Poulos et al 2015.pdf

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Marine and Freshwater Research http://dx.doi.org/10.1071/MF14059

Distribution and spatial modelling of a soft coral habitat in the Port Stephens–Great Lakes Marine Park: implications for management Davina E. Poulos A,D, Christopher Gallen B, Tom Davis C, David J. Booth A and David Harasti B A

School of Life Sciences, University of Technology, Sydney, NSW 2007, Australia. Fisheries Research, Marine Ecosystems, NSW Department of Primary Industries, Locked Bag 800, Nelson Bay, NSW 2315, Australia. C National Marine Science Centre, Southern Cross University, PO Box 4321, Coffs Harbour, NSW 2450, Australia. D Corresponding author. Present address: College of Marine & Environmental Sciences, James Cook University, Townsville, Qld 4811, Australia. Email: [email protected] B

Abstract. Habitat mapping is a useful method for understanding the complex spatial relationships that exist in the marine environment, and is used to evaluate the effectiveness of management strategies, particularly in regards to marine protected areas. This study explored the observed and predicted distribution of an uncommon soft coral species, Dendronephthya australis within the Port Stephens–Great Lakes Marine Park. Dendronephthya australis was mapped by video operated by a SCUBA diver towing a time synchronised GPS. A species distribution model was created to explore the possible occurrence of D. australis outside of the mapped area, using four environmental parameters: bathymetry, slope of seabed, velocity of tidal currents, and distance from estuary mouth. Dendronephthya australis colonies occurred along the southern shoreline in the Port Stephens estuary between Fly Point and Corlette Point, but no colonies were found within sanctuary (no-take) zones within the marine park. The model illustrated limited habitat suitability for D. australis within a larger section of the estuary, suggesting this species has specific environmental requirements survival. Owing to its current threats (anchor damage and fishing line entanglement), implications from these findings will assist future management and protection decisions, particularly in regard to its protection within a marine park. Additional keywords: Dendronephthya australis, estuary, marine protected area, Maxent, species distribution model, towed-GPS. Received 13 May 2013, accepted 9 March 2015, published online 22 June 2015

Introduction Biological, physical and geographical data describing spatial patterns in marine environments are essential in understanding habitat linkages and species distributions (Jordan et al. 2005). Habitat maps can provide information on the location and distribution of habitat types, threatened species, hotspots of habitat diversity, high productivity and spawning aggregation sites, and ultimately aid in the selection of protected areas, thus allowing managers to incorporate spatial patterns into Marine Protected Area (MPA) planning (Wright and Heyman 2008). A framework examining MPAs against ecological criteria (Roberts et al. 2003) such as biodiversity, fisheries threats and ecosystem linkages, has been developed. This is where seabed habitat mapping over a variety of scales is becoming increasingly important in identifying appropriate areas for protection, by examining the distribution and structure of ecosystems (Jordan et al. 2005). Habitat mapping enables planners of MPAs to incorporate Journal compilation Ó CSIRO 2015

samples of all habitat types existing in the area (Stevens and Connolly 2005), as well as allowing scientists to predict impacts on habitats of significant ecological value (Kenny et al. 2003). It is widely accepted that species show associations with the surrounding physical properties of the environment, and understanding the spatial complexity of such distributions is vital for protection of ecologically significant areas (Ierodiaconou et al. 2011) and is in fact at the very heart of ecology (Levin 1992). For example, relating demersal fish assemblages to benthic habitat information can be used to understand local scale richness and abundance assemblages, and can also be used as predictive habitat models (Moore et al. 2010). The production of habitat maps using species distribution models (SDM) enables the prediction of areas where particular species may occur, based purely on the mapped abiotic environmental conditions (Brown et al. 2011). This concept is becoming increasingly popular in identifying areas of high conservation potential (Monk et al. www.publish.csiro.au/journals/mfr

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2011); however, it is often assumed that environmentally different conditions reflect differences in biological characteristics, when in fact species–environmental interactions can be complex and are not always known (Brown et al. 2011). An uncommon soft coral species exists in high abundance in the Port Stephens–Great Lakes Marine Park (PSGLMP) in south-eastern Australia, which is the only known location where this species occurs in such large abundance. The biodiversity value of Dendronephthya australis has previously been explored (Poulos et al. 2013; Harasti et al. 2014), where fish assemblages associated with the D. australis colonies were significantly different to those associated with adjacent sponge, seagrass and sand habitats, potentially providing a unique source of food and shelter for fishes and invertebrates. However, the distribution and extent of D. australis has not previously been investigated. This knowledge has important implications for not only assessing the conservation requirements of this species but also for understanding the specific environmental parameters determining its survival. Macrobenthos plays an important role in estuarine system dynamics but its distribution is not always accounted for in monitoring programs (Ysebaert and Herman 2002). Approximately 125 km2 of offshore habitats of the PSGLMP have been mapped using swath acoustic mapping (Jordan et al. 2010), but acoustic mapping has not been used within the estuary where the present study was conducted. Furthermore, the soft coral habitat is unable to be detected by this method. The PSGLMP multiple-use zoning plan aims to conserve marine biological diversity and marine habitats while providing for their ecologically sustainable use. A key component of this is by aiming to ensure that each type of habitat is represented within sanctuary zones (no-take areas) (NSW Marine Parks Authority 2001). The overall aim of this study was to map the distribution of D. australis within a small area of the Port Stephens estuary where it is known to occur, and create a SDM to predict where else it may occur within a larger area of the estuary based on environmental parameters. In doing so, we will address the following questions, (1) what is the spatial extent of D. australis in Port Stephens and how does this relate to the location of marine park zones? And (2) where else within the Port Stephens estuary is D. australis likely to occur outside of the area mapped? Implications of this research will be addressed in terms of the future protection and management of D. australis. Materials and methods Study site The Port Stephens region in NSW is ,150 km north of Sydney and covers an area of nearly 1000 km2, with the Port Stephens estuary contributing to 15% of the region, making it three times larger than Sydney Harbour (Richins and Mayes 2008). The Port Stephens estuary is a drowned valley (134 km2) influenced by three major rivers. It has an open and untrained entrance ,1.2 km wide with an average depth of 14 m and an average tidal exchange of 1.6 m. The estuary area comprises two distinct basins: eastern and western; each has distinctively different hydrology and sediment substrate type (Austin et al. 2009). The eastern basin (,48.92 km2) is marine dominated with strong

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tidal currents influenced by complex bathymetry including deep narrow channels and shallow sand shoals (Vila-Concejo et al. 2007). The Port Stephens–Great Lakes Marine Park (PSGLMP) extends from Birubi Point at the northern end of Stockton Beach, north to Cape Hawke near Forster, NSW. It was declared on 1 December 2005, and covers ,980 km2 including areas out to 3 nautical miles (,5.5 km) offshore, the state waters boundary, and the estuaries of Port Stephens (see Marine Parks Authority http://www.mpa.nsw.gov.au/psglmp.html, accessed 19 February 2014). This unique and diverse environment exists in a region where temperate, sub-tropical and tropical flora and fauna coexist, including protected and threatened species (Harasti et al. 2012; Harasti and Malcolm 2013). Soft coral data collection and analysis Underwater mapping of the soft coral habitat was conducted in March–April 2011 within the Port Stephens estuary, from Nelson Head, Nelson Bay (328420 36.7100 S, 1528090 37.02700 E), west along the southern shoreline to Corlette Point (328430 00.8800 S, 1528060 24.1100 E) (Fig. 1), where D. australis was known to occur. Additionally, areas through the northern part of the Port Stephens channel were mapped to assess this region for the presence of soft coral colonies, including the sanctuary (no-take) zone and surrounding area of Barnes Rocks and Jimmy’s Beach on the northern side of the estuary. Mapping of the soft coral habitats was undertaken using a towed GPS system, where a video camera (Sony Handycam, HDR-XR550VE Full HD, 12 mega pixels, see http://www.sony. com) in a Light & Motion STINGRAY G2+ underwater housing (http://www.lightandmotion.com/bluefin-housing) was carried by a SCUBA diver towing a GPS unit (Garmin Map60, see http://www.garmin.com) attached to a float at the surface. The camera was held in front of the diver ,1 m above the substrate and pointed in the direction the diver was swimming, slightly angled towards the substrate as the diver swam in a ‘zigzag’ pattern towards and away from the southern shoreline of the estuary. The diver’s line to the surface float was kept taut at all times to ensure that the GPS was directly over the position of the diver, however it is acknowledged that there will be an error margin of a couple of metres as a result of the GPS satellite fix. The time on the video camera was synchronised with the time on the GPS, and this was used to correspond the habitat type to the correct GPS coordinates. Twenty scuba dives were undertaken using this system to map the distribution of D. australis within targeted sections of the Port Stephens estuary. Average dive time was 44 min (2 s.e.) with an average distance covered on each dive of 797 m (59 s.e.). Several exploratory dives (20þ) were also conducted in other areas of Port Stephens, such as Shoal Bay to the east of the study area and the northern sections of the estuary, to search for soft coral habitats. Underwater video footage was analysed as presence or absence of the soft coral so that each GPS coordinate that was mapped was associated with a presence–absence value. GPS data was downloaded from the GPS using the Garmin MapSource program (ver. 6.13.7, 2008) and then exported into Microsoft Excel. Video files were viewed using DVMP Pro software (ver. 5.3, see http://www.dvmp.co.uk/) as this

Management implications of a soft coral habitat


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Fig. 1. Study area in Port Stephens, New South Wales; mapping was conducted in the eastern basin between Nelson Head and Corlette Point. Locations of the Port Stephens–Great Lakes Marine Park (PSGLMP) sanctuary (no-take) zones are also shown. (The shaded region is land.)

software allowed video to be watched in its raw format which included the time stamp on every single frame allowing comparison with the GPS time data. Environmental parameters We obtained data for four environmental parameters that could explain the possible occurrence of D. australis in the greater Port Stephens estuary: depth (bathymetry), slope of the seabed, velocity of tidal currents and distance from the estuary mouth. The bathymetry raster layer was created from hydro-graphic point data (NSW Department of Public Works and Services

1998). This point data was interpolated to a raster grid using the inverse distance weighted (IDW) interpolation tool in spatial analysis tools, ArcGIS (ver. 10.2). Seafloor slope was created from the raster bathymetry layer using the slope function from the spatial analysis tools, surface tool. The inclination of sea floor slope was calculated as a measure of degrees. The maximum velocity of tidal currents at each location was calculated using a tidal flow simulation of the estuary, in the TELEMAC2D hydrodynamic analysis package (Hervouet 2000; see http:// www.opentelemac.org/). The fourth environmental variable, distance, was included because the association of distance from

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Fig. 2. Species distribution model of Dendronephthya australis, illustrating the range of suitable habitat across a larger area of the Port Stephens estuary, based on bathymetry, distance from the estuary mouth, slope of the seafloor and velocity of tidal currents. Red and orange sections represent areas of high habitat suitability.

the estuary mouth is hypothesised to represent the exchange of marine water in a tide dominated estuary (Vila-Concejo et al. 2011). Increasing distance into the estuary was measured from the two prominent headlands of the estuary. Species distribution model A SDM incorporating the four environmental parameters, and D. australis occurrences within the mapped area, was created using the maximum entropy method (Maxent, ver. 3.3.3, see https://www.cs.princeton.edu/,schapire/maxent/) (Phillips et al. 2006). This is a method for using presence-only data to statistically make predictions based on environmental variables (Phillips et al. 2006). Recommended default settings were used (including convergence threshold of 105 and maximum number of iterations of 500), as they have been validated for a diverse range of datasets (Phillips & Dudik 2008). Model predictions are presented as a logistic function, highlighting areas of relative suitability on a scale between 0 and 1. Model results were tested by using 90% of the soft coral occurrence localities as training data and the remaining 10% as test data. The model performance of both training and test datasets were evaluated with receiver operating characteristic (ROC) curves by calculating the area under the curve (AUC). This indicates the model’s predictive accuracy, with an AUC score of 1 being a perfect model fit to the data, and a score of 0.5 being no better than random (Elith et al. 2006). Importantly, AUC values may be higher for species with a relatively narrow range compared to the area explained by the environmental variables. Covariation between environmental variables was analysed in ArcGIS using band collection statistics. Maxent is fairly robust to covarying variables; however model accuracy may be improved by removing highly correlated variables (Merow

et al. 2013). The 10th percentile training value threshold was used to calculate the total area of suitable habitat above a verified threshold, in order to quantify the unknown and possible occurrence of D. australis within the greater Port Stephens estuary. The 10th percentile logistic threshold assumes that 10% of presence localities are inaccurate and excludes predictive values below the highest 10% of records (Bridge et al. 2012). Results Mapped distribution of Dendronephthya australis The soft coral mapping revealed a large abundance and patchy distribution of D. australis colonies occurring from just outside the sanctuary zone at Fly Point, Nelson Bay, west towards Sandy Point, Corlette, along the southern shoreline (see Figs 2, 3). D. australis ranged between depths of 3.2 and 17.7 m (Table 1), with the shallower colonies found alongside seagrass habitats and in some cases interspersed in areas where seagrass (Halophila ovalis) was sparse (D. Poulos, pers. obs.). The soft coral was also observed across a distance of 3.0–6.4 km from the estuary mouth, on a seafloor slope of 0.07–16.188, and where the maximum velocity of the currents were between 0.28 and 0.86 m s1 (Table 1). Model The AUC values for training and test datasets were high (both .0.93). The model indicated a limited availability of suitable habitat for D. australis outside of the area mapped (Fig. 2). An area of 0.73 km2 was found to be highly suitable habitat for the soft coral, which is above the 10th percentile training value threshold (Fig. 3). This is ,1.5% of the total area of the east



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Fig. 3. Tenth percentile training threshold (.0.39) of the species distribution model for Dendronephthya australis, showing the areas of greatest habitat suitability (grey). Red patches indicate the actual mapped aggregations of D. australis.

Table 1. Mean, range, minimum and maximum for bathymetry, distance from estuary mouth, slope of seafloor and velocity of currents, where Dendronephthya australis was observed Observed presence of Dendronephthya australis Environmental variable Bathymetry (m) Distance (km) Slope (8) Velocity (m s1)

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Maximum

9.783 (3.452) 4.324 (0.884) 4.639 (2.924) 0.491 (0.131)

14.524 3.311 16.155 0.518

3.193 3.041 0.066 0.277

17.717 6.352 16.181 0.858

basin. There was some covariation between environmental variables, such as velocity of currents and distance from estuary mouth (0.41), velocity of currents and bathymetry (0.29), as well as for slope of the seafloor and bathymetry (0.29), and velocity of currents and slope of the seafloor (0.21); however, these correlations were not considered strong enough to remove any variables from the model. All other correlations were ,0.1 and considered insignificant. Maxent analysis revealed that slope of the seafloor had the strongest contribution to the model (36.2%), followed by distance from the estuary mouth (33.0%), velocity of tidal currents (20.8%), and bathymetry (10.0%). Response curves (logistic prediction, i.e. probability of D. australis occurrence) illustrate the environmental boundaries and specific ranges of high probability of D. australis presence, for each variable (Fig. 4). The probability of D. australis existing increased with a gradual increase in slope of the seafloor, and was most likely to occur on a slope of 8–148 (Fig. 4a). Probability of presence

generally decreased with increasing distance from the estuary mouth; however, the logistic curve was characterised by numerous peaks and troughs (Fig. 4b). Velocity of currents between 0.35 and 0.85 m s1 was the most likely range for D. australis to occur in, with the highest probability at .0.8 m s1 (Fig. 4c). Predicted probability of D. australis presence was highest at a depth of 10 m, but the species was likely to occur across a depth gradient of 3–18 m (Fig. 4d ). Discussion Distribution of Dendronephthya australis This study found that D. australis covered a restricted, but substantial area of the southern inshore region of the Port Stephens estuary; however, highly suitable habitat for the possible occurrence of D. australis outside of this area was limited. This is not an unexpected result, considering that various other sections of the estuary have been explored (the northern section of the estuary and east in Shoal Bay), in search of this soft coral species. Yet, the area that was mapped in the present study is the only known location where D. australis occurs. The specific environmental conditions required for D. australis to exist are no doubt a limiting factor in its distribution. There are possibly other environmental parameters of significance to the survival of this soft coral that were not incorporated into the model for this study (e.g. turbidity, the presence of other habitat types such as sponge habitats that are common in the study area (Harasti et al. 2012; Harasti et al. 2014) and the benthic substrate). From the Maxent analysis, it was shown that the most likely areas for D. australis to occur in Port Stephens was on a seafloor slope of 8–148, at a depth of ,10 m, a distance of ,3–6 km from the estuary mouth and in an area of high current flow (between 0.35 and 0.86 m s1). The model indicates that the area highly

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suitable for the occurrence of D. australis is very limited in Port Stephens, with only 0.73 km2 considered highly suitable for the species to occur – a mere 1.5% of the east basin of Port Stephens. Predicting habitat or species distribution from spatial modelling techniques in estuarine environments which are susceptible to fluctuating environmental variables, i.e. turbidity, temperature, salinity and highly variable morpho-dynamics, may be more difficult (less accurate) than for marine and terrestrial environments, which are not as dynamic and contextually variable as estuarine environments. Many studies that employ similar modelling methods in the marine environment focus on the relationships between fish assemblages, habitat type and other environmental factors (i.e. Monk et al. 2011; Hattab et al. 2013; Schultz et al. 2014) rather than the prediction of where a particular marine habitat, such as D. australis, might occur. Comparable to the present study, other studies have shown that depth and slope are important factors that can affect species occurrence. Moore et al. (2011) found depth and other landscape features explained ,35% of variation in fish assemblages. A strong positive relationship has been demonstrated between abiotic measures and sessile invertebrate abundance, with vertical relief the most important predictor (Rees et al. 2014). Species distribution modelling is proving useful in conservation efforts around the world (Aı¨ssi et al. 2014; Marshall et al. 2014). Similar to this study, Maxent modelling was recently employed to predict the occurrence of undocumented coral communities along the Great Barrier Reef (Bridge et al. 2012; Harris et al. 2013), and to predict distribution of coral habitats in the north-eastern Atlantic (Rengstorf et al. 2013) and the Gulf of Mexico (Georgian et al. 2014).

There are limitations in the use of Maxent and the type of data it accommodates. The main issues of Maxent are that it uses presence-only data, which may be subject to sampling bias. Presence-only data is less accurate, biologically, than presenceabsence data because absence values are just as important in explaining distribution patterns (Elith et al. 2011). Sampling bias may occur in presence-absence datasets as well; however, its effects are exacerbated in presence-only datasets, whereby the model produced is a combined result of the species distribution, as well as the distribution of sampling effort (Elith et al. 2011). Additionally, Maxent’s logistic output is based on assumptions of occurrence, rather than estimates, because it uses presence-only data (Merow and Silander 2014). Thus its interpretation should not be the probability of occurrence, but rather the level of habitat suitability. There is a strong emphasis in making sure the decisions made when using Maxent are guided biologically, so that the variation in Maxent’s response to different settings is suited to the dataset and hypotheses in question (Merow et al. 2013). In the present study we have ensured the model produced was as accurate as possible by testing 10% of the soft coral data against the model, and examining the AUC value, which we recognise may be higher due to the fact that it does not classify presences v. absences as it was originally intended for (Yackulic et al. 2013). We also tried to simplify our model so that it could be easily interpreted, by using correlation analysis, and thereby deducing that the environmental variables were not sufficiently highly correlated with be removed, as well as by not using hinge features alone, which create complex, but smooth response curves (Merow et al. 2013).


During mapping surveys, we observed shallow patches (,5 m) of D. australis restricted alongside or interspersed within seagrass habitats, particularly H. ovalis seagrass, possibly a result of the changes in sediment, depth, current strength or other abiotic conditions that are not preferable for this soft coral species, limiting its distribution to certain areas. Additionally, competition for space may exist between these two converging habitats, which might explain the absence of D. australis in the areas where seagrass exists, although further investigation of this process was not within the scope of this study. Seagrass species in NSW have been shown to be quite resilient to encroachment from other habitat types (Glasby 2013) and the substantial primary foliage (i.e. shoots) and density of seagrasses most likely make it difficult for D. australis to colonise seagrass meadows. Despite D. australis colonies existing at a maximum depth of 18 m, it is unknown whether this is close to the maximum depth range for this species or whether it simply was not observed in deeper areas, due to other unfavourable conditions. The Port Stephens estuary extends down to 30 m off Fly Point; however, this species has never been recorded in the estuary .18 m. Many azooxanthellate corals are capable of living at great depths, and some have been found 6 km below the surface, although most occur no deeper than several hundred metres (Roberts and Hirshfield 2004), suggesting the high possibility that D. australis is capable of inhabiting much deeper waters. Deeper soft-sediment and reef habitats are common along the NSW coast (Jordan et al. 2010; Malcolm et al. 2010; Schultz et al. 2012); however, D. australis is yet to be confirmed occurring in deeper offshore waters. The distribution patterns observed adjacent to the estuary’s southern shoreline where D. australis patches progressively reduce in size and frequency towards the west may indicate the gradual decline in suitable environmental conditions further upstream. A recent study investigating the hydrodynamics of the Port Stephens estuary suggested that the complex estuarine bathymetry and geometry give rise to spatial variations in the tidal currents and a noticeable irregularity between the ebb and flood tidal flows (Jiang et al. 2013). Studies on channel morphology identify a relationship between channel bathymetry and tide phase dominance, deeper channels are typically ebb dominated (out flowing) and shallower areas are flood tide (inflowing) dominated (Austin et al. 2009). Variation in channel bathymetry within an estuary will therefore result in spatially variable tide phase dominance. It could be this characterisation of tide phase dominance that may also be influencing the distribution of D. australis within the Port Stephens estuary. The conditions in the most western area surveyed, near Corlette Point, can be characterised by increased turbidity and changes in sediment size and current strength (Roy and Boyd 1996; Austin et al. 2009). The section to the east of the survey area, known as Shoal Bay, is dominated by shallow seagrass meadows; however, in the deeper sections it has been shown there is considerable shifting sand movement driven by currents and wave action (Vila-Concejo et al. 2007). It is unlikely that D. australis would be able to in-habitat areas with considerable sand movement as they are likely to be buried under inundating sand – as was found in this study at the ‘Pipeline’ dive site, where soft coral colonies were completely buried several months after mapping was conducted.


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Soft coral colonies were found to attach to various anchor points within the sand habitats such as oyster shell beds and old pieces of wood. Several instances were found where boats had anchored and dragged the chain and anchor through the soft coral habitats uprooting and dislodging individual colonies from the substrate. In most cases the dislodged corals are unable to reattach to the substrate and perished (D. Harasti, pers. obs.). Additionally, fishing line was encountered at several locations tangled around the soft coral colonies and on a few occasions had cut though the soft skeletal tissue. Sightings of D. australis have previously been recorded in areas closer to and within Sydney, NSW, in areas of Sydney Harbour (Balmoral and near Watson’s Bay) in the 1970s (R. Kuiter, pers. comm.), and more recently in Lake Macquarie (H. Beck, pers. comm.) and Brisbane Waters (D. Harasti, pers. obs.). These have not been documented and have become known only by photographic evidence, and only reported as the presence of several small individual colonies. From 2005 to 2009, Harasti et al. (2012) undertook extensive diving surveys of 24 estuaries along the NSW coast to search for the presence of the seahorse Hippocampus whitei and the only location where D. australis was found to occur was along the southern shoreline of Port Stephens. With the development of a SDM for D. australis, this model could be applied in the future to investigate other estuaries in NSW for the occurrence of D. australis. Surveys could target areas that are considered to have the most suitable depth, slope, current velocity and distance from estuary mouth as indicated in this study. Current strength and direction are known to have a strong influence on the occurrence of soft coral habitats (Fabricius et al. 1995; Fabricius and Alderslade 2001). Future management and protection MPAs are put in place to conserve biodiversity, and can be small highly protective areas focussed on one species or community (Kelleher 1999), or they can encompass large areas focussed on conserving ecosystem and habitat linkages (Day et al. 2002). MPAs harbour numerous benefits, and those of particular benefit to the implications of this study include assistance in ecosystem recovery from natural and anthropogenic impacts (Edgar et al. 2010), increased protection of threatened and important species and habitats (Barrett and Edgar 2010), and the ability to evaluate threats to biodiversity through the use of reference sites (Babcock et al. 2010; Kelaher et al. 2014). There are several instances where spatial data has been used to contribute to the protection of benthic habitats and communities (Hewitt et al. 2004; Howell et al. 2010; Malcolm et al. 2012), such as the development of the Kent Group MPA, where habitats were extensively mapped and MPA boundaries were defined to ensure the MPA was comprehensive, adequate and representative of all habitat types (Jordan et al. 2005). However, it appears that the creation of protected areas for the preservation of soft coral habitat is yet to occur. The unique status of this soft coral habitat, the direct anthropogenic threats facing it (boat anchor and fishing line damage), the unknown nature of its occurrence outside of the eastern section of the Port Stephens estuary, and its occurrence outside of marine park sanctuary zones, all lead to the suggestion

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that increased protection of D. australis should be considered. Additionally, there is the more recent threat of sand inundation smothering portions of the soft coral habitat, caused by a combination of complex factors such as ongoing erosion at Shoal Bay and past nourishment there, stabilisation of Zenith Beach sand dunes, and interaction of wave climate changes (Wainwright 2011). The existence and abundance of D. australis is likely to be sustained or even improved if increased protection was provided around the large dense D. australis patches found off Nelson Bay. This could be achieved through the establishment of habitat management (such as no-anchoring areas or sanctuary zones) that reduce anthropogenic impacts such as anchoring and fishing line entanglement. Under the existing PSGLMP zoning plan, sanctuary zones were created to protect important estuarine habitats such as seagrasses (Posidonia australis and Zostera capricornia); however, no sanctuary zones were constructed in the establishment of the marine park zoning plan with the purpose of D. australis protection. Given that there are no large aggregations of D. australis known to occur outside of Port Stephens, it is essential to ensure that this fragile marine habitat is provided with some form of protection from anthropogenic threats. Future surveys of other estuaries from Port Stephens to Port Hacking (Sydney) would be useful to determine if Port Stephens is currently the only locality where this species occurs in high abundance. Acknowledgements We thank the Hunter-Central Rivers Catchment Management Authority (CMA) who financially supported this research and the NSW Marine Parks staff for their time in assisting with collection of data. In particular, thanks go to A. Jordan for being a constant source of advice, and to M. Linklater, G. West and A. Reside for assistance with data analysis. Comments from two anonymous reviewers greatly improved the manuscript.

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