Small-scale patterns of biodiversity in sponges (Porifera), from the Sunshine Coast, southeast...

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© CSIRO 2002 10.1071/IS02015 1445-5226/02/040637

Invertebrate Systematics, 2002, 16, 637–653

IS02015Small-scale sponge biodivers ity patternsJ. N. A. Hooper and J. A. Kennedy

Small-scale patterns of sponge biodiversity (Porifera) onSunshine Coast reefs, eastern Australia

John N.A. HooperA and John A. Kennedy

Biodiversity Program, Queensland Museum, PO Box 3300, South Brisbane, Qld 4101, Australia.ATo whom correspondence should be addressed. Email: [email protected]

Abstract. Ten reefs off the Sunshine Coast, south-eastern Queensland (26.2–26.8°S, 153.2°E), Australia, weresampled from 1991 to 2000. They were found to contain a rich fauna of 247 species of marine sponges (Porifera)in 97 genera, 44 families and 14 orders, with 51% of species not yet recorded elsewhere from the Indo-west Pacific,representing a highly unique fauna in this biogeographic transition zone between the Solanderian and Peronianprovinces. Reefs were relatively heterogeneous in species richness (18–83 species/reef, mean 41 species/reef),despite equivalent collection effort, and were highly heterogeneous in taxonomic composition (34% mean ‘apparentendemism’/reef), with only 15 species co-occurring in more than five reefs. Sixty per cent of species were ‘rare’(found only on single reefs) and only 19% of species co-occurred in the adjacent Moreton Bay region. Gradients inspecies richness and taxonomic composition were not correlated with the distance between reefs or their latitudeand only partially correlated with their distance from the shore, but they were highly correlated when sites werecombined on the basis of both distance from shore and latitude. Two southern outer reefs (5.5–9 km from the coast)and four northern inner reefs (0.5–1.25 km from the coast) had highly distinctive faunas (richness and taxonomiccomposition), with a gradual gradient of dissimilarities for reefs intermediate between these two groups of sites,similar to sponge faunal patterns from other studies conducted at much larger spatial scales. One southern outerreef, Flinders Reef, was anomalous compared with the general regional fauna. Flinders Reef had low speciesrichness, the highest taxonomic distinctness and the least heterogeneity in terms of taxonomic composition atspecies, genus and family levels, with affinities closer to the southern Great Barrier Reef than to the Sunshine Coastor Moreton Bay reefs. This finding is significant because Flinders Reef is the only designated highly protectedmarine area outside of Moreton Bay and is allegedly representative of the marine biodiversity of the whole region,yet contains few of the sponge genetic resources of the region, which has implications for the design and scale ofmarine reserves. Family-level taxa were poor surrogates of species diversity. Factors potentially responsible forspatial heterogeneity of sponge faunas between groups of reefs are discussed, including gradients in water quality(light, turbidity, siltation) and requirements for habitat specialisation by some species.

Introduction

Tropical Indo-Pacific marine faunas are often thought to behighly diverse and to occupy a more-or-less uniformenvironment (reviewed in Taylor 1997). Yet we know thatcoral reefs in particular, and many other marineenvironments in general, are substantially heterogeneous intheir diversity and faunal composition, often with littletaxonomic overlap between adjacent areas, geographicallyand bathymetrically (e.g. Huston 1985; Grassle 1991;Guichard and Bourget 1998; Richer de Forges et al. 2000;Gray 2001). Good baseline data to support this hypothesisexist for a few marine taxa including corals, fishes and somegroups of molluscs (e.g. Veron 1993, 1995; Meyer and

Paulay 2000; Bellwood and Hughes 2001). However, formost marine phyla we are still uncertain even of themagnitude of marine biodiversity (Kohn 1997), let alonetheir dynamics and interdependencies, their interactions andresponses to environmental factors, or the appropriate spatialscales needed to study them as essential prerequisites toaddress pressing conservation and management issues (e.g.Jensen et al. 1999; Anon. 2001).

In this category are included the sponges (Porifera), ahighly diverse and ancient group of primitive metazoans thathave survived largely unchanged in their fundamental‘bauplans’ since the Late Cambrian (509 million years ago).Once important reef builders during the Phanerozoic – a role

638 J. N. A. Hooper and J. A. Kennedy

subsequently usurped by the faster-growing scleractinariancorals during the late Mesozoic – sponges have radiated anddiversified in Recent seas, with representatives now found inall aquatic habitats, from ephemeral (quasi-terrestrial)habitats to the more stable marine abyssal zones (reviewed inHooper and Van Soest 2002). There are currently about 7000described (‘valid’) species worldwide but recent estimatesfrom the various museum collections suggest that the extantfauna is at least twice as diverse as this (e.g. Hooper and Lévi1994; Hooper and Wiedenmayer 1994; Van Soest 1994). Therenewed interest in the phylum and the accelerated discoveryof species over the past few decades has largely been drivenby their huge potential as sources of therapeutic drugs (e.g.Munro et al. 1999). Conversely, their systematic frameworkis still poorly resolved (Hooper and Van Soest 2002), andknowledge of their biodiversity (richness, endemism, spatialdistributions) (e.g. Hooper et al. 2002) and historicalbiogeographic affinities (e.g. Van Soest and Hajdu 1997) isstill rudimentary.

Over the past decade there have been several studies onsponge biodiversity and biogeography (i.e. contributing dataon species richness, abundance, endemism, taxonomicrelationships between marine areas), at various spatialscales. These studies have revealed that sponges formspatially heterogeneous, relatively highly ‘apparentlyendemic’ communities, with potential connectivity betweenadjacent communities also hampered by their reportedlyvery limited sexual reproductive dispersal capabilities (e.g.Sarà and Vacelet 1973; Zea 1993, 2001; but see Davis et al.1996). At larger spatial scales, sponge community patternshave been linked to such factors as historic and modern daywater current patterns, historical biogeography of the faunasand the regions they occupy, introduced ‘exotic pests’through human activity, etc. (e.g. Van Soest 1993, 1994;Maldonado and Uriz 1995; Van Soest and Hajdu 1997;Hooper et al. 1999b, 2000, 2002; Zea 2001). At smallerspatial scales, certain pivotal environmental variables havebeen linked to sponge community heterogeneity. Theseinclude light, depth, substrate quality, local reefgeomorphology, water quality, food particle size availability,larval recruitment and survival, etc. (e.g. Wilkinson andVacelet 1979; Wilkinson and Cheshire 1989; Hooper 1994;Maldonado and Young 1996; Roberts and Davis 1996; deVoogd et al. 1999; Lehnert and Fischer 1999). Thisdemonstrates that many (although not all) sponge specieshave very specific habitat requirements, thus making themuseful tools for monitoring environmental stress (reviewed inCarballo et al. 1996).

This present study complements an earlier analysis thatexamined changes to the sponge community along a largespatial scale (seascape or γ-scale; see Gray 2001) throughoutnorth-eastern Australia (Hooper et al. 1999b) (investigatingpatterns of richness, endemism and taxonomic composition),and a still larger, regional (continental or ε-scale) study of

provincial faunas throughout tropical Australasia, thewestern Pacific and South-East Asia (Hooper et al. 2002).Here we focus on small-scale (intra-regional or α-scale)patterns of biodiversity by comparing sponge faunas from 10more-or-less adjacent discrete reefs lying off the SunshineCoast of Australia (26.2–26.8°S, 153.2°E) (Fig. 1). Weinvestigate more closely the influence of high speciesheterogeneity between similar and/or adjacent reef systems,the taxonomic distinctness of the faunas that occupy thesereefs, and the impact of unique or rare species onbiodiversity (numerical) models and its implication for thedesign of small-scale marine protected areas.

Methods

Sponges were sampled from ten reefs lying off the greater SunshineCoast region, extending from South Halls to the north (off Noosa;26°22′S) to Hutchinson Shoals and Flinders Reef to the south (off CapeMoreton; 26°58′S) (Fig. 1). Sponges were collected using SCUBAbetween September 1991 and October 2000, photographed in situ,identified, registered and databased in the collections of the QueenslandMuseum (QM). Identifications were made to the lowest taxonomiclevel possible, with each ‘morphospecies’ assigned a unique codeirrespective of whether it could be identified as a known (described)taxon or was new to science. The majority of the Australian spongefauna (possibly >75%) still remains undescribed (Hooper andWiedenmayer 1994). Conspecificity between regional faunas, at allspatial scales (Hooper et al. 1999b, 2002) was determined using theMuseum’s unique taxonomic knowledge base (i.e. a propriety databasedescribing and illustrating morphology, histology, life characteristics,locality and habitat information, etc. for every specimen collected, withemphasis on differentiating morphospecies).

In this study, species diversity or biodiversity refers to the speciesrichness, taxonomic composition, levels of ‘apparent endemism’ andtaxonomic distinctness between small (α-scale) faunas occupyingdiscrete reefs within an approximate 50 km radius of each other. It doesnot include a measure of abundance of species populations frequentlyused to derive a synthetic diversity measure (e.g. Clifford andStephenson 1975), which was not possible to calculate using our divingprotocols. Similarly, ‘apparent endemism’ refers to geographicallyrestricted taxa that were not recorded outside a particular site relative toother sites investigated (Hooper et al. 2002) and does not necessarilyimply a wider phylogenetic interpretation of ‘endemism’, which for themarine biome (and for sponges in particular) is still highly contentious(Hooper 1994; Van Soest and Hajdu 1997).

Definitions of the spatial scale terminology follow Gray (2001),which were modified from his earlier classification (Gray 1997). Theserevised scales are defined as: point-diversity (a single sample within ahabitat); α-scale diversity (within-habitat diversity, or a number ofsamples within a habitat, where species are presumed to interact andcompete for similar limiting resources); γ-scale (within-region orlandscape/seascape diversity, where evolutionary processes becomeincreasingly important); and ε-scale diversity (regional orbiogeographic provincial diversity, or the total species richness of agroup of large areas). Gray’s (1997) previous use of the term β-scalediversity (between-habitat diversity, where community boundaries arecrossed and sampling covers more than one habitat or community; usedin a recent sponge biodiversity study; Hooper et al. 2002) has beenabandoned as a spatial descriptor because β-diversity refers to changesin the identity of species or the spatial turnover of species, along anenvironmental gradient, such as from site to site (e.g. Gray 2001; Izsakand Price 2001); it is used here in this revised sense. This, then, leavesus with a gap in the terminology for groups of small-scale sites that

Small-scale sponge biodiversity patterns 639

encompass more than one habitat, or between-habitats, previouslyreferred to by Gray (1997) as β-scale diversity. The term ‘meso-scalediversity’ is used here in the interim.

Dichotomous (presence/absence) data were tabulated for each of theten reefs investigated, providing information on intra-reef (α-scalediversity) and inter-reef differences (meso-scale diversity) in speciesdiversity and levels of ‘apparent endemism’. These data providedinformation on numbers of species, number and percentage of‘apparent endemic’ species in each site (Table 1). Collection effortrefers to the number of dives made at each site multiplied by the numberof divers used for each survey. Collection effort was obviously biasedby depth, whereby dive times decrease with increasing depth, but thisbias is consistent and definable, and collection effort was standardisedaccordingly. Median distances between reef sites were calculated usinga GIS (geographic information system) mapping tool (BioLink, CSIROPublishing: Melbourne).

To initially test whether species distributions were heterogeneous,Cochran’s Q test (designed specifically for dichotomous variables; Zar1999) was used. The null hypothesis was that there were no differencesin sponge species distributions between different reef sites. Clustering,ordination and biodiversity statistical analyses were then conducted toexamine biotic affinities between reef sites and to test hypothesesconcerning area relationships and reef connectivity. To estimate faunalsimilarity between pairs of reefs, we firstly used the Jaccard coefficientor similarity index (a/(a + b + c), where b and c are the total number ofspecies occurring at each reef and a is the number of species shared byboth reefs; e.g. Clifford and Stephenson 1975). This index was used inpreference to the Greig-Smith index, also known as the Sorensen’s,Dice’s and Bray–Curtis index (2 × a/(b + c)), used in some previoussponge biodiversity studies (e.g. Maldonado and Uriz 1995; Hooperet al. 1999b). Although both indices are apparently appropriate fordichotomous data, and both neglect conjoint absences of speciesbetween pairs of sites, the former gives equal weight to shared andnon-shared groups of fauna, is semi-metric, the most suitable forpresence/absence data and suitable for subsequent extrapolation toordination spaces such as multi-dimensional scaling (MDS) (Pielou1977, 1984) (Table 3). Although more widely employed, also metricand excluding conjoint absences, the Bray–Curtis index is not used herebecause it is more appropriate for semi-quantitative data (Maldonadoand Uriz 1995), is conservatively biased when the sampling and numberof species at two sites differs significantly (Richer de Forges et al. 2000)and overestimates the importance of shared taxa in the analysis offaunistic similarity (i.e. underestimates the contribution of unique or‘apparently endemic’ species) (Pielou 1984). Similarity analysis(hierarchical clustering using unweighted group average linkage) wasperformed on the Jaccard similarity matrix to investigate faunisticrelationships among the different sponge communities. Confidencelimits for the cluster analysis classification were obtained frombootstrapping routines from PAUP (version 4.0b8; Swofford 2001),with bootstrap values >85% apparently equivalent to 95% confidencelimits (Hillis and Bull 1993). PAUP analysis was set to perform aheuristic search with optimality set to distance, negative branch lengthsset to zero for tree score calculation, distance measure set to meancharacter difference, starting tree via neighbour-joining, TBR branchswapping, multrees option, trees unrooted and 1000 bootstrap replicatescalculated. Ordination analyses (2-D MDS) were also performed on theJaccard matrix, with bubble plots used to depict species richnesssuperimposed on MDS ordination space. The contribution of particularspecies to the observed assemblage differences from both cluster andMDS analyses was investigated using a similarity percentage(SIMPER) routine (Clarke and Warwick 2001), allowing species to beranked in decreasing order of importance according to the overallpercentage contribution each species makes to the average similarity(i.e. defining clusters) and dissimilarity between two groups (i.e.differentiating clusters).

Geographic and faunistic distances between pairs of reef sites werefurther explored using hypothesis tests on variation in biodiversityrelationships as an indicator of potential dispersal and connectivitybetween these α-scale sponge communities. Initially, a regressionanalysis of all pairwise comparisons between sites was undertaken ontabulated data of distance between each reef site (km) and pairwiseβ-diversity (calculated as the number of species common to two sitesfor all pairwise permutations of sites over the total area sampled; e.g.Schlacher et al. 1998). Secondly, the two independent data matrices(pairwise comparisons of the Jaccard similarity index and the pairwisedistances between reefs) were compared using a Kendallnon-parametric correlation statistic (rho, ρ), which measures therelative degree of agreement or disagreement between pairs ofvariables, with –1 being perfect disagreement and +1 being perfectagreement. Pairwise comparisons between individual reefs were alsoexamined using a Kendall rank correlation statistic (tau, τ). Thirdly,sites were grouped into classes according to a priori criteria (latitudinalclusters, distances from shore). Non-parametric one-way analyses ofsimilarity (ANOSIM), roughly analogous to standard one-wayANOVA, provided a statistical test of the null hypothesis – that there areno assemblage differences between groups of samples (sites) – andbecause they are permutation/randomisation tests, they make minimumassumptions and focus on the ranks of the biotic similarity matrices thatcontain the primary information on assemblage relationships betweensamples (Clarke and Warwick 2001). According to these authors, thepairwise global R-value is the pivotal statistic because it gives anabsolute measure of how separated the groups are, on a scale of 0(indistinguishable) to 1 (all similarities within groups are less than anysimilarities between groups). These tests also identify which of thefactors (groups of sites) are significantly different from other sites.

Finally, a taxonomic distinctness analysis (Clarke and Warwick2001) was conducted on presence/absence data for species-, genus- andfamily-level taxa. Taxonomic distinctness, an average measure of therelatedness between any two species in a community sample (Izsak andPrice 2001), was measured using two indices: average taxonomicdistinctness (AvTD or delta+) and variation in taxonomic distinctness(VarTD or lambda+) (Clarke and Gorley 2001). These indicesincorporate taxonomic or phylogenetic information by computing apath length, or relative taxonomic distance, between any two species.They are independent of sampling effort and therefore less susceptibleto possible biases from sampling than are species richness indices,including the Jaccard index of similarity (Clarke and Warwick 1998;Warwick and Clarke 1998; Clarke and Gorley 2001; Izsak and Price2001), and provide effective comparisons of biodiversity betweenlocalities at various spatial scales. These indices reflect both therichness in higher taxa and the evenness component of diversity, but areultimately a function of pure taxonomic relatedness of individuals(Warwick and Clarke 2001). Average taxonomic distinctness is theaverage taxonomic path length (in a Linnean or a phylogeneticclassification) between any two randomly chosen species. The index ismost effective for comparing sets of data where there are a restrictednumber of higher taxa for a given number of species, but is lesseffective when there is an uneven distribution of species taxa amonghigher taxa, where some taxa are over-represented and othersunder-represented in comparison to the species pool for the geographicregion (e.g. effects of habitat heterogeneity). Variation in taxonomicdistinctness therefore measures the evenness of the distribution of taxaacross the hierarchical taxonomic tree, and is also independent ofsample size. The two indices used together are considered to be astatistically robust summary of taxonomic relatedness patterns acrossan assemblage and appropriate to historical data and simple species lists(Warwick and Clark 2001). The null hypothesis tested is that a specieslist from a particular site, which may be incomplete, nonetheless has thesame taxonomic distinctness structure as the master list from which itis drawn (i.e. for all species from all sites in that geographic region)


(Clarke and Gorley 2001). Using a series of randomisation tests thatsample the whole data set from the geographic region, repeated for arange of random sample subsets (M = 20–100), a 95% confidence rangeof possible (‘expected’) values of both AvTD and VarTD wascalculated. These probability envelopes (funnel and ellipse functions)were plotted over the real data set for each set of taxa (sites), withsamples below or above these intervals representing biodiversitymeasures below or above expectation respectively. These data werecompared at species, genus and family levels.

Calculations were performed using several software packages:Primer 5.0 (Plymouth routines in multivariate ecological research)(Primer-E Ltd, Plymouth Marine Laboratory, UK); Statgraphics (STSCCorp., New York, USA); and Systat 9.0 (SPSS Inc., Chicago, USA).

Results

Species composition

Our various surveys of sponges from the Sunshine Coastregion recorded 247 species in 97 genera, 44 families and 14orders associated with fringing coral reefs, hard (rocky)bottoms and soft bottom substrata. Saenger (1991) andBanks and Harriott (1995) provide more detaileddescriptions of the habitats. Of these sponge species, 226were collected from reefs lying directly off the SunshineCoast (Fig. 1), and an additional 21 species were collectedfrom the more southern reefs lying off Moreton Island(Hutchinson Shoal and Flinders Reef, approximately 50 kmsouth of the Sunshine Coast reefs). Of the Sunshine Coastreefs 115 species (51%) were ‘apparent endemics’, i.e.restricted to these reefs, and 47 were also previouslyrecorded from the Moreton Bay fauna.

Species richness varied considerably between each of the10 localities (Table 1), ranging from 18 species (OuterGneering Shoals) to 83 species (Jew Shoal; Fig. 1) (mean 41species). Only fifteen species were ‘common’, i.e.co-occurred in more than five different sites (or 50% ofsites), and can be considered as the ‘typical’ Sunshine Coastfauna: Acanthella cavernosa; Acanthella klethra; Agelasmauritiana; Aplysinella sp. (# 1194); Cliona (Rhaphyrus)hixonii; Dendrilla rosea; Echinoclathria (Echinoclathria)intermedia; Echinodictyum mesenterinum; Halichondria, sp.nov. (#1437); Haliclona sp. (#1381); Ianthellaquadrangulata; Leucetta microrhaphis; Raspailia(Clathriodendron) digitatus; Rhabdastrella globostellata;Rhaphidotethya enigmatica; and Trachycladus digitatus. Themajority of species (147, or 60%) are ‘rare’, found only froma single reef system (Fig. 2).

Fig. 1. Location of reefs sampled off the Sunshine Coast,south-eastern Queensland in the present study (with abbreviated sitenames) and those studied in other surveys of south-easternQueensland (mentioned in the text).

Table 1. Summary statistics for the 10 reefs surveyed for sponges off the Sunshine Coast, eastern Australia, ordered by distance from shore

Median coordinates of latitude and longitude are averages from several collection sites on each reef

Locality Depth range sampled (m)

Median distance

from shore (km)

Median latitude (decimal deg. S)

Median longitude (decimal deg. E)

Collecting effort (person

dives)

No. species

No. ‘endemic’

species

% ‘endemic’ species

North Halls (Nth) 12–24 0.50 26.205 153.040 08 030 011 37Jew Shoal (Jew) 12–29 1.20 26.218 153.066 08 083 034 41Peregian Reef (Per) 12–17 1.20 26.290 153.060 07 023 009 39Mudjimba Island (Mud) 10–16 1.25 26.367 153.068 14 067 024 36Sunshine Reef (Sun) 20–27 3 26.245 153.083 13 067 024 36Inner Gneering Shoals (Ing) 10–19 5 26.388 153.095 11 044 014 32Flinders Reef (Fli) 04–20 5.50 26.586 153.292 14 028 016 57Outer Gneering Shoals (Out) 25–28 8.50 26.395 153.129 07 018 004 22Murphy's Reef (Mur) 25–30 9 26.406 153.141 07 032 006 19Hutchinson Shoal (Hut) 18–28 9 26.560 153.290 07 021 005 24Total 64 247 147 Mean 34.3%


Given the close proximity of some of these reefs to eachother, it was initially hypothesised that this observedheterogeneity might be a result of variable collection efforts,such as depth bias where deeper reefs were sampled for ashorter duration than shallow reefs, or differences inperson-dives between localities. Superficial comparison ofcollection effort versus species richness between sites(Fig. 3) indicates that these data may be dependent (i.e. theincrease in species richness corresponds to an increase incollection effort for a particular site), but regression analysesshow that correlations were not statistically significant (R2 =0.1987, P > 0.05; Fig. 3A). Removal of the two extreme datapoints from the analyses (the most diverse site, Jew Shoal,and the least diverse site, Flinders Reef) (Fig. 3B) produceda highly significant correlation (R2 = 0.9406, P < 0.0001),inferring that species richness was not completely dependenton collection effort. Other factors, such as local (small-scale)environmental differences between reefs (geomorphology,water quality, substrate type, etc.), may also influencesponge species composition and richness, even at the smallscale (point- and α-scales of diversity). However, it ispresently impossible to separate the relative contribution ofthese factors without accompanying data on species’abundances (which are not routinely acquired duringmuseum biodiversity surveys where the ultimate aim is tomaximise discovery of new taxa using the most efficientcollection effort).

Species richness was generally lowest on the outer-mostreefs (5.5–9 km from nearest shore) and some of theinnermost reefs (0.5–1.25 km) (Table 1), with some inner-and both the mid-shelf reefs (3–5 km) containing the richestfaunas. To some extent, these trends correlate with physical

differences between reefs and proximity to the coast,whereby outer reefs (Outer Gneering Shoals, HutchinsonShoal, Flinders Reef, Murphy’s Reef) have a relatively lowdiversity of habitats (mostly hard corals) and relativelyuniform topography (i.e. they are either completely flat orhave only slight slope). Similarly, two of the inner reefs(Peregian Reef and North Halls) are situated closer to riveroutflows with consequent higher impacts from turbidity andsiltation. Reefs with the highest species richness (two innerreefs, Mudjimba Island and Jew Shoal; and two middle reefs,Inner Gneering Shoals, Sunshine Reef) also have a greaterdiversity of habitat types and rugged topography (i.e.containing habitats with overhangs, caves, gullies, greaterslope etc.), providing refuge to cryptic taxa such as theencrusting and sciaphilic communities. This is especiallytrue of Jew Shoal, which has the highest species richness andgreatest variability in bottom topography. Although this siteis also in proximity to the Noosa River, it is not directly in itspath, as is the case for North Halls, where the heterotrophicfilter-feeding sponge community potentially benefits fromthe higher nutrient river discharge. Both these reefs hadidentical collection effort but the former had nearly threetimes species richness (Table 1). These hypotheses aboutchanges in community patterns along two environmentalgradients (latitude of sites and their distance from the coast)are specifically tested further below.

Fig. 2. Frequency distribution of sponge species co-occurring inthe 10 reef sites.

Fig. 3. A, Regression analysis of species diversity against collectioneffort (person dives) for all 10 reef sites (R2 = 0.1987, P > 0.05); B,regression analysis for eight reef sites, the most diverse and leastdiverse sites (Flinders Reef and Jew Shoal) are excluded from analysis(R2 = 0.9406, P < 0.0001). (Refer to Fig. 1 for key to abbreviated sitenames.)


Generally, ‘apparent endemism’ (numbers of uniquespecies found at a particular site), appears to be closelyrelated to the total species richness at each site, such that theeffects of the two parameters could not be differentiatedreliably (R2 = 0.8998; P < 0.0001; Fig. 4). In other words,although middle- and some inner-reef sites had highest‘apparent endemism’, they also contained the greatestnumbers of species compared with other inner- and theouter-reefs. An exception is Flinders Reef, which hadcomparatively low species diversity but high levels of‘apparent endemism’, and in this case its unique fauna maypartially be a result of its more southerly, isolated location,more closely connected to the Moreton Bay faunas than tothose of the Sunshine Coast. Unfortunately, no abundancedata were collected to test whether species accumulationcurves were saturated (reaching an asymptote), to ascertainthe impact of potential sampling bias on unique speciesversus total numbers of species at each site. These data alsoindicate that unique or rare species may significantly biasbiodiversity estimates when applied to small (α-scale)

faunas (point collection data and single reef systems). Thesignificant contribution of rare or unique species tosmall-scale faunas is illustrated by their frequencydistributions (Fig. 2), showing that of the 247 speciescollected from 10 sites, 81% (202 species) are ‘unique’ or‘rare’, occurring in only one or two sites, and only 19% (45species) are found in three or more sites. Hence, the 147(60%) ‘apparently endemic’ species (i.e. found only in onesite) do not contribute to biodiversity models using similarityanalyses, with analyses based on affinities between spongecommunities solely dependent on information from the 100species that occur at two or more sites. To some extent, thesequestions are answered using the non-parametric taxonomicdistinctness analysis, below.

Community classification

Sponge community structure (mean diversity) differedsignificantly over all reef sites (ANOVA, F = 65.337,d.f. = 10,237, P < 0.0001; Cochran’s Q statistic = 133.25,P < 0.0001). Cluster analysis of reef sites (Fig. 5) based onJaccard similarity matrix (Table 2) yielded three groups,although bootstrapping support for branch dichotomies waslow (36%, 48%, 23%), probably reflecting the highproportion of ‘unique’ or ‘rare’ species on all reefs (meanvalue of 34% ‘apparent endemism’ for all reefs). The outerreefs (Group 1 cluster) are most similar, with the southernreefs of Flinders and Hutchinson having strong bootstrapsupport, and the Outer Gneerings less similar within thisgroup. This pattern is also reflected in the MDS ordination(stress of configuration 0.15, proportion of variance (RSQ)= 0.804; Fig. 6, with higher species richness represented bylarger bubbles). The Outer Gneerings Shoals is closest andmost similar in depth and habitat profile to Murphy’s Reef(Fig. 1) (Table 1), yet surprisingly both cluster and MDSshow they are dissimilar in both species richness and

Fig. 4. Regression analysis of species richness against the numberof unique species collected from each reef site (R2 = 0.8998, P <0.0001). (Refer to Fig. 1 for key to abbreviated site names.)

Table 2. Similarities in composition of sponge species between 10 reef sites off the Sunshine Coast, eastern Australia

Pairwise comparisons between sites show numbers of co-occurring species (upper half of matrix, roman text) and percentage similarity between faunas (lower half of matrix, italic text); the latter are based on the Jaccard

similarity index. Species richness at each site is shown in bold (numbers in diagonal row)

Number of shared speciesLocality Nth Jew Sun Per Mud InG Mur Out Hut Fli

North Halls (Nth) 30.99 11.99 08.99 02.88 10.88 04.99 04.77 02.66 02.88 01Jew Shoal (Jew) 10.78 83.88 22.99 08.77 18.77 13.99 14.88 07.88 07.88 04Sunshine Reef (Sun) 08.99 17.19 67.88 02.77 18.77 10.99 11.66 09.88 04.77 03Peregian Reef (Per) 03.92 8.16 02.27 23.88 05.88 01.88 04.88 01.77 01.66 00Mudjimba Island (Mud) 11.49 13.64 15.52 05.88 67.00 13.99 07.55 07.66 06.66 02Inner Gneerings (InG) 05.71 11.40 09.90 01.52 13.27 44.66 03.77 01.66 06.66 02Murphy’s Reef (Mur) 06.90 13.86 12.50 07.84 07.61 04.11 32.66 01.66 03.66 03Outer Gneerings (Out) 04.35 07.45 11.84 02.50 08.97 01.64 02.04 18.66 02.77 02Hutchinson Reef (Hut) 04.08 07.22 04.76 02.33 07.32 10.17 06.00 05.41 21.66 07Flinders Reef (Fli) 01.75 03.74 03.26 00.00 02.15 02.86 05.26 04.55 16.67 28

Jaccard similarity index (%)


taxonomic composition, with Murphy’s Reef being moresimilar to the inshore Peregian Reef (with 50% bootstrapsupport; Fig. 5) (Group 3 cluster), although this trend is notso strong in ordination space (Fig. 6). The middle cluster(Group 2) has very low bootstrap support (23%; Fig. 5), withsites not showing any strong affinities in ordination space(Fig. 6), and with North Halls and the Inner Gneeringsrelatively more dissimilar than the other three reefs(Mudjimba Island, Jew Shoal, Sunshine Reef) – the lattersupported by 42% and 62% bootstrapping respectively.Multi-dimensional scaling ordination of this group is moreinformative (Fig. 6), with the likelihood that the former tworeefs should be excluded from this similarity cluster (this istested further below). Interestingly, MDS ordination patternswere identical irrespective of whether the Jaccard orBray–Curtis indices were used, although percentage

similarities were significantly higher using the Bray–Curtisindex (data not shown).

Patterns of sponge community structure between reefsites (measured as the number of species common to twosites for all pairwise permutations), was not uniformlyrelated to the distance between reefs, such that the mostadjacent reefs do not, on average, have the most similarfaunas (R2 = 0.1127, P > 0.01; Fig. 7). In some cases, thereis an obvious relationship between faunal similarity andproximity between reefs, such as between the adjacent reefsof Jew Shoal and Sunshine Reef, only 2.5 km apart, asevidenced by both cluster and MDS analyses (Figs 5, 6), inwhich case there is clearly some level of connectivitybetween their respective faunas. In other cases the inverse istrue, such as the adjacent reefs of Inner Gneerings and OuterGneerings Shoals, only 1.1 km apart, which have low faunalsimilarities and which is presumably indicative of somesignificant environmental and/or geomorphologicaldifferences between these two reef sites. These conclusionsare supported by the less powerful but possibly also less

Group 1

Group 2

Group 3

}

}}

Fig. 5. Cluster analysis of similarities between sponge communities on the Sunshine Coast(Jaccard similarity index). Numbers refer to bootstrapping (from PAUP), where 85% isroughly equivalent to 95% confidence limits. (Refer to Fig. 1 for key to abbreviated sitenames.)

Fig. 6. Multi-dimensional scaling (MDS) ordination of similaritymatrix (Jaccard index) for sponge communities on the Sunshine Coast(refer to Fig. 1 for key to abbreviated site names). Final stress ofconfiguration = 0.15; proportion of variance (RSQ) = 0.804. Bubbleplots depict species richness at each site.

Fig. 7. Regression of β-diversity (number of species common to twosites for all pairwise permutations of sites over the total area sampled)in relation to distance (km) between reefs, from pairwise comparisonsbetween each reef system (R2 = 0.1127, P = 0.011).


biased non-parametric Kendall rank correlation statistic (ρ =–0.263, P > 0.025), which found that in fact only six pairs ofreef sites had any significant positive or negative correlationbetween them with regard to their relative proximity andsimilarity in species composition. Sites showing negativecorrelation were Peregian Reef v. Sunshine Reef, andFlinders Reef v. Jew Shoal, Sunshine Reef and Mudjimba.Sites showing positive correlation were the Outer GneeringShoals v. Sunshine Reef, and Flinders Reef v. HutchinsonReef; Table 3). Analysis of Similarity (ANOSIM) found nosignificant differences in faunal similarity between sitesgrouped according to their latitudinal position (i.e. northern,central and southern reefs; global R = 0.14, P = 0.217), ordistance from the coast (i.e. inner, mid and outer reefs; globalR = 0.19, P = 0.138), that were not greater than within-group

similarities. By comparison, when sites were grouped usingboth factors there were slightly more significant differencesbetween sites (global R = 0.33, P = 0.064), particularlybetween the southern outer reefs (Flinders and Hutchinson)and the northern inner reefs (North Halls, Jew, Peregian,Mudjimba) (R = 0.821, P = 0.067; Table 4; Fig. 8).

A posteriori grouping of sites based on latitude anddistance criteria produced four ‘meso’-scale faunas(northern-inner, northern-mid, northern-outer and southern-outer reefs). Similarity percentage (SIMPER) analysisshowed that only a relatively small proportion of speciescommon to all pairwise comparisons within and betweenthese sites were significant contributors to their similarities(Table 5) (i.e. defining within-group similarities), ordissimilarities (Table 6) (i.e. differentiating between groups),with groups determined by both cluster (Fig. 5) and MDSanalyses (Fig. 6). Not surprisingly, many of these species thatboth define and differentiate the cluster groups are also themost ‘typical’ species for the Sunshine Coast reefs. In otherwords, the contribution of the 60% of ‘rare’ species indefining and differentiating these ‘meso’-scale faunas isnegligible.

Taxonomic distinctness analyses

For species-level taxa (Fig. 9A–B) most sites fall within the95% predicted range for average taxonomic distinctness(AvTD, or delta+), with only Flinders Reef (highest

Table 3. Summary of correlation analysis testing sponge faunal affinities between pairs of reef sites off the Sunshine Coast, eastern Australia

Pairwise comparisons between sites show median distances between each reef (upper half of matrix, roman text) and Kendall rank correlation coefficient (upper numeral) and significance level (italicised lower numeral, lower half of matrix), with sample size = 247 for all sites. Species

richness at each site is shown in bold (numbers in diagonal row)

Median distance between reefs (km)Locality Nth Jew Sun Per Mud InG Mur Out Hut Fli

North Halls (Nth) 30 2.600 5.100 9.500 18.6000 21.0000 24.3000 22.1000 46.1000 49.3Jew Shoal (Jew) 0.0241 83 2.500 6.900 16.0000 18.4000 21.7000 19.5000 43.5000 46.7

0.7053Sunshine Reef (Sun) –0.0038 –0.0099 67 4.400 13.5000 15.9000 19.2000 17.0000 41.0000 44.2

0.9520 0.8764Peregian Reef (Per) –0.0338 0.0080 –0.1328 23 9.1000 11.5000 14.8000 12.6000 36.6000 39.8

0.5955 0.9001 0.0372Mudjimba Island (Mud) 0.0519 –0.0485 –0.0036 –0.0388 67000 2.4000 5.7000 3.5000 27.5000 30.7

0.4155 0.4471 0.9554 0.5426Inner Gneerings (InG) –0.0435 –0.0400 –0.0461 0.0491 0.0491 44 3.3000 1.1000 25.1000 28.3

0.4155 0.5305 0.4701 0.4409 0.4409Murphy's Reef (Mur) 0.0042 0.0829 0.0629 –0.0184 –0.0851 –0.0851 32 2.2000 21.8000 25.0

0.9477 0.1937 0.3238 0.7724 0.1820 0.1820Outer Gneerings (Out) –0.0089 0.0314 0.1442 0.0742 –0.0898 –0.0898 –0.0618 18 24.0000 27.2

0.8892 0.6227 0.0237 0.2447 0.1589 0.1589 0.3326Hutchinson Reef (Hut) –0.0245 –0.0017 –0.0227 –0.0477 0.0426 0.0857 0.0121 0.0262 21 03.2

0.7012 0.9782 0.7214 0.4541 0.5044 0.1789 0.8498 0.6808Flinders Reef (Fli) –0.0939 –0.1462 –0.1320 –0.1146 –0.1320 –0.0997 –0.0239 –0.0020 0.2115 28

0.1410 0.0218 0.0384 0.0723 0.0384 0.1178 0.7082 0.9751 0.0009Kendall rank correlation coefficient (and significance level)

Table 4. Global R-values and significance levels for analysis of similarity (ANOSIM) tests, comparing groups of sites arranged

a priori by site characteristics

Group Global R value Significance level

A, Latitude 0.14 21.7%B, Distance from coast 0.19 13.8%C, Latitude + distance 0.63 06.4%

A, Latitude (northern, central and southern reefs); B, distance fromcoast (inner, mid and outer reefs); C, combined latitude and distancefrom coast (northern inner, northern mid, northern outer andsouthern outer reefs).


distinctness) and Mudjimba Island (lowest distinctness)deviating from the general area pattern. By comparison,Flinders is under-represented and Mudjimba isover-represented in terms of habitat heterogeneity (orunevenness), as measured by variation in taxonomicdistinctness (VarTD, or lambda+): the former the leastheterogenous and the latter the most heterogeneous. Boththese sites, for both indices, are statistically significant(P < 0.002 and 0.01 for Flinders, P < 0.042 and 0.006 forMudjimba, for AvTD and VarTD respectively). Thesimulated means (dashed lines, Fig. 9A–B) are also close tothe master list values of 94.8 (AvTD) and 191 (VarTD),reflecting independence of the statistic on differing samplesize between sites (Warwick and Clarke 1998). At the genuslevel (Fig. 9C–D), only Flinders Reef deviates from theexpected null distribution (P < 0.008 (AvTD) and 0.028(VarTD)). At the family level (Fig. 9E–F), Flinders Reef,again, is significantly different (P < 0.032 (AvTD) and 0.02(VarTD)), as is Peregian Reef (P < 0.013 (AvTD) and 0.019(VarTD)), the latter result suggesting that the distribution ofsamples within family groups at Peregian Reef differssignificantly from the geographic region in general.Two-dimensional plots of 95% probability ellipses from thesimulated distribution (for sampling subsets M = 20–100),with real values of the AvTD and VarTD pairs superimposed

within probability ellipses (Fig. 10), show that forspecies-level taxa, all faunas lie within the M = 50 simulationenvelopes, except Flinders (well outside the M = 30envelope), and Peregian and Mudjimba (outside M = 50).These data simply reflect the higher taxonomic distinctness(AvTD) and unevenness (VarTD) of Flinders Reef, and theconverse for Mudjimba and Peregian Reefs. For genus-leveltaxa, the Flinders Reef fauna is again exceptional (withPeregian marginal), whereas for family-level taxa onlyPeregian Reef lies outside of the 95% contour at M = 20owing to the highly uneven taxonomic distribution of itsfauna, as noted earlier. Thus, with the exception of FlindersReef at the species and genus level, and Peregian Reef at thefamily level, the modelled 95% probability contour is areasonable fit for these sponge faunal distributions.

Discussion

Sunshine Coast benthic faunas and their biogeographic affinities

The Sunshine Coast benthic fauna is rich (Banks andHarriott 1995), although its taxonomic description is stillrudimentary in comparison with the adjacent, better-knownMoreton Bay region and the southern Great Barrier Reef.Existing collections from this region far exceed taxa

Freq

uenc

yFr

eque

ncy

Freq

uenc

y

R

Fig. 8. Frequency histograms of analysis of similarity (ANOSIM) tests, comparing groups ofsites arranged a priori by site characteristics. A, Latitude (northern, central and southern reefs);B, distance from coast (inner, mid and outer reefs); C, combined latitude and distance from coast(northern inner, northern mid, northern outer and southern outer reefs). Refer to Table 4 forglobal R-values and significance levels.


formally described in the literature (QM database). This richfauna is a product of mixing of biota from the major northern(Solanderian) and southern (Peronian) biogeographicprovinces, as well as a suite of endemic species (e.g. Davieand Hooper 1998; Davie 1998). There is also an observeddepth stratification of tropical and temperate faunas inshallow and deeper waters on many reefs off the greaterBrisbane area respectively (J. N. A. Hooper, personalobservation). With the possible exception of algae andscleractinian corals, much remains to be done to achieveanything approaching a comprehensive inventory of itsbiota. Some pertinent literature includes: macroalgae (Cribband Cribb 1985; Saenger 1991; HERBRECS in Dennisonand Abal 1999); sponges (Hooper 1991, 1996; Hooper et al.1999b); coralliomorpharians, actiniarians and zoantharians(Carlgren 1950; Richardson et al. 1997); hydroids(Pennycuik 1959); scleractinian corals (Wallace 1978; Veron1993; Banks and Harriott 1995); and ascidians (Kott 1985,1990, 1992) (with additional literature cited in Davie andHooper 1998).

A few studies have also explored community structure ofthese reefs. Banks and Harriott (1995) determined patternsof scleractinian coral distributions (and also reworkedSaenger’s 1991 macroalgae dataset), delineating fourcommunities with different faunal patterns: (1) off-shore

(Murphy’s Reef and Outer Gneering Shoals); (2) near-shore(Point Cartwright); (3) sites intermediate between near-shoreand off-shore (Inner Gneering Shoals and Maroochy Reef);and (4) Mudjimba Island (refer to these localities marked inFig. 1). They also found that coral and algal species diversityat the Inner Gneerings was lower than that on Flinders Reef,and also the southern Great Barrier Reef (about 300 km tothe north). Algae contributed most to benthic cover at themajority of these sites, and the dynamics of the algalcommunity was apparently a key determinant for the wholeof their community classification. Richardson et al. (1997)found for subtropical giant anemones that offshore sitesgenerally had comparatively higher species diversity thanin-shore sites, and that diversity was lower on reefs off theSunshine Coast than those further to the north, in the tropics.

Similarly, patterns of change (or β-diversity) andbiogeographic affinities of the sponge communities wereexplored by Hooper et al. (1999b) for 18 ‘meso’-scale faunas(clustered groups of α-scale communities), extending fromSydney (34°S, 151°E) to the Gulf of Carpentaria (11°S,141°E), and including some sites on the Sunshine Coast.They delineated six distinct regional (or γ-scale) spongefaunas in NE Australia. Most of the 18 ‘meso’-scale faunascontained relatively high proportions (mean 33%) of‘apparently endemic’ species (i.e. species found in only one

Table 5. Summary of similarity percentage (SIMPER) analysis, ranking species according to the overall percentage contribution each makes to the average within-group similarity (i.e. contributing to defining groups from cluster (Fig. 5) and multi-dimensional scaling

(MDS) analyses (Fig. 6))

Cluster and MDS group

Sites included Average within-sitesimilarity

No. spp.in group

Species that contribute >95% similarity to define within-group similarity (% contribution)

No. spp. contributing significantly to within-group similarity (% contribution)

A. Northern-inner reefs

North Halls (Nth), Jew Shoals (Jew), Peregian Reef (Per), Mudjimba Island (Mud)

16.75% 149 Cliona (Rhaphyrus) hixonii (7.39%), Psammocinia sp. #1407 (5.41%), Haliclona sp. #1381 (5.41%), Raphidotethya enigmatica (5.14%), Raspailia (Clathriodendron) digitatus (5.14%), Raspailia (Raspaxilla) sp. #1081 (5.14%), Phakellia cavernosa (5.14%), Echinodictyum mesenterinum (5.14%), Aplysinella sp. #814 (5.14%), Hyattella sp. #2763 (3.75%), Dysidea sp. #1400 (2.21%)

35 (95% similarity) (11 spp accounting for 55.1% similarity)

B. Northern-mid reefs

Sunshine Reef (Sun), Inner Gneering Shoals (Ing)

18.02% 101 Trachycladus digitatus (10%), Siphonochalina sp. #2672 (10%), Raphidotethya enigmatica (10%), Phakellia klethra (10%), Euryspongia deliculata (10%), Dysidea sp. #2669 (10%), Cribrochalina sp. #2666 (10%), Cinachyrella sp. #180 (10%), Agelas sp. #2480 (10%), Aplysinella sp. #1194 (10%)

10 (100%)

C. Northern-outer reefs

Murphys Reef (Mur), Outer Gneering Shoals (Out)

28.57% 049 Haliclona sp. #1381 (94.6%), Strongylacidon sp. #2900 (2.4%), Stylinos sp. #252 (1.5%), Stylotella sp. #2514 (1.5%)

4 (100%)

D. Southern-outer reefs

Flinders Reef (Fli), Hutchinson Reef (Hut)

28.57% 028 Rhabdastrella globostellata (14.29%), Myrmekioderma granulata (14.29%), Erylus amissus (14.29%), Desmapsamma sp. #1125 (14.29%), Cliona sp. #2337 (14.29%), Cribrochalina sp. #2178 (14.29%), Agelas mauritiana (14.29%)

7 (100%)


of the 18 regions), with geomorphology and biogeographicfactors suggested to be largely responsible for inter-regionaldifferences. Of the Sunshine Coast sites, they reported afauna consisting of 106 species, of which 35 species (33%)were ‘apparent endemics’. Not surprisingly, the fauna wasmost similar in terms of species composition to the adjacentMoreton Bay region, with 13% (31) of species shared from acombined fauna of 241 species (approximately 10% Jaccardsimilarity). On this basis, the two regions were subsequentlycombined into a single ‘Moreton Bay Provincial fauna’(Hooper et al. 1999b). Subjectively, however, these data werequestioned by the authors given some substantial differencesbetween the Sunshine Coast and Moreton Bay regions interms of the diversity, environmental conditions andgeomorphologic structures of their marine habitats,irrespective of their relatively close proximity (i.e. potentialconnectivity between their faunas). From the present data wesuggest that the high levels of ‘apparent endemism’ ofsponges observed on Sunshine Coast reefs, and relativelylow similarities with adjacent regions, indicate that thissponge fauna is only marginally more closely related to theMoreton Bay province than with other coastal and reef

faunas further to the north and south, and there is probablyno justification to combine their faunas into a singlehomogeneous marine area (i.e. only 19% of species from theSunshine Coast reefs are also found in Moreton Bay (11%Jaccard similarity; present data), 7% are found in the coastalfauna from the Fraser Island–Hervey Bay region (6%Jaccard similarity; Hooper et al. 1999b), 7% in theCapricorn–Bunker Group fauna, southern Great BarrierReef (6% Jaccard similarity; Hooper et al. 1999b), and 9%further south in the Tweed River–Byron Bay region (8%Jaccard similarity; Hooper et al. 1999b).

Sponge biodiversity

The present study more than doubles the region’s knownsponge diversity (247 species) with an additional 141 speciesdiscovered since the earlier study (Hooper et al. 1999b), andreveals significantly higher levels of spatial heterogeneitybetween reefs at the α-scale than previously acknowledgedfor sponges within the Indo-west Pacific fauna (e.g.Wilkinson 1983, 1988; Wilkinson and Cheshire 1989;Hooper 1994; Hooper et al. 1999b), although also recentlyreported for a Caribbean sponge fauna (Zea 2001). Of these

Table 6. Summary of similarity percentage (SIMPER) analysis, ranking species according to the overall percentage contribution each makes to the average between-group dissimilarity (i.e. their proportional contributions to differentiating ‘meso’-scale groups, as defined

by cluster (Fig. 5) and multi-dimensional scaling (MDS) analyses (Fig. 6))Refer to Table 4 for key to group and site nomenclature

Pairwise group comparisons

Averagegroup dissimilarity

Species contributing most to defining between-group similarity(% contribution)

No. spp. accountingfor between-group dissimilarity (% dissimilarity)

A v. B 82.92% Siphonochalina sp. #2672 (1.22%), Euryspongia deliculata (1.22%), Dysidea sp. #2669 (1.22%), Cinachyrella sp. #180 (1.22%), Agelas sp. #2480 (1.22%), Phakellia klethra (1%), Cribrochalina sp. #2666 (1%), Aplysinella sp. #1194 (1%), Cliona (Rhaphyrus) hixonii (0.97%), Psammocinia sp. #1407 (0.86%), Raspailia (Raspaxilla) sp. #1081 (0.83%), Aplysinella sp. #814 (0.83%)

12 (12.8%)

A v. C 85.61% Psammocinia sp.#1407 (1.21%), Hyattella sp.#2763 (1.16%), Raphidotethya enigmatica (1.13%), Raspailia (Clathriodendron) digitatus (1.13%), Raspailia (Raspaxilla) sp.#1081 (1.13%), Phakellia cavernosa (1.13%), Aplysinella sp.#814 (1.13%), Spirastrella vagabunda (0.98%), Plakinastrella sp.#2677 (0.98%), Ircinia wistari (0.98%), Euplacella sp.#2675 (0.98%), Cliona sp.#2676 (0.98%)

12 (12.9%)

A v. D 92.66% Myrmekioderma granulata (1.61%), Erylus amissus (1.61%), Desmapsamma sp.#1125 (1.61%), Cliona sp.#2337 (1.61%), Cribrochalina sp.#2178 (1.61%), Cliona (Rhaphyrus) hixonii (1.32%), Psammocinia sp.#1407 (1.12%), Haliclona sp.#1381 (1.12%), Rhabdastrella globostellata (1.07%)

09 (12.7%)

B v. C 86.37% Trachycladus digitatus (1.48%), Siphonochalina sp.#2672 (1.48%), Raphidotethya enigmatica (1.48%), Euryspongia deliculata (1.48%), Dysidea sp.#2669 (1.48%), Cribrochalina sp.#2666 (1.48%), Cinachyrella sp.#180 (1.48%), Agelas sp.#2480 (1.48%), Aplysinella sp.#1194 (1.48%)

09 (13.3%)

B v. D 89.58% Trachycladus digitatus (1.43%), Rhabdastrella globostellata (1.43%), Siphonochalina sp.#2672 (1.43%), Phakellia klethra (1.43%), Myrmekioderma granulata (1.43%), Euryspongia deliculata (1.43%), Dysidea sp.#2669 (1.43%), Erylus amissus (1.43%), Desmapsamma sp.#1125 (1.43%)

09 (12.85%)

C v. D 89.93% Haliclona sp.#1381 (2.3%), Erylus amissus (2.3%), Desmapsamma sp.#1125 (2.3%), Cliona sp.#2337 (2.3%), Cribrochalina sp.#2178 (2.3%), Xestospongia testudinaria (1.32%)

06 (12.84%)


species, 115 (51%) are so far only known from this region(consisting mostly of the rare species found from singlesites), with 47 (19%) of species also occurring in theMoreton Bay fauna. This level of ‘apparent endemism’ isexceptional compared with the lower proportion of endemictaxa of other phyla known from this region (e.g. Davie andHooper 1998), but is possibly over-inflated given that someof these ‘apparently endemic’ taxa are periodicallydiscovered elsewhere throughout tropical Australasia as ourongoing surveys become more comprehensive (but seeHooper et al. 1999b, 2002), and it is anticipated that thisnumber might decrease to about 30% ‘apparent endemism’,as a median observation for many other sponge faunas in theIndo-west Pacific (e.g. Hooper and Lévi 1994; Hooper et al.2000).

Patterns of similarities in taxonomic compositionbetween reefs were not generally related to the distancebetween them, such that the most adjacent reefs often did nothave the most similar faunas. Other environmental gradientsappear to be more significant to community patterns ofrichness, endemism and taxonomic affinities. To someextent, species richness is inversely correlated with distancefrom the nearest shore, where the outer-most reefs (5.5–9 kmfrom the coast) contain lower species richness than themid-shelf reefs (1.25–5 km) and some of the inner reefs(0.5–1.25 km) (although one inner reef, Jew Shoals, was alsothe richest, another inner reef, North Halls, was the least richof reefs with comparable collection effort). This pattern wasalso partially evident from similarity analyses (clustering,MDS) of taxonomic composition between sites, with threegroups of faunas delineated. Outer-reef sites were well

differentiated from the others, but the inner- and middle-reefsite clusters were not fully resolved, nor was there completeagreement between clustering and MDS ordination of theselatter data. By comparison, neither numerical similarity datanor non-parametric analyses (ANOSIM) found anysignificant patterns or trends in β-diversity based onlatitudinal gradients, although this is probably notunexpected given that the most distant reefs were only about50 km apart. However, when sites were grouped by bothlatitude and distance from the coast, there were slightly moresignificant differences in taxonomic composition betweensome groups, the southern outer reefs (Flinders andHutchinson) and the four northern inner reefs in particular,with a gradual gradient of dissimilarities for reefsintermediate between these two groups of sites. This patternmay partially reflect that these two groups of reefs are alsothe most distant from each other, but it is also likely that theyare a result of the different prevailing environmentalconditions. Some of these include geomorphology andphysico-chemical differences between sites. The outer-mostreef sites are dominated by hard corals, have flatter reeftopography and probably clearer waters (this latterassumption has not yet been empirically tested). Theinnermost reef sites are situated closer to river discharge withhigher impacts from siltation and turbidity. Both groups ofsites have lowest species richness, whereas the middle-reefsites have conditions intermediate to these and highestrichness. An anomaly to this pattern is Jew Shoals, aninner-reef site, with the highest richness and comparablecollection effort to other sites. It is hypothesised that itslocation in proximity to, but not in the path of the Noosa

Fig. 9. Probability funnels (95% confidence interval) of average taxonomic distinctness (delta +)and variation in taxonomic distinctness (lambda +) values for species-level (A–B), genus-level (C–D)and family-level taxa (E–F). Dotted lines indicate the simulated mean value from 5000 randomselections from the master list of 247 species for each sublist (M = 10–100). Intervals within which95% of the simulated values lie are represented by a probability funnel (solid lines).


River may benefit its heterotrophic sponge communitythrough higher nutrient discharge, without the more severeimpacts of scouring, turbidity and siltation that may occur forreefs in the direct path of the river (e.g. North Halls).

There is now a significant body of literature in whichmany deterministic and stochastic processes have been usedto explain observed heterogeneity among spongecommunities. These include terrestrial influences (such asimpacts of freshwater runoff, nutrient levels, turbidity etc.),geomorphological differences between reefs (microhabitatavailability, aspect of seabed, exposure to waves and currentsetc.), and random events (such as patterns and timing ofarrival and survival of larvae and asexual propagules, effectsof severe storm events on fragmentation and dispersal etc.).Of relevance here are the reports on environmental gradientsacross the shelves of the Great Barrier Reef and SpermondeArchipelago, Indonesia (Wilkinson 1983; Wilkinson andEvans 1989; Wilkinson and Cheshire 1989; de Voogd et al.1999), temperate reefs in south-eastern Australia (Robertset al. 1994; Roberts and Davis 1996), coral reefs in the

Caribbean (reviewed in Zea 2001) and soft bottomcommunities in the Indian Ocean (Van Soest 1993). Thestudies of Wilkinson et al. (listed above) found that innerreefs (approximately 20 km from the coast) had highestrichness and biomass, middle reefs had highest abundanceand outer reefs (up to 200 km from shore) were poorest insponge diversity. However, their inner reefs are probablymore closely comparable to our middle reefs in terms ofproximity to the coast and terrestrial influences on themarine fauna. Their studies are also not directly comparableto the present data or to the findings of Hooper et al. (1999b)given that they surveyed only the highly visible, largelyphototrophic sponge fauna, ignoring the many hundreds ofother megabenthic, cryptic and heterotrophic species presentin each reef system. De Voogd et al. (1999) also observedthat sponge species richness and abundance increased withdistance from shore, up to about 20 km offshore, and thendecreased in outer reefs (40 km from the coast). These andother authors (e.g. Roberts and Davis 1996) also noted apositive correlation between species richness and abundancewith increasing depth. Wilkinson and Cheshire (1989) andde Voogd et al. (1999) hypothesised that excessiveturbulence and light may be responsible for the depth-relatedenvironmental gradient, owing to factors such as highernutrient levels and lower wave action in near-shore waters,and with communities further from the coast, in the clearer,more oligotrophic conditions, dominated more byphototrophic species. In Caribbean coral reefs, at least, thisbathymetric gradient reverses at about 40–60 m depth(Lehnert and Fischer 1999), depending on light penetrationthrough the water column from site to site, where spongediversity decreases and taxonomic composition changessignificantly with increasing depth. There are as yet nodirectly comparable ecological studies of sponge β-diversityacross similar depth profiles for tropical Indo-Pacific reefs,but studies of temperate sponge populations in south-easternAustralia (intertidal to 50 m depth) found that speciesrichness increased with depth, abundance decreased andgrowth forms became increasingly erect in deeper waters(Roberts et al. 1994; Roberts and Davis 1996). This wassuggested to be a result of differences in habitatmicro-topography related to exposure to wave energy,turbulence and siltation. Zea (2001) found weak correlationbetween environmental variables and the observedheterogeneity in sponge community structure on threeisolated Caribbean seamounts, but reported that mostsponges had strong habitat preferences despite patchyα-scale (within-habitat) distributions. Van Soest (1993)suggested changes in species composition and abundance ofsoft bottom sponge communities of the Mauritian shelf werepartly a result of restricted larval dispersal imposed by mudbarriers separating adjacent sites, and Uriz et al. (1998)showed that differences in the distribution of particularspecies in the Mediterranean were related to the dispersal

Fig. 10. Average taxonomic distinctness (delta +) and variation intaxonomic distinctness (lambda +) plots of species-level (A),genus-level (B) and family-level (C) taxa for reef sites, superimposedon 95% probability ellipses from simulated data for each sublist (M =20–100). Number of species in each site is indicated in brackets.


ability of their respective larvae. Zea (1993, 2001) furthersuggested that small-scale random factors related to larvaldispersal and settlement in Caribbean sponge communitieswere also pivotal to unexplained heterogeneity among thesecommunities.

Small-scale endemism

Small-scale endemism is less widely reported in the spongeliterature, perhaps in part due to the appalling difficultiesassociated with sponge taxonomy, such as the uncertainty ofconspecificity when comparing faunas described bydifferent authors, especially from literature published overseveral generations (Hooper and Van Soest 2002), and alsoin part because marine taxonomists tend to describe faunasat larger spatial scales (e.g. biogeographic provinces),compared with many ecological studies for example. Overthe last two centuries, taxonomic collections made at point-and α-scales were frequently (mis-)interpreted as being‘representative’ of faunas over much larger spatial scales.For example, for many decades the Low Isles sponge fauna(Cairns Sector, Great Barrier Reef) was considered to be‘typical’ of the entire Great Barrier Reef, until acomprehensive survey (Hooper et al. 1999a) found that only23% of species were ‘typical’ (from a fauna of 134 species):48% were found only in the northern Great Barrier Reef and32% were ‘apparent endemics’ to this α-scale fauna. As thegeographic gaps in our knowledge of Indo-Pacific marinebiodiversity are gradually being filled, we have increasinglyrecognised both high spatial heterogeneity (richness andabundance) and ‘apparent endemism’ among and betweensponge faunas, even at small spatial scales.

On the Sunshine Coast reefs the contribution of unique orrare species to local or small-scale patterns in spongebiodiversity is considerable; up to 60% of species are foundonly on one or two reefs. As previously noted, this proportionof rare species is expected to fall over time as further spongesurveys of tropical Australasia are completed, butnevertheless this proportion is exceptional and may reflectthe unique biogeographic transition zone in the Moreton Bayregion, with mixing and potential hybridisation ofSolanderian and Peronian faunas (Davie and Hooper 1998).Species that contributed most to defining ‘meso’-scalefaunas (i.e. α-scale faunas grouped according to variousfactors) were also the most common or ‘typical’ of the wholeSunshine Coast fauna. Northern-inner reefs had only 11species accounting for over 50% of their similarity (from acombined ‘meso’-scale fauna of 149 species); northern-midreefs had 10 species accounting for 100% similarity (with atotal fauna of 101 species); northern-outer reefs had onlyfour species defining 100% similarity (with a total fauna of49 species); and southern-outer reefs had seven speciesaccounting for 100% of similarity (with a total fauna of only28 species). Clearly, the grouped (‘meso’-scale) sites ofnorthern-mid and northern-outer reefs are artificial given

their low within-group taxonomic similarities (from cluster,MDS and SIMPER analyses and ANOSIM hypothesistesting), lending support to Banks and Harriott’s (1995)suggestion (based on scleractinian coral and macroalgaedistributions) that these northern-mid and -outer reefs are‘sites intermediate between near-shore and off-shore’ anddeserve no other formal recognition as distinct zones.

The proportion of unique species (‘apparent endemism’)for most sites is generally related to the total species richnessof each site, such that sites containing greater diversity alsocontained a greater proportion of species not foundelsewhere. A notable exception to this pattern was FlindersReef (with low diversity but high ‘apparent endemism’),which might be explained by its greater distance offshoreand/or its closer proximity to the Moreton Bay fauna than tomore northerly reefs. This observation is supported by thefindings of Banks and Harriott (1995), who recorded asignificantly higher number of scleractinian coral speciesfrom Flinders Reef than from any other reefs on the SunshineCoast or in Moreton Bay. They suggested that its locationfurther off-shore provided greater exposure to the EastAustralian Current, carrying warmer water and larvae (andprobably, in the case of sponges, asexual propagules) fromthe tropics and thus also resulting in a better coral reefstructure than any of the other sites. This phenomenon mayexplain the relatively high proportion of unique spongespecies on Flinders Reef, but not its relatively low spongediversity in comparison to reefs further north. Conversely,the adjacent Hutchinson Shoal (only 3.2 km away, butslightly deeper than Flinders Reef) has both low richness andlow ‘apparent endemism’. Flinders Reef is clearlyanomalous compared with the general regional fauna. It isthe most taxonomically distinct (AvTD) and leastheterogeneous (VarTD) in terms of sponge taxonomiccomposition (for species-, genus- and family-level taxa), andmay have closer taxonomic affinities to the southern GreatBarrier Reef than to the Sunshine Coast or more southerlysites. By comparison, Mudjimba Island (at species level) andPeregian Reef (at species, genus and family levels) have theleast taxonomically distinct and most heterogeneous faunas.These differences in taxonomic distinctness at differenttaxonomic levels also indicate that family-level taxa are noteffective surrogates for true (species level) taxonomicdiversity.

Implications of heterogeneity to conservation biology

It has been suggested that rare or unique species maysignificantly bias biodiversity models when using onlysimilarity index data to explore β-diversity or communitypatterns across environmental gradients (Clarke and Gorley2001). In the present study, only 40% of species were foundat two or more sites, with 60% of species not contributing tocluster or MDS groups. A posteriori (SIMPER) analysesshowed that only a relatively small proportion of species


common to all pairwise comparisons within and betweensites were significant contributors to defining ordifferentiating these hypothesised spatial groups, and manyof these species were also the most ‘typical’ for the region asa whole. This is a general problem for sponge ecological andbiogeographic studies given the observed high taxonomicheterogeneity between habitats and high ‘apparentendemism’, even at small spatial scales (Wilkinson andCheshire 1989; Alcolado 1990; Zea 1993, 2001; Hooper1994; Carballo et al. 1996; Hooper et al. 1999b), even whenthere are no obvious environmental gradients (e.g. Reed andPomponi 1997) and even for cases in which abundance dataare collected that may provide more informative assessmentsof community evenness and dominance (Maldonado andUriz 1995; Roberts et al. 1998; de Voogd et al. 1999; Lehnertand Fischer 1999) than solely presence/absence data.Without abundance data to demonstrate that speciesacquisition curves are saturated for every study site, it isequivocal whether samples are accurate representations ofthe true biodiversity, or to what extent local biodiversityestimates are biased by these rare or unique species. Anincreasingly popular alternative, the use of broad-scale(RAPD) survey techniques (e.g. Ward et al. 1998), is alsogenerally unreliable for studies on sponges where it isessential that histological comparisons are made to verifyidentifications, or better still, genetic markers are used todetect cryptic species hidden among sponge morphospecies.For example, it is becoming increasingly evident that thesponge genotype is not necessarily manifested at thephenotypic level (e.g. Alvarez et al. 2002), and conversely,that even small morphometric differences may translate intosignificant genetic differences (e.g. Wörheide et al. in press).Thus, any measure of biodiversity based solely onmorphology, as presented here, is likely to be anunderestimate of true taxonomic diversity and communitypatterns presented here are probably minimal hypotheses.

The preponderance of unique or rare species at theα-scale of diversity has implications on the effectiveness ofdesignated small-scale marine reserves, which (from ourdata at least) may not contain an adequate representation ofthe ‘meso’-scale regional biodiversity. A case in point isFlinders Reef, which is the only legislated highly protectedmarine area (Protection zone) outside of Moreton Bay, butcontains few of the sponge genetic resources found in theadjacent Sunshine Coast reefs (or for that matter, reefs withinMoreton Bay; Davie and Hooper 1998). This findingsupports previous contentions that small, isolated reefscontain unique sponge genetic resources that are notnecessarily contained within broad ‘coral reef’ conservationstrategies (Hooper 1994), but when α-scale spongebiodiversity data are amalgamated into broader γ- andε-spatial scales (e.g. Hooper et al. 1999b, 2002), it is possibleto obtain more accurate estimates of regional biodiversitysince the data sets are much larger and a greater proportion

of species occur in more than one site. There is an increasinglikelihood that species accumulation curves approachsaturation over larger scales, and thus rare or unique speciesare less significant to biodiversity analyses.One of the primary considerations for implementing marinereserves concerns protecting critical habitats and specialfeatures representative of the particular biogeographic zone,including consideration of the rarity of the species to beprotected (Anon. 2001). Clearly, for sponges, small-scalereserves like Flinders Reef fail to meet this criterion.Furthermore, assumptions about wide connectivity betweenmarine protected areas (compared with terrestrial protectedareas, which have more discrete boundaries; Anon. 2001) arelargely based on phyla capable of widespread dispersal (e.g.Palumbi 1992). Taxa with reportedly very limited larvaldispersal potential, such as sponges (e.g. Zea 1993), requiremanagement on much finer spatial scales than previouslyassumed (Anon. 2001). These considerations are pivotal tothe design and size of marine reserves (Roberts and Hawkins1997) that need to take into account the scale of the reserveand biology of the species that inhabit them (Ray 1999), andplace them into the larger context of the ecosystems theyallegedly represent (Anon. 2001).

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Manuscript received 4 April 2002; revised and accepted 24 June 2002.

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