The authors have declared that no competing interests exist.
Conceived and designed the experiments: AS HM DB SS. Performed the experiments: AS. Analyzed the data: AS HM ML SS. Wrote the paper: AS HM ML SS.
Where biological datasets are spatially limited, abiotic surrogates have been advocated to inform objective planning for Marine Protected Areas. However, this approach assumes close correlation between abiotic and biotic patterns. The Solitary Islands Marine Park, northern NSW, Australia, currently uses a habitat classification system (HCS) to assist with planning, but this is based only on data for reefs. We used Baited Remote Underwater Videos (BRUVs) to survey fish assemblages of unconsolidated substrata at different depths, distances from shore, and across an along-shore spatial scale of 10 s of km (2 transects) to examine how well the HCS works for this dominant habitat. We used multivariate regression modelling to examine the importance of these, and other environmental factors (backscatter intensity, fine-scale bathymetric variation and rugosity), in structuring fish assemblages. There were significant differences in fish assemblages across depths, distance from shore, and over the medium spatial scale of the study: together, these factors generated the optimum model in multivariate regression. However, marginal tests suggested that backscatter intensity, which itself is a surrogate for sediment type and hardness, might also influence fish assemblages and needs further investigation. Species richness was significantly different across all factors: however, total MaxN only differed significantly between locations. This study demonstrates that the pre-existing abiotic HCS only partially represents the range of fish assemblages of unconsolidated habitats in the region.
To adequately represent the entire range of biota present, conservation planning for sub-tidal marine ecosystems should be based on comprehensive understanding of species, habitats, ecosystems and associated ecological processes
Sub-tidal sediments are the most extensive benthic habitat worldwide
Most data on demersal fish assemblages of unconsolidated habitats have been collected by trawling
The Solitary Islands Marine Park (SIMP) in the Tweed-Moreton Bioregion of northern New South Wales, eastern Australia, comprises both State waters (to 3 nautical miles from shore), and the adjacent Solitary Islands Marine Reserve (SIMR) in Commonwealth waters. The SIMP is unique among NSW marine parks in that it includes several islands up to 12 km from the mainland coastline. The dominant system of currents, which includes strong influence of the East Australian Current (EAC)
Unconsolidated sediments dominate sub-tidal habitats in the SIMP
The focus of this research, therefore, was to examine fish assemblage structure of unconsolidated habitats to determine patterns over factors known to affect their reef counterparts (depth, distance from shore), and to determine how consistent they are over medium spatial scales (km). As data on the broad composition of unconsolidated habitats has recently been generated using swath acoustic multi-beam sidescan sonar
The SIMP covers an area of approximately 71,000 hectares, within a broad ecotone between tropical and temperate assemblages
The northern sampling site is approximately adjacent to Station Creek, and the southern sampling site is approximately adjacent to Bare Bluff. The insert box (right side of image) displays the full extent of the SIMP in grey, with the complete sampling area indicated within the red box. The second insert box (top of image) shows the study position in relation to Australia.
Factor | Level | Number of Replicates |
Depth | 10–30 m | 18 |
30–50 m | 24 | |
50+m | 23 | |
Location | North | 29 |
South | 35 | |
Backscatter Intensity | 0–50 | 50 |
50–100 | 6 | |
100–150 | 8 | |
150+ | 1 |
Each BRUV drop consisted of a mini-DV video camera with a wide angle lens, within an underwater housing with flat acrylic end ports, an attachment frame, a bait pole and mesh bag with bait, and a rope and float system linking the unit to the surface
Files were analysed using Eventmeasure (SeaGIS Pty Ltd, Version 3.31), and the identity and MaxN of each fish species was recorded. MaxN is the maximum number of fish of each species within the field-of-view at any one time during the 30-min recording, which removes the possibility of recounting the same fish. As counts reflect relative abundance and not density, we expect our data to be robust to variability in observing the field-of-view
The placement for all BRUV deployments was determined using GIS high-24 resolution imaging software (ArcGIS v9.3, ESRI software, USA) which incorporated metrics for the factors of primary interest: depth; distance from shore; and along-shore location (as distance from latitude -29 S – hereafter termed location). Additional factors, including fine-scale bathymetric range, rugosity and backscatter were also considered. Bathymetry and backscatter data (5 m cell size) were provided by the NSW Office of Environment and Heritage (2012). Rugosity was derived from the bathymetry layer using the Benthic Terrain Modeller extension 1.2
Both multivariate and univariate analyses were performed using procedures in the PRIMER 6.0 software
Distance based redundancy analysis (dbRDA) biplots were generated to visually display the direction and magnitude of the relationship between habitat factors and individual fish species
Univariate permutational multivariate analysis of variance (PERMANOVA) (using Euclidean distance as the similarity measure) was performed to test for significant differences in Species Richness and Total MaxN between factors that were significant within the DistLM regression. Species Richness is calculated as the total number of different species viewed on each replicate video, and Total MaxN is calculated as the sum of MaxN for all species per replicate. Differences across bathymetry and rugosity categories were not analysed as the range of values was very low.
One-way PERMANOVA
Multivariate assemblage structure was visually examined using non-metric multi-dimensional scaling (nMDS) ordination. Data were square-root transformed, and a dummy variable was added, prior to the generation of Bray-Curtis similarities. The resultant nMDS was used to examine differences in assemblage structure across factors that were significant in the DistLM analysis. Depth-related patterns were visually examined to see how well they fitted the current depth-based habitat classification categories
A total of 16 teleost and elasmobranch species, from 12 families, was recorded from BRUVs deployments. Platycephalidae was the most speciose family (3 species). Three species were numerically abundant and ubiquitous across all BRUV positions in the study –
Marginal tests in distance-based linear modelling (DistLM) showed that, of the four environmental factors retained for analysis, three were significant in isolation (
Vectors are overlaid to represent the different environmental variables most important in each modelling approach. Length and direction of vectors indicate the strength and direction of the relationship. Numbers 1–4 within the key indicate backscatter intensity.
MARGINAL TESTS | ||||||
Depth | 8086.4 | 8.8324 | 0.12296 | |||
Location | 4927.5 | 5.1026 | 7.49E–02 | |||
Backscatter Range 10 m | 3364.4 | 3.3966 | 5.12E–02 | |||
Rugosity | 1205.7 | 1.1766 | 0.3168 | 1.83E–02 | ||
Depth | 8086.4 | 8.8324 | 0.12296 | |||
Location | 4927.5 | 5.1026 | 7.49E–02 | |||
Backscatter Range 10 m | 3364.4 | 3.3966 | 0.011 | 5.12E–02 | ||
Rugosity | 1205.7 | 1.1766 | 0.314 | 1.83E–02 |
SEQUENTIAL TESTS | ||||||
0.10904 | Depth | 8086.4 | 8.8324 | 0.12296 | 0.12296 | |
0.17173 | Location | 4909.8 | 5.7687 | 7.47E–02 | 0.19761 | |
0.17258 | Rugosity | 904.3 | 1.0636 | 0.3836 | 1.38E–02 | 0.21137 |
0.17937 | Backscatter Range 10 m | 1269.2 | 1.5051 | 0.1998 | 1.93E–02 | 0.23066 |
445.24 | Depth | 8086.4 | 8.8324 | 1.23E–01 | 0.12296 | |
441.45 | Location | 4909.8 | 5.7687 | 7.47E–02 | 0.19761 |
Significant values are shown in bold.
By examining the raw Pearson correlations for different species, we could identify those most responsible for driving the assemblage response to each environmental factor: these are displayed as vectors in
This is as seen in Fig. 2, however here vectors for the 13 most influential fish species in the analysis are overlaid. Length and direction of vectors indicate the strength and direction of the relationship. Numbers 1–4 within the key indicate backscatter intensity.
Family | Genus | Species | Common Name | Correlation | |
Myliobatididae | Eagle Ray | 0.44 | 0.55 | ||
Platycephalidae | Long-Spine Flathead | 0.31 | 0.33 | ||
Scombridae | Australian Bonito | 0.26 | 0.22 | ||
Urolophidae | Common Stingaree | 0.19 | 0.30 | ||
Rachycentridae | Black Kingfish | 0.19 | 0.32 | ||
Platycephalidae | Blue-Spotted Flathead | −0.54 | −0.47 | ||
Triakidae | Gummy Shark | −0.25 | −0.25 | ||
Rhinobatidae | Eastern Shovelnose Ray | −0.25 | −0.15 | ||
Aracanidae | Eastern Smooth Boxfish | −0.04 | −0.11 | ||
Tetraodontidae | Silver Pufferfish | −0.10 | −0.16 | ||
Tetraodontidae | Silver Pufferfish | 0.24 | 0.26 | ||
Aracanidae | Eastern Smooth Boxfish | 0.21 | 0.19 | ||
Pinguipedidae | White-Streaked Grubfish | 0.12 | 0.08 | ||
Scorpaenidae | Fortesque | 0.03 | 0.08 | ||
Urolophidae | Common Stingaree | −0.34 | −0.16 | ||
Rachycentridae | Black Kingfish | −0.34 | −0.10 | ||
Sillaginidae | Whiting spp. | −0.34 | −0.29 | ||
Myliobatididae | Eagle Ray | −0.31 | −0.10 | ||
Rhinobatidae | Eastern Shovelnose Ray | −0.19 | −0.20 | ||
Rhinobatidae | Eastern Fiddler Ray | −0.19 | −0.16 | ||
Rachycentridae | Black Kingfish | 0.27 | |||
Urolophidae | Common Stingaree | 0.17 | |||
Tetraodontidae | Silver Pufferfish | 0.16 | |||
Aracanidae | Eastern Smooth Boxfish | 0.09 | |||
Paralichthyidae | Large-Tooth Flounder | −0.20 | |||
Triakidae | Gummy Shark | −0.16 | |||
Platycephalidae | Blue-Spotted Flathead | −0.13 | |||
Pinguipedidae | White-Streaked Grubfish | −0.12 | |||
Paralichthyidae | Large-Tooth Flounder | 0.39 | |||
Myliobatididae | Eagle Ray | 0.17 | |||
Rhinobatidae | Eastern Shovelnose Ray | 0.10 | |||
Pinguipedidae | White-Streaked Grubfish | 0.03 | |||
Platycephalidae | Blue-Spotted Flathead | −0.21 | |||
Urolophidae | Common Stingaree | −0.10 | |||
Aracanidae | Eastern Smooth Boxfish | −0.08 | |||
Triakidae | Gummy Shark | −0.08 |
Species richness was generally higher in the south of the park (
The graph for depth also show differences between northern (black bars) and southern (grey bars) locations within these factors. Data from northern and southern locations has been combined for backscatter intensity due to unbalanced numbers of replicates within the four levels of intensity.
PERMANOVA revealed a significant difference between species richness for all four factors of interest (
Source | df | SS | MS | Pseudo-F | P(perm) | |
Species Richness | Depth | 2 | 0.9015 | 0.45075 | 3.1429 | |
Res | 62 | 8.8921 | 0.14342 | |||
Total | 64 | 9.7936 | ||||
Location | 1 | 1.7317 | 1.7317 | 13.533 | ||
Res | 63 | 8.0618 | 0.12797 | |||
Total | 64 | 9.7936 | ||||
Backscatter | 3 | 1.2872 | 0.42908 | 3.077 | ||
Res | 61 | 8.5063 | 0.13945 | |||
Total | 64 | 9.7936 | ||||
Total MaxN | Depth | 2 | 3.8573 | 1.9287 | 2.4472 | 0.092 |
Res | 62 | 48.862 | 0.7881 | |||
Total | 64 | 52.72 | ||||
Location | 1 | 12.859 | 12.859 | 20.323 | ||
Res | 63 | 39.861 | 0.63271 | |||
Total | 64 | 52.72 | ||||
Backscatter | 3 | 5.8483 | 1.9494 | 2.5371 | 0.065 | |
Res | 61 | 46.871 | 0.76838 | |||
Total | 64 | 52.72 | ||||
Depth | 2 | 8532.2 | 4266.1 | 6.8736 | ||
Res | 62 | 38480 | 620.65 | |||
Total | 64 | 47013 | ||||
Location | 1 | 5203.1 | 5203.1 | 7.8402 | ||
Res | 63 | 41810 | 663.64 | |||
Total | 64 | 47013 | ||||
Backscatter | 3 | 5905 | 1968.3 | 2.9208 | ||
Res | 61 | 41108 | 673.9 | |||
Total | 64 | 47013 |
Significant results are shown in bold.
Groups | t | P(perm) | perms | ||
Depth | Shallow, Int. | 2.0833 | 850 | ||
Shallow, Deep | 0.53341 | 0.598 | 833 | ||
Int., Deep | 2.3135 | 543 | |||
Dist. From Sh. | Inshore, Midshelf | 1.7718 | 0.112 | 477 | |
Inshore, Offshore | 3.0799 | 378 | |||
Midshelf, Offshore | 8.43E–02 | 0.917 | 517 | ||
Backscatter | 0–50, 50–100 | 2.6171 | 280 | ||
0–50, 100–150 | 0.7656 | 0.415 | 374 | ||
0–50, 150+ | 1.4333 | 0.211 | 7 | ||
50–100, 100–150 | 2.0349 | 0.06 | 222 | ||
50–100, 150+ | 2.1264 | 0.156 | 5 | ||
100–150, 150+ | 0.73973 | 0.784 | 6 | ||
Depth | Shallow, Int. | 2.377 | 4989 | ||
Shallow, Deep | 2.8837 | 4992 | |||
Int., Deep | 2.5972 | 4990 | |||
Dist. From Sh. | Inshore, Midshelf | 1.9663 | 4622 | ||
Inshore, Offshore | 3.2945 | 4990 | |||
Midshelf, Offshore | 2.3576 | 4987 | |||
Backscatter | 0–50, 50–100 | 2.5076 | 4983 | ||
0–50, 100–150 | 1.6913 | 4983 | |||
0–50, 150+ | 1.1757 | 0.2954 | 49 | ||
50–100, 100–150 | 1.0779 | 0.346 | 2408 | ||
50–100, 150+ | 1.414 | 0.1356 | 7 | ||
100–150, 150+ | 0.55622 | 1 | 9 |
Significant results are shown in bold.
Total MaxN values were slightly higher in the south of the park, and showed trends similar to those seen for species richness (
PERMANOVA revealed significant effects for all three selected factors (
Some trends were evident in the nMDS ordination (
Data were square-root transformed prior to analysis. Lines represent 60% similarity. Numbers 1–4 within the key indicate backscatter intensity.
Patterns of fish assemblage structure in unconsolidated habitats within the SIMP are clearly influenced both by depth and factors operating over medium spatial scales (i.e. the distances between our locations). Additionally, backscatter intensity, while not a primary driver, was highlighted as potentially explaining some of the differences in fish assemblages across the scales of the study.
Worldwide, fish assemblage patterns of both reef and unconsolidated habitats have often been found to be correlated with depth
The importance of depth in structuring assemblages reflects what is known from other habitats in the SIMP and elsewhere. Strong depth-related patterns have been detected in reef fishes
With strong co-linearity between depth and distance from shore within our study, it is difficult to distinguish which factor primarily drives the observed patterns. Shelf position is included in the current HCS for the SIMP, as distinct cross-shelf patterns have been demonstrated for corals
The correlation with location in our modelling contradicts findings for reef fish communities in the SIMP
Our modelling also suggests the possibility that sediment grain size, for which backscatter intensity is an appropriate surrogate, may influence fish communities of unconsolidated habitats. Broad-scale bathymetric mapping of the seafloor within the SIMP has shown that unconsolidated habitats show distinct variability in sediment type, as revealed in acoustic backscatter
When compared to reefs, unconsolidated habitats, particularly in a high energy environment, have lower topographical complexity, support fewer sessile biota, and provide limited foraging opportunities and protection from predation
The current Habitat Classification System (HCS) for the SIMP has been developed and adjusted primarily based on fish assemblage patterns associated with hard substrata
The availability of high resolution benthic mapping has allowed us to examine the influence of a range of factors, which would otherwise be difficult and expensive to assess, on the poorly described fish assemblage of unconsolidated habitats. The patterns evident within the study endorse the relevance of depth and distance from shore as categories currently used as biophysical surrogates to represent discrete assemblages in marine parks in NSW. However, the study also indicates that the inclusion of habitat type, based on backscatter and possibly other remotely-sensed metrics, will lead to better representation of assemblages of unconsolidated habitats.
This paper comprises part of a PhD project by the first author. Field assistance was provided by Andrew Cox and Mitch Young. Logistic and administrative assistance was provided by the staff at the Solitary Islands Marine Park office and the National Marine Science Centre. We thank Peter Davies, Tim Ingleton and Edwina Foulsham from the NSW Office of Environment and Heritage for provision of bathymetric and habitat data. Thanks also to Alan Jordan and Nicola Johnstone for very helpful comments on this manuscript. Technical assistance was provided by Ken Cowden and Gary Shipley. We gratefully acknowledge Jim Seager (SeaGIS) for providing a student license for Eventmeasure for our video analysis.