Classifying benthic biotopes on sub-tropical continental shelf reefs: How useful are abiotic surrogates?

Estuarine, Coastal and Shelf Science 138 (2014) 79e89

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Classifying benthic biotopes on sub-tropical continental shelf reefs: How useful are abiotic surrogates? Sarah Richmond*, Tim Stevens Griffith School of Environment and Australian Rivers Institute, Griffith University, Gold Coast Campus, QLD 4222, Australia

a r t i c l e i n f o

a b s t r a c t

Article history: Received 18 September 2013 Accepted 22 December 2013 Available online 30 December 2013

Biodiversity of marine areas beyond the reach of conventional diving technology (>30 m) is poorly known, yet subjected to increasing stresses from expanding recreational and commercial fishing, minerals exploration and other anthropogenic influences. In part, resource managers address this by using abiotic surrogates for patterns of biodiversity in planning marine protected areas or other management measures. However, the efficacy of these surrogates varies from place to place, and is often not quantified at the scale used by MPA designers and managers. This study surveyed and classified benthic assemblages of continental shelf rocky reefs across three depth categories from 30 to 70 m, using a suspended HD camera array, which is both non-destructive and cost-effective compared to any other methods of sampling at these depths. Five distinct benthic biotopes were defined, characterised primarily by variations in abundances of sea whips, sponges, kelp, and urchins. Derived patterns of benthic assemblage structure were compared to abiotic surrogates available at the scale (local) used in MPA planning. The individual factors with most influence on the classification were recreational fishing pressure, water temperature at the bottom, and distance from nearest estuary. The best combination of abiotic surrogates had a relatively strong relationship with the benthic assemblage, explaining 42% of the variation in assemblage structure (BIOENV r ¼ 0.65), however the performance of a classification based on commonly used physical surrogates was relatively poor, explaining only 22% of variation. The results underline the limitations of using abiotic variables for habitat mapping at the local scale, and the need for robust surveys to quantify patterns of biodiversity. Ó 2014 Elsevier Ltd. All rights reserved.

Keywords: benthic communities abiotic surrogates marine protected areas conservation classification systems biotopes

1. Introduction Coastal ecosystems around the world are under increasing stress from anthropogenic influences, such as global climate change (Pandolfi et al., 2003; Hoegh-Guldberg et al., 2007) and the effects of over-fishing (Pauly et al., 1998; Jackson et al., 2001; Daskalov et al., 2007) and this is most evident in well-studied shallow water (<30 m) habitats. As a result, habitats in the mesophotic zone, defined as 30e150 m depth and characterised by the presence of a light-dependent community (Hinderstein et al., 2010) are coming under increasing scrutiny, for two reasons. As fishing effort is displaced offshore by declines in shallow water stocks (Koslow et al., 2000; Roberts, 2002), understanding patterns of biodiversity and ecological processes in newly exploited, deeper habitats is critical. In addition it has been suggested that mesophotic habitats,

* Corresponding author. E-mail addresses: sarah.richmond@griffith.edu.au (S. Richmond), t.stevens@ griffith.edu.au (T. Stevens). 0272-7714/$ e see front matter Ó 2014 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ecss.2013.12.012

especially reefs, might function as refugia from some of these perturbations, in particular climate change (Bongaerts et al., 2010), and be a crucial source of recruits for shallow water ecosystems (Van Oppen et al., 2011). However, conducting investigations at depths beyond the range of conventional SCUBA is logistically complex and relatively expensive, and so the number and extent of studies within this zone is orders of magnitude less than shallow water (Menza et al., 2007). To date, little systematic survey work has been carried out on subtropical mesophotic or deep-water reefs. Rocky reef ecology has focused on the intertidal (Jones, 1988; Underwood and Kennelly, 1990), temperate inshore reefs (Harriott et al., 1999; Levin et al., 2002) and kelp beds (Estes and Palmisano, 1974; Dayton, 1985; Wernberg et al., 2003). Most deep-water surveys have concentrated in tropical (Lesser et al., 2009; Bare et al., 2010) and temperate areas (Williams et al., 2010; Schlacher et al., 2010b; Bridge et al., 2011), with a particular focus on seamounts (Schlacher et al., 2010a). There is therefore a conspicuous gap in our knowledge of subtropical continental shelf reefs beyond the reach of conventional SCUBA-based surveys.

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This gap in our knowledge of mesophotic reefs is an important issue, especially in the context of spatial management of marine resources. Marine protected areas (MPAs) are considered a successful tool for the conservation and sustainable use of marine ecosystems (Gray, 1997; Boersma and Parrish, 1999; Stevens, 2002). Over the last decades MPA design and selection tools have evolved (Possingham et al., 2000; Fernandes et al., 2005) to better address global concerns that ad hoc approaches to marine resource management are failing (Beaelde, 2005; Gladstone, 2007). This ‘ecosystem’ approach sees the management of large, multiple use areas with varying levels of protection (Butler et al., 2010). A key plank of the ecosystem approach is the inclusion of representative samples of all identified habitat types within MPA designs (e.g. Stevens, 2002), and the adoption of structured classification schemes to allow consistent descriptions across differing spatial and organisational scales (Howell, 2010; Last et al., 2010). Progressively, management of marine ecosystems is being extended beyond shallow coastal waters, in recognition that increasing anthropogenic impacts offshore has led to increased stress in these more isolated ecosystems. To address this growing need in Australia, declaration of a national system of MPAs designed to represent marine habitats from the coastline to the limit of the exclusive economic zone (EEZ) is underway (ANZECC, 1999; Barr and Possingham, 2013). Spatial management of marine ecosystems requires detailed information about the distribution of marine assemblages (or communities, habitats, or biotopes, depending on the terminology applied) at spatial scales relevant to the scale at which management occurs e habitat maps, in other words (Stevens, 2002; Bianchi et al., 2012). Broad scale surveys of marine biota, however, are prohibitively expensive, complex, and logistically difficult (Brown et al., 2011), and are therefore rarely undertaken. Thus, species’ distributions, as well as information about key ecological processes (recruitment, dispersion, herbivory) are poorly known in deeper waters (Conroy and Noon, 1996), with the exception of deep sea trawl fishery resources (Koslow et al., 2000). To address this, the production of habitat maps in the absence of detailed biological survey data, relies increasingly on models (e.g. Pitcher et al., 2012; Parravicini et al., 2012) using more easily measured physical, chemical or biological attributes as surrogates for patterns of biodiversity (Kenny et al., 2003), sometimes in combination with biological data (Howell, 2010). Typically, these use information readily available from remote sensed sources, such as satellite data (e.g. SST, currents, Chl a), geomorphic information (detailed bathymetry, slope, aspect from swath mapping or sidescan, “hardness” derived from acoustic data), as well as grabsampled attributes of sediment type or local geology (McArthur et al., 2010). In combination, these can be used to predict patterns in biological assemblages over large spatial scales, and within poorly understood environments (McArthur et al., 2010; Schlacher et al., 2010b; Przeslawski et al., 2011; e.g.; Stevens and Connolly, 2004; Williams et al., 2009). While these habitat-level surrogates can be cost-effective for pinpointing candidate MPAs (Ward et al., 1999), the legitimacy of using such surveys as predictors of benthic assemblage structure is often not well tested at the scale of management (Stevens and Connolly, 2004). Additionally, abiotic surrogates can be coarse in detail (e.g. rock vs. sand) resulting in biologically relevant differences in assemblage structure being overlooked. This study has two key objectives: 1) to investigate patterns of benthic biodiversity in continental shelf reef ecosystems, in this case adjacent to a major urban and recreational centre, using towed video sampling and 2) to evaluate the robustness of marine conservation planning based on the use of abiotic surrogates for patterns of biodiversity. Specific aims are to:

1. Survey and classify benthic assemblages on inner-continental shelf hard substrate mesophotic reefs; 2. Describe any distinct biotopes occurring on these reefs; 3. Establish links between derived patterns of benthic assemblage structure and putative abiotic drivers, especially those used by MPA designers and managers, and thereby; 4. Assess the value of abiotic surrogates for benthic habitat mapping and MPA design in subtropical deep water rocky environments.

2. Methods 2.1. Study locations To quantify benthic assemblage structure on continental shelf rocky reefs, we surveyed four locations (Fig. 1) selected to represent the geographical range of hard substrates in South East Queensland (SEQ); offshore areas from 1) Moreton Island, 2) North Stradbroke Island, 3) the Gold Coast Seaway, and 4) the Tweed River. To examine cross-shelf patterns in hard-substrate biodiversity, three sites were selected at each location, corresponding to bands of exposed rock strata at increasing depths running roughly parallel along the SEQ coastline; Inner (30e40 m depth), Mid (45e55 m) and Outer (60e70 m). At the Moreton location we found no hard substrate band corresponding to the “Mid” depth range, so only an inner and outer site were surveyed for this location. There is no existing habitat mapping across the study area; inshore sites adjacent to Moreton and North Stradbroke Islands are included within the QEPA (2008) habitat map, but all hard substrate types are classified together as “offshore reef”. 2.2. Benthic assemblage structure 2.2.1. Sampling methods and design Surveys of continental shelf reef biota were conducted using a towed video array based on the design of Stevens and Connolly (2004), but mounted on a lightweight, flexible PVC frame. The towed-video technique has been widely used for characterising benthic habitats at depths beyond the reach of conventional SCUBA (e.g. Bax and Williams, 2001), is non-destructive, and allows rapid surveys of relatively large areas (Barker et al., 1999), although there is an acknowledged tradeoff in taxonomic resolution because specimens are not retrieved (Sheehan et al., 2010). The array was deployed as a suspended camera arrangement because of the high relief topography. Elevation of the array above the substrate was manually controlled using an electric winch while watching the live feed on a video monitor. A ten kilogram weight was attached to the array, to keep the winch line nearly vertical. Video imagery was collected using a GoPro HD Hero camera with 127 wide angle view mounted in a deep-water housing (iQsub, supplied by Golem Gear Inc, Florida, USA), waterproof to 150 m, which corrected optical distortion and allowed us to work beyond the range of the standard GoPro housings. This camera was set to record at a resolution of 960 lines (4:3 ratio) as it avoided line interlacing on still images. An additional VGA resolution (480 lines) video sensor was fixed beside the GoPro, providing a live video feed to an on-board monitor so that operators were able to check orientation and elevation of the array, and avoid obstacles where necessary. Four green laser line diodes (Roithner lasertechnik, CW532-005L) were mounted in waterproof encapsulant and projected a constant 1 m2 quadrat onto the seafloor to allow for scaling of the video images. Although usable images were obtainable in natural light at the maximum depth reached in the study, LED camera lights (Nocturnal Lights SLX 800) were fitted to the array to

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Fig. 1. Map of South East Queensland coastline illustrating locations and transect sites with gross bathymetry.

provide consistent colour at depth to aid taxon identification. A conductivity, temperature and depth (CTD) sensor (RBR XR420) was mounted on the array and recorded these variables every 10 s to allow evaluation of their utility as abiotic surrogates (Section 2.3). A total of 53 transects covering 11.24 km was surveyed on 12 field days from June to August 2012. It should be noted that the fieldwork was carried out a few months after one of the wettest periods on record, but that at all sites there was sufficient light penetration to record clear images at up to 70 m without additional lighting. Five transects were surveyed at each of the three sites within a location (due to the excessive speed over the ocean floor, two transects were unable to be used). Transect positions were selected from existing shoal mapping and using waypoint data provided by local charter fishing operators who target these reefs. On-site, the array was deployed where depth sounder returns indicated hard substrate. The direction of transects were

haphazard, as the vessel was allowed to drift with wind and current so that speed over the bottom was not excessive. GPS positions were taken at the start and finish of each transect and nominal transect length was 200 m, but this varied with topography and drift speed. Transects were positioned opportunistically depending on the orientation of hard substrate, so distance between each transect varied; however, the distance between them was always greater than the transect length to ensure independence. Most transects within a site were more than 500 m apart. 2.2.2. Extraction of biological data Taxon abundance (individuals m2) was obtained from still frames manually extracted from the video stream (mean 59.35 frames.transect1  4.39). To ensure no overlap of images the video was paused at 3e5 s intervals (depending on speed of camera over substrate) and the clearest image was sought within 1 s of that point. To ensure a consistent approach to the treatment of video

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Table 1 Selected abiotic variables. Variable

Units

Definition

Source

Depth

m

From CTD data

Salinity at bottom

&

Temperature at bottom



Distance from shore

km

Distance from estuary

km

Distance from continental shelf break Relief

km

Mean CTD readings from beginning to end of transect Mean CTD readings from beginning to end of transect Mean CTD readings from beginning to end of transect Measurement from transect centroid to closest point on coastline Measurement from transect centroid to closest major river mouth or ocean entrance Measurement from transect centroid to closest point on 200 m isobath Flat, low, medium, high, scored as 0, 1, 2, or 3. Mean of start, middle and end readings Substrate labelled as either; Sandstone, Limestone, Basalt or Rubble Estimated number of recreational fishing vessels surveyed for each location

Substrate type Fishing effort

C

Ordinal scale Nominal scale (Rock type) Number of fishing boats/per year

imagery, decision rules were set up a priori to guide the selection of usable images. Images were removed from the analysis unless all the following rules were satisfied:  Image must be well focused and evenly lit  Frame must be >50% hard substrate  Image must be clear of anything obstructing view of benthos (e.g. large fish, debris)  The projected laser lines must be visible, and the 1 m2 area represent at least half the frame Taxa were identified to the lowest taxonomic level possible from visual identification (morphospecies), with the assistance of specialist staff from the Queensland Museum where necessary. Where species identification could not be made with confidence, taxa were placed into a higher taxonomic group and assigned a unique identifier (e.g. gorgonian sp. 1) that was used consistently for the remainder of the survey. Abundance of each morphospecies was quantified by counting the number of individuals of each within the 1 m2 quadrat, defined by the laser lines, in each frame selected. Abundance data from all the frames within each transect were averaged to give a transects by morphospecies data matrix.

2.2.3. Analytical methods Patterns of benthic assemblage structure were examined using tools within the PRIMER v.6 software package (Clarke and Gorley, 2006) with permuted multivariate analysis of variance (PERMANOVA, Anderson, 2001). PERMANOVA is robust for datasets that violate the assumptions of an ANOVA, while allowing the incorporation of covariates and complex experimental designs (Anderson, 2005). It can also be used for analyses of a single response variable in combination with a Euclidean distance measure, in which case it yields Fisher’s univariate F statistic, but with significance values derived from permutation (Anderson, pers comm). Univariate analyses on total abundance and taxon richness (calculated for each transect) tested for differences between site and location in a two-way design. Multivariate analyses of assemblage structure were conducted on BrayeCurtis similarity matrices from both untransformed data and 4th-root transformed data to allow for influences of both abundant and rare taxa in the dataset. In addition, patterns in multivariate assemblage structure were visualized using ordination techniques (nMDS and cluster analysis),

From CTD data From CTD data GIS using Auslig Coastline 2004 GIS using Auslig Coastline 2004

GIS using Auslig Bathymetry 2004 Visually assessed Visually assessed Taylor et al. (2012) and VMR vessel logs 2012.

and the validity of derived groups tested using PERMANOVA. The relative influence of individual taxa on the groups derived from ordination analyses were assessed using similarity percentage analysis (SIMPER). This was used to highlight the main taxa responsible for within- and between-group similarity. 2.3. Utility of abiotic surrogates 2.3.1. Selection of abiotic variables The abiotic factors used to test predictive ability were chosen on the basis of their relevance and availability at the required scale. Factors selected were; depth, salinity at bottom, temperature at bottom, distance from shore, distance from nearest estuary, distance from the continental shelf break (200 m isobath), relief, substrate type and fishing pressure (Table 1). All variables were standardised by rank to give them equal weight in the analysis. Correlation analysis was used to check that factors didn’t autocorrelate too highly (>0.70). The substrate type category was comprised of four variables corresponding to each rock type, since these cannot be placed on an ordinal scale. So that the four variables did not overly influence the analysis, they were de-weighted to a maximum of 0.25 each. Fishing effort data (as fisher-event-days year1) from Queensland Fisheries surveys (Taylor et al., 2012) were available from Queensland only, so this information was calibrated with vessel movement information from Volunteer Marine Rescue (VMR) logs of vessel movements, to account for fishers from New South Wales using sites adjacent to the Tweed River, which forms the border between the two states. Fishing effort data were only available at the scale of locations; there was not sufficient information in the source data to confidently assign events to the different sites within locations. This is therefore a coarser surrogate than the others used. 2.3.2. Relationship with patterns of biodiversity The relationship between this suite of abiotic variables and the biological assemblage structure was assessed using the BIOENV procedure (Clarke and Ainsworth, 1993), which compares a biological similarity matrix (from Section 2.2) with abiotic similarity matrices derived from the selected surrogates, in this case using normalised Euclidean distance. BIOENV returns a Spearman’s ranked correlation value (r). Initial analyses focused on physical attributes used as surrogates in benthic habitat classifications, in this case depth, substrate, relief,

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Fig. 2. a) Mean organism abundance for each site across locations, b) Mean species richness for each site across locations.

Table 2 Pairwise PERMANOVA for Mean Total Abundance on a) interaction at the site level, and b) interaction at the location level. Statistically significant values in bold. Groups a) Seaway

Tweed

North Stradbroke

Moreton b) Inner

Mid

Outer

Inner, Mid Inner, Outer Mid, Outer Inner, Mid Inner, Outer Mid, Outer Inner, Mid Inner, Outer Mid, Outer Inner, Outer

Seaway, Tweed Seaway, North Stradbroke Seaway, Moreton Tweed, North Stradbroke Tweed, Moreton North Stradbroke, Moreton Seaway, Tweed Seaway, North Stradbroke Tweed, North Stradbroke Seaway, Tweed Seaway, North Stradbroke Seaway, Moreton Tweed, North Stradbroke Tweed, Moreton North Stradbroke, Moreton

t value

Degrees of freedom of denominator

p value from permutation

0.346 2.907 4.005 1.314 5.198 7.937 0.627 0.696 1.977 0.650

8 6 8 8 7 7 9 9 8 7

0.798 0.030 0.011 0.260 0.007 0.006 0.567 0.531 0.082 0.544

2.122 0.782 0.340 1.000 4.812 1.276 2.166 0.436 2.020 2.920 2.627 6.746 5.959 10.690 4.505

7 8 7 9 8 9 9 9 8 6 7 6 7 6 7

0.031 0.456 0.864 0.380 0.009 0.228 0.047 0.697 0.088 0.029 0.053 0.027 0.008 0.029 0.007

temperature and salinity. The variation in biological assemblage structure highlighted in the preceding analyses was not well explained by any single physical attribute, or any combination, we therefore included geographic attributes relating to structuring processes from the adjacent coast, from riverine input, or open ocean influences. These factors were distance from shore, distance from estuary and distance from the continental shelf break. Finally we included recreational fishing effort as a possible structuring process. 3. Results A total of 11,411 individual organisms, belonging to 94 morphospecies, were identified in the 53 transects surveyed (covering 11.24 km, mean transect length was 212 m  13 SE). The most common taxa found were the gorgonian, Junceella fragilis, followed by the macroalga, Ecklonia radiata. Least frequently occurring were the sponge Speciospongia papillosa (only recorded once) and the ascidian Pyura spinifera (only in three transects).

3.1. Geographic and cross-shelf patterns 3.1.1. Total abundance and taxon richness Total abundance of benthic taxa varied greatly across locations and sites (Fig. 2a). The Seaway and Tweed locations had higher overall mean abundance (3.25 ind m2 0.31 SE and 4.52  0.47 respectively) than North Stradbroke (3.19  0.20) and Moreton (2.44  0.06). Mean abundance at inner sites was highest at the Tweed (3.75  0.27) and lowest at Moreton (2.47  0.10). Mid sites at North Stradbroke and Tweed had the lowest mean abundance (2.83  0.22 and 3.34  0.12) of all sites within those locations. Outer sites at North Stradbroke, Seaway and Tweed had the highest mean abundance (3.49  0.25, 4.72  0.40 and 6.94  0.66, respectively) of all sites within those locations. A two-way (location  site) PERMANOVA for total abundance showed a significant interaction (p ¼ 0.001) between location and site. Pairwise analyses showed that in all locations the difference in mean abundance between inner and mid sites was not significant, and within the North Stradbroke and Moreton locations there was

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Table 3 Pairwise PERMANOVA for Taxon Richness on location term. Statistically significant values in bold.

value (0.17); these patterns were consistent across data transformations and agree with cluster analyses.

Groups

t value

Degrees of freedom of denominator

p value from permutation

3.2. Distinct biotopes derived from the classification

Seaway, Tweed Seaway, North Stradbroke Seaway, Moreton Tweed, North Stradbroke Tweed, Moreton North Stradbroke, Moreton

2.3545 3.3489 5.2386 5.6496 9.318 1.7391

22 24 18 24 18 20

0.0209 0.0020 0.0001 0.0001 0.0001 0.0885

There were five very clear groups within the nMDS (Fig. 3, pictorially in Fig. 4), and reflected in the cluster analysis interpreted at 50% similarity. A one-way PERMANOVA (p < 0.05) and pairwise tests (all pairs p < 0.01) confirmed that these groups were statistically distinct. The biological assemblages found within these groups represent distinct biotopes as characterised by the taxa highlighted in the SIMPER analyses.

no difference between inner, mid and outer sites (Table 2). Total abundance at Seaway and Tweed inner sites was significantly different (p ¼ 0.031), as was the case at Tweed and Moreton (p ¼ 0.009). Only the Seaway and Tweed mid sites were significantly different (p ¼ 0.047), and neither was different to North Stradbroke. With the exception of the Seaway and North Stradbroke (p ¼ 0.052) all outer sites were significantly different (p < 0.05) from each other. Mean taxon richness (Fig. 2b) was also higher in the Seaway (22  1.68) and Tweed (28.14  1.54) locations compared to North Stradbroke (14.13  1.67) and Moreton (9.56  0.85). The highest mean taxon richness was seen in the outer sites at the Seaway (24.75  1.24) and Tweed (30.75  2.18). A two-way (location  site) PERMANOVA showed no significant interaction (p ¼ 0.357), and no significant difference (p ¼ 0.258) between sites. Locations were found to be significantly different (p < 0.001), and pairwise testing established that all locations were significantly different (p < 0.05) from each other, with the exception of Moreton and North Stradbroke (p ¼ 0.089) (Table 3). 3.1.2. Assemblage structure Two distinct trends are apparent in the nMDS (Fig. 3). There was a defined geographical trend with the northern locations (Moreton and North Stradbroke) clearly separate from those in the south (Seaway and Tweed). Secondly, a distinct cross-shelf pattern was detected within the southern locations, in that outer sites were clearly separate from the inner and mid sites. The nMDS for 4throot transformed data is illustrated because it has the lowest stress

3.2.1. Biotope 1 (Ecklonia beds) The sites at Moreton (all) and North Stradbroke (mid and outer) were dominated by the presence of kelp Ecklonia radiata, with an individual contribution of 28.27%, followed by hydroid Gymnangium longicorne (17.88%) and an encrusting algae (ID: encrusting algae sp. 2) (16.09%). There was an observed difference in morphology, with regards to stipe length and number of lamina, between E. radiata forests found at the mid site at North Stradbroke compared to those found at the deeper outer sites at North Stradbroke and Moreton. 3.2.2. Biotope 2 (Encrusting sponge and alga flats) Sponges with a collective contribution of 33.22% dominated the assemblage at the inner site at North Stradbroke. Of this collective contribution, 18.84% was contributed by encrusting sponge growth forms. Sponges that were identified include the large sponge Spheciospongia montiformis (7.20%) and Aulospongus similiaustralis (4.08%). Encrusting alga showed a noticeable contribution to the grouping (20.12%) followed by various ascidians (14.23%) such as a Didemnum sp. (6.12%) and Cnemidocarpa stolonifera (5.01%). 3.2.3. Biotope 3 (Sponge gardens) Inner and mid sites at the Seaway were driven primarily by various sponge species (45.43%). Examples include a vase sponge (ID: Sponge sp.1) (8.82%), Spheciospongia montiformis (4.51%), the cup-like sponge Strepsichordaia lendenfeldi (4.33%) and bulbous growing sponge from the genus Psammoclema (ID: Sponge sp.19). This biotope also had a noticeable contribution by encrusting alga species (28%). 3.2.4. Biotope 4 (Sponge garden with urchin population) The inner and mid sides at Tweed were dominated by sponges (38.52%) and various encrusting algae species (combined contribution of 23.2%). There was a noticeable (8.96%) contribution by the large sponge Spheciospongia montiformis. This biotope is primarily distinguished from biotope 4 by the abundance of urchin species (5.55%). Three urchin species were detected within the study: Phyllacanthus parvispinus, Centrostephanus rogersii, and Tripneustes gratilla. Of the 289 individual urchins recorded, 230 were found within the inner and mid sites at Tweed and 51 found across all sites at the Seaway. The majority (n ¼ 149) of urchins at the inner site of the Tweed were C. rogersii.

Fig. 3. nMDS of BrayeCurtis similarity from abundances of 94 morphospecies. Abundance data was 4th root transformed. Derived biotope groups from 45% similarity are shown.

3.2.5. Biotope 5 (Whip forest) The outer sites at both the Tweed and Seaway showed similar assemblages, dominated mostly by various tall standing sea whips (combined contribution of 41.53%) and sponges (combined contribution of 27.04%). Gorgonian species Junceella fragilis alone contributed 11.51% to the grouping. Other high contributing taxa include; an unidentified sea whip (ID: Gorgonian sp.3) (8.61%), Dichotella gemmacea (6.47%), Cirrhipathes spiralis (9.49%), Antipathes sp. (black coral, 5.45%), and sponge Acanthella costata

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Fig. 4. Images extracted from each biotope. A ¼ Biotope 1, Ecklonia beds; B ¼ Biotope 2, Encrusting sponge and alga flats; C ¼ Biotope 3, Sponge gardens; D ¼ Biotope 4, Sponge garden with urchin population; E ¼ Biotope 5, Whip forest.

(3.69%). Sites within this biotype recorded the highest organism abundance (ind m2) of all sites across all locations. The cluster dendogram (Fig. 5) illustrates the relationship between these biotopes. It is apparent that the Ecklonia beds are characteristic of the northern sites, except the inner North Stradbroke site (encrusting sponge dominated), which is intermediate

between the northern and more variable southern sites. In the south, the strong cross-shelf gradient is apparent, with the outer sites (whip forest) presenting a distinctly different assemblage to the inner and mid sites. Inner and mid sites at the Tweed (sponges and urchins) are related to, but distinct from, inner and mid sites at the Seaway (sponge gardens). When analysed at a higher (phylum)

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Fig. 5. Cluster dendogram of BrayeCurtis similarity from abundances of 94 morphospecies, pooled by location and site for clarity. Abundance data was 4th root transformed.

taxonomic level, the distinction between the sponge-dominated biotopes (2, 3 and 4) is not apparent, but biotopes 1 and 5 remain separate. 3.3. Utility of abiotic surrogates Analyses of individual surrogates showed that fishing effort had the highest BIOENV correlation with the biological data (Spearman’s r ¼ 0.54, p ¼ 0.001), meaning that 29% of the variation in assemblage structure was explained by fishing effort (R2 ¼ 0.29), followed by temperature and distance from estuary (both 0.41, p ¼ 0.001) explaining 17% and distance from shelf break (0.40, p ¼ 0.001) explaining 16% (Table 4). Relief and salinity had the lowest correlations of 0.09 and 0.08 respectively, although these were still statistically significant (p ¼ 0.001). The best-fit BIOENV utilising only the physical abiotic variables was a combination of temperature and substrate type (r ¼ 0.47, p ¼ 0.001), explaining 22% of the variation in assemblage structure. The addition of geographic factors improved the fit; the combination of temperature, substrate type, distance from estuary and distance from continental shelf break (r ¼ 0.58, p ¼ 0.001) explained 33% of assemblage structure. The addition of fishing effort into the abiotic matrix resulted in a best fit correlation of r ¼ 0.65 (p ¼ 0.001), meaning that the combination of fishing effort, temperature and distance to shelf break explained 42% of the patterns of biological assemblages across the study site (Table 4). All of the best solutions (r > 0.6) included temperature and fishing effort.

200 km south of our southernmost sites. They attributed this difference to the East Australian Current (EAC) influencing outer sites (representing a tropical dominated assemblage) and cool northward-flowing counter currents driving assemblages at the inner sites (representing a temperate dominated assemblage). Our findings are consistent with this interpretation, especially the abundance of particular octocorals at the outer sites (e.g. Junceella fragilis). With observed and predicted intensification of EAC flow, there are clear implications for changes in range of tropical taxa with planktonic larval forms (O’Kane et al., 2011; Ridgway and Hill, 2012) including the taxa characteristic of this biotope. Nevertheless, the existence of five statistically distinct biotopes illustrates that there is considerable variation in benthic assemblages across a relatively small survey footprint (<120 km northsouth, <20 km eastewest at the widest point). While a crossshelf pattern was to be expected, the discontinuous kelp distribution was not, and nor was the variability in mid- and inner-shelf sites. This finding underlines the importance of conducting biological surveys at management-relevant scales in order to understand local influences on marine biodiversity. This study has expanded on the methods used by Barker et al. (1999) and Stevens and Connolly (2004) to define novel subtropical continental shelf assemblages using a quantitative and costeffective approach. The information derived is directly applicable to the design of marine reserves to represent patterns of biodiversity. In addition, economically important fisheries for snapper (Pagrus auratus) (Campbell et al., 2009) and eastern king prawn (Penaeus plebejus) (O’Neill and Leigh, 2007) are located within parts of the study area; understanding local influences on biodiversity is

4. Discussion 4.1. Classification of benthic biotopes This study has surveyed and classified the benthic assemblages of inner continental shelf subtropical rocky reefs from three depth bands (30e70 m). It has, for the first time, quantified the considerable variation in assemblage structure within the study area, and described five distinct biotopes. Two trends in assemblage structure were immediately obvious across the study site; 1) there was a broad dichotomy between northern and southern locations characterised most obviously by the presence or absence of the kelp Ecklonia radiata, found here at close to the northern limit of its eastern Australian distribution (Bostock et al., 2007); and 2) a distinct cross-shelf trend in the southern locations was evident between the high diversity outer sites, and the inner and mid sites, suggesting that different influences are at play. Harrison and Smith (2012) and Malcolm et al. (2011) described a shift from a temperate dominated assemblage (of molluscs and reef fish, respectively) to a tropical dominated assemblage with increasing depth in the Solitary Islands, about

Table 4 Tabulated BIOENV results for abiotic variables tested independently (left) and in combination (right). Combination of best fit run using three differing combinations of variables; All variables ¼ all measured variables listed in Table 1, Physical variables only ¼ temperature, substrate type, depth, relief, salinity; Geographical variables ¼ distance to estuary, distance to shore, distance to shelf break; Anthropogenic variable ¼ fishing effort. Biological data is 4th-root transformed, p < 0.001 in every case. Refer to Table 1 for abbreviations. Combinations of best fit

Individual Variable

Spearman’s r

Variable

Spearman’s r

Fishing effort Temp Dist. estuary Dist. shelf Substrate

0.54 0.41 0.41 0.40 0.34

Physical variables Temp, substrate type

0.47

Depth Dist. shore Relief Salinity

0.21 0.15 0.09 0.08

Including geographical variables Temp, Substrate type, Dist. 0.58 estuary, Dist. shelf Including anthropogenic inputs Temp, Fishing effort, Dist. shelf 0.65 Temp, Fishing effort, Dist. 0.64 shelf, Dist. estuary

S. Richmond, T. Stevens / Estuarine, Coastal and Shelf Science 138 (2014) 79e89

87

Table 5 Comparison of surrogacy studies in which biological assemblage patterns were analysed against abiotic surrogates. The figure in the variability column refers to the R2 term for regression analyses, or the square of the value of the correlation measure used. Best variables

Component of biodiversity investigated

Variability accounted for

Source

Gravel (proportion in sample) Depth, rhodolith biomass, longitude

Infauna Epibenthos (algae, faunal and functional groups tested) Soft sediment epibenthos Soft sediment and reef epibenthos Soft/Gravel sediment epibenthos Continental shelf reef epibenthos Soft/Gravel sediment epibenthos Reef Fish

14% 28%

Przeslawski et al., 2013 Barbera et al., 2012

30% 36% 38% 42% 56% 56%

Stevens and Connolly 2004 Post et al., 2006 Beaman and Harris 2007 Present study Beaman and Harris 2007 Malcolm et al., 2011

% Mud, distance to ocean % Mud, % gravel, depth, seabed exposure Slope, gravel weight, transmission Temperature, fishing effort, distance to shelf Slope, gravel weight, sand CaCo3 Depth, dist from shore

also important for management of these resources, particularly given that we found recreational fishing pressure to be a possible structuring factor (Section 4.2). 4.2. Abiotic drivers The performance of individual abiotic factors at predicting observed patterns of benthic assemblage structure varied from very poor (relief, r ¼ 0.08) to moderately good (fishing effort, r ¼ 0.54). We found the combination of abiotic factors that best explained the observed patterns of benthic assemblage structure were fishing effort, temperature and distance to shelf break (r ¼ 0.65), rather than more conventional factors that describe the nature of the local environment (depth, substrate, etc). Here we discuss possible mechanisms for the four most important surrogates. 4.2.1. Fishing effort The strong influence of fishing effort in the BIOENV (both individually, and in combination) suggests that recreational and/or commercial line fishing on these rocky reefs may have a structuring role. Fishing effort in the southern locations where kelp is absent (Seaway, Tweed) was almost twice that of the northern locations (Moreton, North Stradbroke). This is consistent with a trophic cascade scenario where commercially or recreationally valued predatory fish (e.g. Serranidae, Lutjanidae, Lethrinidae) are selectively fished (Tegner and Dayton, 2000; Shears and Babcock, 2003; Hereu et al., 2005), removing controls on populations of herbivorous fish or urchins and leading to the absence of macroalgae. Paradoxically, benthic biodiversity is higher in the areas with greater fishing pressure, as Ecklonia appears to inhibit the growth of other attached macrobenthic forms. However, we acknowledge that there are alternate, and more prosaic, explanations for the observed distributions, especially oceanographic influences (Sections 4.2.2 and 4.2.4). Further work is in train to investigate the abundance and behaviour of both herbivores and predatory fish on these reefs. 4.2.2. Temperature The significant influence of water temperature within the BIOENV may be a pointer to differing water masses, both across the shelf (EAC offshore, northward drift inshore), and between northern and southern sites as it may reflect the influence of cooler deep water upwelling over the shelf edge (Babcock, pers comm.; Rattray et al., 2009; Roughan and Middleton, 2002) which is closer in the northern locations (Section 4.2.4). Counter-intuitively, bottom water temperature at the outer/deeper sites was approximately 3  C lower in the northern locations than those in the south. While this aligns neatly with the observed Ecklonia distribution, we do not understand the local oceanographic processes that drive this. This will require further, directed investigations, for instance repeated CTD casts across the continental shelf to confirm if these

temperature differences are consistent, and/or follow some seasonal pattern. Furthermore, the water temperature values used in our analyses were measured in situ and so represent a snapshot in time, whereas long-term average or seasonal values are more likely to reflect its influence on community structure. 4.2.3. Distance from nearest estuary Distance from estuary had the third highest influence in the individual BIOENV; this is interesting, as continental shelf reef systems have been considered to be largely independent of terrestrial inputs. However, there is evidence of trophic linkages between reefs and their adjacent habitats (Posey and Ambrose, 1994), particularly for the supply of terrestrially derived allochthonous nutrients (Waples and Hollander, 2008). Therefore, there may be closer trophic coupling between land-based nutrient sources (Beaudreau and Essington, 2011) and (particularly inner) continental shelf reefs than previously supposed, periodically at least. The distance from estuary variable may also be related to fishing pressure and its possible influence (Section 4.2.1), since these estuaries also represent the key entry points for recreational and commercial fishers (although not highly autocorrelated, R ¼ 0.43). 4.2.4. Distance to shelf break (200 m isobath) The factor distance to shelf break was included to allow for open ocean or deep water influences, for instance the upwelling of cold, nutrient rich waters onto the continental shelf (Section 4.2.2). Seasonal upwelling events occur on the east coast of Australia, and are often attributed to EAC intrusions via narrowing shelf widths (Rochford, 1975; Boland, 1979; Roughan and Middleton, 2002). The prominence of this factor in the BIOENV analysis, both individually and in combination, suggests that upwelling events have a structuring influence, in particular, in favouring the persistence of deep macroalgal canopies. 4.3. Are abiotic surrogates an effective tool for benthic habitat mapping in mesophotic rocky reef habitats? Contemporary approaches to marine conservation planning frequently used formalized hierarchical frameworks designed to classify marine biodiversity in nested scales (e.g. Last et al., 2010) from global to genetic. Typically, physical or other abiotic surrogates are used most widely at higher levels of the classification, and biological or site specific information at lower levels (McArthur et al., 2010). In such a scheme, the present study provides information at the secondary biotope level (level 5) and uses local scale biotic units of the seafloor (biotopes, in our terminology) and relates them to a suite of physical surrogates. Most area-based management occurs at this local scale (10 s of km) or finer, so habitat (or biotope) survey and mapping for MPA design or management should logically be carried out at these scales, yet this is

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often not the case (Stevens, 2002). In order for these biotopes to be included in the classification, these (local-scale) biological surveys have to be carried out, and relationships between patterns of biodiversity and available surrogates established, as in this study. In the context of other studies (Table 5) that examine the performance of abiotic surrogates in explaining patterns of biodiversity, this study has performed moderately well. We found a modest correlation between the full suite of abiotic factors and benthic assemblages (r ¼ 0.65, R2 ¼ 42%), placing it in the mid-range of comparable studies (Table 5). However, the predictive power of physical surrogates commonly used in such classification schemes was not high (Table 4), explaining just 22% of variation in assemblage structure. Moreover, the relationship between biodiversity and abiotic factors was, in some cases, contrary to expectations (e.g. areas with higher fishing pressure showed higher taxon richness and overall abundance). The classification needed to include anthropogenic factors that vary markedly from place to place (e.g. fishing effort), to explain benthic assemblage structure. That is to say, a classification of benthic habitats based on available conventional physical surrogates would have failed to identify clear patterns of benthic biodiversity, and any representative reserve system based on the surrogates, but without the biological information, would therefore be flawed. Other comparable surrogacy studies (Table 5) have used a range of predictor factors, depending on the location, substrate types, component of biodiversity being compared, and the availability of data at the target scale. Few surrogacy studies have been conducted exclusively on hard substrates, and even fewer on reefs in the mesophotic zone. Even given this range of approaches, the ability of abiotic surrogates to explain patterns of biodiversity varies enormously from place to place. Therefore, the relationship between abiotic surrogates and patterns of biodiversity must be established in each situation if surrogate-based habitat classifications are to be validly used in making MPA or other marine conservation planning decisions (Leaper et al., 2012). This is just as important, but more difficult, in situations such as this study where collection of quantitative biological information is more complex and expensive, and where therefore abiotic surrogates may be given greater weight in classification schemes. In an Australian context, this is especially relevant given the recent declaration of a network of offshore MPAs designed to represent the range of Australia’s marine biodiversity and for which management plans are (at the time of writing) under preparation (Anon, 2013). Acknowledgements The authors would like to thank the following;  Queensland Museum staff Dr John Hooper and Dr Merrick Ekins, and Professor Tony Carroll of the Griffith School of Environment, for assistance with species identifications;  Dr Jan-Olaf Meynecke and the Griffith Centre for Coastal Management for loan of equipment;  Dr James Webley from the Queensland Department of Fisheries and Forestry for extracting data on recreational fishing effort;  Staff of the Moreton Bay Research Station for field assistance. The study was funded by the Australian Rivers Institute (ARI). We are grateful to Associate Professor Ian Tibbets, Professor Rod Connolly, Ben Gilby and Paul Maxwell for constructive comments

on the manuscript at various stages. Gastronomic support was provided by Pixie Stevens.

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