Coral Reefs

, Volume 29, Issue 2, pp 427–436 | Cite as

Beta diversity of cold-water coral reef communities off western Scotland

  • Lea-Anne Henry
  • Andrew J. Davies
  • J. Murray Roberts


Spatial heterogeneity in coral reef communities is well documented. This “species turnover” (beta diversity) on shallow warm-water reefs strongly conforms to spatial gradients in the environment as well as spatially autocorrelated biotic processes such as dispersal and competition. But the extent to which the environment and spatial autocorrelation create beta diversity on deep cold-water coral reefs such as those formed by Lophelia pertusa (Scleractinia) is unknown. The effects of remotely sensed and ground-truthed data were tested on the community composition of sessile suspension-feeding communities from the Mingulay Reef Complex, a landscape of inshore Lophelia reefs off the Scottish west coast. Canonical correspondence analysis determined that a statistically significant proportion (68%) of the variance in community composition could be explained by remotely sensed environmental variables (northerly and easterly aspect, seabed rugosity, depth), ground-truthed environmental variables (species richness and reef macrohabitat) and geospatial location. This variation was further partitioned into fractions explained by pure effects of the environment (51%), spatially structured environmental variables (12%) and spatial autocorrelation (5%). Beta diversity in these communities reflected the effects of both measured and unmeasured and spatially dependent environmental variables that vary across the reef complex, i.e., hydrography. Future work will quantify the significance and relative contributions of these variables in creating beta diversity in these rich communities.


Beta diversity Lophelia pertusa Cold-water corals Acoustic remote sensing Variance partitioning 


Spatial heterogeneity in coral reef communities

The distribution of species inhabiting coral reefs is often heterogeneous. Because species tend to occupy particular environmental niches, the spatial structure of reef communities is largely controlled by environmentally deterministic processes governed by species’ traits. Thus, on shallow warm-water reefs, environmental gradients explain significant amounts of variation in community composition or “beta diversity” (Adjeroud 1997; Cleary and de Voogd 2007). Beta diversity itself can vary on these reefs and closely corresponds to the degree of environmental similarity and heterogeneity (Becking et al. 2006; Harborne et al. 2006; de Voogd et al. 2006).

Heterogeneous reef communities can also reflect the effects of spatial autocorrelation. In this instance, processes governed by species’ traits (e.g., growth, predation, differential mortality) and/or random but spatially autocorrelated neutral processes (e.g., spatially limited dispersal) produce heterogeneous communities that vary across purely spatial gradients (Borcard et al. 1992; Hubbell 2001; Dray et al. 2006). Neutrality assumes demographic equality, whereby spatial autocorrelated communities are created through “spatially limited dispersal of species drawn at random from a metacommunity, plus possibly the appearance of newly evolved species in different areas” (Legendre et al. 2005). There is presently minor support for spatially autocorrelated coral reef communities (e.g., Schlacher et al. 1998), but it has been difficult to resolve whether this actually reflects spatial autocorrelation or the effects of unmeasured environmental variables that are often themselves spatially dependent (Becking et al. 2006; Cleary and de Voogd 2007), which confounds any pure spatially explicit effects on beta diversity. However, more studies are needed, and across a broader range of species, communities and regions that simultaneously test the significance and relative importance of these processes in order to refine the understanding of beta diversity on coral reefs worldwide.

Beta diversity on Lophelia reefs

Deep cold-water coral reefs formed by the colonial azooxanthellate scleractinian Lophelia pertusa are globally ubiquitous (Roberts et al. 2006, 2009a, b), but until recently their remoteness has meant that these habitats have received much less attention than their shallow warm-water counterparts. Lophelia is typically distributed 200–1,000 m deep and can form reefs tens of kilometres long, some of which have been growing sporadically since the early Pleistocene (Kano et al. 2007). Like shallow warm-water corals, Lophelia creates structural habitat that alters local hydrography (Davies et al. 2009). As such, Lophelia is an important “ecosystem engineer” (van Oevelen et al. 2009): it creates and modifies the abiotic environment in ways that favour colonisation of its reefs by species-rich fish and invertebrate communities, with the richness of some taxa rivalling that found on shallow warm-water coral reefs (Jensen and Frederiksen 1992; Henry et al. 2008; Gheerardyn et al. 2009).

Cyclical Lophelia reef growth produces a dynamic mosaic of habitats that results in the vertical zonation of four major “macrohabitats” and therefore zones of significant invertebrate species turnover ranging from the reef summit to its flank: (1) living coral framework, (2) sediment-clogged mostly dead coral framework, (3) coral rubble and (4) underlying sediments (Mortensen et al. 1995; Raes and Vanreusel 2005; Roberts et al. 2006). Beta diversity on Lophelia reefs is also created by gradients in live coral cover and depth (Jonsson et al. 2004; Raes and Vanreusel 2005; van Soest and Lavaleye 2005; Mortensen and Fosså 2006; Henry and Roberts 2007; van Soest et al. 2007; Roberts et al. 2008; Weinberg et al. 2008). Beta diversity on Lophelia reefs also varies, with communities becoming more dissimilar from each other as one moves from the reef summit to flank macrohabitats, and as one compares communities from increasingly dissimilar or distant environments (Jonsson et al. 2004; Mortensen and Fosså 2006; Henry and Roberts 2007; van Soest et al. 2007; Cordes et al. 2008; Roberts et al. 2008). However, none of these studies have tested the significance of spatial autocorrelation or the effects of remotely sensed environmental data in creating beta diversity in these habitats. These omissions present new opportunities for the cold-water coral reef research community to contribute to the knowledge about the mechanisms controlling beta diversity on such rich yet remote coral reefs.

Acoustic remote sensing of cold-water coral reefs

Remotely sensed environmental data can explain significant amounts of variation in invertebrate species composition and beta diversity itself on shallow warm-water coral reefs (Becking et al. 2006; Harborne et al. 2006; de Voogd et al. 2006; Cleary and de Voogd 2007; also see review by Knudby et al. 2007) and have also been used to predict and map beta diversity of coral reef species (Mumby et al. 2004; Harborne et al. 2006). But the measurement of environmental variables on an aphotic deep cold-water coral reef excludes the use of airborne or satellite-based remote sensing methods that are effective on shallow marine ecosystems. Measurements at the depths of most Lophelia reefs require shipboard, towed or underwater vehicle-mounted acoustic remote sensing devices such as multibeam echosounders and side-scan sonar to characterise seafloor properties. Acoustic remote sensing has been vital to the recent discovery, habitat mapping and geological characterisation of Lophelia reefs (Fosså et al. 2005; Foubert et al. 2005; Roberts et al. 2005a, 2008; Beyer et al. 2007), and has overall positive implications for conceptual and predictive modelling, cruise planning and overall conservation of coral reefs worldwide (Andréfouet and Riegl 2004). Bathymetric terrain features, e.g., depth, slope, aspect and rugosity, can all be predicted from acoustic data and have been used to model habitat associations of the cold-water corals themselves (Wilson et al. 2007; Davies et al. 2008; Dolan et al. 2008; Guinan et al. 2009).

An interdisciplinary approach combining ground-truthed (photographs, videos, benthic sampling) and acoustically acquired data allows one to test the effects of numerous bathymetric and seabed features on deep cold-water coral reef communities. The present study integrated these data into a model of community composition variance to test whether cold-water coral reef communities show significantly heterogeneous spatial structure, and the significance and relative importance of environmental and spatial autocorrelation in creating beta diversity.

Materials and methods

Study site

The Mingulay Reef Complex (MRC) is an inshore landscape of Lophelia pertusa reefs in the outer Hebrides off the west coast of Scotland in the northeast Atlantic (Fig. 1) and has been the subject of recent intensive study (Roberts et al. 2009a, b). Each reef is formed by seabed mounds up to five metres high that likely have been growing since the early Holocene (Roberts et al. 2005a). Reef invertebrates broadly conform to local habitat classes: these range from bioturbated muds inhabited by Nephrops norvegicus, seapens, crinoids and other burrowing megafauna, to rocky substrata encrusted by large erect sponges (Phakellia sp., Axinella sp.) and zoanthids, to living Lophelia reefs and coral rubble inhabited by diverse suspension-feeding communities (Roberts et al. 2005a). Sponges are particularly diverse on the MRC, with 70 species being recorded to date (Maier et al. 2006), at least one of which was new to science (van Soest and Beglinger 2008). Two of the reefs that were mapped in detail using multibeam bathymetry in June–July 2003, Mingulay Reef 1 and Mingulay Reef 5 North (Roberts et al. 2005a), are considered in the present study, and herein called MRC1 and MRC5. The survey was conducted using an EM2000 Multibeam Echo Sounder (MBES, Kongsberg-Simrad Ltd.) with hull-mounted transducers on the RV Lough Foyle (Roberts et al. 2005a).
Fig. 1

Regional context of the MRC (a) with study area (see inset b) indicated by a black dot. Detailed multibeam bathymetry map (b) of MRC1 and MRC5 showing locations of grab samples

Grab sampling and faunal identification

A total of 18 Day grab samples (each 100 cm2) of invertebrates were collected from the reefs (Fig. 1). Latitude and longitude of each grab were recorded and based on the ship’s differential GPS position. Macrohabitat data were ground-truthed, based on the predominant seabed type recovered in the grab (coral framework, coral rubble, rock or muddy sand). Samples were washed through a 1-mm sieve, fixed in 4% borax-buffered solution of formalin and seawater, and stored in alcohol. Roberts et al. (2005a) enumerated subsampled fauna from these grabs: in the present study, the full sample of grab taxa was sorted and identified to the lowest possible taxonomic level. A total of 223 species were identified but analyses were restricted to the 77 species of attached epibenthic sponges, cnidarians, bryozoans and tunicates that represented a functionally equivalent “ecological community” (Hubbell 2005) of sessile suspension feeders. Every taxonomically distinct species was recorded as either present or absent in each grab (Electronic Supplemental Material; ESM, Appendix 1).

Digital terrain analysis

Supplementing the processed depth values from the multibeam data, several topographic variables were derived within a geographical information system (ArcGIS 9.2, ESRI Software). A buffer of 10 m diameter around each grab location was used, and the mean was calculated for each of the following derived variables within the buffer distance (ESM, Appendix 2). Calculations were performed using the Benthic Terrain Modeler (BTM) extension for ArcGIS 9.2 using routines from Jenness (2002). “Aspect” is the direction of slope measured in degrees from north ranging from zero to 359.9 (“general aspect”) but only aspects measured in continuous radians ranging from −1 to 1 (“easterly” and “northerly aspect”) were used in the statistical analyses. Each aspect was calculated using a 3 × 3 cell neighbourhood to determine the rate of change from each cell to its neighbours. “Slope” is the maximum change from each cell using a 3 × 3 neighbourhood. Slope values range from zero (flat surface) to 90° (vertical surface). “Rugosity” is the ratio of surface area to planar area and provides an index of how variable the surrounding area is within a 3 × 3 neighbourhood (Jenness 2002). Using this method, flat areas had a value close to one, while high relief areas had higher values of rugosity. “Bathymetric position index” (BPI) is derived from the topographic position index and provides a measure for a location relative to the other locations surrounding it, which can be part of a positive (e.g., living reef) or negative (e.g., depression) feature of the terrain surface. Values near zero represent flat areas or areas of constant slope. The BPI was calculated using the BTM extension with a 3 × 3 cell inner neighbourhood, and an outer neighbourhood of 5 × 5 that focused the analysis on fine-scale rather than broad-scale terrain features to correspond with the small spatial scale from which the reef communities were sampled. Geo-referenced environmental data for each grab sample are reported in ESM, Appendix 2.

Statistical analysis

Community characterisation and explanatory variable correlations

PRIMER v6 (Clarke and Gorley 2006) was used to ordinate Sørensen indices into a non-metric multidimensional scaling (nMDS) plot, and the similarity of percentages analysis (SIMPER) was used to identify species that contributed up to approximately 50% of the similarity within groups of grab samples. The Pearson correlation coefficient rho (ρ) and its statistical significance at α = 0.05 were measured between all spatial and environmental variables (including the number of species or “species richness”, but excluding the categorical variable macrohabitat) using XLSTAT-2008® Addinsoft SARL.

Beta diversity

A raw-data canonical approach was used to explain beta diversity in relation to environmental and spatial variables. Canonical correspondence analysis (CCA, described by Ter Braak 1986) partitions the variance in community composition due to constrained variables of interest and unconstrained residual inertia. This constrained variance can be further partitioned into three fractions of pure environmental, spatial and spatially structured environmental effects (Borcard et al. 1992; Legendre 2008). In the present study, these fractions were estimated by measuring the variance explained by the matrix of environmental variables, the matrix of the spatial variables, the environmental matrix (with co-varying spatial variables) and the spatial matrix (with spatial variables co-varying): the latter two are partial analyses or pCCA, described by Borcard et al. (1992), each of which generates the contribution of purely environmental (or geospatial and thus spatially autocorrelated) effects uncoupled from geospatial (or environmental) effects.

The response variables were species presence–absence data. Species that occurred in only one grab were excluded from the CCA and pCCA analyses, as these disproportionately inflate the effects of rare species on canonical ordinations (Legendre and Gallagher 2001).

The explanatory variables included both spatial and environmental data (including species richness). Any redundant environmental variable, i.e., one that was significantly correlated to another environmental variable already in the model, was excluded. This reduced any effects of multicollinearity that otherwise makes variable coefficients unstable, and thus their degree to which they “explain” variance in community composition.


Community characterisation and explanatory variable correlations

Three community types (grabs 1–5, grabs 6–10 and grabs 10–18) were visible in the nMDS (Fig. 2): grabs 1–5 were characterised by the bryozoans Pyripora catenularia, Disporella hispida, Cheilostomata sp. 5 and Diplosolen obelia, and the hydroids Clytia hemisphaerica and Modeeria rotunda; grabs 6–10 were characterised only by the zoanthid Parazoanthus anguicomus; grabs 11–18 were characterised by P. anguicomus, the bryozoan Chartella barleei and the hydroid Halecium muricatum.
Fig. 2

Non-metric MDS ordination of grab samples. Closed squares = MRC1, open squares = MRC5. High stress (0.16) and overlap of samples from MRC1 and MRC5 indicate high similarity of the two reef communities

UTM northing and BPI (ρ = −0.559), UTM easting and richness (ρ = −0.811), rugosity and slope (ρ = 0.973), BPI and depth (ρ = −0.599) were all significantly correlated.

Beta diversity

Slope and depth were excluded from the model due to their significant correlations with rugosity and BPI, respectively. Linear combinations of northing, easting, rugosity, northerly aspect, easterly aspect, BPI, macrohabitat and species richness explained 68.1% of the variation in community composition, and this was statistically significant (Fig. 3; P = 0.003). Partial CCA determined that pure environmental effects explained 51.1% of the variation, but this was not statistically significant, at least at a type I error rate of 0.05 (P = 0.106). Pure spatial effects only accounted for 5.1% of the variance, and this was also not statistically significant (P = 0.983). Pure effects of spatially structured environmental effects explained an additional 11.9% of the variation; however, their statistical significance cannot be measured (Borcard et al. 1992), leaving 31.9% of the total variation unexplained.
Fig. 3

Two-dimensional canonical biplots of constrained inertia with ordination of sites and then species along the first two canonical axes (percentage of constrained variance explained given in parentheses). Path length indicates the correlation between a predictor variable and a canonical axis. The correlation between a categorical variable and a canonical axis is given as a single point. Species are abbreviated as follows: P. anguicomus = Parazoanthus anguicomus; B. muscus = Bougainvillia muscus; C. hemisphaerica = Clytia hemisphaerica; L. dumosa = Lafoea dumosa; C. panicula = Campanulina panicula; P. setacea = Plumularia setacea; H. muricatum = Halecium muricatum; M. rotunda = Modeeria rotunda; S. tenella = Sertularella tenella; C. eburnea = Crisia eburnea; O. dilatans = Oncousoecia dilatans; I. atlantica = Idmidronea atlantica; B. ciliata = Bicellaria ciliata; D. obelia = Diplosolen obelia; E. deflexa = Entalophoroecia deflexa; D. hispida = Disporella hispida; P. catenularia = Pyripora catenularia; C. barleei = Chartella barleei; A. imbellis = Alderina imbellis; A. flemingii = Amphiblestrum flemingii; R. minax = Ramphonotus minax; C. ellisii = Caberea ellisii; H. flagellum = Hippothoa flagellum; E. octodentata = Escharella octodentata; S. reticulata = Smittoidea reticulata; S. linearis = Schizomavella linearis; T. avicularis = Turbicellepora avicularis; O. ramulosa = Omalosecosa ramulosa; R. beaniana = Reteporella beaniana; D. fragilis = Dysidea fragilis; H. paupertas = Hymedesmia paupertas; P. pomaria = Polycarpa pomaria

The first two canonical axes contributed 33.9% of the constrained inertia (explained variance) and 23.1% of the total inertia. Spatially structured environments on the MRC were evident from the gradients that characterised these axes. Axis 1 was largely characterised by gradients in seabed topography (rugosity, BPI) and macrohabitat that were themselves spatially structured across a longitudinal gradient. Axis 2 was also largely characterised by spatially structured gradients but these were due to changes in aspect, richness and macrohabitat (Fig. 3) that varied along a latitudinal gradient. The encrusting sponge Hymedesmia paupertas was positively associated with both axes, being found only at northeastern sites (grabs 8 and 9) on rocky and coral framework macrohabitats that were also among the more species-rich communities, in highly rugose and variable topographical areas with northwesterly aspects (Fig. 3). A group of small (mostly < 5 mm) epizoic hydroids (Bougainvilliidae sp., Laomedea sp., erect thecate sp. and Tubulariidae sp.) were strongly associated with northwestern locations that were also the most speciose communities, from muddy sand macrohabitat and relatively homogenous areas of surrounding seafloor. Another group of small epizoic hydroids (Bougainvillia muscus, Clytia hemisphaerica and Campanularia sp.) were also found in relatively homogenous areas of seafloor, but on less speciose coral rubble macrohabitats with more southeasterly aspects. The tunicate Polycarpa pomaria and the lobate bryozoans Chartella barleei and Caberea ellisii were found at the easternmost locations, on species-poor coral rubble macrohabitats distributed in highly rugose areas of varied surrounding topography (Fig. 3).


Bathymetry-mediated control of community composition

The present study demonstrated predominantly bathymetry-mediated control of beta diversity patterns on the MRC, which is consistent with studies from shallow warm-water coral reefs. Seabed properties that directly relate to a sessile suspension feeder’s hydrographical niche seem to play especially important roles and are all spatially structured on the MRC.

Variation in community composition due to changes in aspect strongly relates to how species respond to differences in water flow (Best 1988; Glasby 2000; Glasby and Connell 2001). On the MRC, differences in flow arise through variations in the orientation of communities into the prevailing currents. Since biological functioning of suspension feeders such as feeding efficiency can vary between upstream and downstream flow (Holland et al. 1987), aspect creates beta diversity in coral reef communities (Cleary and de Voogd 2007). On the MRC, currents are tidally driven and flow in an approximately south to north direction (Davies et al. 2009). Differences in aspect polarise northward-facing sites from those with more southward-facing orientations because the former are oriented downstream away from the prevailing currents and are thus more sheltered, while the latter are oriented upstream into the current and are thus more exposed. In the present study, the encrusting sponge Hymedesmia paupertas only occurred in grabs 8 and 9, which were northward-facing and thus less exposed sites. The present study not only demonstrates the concept of current-mediated control on this species’ distribution, but also is supported by the habitat distribution of H. paupertas in British waters (Picton and Morrow 2007).

Varied slope, rugosity and bathymetric position also create beta diversity on cold-water coral reefs. The slope or inclination of a substratum produces differential larval recruitment and feeding ability in sessile suspension-feeding fauna. Like the variable “aspect”, these differences arise due to species-specific niches related to water flow, and result in increasingly dissimilar communities as slope and its variance increase between sites, for example, in subtidal algal/invertebrate assemblages (Balata et al. 2007) and sponges from bathyal shelf and canyon ecosystems (Schlacher et al. 2007). Seabed rugosity also has profound implications for benthic biodiversity or marine ecosystems from rocky shores (Beck 2000) to coral reefs (Alvarez-Filip et al. 2009), and as a proxy for hard-bottom marine habitats in general, is vital for models of regional marine biodiversity (Dunn and Halpin 2009). Varied bathymetric position creates beta diversity by increasing seabed heterogeneity between sites with positive versus negative relief features and is known to affect benthic species’ distribution and community structure on offshore banks (Buhl-Mortensen et al. 2009). Lophelia itself thrives on topographic highs and irregularities associated with positive relief seafloor features such as ridges (Frederiksen et al. 1992; Mortensen et al. 2001; Thiem et al. 2006; Davies et al. 2008; Guinan et al. 2009). In the present study, live Lophelia was only collected from grab 8 from a moderately rugose and sloped site (ESM, Appendix 1 and 2). Similarly, Hymedesmia paupertas was only collected from grabs 8 and 9 from areas of deeper waters and moderately rugose and sloped sites (Appendix 1 and 2), which corresponds to its known habitat in British waters including the MRC (Maier and party 2006). Although the larval ecology and natural history of many species from the MRC are unknown, the occurrence of the bryozoan Plagioecia patina at a steeply sloped site with high BPI (Appendix 1 and 2) is supported by evidence for enriched larval settlement patterns in cryptic or downwardly concave or lateral surfaces (McKinney and McKinney 2002).

Water depth was related to changes in sponge community composition on bathyal Lophelia reefs off southwest Ireland (van Soest et al. 2007). The present study’s depth range of 76 m was also large enough to elicit significant changes in community composition, which are related to species’ habitat requirements. Several species found at the deeper sites on the MRC are typically deep-water and even bathyal species, also occurring on other deeper Lophelia reefs including Axinella infundibuliformis, Hymedesmia paupertas, Plagioecia patina and Smittina crystallina. But depth was excluded from the canonical model in preference for BPI (see Materials and methods) because the former is actually a composite variable: water temperature, salinity, nutrient input or the water masses themselves could all vary with depth on the MRC. Current speeds and turbidity are known to decrease with depth and are spatially structured on the MRC as a consequence of the interplay between reef topography and local hydrography (Davies et al. 2009); such gradients surely pose niche consequences for suspension-feeding reef fauna as they seem to, for example, in the local deep-water bryozoan fauna (Hughes 2001). Depth-related changes in sediment oxygenation and food availability are also invoked to explain bathymetric shifts in benthic community structure in marine ecosystems ranging from coastal to slope ecosystems, and even across a basin-wide scale (Blake and Grassle 1994; Vanaverbeke et al. 1997a, b; Bonsdorff et al. 2003; Danovaro et al. 2008). Long-term in situ monitoring of near-seabed environments on the MRC and other Lophelia reefs using e.g., benthic landers (Roberts et al. 2005b; Linke et al. 2006; Davies et al. 2009) will be critical for identifying important variables encompassed by the depth factor.

The present study confirms that spatial heterogeneity in the distribution of Lophelia reef macrohabitats creates significant beta diversity in the sessile fauna inhabiting these niches. Substratum heterogeneity creates beta diversity in all marine ecosystems because many species have specific habitat requirements. Consequently, much of the beta diversity observed in marine ecosystems can be explained by heterogeneity in seabed types, including those that are inhabited by cold-water corals (Serrano et al. 2006; 2009). The present study also points to the importance of species richness, which for the macrofauna, is generally highest in macrohabitats with less live coral cover (Mortensen and Fosså 2006; van Soest et al. 2007; Roberts et al. 2008) and is spatially structured on the MRC (Fig. 3). The most speciose communities were found in grabs 2, 3 and 4 (rock and coral rubble macrohabitats), all of which were located in the westernmost portion of the study area (Fig. 1; ESM, Appendix 1). The significant decline in richness with easting may have created beta diversity simply because there were fewer species in the easternmost reaches of the MRC, and thus communities vary along this longitudinal gradient. Alternatively, another unmeasured environmental variable that closely correlates with species richness (e.g., disturbance) actually controls community composition on the MRC and is also longitudinally structured.

The role of unmeasured environmental and historical variables

Variance partitioning demonstrated a very minor role of spatial autocorrelation in creating beta diversity on the MRC. Furthermore, despite over half of the variance in community composition being explained by purely environmental variables, beta diversity was not significantly controlled by the measured variables. This leaves one to consider the significance of spatially structured environmental variables. But perhaps more important are the effects of the unmeasured environmental variables that may too be spatially structured. For example, current speeds are known to be reduced in the easternmost reaches of MRC1 (Davies et al. 2009). This variable likely represents an important unmeasured but spatially structured environmental variable that induced spatial dependence in the sessile suspension-feeding communities, as it was for sponges in shallow water coral reefs (Cleary and de Voogd 2007). There may be spatial gradients in other unmeasured variables e.g., the distribution of food resources that create biodiversity in marine ecosystems (Hewitt et al. 2005), which also vary beta diversity itself between areas with patchy versus continuous resources (Harrison 1997). Statistical methods that can integrate directional forces in environmental variables such as current regimes using, for example, an asymmetric eigenvector map method (Blanchet et al. 2008) will be integral for future work given the spatially structured nature of hydrodynamics across the MRC (Davies et al. 2009), but this invokes the fundamental need for further in situ deployment of instrumentation on the reefs themselves to measure these environmental variables.

Beta diversity was best explained by multibeam bathymetry data estimated at a small buffer size. This suggests that beta diversity of Lophelia reef communities on the MRC is created by local scale environmental heterogeneity. However, regional processes can still exert control over local community biodiversity (Gage 2004; Leibold et al. 2004; Harrison et al. 2006), and this may be the case for Lophelia reefs as well in both an environmental and historical context. Regional environmental factors on these reefs include large-scale hydrographical variables such as tidal regimes, water masses and currents. Forces that are temporally variable over palaeoecological time scales can also influence beta diversity of marine communities, particularly if these forces themselves varied across between regions (Danovaro et al. 2008). The importance of spatial autocorrelation may be more pronounced in historically dispersal-limited species (Baselga and Jiménez-Valverde 2007; Wang et al. 2008); contemporary Lophelia reefs are colonised by a range of species that vary in their dispersal potentials and those with highly finite dispersal ranges would have been more unlikely to overcome historical dispersal barriers created by, e.g., glacial periods that impoverished reef fauna and significantly reduced suitable habitats. Thus, a function of both regional (hydrography, distribution of Lophelia reefs) and local (species-determined traits) processes has produced extant local communities derived from a common regional species pool wherein species associations are overwhelmingly facultative (Henry and Roberts 2007). Basin-wide analysis of the fauna on Lophelia reefs across long temporal scales will better illuminate the roles of such historical and regional processes, bearing in mind that methodologies must advance to study in situ dispersal and recruitment limitation on cold-water coral reefs across multiple spatial scales.



We are grateful for the helpful criticisms provided by the Editors of Coral Reefs and from two anonymous reviewers. Funding was provided by the European Commission Sixth Framework Programme “Structuring the European Research Area” to L. -A. Henry through a Marie Curie fellowship (contract no. MIF1-CT-2004-002469), to A. J. Davies and J. M. Roberts through the European HERMES project (contract no. GOCE-CT-2005-511234) under the priority “Sustainable Development, Global Change and Ecosystems”, and to J. M. Roberts through the TRACES project (contract no. MOIF-CT-2006-040018).

Supplementary material

338_2009_577_MOESM1_ESM.doc (290 kb)
DOC 290 kb


  1. Adjeroud M (1997) Factors influencing spatial patterns on coral reefs around Moorea, French Polynesia. Mar Ecol Prog Ser 159:105–119CrossRefGoogle Scholar
  2. Alvarez-Filip L, Dulvy NK, Gill JA, Côté IM, Watkinson AR (2009) Flattening of Caribbean coral reefs: region-wide declines in architectural complexity. Proc R Soc B. doi: 10.1098/rspb.2009.0339
  3. Andréfouet S, Riegl B (2004) Remote sensing: a key tool for interdisciplinary assessment of coral reef processes. Coral Reefs 24:1–4CrossRefGoogle Scholar
  4. Balata D, Piazzi L, Benedetti-Cecchi L (2007) Sediment disturbance and loss of beta diversity on subtidal rocky reefs. Ecology 88:2455–2461CrossRefPubMedGoogle Scholar
  5. Baselga A, Jiménez-Valverde A (2007) Environmental and geographical determinants of beta diversity of leaf beetles (Coleoptera: Chrysomelidae) in the Iberian Peninsula. Ecol Entomol 32:312–318CrossRefGoogle Scholar
  6. Beck MW (2000) Separating the elements of habitat structure: independent effects of habitat complexity and structural components on rocky intertidal gastropods. J Exp Mar Biol Ecol 249:29–49CrossRefPubMedGoogle Scholar
  7. Becking LE, Cleary DFR, de Voogd NJ, Renema W, de Beer M, van Soest RWM, Hoeksema BW (2006) Beta diversity of tropical marine benthic assemblages in the Spermonde Archipelago, Indonesia. Mar Ecol 27:76–88CrossRefGoogle Scholar
  8. Best B (1988) Passive suspension feeding in a sea pen: effects of ambient flow on volume flow rate and filtering efficiency. Biol Bull 175:332–342CrossRefGoogle Scholar
  9. Beyer A, Chakrabotry B, Schenke HW (2007) Seafloor characterization of the mound and channel provinces of the Porcupine Seabight - an application of the multi-beam angular backscatter data. Int J Earth Sci 96:11–20CrossRefGoogle Scholar
  10. Blake JA, Grassle JF (1994) Benthic community structure on the US South Atlantic slope off the Carolina: spatial heterogeneity in a current-dominated system. Deep-Sea Res (II) 41:835–874CrossRefGoogle Scholar
  11. Blanchet FG, Legendre P, Borcard D (2008) Modelling directional spatial processes in ecological data. Ecol Model 215:325–336CrossRefGoogle Scholar
  12. Bonsdorff E, Laine AO, Hänninen J, Vuorinen I, Norkko A (2003) Zoobenthos of the outer archipelago waters (N. Baltic Sea)-the importance of local conditions for spatial distribution patterns. Bor Env Res 8:135–145Google Scholar
  13. Borcard D, Legendre P, Drapeau P (1992) Partialling out the spatial component of ecological variation. Ecology 73:1045–1055CrossRefGoogle Scholar
  14. Buhl-Mortensen P, Dolan M, Buhl-Mortensen L (2009) Prediction of benthic biotopes on a Norwegian offshore bank using a combination of multivariate analysis and GIS classification. ICES J Mar Sci 66:2026–2032CrossRefGoogle Scholar
  15. Clarke KR, Gorley RN (2006) PRIMER v6: user manual/tutorial. PRIMER-e Ltd, PlymouthGoogle Scholar
  16. Cleary DFR, de Voogd NJ (2007) Environmental associations of sponges in the Spermonde Archipelago, Indonesia. J Mar Biol Assoc UK 87:1669–1676CrossRefGoogle Scholar
  17. Cordes EE, McGinley MP, Podowski EL, Becker EL, Lessard-Pilon S, Viada ST, Fisher CR (2008) Coral communities of the deep Gulf of Mexico. Deep Sea Res (I) 55:777–787CrossRefGoogle Scholar
  18. Danovaro R, Gambi C, Lampadariou N, Tselepides A (2008) Deep-sea nematode biodiversity in the Mediterranean basin: testing for longitudinal, bathymetric and energetic gradients. Ecography 31:231–244CrossRefGoogle Scholar
  19. Davies AJ, Wisshak M, Orr JC, Roberts JM (2008) Predicting suitable habitat for the cold-water coral Lophelia pertusa (Scleractinia). Deep Sea Res (I) 55:1048–1062CrossRefGoogle Scholar
  20. Davies AJ, Duineveld G, Lavaleye M, Bergman M, van Haren H, Roberts JM (2009) Downwelling and deep-water bottom currents as food supply mechanisms to the cold-water coral Lophelia pertusa (Scleractinia) at the Mingulay Reef Complex. Limnol Oceanogr 54:620–629Google Scholar
  21. Dolan MFJ, Grehan AJ, Guinan JC, Brown C (2008) Modelling the local distribution of cold-water corals in relation to bathymetric variables: Adding spatial context to deep-sea video data. Deep Sea Res (I) 55:1564–1579CrossRefGoogle Scholar
  22. Dray S, Legendre P, Peres-Neto PR (2006) Spatial modelling: a comprehensive framework for principal coordinate analysis of neighbor matrices (PCNM). Ecol Model 196:483–493CrossRefGoogle Scholar
  23. Dunn DC, Halpin PN (2009) Rugosity-based regional modeling of hard-bottom habitat. Mar Ecol Prog Ser 377:1–11CrossRefGoogle Scholar
  24. Fosså JH, Lindberg B, Christensen O, Lundålv T, Svellingen I, Mortensen PB, Alvsvåg J (2005) Mapping of Lophelia reefs in Norway: experiences and survey methods. In: Freiwald A, Roberts JM (eds) Cold-water corals and ecosystems. Springer-Verlag, Berlin Heidelberg, pp 359–391CrossRefGoogle Scholar
  25. Foubert A, Beck T, Wheeler AJ, Opderbecke O, Grehan A, Klages M, Thiede J, Henriet JP, The Polarstern ARK-XIX/3a Shipboard Party (2005) New view of the Belgica Mounds, Porcupine Seabight, NE Atlantic: preliminary results from the Polarstern ARK-XIX/3a ROV cruise. In: Freiwald A, Roberts JM (eds) Cold-water corals and ecosystems. Springer-Verlag, Berlin Heidelberg, pp 403–415CrossRefGoogle Scholar
  26. Frederiksen R, Jensen A, Westerberg H (1992) The distribution of the scleractinian coral Lophelia pertusa around the Faeroe Islands and the relation to internal tidal mixing. Sarsia 77:157–171Google Scholar
  27. Gage JD (2004) Diversity in deep-sea benthic macrofauna: the importance of local ecology, the larger scale, history and the Antarctic. Deep Sea Res (II) 51:1689–1708CrossRefGoogle Scholar
  28. Gheerardyn H, de Troch M, Vincx M, Vanreusel A (2009) Harpacticoida (Crustacea: Copepoda) associated with cold-water coral substrates in the Porcupine Seabight (NE Atlantic): species composition, diversity and reflections on the origin of the fauna. Sci Mar 73:747–760Google Scholar
  29. Glasby TM (2000) Surface composition and orientation interact to affect subtidal epibiota. J Exp Mar Biol Ecol 248:177–190CrossRefPubMedGoogle Scholar
  30. Glasby TM, Connell SD (2001) Orientation and position of substrata have large effects on epibiotic assemblages. Mar Ecol Prog Ser 214:127–135CrossRefGoogle Scholar
  31. Guinan J, Grehan AJ, Dolan MFJ, Brown C (2009) Quantifying relationships between video observations of cold-water coral cover and seafloor features in Rockall Trough, west of Ireland. Mar Ecol Prog Ser 375:125–138CrossRefGoogle Scholar
  32. Harborne AR, Mumby PJ, Żychaluk K, Hedley JD, Blackwell PG (2006) Modeling the beta diversity of coral reefs. Ecology 87:2871–2881CrossRefPubMedGoogle Scholar
  33. Harrison S (1997) How natural habitat patchiness affects the distribution of diversity in Californian serpentine chaparral. Ecology 78:1898–1906CrossRefGoogle Scholar
  34. Harrison S, Davies KF, Safford HD, Viers JH (2006) Beta diversity and the scale-dependence of the productivity-diversity relationship: a test in the Californian serpentine flora. J Anim Ecol 94:110–117Google Scholar
  35. Henry L-A, Roberts JM (2007) Biodiversity and ecological composition of macrobenthos on cold-water coral mounds and adjacent off-mound habitat in the bathyal Porcupine Seabight, NE Atlantic. Deep Sea Res (I) 54:654–672CrossRefGoogle Scholar
  36. Henry L-A, Nizinski MS, Ross SW (2008) Occurrence and biogeography of hydroids (Cnidaria: Hydrozoa) from deep-water coral habitats off the southeastern United States. Deep Sea Res (I) 55:788–800CrossRefGoogle Scholar
  37. Hewitt JE, Thrush SF, Halliday J, Duffy C (2005) The importance of small-scale habitat structure for maintaining beta diversity. Ecology 86:1619–1626CrossRefGoogle Scholar
  38. Holland ND, Leonard AB, Strickler JR (1987) Upstream and downstream capture during suspension feeding by Oligometra serripinna (Echinodermata: Crinoidea) under surge conditions. Biol Bull 173:552–556CrossRefGoogle Scholar
  39. Hubbell SP (2001) The unified neutral theory of biodiversity and biogeography. Princeton University Press, PrincetonGoogle Scholar
  40. Hubbell SP (2005) Neutral theory in community ecology and the hypothesis of functional equivalence. Funct Ecol 19:166–172CrossRefGoogle Scholar
  41. Hughes DJ (2001) Quantitative analysis of a deep-water bryozoan collection from the Hebridean continental slope. J Mar Biol Assoc UK 81:987–993Google Scholar
  42. Jenness J (2002) Surface areas and ratios from elevation grid (surfgrids.avx) extension for ArcView 3.x. Jenness Enterprises.
  43. Jensen A, Frederiksen R (1992) The fauna associated with the bank-forming deepwater coral Lophelia pertusa (Scleractinia) on the Faroe Shelf. Sarsia 77:53–69Google Scholar
  44. Jonsson LG, Nilsson PG, Floruta F, Lundälv T (2004) Distributional patterns of macro- and megafauna associated with a reef of the cold-water coral Lophelia pertusa on the Swedish west coast. Mar Ecol Prog Ser 284:163–171CrossRefGoogle Scholar
  45. Kano A, Ferdelman TG, Williams T, Henriet J-P, Ishikawa T, Kawagoe N, Takashima C, Kakizaki Y, Abe K, Sakai S, Browning EL, Li X, Integrated Ocean Drilling Program Expedition 307 Scientists (2007) Age constraints on the origin and growth history of a deep-water coral mound in the northeast Atlantic drilled during Integrated Ocean Drilling Program Expedition 307. Geology 35:1051–1054CrossRefGoogle Scholar
  46. Knudby A, Le Drew E, Newman C (2007) Progress in the use of remote sensing for coral reef biodiversity studies. Progr Phys Geogr 31:421–434CrossRefGoogle Scholar
  47. Legendre PD (2008) Studying beta diversity: ecological variation partitioning by multiple regression analysis and canonical analysis. J Plant Ecol 1:3–8CrossRefGoogle Scholar
  48. Legendre PD, Gallagher ED (2001) Ecologically meaningful transformations for ordination of species data. Oecologia 129:271–280CrossRefGoogle Scholar
  49. Legendre P, Borcard D, Peres-Neto PR (2005) Analyzing beta diversity: partitioning the spatial variation of community composition data. Ecol Monogr 75:435–450CrossRefGoogle Scholar
  50. Leibold MA, Holyoak M, Mouquet N, Amarasekare P, Chase JM, Hoopes MF, Holt RD, Shurin JB, Law R, Tilman D, Loreau M, Gonzalez A (2004) The metacommunity concept: a framework for multi-scale community ecology. Ecol Lett 7:601–613CrossRefGoogle Scholar
  51. Linke P, Pfannkuche O, Beuck L, Karstensen J (2006) Longterm observation of a cold-water coral mound in the Porcupine Seabight and implications for ESONET/CeltNet. In: Proceedings of the Fourth International Workshop on Scientific Use of Submarine Cables and Related Technologies, Dublin, 7–10 February 2006Google Scholar
  52. Maier C, shipboard scientific party (2006) Biology and ecosystem functioning of cold water coral bioherms at Mingulay (Hebrides), NE Atlantic. Cruise Report, BIOSYS, 2006. Cruise 64PE250 on R/V Pelagia. Oban-Oban. 7–23 July 2006Google Scholar
  53. McKinney FK, McKinney MJ (2002) Contrasting marine larval settlement patterns imply habitat-seeking behaviours in a fouling and a cryptic species (phylum Bryozoa). J Nat Hist 36:487–500CrossRefGoogle Scholar
  54. Mortensen PB, Fosså JH (2006) Species diversity and spatial distribution of invertebrates on deep-water Lophelia reefs in Norway. Proc 10th Int Coral Reef Symp 1849–1868Google Scholar
  55. Mortensen PB, Hovland M, Brattegard T, Farestveit R (1995) Deep water bioherms of the scleractinian coral Lophelia pertusa (L.) at 64° N on the Norwegian shelf: structure and associated megafauna. Sarsia 80:145–158Google Scholar
  56. Mortensen PB, Hovland MT, Fosså JH, Furevik DM (2001) Distribution, abundance and size of Lophelia pertusa coral reefs in mid-Norway in relation to seabed characteristics. J Mar Biol Assoc UK 81:581–597CrossRefGoogle Scholar
  57. Mumby PJ, Skirving W, Strong AE, Hardy JT, LeDrew EF, Hochberg EJ, Stumpf RP, David LT (2004) Remote sensing of coral reefs and their physical environment. Mar Pollut Bull 48:219–228CrossRefPubMedGoogle Scholar
  58. Picton BE, Morrow CC (2007) Encyclopedia of Marine Life of Britain and Ireland.
  59. Raes M, Vanreusel A (2005) The metazoan meiofauna associated with a cold-water coral degradation zone in the Porcupine Seabight (NE Atlantic). In: Freiwald A, Roberts JM (eds) Cold-water corals and ecosystems. Springer-Verlag, Berlin Heidelberg, pp 821–847CrossRefGoogle Scholar
  60. Roberts JM, Brown CJ, Long D, Bates CR (2005a) Acoustic mapping using a multibeam echosounder reveals cold-water coral reefs and surrounding habitats. Coral Reefs 24:654–669CrossRefGoogle Scholar
  61. Roberts JM, Peppe OC, Dodds LA, Mercer DJ, Thomson WT, Gage JD, Meldrum DT (2005b) Monitoring environmental variability around cold-water coral reefs: the use of a benthic photolander and the potential of seafloor observatories. In: Freiwald A, Roberts JM (eds) Cold-water corals and ecosystems. Springer-Verlag, Berlin Heidelberg, pp 483–502CrossRefGoogle Scholar
  62. Roberts JM, Wheeler AJ, Freiwald A (2006) Reefs of the deep: the biology and geology of cold-water coral ecosystems. Science 312:543–547CrossRefPubMedGoogle Scholar
  63. Roberts JM, Henry L-A, Long D, Hartley JP (2008) Cold-water coral reef frameworks, megafaunal communities and evidence for coral carbonate mounds on the Hatton Bank, north east Atlantic. Facies 54:297–316CrossRefGoogle Scholar
  64. Roberts JM, Wheeler AJ, Freiwald A, Cairns SD (2009a) Cold-water corals: the biology and geology of deep-sea coral habitats. Cambridge University Press, CambridgeGoogle Scholar
  65. Roberts JM, Davies AJ, Henry LA, Dodds LA, Duineveld GCA, Lavaleye MSS, Maier C, van Soest RWM, Bergman MJN, Hühnerbach V, Huvenne VAI, Sinclair DJ, Watmough T, Long D, Green SL, van Haren H (2009b) Mingulay reef complex: an interdisciplinary study of cold-water coral habitat, hydrography and biodiversity. Mar Ecol Prog Ser 397:139–151CrossRefGoogle Scholar
  66. Sánchez F, Serrano A, Gómez Ballesteros M (2009) Photogrammetric quantitative study of habitat and benthic communities of deep Cantabrian Sea hard grounds. Cont Shelf Res 29:1174–1188CrossRefGoogle Scholar
  67. Schlacher TA, Newell P, Clavier J, Schlacher-Hoenlinger MA, Chevillon C, Britton J (1998) Soft-sediment benthic community structure in a coral reef lagoon-the prominence of spatial heterogeneity and ‘spot endemism’. Mar Ecol Prog Ser 174:159–174CrossRefGoogle Scholar
  68. Schlacher TA, Schlacher-Hoenlinger M, Williams A, Althaus F, Hooper JNA, Kloser R (2007) Richness and distribution of sponge megabenthos in continental margin canyons off southeastern Australia. Mar Ecol Prog Ser 340:73–88CrossRefGoogle Scholar
  69. Serrano A, Sánchez F, García-Castrillo G (2006) Epibenthic communities of trawlable grounds of the Cantabrian Sea. Sci Mar 70(S1):149–159CrossRefGoogle Scholar
  70. Ter Braak CJF (1986) Canonical correspondence analysis: a new eigenvector technique for multivariate direct gradient analysis. Ecology 67:1167–1179CrossRefGoogle Scholar
  71. Thiem Ø, Ravagnan E, Fosså JH, Berntsen J (2006) Food supply mechanisms for cold-water corals along a continental shelf edge. J Mar Syst 26:1481–1495Google Scholar
  72. van Oevelen D, Duineveld G, Lavaleye M, Mienis F, Soetaert K, Heip CHR (2009) The cold-water coral community as a hot spot for carbon cycling on continental margins: A food-web analysis from Rockall Bank (northeast Atlantic). Limnol Oceanogr 54:1829–1844Google Scholar
  73. van Soest RWM, Beglinger EJ (2008) New bioeroding sponges from Mingulay coldwater reefs, north-west Scotland. J Mar Biol Assoc UK. doi:  10.1017/S0025315408002725
  74. van Soest RWM, Lavaleye MSS (2005) Diversity and abundance of sponges in bathyal coral reefs of Rockall Bank, NE Atlantic, from boxcore samples. Mar Biol Res 1:338–349CrossRefGoogle Scholar
  75. van Soest RWM, Cleary DFR, de Kluijver MJ, Lavaleye MSS, Maier C, Van Duyl FC (2007) Sponge diversity and community composition in Irish bathyal coral reefs. Contrib Zool 76:121–142Google Scholar
  76. Vanaverbeke J, Arbizu PM, Dahms H-U, Schminke HK (1997a) The metazoan meiobenthos along a depth gradient in the Arctic Laptev Sea with special attention to nematode communities. Polar Biol 18:391–401CrossRefGoogle Scholar
  77. Vanaverbeke J, Soetaert K, Heip CHR, Vanreusel A (1997b) The metazoan meiobenthos along the continental slope of the goban spur (NE atlantic). J Sea Res 38:93–107CrossRefGoogle Scholar
  78. Voogd N, de Cleary DFR, Hoeksema BW, Noor A, van Soest RWM (2006) Sponge beta diversity in the Spermonde Archipelago, SW Sulawesi, Indonesia. Mar Ecol Prog Ser 309:131–142CrossRefGoogle Scholar
  79. Wang J, Wu Y, Jiang H, Li C, Dong H, Wu Q, Soininen J, Shen J (2008) High beta diversity of bacteria in the shallow terrestrial subsurface. Environ Microbiol 10:2537–2549CrossRefPubMedGoogle Scholar
  80. Weinberg C, Beuck L, Heidkamp S, Hebbeln D, Freiwald A, Pfannkuche O, Monteys X (2008) Franken Mound: facies and biocoenoses on a newly-discovered “carbonate mound” on the western Rockall Bank, NE Atlantic. Facies 54:1–24CrossRefGoogle Scholar
  81. Wilson MFJ, O’Connell B, Brown C, Guinan JC, Grehan AJ (2007) Multiscale terrain analysis of multibeam bathymetry data for habitat mapping on the continental slope. Mar Geodesy 30:3–35CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • Lea-Anne Henry
    • 1
  • Andrew J. Davies
    • 1
    • 2
  • J. Murray Roberts
    • 1
    • 3
    • 4
  1. 1.Scottish Association for Marine Science, Dunstaffnage Marine LaboratoryOban, ArgyllUK
  2. 2.School of Ocean SciencesBangor UniversityMenai BridgeUK
  3. 3.Center for Marine ScienceUniversity of North Carolina-WilmingtonWilmingtonUSA
  4. 4.Centre for Marine Biodiversity and Biotechnology, School of Life SciencesHeriot-Watt UniversityEdinburghUK

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