Classification of marine bioregions on the east coast ...

African Journal of Marine Science

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Classification of marine bioregions on the east coast of South Africa T-C Livingstone, JM Harris, AT Lombard, A J Smit & DS Schoeman To cite this article: T-C Livingstone, JM Harris, AT Lombard, AJ Smit & DS Schoeman (2018) Classification of marine bioregions on the east coast of South Africa, African Journal of Marine Science, 40:1, 51-65, DOI: 10.2989/1814232X.2018.1438316 To link to this article: https://doi.org/10.2989/1814232X.2018.1438316

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African Journal of Marine Science 2018, 40(1): 51–65 Printed in South Africa — All rights reserved

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AFRICAN JOURNAL OF MARINE SCIENCE

ISSN 1814-232X EISSN 1814-2338 https://doi.org/10.2989/1814232X.2018.1438316

Classification of marine bioregions on the east coast of South Africa T-C Livingstone1*, JM Harris1,2, AT Lombard2, AJ Smit3 and DS Schoeman4,5 Ezemvelo KZN Wildlife, Pietermaritzburg, South Africa Institute for Coastal and Marine Research, Nelson Mandela University, Port Elizabeth, South Africa 3 Department of Biodiversity and Conservation Biology, University of the Western Cape, Cape Town, South Africa 4 School of Science and Engineering, University of the Sunshine Coast, Sippy Downs, Australia 5 Centre for African Conservation Ecology, Department of Zoology, Nelson Mandela University, Port Elizabeth, South Africa * Corresponding author, e-mail: [email protected] 1

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Marine bioregional planning requires a meaningful classification and spatial delineation of the ocean environment using biological and physical characteristics. The relative inaccessibility of much of the ocean and the paucity of directly measured data spanning entire planning regions mean that surrogate data, such as satellite imagery, are frequently used to develop spatial classifications. However, due to a lack of appropriate biological data, these classifications often rely on abiotic variables, which act as surrogates for biodiversity. The aim of this study was to produce a fine-scale bioregional classification, using multivariate clustering, for the inshore and offshore marine environment off the east coast of South Africa, adjacent to the province of KwaZulu-Natal and out to the boundary of the exclusive economic zone (EEZ), 200 nautical miles offshore. We used remotely sensed data of sea surface temperature, chlorophyll a and turbidity, together with interpolated bathymetry and continental-slope data, as well as additional inshore data on sediments, seabed oxygen and bottom temperature. A multivariate k-means analysis was used to produce a fine-scale marine bioregionalisation, with three bioregions subdivided into 12 biozones. The offshore classification was primarily a pelagic bioregionalisation, whereas the inshore classification (on the continental shelf) was a coupled benthopelagic bioregionalisation, owing to the availability of benthic data for this area. The resulting classification was used as a base layer for a systematic conservation plan developed for the province, and provided the methods for subsequent planning conducted for the entire South African EEZ. Validation of the classification is currently being conducted in marine research programmes that are sampling benthic biota and habitats in a sampling design stratified according to the biozones delineated in this study. Keywords: benthopelagic zone, bioregionalisation, biophysics, continental shelf, habitat maps, KwaZulu-Natal, marine environment, remotely sensed data, spatial distribution, conservation planning Online supplementary material: Supplementary information for this article is available at https://doi.org/10.2989/1814232X.2018.1438316. Data obtained from Birch (1996) were interpolated to produce a series of maps covering the distribution of sand, mud, clay, gravel, organic carbon and phosphate (Figures S1–S6, respectively) over the continental shelf off KwaZulu-Natal, South Africa.

Introduction The development of an ecologically based classification forms part of the marine spatial-planning process for the waters offshore of KwaZulu-Natal (KZN), South Africa, and provides a basis for assessing the spatial distributions of biophysical patterns and processes that shape marine biodiversity distributions within this region. Spatial frameworks require mapping of habitats, species, processes and/or relevant ecosystem classifications to plan efficiently. The expanse of the offshore environment, however, presents a challenge owing to the deficiency of spatial data. Understanding the physical and biological patterns and processes that sustain the offshore marine environment, as well as their spatial distribution, is essential yet made difficult by the paucity of biotic data. Abiotic data help to infer biological patterns from physical properties under the premise that community types depend on certain habitat characteristics (Day and Roff 2000). These

datasets provide an understanding of heterogeneity within an environment and thus the ecology of the region (Grant et al. 2006). Used in this way, remotely sensed data act as surrogates, providing spatial and temporal coverage of the biophysical characteristics of ocean surface waters (Longhurst 1995; Turner et al. 2003; Snelder et al. 2004, 2007; Grant et al. 2006; Allee et al. 2014; Roberson et al. 2017). In the pelagic realm, the use of satellite imagery of oceanographic features (e.g. Day and Roff 2000; Roff and Taylor 2000; Jackson and Lundquist 2016) has facilitated the development of ecosystem classifications required for biodiversity assessments and plans (Ferrier 2002; Roberts et al. 2003; Lombard et al. 2004; Sink and Attwood 2008; Game et al. 2009; Sink et al. 2011; Roberson et al. 2017). Within the benthic environment, habitats can be defined using their sediment and textural characteristics (Hewitt et al. 2004; MacKay et al. 2016; Moore et al. 2016), and

African Journal of Marine Science is co-published by NISC (Pty) Ltd and Informa UK Limited [trading as Taylor & Francis Group] Published online 29 Mar 2018

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together with additional abiotic data, such as temperature and oxygen, can provide robust classifications of the marine benthic environment and its associated communities. Biogeographic classifications are used to divide a spatial area into statistically distinct regions using a range of abiotic and biotic variables (Grant et al. 2006). These types of classifications can assist in marine conservation planning (Grant et al. 2006; Copeland et al. 2013) by providing surrogate habitat or ecosystem maps for unmapped and unknown biodiversity distributions. In addition to their role in marine conservation and spatial planning, biogeographical classifications can also provide a framework for the assessment of the status and trends of threats to biodiversity at the regional scale, as well as providing spatial units for ecosystem-based management of human activities, research and forecasting, and input into the design and establishment of marine protected areas (MPAs) (Copeland et al. 2013). Although the validity of biogeographical classifications as suitable surrogates for biodiversity is an area for further investigation (Malcolm et al. 2011), such classifications are important as the first spatial delineations (Bridge et al. 2016) for conservation planning and the basis for additional refinement. Broad-scale bioregional classifications have divided the world’s oceans into subregions based on physical and biological characteristics. Some are benthopelagic classifications (Longhurst 1995; Sherman et al. 2003; Breen et al. 2004; Devred et al. 2007; Spalding et al. 2007), while others separate the benthic and pelagic systems (Heap et al. 2005; UNESCO 2009; Spalding et al. 2012). All of these classifications focus on major oceanographic variables and provide a broad-scale approach and basis for bioregionalisation. These global marine biogeographic classifications are summarised in a UNESCO report (UNESCO 2009), and many are limited to continental-shelf regions. Classifications that extend further offshore include both pelagic (Alidina and Roff 2003; Vincent et al. 2004; Lyne and Hayes 2005; Connor et al. 2006; Populus et al. 2017) and benthic components (Alidina and Roff 2003; Beaman 2005; Connor et al. 2004, 2006; Vasquez et al. 2015; EMODnet 2016), and they can be either hierarchical, such as the EUNIS classification (Davies et al. 2004), or non-hierarchical, such as that developed by Powles et al. (2004). In South Africa, biogeographical classifications were originally limited to the coastal or shelf regions where most research occurs and most data are collected. These were based on selected species (Bolton 1986; Hommersand 1986; Turpie et al. 2000; Bolton et al. 2004), intertidal community structure (Bustamante and Branch 1996; Chassot et al. 2010) or habitat analyses (Riegl and Schleyer 1995; Sink et al. 2005). These classifications originally divided South Africa into three biogeographical provinces (Brown and Jarman 1978; Emanuel et al. 1992; Bustamante and Branch 1996), with Lombard et al. (2004) and Spalding et al. (2007) recognising a fourth inshore province called the Delagoa Bioregion. The marine component of the South African National Spatial Biodiversity Assessment 2004 (Lombard et al. 2004) not only delineated inshore marine bioregions but also categorised South Africa’s offshore waters, resulting in the definition of nine bioregions around South Africa. Within the political province of KZN, these include two inshore

Livingstone, Harris, Lombard, Smit and Schoeman

(continental-shelf) bioregions (the Delagoa Bioregion north of Cape Vidal, and the Natal Bioregion from Cape Vidal southwards to Port St Johns in the Eastern Cape Province), and three offshore bioregions (the South-West Indian, West Indian and Indo-Pacific). The present study aimed to improve on previous bioregional delineations for the waters off KZN by performing an environmental classification using remotely sensed physical and biological datasets, and to produce a finer-scale marine ecosystem classification required to meet provincial management goals. We originally considered separating our analyses into a benthic and pelagic classification, such as that developed at a national scale for the National Biodiversity Assessment 2011 (published in 2012) (Driver et al. 2012). However, benthic data at our spatial scale (other than bathymetry) were not available for the whole planning region. Our initial bioregional classification should thus be considered a pelagic bioregionalisation of the surface waters because we used only superficial sea-surface biophysical variables from satellite imagery and bathymetry data (depth and slope) for the benthic realm. The development of many pelagic bioregional classifications has acknowledged the influence of benthic-zone variables (e.g. depth and slope) on the pelagic realm (Alidina and Roff 2003; UNESCO 2009; Roberson et al. 2017), and Allee et al. (2014) proposed that the addition of such data could contribute to more robust pelagic habitat maps. Despite the clear limitations when seafloor data are lacking, surface data derived from satellite imagery has significant value in discriminating levels of biogeographic provinces (Oliver and Irwin 2008). However, our study assumes that the validity of the resultant bioregions is most accurate in the upper mixedlayer of the water column (Roberson et al. 2017). Our secondary classification into biozones is also a pelagic bioregionalisation, except within the continental-shelf region, classified Bioregion A in the initial analyses. Within this bioregion the secondary classification is considered a benthopelagic bioregionalisation, because we included seabed and benthic data, which were available for this area only. We consider that the stronger benthopelagic coupling in shelf ecosystems (Spalding et al. 2007; de Lecea et al. 2016; MacKay et al. 2016) justifies this combined benthopelagic classification. The marine bioregionalisation derived from the present study informed the KZN Coastal and Marine Biodiversity Plan (Harris et al. 2012), also known as the SeaPlan project, which in turn informed the delineation of 21 offshore MPAs (RSA 2016a) proposed under South Africa’s Operation Phakisa (http://www.operationphakisa.gov.za). The importance of such classifications is driven by the need for resource protection and spatial frameworks, which requires adequate mapping of habitats and species to better delineate MPAs. International agreements such as the Convention on Biological Diversity have recommended that 10% of the global marine environment be proclaimed as MPAs by 2020 (Secretariat of the Convention on Biological Diversity 2010), though in 2014 the World Parks Congress recommended increasing this to 30% (IUCN 2014). In South Africa, however, only 0.4% of the ocean is within an MPA, and the majority of MPAs are concentrated along coastlines and fall short of adequately representing

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the full diversity of the marine environment. The most recent National Biodiversity Assessment (Driver et al. 2012) highlighted the poor protection level of the offshore ecosystems and the need for a more representative set of MPAs, as well as new measures to protect the valuable ecosystem services and key ecological infrastructure of South Africa’s ocean environment. South Africa aims to address these shortcomings through the National Protected Area Expansion Strategy, which aims to expand the protected area network in the most cost-effective way by improving the representation of different ecosystems and ensuring ecological sustainability and adaptation to climate-change conditions (DEA 2016). The basis of this is a systematic approach to conservation planning that relies on habitat mapping and/or the creation of bioregional habitat maps. Methods Study area The study area encompassed the South African exclusive economic zone (EEZ) adjacent to the province of KwaZuluNatal, on the northeast coast of South Africa. The study area is bounded on the landward side by 640 km of coastline stretching from the Mozambique/South Africa border in the north to Port Edward in the south, and on the seaward side by the 200-nautical mile outer limit of the EEZ. The northern boundary was defined by the South African Navy Charts country boundary, and the southern boundary was drawn perpendicular to the general direction of the shoreline in that region. Owing to the resolution of the satellite imagery and missing data pixels along the shoreline, the planning region begins approximately 3 km offshore (Figure 1). Collation and preparation of data At the time of data collection, the highest resolution available for Sea-viewing Wide Field-of-view Sensor (SeaWiFS) data was 1.021 km2, which is an appropriate resolution to capture broad-scale patterns within the entire EEZ while still preserving the finer-scale features. Datasets were thus all resampled to a 1.021 km2 planning-unit grid and the final data layer was produced at this resolution. Ocean colour data consisted of a short-term time-series collected by the Advanced Very High Resolution Radiometer (AVHRR), including information on sea surface temperature (SST) and SeaWiFS chlorophyll a data, obtained from the NOAA website and then processed by the OceanSpace Institute at the University of Cape Town, South Africa. Data were supplied as text files, representing the best single-day image per month, over a four-year period, from January 2001 to December 2004. Data interpolation of missing pixels owing to cloud presence was performed using ordinary kriging (Eastman 2003), and a total of 46 AVHRR SST images and 51 SeaWiFS chlorophyll a images were used for the final analysis. Thirty monthly composites of MODIS-Aqua diffuseattenuation data were obtained from the Level-3 browser of NASA’s OceanColor Web over a two-year period, from July 2002 (first month of availability) to December 2004. Seabed data consisting of bottom-oxygen and bottomtemperature values for the KZN continental shelf were

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obtained from the Bayworld Centre for Research and Education (Cape Town, South Africa). These data were a compilation of hydrographic data from a number of surveys, indicating the maximum, minimum and mean values, over the period 1930–2005. Data were interpolated using the nearest neighbour interpolation. Bathymetric data were compiled by Young (2009) and consisted of a compilation of 32 datasets. Twenty-nine of these datasets consisted of nearshore data points, variously acquired though the Council for Geoscience, the South African Navy, and the African Coelacanth Ecosystem Programme (ACEP). The deep-water datasets consisted of digitally acquired grids and a digitised contour set from the Natal Valley bathymetric contour map. Slope data were determined from the bathymetric data using the IDRISI GIS slope/surface tool set. This tool uses Rook’s Case procedure (Eastman 2003) to determine the slope, based on the resolution and value of immediate neighbouring cells. The resulting slope data are based on the vector slopes along the X and Y gradient (Eastman 2003). Sediment data were obtained from Birch (1996), which comprised hard-copy data on sediment-sample GPS coordinates and associated sediment readings. These data were imported into ArcGIS and mapped according to the coordinates provided. A standard kriging method was used to interpolate the data over the continental shelf. The results produced a series of maps of sand, mud, clay, gravel, organic carbon and phosphate distribution (Supplementary Figures S1–S6, respectively). These maps represent a modelled gradient between the data points, and we used these data instead of the original vector drawings, which were based on qualitative interpretations of the same data. Selection of variables Variables for our analyses were selected based on two criteria: (1) assumed ability of the variables to act as surrogates for unsampled marine biodiversity patterns and processes (variables were chosen in consultation with experienced marine scientists during expert workshops); and (2) dataset availability at the appropriate spatial and temporal scale within the region. The variables and their assumed ecosystem properties are described in Table 1 and the datasets used are presented in Table 2. Clustering technique A quantitative multivariate clustering technique was used to provide a robust repeatable classification, which can be refined in future as new data become available. Data clustering techniques group similar sets of points, patterns or objects, and are thus well suited to bioregionalisation studies as they are designed to partition large datasets into smaller clusters that contain elements with common characteristics (Zhao et al. 2005; Grant et al. 2006; Holland 2006). The benefit of clustering techniques with regard to bioregionalisation is that they allow for areas of similar environments to be defined regardless of their geographic location and size, thereby producing an output representative of intrinsic spatial patterns and environmental variables (Leathwick et al. 2003; Snelder et al. 2004, 2007). Here we use the standard k-means classification (MacQueen 1967) within the statistical program R 2.7.0

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Livingstone, Harris, Lombard, Smit and Schoeman

KwaZulu-Natal

0

50

MOZAMBIQUE

SWAZILAND

AFRICA

SOUTH AFRICA

100 km

South Africa

Ponta do Ouro

Mpumalanga

Lake St Lucia

28° S

Cape Vidal

KwaZulu-Natal

Th

St Lucia Estuary

Richards Bay

uk

29° S

el

a

Thukela Mouth

Durban

INDIAN OCEAN 30° S

Scottburgh

Port Shepstone 31° S

25

0m

Port Edward

Planning domain

32° S

Shelf edge

31° E

32° E

33° E

34° E

35° E

36° E

33° S

Figure 1: The project study area is situated on the east coast of South Africa and stretches from the coastline of the province of KwaZuluNatal (KZN) (from the Mozambican border at Ponto do Ouro in the north, to Port Edward in the south) to the outer margin of South Africa’s exclusive economic zone (EEZ), 200 nautical miles offshore

(R Core Team 2013) for the analysis. K-means clustering is a data-mining method that classifies the data (n) into a number of predetermined clusters (k), so that data points within a cluster are more similar to each other (in

multivariate space) than they are to data points in other clusters (i.e. positions of the k-cluster centres in multivariate space are chosen so as to minimise the squared Euclidean distance to the data points within each cluster).

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African Journal of Marine Science 2018, 40(1): 51–65

Table 1: Variables used in the marine bioregionalisation analyses and their associated ecosystem properties Data Temperature

Chlorophyll a and turbidity

Sediment texture

Inshore organic carbon, phosphate and oxygen

Bathymetry and slope

Ecosystem properties Temperature is a driver of biogeographical patterns and biological diversity (Tittensor et al. 2010) and a determinant of marine ecosystem structure (Richardson 2008; Boyce et al. 2010). Sea surface temperature (SST) is commonly used as a surrogate for ocean temperature and to infer biological patterns (Snelder et al. 2004), intertidal (Blanchette et al. 2006) and plankton community structures (Bouman et al. 2003; Richardson and Schoeman 2004; Boyce et al. 2010), and zooplankton production (Davis 1987). SST has been used to describe global, temporal and spatial patterns of marine biodiversity (Boyce et al. 2010). Similarly, seabed temperature affects the distribution of benthic species and communities in space and time (Clarke et al. 2009). The maximum SST was extracted, thus providing information on the highest temperatures that communities are exposed to (global warming and increasing temperatures were the driving factors), and also mean SST, which provides information on the average temperature gradient, and the coefficient of variation for this value, which determines the temporal variability. Ocean-colour remotely sensed data provide a measure of phytoplankton biomass based on the absorption of wavelengths by photosynthetic pigments, such as chlorophyll a (Falkowski et al. 2000), and are thus used to measure ocean productivity, linking its variability to environmental factors (Behrenfeld et al. 2006). K490 diffuse attenuation data were used as a surrogate for turbidity, as these satellite data provide information on the reflection of sediments within the water column. Chl a and K490 data can be correlated with water circulation and the biological response to physical processes, such as upwelling, fronts, eddies, wind-induced mixing and riverine inputs, and the measure of these variables can provide information on the location, duration and amplitude of these types of events (Pauly 1999; Campillo-Campbell and Gordoa 2004; Welch et al. 2016). The combination of these variables thus provides information on ocean dynamics. The average amounts of sediment and Chl a within the water column, as well as the durations, were seen as the most meaningful variables affecting community distributions, therefore the means and coefficients of variation for Chl a and turbidity were used. Sediment texture is an important attribute of the coastal environment and is used in habitat studies, such as the European Nature Information System (EUNIS) (Davies et al. 2004), and for providing information on the distribution and abundance of biological resources (Smith and McConnaughey 1999; Roland et al. 2012). Beaman (2005) and MacKay et al. (2016) confirmed the importance of substrata in determining assemblages of benthic organisms and hence their usefulness as a biological proxy. These data were available only for the continental shelf and were accordingly used only for the inshore analysis. High-productivity areas over the continental shelf are associated with high organic content within the continental-shelf sediments (Gray 1981), and the relationship between organic carbon, phosphorous and nitrogen (Gray 1981) can be used as a surrogate for benthic patterns. Seabed oxygen influences large-scale diversity patterns (Levin et al. 1991), and together with sediment organic carbon is an important factor in macrobenthic species richness (Levin and Gage 1998) and macrofaunal community structure (Levin et al. 1991). These data were available only for the continental shelf and were consequently used only for the inshore analysis. Water depth and bathymetric patterns affect the vertical distribution of species, with different species assemblages occurring in different depth zones (Sanders 1968; Malcolm et al. 2011; Fitzpatrick et al. 2012; Kaunda-Arara et al. 2016; MacKay et al. 2016) and changes in community structure occurring between the continental-shelf and ocean-basin areas (Thistle 2003; Beaman 2005; Post 2008). Thus, depth and slope data act as broad surrogates for benthic patterns (whereas features such as canyons can have finer-scale impacts on marine biodiversity).

As opposed to hierarchical clustering methods, which depend on pairwise distances between data points to merge or divide data into a series of clusters (Fraley and Raftery 1998), k-means clustering cannot be represented by a dendrogram (because it does not produce pair-wise distances), and thus the outputs from these analyses have been represented in geographical space. The number of clusters for the bioregions and biozones were determined using an approach similar to that of Hartigan and Wong (1979), who plotted changes in the total within-cluster

sum-of-squares values against k to determine the optimal number of clusters for the k-means analyses (i.e. the point at which the curve starts to plateau, indicating the point at which additional clusters provide little additional explanatory power). Though this approach remains somewhat subjective, it allows the analyst to identify a reasonable tradeoff between simplicity and explanatory power. A k-means clustering analysis was performed using the ‘kmeans’ function in R. All datasets were standardised to zero mean and unit standard deviation to eliminate issues

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Table 2: Data layers used in the marine bioregionalisation analyses. Chl a = chlorophyll a concentration; CV = coefficient of variation; SST = sea surface temperature Data Advanced Very High Resolution Radiometer (AVHRR) SST data

Variable Maximum, mean, CV

Resolution and extent 1.021 km2 raster grids: 26° E, 31° S–35° E, 32° S

SeaWiFS Chl a imagery

Mean, CV

1.021 km2 resolution: 26° E, 31° S–35° E, 32° S

MODIS-Aqua diffuse Mean, CV attenuation coefficient for photosynthetically available radiation (PAR), as a surrogate for turbidity Inshore sediment data Percentages of mud, silt/clay, sand/gravel, organic carbon and phosphate Inshore seabed data

Bathymetric data

Slope data

Oxygen and temperature

K490 data available at 4 km2 level of resolution resampled to the 1.021 km2 grid: 26° E, 31° S–35° E, 32° S Birch (1996) manuscript captured electronically to produce gradient maps at 1.021 km2 level of resolution Raster grid at 0.005-m resolution resampled to the 1.021 km2 grid Raster grid at 1.021 km2 level of resolution: 26° E, 31° S–35° E, 32° S Raster grid at 1.021 km2 level of resolution: 26° E, 31° S–35° E, 32° S

associated with units of measurements and to ensure equal weighting in the analyses. The output produced a set of cluster-centre values for each cluster; these values represent the mean of a set of data points closest to the cluster centre, with values farther from the mean indicating variables with the greatest influence on the formation of that cluster. The k-means clustering was used initially to divide the planning region into a set of three broad or core clusters called bioregions. Bioregion A is the inshore bioregion, Bioregion B is situated off the continental shelf within the Agulhas Current, and Bioregion C is positioned in the deeper waters offshore of KZN. A second k-means clustering was then performed within each of these bioregions, dividing them into biozones and allowing for identification of areas of subtle change within each of the bioregions to emerge. Thus the biozones are nested within the bioregions. Additional data were available over the shelf region within Bioregion A; hence, these data (sediment composition, bottom oxygen concentration and bottom temperature) were included in the second k-means cluster analysis for Bioregion A. Results Bioregions The first k-means cluster analysis divided the planning region into three main clusters, herein referred to as bioregions A (Inshore Bioregion), B (Agulhas Current Bioregion) and C (Deep-Water Bioregion) and shown in

Time period Source Single-day imagery (best OceanSpace Institute at cloud-free image per the University of Cape month) over a three-year Town period, January 2001– December 2004. Single-day imagery (best OceanSpace Institute at cloud-free image per the University of Cape month) over a three-year Town period, January 2001– December 2004. 30 monthly composites, Level-3 browser in from July 2002 (first date OceanColor Web (http:// these data were available) oceancolor.gsfc.nasa. to December 2004. gov/cgi/level3.pl) Single source, 1996

Birch (1996)

Fiona Duff, University of Cape Town Young (2009)

Interpolated from bathymetric data

Figure 2. Table 3 shows the key characteristics of each bioregion, as defined by the cluster-centre values. The Inshore Bioregion (A) is a shallow bioregion situated along the continental shelf (200 m) to approximately the 2 000 m isobath. It is driven by high SST values (average of 24.7 °C for min. SST, and 28 °C for max. SST), while its lower Chl a (average of 83.7 mg m–3 for mean Chl a) and turbidity (average of 27.5 m–1 for mean turbidity) separate it from Bioregion A. The Deep-Water Bioregion (C) is a very deep area beyond the 2 000 m depth contour, with relatively cool surface waters (average of 23.9 °C for min. SST, and 27.4 °C for max. SST) and fluctuating temperatures (indicated by a high value for the coefficient of variation of SST), and low Chl a (average of 82.4 mg m–3 for mean Chl a) and turbidity (average of 27 m–1 for mean turbidity). Biozones The second k-means cluster analysis and within-cluster sum of squares produced a final output of four biozones

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African Journal of Marine Science 2018, 40(1): 51–65

Ponta do Ouro

Cape Vidal St Lucia Estuary Richards Bay Thukela Mouth Durban

INDIAN OCEAN

A

B

30° S

Scottburgh

C

Port Shepstone Port Edward

Bioregions A B 90 km

C 33° E

Figure 2: The three bioregions defined by k-means cluster analysis for the portion of South Africa’s exclusive economic zone off KwaZulu-Natal. Bioregion A is the inshore cluster, Bioregion B is the Agulhas Current cluster, and Bioregion C is the deep-water cluster

Table 3: Bioregions and their associated cluster-centre values (cluster centre values represent the number of standard deviations the centres are from the mean for that cluster). Bold font indicates values that had the largest standard deviation and therefore the greatest significance within that cluster. Chl a = chlorophyll a concentration; CV = coefficient of variation; SST = sea surface temperature

Variable Maximum SST Mean SST CV SST Mean Chl a CV Chl a Mean turbidity CV turbidity Bathymetry Slope

Inshore Bioregion A −0.47 −0.51 0.12 4.03 0.51 4.03 −2.71 2.34 −0.04

Agulhas Current Bioregion B 0.79 0.86 −0.73 −0.11 0.19 −0.10 −0.24 0.54 0.24

Deep-Water Bioregion C −0.74 −0.81 0.72 −0.25 −0.24 −0.27 0.48 −0.75 −0.24

(A3–A6) within the Inshore Bioregion; four biozones (B1–B4) within the Agulhas Current Bioregion; and two biozones (C1–C2) within the Deep-Water Bioregion. The withincluster sum of squares obtained for bioregions B and C did allow for a greater subdivision into even more biozones. However, for these bioregions the resulting additional cluster centres for each variable were so similar that it was considered not sufficiently robust to recognise them as different biozones. They were therefore aggregated to the level at which differentiation was clearly apparent in the cluster-centre table.

Owing to the resolution of the satellite imagery and a lack of data for waters closest inshore (0–3 km from the shoreline and approximately 0–30 m depth), we used the inshore bioregional break (Cape Vidal), as defined by Sink et al. (2005), to define two additional inshore biozones (A1– A2) which run parallel to the coast inshore of Bioregions A and B. This break was also recognised by Lombard et al. (2004) and allowed us to extend our analyses inshore to the low-water mark. The final map of 12 biozones (Figure 3) thus consists of 10 clusters derived from the second k-means analysis, as well as the additional two inshore biozones. Table 4 lists the cluster-centre values from the second k-means analysis, with the highest and lowest values indicating those variables that are farthest away from the mean and thus have the greatest influence on a cluster formation. The Natal Biozone (A1) stretches from Port Edward in the south to Cape Vidal in the north, and from the shoreline out to the 30-m depth contour, and is based on the bioregions identified by Lombard et al. (2004). The Delagoa Biozone (A2) stretches from Cape Vidal in the south to Mozambique in the north, and from the shoreline out to the 30-m depth contour, and is also based on the bioregions identified by Lombard et al. (2004). The Inshore High Chl a Biozone (A3) is distinguished by the highest Chl a and turbidity (i.e. diffuse attenuation coefficient, K490) (Chl a range 19.3–230.72 mg m–3, mean 143 mg m–3; max. K490 72.6 m–1) and a SST range of 19.6–27.9 °C (average 23.4 °C). It represents a mixture of sediment types, with slightly larger amounts of mud, silt and clay than found in the other biozones. This biozone has the highest and greatest range of seabed temperatures (average 20.5 °C, range 17.3–35.4 °C), the highest organic carbon content (average 0.4 mg l–1, max. 1.23 mg l–1), relatively low but the widest-ranging seabed oxygen levels (average 4.9 mg l–1, range 0.17–8.2 mg l–1), and relatively low phosphate (average 0.11 µM). The Sandy Inshore Biozone (A4) is a relatively shallow area, with the highest content of sand and gravel and the lowest content of soft sediments as compared with the other biozones. It has warm SSTs (range 23.8–28 °C); slightly lower but wider-ranging Chl a (31.08–229.2 mg m–3) and lower turbidity (range 46.1–66 m–1) as compared with Biozone A3; low organic carbon (average 0.2 mg l–1) and phosphate (average 0.14 µM); the highest levels of seabed oxygen (average 5.4 mg l–1, range 3.5–10.39 mg l–1) and moderate seabed temperatures (average 18 °C). The Inshore Mud Biozone (A5) has the largest proportion of soft sediments (mud, silt and clay) and the lowest portion of coarse sediment of all the shelf biozones. This area starts on the shelf at about 100 m depth and extends over the shelf edge to about 300 m. It has high levels of organic phosphate (average 0.17 µM) and the highest carbon values (average 0.5 mg l–1) across the shelf biozones, with low seabed oxygen (average 4.17 mg l–1) and low seabed temperatures (average 13.4 °C). The SST is relatively high (range 20.3–28.06 °C), while Chl a (average 110.8 mg l–1) and turbidity (average 22.8 m–1) are lower than those of the biozones inshore of this area. The Inshore Slope Biozone (A6) occurs at depths of 20–200 m and is associated with the continental-shelf edge

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Livingstone, Harris, Lombard, Smit and Schoeman

Ponta do Ouro

(a)

Biozones

Cape Vidal St Lucia Estuary Richards Bay

Durban

B1

C1

A2

B2

C2

A3

B3

A4

B4

Cape Vidal

A5

Thukela Mouth

30° S

A1

A6

INDIAN OCEAN

St Lucia Estuary

(b)

(b)

Richards Bay

Scottburgh Port Shepstone Port Edward

Thukela Mouth

Durban

INDIAN OCEAN

90 km Scottburgh

60 km

33° E

Figure 3: The 12 biozones, of which 10 were defined by a second k-means cluster analysis, for the portion of South Africa’s exclusive economic zone off KwaZulu-Natal. The biozone numbers are prefixed by the letter (A, B or C) that corresponds to the bioregion in which they occur; inset shows the six biozones within Inshore Bioregion A Table 4: Biozones and associated cluster-centre values (cluster-centre values represent the number of standard deviations the centres are from the mean for that cluster). Biozones A1 and A2 were derived from previous analyses (Lombard et al. 2004) and are not listed here. Chl a = chlorophyll a concentration; CV = coefficient of variation; SST = sea surface temperature Biozones Maximum SST Mean SST CV SST Mean Chl a CV Chl a Mean turbidity CV turbidity Depth Slope Mud, silt and clay Sand and gravel Phosphate Organic carbon Seabed oxygen Seabed temperature

A3 −1.09 −1.04 1.15 1.38 −1.27 1.43 −1.26 0.68 −0.57 0.33 −0.44 −0.60 0.54 −0.13 1.03

A4 −0.06 −0.07 −0.20 −0.07 0.34 −0.11 0.22 0.56 −0.38 −0.60 0.54 0.01 −0.57 0.38 0.44

A5 0.69 0.62 −0.35 −0.65 0.21 −0.63 0.22 −0.72 0.15 1.23 −0.94 0.84 0.98 −0.88 −0.85

A6 0.69 0.71 −0.58 −0.80 0.59 −0.79 0.80 −1.14 1.20 −0.41 0.38 −0.14 −0.46 0.27 −1.16

and slope. The average SST is slightly higher (24.2 °C, range 19.9–28.05 °C) than in biozones A3 and A4, and Chl a is widely fluctuating (range 38.2–173.4 mg m–3, average 108.4 mg m–3). Turbidity (average 42.4 m–1) is similar to that in biozones inshore of this area. The sediments are similar

B1 0.14 0.12 −0.07 2.15 1.15 2.23 0.16 1.01 1.92

B2 0.09 0.97 −1.04 0.21 0.06 0.17 −0.39 0.57 −0.10

B3 0.61 −0.02 0.65 −0.79 −0.89 −0.83 −0.59 0.03 −0.46

B4 −0.66 −0.92 0.42 −0.14 0.39 −0.10 0.84 −0.86 −0.09

C1 −0.30 0.72 0.03 −0.75 −0.05 −0.90 −0.28 0.97 0.24

C2 0.23 −0.55 −0.02 0.57 0.04 0.68 0.21 −0.73 −0.18

to Biozone A4, with a high proportion of coarse sediments and small proportion of soft sediment as compared with the other shelf biozones. The surficial sediment consists of intermediate levels of phosphate (average 0.13 µM) and relatively low organic carbon (average 0.2 mg l–1). Average

African Journal of Marine Science 2018, 40(1): 51–65

seabed oxygen (5.2 mg l–1) is higher than that of Biozone A5 but within a similar range; the seabed temperatures (average 12.2 °C) are the lowest of the shelf biozones though similar to Biozone A5. The Slope-Edge Biozone (B1) is associated with the continental shelf and slope edge down to about 300 m. The SST (range 19.7–29 °C), Chl a (average 92.9 mg m–3) and turbidity (average 34.1 m–1) are lower than that of the biozones inshore of this region, but higher than in the other current biozones. The Current-Core Biozone (B2) has SSTs (range 20.1–28.8 °C) similar to Biozone B1 and lower Chl a (average 84.6 mg m–3) than biozones inshore of it. Average turbidity is 28.2 m–1 (range 16.1–50.6 m–1). The Warm-Eddy Biozone (B3) has the highest SST (range 19.4–29.6 °C, average 28.2 °C for the maximum SST layer); Chl a is 25.4–125.12 mg m–3 and turbidity is 16.2–42 m–1 (average 25.4 m–1). The Current-Edge Biozone (B4) is situated along the edge of the Agulhas Current and is associated with deeper waters along the 2 000-m depth contour and with less of a slope. It has warm SSTs (range 19.3–29 °C, average 28 °C for the maximum SST layer); Chl a (average 83.1 mg m–3) and turbidity (average 27.47 m–1, range 13.4–51.2 m–1) are similar to values for Biozone B2. The Mozambique Ridge Biozone (C1) is situated along the Mozambique Ridge down to 2 500 m. This biozone has cooler surface waters than the biozones inshore of it, with SST averaging 24 °C (range 18.9–27.8 °C). Average Chl a is 81.4 mg m–3 while turbidity averages 13.1 m–1 (range 4.8–15.3 m–1). The Deep Ocean Basin (C2) is the deepest biozone and occurs in the deep ocean basin (at 3 000–3 500 m). It has slightly cooler SSTs (range 18.7–28.3 °C) than Biozone C1, and slightly higher Chl a (average 83.2 mg m–3), and turbidity (average 27.5 m–1) is similar to that in Biozone B4 but with a lower maximum (range 17.3–47.7 m–1). Discussion Our multivariate marine bioregionalisation analysis for the province of KwaZulu-Natal, South Africa, defined three broad bioregions and 12 finer-scale biozones. Bioregion A is associated with the continental shelf, Bioregion B is associated with the warm waters of the Agulhas Current, and Bioregion C defines the deep-water regions farther offshore. The Inshore Bioregion (A) (Figure 2), excluding the very narrow and coastal Biozone A2 (see Figure 3), begins at Cape Vidal in the north, stretches offshore to include the widened area of the shelf known as the KZN Bight, and narrows inshore just south of Port Shepstone to include the southern continental shelf as far as Port Edward in the south. This bioregion groups the shelf waters of the KZN Bight with those of the southern continental shelf, and distinguishes the shelf waters from those farther offshore within the Agulhas Current. Cooler surface waters and higher values of Chl a and turbidity characterise this bioregion. The southern half of KZN has many rivers and estuaries carrying nutrient-rich waters and sediments into the ocean. The surface waters within the KZN Bight originate from the inshore edge of the Agulhas Current

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(Lutjeharms et al. 2000a) and are generally cooler than the Agulhas Current waters, with slightly lower salinity, which decreases even more during the summer months owing to high rainfall and increased river input (Brown and Jarman 1978). The relatively high levels of chlorophyll a and sediments (turbidity) in this bioregion may be due to the riverine input; hence, the grouping of the southern continental-shelf waters with the cooler nutrient-rich waters of the KZN Bight is possibly due to the high nutrient and sediment loads in this region (de Lecea et al. 2016; MacKay et al. 2016; Scharler et al. 2016). The Agulhas Current Bioregion (B) has high maximum and mean SST values, which is indicative of the trajectory of the Agulhas Current as it moves southward along the east coast of South Africa (Weeks et al. 1998; Lutjeharms et al. 2000b). This bioregion is therefore assumed to be closely associated with the Agulhas Current. The lack of distinction between the continental-shelf waters north of Cape Vidal and those associated with the Agulhas Current suggests a strong relationship between the northern continental-shelf waters and the Agulhas Current. The continental shelf in northern KZN is approximately 11 km wide whereas the KZN Bight extends to almost 45 km offshore (Gründlingh 1992). The Agulhas Current flows close inshore, near the 200-m isobath, with mean speeds of about 1.4–1.6 m s–1 (Lutjeharms 2006). The sharp incline in the continental shelf is thought to have a stabilising effect on the position of the current (de Ruijter et al. 1999), with little meandering on either side of this isobath (Gründlingh 1983). The lack of distinction between the current and the shelf waters in the north may be due to the resolution of the satellite imagery (which is not accurate inshore of approx. 3 km), as well as the close association between continental-shelf waters and the inner edge of the Agulhas Current (Lutjeharms et al. 2000a). The Agulhas Current is an important transport mechanism for many species, from large fauna such as sea turtles (Lambardi et al. 2008) to smaller fauna such as fish larvae (Hutchings et al. 2002). However, instabilities resulting in meandering of the current and the transport of cyclonic eddies known as the Natal Pulse (Lutjeharms and Roberts 1988; Roberts et al. 2010) may result in transport of shelf water offshore and the loss of shelf biota (Hutchings et al. 2002; Roberts et al. 2016). Many species have adapted to this type of environment and broadcast spawning occurs near the edge of the continental shelf, taking advantage of seasonal variations in the current. Thus, the inshore edge of the Agulhas Current plays an important role in the transport of larvae back onto the shelf and this, together with cyclonic eddies, wind direction, current reversals and some upwelling occurring in the northern section of the KZN Bight (Hutchings et al. 2002; Roberts et al. 2016), produces enhanced phytoplankton levels (Lutjeharms et al. 2000b; de Lecea et al. 2016; Scharler et al. 2016). This increases the survival of larvae and juveniles of pelagic spawning species that use the KZN Bight as an important nursery area (Hutchings et al. 2002). The categorisation of the Deep-Water Bioregion (C) is mainly driven by depth and fluctuating SST. This bioregion exhibits slightly cooler SST than that of the Agulhas Current Bioregion (B), with similar levels of Chl a and turbidity.

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This bioregion is also driven by the high SST coefficient of variation, which signifies variable SSTs, most probably associated with the occasional movement of large anti-cyclonic eddies from the east, and the Mozambique Channel carrying warmer surface waters and causing fluctuating temperatures during these eddies (de Ruijter et al. 1999; Lutjeharms 2006). Mesoscale eddies are large, time-dependent structures (Tew Kai and Marsac 2010), recognised by their spatial variability in physical, chemical and biological properties (Benitez-Nelson and McGillicuddy 2008). They act as an important process influencing the pelagic community by converting physical energy into trophic energy (Bakun 2006) through the supply of nutrients to the euphotic zone (Tew Kai and Marsac 2010). This results in areas of ’enhanced primary and secondary activity’ (Yen et al. 2006), which in turn attracts upper-trophic-level predators, such as sea birds (Jaquemet et al. 2004; Yen et al. 2006; Bost et al. 2009; Tew Kai and Marsac 2010), sea turtles (Lambardi et al. 2008), fishes (Tew Kai and Marsac 2010; Lan et al. 2012) and marine mammals (Etnoyer et al. 2004; Bluhm et al. 2007; Cotté et al. 2011). These features consequently act as hotspots of biological and biogeochemical activity (Benitez-Nelson and McGillicuddy 2008) and play an important role in the transport of fish larvae (Bakun 2006) and top-predator feeding. Fishing data for the waters within this bioregion indicate that this area is used within the large-pelagic fishery, but further analysis is suggested using top-predator data to better understand such features and the ability of SST and Chl a data to determine these. Within the Inshore Bioregion (A), the Inshore High Chl a Biozone (A3) consists of cooler water with seabed and surface water temperatures similar to each other, and this, together with the mixture of sediment types, suggests good mixing of top and bottom water and a close benthic–pelagic coupling. The Sandy Inshore Biozone (A4) occurs both north and south of Biozone A3 and is characterised by sand and gravel sediments. Flemming and Hay (1988) and Ramsay (1995) described a band of high gravel content near the 65-m depth contour related to the position of historical mean sea levels. This band falls in the centre of this biozone and runs adjacent to the coast. The Inshore Mud Biozone (A5) is situated along the edge of the continental shelf, stretching from the Thukela River mouth southwards, and widening farther off the shelf, out to about 500-m depth near Durban. This biozone has the largest portion of soft sediment, which may be related to the offshore transport of these sediments from the Thukela River (Flemming and Hay 1988). The Inshore Slope Biozone (A6) stretches from Durban, near the edge of the continental shelf, out to about 900 m depth, and narrows shoreward south of Bazley Beach to form a thin strip over the continental shelf stretching down to Port Edward. In the northern section of this biozone the continental shelf begins to narrow, and the Agulhas Current overshoots the shelf edge causing warmer surface waters to be advected onto the shelf in the southern half of the KZN Bight (Heydorn et al. 1978; Lutjeharms and Connell 1989; Lutjeharms et al. 2000b). This may explain the warm sea surface temperatures occurring within this biozone and in the

Livingstone, Harris, Lombard, Smit and Schoeman

Inshore Mud Biozone (A5). Ekman veering along the full length of the shelf slope, as well as the topographically induced upwelling cell between St Lucia and Richards Bay (Lutjeharms and Roberts 1988; Meyer et al. 2002), pushes cold, nutrient-rich water onto the shelf (Flemming and Hay 1988; Schumann 1988), which may explain the cooler seabed waters associated with the Inshore Mud (A5) and Inshore Slope (A6) biozones. Several studies have suggested the existence of a subsurface eddy 20–40 km offshore of Durban (Pearce 1978), where the shelf begins to narrow. This eddy stretches southward, bringing cooler nutrient-rich bottom water onto the shelf (Schumann 1988; Lutjeharms and Connell 1989; Lutjeharms et al. 2000b; Lutjeharms 2006). When this eddy is absent the shelf exhibits a well-developed mixed surface layer in contrast to the reduced mixed layer and nutricline depth (Meyer et al. 2002). The warm surface waters of the Inshore Mud (A5) and Inshore Slope (A6) biozones, together with the much cooler seabed temperatures, suggest less vertical mixing of seawater and the possibility of a cold-core eddy in this area. Although biozones A5 and A6 have similar pelagic properties, they differ in their sediment types, with Biozone A6 exhibiting a much smaller proportion of soft sediment. The Agulhas Current Bioregion (B) was subdivided into four biozones separated by their variability in temperature and depth, with temperatures up to 1 °C higher than those within the Inshore Bioregion (A). The Slope-Edge Biozone (B1) is closely associated with the shelf edge and shallower water, and runs the entire length of the planning region, whereas the Current-Core Biozone (B2) is limited to the northern half of this bioregion. Higher values of Chl a and turbidity within B1 indicate its close association with shelf waters, yet higher SSTs separate it from waters closer inshore. Lutjeharms (2006) refers to the northern half of the Agulhas Current as consisting of three core areas: the inshore boundary consisting of high thermal and velocity gradients, the current core, and the anticyclonic shearedge zone on the seaward edge. These zone delineations are slightly different from our biozone descriptions, but there are some similarities. The Current-Core Biozone (B2) has higher temperatures than the rest of the biozones within the Agulhas Current Bioregion (B), suggesting its association with the core of the current and separation from the slightly cooler waters of the Slope-Edge (B1) and Current-Edge (B4) biozones. The Agulhas Current slowly loses heat as it moves southwards (Lutjeharms 2006), which may explain why Biozone B2 does not extend over the entire length of the planning domain. The Warm-Eddy Biozone (B3) is situated in the northern part of the planning region and has slightly higher SST values than the Current-Edge Biozone (B4). The positive coefficient of variation (Table 4) for both biozones B3 and B4 suggests fluctuating surface temperatures, which are most likely related to the movement of offshore eddies shed from the Mozambique and East Madagascar currents that move westward to join and form part of the Agulhas Current (Lutjeharms 2006). These eddies are often present in the northern section of the planning region and move southwards, but they are not consistent over time, which is evident in the fluctuating SST and Chl a data, with higher observed values when these eddies are present.

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The Current-Edge Biozone (B4) is in deeper waters along the 2 000-m isobath and over an area with less of a slope as compared with biozones B1–B3. The surface temperatures, although similar to those of Biozone B3, have a lower average for maximum SST, indicating that they are starting to cool towards the edge of the Agulhas Current. The Deep-Water Bioregion (C) is subdivided into the Mozambique Ridge Biozone (C1) and the Deep Ocean Biozone (C2). Biozone C1 is associated with cool sea surface temperatures and a high coefficient of variation for SST data. Biozone C2 is the deepest biozone and is situated offshore in the southeastern part of the planning region, covering depths of 3 000–3 500 m. The main driver of these two biozones is depth, with very marginal differences in SST and Chl a. The SST is marginally cooler (0.6 °C) in the Deep Ocean Biozone (C2), with a slightly higher average Chl a, although in a smaller range of values. The biozones on the shelf and within the Inshore Bioregion (A) were more refined than the offshore biozones owing to data availability, but further improvement of the shelf classification could be made with the acquisition of additional and more recent sediment data along the entire length of the coastline as well as an accurate map of rocky reef areas. Reef points have been collected during the SeaPlan project (Harris et al. 2012), but accurate mapping of reef areas is still required. The addition of more data could provide a greater understanding of the shelf areas and possibly create more subdivisions, which would be valuable to spatial planning in this region, where human use is most intense and diverse. The data collated for the analysis consisted mainly of physical data, with biological data integrated in the form of surrogates, such as chlorophyll a concentration, to provide ecological profiles. While it is understood that many of the chosen variables are not direct drivers of biological patterns, they are correlated with various water-column (Bouman et al. 2003; Richardson and Schoeman 2004; Snelder et al. 2004; Boyce et al. 2010) and benthic properties (Smith and McConnaughey 1999; Beaman 2005; Roland et al. 2012), and have thus been used as proxies to infer ecological processes (Snelder et al. 2007). It is acknowledged that additional testing of these variables as accurate predictors of biological patterns is needed, and this will be addressed by ongoing research. This ongoing research will initially focus on the benthopelagic classification on the continental shelf using benthic communities (macrofauna, fish species and larvae) to compare their spatial distribution patterns to the defined biozones. Results from these benthic-zone validation studies will subsequently inform the future use of marine bioregionalisation classifications in South Africa’s developing marine spatial-planning framework (RSA 2016b). Conclusions Biogeographic classifications act as benchmarks and are constantly evolving as new data and analyses become available (Spalding et al. 2012). It is understood that the boundaries defined in this study are not fixed in space or time, and have not yet been validated with biological data, so their value as biodiversity surrogates remains untested.

However, validation studies are currently underway, and given the lack of offshore biodiversity data at the inception of this project, a quantitative bioregionalisation was the best available tool we could use to inform marine conservation planning initiatives that were proceeding for offshore marine regions. While Oliver and Irwin (2008) demonstrated the value of pelagic bioregions in representing biogeographic provinces generally, recent studies have indicated separation between the benthic and pelagic systems and the processes that dominate them, suggesting that separate classifications for the two systems would be appropriate (Spalding et al. 2012). Future work examining links between the benthic and pelagic systems is encouraged to provide evidence and justification for separating or combining the systems when developing spatial classifications to represent biological patterns in the ocean. Measuring the effectiveness of surrogate data as substitutes for biodiversity patterns helps to improve our understanding of the usefulness of these data to conservation planning (Ferrier 2002; Calvert et al. 2015; Jackson and Lundquist 2016). However, given a lack of more detailed biodiversity information, and the increased human pressures on marine systems (Halpern et al. 2008, 2015; Borja 2014), it is imperative that we use the best available biodiversity data, coupled with quantitative models (such as bioregionalisations) of unmapped biodiversity, to guide current efforts in marine spatial planning. This would reaffirm the use of biodiversity surrogates and classifications in regional planning. The analysis presented in this study has provided a framework for stratifying marine biodiversity censuses in the region (now part of ongoing research programmes to validate the boundaries of bioregions and biozones), and has also provided the foundation for further bioregionalisation analyses for the entire South Africa EEZ (Roberson et al. 2017) and the southern Benguela region (Lagabrielle et al. 2012). Finally, results from current validation studies will be used to inform future bioregionalisations in South Africa’s EEZ. Acknowledgements — We would like to thank the following: Ezemvelo KZN Wildlife for financial and technical support; Erwann Lagabrielle for assistance with the SeaPlan project and provision of data layers, as well as analysis expertise; the University of KwaZulu-Natal and Paul Young for bathymetric data, and Fiona Cuff from the Bayworld Centre for Research and Education for seabed data; Marine Geosolutions for technical support; and the African Coelacanth Ecosystem Programme, KwaZulu-Natal Surrogacy Project, for assistance and funding related to this project.

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Manuscript received March 2017 / revised September 2017 / accepted December 2017