for temporally autocor- percentage variation explained by region represents the fraction related observations. Inferential tests for terms in the models of the community data that could be explained by regional were based on 1000 permutations. Due to the structural imbal- effects independent of any habitat effects. The interaction term ance in the sampling design, terms were fitted sequentially such estimates the fraction of the variation in the community data that the effects were tested conditional only on the preceding that was shared by both habitat and regional effects. Therefore, effects in the model. Potential effects on the order of entry of following Schluter & Ricklefs (1993), the total influence of factors into the model were examined. Order of entry did not region includes the variation explained by region plus its inter- affect the results presented here in any material way, and there- action with habitat. The percentage variation explained by time, fore is not considered further. and the interactions with spatial factors, estimates the degree to which community structure changed in response to short-term processes such as disturbance. It should also be noted that all Concordance between fish and coral communities estimates of relative variance include sampling error, but this is Given that similar components of variance due to habitat, minimized though sampling permanently marked transects and region and time were evident between fish and coral communi- using consistent methodologies. Using partial redundancy ties (see Results), we were interested in assessing how much of analysis, where the terms are fitted sequentially and measures of this similarity could be attributed to the similarity of paired variation are based on different degrees of freedom, does not communities of fishes and corals on the same reefs. To this end, allow for direct comparison of the magnitudes of relative pro- Procrustes analysis was used on the nMDS ordination solutions portions of variation explained by the spatial model terms described above. Procrustes analysis is similar to the Mantel test, within each community (Legendre & Legendre, 1998). It does, though it has the advantage of assessing concordance between however, allow comparisons among communities in the magni- direct observations and their ordination solutions (rather than tude of relative proportions of variation explained by a particu- transforming data into distance matrices, as is done in the lar spatial term and comparison of spatial versus temporal Mantel test). Procrustes analysis was used to scale and rotate the variation. Prior to pRDA, the percentage cover of benthic taxa nMDS ordinations of fish and coral communities to find the was arcsine square-root transformed. Abundances of each fish optimal superimposition that minimized the sums of squared species were transformed using the Hellinger metric, which differences. The sum of the squared residuals between the final standardizes the data to a measure of relative abundance (i.e. configurations (m2) was then used as a measure of association, row standardized), downscaling the influence of very abundant such that the greater the similarity between the configurations, species. the lower the m2 value (Jackson & Harvey, 1993; Peres-Neto & Jackson, 2001). Residuals are presented as a proportion of the maximum value (standardized residuals). To estimate the sig- Significance tests nificance of the Procrustean fit (m2), a permutation procedure To assess the significance of spatial and spatio-temporal differ- (protest)wasused(Jackson,1995;Peres-Neto&Jackson, ences in the structure of fish and coral communities, eigenvalues 2001). This procedure repeatedly randomizes the configuration from each canonical axis in the pRDA were tested for signifi- of one matrix, by randomly permuting the observations within cance using permutation tests within a multivariate analyses of rows in relation to each other which maintains its within-matrix variance framework (Legendre & Legendre, 1998; Anderson & covariance structure, and recalculates the m2 value. The percent- ter Braak, 2003). The null hypothesis tested was that there is no age of m2 values equal to or less than the observed m2 value is the

Global Ecology and Biogeography, 19,363–375,©2010BlackwellPublishingLtd 367 S. C. Burgess et al. significance level of the test. We used 1000 permutations. The types (Fig. 2). Generally, outer-shelf reefs were more similar to null hypothesis was a random association between fish and coral mid-shelf reefs than they were to inner-shelf reefs. However, communities. To visualize the concordance between fish and some reefs within a particular habitat type were just as dissimilar coral community structure at each reef, nMDS ordinations are to each other as they were to reefs in another habitat type. These presented in their optimal superimposition (i.e. after the Pro- within-habitat type differences related to regional sectors so that crustes rotation of the fish ordination). All analyses were per- reefs within each locality were generally more similar to each formed in R 2.4.1 (R Development Core Team, 2006). other than to reefs in other localities. This pattern was consistent for ordinations of both fish and coral communities. RESULTS Variance partitioning Spatial and temporal patterns in fish and coral Habitat, region and time all contributed significantly to the community structure structure of the fish and coral communities studied here nMDS ordinations of both fish and coral communities revealed (Table 2). The proportion of total variation attributed to each a gradient in the structure of these communities across habitat factor was also qualitatively similar for the fish and coral com-

0.2 0.20 TO.I a SW.M b

WH.M SW.M TO.I CL.M 0.15 CL.M WH.M CA.M TO.ITO.I 0.1 CL.I WH.I WH.MTO.MCL.I 0.10 CA.I WH.I SW.M TO.IWH.I TO.M SW.M TO.M SW.M WH.I CB.O SW.M 0.0 CB.O CA.I CA.I 0.05 CA.I CA.ICA.MCA.I CB.O CL.O TO.I CB.O CL.O TO.O WH.I CB.O CL.MCL.M WH.I TO.OTO.O CB.O CL.I CB.O CL.O CA.M CL.M CL.I CA.M CB.O CL.M 0.00 TO.MTO.M WH.M CA.MSW.M SW.O SW.MTO.M -0.1 SW.O WH.M SW.O CA.O WH.OSW.MSW.M CA.O CA.O -0.05 WH.OSW.O TO.O TO.OWH.OCA.M WH.M CL.O CA.O WH.O CA.M CA.O CL.O CL.OCA.O CA.MTO.O -0.2 stress = 0.13 -0.10 stress = 0.16

-0.4 -0.2 0.0 0.2 0.4 -0.2 -0.1 0.0 0.1 0.2 nMDS axis 2 0.4 0.20 c d GB_PORB 0.3 G_LEPTG_PACHY 0.15 Hemigymnus.melapterus

GB_ACX GB_ECF 0.2 F_DEN 0.10 Acanthochromis.polyacanthus F_PECT F_FUNG G_MONT G_GONALV Plectropomus.leopardus GB_ACB GB_PORM 0.1 GB_ECE 0.05 Neoglyphidodon.melas F_MUS Other_CEOther_CB G_PLATETC G_SARC Chlorurus.microrhinos 0.0 GB_PORFES 0.00 Chaetodon.trifasciatus Neopomacentrus.azysron G_POC F_GOR Scarus.niger GB_ACT GB_ISOSM Scarus.chameleon GB_ACD Chlorurus.sordidus -0.1 -0.05 Pomacentrus.lepidogenys Scarus.psittacus

-0.2 GB_ISOE -0.10

-0.4 -0.2 0.0 0.2 0.4 -0.2 -0.1 0.0 0.1 0.2

nMDS axis 1

Figure 2 Non-metric multidimensional scaling (nMDS) ordination of (a) coral and (b) fish community structure (relative abundances) at each reef using a time-averaged dataset. Solid lines group similar habitat types (inshore, mid-shelf or offshore). Each reef is coded according to its locality using a combination of a code for region (CL – Cooktown-Lizard Island, CA – Cairns, TO – Townsville, WH – Whitsundays, SW – Swains, CB – Capricorn-Bunkers) and for shelf position (I = inner, see Fig. 1). The nMDS ordination of the fish community (b) is presented in its optimal configuration after Procrustes rotation (m2 = 0.67, P < 0.001; see ‘Concordance between fish and coral communities’ in Methods for further details) to visualize the concordance between spatial patterns of fish and coral community structure. Panels (c) and (d) show the weighted species scores for influential coral taxa (see Table 1 for classifications) and fish species, respectively.

368 Global Ecology and Biogeography, 19,363–375,©2010BlackwellPublishingLtd Fish and coral community structure munities. The proportion of variation attributable to spatial communities (fishes = 21.2%, corals = 26.1%). There were also factors was much larger than that attributable to temporal significant temporal effects with habitat, region and their inter- effects in both communities (i.e. fishes = 85.5% [space] vs. 7.9% action, though these made a small contribution towards the [time]; corals = 82% [space] vs. 9.6% [time], Table 2). The pro- total variation. portion of total variation attributable to regional effects was Spatial patterns of variance partitioning in fish and coral similar for both communities (fishes = 33%, corals = 36.9%). community structure were driven by contrasting patterns of This regional component was made up of a component relative abundance of constituent species. Figures 3 and 4 sum- attributable to the unique influence of region (fishes = 19.9%, marize general trends for some of the more abundant species. corals = 20.3%) plus a component attributable to the shared Some species of coral (e.g. tabulate Acropora spp.) and fish (e.g. influence of habitat and regional setting (interaction between Scarus globiceps) showed increasing relative abundance from habitat and region: fishes = 13.1%, corals = 16.3%). inshore to offshore habitats and with increasing latitude. Other The proportion of variation among habitats that was consis- species of both coral (e.g. branching Porities spp.) and fish (e.g. tent across different regions covering gradients in species rich- Pomacentrus moluccensis) showed decreasing relative abundance ness was slightly greater in the fish communities than in the from inshore to offshore habitats which was fairly consistent coral communities (fishes = 31.3%, corals = 20.1%), whereas the across regional sectors. The remaining taxa (e.g. soft corals and interaction of habitat with region contributed slightly less in the Neopomacentrus azysron) had relative abundance patterns fish community. Within-habitat variation (i.e. among reefs across habitats that differed among regional sectors within localities) also contributed a similar amount in both (Figs 3 & 4).

Table 2 Nonparametric multivariate analysis of variance (based on eigenvalues from each canonical axis in the partial redundancy analysis (pRDA) of spatial and temporal variation in (a) reef ﬁsh relative abundance and (b) percentage cover of 36 coral taxonomic categories.

Source d.f. SS MS F Variation (%)

(a) Reef ﬁshes Habitat 2 70.68 35.34 23.54 31.3a *** Region 5 44.91 8.98 5.98 19.9 *** Habitat ¥ region 7 29.54 4.22 2.81 13.1 *** SUBTOTAL 33b Reef (habitat ¥ region) 33 48.04 1.50 21.2 *** 85.5c Year 9 2.21 0.25 4.51 1.0 *** Year ¥ habitat 18 2.46 0.14 2.51 1.1 *** Year ¥ region 45 7.54 0.17 3.07 3.3 *** Year ¥ habitat ¥ region 63 5.64 0.09 1.64 2.5 *** SUBTOTAL 7.9d Residuals 290 15.12 0.06 6.6 *** (b) Corals Habitat 2 1328.9 664.46 12.68 20.1a *** Region 5 1345.6 269.11 5.14 20.3 *** Habitat ¥ region 7 1080.7 154.39 2.95 16.3 *** SUBTOTAL 36.9b Reef (habitat ¥ region) 33 1728.7 52.39 26.1 *** 82.8c Year 9 112.0 12.45 7.21 1.7 *** Year ¥ habitat 18 133.8 7.43 4.31 2.0 *** Year ¥ region 45 225.3 5.01 2.90 3.4 *** Year ¥ habitat ¥ region 63 165.7 2.63 1.52 2.5 *** SUBTOTAL 9.6d Residuals 290 500.5 1.73 7.6

SS, sums of squares; MS, mean squares. The percentage variation explained by each factor is the (constrained) eigenvalues for the respective canonical axis divided by the sum of all (unconstrained) eigenvalues in the community (response) matrix. Inferential tests were based on 1000 permutations of residuals under a reduced model and are represented by asterisks. ***P < 0.001. Subtotals give the summed percentage of total variation due to ashelf habitat, bregional setting, cspatial factors, and dtemporal factors across local habitat and region.

Tabulate Acropora spp. Branching Porites spp. Soft Corals 0.7 0.10 0.6 Cooktown Lizard 0.6 0.08 0.5 0.5 0.4 0.4 0.06 0.3 0.3 0.04 0.2 0.2 0.1 0.02 0.1 0.0 0.00 0.0 0.7 0.10 0.6 Cairns 0.6 0.08 0.5 0.5 0.4 0.4 0.06 0.3 0.3 0.04 0.2 0.2 0.1 0.02 0.1 0.0 0.00 0.0 0.7 0.10 0.6 Townsville 0.6 0.08 0.5 0.5 0.4 0.4 0.06

over 0.3 0.3 0.04 t c 0.2 0.2 0.1 0.02 0.1 0.0 0.00 0.0 0.7 0.10 0.6 Whitsundays 0.6 0.08 0.5

Relative percen Relative 0.5 0.4 0.4 0.06 0.3 0.3 0.04 0.2 0.2 0.1 0.02 0.1 0.0 0.00 0.0 0.7 0.10 0.6 0.6 Swains 0.08 0.5 Figure 3 Patterns of relative percentage 0.5 0.4 0.4 0.06 o 0.3 cover (temporally averaged at each reef) 0.3 0.04 0.2 for three main coral taxa (tabulate 0.2 o 0.1 0.02 0.1 Acropora spp. [GB_ACT], branching 0.0 0.00 0.0 Porities spp. [GB_PORB], and soft corals 0.7 0.10 0.6 [GR_SC], see Table 1 for category 0.6 Capricorn Bunkers 0.08 0.5 0.5 0.4 descriptions) across shelf habitats 0.06 0.4 0.3 (x-axis) and regional sectors (arranged 0.3 0.04 0.2 0.2 north to south down the page). Dark 0.02 0.1 0.1 bars represent the median, the box 0.0 0.00 0.0 represents 50% of the data, and the Inner Mid Outer Inner Mid Outer Inner Mid Outer whiskers represent the minimum and Shelf Habitat maximum values.

Concordance between fish and coral communities DISCUSSION protest analyses indicated that in every year of sampling, and for the time-averaged sample, there were non-random similari- Our exploration of macroecological patterns of these fish and ties between fish and coral community structure (Table 3). coral communities revealed strongly consistent patterns in the These results indicate that similarities between communities in partitioning of their structure into habitat, region and time the proportion of variation attributed to habitat, region, time effects and their interactions. Generally, spatial patterns in the and their interactions, was related to the similarity of the struc- community structure of corals were correlated with those of the ture of coral communities on particular reefs with the structure fish communities. Our results demonstrate that not only are the of the fish communities on the same reefs. Analysis of the numbers of species in local coral reef communities regionally residuals from the Procrustes procedure on time-averaged enriched (Cornell & Karlson, 1996; Karlson et al.,2004),but datasets indicated that the concordance between fish and coral there is also a large regional effect reflected in local community communities was greatest in localities in northern regions structure across habitat types and a comparatively small tempo- (Cairns inner and mid-shelf, Cooktown Lizard outer-shelf) and ral component, at least over little more than a decade. The results least in southern regions (Capricorn Bunkers outer shelf, reported here extend previous studies on coral reefs that have Townsville inner shelf and Whitsunday mid-shelf) (Figs 5 & 6). found similar patterns between fish and coral richness (Bellwood There was no consistent pattern in the degree of concordance & Hughes, 2001; Karlson et al.,2004),SADs(Connollyet al., across habitats: in the Townsville region, similarity increased 2005) and compositional patterns (Bellwood & Hughes, 2001). (decreasing residuals) from inshore to offshore habitat, while Variation in community structure associated with different the reverse was true in the Whitsunday and Cairns regions. habitats across the continental shelf of the Great Barrier Reef

Scarus globiceps Pomacentrus moluccensis Neopomacentrus azysron 0.015 1.0 0.6 Cooktown Lizard 0.8 0.5 0.010 0.4 0.6 0.3 0.005 0.4 0.2 0.2 0.1 0.000 0.0 0.0 0.015 1.0 0.6 Cairns 0.8 0.5 0.010 0.4 0.6 0.3 0.005 0.4 0.2 0.2 0.1 0.000 0.0 0.0 0.015 1.0 0.6 Townsville 0.8 0.5 0.010 0.6 0.4 0.3 0.4 0.005 0.2 0.2 0.1 0.000 0.0 0.0 0.015 1.0 0.6 Whitsundays 0.8 0.5

Relative abundance Relative 0.010 0.6 0.4 0.4 0.3 0.005 0.2 0.2 0.1 0.000 0.0 0.0 0.015 1.0 0.6 Swains 0.8 0.5 0.010 0.6 0.4 0.4 0.3 Figure 4 Patterns of relative abundance 0.005 0.2 0.2 (temporally averaged at each reef) for 0.1 o 0.0 three abundant fish species (Scarus 0.000 0.0 globiceps, Pomacentrus moluccensis and 0.015 1.0 0.6 Capricorn Bunkers 0.5 Neopomacentrus azysron)acrossshelf 0.8 0.010 0.6 0.4 habitats (x-axis) and regional sectors 0.3 0.4 (arranged north to south down the 0.005 0.2 0.2 0.1 page). Dark bars represent the median, 0.000 0.0 0.0 the box represents 50% of the data, and Inner Mid Outer Inner Mid Outer the whiskers represent the minimum Inner Mid Outer and maximum values. Shelf Habitat could occur through spatial niche separation and/or spatially 2002). Theory suggests that the homogenizing influences of limited dispersal (Leibold et al.,2004;Legendreet al., 2005). dispersal within a shelf zone could then generate differences Studies of fish (Williams, 1982) and hard (Done, 1982) and among shelf habitats, independent of, or in conjunction with, soft (Dinesen, 1983) corals have previously nominated differ- other environmental drivers (Leibold et al.,2004;Legendre ences in wave energy, sediment loads and water clarity as pos- et al., 2005). Such an effect could also explain the similarity of sible environmental drivers of niche separation among species, pattern reported here since the only means of large-scale and consequently differences in community structure across movement among reefs in the large majority of species in both these habitat types. The strikingly similar patterns reported communities is via larval dispersal. here between fish and coral community structure could arise at Why did the effect of habitat interact with region in these least in part if the relative importance of these cross-shelf coral and fish communities? At least three sources of these dif- niche-based processes were similar and function in similar ferences are possible in both communities. First, differences ways for these two communities despite clear differences in the between regions in the numbers and types of taxa present could biology of their component species. In addition, spatial varia- lead to different community structures in the absence of any tion in patterns of larval dispersal may also account for some habitat differences (Schluter & Ricklefs, 1993). Second, large- of the spatial effects reported here. Regional-scale modelling of scale climatic variables that differ among regions may influence larval reef fish dispersal on the northern GBR suggests that a species’ probability of colonizing similar habitats in different cross-shelf mixing is much less than along-shelf mixing, so regions. For example, inshore habitats along the entire coast, that dispersing larvae are likely to have a much greater prob- while similar in their position across the continental shelf, expe- ability of settling on a reef in the same shelf zone (i.e. inner-, rience different magnitudes of terrestrial input (e.g. freshwater, mid- or outer-shelf positions) than in another (James et al., sediment and nutrients related to patterns of rainfall), which

Table 3 Concordance between non-metric multidimensional- 1.0 Cooktown Lizard scaling (nMDS) ordinations of ﬁsh and coral community 0.8 structure for each year and for all years combined 0.6 (time-averaged). Stress values are in percentage. 0.4 nMDS stress 0.2 No. of 0.0 2 Year reefs Benthic Fish m P-value 1.0 Cairns 0.8 1994 29 16.13 12.77 0.46 0.003 1995 44 16.26 13.93 0.46 < 0.001 0.6 1996 45 15.60 14.58 0.46 < 0.001 0.4 1997 43 15.15 13.74 0.62 < 0.001 0.2 1998 46 14.25 16.38 0.53 < 0.001 0.0 1999 45 13.95 15.22 0.53 < 0.001 1.0 2000 46 14.48 15.45 0.62 < 0.001 Townsville 2001 45 14.23 16.40 0.50 < 0.001 0.8 2002 45 13.09 16.31 0.52 < 0.001 0.6

2003 46 15.24 15.79 0.52 < 0.001 0.4

s l

2004 45 13.97 17.04 0.42 0.001 a

u 0.2 d Time-averaged 46 13.12 15.58 0.64 0.001 i

< s

e 0.0

e 1.0

Concordance is based on Procrustes rotation analysis (see Methods for s

i Whitsunday

2 d

details) where m is a measure of association between ﬁsh and coral r 0.8 a nMDS ordinations (lower values indicate a greater similarity between d

n 0.6 a ordinations). P-values indicate the significance level of the observed m2 t S 0.4 value. All P-values are less than 0.05 indicating non-random similarities in pattern between fish and coral communities. 0.2 0.0 1.0 potentially adds another level of environmental variability Swains 0.8 within this habitat zone (Fabricius & De’ath, 2001). Third, the 0.6 distance between habitats increases from the northern regions to the southern regions as the continental shelf widens (Fig. 1), so 0.4 that shelf habitats potentially become more isolated (James 0.2 et al.,2002)and,throughspatiallylimiteddispersal,mayleadto 0.0 differences in the degree to which one region exhibits commu- 1.0 Capricorn Bunkers nity variation across shelf habitats compared with another 0.8 (Leibold et al., 2004). It might be expected, however, that corals 0.6 have less potential than fishes for ecologically significant levels 0.4 of connectivity among shelf habitats due to their shorter pelagic 0.2 larval durations. So the effects of spatially limited dispersal 0.0 should be greater for corals than fishes and may explain why the Inner Mid Outer interaction between habitat and region contributed slightly less in the fish communities. Figure 5 Standardized residuals following Procrustes rotation It is also possible that the dynamics of each community are on time-averaged nMDS ordinations showing the degree of not independent of each other. The commonality of community association between fish and coral community structure across shelf positions (x-axis) and regional sectors (arranged north to patterns reported here could reflect a reliance of reef fishes on south down the page). Residuals are the differences, at each reef, live corals for food and shelter. If so, environmental gradients between the fish and coral nMDS ordinations after the optimal and disturbances influencing these corals could influence the Procrustes rotation was found (see Methods for details). structure of these fish communities (Jones et al.,2004;Wilson Standardized residuals are the residuals as a proportion of the et al., 2006, 2008; Graham et al., 2007; Munday et al., 2008). maximum residual (which occurred at reef 19–131, Fig. 6). Lower Even though a low proportion of reef fish species on the GBR are residuals indicate where reefs with similar coral communities had obligate live-coral-dwellers and coral-feeders (Munday et al., similar fish communities. 2008), declines in abundances of reef fish species in response to coral loss often occur in species that do not directly use live corals for food and shelter (Wilson et al., 2006), suggesting that the reliance of fishes on corals may involve indirect pathways. Because of the observational nature of this study, it is not

1.0 0.8 0.6 0.4 0.2

Standardized vector residuals 0.0 DIP RIB REBE HYDE 19138 20104 21529 19131 SNAKE LINNET YONGE DAVIES MIDDLE MARTIN CARTER OPAL (2) MACKAY CHICKEN NO NAME GREEN IS LIZARD IS WRECK IS PANDORA EAST CAY HASTINGS HAYMANIS CHINAMAN THETFORD FITZROY IS MYRMIDON ST. CRISPIN LOW ISLETS ONE TREE IS HORSESHOE TURNER CAY GANNET CAY MICHAELMAS BROOMFIELD HAVANNAH IS BORDER IS (A) JOHN BREWER MACGILLIVRAY AGINCOURT NO. 1 LADY MUSGRAVE IS NORTH DIRECTION IS LANGFORD AND BIRD IS'S

Figure 6 Standardized residuals from Procrustes analysis on time-averaged data showing the degree of association between fish and coral community structure at each reef. Residuals are the differences, at each reef, between the fish and coral nMDS ordinations after the optimal Procrustes rotation was found (see Methods for details). Standardized residuals are the residuals as a proportion of the maximum residual (which occurred at reef 19–131, Fig. 6). Lower residuals indicate where reefs with similar coral communities had similar fish communities.

possible to say whether the patterns observed here represent an than a decade in the absence of subsequent disturbances indirect link between the environment and fish expressed (Connell et al.,1997;Halfordet al., 2004; Sweatman et al., 2008) through corals as intermediaries, or a manifestation of some and the recovery of fish communities may lag that of coral other environmental variable directly affecting the partitioning communities since many fish species depend on corals for of variation in community structure in these two communities shelter and/or food (Graham et al.,2007;Wilsonet al., 2008). in a similar way. It is interesting to note, though, that across the In summary, our exploration of macroecological patterns in Indo-Pacific similar species abundance and resource use distri- these fish and coral communities indicates that either both butions have been reported for corals and Labrid reef fishes communities were structured in similar ways by processes (Connolly et al., 2005), as well as a similar representation of associated with region, habitat and time, or that there is an common families of fishes and corals across regional species indirect link between the environment and the structure of fish richness gradients (Bellwood & Hughes, 2001). Irrespective of communities mediated by corals. The consistency of the pat- the source(s) of this common partitioning, more research is terns reported here, and previously for local–regional species required on the importance of benthic communities in reef fish richness relationships (e.g. Caley and Schluter, 1997), makes a demography for determining the distribution and abundance of compelling argument for the need to better understand factors fishes within and among multiple regions (Wilson et al., 2008). affecting the structure of biological communities, and not just Compared with spatial factors, the inclusion of time in our species richness, across a diversity of scales. Understanding the analyses contributed comparatively little to improving the causes of these patterns and their consistency would be par- amount of variation in community structure that could be ticularly useful in the spatial management of a diverse range of explained. Although studies have shown that temporal biological communities facing increased degradation and loss sequences of disturbance and recovery of local communities on world-wide. reefs can be dramatic and important to their local ecologies (Connell et al., 1997), our results indicate that such temporal dynamics are less important to the overall structuring of reef ACKNOWLEDGEMENTS communities than are larger-scale spatial processes. This is despite frequent disturbances from multiple agents to GBR We thank all members of the Australian Institute of Marine communities. The most notable being cyclones affecting Science Long-Term Monitoring Programme (AIMS LTMP) who 20–25% of the sampled reefs in the 1996–97 and 1999–2000 have contributed to the collection of the data used here and sampling seasons, bleaching occurring on 15% of the sampled Steven Delean for help with statistical analyses. We thank Ron reefs in 1997–98 and 2002–03 sampling seasons, outbreaks of Karlson and three anonymous referees for comments that crown-of-thorns starfish predation, peaking at 31% of the greatly improved the quality of the manuscript. The order of sampled reefs in the 1999–2000 sampling season, and increased authorship follows a ‘first-last-author-emphasis’ approach. This prevalence of coral disease incidences since 1998–99 (Dolman work forms part of the AIMS LTMP. et al.,unpublisheddata).Therefore,theimpactsofdisturbances and recovery from them at local scales appear to be bounded to REFERENCES some extent by habitat effects and regional setting. It is important to realize, though, that the full spectrum of disturbance and Adbo, D., Burgess, S., Coleman, G. & Osborne, K. (2004) Surveys recovery dynamics of these communities may not have been of benthic reef communities using underwater video. Long-term captured during these 11 years of monitoring. Recovery of coral monitoring of the Great Barrier Reef, 3rd revised edn. Austra- and fish communities to pre-impact conditions can take longer lian Institute of Marine Science, Townsville, Australia.

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