Multi-scale multi-level marine spatial planning: A novel methodological approach applied in South Africa
Multi-scale multi-level marine spatial planning: A novel methodological approach applied in South Africa
Erwann Lagabrielle 0 1 2
Amanda T. Lombard 1 2
Jean M. Harris 2
Tamsyn- Claire Livingstone 2
0 UMR ESPACE-DEV 228, Universit eÂ de La ReÂunion, IRD, La Reunion, France, 3 Scientific Services , Ezemvelo KZN Wildlife, Pietermaritzburg, South Africa, 4 Wildlands Conservation Trust, Hilton , South Africa
1 Institute for Coastal and Marine Research, Nelson Mandela University , Port Elizabeth , South Africa
2 Editor: Christopher A. Lepczyk, Auburn University , UNITED STATES
This study proposes and discusses a multi-scale spatial planning method implemented simultaneously at local and national level to prioritize ecosystem management actions across landscapes and seascapes. Mismatches in scale between the occurrence of biodiversity patterns and ecological processes, and the size and nature of the human footprint, and the different levels and scope of governance, are a significant challenge in conservation planning. These scale mismatches are further confounded by data resolution disparities across and amongst the different scales. To address this challenge, we developed a multiresolution scale-linked marine spatial planning method. We tested this approach in the development of a Conservation Plan for a significant portion of South Africa's exclusive economic zone, adjacent to the east coast province of KwaZulu-Natal (the SeaPlan project). The study's dataset integrated the geographic distribution of 390 biodiversity elements (species, habitats, and oceanographic processes) and 38 human activities. A multi-resolution system of planning unit layers (PUL), with individual PUs ranging in resolution from 0.2 to 10 km, was designed to arrange and analyse these data. Spatial priorities for conservation were selected incrementally at different scales, contributing conservation targets from the fine-, medium- and large-scale analyses, and from the coast to the offshore. Compared to a basic single-resolution scale-unlinked plan, our multi-resolution scale-linked method selects 6% less conservation area to achieve the same targets. Compared to a multi-resolution scale-unlinked plan, our method requires only an additional 5% area. Overall, this method reflects the multi-scale nature of marine social-ecological systems more realistically, is relatively simple and replicable, and serves to better connect fine-scale and large-scale spatial management policies. We discuss the impacts of this study on protected area expansion planning processes in South Africa. This study showcases a methodological advance that has the potential to impact marine spatial planning practices and policies.
Grant (EL). http://www.nrf.ac.za/. Western Indian
Ocean Marine Science Association (WIOMSA)
Marine and Coastal Science for Management
Project Grant for the MOZALINK project (Grant NÊ:
MASMA/OP/2013/02) (EL). http://www.wiomsa.
org/, European Union's European Maritime and
Fisheries Fund Project Grant (EL) for the OCEAN
METISS project (Grant NÊ: EASME/EMFF/2016/
18.104.22.168/04/SI2.766484 - OCEAN METISS). https://
Marine spatial planning (MSP) is a decision support process for integrated ocean governance
and aims to allocate marine and coastal resources sustainably and efficiently through space
and time, to achieve social, economic, strategic and ecological objectives [
]. Within MSP
frameworks, the focus of conservation planning is to identify spatial priorities for the
protection of natural assets, and to identify spatial contraints for human activities in order to
promote the persistence of functional coastal and marine social-ecological systems [2±3]. A key
challenge in developing marine spatial management plans that are implementable and lead to
action, is the integration of different scales of ecological and social systems [
]. There is
growing recognition that conservation planning concepts and tools that address cross-scale
dimensions are needed [
] to account for the multiscalar nature of conservation problems and to
balance divergent priorities at multiple spatial scales [
]. Spatial conservation planning
exercises generally address fine- and large-scale domain in separate planning processes, and social
frameworks (e.g. legal, institutional, political) are often poorly integrated across local, national,
regional and global scales [
]. Ecosystems, however, are increasingly threatened at all scales
 whereas local actions can have combined and cumulative impacts at broader scale, and
global drivers of change, such as climate-related drivers, affect social-ecological systems locally
. Therefore, to maintain adaptive social-ecological systems, societies need to address the
challenge of fitting the scale of their conservation actions to the spatial and temporal scale of
social-ecological processes [
6, 10, 11
The need for conservation planning to address cross-scale dimensions is widely
acknowledged and methods to balance divergent priorities at multiple spatial scales have been
] but rarely implemented in practice . As a result, spatial conservation plans often
make recommendations that do not fit the scale of local management, and local decisions
often fail to address broader scale issues [
]. Scale mismatches can significantly limit the
applicability and implementation of spatial plans in the real and complex world [
5, 6, 7
problem arises largely because conventional spatial planning methods typically channel all data
into a single-scale framework, and methods that effectively address the multi-scale and
multiresolution nature of data are lacking. In the marine environment, the data resolution and scale
mismatch is particularly problematic. It is therefore ªtimely to explore tools and approaches
that can help deal with scale mismatches that impede effective implementationº [
generally, the multi-scale nature of social-ecological systems has raised increasing scientific
interest from various fields, including mathematics, astronomy, physics, ecology, economics and
]. Scale research has progressed with the development of fractal theory that
defines the concepts and metrics for the analysis of multi-scale phenomena within a single
notional framework . In the fields of ecology, landscape ecology and biogeography, the
analysis of ecological patterns and processes across scales has always been a central problem
] both in the terrestrial and marine realm [17±18]. An abundant literature describing the
concepts, challenges and issues related to the multi-scale organisation of complex and coupled
social, jurisdictional, political, cultural and ecological systems has emerged in the past thirty
6, 8, 10
In practice, regional and global MSP processes generally use large-scale data (large area
coverage) with a low spatial resolution (10×10 km resolution and broader) whereas local or even
national plans are generally based on fine-scale high-resolution data (e.g. 1×1 km resolution)
(see [19±23]). The move towards multi-scale multi-resolution MSP is technically facilitated by
the increasing availability of data for both social and ecological systems that spans diverse
scales and resolutions in both space and time [24±26]. Indeed, spatial marine ecology data
from remote sensing satellite and ecological niche models [
], oceanographic circulation
2 / 29
models , transport simulation models [
] and tracking devices [
] have also become
increasingly available, allowing the monitoring of biophysical oceanographic parameters and
biota movement across entire oceans. In addition, geographic data on human uses of the sea
(e.g. Automatic Identification System ship location data) and management zonation data have
become available and accessible. Exploring this data and using it to support decision requires
integrative multi-scale multi-resolution methods.
This study proposes and discusses a multi-scale spatial planning method implemented
simultaneously at local and national level to prioritize ecosystem management actions across
landscapes and seascapes. This approach is tested in the development of a Conservation Plan
for a significant portion of South Africa's exclusive economic zone, adjacent to the east coast
province of KwaZulu-Natal (the SeaPlan project). The hypothesis defended in this research is
that a multi-scale and multi-resolution approach to spatial planning from its early steps (e.g.
problem assessment and formulation [
]) can improve the efficiency of the planning process
at the later implementation stages. In this study, the scale refers to the size of a spatial extent
and the resolution refers to the smallest identifiable element of a spatial dataset. We present a
technical procedure that links the systematic conservation planning , spatial prioritisation
process across different scales. Firstly, we describe the method (i.e., multi-resolution
scalelinked (ML)), the stakeholder involvement strategy, the data collection, analysis and
modelling, and the decision implementation process. Secondly, we assess the advantages of the ML
approach by comparing the outputs of the ML method with that derived from conventional
single-scale single-resolution methods [
]. Finally the impacts of the plan on policy and
future methodological developments are discussed.
Study area and project context
The study area is adjacent to the province of KwaZulu-Natal (KZN) on the east coast of South
Africa, covering 640 km of coastline and 233,747 km2 of (EEZ) waters (Fig 1). The seafloor is
characterised by a narrow continental shelf (<200 m) and steep continental slope, incised by
canyons running down to the abyssal plain (maximum depth of -3,668 m). The offshore waters
of KZN are dominated by the fast-flowing southward Agulhas current, which is largely formed
by the recirculation of the South West Indian Ocean sub-gyre, with rings shed from the
Mozambique and East Madagascar current [
]. The KZN waters are a typical oligotrophic
system with low productivity. Owing to its strength and extent, the Agulhas current plays an
important role in the distribution of species in the KZN region by transporting warm, salty
tropical waters southward along the KZN coastline [
On the continental shelf, rocky reefs are distributed throughout the province, whereas the
occurrence of coral reefs increases in the northern part of the study area [
]. Top predators
such as tuna and seabirds concentrate in the more productive areas characterised by strong
eddies . The KZN marine environment consists of an estimated 1,640 marine fish species
]. Coelacanths have been found in the northern canyons at depths of -54 to -359 m [
Loggerhead (Caretta caretta) and leatherback (Dermochelys coriacea) turtles nest along the
northern stretches of KZN and 15 cetacean species have been found in KZN waters [
The coastline and nearshore have high habitat biodiversity, including rocky, sandy and
mixed shores, rock platforms, mangroves and estuaries. The superficial coastal sediments
consist mainly of sand with gravel and mud. Sediment inputs are mainly of fluvial origin [
sediment habitats are often ephemeral owing to the effects of wave and tidal action on sand
transport. These habitats are distributed throughout the province, and are typically inhabited
by burrowing and benthic species [
3 / 29
Fig 1. Study area map. Study area off the KwaZulu-Natal Province in the EEZ of South Africa, with a three-dimensional representation of the planning domain.
The Province contains 74 river systems and associated estuaries, 13 of which are
permanently open. The main river is the uThukela, contributing to more than 38% of the provincial
freshwater flow. Estuaries form a vital connection between the different life cycle stages for
many fish species. Some marine species spawn within estuarine systems and many breed at
sea, with their juveniles using estuaries as nursery areas [
]. The largest mangrove areas in
South Africa occur in northern KwaZulu-Natal estuaries, covering about 1391 ha [
Social system and pressures on marine ecosytems
The human population in KZN has increased from 8.4 million inhabitants in 1996 to 10.5
million in 2013 (density of 110/km2) with 3.5 million located in the coastal Durban metropolitan
]. The economy of KZN has been growing rapidly as a result of the post-apartheid
political change and the integration of the South African economy within the global market
]. In 2011, the average annual income per household per year was about 2,500 US
dollars but 48.4% of the population still lives in poverty [
]. Agriculture (mostly sugar cane)
and forestry remain central to the economy. Tourism accounted for 5.1% of the Gross
Domestic Product in 2013 (IHS Global Insight database). In the marine environment, Durban and
4 / 29
Richards Bay harbours are respectively one of the ten largest container terminals in the world
and the area has largest coal export facility in Africa.
Offshore mining of heavy mineral sands, and increasing seismic activity for oil and gas
industries, are placing coastal and marine ecosystems under increasing pressure. The
cumulative threats to sandy beaches in South Africa are highest in the southern half of the KZN
]. Several marine fish stocks in KZN waters have declined or collapsed, with many no
longer financially viable [
]. This trend has been exacerbated by new developments in
artisanal fishing gear. International and local commercial fishing fleets target swordfish, sharks
and tuna and are regarded as a potential threat to marine ecosystems. Commercial prawn
trawling on the uThukela Banks has been shown to be severely detrimental to several fish and
shark species owing to the industry's high bycatch [47±49]. Shark nets pose a threat to many
marine species, but particularly the larger non-target species such as turtles and cetaceans [
], which are exposed to additional pressures including vessel collisions , plastic [
noise pollution [
Although fishing remains the greatest direct pressure on marine biodiversity, climate
change has strong ecological, fisheries, resource management and socio-economic
implications. In KZN, sea surface temperature (SST) has risen along the east coast [
] and is expected
to result in the southward expansion of the ranges of tropical intertidal species, as has been
recorded for some fish species [
]. Coral communities have experienced less bleaching than
those in southern Mozambique, but the emerging trend is one of increasing frequency and
intensity of bleaching [
]. Along the coastline, including the Durban metropolitan area, areas
within 100 m of the shoreline are particularly vulnerable to sea-level rise and increased coastal
erosion linked to an increased frequency and severity of storms [
Current MPA network
As in the rest of the world, the establishment of MPAs in KZN has fallen far behind the
proclamation of terrestrial protected areas [
]. Nevertheless, the rapid increase in KZN's coastal
population and the mounting demands made on the marine environment led to the realisation
of the need to establish a system of MPAs along the coast during the late 1960s . KZN
currently has three MPAs all of which occur inshore. In the North, covering the entire coastline of
the Delagoa Bioregion [
], the marine component of the iSimangaliso Wetland Park (a
UNESCO World Heritage Site) is divided into the St Lucia MPA (441 km2, proclaimed in
1979) and the Maputuland MPA (384 km2, proclaimed in 1986), both extending 3 M (nautical
mile, 1 M = 1852 m) offshore. In the Natal Bioregion further South, there are two smaller
MPAs: the Trafalgar MPA (proclaimed in 1979) is only 8.1 km2, 4.5 km long and extends 1 M
offshore, and the Aliwal Shoal MPA (proclaimed in 2004) is 125 km2, 18 km long and extends
to 4 M offshore. All four of the above mentioned MPA's have been proclaimed (or
re-proclaimed) under Section 43 of the Marine Living Resources Act (Act 18 of 1998) and the two
MPAs within the iSimangaliso Wetland Park have also been proclaimed under the World
Heritage Convention Act (Act 49 of 1999). It is important to note that the Pondoland MPA
(currently South Africa's largest continental MPA in terms of surface area ~900 km2) was also
proclaimed in June 2004 just south of the KZN/Eastern Cape border. Also of relevance is the
recent proclamation of the Ponta do Ouro Partial Marine Reserve in Mozambique extending
northwards from the RSA/Mozambique border to inland of Inhaca Island.
The SeaPlan marine spatial planning project
In the late 1990s, Ezemvelo KZNWildife (EKZNW), the conservation authority for South
Africa's Kwazulu-Natal (KZN) Province, initiated the SeaPlan marine conservation planning
5 / 29
], to be implemented along the 640 km coastline of KZN and its Provincial waters,
including all marine habitats (coastal, estuarine, benthic and pelagic), from the shoreline out
to the offshore limits of the EEZ. EKZNW has the institutional mandate to implement marine
conservation actions, to facilitate stakeholder participation, and to recommend plans for
resource use and protection of biodiversity. EKZNW is contracted by the national
environmental agency to manage proclaimed MPAs in the province of KZN.
SeaPlan aimed to 1) assess the status of marine biodiversity protection in the province; and
2) identify spatial priorities for future marine conservation actions. The overarching challenge
was to achieve these objectives across spatial scales and levels of governance, while engaging
meaningfully with stakeholders from many sectors of society. As the first marine spatial
planning process in South Africa, SeaPlan also aimed to contribute methodological guidelines
toward the integration of biodiversity conservation priorities with other MSP processes at
Provincial and National levels, for example, the National Protected Area Expansion Strategy [
and more recently, the ªMarine Protection and Governanceº component of the National
Phakisa Operation (http://www.operationphakisa.gov.za) which aims to promote a sustainable
ocean economy through MSP. The need for an expansion of the MPA estate in the Natal
Bioregion of KZN, and particularly its offshore areas, is recognized both Nationally and
Provincially. Although the entire Delagoa Bioregion in the North falls within a protected area (Fig 1),
only 6% of the Natal Bioregion falls in a MPA and only 0.28% of offshore areas are protected.
In addition, the Natal Bioregion MPAs do not include any fully protected (or ªno-takeº)
The SeaPlan development process follows adaptive management principles. Its host
(EKZNW) has the research capacity to collaborate, gather and analyse data, respond to
opportunities and constraints, engage with stakeholders, update plans, and make recommendations
to either local or national government for MPA expansion, in an on-going process which is
part of its mandate [
]. South Africa's national marine planning products [
] provide the
most complete MSP study for the EEZ, but do not integrate inshore and offshore planning.
SeaPlan is currently the only study that spans the coastal-inshore-offshore continuum, making
use of all available data for both biodiversity and human use. The current iteration of SeaPlan
aimed to develop a multi-scale multi-resolution conservation assessment and plan for the next
20 years. Here we present the methodology developed to achieve this objective.
Material and methods
SeaPlan used a five-step method to identify and implement future priority areas for
conservation action across different scales in the study area. These steps follow the systematic
conservation planning framework [
22, 24, 25, 32
]. The steps are summarised in Fig 2, and then detailed
in following sections. Step 1 was initiated by EKZNW in the late 1990s, involving multi-level
decision-makers and stakeholders operating or managing coastal and offshore activities. Step 2
collected or collated spatial distribution data on biodiversity (species, habitats, oceanographic
processes) and human activities in the marine environment: 85 marine species, 264 habitats
(shoreline, benthic and pelagic) and 38 human activities (presence/absence, with intensity
gradients for 15 of them). At least ten years were required to assemble these data sets. Step 3
developed an assemblage of three spatially nested planning unit layers (PUL): a fine-scale
highresolution PUL (0.2×0.2 km), a medium-scale medium-resolution PUL (1×1km), and a
largescale low-resolution PUL (10×10 km). Data layers from Step 1 were allocated to one of the
three PULs, in what we refer to as a ªscale allocationº process. Conservation targets
(quantitative expressions of conservation goals) were set to specify how much of each biodiversity
6 / 29
Fig 2. Key steps within the multi-scale marine spatial planning (MSP) process. Launch the MSP process (step 1), Describe present conditions (step 2), Analyse
present conditions (step 3), Explore future conditions (step 4) and Implement, monitor and adapt (Step 5). Generic steps were adapted and expanded from [
]. Number in white refer to the eleven-stages of Systematic Conservation Planning by [
] expanded from .
element (species, habitat types, oceanographic processes) should be protected. Targets were set
as a percentage of the distribution area of each element, and the target achievement within the
current MPA network was assessed. In Step 4 an efficient and practical spatial arrangement of
additional multi-resolution PUs was identified to meet all (currently unmet) targets in the
entire EEZ. The final network of MPAs maximised target achievement within sites that were
spatially clustered at all scales (i.e. fine, medium, large) while minimizing overlaps with other
human activities. Step 5 entails the implementation, monitoring and adaptation of the
conservation actions planned in selected PUs. This final step underway is described in the Discussion
section. All analysis were conducted using ArcGIS 10 (Environmental Systems Research
Institute, Redlands, California).
Step 1: Launch the spatial planning process
The objective was to develop a multi-scale vision for the marine spatial planning process
through cross-sectoral engagement with stakeholders. This step encompasses the first four
SCP steps [
22, 24, 25, 32
], i.e. scoping and costing the planning process; identifying and
involving stakeholders; describing the context for conservation areas; and identifying conservation
goals. To initiate the process, individual interviews with specialist scientists and small
workshops (less than 20 participants) with relevant user groups were held to identify, collect or
build spatial datasets on ecological features and human activities, for example, coral reef
distribution, rocky reef location and shore angling effort. This initial step identified realistic
7 / 29
expectations for the conservation plan and assessed the legal mandates and political capacities
of the implementing institution.
Public participation was encouraged to set up a collective vision and translate it into
measurable objectives. Since its inception, SeaPlan has directly involved over one hundred stakeholders
operating at different scales in the marine and coastal domain, including the commercial fishing
sector, the recreational sector (e.g. fishers, scuba divers, surfers, etc.), conservation scientists,
MPA managers, conservation non-government organisations (NGOs), urban planners, and
other government sectors. Participants were kept informed of the spatial planning steps and
preliminary products through regular emails and involvement in collaborative workshops. A
larger annual workshop was held each year during the planning process from 2009 to 2012. In
2009, the multi-scale conservation assessment results and the site prioritisation method were
presented at a a two-day public workshop to over 100 participants. In 2010, the outputs of the
spatial prioritisation process were presented and discussed with a core group of 50
multi-sectoral stakeholders, MPA managers and scientists. Preliminary maps of proposed MPAs were
presented during this meeting, and the outputs were adjusted to accommodate additional
information and stakeholder interests. In 2011 a meeting was held to discuss final changes to the
conservation plan and set of proposed new MPAs (50 participants). In 2012, a final stakeholder
workshop was held to discuss processes for future updates to the conservation plan and
strategies to implement a prioritised subset of selected proposed MPAs (50 participants).
Step 2: Describe present conditions
Collection of data on social and ecological systems. Individual interviews with specialist
scientists and small workshops (less than 20 participants) with relevant user groups were held
to identify, collect or build spatial datasets on ecological elements and human activities, for
example, coral reef distribution, rocky reef location and shore angling effort. Various types of
spatial data were collected or collated at different scales on the administrative, ecological and
social systems. Administrative data describe the spatial distribution of management regimes in
the seascape (MPAs for instance). Ecological data described habitats (water masses,
oceanographic processes, shoreline habitats, coral reefs, rocky reefs, submarine canyons and estuaries)
and species distribution (fish, turtles and cetacean) (S1 Appendix). Data on social system
focused mostly on human uses (mapping of activities) (S2 Appendix).
Existing and proposed MPA boundaries were mapped. All these protected areas comprise
coastal and marine areas between the vegetation line and the limit of the EEZ. Existing MPA
boundaries were extracted from the South African Government Gazette (Marine Living
Resources Act). Spatial data were collected for other protected areas that overlapped
marginally with the planning domain: terrestrial protected areas (data from EKZNW), and Admiralty
reserves and State Land (data provided by the KZN Provincial Planning & Development
Commission). The boundaries of six proposed MPAs were mapped with MPA proposers.
Modelling spatial distributions of biodiversity elements and human activities. A range
of ecological modelling techniques and tools were used to model the distribution of habitats
and species (time series analysis, cluster analysis, boolean rules, etc.). S1 Appendix describes
the ecological data that were collected or modelled (biodiversity conservation elements). S2
Appendix describe the method for mapping the presence/absence or the intensity of 38 human
activities that can impact marine ecosystem structure and function negatively.
Step 3: Analyse present conditions
Planning domain delineation. The planning domain stretched from the coastal
vegetation line, delineated from aerial photography, to the EEZ (Maritime Boundaries Geodatabase
8 / 29
]). The planning domain was first divided into four depth zones (DZ): DZ one (vegetation
line to -2 m depth), DZ two (-2 m to -30 m), DZ three (-30 m to -200 m) and DZ four (-200 m
and deeper) (Fig 3). The -30 m and -200 m depth cuts were based on the following rationale:
-30 m is the offshore limit of the deep photic zone in this region, where the influence of waves
and light attenuates [
], and very little macroalgae occurs below 30 m. Additional biological
rationale for this depth cut is that the sardine run occurs to this depth, and coral reefs occur up
to about 30 m (although non-reef corals do occur in deeper subtidal habitats). The 200 m
depth is the approximate offshore limit of the continental shelf in the Province.
Multi-resolution planning units layers. Three PULs were generated with squares at three
different resolutions (Fig 3): the high-resolution PUL (0.2×0.2 km) covered DZ one and two,
the medium-resolution PUL (1×1 km) covered DZ two and three, and the
low-resolutionPUL (10×10 km) covered DZ three and four. Each of the PULs was then intersected with the
boundaries of the existing network of conservation areas. This network included: MPAs (with
internal zonation as described below), terrestrial protected areas, admiralty zones and state
land. Depth contours were extracted from the bathymetry layer described previously. PUs
smaller than 5,000 m2 were then dissolved into adjacent polygons, but keeping MPA
boundaries, depth zone boundaries and the planning domain boundary intact. In addition, the three
PULs were combined to form a unique ªmixed-resolutionº large-scale PUL required to
perform the conservation assessment. The highest resolution PU taking precedence where there
was overlap.This mixed-resolution PUL contained 64,189 planning units. It was not used in
the site priorisation process that articulates three distinct PULs (fine-, medium- and
Allocating data to multi-resolution planning unit layers. Data were verified and
converted into raster or vector layers in ArcGIS. Spatial layers for each biodiversity element were
overlayed with the PU layer of the appropriate resolution and aerial coverage (Fig 4). Thus, the
0.2 km PUL was overlaid with fine-scale data along and close to the shoreline (e.g. the
shoreline habitat data); the 1 km PUL with medium-scale data on the shelf (e.g. reefs), and the 10
km PUL with large-scale data offshore (e.g. oceanographic eddy distribution data). This ªscale
allocationº of data to PUs of appropriate resolution was a key step in the structuring of our
multi-scale planning process. The mean cost value from the cost map was calculated for each
PU at all three levels of resolution, based on the assumption that pressures are cumulative and
can potentially affect marine ecosystems at all operational scales.
Assessing cumulative anthropogenic pressures. Data on the spatial extent and intensity
of drivers of change (i.e. human activities) were combined into an index of cumulative
anthropogenic pressure, which used an expert-driven weighting approach to assign higher values to
activities with known high impacts (e.g. trawling), and lower values to activities with lower
impacts (e.g. scuba diving) (see S2 Appendix). This method assumes that high cost values are a
proxy for actual impacts, as well for areas where high socio-economic costs would be incurred
in the event of conservation management measures being instituted. The weight was attributed
to each of the 38 individual human activities (weights from 5 to 21.5) by a panel of experts
according to their relative assumed impact on marine ecosystems. Layers were then
weightsummed to create a single cumulative anthropogenic pressures map (with values linearly
scaled from 0 to 100) that was linked to the main conservation elements (species, habitats and
oceanographic processes) and latter used as a cost map in the priority sites selection
Setting and assessing conservation targets. Conservation targets were set under the
authority of Ezemvelo KZNWildife (EKZNW), the conservation authority for South Africa's
Kwazulu-Natal (KZN) Province. Targets were set to comply with the objectives of the
Convention of Biological Diversity (CBD), using IUCN status and through discussions with
9 / 29
Fig 3. Multi-resolution nested planning units layers. The planning domain is divided into fours depth zones (DZ) and are covered by three partially overlapping
multi-resolution planning unit layers. The high-resolution planning units layers (red) covers DZ 1 and 2 (vegetation line to -30 m). The medium-resolution planning
units layers (blue) covers DZ 2 and 3 (-2m to -200 m). The low-resolution planning units layers (yellow) covers DZ 3 and 4 (-30 m to the limits of the Economic
Exclusive Zone). ªVeg. lineº stands for vegetation line and ªEEZº for Economic Exclusive Zone.
internationally recognized south african experts. Stakeholders validated conservation targets
during SeaPlan public workshops but did not contribute to build them.
The Aichi-CBD targets state that by 2020, at least 10 per cent of coastal and marine areas
should be conserved through effectively and equitably managed, ecologically representative
and well-connected systems of protected areas and other effective area-based conservation
measures, and integrated into the wider seascapes. Previous international policy statements
issued by the World Summit on Sustainable Development (2002) and the World Parks
Congress (2004) set a target for governments to protect 20±30% of all marine habitats under their
jurisdiction (i.e. including Exclusive Economic Zones). In order to minimize the risk of a
fishery collapsing owing to overexploitation, and recruitment overfishing, early calculations
suggested that at least 20% of the spawner biomass of the unexploited stock is required to ensure
]. This does not necessarily directly translate into a spatial requirement but
has been used as the basis on which the World Parks Conference recommended that at least
20% of the waters within a country's jurisdiction be protected . Consequently, a baseline
target of 20% was used for most of our conservation planning analyses.
The initial status of PUs was referred to as ªavailableº or ªconservedº. Within KZN, MPAs
(or zones within MPAs) are classified as either Types A, B or C. There is currently no National
standard for MPA zonation, but within KZN a consistent zonation is applied across the 3
10 / 29
Fig 4. Example of multi-resolution spatial data. Maps showing (from A to I) extracts of the multi-resolution spatial distribution data for habitats, species and human
uses at fine-, medium- and large-scale. Colors of map frames indicate the high-resolution planning units layers (red), the medium-resolution planning units layers (blue)
and the low-resolution planning units layers.
existing MPAs. Type A MPAs are similar to no-take sanctuary areas with no extraction
allowed, Type B MPAs allow restricted extraction activities that are compatible with the
objectives of the MPA, and Type C MPAs allow additional regulated human uses. Note that only
PLOS ONE | https://doi.org/10.1371/journal.pone.0192582
Types A and B were considered to contribute to meeting conservation targets within SeaPlan,
because Type C MPAs currently do not provide any more biodiversity protection than areas
A baseline target of 20% of a biodiversity element's distribution area was applied to all
elements. This baseline target was adjusted for some elements (by adding an additional 0±80%)
based on their rarity, endemism, specialisation, localised distribution, and intrinsic
vulnerability, as advised by experts. Final targets for each element had to be met equally within Types A
and B MPAs (i.e. half the target in each Type) unless otherwise stated by experts (S3
Appendix). The existing MPA network in the study area was overlaid on all biodiversity elements to
determine how much of A and B targets for each element were already met in Types A or B
MPAs. Then, for each biodiversity element, the proportion of target achieved/unachieved was
Targets for habitats were fixed at 20% for large-scale marine habitats, rock reefs and
finescale shoreline habitats (except for foredune habitats, surf-zone habitats, and submerged and
emerged rocks which had 10% targets owing to the fact that they are common and widely
distributed and considered more resilient than the other habitats). Coral reef targets ranged from
80% to 100% owing to their uniqueness, low resilience, high vulnerability and exposure to
direct and indirect threats in the KZN region [
]. Canyon targets were set as follows in each
bioregion (Natal and Delagoa): one canyon with 100% Type A protection and another with
100% Type B protection. Canyons were manually selected based on their proximity to existing
MPAs. Targets for the modelled marine influence of 17 estuaries (of a total of 74) were set to
20% (of their area) within Type B protection (both estuaries in the Delagoa bioregion, and 15
of the 72 estuaries in the Natal bioregion). These 17 estuaries were identified for inclusion in
SeaPlan by the Estuaries Spatial Conservation Plan [
], based on their irreplaceability value.
Baseline targets for fish and cetacean species were fixed at 20%, with an adjustment target for
each species based on the following criteria (additional 0±10% per criterion): endemicity,
rarity, specialisation, localised distribution and intrinsic vulnerability [
]. Targets for areas
supporting semi-permanent oceanographic processes (zones with frequent fronts or gyres) were
set to only 10% of their distribution, but given that these areas are important for pelagic
ecosystems and are species aggregation sites, we assigned the full target to Type A MPAs. The target
for turtle nesting sites was set to 80% divided into 20% for Type A and 60% for Type B
Step 4: Explore future scenarios and solutions
Decision support system. We used MARXAN software [
] to identify networks of
additional protected areas (i.e. reserves) that best achieved conservation targets. MARXAN was
specifically designed to compute combination calculations for complex site priorisation
problems. The software aims to solve the ªminimum set reserve designº problem [
]. In this study,
the use of Marxan contributed to ensure an unbiased and controlled selection of planning
units along the multi-scale spatial planning procedures. Nevertheless, the multi-scale method
exposed remains independant from the use of Marxan or any other spatial optimisation
software. MARXAN algorithm uses a mathematical approach called simulated annealing [
identify near-optimal reserves portfolios (i.e. networks of protected areas) that minimize a cost
function . The cost function in MARXAN is the sum of an overall penalty factor (a cost is
allocated if a conservation target remains unachieved) with an overall planning units cost. A
planning unit cost can be a measure of any of its aspects, such as its area or perimeter, the
probability of being impacted by human activities, or the opportunity cost resulting from its
]. Each run of MARXAN produces a near-optimal reserve portfolio named solution.
12 / 29
MARXAN produces two outputs: the ªbest solutionº (i.e. the less costly solution calculated
along the runs) and the ªsummed solutionº (i.e. the cumulated number of selections of
planning units as part of a solution along the runs). The number of runs and the calculation of
costs can be parameterized heuristically by the user (see [
] for more details on MARXAN
good practices). In our study MARXAN parameters were tuned with members of the Scientific
Services unit of Ezemvelo KZN Wildlife.
Multi-resolution site selection procedure. A bottom up site selection procedure was
designed to identify conservation areas incrementally from the high-resolution PUL to the
low-resolution PUL. In this procedure a PUL inherits mandatory planning units from the
lower scale PUL. First, Marxan was run in the high-resolution PUL to select additional
conservation areas that achieve targets for the fine scale biodiversity elements (Fig 5). Next, we ran
Marxan on the medium scale elements with the medium resolution PUL (Run 2), keeping the
mandatory sites from Run 1. Mandatory sites in Run 2 included MPAs and planning unit
overlapping (>50% treshold) with the best solution identified in Run 1. The process was
repeated for large-scale elements with the low-resolution PUL (Run 3).
The fine scale plan was run first because there are more constraints in the coastal and
shallow areas given the proximity effects of increased human activity, whereas the large-scale plan
encompasses the pelagic domain where more management options are available for
conservation. Runs 1, 2 and 3 were performed to meet A targets and a second time to meet B targets
(where the ªbest solutionº for meeting A targets was retained, to ensure its inclusion in the
final solution). The results of the fine, medium and large-scale plans were combined into the
single multi-resolution PUL reflecting the ªselection frequencyº and ªbest solutionº results for
all PUs. The conservation penalty factor (PF), cost per PU, and the boundary length modifier
(BLM), all required by Marxan, were assigned as follows: the PF was set very high (10 million
per biodiversity element) to ensure that all conservation targets were met; the cost of each PU
used the cumulative anthropogenic pressures map as previously described; and the BLM varied
in relation to the PU resolution. A high BLM promotes compactness in the final selection of
PUs. The BLM was set as follows for each PU layer (fine, medium, large scale):
BLM = 100 × PU resolution (in metres).
Comparing different methods of addressing multiple scales in spatial planning
For the purpose of this study, the outputs of four different methods of addressing scale in
spatial planning were compared (Fig 6). The comparisons were made in terms of total area and
spatial distribution patterns of selected areas required to meet conservation targets (in Type A
MPAs). Method 1 was the multi-resolution scale-linked (ML) method used in this study,
where planning outputs from finer scales were inherited by broader scale. Method 2 was the
same as Method 1 except that planning outputs from finer scales were not inherited by broader
scale PUs (i.e. the multi-resolution scale-unlinked method, MU). Method 3 used only one
PUL (the low-resolution 10×10 km PUL) and as a result, no finer scale planning outputs could
be passed to broader scales (i.e. the single resolution scale-unlinked method, SU). Method 3 is
the most commonly used methodology in spatial conservation planning. Finally, priority
conservation areas identified using Method 1 (i.e. the ML) were compared with a large-scale
expert-driven MPA proposal (Method 4) developed by experts from the Oceanographic
Research Institute (ORI), independently from the SeaPlan process (Fig 5). The implementation
of the decisions drawn from the multi-resolution scale-linked (ML) SeaPlan project in the
study area are currently underway. This aspect is discussed in the Discussion section.
13 / 29
Fig 5. The multi-resolution site selection procedure to develop and coordinate the fine-, medium- and large-scale conservation plans. Maps showing the planning
units status (A, D, G), the selection frequency of planning units (B, E, H) from MARXAN runs and the `best solution' identified (C, F, I). Broader-scale planning units
inherit a mandatory protection status if 50% of their area is selected as part of the ªBest solutionº at finer scale. Colors of map frames and map extent indicate the
high-resolution planning units layers (red), the medium-resolution planning units layers (blue) and the low-resolution planning units layers (yellow). Dotted green lines
indicate the MPA proposal made by ORI.
Current target achievement
Overall, existing MPAs in the study area are biased toward coastal areas and the northern
KZN Delagoa Bioregion (Fig 1). Of 390 biodiversity elements assigned a A target, only 48
(11%) had their target achieved in the current MPA network and only 83 (24%) of 338
biodiversity elements with a B target had their target achieved. Target achievement of biodiversity
elements is summarized in Table 1 (per main categories) and S3 Appendix (per element).
None of the 10 large-scale marine habitats have their A and B targets met. Areas supporting
semi-permanent oceanographic processes (eddies, chlorophyll-a fronts and sea surface
temperature fronts) located on the outer margin of the continental shelf have no protection. On
the continental shelf, only three of nine rocky reef classes meet their conservation targets (both
A and B). With only 15% (on average) of their area protected within Type A or B MPAs, the
four sub-marine canyons targeted in SeaPlan are poorly protected. Sixteen of 27 coral reef
classes achieve A targets. Worth noting is that these 16 coral reef classes have only partial
protection within Types B and C zoning of the same MPA. Along the shoreline, only 21 of the 74
14 / 29
Fig 6. Three methods for addressing scale in spatial conservation planning. (A) The multi-resolution scale-linked (ML) planning method used for SeaPlan is
described in this study (dashed lines arrows indicate inter-scale linkages), (B) The multi-resolution scale-unlinked (MU) planning method is similar but without any
transfert of information among planning units layers (PUL). (C) The single-resolution scale-unlinked (SU) method uses only one low-resolution planning unit layer.
fine-scale habitat classes have their targets met by the current MPA network, mostly within the
iSimangaliso MPA (St. Lucia and Maputuland) in the Delagoa bioregion. Only one of the six
sandy shore classes (i.e. intermediate sandy shore) achieves its Type B target. Only one of the
17 targeted estuaries meets its target, namely the Kosi River estuary in the St. Lucia MPA. The
15 estuaries located in the Natal bioregion have no protection.
Categories of biodiversity elements are allocated to a planning unit layer (PUL). For each
category of biodiversity elements, the table shows the number of targets achieved and the
number of targets set. The average proportion of current distribution area of biodiversity elements
in MPA (type A, B and C) is provided per category of biodiversity elements. Targets are
expressed as a percentage of the current distribution area.
None of the 66 species of fish or 14 shark species included in this analysis achieves their
conservation targets in existing MPAs. On average, 10% of the distribution areas of 45 fish
species with mapped distribution areas [
] are included within Type A or B MPAs. A detailed
analysis of the 21 species of fish with mapped Species Life Cycle Envelopes (SLICEs) shows
that sites supporting key life stages are insufficiently protected [
]. On average, only 3% of
fish nursery distribution areas are currently protected within Type A MPAs. Apart from the
spatial extent of the ªmigration pathway to spawning areaº of Petrus rupestris that is included
within the iSimangaliso MPA, a very small proportion (less than 2%) of the migration
pathways that spatially link different fish life cycle stages sites is currently protected. Results for the
14 species of sharks reveal similar trends with less than 8% of their distribution areas located
within Type A or B MPAs. Two noticeable exceptions are Carcharhinus obscurus (26% of its
distribution area falls within Type A or B MPAs) and Carcharias taurus (32% of its mating
area falls within Type A or B MPAs). Turtle nesting sites are well protected with 74% of their
distribution area falling within Type A or B zones of the iSimangaliso MPA. Turtle
distributions at sea were not considered in this analysis. Less than 6% of cetacean distribution areas
are covered by Type A or B MPAs, and only 16% of the migration pathway of the humpback
whale is currently protected (within the iSimangaliso MPA).
Index of cumulative anthropogenic pressures
The mean value for the index of cumulated anthropogenic pressures across the entire study
area is 15 (standard deviation = 6, minimium = 0, maximum = 100,) (Fig 7A). Areas with high
15 / 29
Area in Area in
MPA type A MPA type B
Target Number of target Number of target
B A achieved/ B achieved/
(%) unachieved unachieved
6% 0/7 0/3
with a target
index values ( 40) are distributed along the shoreline close to the main coastal cities (i.e.
Durban and Richards Bay), and in the vicinity of harbours and industrial pipelines, and in easily
accessible coastal areas. High pressure areas are also located offshore in zones targeted by
industrial fishing vessels and along major shipping routes linking Durban to ports located in
East Asia. Not surprisingly, low pressures areas (close to 0) are located within Type A and B
MPAs. It is noticeable that pressure scores inside Type C MPAs are higher than within
adjacent areas located outside the MPA [
], due to the presence of attractive recreational
infrastructures (e.g. parking access and boat launching sites for instance).
Proposed priority areas for conservation
This section presents the best solution for a spatial network of protected areas identified using
the multi-resolution scale-linked (ML) planning method to achieve the full set of conservation
targets (Fig 7B, Table 2). Currently only 0.1% of the planning domain is made up of Type A
MPA zones. A total of a further 11.3% (26,341km2) of the planning domain is selected as
priority areas to achieve Type A targets. Only 16.8% of the shallowest depth zone 1 is selected;
whereas greater than 40% of depth zones 2 and 3 were selected (43.5 and 48.6%, respectively).
Only 9.8% of depth zone 4 is selected, reflecting the lower targets for habitats in the deeper
Area of planning domain zones (in km2 and in percentage) selected using three alternative
multi-scale spatial planning methods: single-resolution scale-unlinked (SU), multi-resolution
scale-unlinked (MU) and multi-resolution scale-linked (ML). The ML output is used as
benchmark for comparison. DZ 1, 2, 3 and 4 are depth zones from the inshore to the offshore with
17 / 29
Fig 7. Maps of index of cumulated anthropogenic pressures and priority sites for conservation. The map of index of anthropogenic
pressures indicates areas under human-induced impacts from blue (low impact) to red (high impact) (A). The map of selection frequency
(maximum value) along MARXAN runs (at fine, medium and large-scale) indicates higher priority areas for conservation from blue (low
priority) to red (high priority) (B).
the following limits: Vegetation line, -2m, -30m, -200m and outer boundary of the Exclusive
Economic Zone. Outputs were obtained using the MARXAN best solution for A targets.
Priority areas were identified incrementally for the fine-, medium- and large-scale PUL's.
The fine-scale PUL covers an area of 1,786 km2 and includes depth zones 1 (140 km2) and 2
(1,646 km2) (Fig 3), and consists entirely of high resolution PUs. A proportion of 12% of Zone
1 has Type A MPA protection. In the conservation plan, an additional 17% is selected to
achieve Type A conservation targets for estuaries and fine-scale shoreline habitats. These
additional priority areas are selected on sections of coastline with a high diversity of shoreline
habitat types, and in the vicinity of an estuary (Fig 5, S1 Appendix) and a low impact score (S2
Appendix). Some shoreline habitat types are rare and this causes additional isolated sites to be
selected along the shoreline. A small proportion (5%) of zone 2 has Type A MPA protection
and 65 km2 (4%) is selected as part of the `best solution' in the high-resolution PUL, including
62 km2 of existing Type B protection located within the iSimangaliso MPA (the additional 3
km2 required is to achieve conservation targets for the marine extent of estuaries).
The medium-scale PUL covers an area of 8,762 km2 and includes depth zones 2 and 3 (Fig
3). This PUL overlaps with the high-resolution PUL in zone 2. In this zone 2, 8% of the area is
pre-selected for the conservation plan run, because a number of planning units overlap by
more than 50% with the ªbest-solutionº identified by the conservation plan run at the finer
scale. In addition to this 8%, another 35% is selected to achieve Type A conservation targets. In
zone 3, 163 km2 (2% of this zone) is covered by existing Type A MPA and an additional 18% is
selected to achieve Type A conservation targets. At this stage of the site selection procedure,
conservation targets are achieved for the following medium-scale elements: rocky reefs, coral
reefs, submarine canyons, large estuaries, fish and cetaceans species (Fig 5, S1 Appendix). A
visual analysis of the spatial patterns of priority areas shows that the northern part of the Natal
(km2) (% of
(km2) (% of
Difference to area
selected in ML
(km2) (% of benchmark area)
19 / 29
Bight (North of Durban) is a focal area to achieve conservation objectives for elements
included in the medium-scale PUL (Figs 4, 5 and 6B). This area combines a high diversity of
fish, coral reef habitats and the influence of the Mzimkhulu and uThukelaRivers. In the south,
close to Port-Shepstone, the spatial clusters of priority areas linking the shoreline to the -200 m
depth line offshore are explained by the proximity of targeted estuaries, submarine canyons
and higher fish species richness (Figs 4, 5 and 6B, S1 Appendix).
The large-scale PUL covers an area of 224,985 km2, from the -30 m depth line up to the
offshore limit of the EEZ (zones 3 and 4 in Fig 3). Currently, there are no MPAs in zone 4. The
large-scale PUL overlaps with the medium-scale PUL in zone 3 and sites selected from the
medium-scale PUL cover an area of 1,087 km2 (15% of this zone). Selected additional sites for
conservation cover an area of 25,372 km2, including 3,229 km2 in zone 3 (46% of this zone)
and 22,143 km2 in zone 4 (10% of this zone). Priority areas in zone 4 are associated with
semipermanent eddies (Figs 5 and 6B, S1 Appendix). Priority areas in zone 3 are spatially
aggregated on the northern extent of the Natal Bight, expanding southward to integrate areas
supporting semi-permanent sea surface temperature fronts bordering the landward side of the
Aghulhas current (Figs 5 and 6B, S1 Appendix).
Comparison of multi-scale planning methods
The SU, MU and ML methods select 27,945 km2, 24,938 km2 and 26,341 km2 for conservation
respectively (Table 2, Fig 6). Using the ML method as a benchmark, the SU method selects
more area (1,604 km2, +6%) and the MU method less area (-1,403 km2, -5%) to achieve the
same conservation targets. As would be expected, the SU method selects large areas in coastal
zones owing to the large size of planning units (10×10 km) where the MU and ML methods
have smaller planning units (0.2×0.2 km and 1×1km) that better match fine and medium scale
distribution data of high and medium resolution. For instance, along the coastline, the SU
method selects 39% of zone 1, versus only 17% using the MU and ML method. With the ML
method, the mandatory selection of finer-scale PUs adds these PUs to those needed to achieve
conservation targets at the broader scales in areas that may not match spatially. While
requiring slightly more area to achieve targets compared to the MU plan, the ML plan ensures a
spatial continuum of priority areas linking the inshore to the offshore domain, which was an
initial objective of SeaPlan (Fig 7B). This continuity is not achieved in the MU plan with a
more spatially scattered `best solution'. We interpret the additional area selected in the ML as
the spatial cost of linking inshore and offshore conservation priorities.
Priority areas for conservation identified using the ML method were compared with a
proposed MPA delineated by marine conservation scientists at ORI, who used a more intuitive
expert knowledge-driven, and not a systematic conservation planning approach. Fig 5 and Fig
7B show good spatial overlap between the initial ORI proposal and the priority areas (best
solution and selection frequency) identified by SeaPlan on the uThukela banks using the ML
method. However, the SeaPlan output was generally more spatially efficient representing a
subset of a greater area with core priorities within the focal area highlighted.
Why multi-scale spatial planning?
The need to develop multi-scale approaches to marine spatial planning has been advocated to
account for mismatches between the nested and multiple scales of biodiversity pattern and
process variation through space, the disparate scales over which different anthropogenic
impacts operate, and the scales (local to global) at which interventions are proposed. In the
inshore, particularly the shoreline, scales of change in biological patterns are generally much
20 / 29
finer than offshore, and stakeholders are strongly invested in local issues and interested in
patterns of the biodiversity that are clearly visible to them. Accounting for, and generating action
to respond to, the many diverse local conservation issues within the development of broader
scale plan, without obfuscating them by absorbing them in lower resolution information
packages, is a crucial but complex task. To address this challenge, this study developed and tested a
method for identifying priority areas for conservation simultaneously and systematically at
fine, medium and broad scales, across the whole planning domain from the shoreline to the
offshore limit of the EEZ (covering coastal and offshore environments), while taking into
account the nature and scale of anthropogenic drivers of ecosystem change. This multi-scale
multi-resolution spatial planning method was developed and tested in the preparation of a
marine conservation plan for the eastern seaboard of South Africa (the SeaPlan project), but
provides an approach of general relevance.
Like many countries, previous marine conservation assessments and plans in South Africa
have been undertaken independently at different scales (e.g. national, provincial, local), using
data of different resolutions aggregated to a single resolution, and addressing various
components of the marine environment (e.g. bioregions, inshore/offshore, benthic/pelagic) and
human use patterns (e.g. fishing pressure) seperately. A national marine assessment providing
a very broad framework for biodiversity conservation planning exists, but it is limited by the
scarcity of consistent fine-scale data that cover the whole planning region [
]. As is the
trend world-wide, the existing MPAs within the study area are strongly biased towards the
coast with offshore protection being virtually non-existent. The extent and distribution of the
coastal MPAs is also biased, with high protection in the northern Delagoa bioregion and little
protection in the Natal bioregion. Unsurprisingly, the highest levels of anthropogenic
pressures are experienced along the shoreline, close to the main cities (Durban and Richards Bay)
(Fig 7A). This has two implications: first, many options for marine conservation are still
available in the offshore domain (but these options are starting to foreclose owing to escalating
offshore activities) and, second, inshore conservation actions are severely spatially constrained.
Priority areas for conservation shown on Fig 7B can serve as a basis for future MPA expansion.
In comparison with the offshore, the implementation of shoreline and inshore marine
protected areas require finer scale identification of spatial priorities (to improve efficiency and
limit conflict with users in a crowded space), more rapid action and opportunistic timing (to
get ahead of looming fore-closure on options), and the ability to demonstrate tangible linkages
between condition of biodiversity and benefits of protection (to garner local stakeholder
support). The multi-scale nature of our method, which explicitly identifies and assigns spatial
conservation priorities along the shoreline and the inshore first, embeds these priorities as key
drivers of the selection process for offshore spatial conservation priorities. This ensures that
the priorities in the area of the planning domain that has least options for protection and most
pressure are ªhard-wiredº into the plan up front, while spatial continuity is achieved by
adjacent selection of priorities in the areas where more options exist for meeting conservation
targets, and where there is greater scope for adaptation.
Improving the spatial efficiency of marine zoning
Efficiency in space allocation for different uses of the ocean is an important consideration in
an ocean that is becoming crowded with competing activities [
]. Efficient spatial planning
has the added advantage of facilitative achievement of efficiencies in deployment of
management capacity. We found that our new ML spatial planning approach was more efficient
compared to a conventional SU method, and in our case study 6% less area was selected to achieve
the same conservation targets. This result is owing to the selection of only the smaller
(high21 / 29
resolution) priority planning units in the ML method, compared to selection of the larger (low
resolution) planning units within which small-scale elements are absorbed in a SU plan.
Compared to a scale-unlinked version (MU) of the multi-resolution approach, the ML plan
required more area (5%) to meet conservation targets. This apparent reduced spatial
inefficiency is however mitigated by the gains in terms of ecological connectivity achieved between
identified priority conservation areas that occur across different spatial scales.
To promote efficiencies, many spatial conservation planning exercises entrench existing
MPAs as mandatory for selection, thereby reducing the selection of new areas to achieve targets.
In the SeaPlan case-study, the ML approach reinforced this effect, because existing MPAs in the
planning area are all coastal. By prioritising expansion of the existing coastal MPAs over the
creation of new and isolated MPAs, the positions of the coastal MPAs strongly influenced selection
of offshore priority areas to achieve targets. The resultant ML spatial plan meets most
conservation targets by expanding existing coastal MPAs into the offshore environment (Fig 6).
In the study area, some conservation targets can be addressed simply by rezoning
appropriate areas of current MPAs (for instance the coral reefs in the Delagoa region). Additional
coastal protection is required in the south of the Natal bioregion, again best achieved adjacent
to the existing MPAs. In the offshore, the plan identified unique areas that are distant from
existing MPAs, and merit conservation management. In the southern offshore, the area of
interest is defined by the Protea Banks reef system and associated deep canyons on the
continental shelf edge, where the dominant southward flowing warm Agulhas current moves close
inshore. Further north, still within the Natal bioregion, an additional large MPA should be
implemented to conserve a spatial ecological continuum from the uThukela River up to the
offshore limit of the KZN Bight, thus linking up with the semi-permanent sea surface and
chlorophyll-a fronts associated with the Agulhas current (Fig 5). The resulting MPA network
should contribute to the persistence of ecological processes across ecological realms, along the
terrestrial-marine-freshwater continuum [
Strenghtening political buy-in and social acceptance related to marine conservation
Adopting a ML method reduced political risk (loss of political support) owing to regular
interactions with decision makers and stakeholders across spatial scales. The multi-scale approach
ensured a strategic and stable positioning of the project on the political agenda at national,
provincial and local scale over years, throughout a constantly changing social and political
landscape. This was also made possible through a deep rooting of the SeaPlan project within a
single conservation planning agency (EKZNW), that had the required capacity at a provincial
level and was committed to contribution to national objectives.
Once the results of SeaPlan emerged, EKZNW prepared a MPA Expansion Plan based on
the results, and motivated to the national Department of Environmental Affairs to start the
process of proclaiming new and expanded MPAs. The national Department supported an
initial process of stakeholder engagement to develop proposals for two new MPAs, over the
uThukela Banks and the Protea Banks. This process was then absorbed in 2014 into a national
process initiated by the Office of the Premier to fast-track the unlocking of the Ocean
Economy in South Africa (i.e. Operation Phakisa). Phakisa was aimed at stimulating commercial
industries, but also provided an important opportunity to articulate the potential negative
impacts and risks of rapidly emerging industries such as mining and aquaculture, and to
consider mitigation measures. Phakisa gained political and public support to initiate the
establishment of an expanded network of MPAs, to increase offshore protection within South African
waters from 0.4% to 5% within 3 years.
22 / 29
Of the twenty-one areas included in the Phakiza proposed national MPA network, which
was refined from existing products in very short period (six weeks) in 2014, four had been
identified by the SeaPlan project. The rapid devlopment of this proposed MPA network design
was possible owing to the high state of technical readiness amongst the team of conservation
planners working collaboratively across scales, as well as to the availability of existing spatial
products developed collectively by the country's marine conservation planners over the
preceding decade [
60, 63, 64
]. It was also facilitated by an established dialogue between national
and provincial government agencies, because the SeaPlan priority areas had already been
submitted to and supported by national government and embedded in the national planning
The concurrence and synergies between the coarse large-scale national MPA expansion
priorities, and those identified by SeaPlan at a finer scale, meant that there was confidence in
including overlapping areas such as Protea Banks and uThukela in the Phakisa MPA network.
In addition, the expansion of the Aliwal Shoal and iSimangaliso MPAs were also validated and
included in the Phakisa MPA network. These successes demonstrated the value of working at
different scales, both with respect to technical analyses and decision-making spheres.
Although this study doesn't emphasize the participation of stakeholders, they were strongly
involved in the SeaPlan planning process. This participation is vital for the development and
implementation of conservation plans and it guarantees its legitimacy [
4, 8, 26
]. Among the
benefits of the multi-scale approach, is that it provides for understanding by stakeholders of
conflicts between and synergies amongst their spheres of interest and influence, and those of
other stakeholders who they previously regarded as unrelated or unapproachable. We have
found that stakeholders are more strongly invested in knowledge at a scale relevant to their
specific activity, for example shore users showed a strong interest in localised intertidal areas
that were clearly visible and known to them (at a scale of 100 m-10 km), whereas boat-based
fishers, while interested in the shoreline from the perspective of safe launch sites, had a larger
area of interest (10±100 km) and were less interested in detailed elements at the scale of
hundreds of metres. Similarly, further offshore trawlers (who operate at scales of >10 kms) and
seafloor mining (that operates at scale of >100 km) had overlapping areas of interest for their
activities but were not invested in the fine scale features of the plan. By analysing and
displaying the data and plan at multiple scales, such users were able to articulate and engage with one
another about the relative impacts of each other's activities on one another as well as proposed
management actions. This facilitated dialogue about overlaps, conflicts and common interest,
and created stakeholder interest and alliances in broader scale planning issues, thus adressing
constraints identified by [
]. This interplay among a wide array of sea users operating at
different scales strengthened the SeaPlan spatial process.
Future uses and developments of the method
Conducting spatial planning across multiple scales involves additional costs to achieve
expected benefits, compared with single resolution spatial planning, specifically with respect to
the costs of time and expertise for data collection, analysis and stakeholder engagement [
The ML method used in this study involved more stakeholders (and at different levels) and
required more frequent and complex interactions with them. While we are currently unable to
translate these costs into monetary values, we believe that any additional costs need to be seen
in the context of the long-term benefits gained from the ecosystem services supported by cross
realm planning [
The implementation of the ML method, compared with other methods (SU and MU),
resulted in a better overall spatial fit of the proposed MPA network to biodiversity targets and
23 / 29
constraints across coastal and offshore marine realms [
]. The benefits of this method are
more easily assessed in terms of planning cost avoidance: SeaPlan tackled fine, medium and
broad scales within a single spatial planning process, thus avoiding the cost of three separate
spatial plans. In addition, the ML method made the best use of all available data and no time
was wasted in collecting data that were not used in the planning process. Comparison of the
ML plan with the expert-driven plan showed good congruence, although the ML-derived plan
was more conservative in the size of the area selected for protection. Whereas, the
expertmethod was applied only in a restricted area generally considered to be of conservation
significance, the ML method had the advantage of being systematically replicable over the entire
planning domain, and its selection procedure was explicit.
The ML method contributes to building technical solutions to scale mismatch problems
that impede effective implementation of spatial plans [
4, 5, 6
]. For instance, the difficulty of
mixing Integrated Coastal Zone Management and broader Marine Spatial Planning [
be conceptualized as a case of scale mismatche that could be addressed using the ML method.
The ML method is relatively simple and can act as a technical bridge to link distinct
conceptual, technical and social processes operating at different scales [
]. In a rapidly changing
world, this marine spatial planning approach addresses the urgent need for integrating coastal
and offshore conservation strategies [
1, 2, 3
]. Coordination across scale does not increase the
area selected to achieve targets by much, but it does support better choices in spatial allocation
of marine management actions.
Future developments of the ML method should focus on coupling the social and technical
dimensions of the multi-scale planning process [
]. Another direction to explore is the
development of routines and data formats within current GIS software and spatial optmization
tools to ease multi-resolution data management and analysis. The time invested in the
organisation, formatting and analysis of multi-scale multi-resolution data could then be invested in
more effective tasks. We also suggest that post-hoc assessments be done of marine spatial
plans, across scales, to ensure that they are self-consistent.
Multi-scale multi-resolution spatial planning is challenging. The methodology developed in
this study allows the identification of priority areas for conservation at fine, medium and large
scales within a single multi-scale multi-resolution spatial planning framework. The method
enables incorporation of diverse biodiversity elements, ecological processes and human use
patterns, that occur or operate across different scales and vary across their distribution at
different rates, while at the same time maintaining the data resolution integrity for these
elements. This approach reflects the multi-scale nature of coupled social and ecological systems.
Our multi-resolution scale-linked (ML) planning method delivers a more efficient spatial
solution for achieving conservation targets within marine protected areas, than a single
resolution scale-unlinked plan. Whereas the ML method requires slightly more area, when compared
with a multi-resolution scale-unlinked method, it promotes the ecological connectivity of the
future MPA network across spatial scales. Although the monetary costs of developing and
implementing a ML spatial plan compared to scale-unlinked methods were not quantified, the
ecological, social and political benefits (and cost avoidance) of the ML method are likely to
outweigh any additional costs that may be incurred for skills, data collection and social
This study provides a useful, repeatable and transferable approach that is applicable to
multi-scale spatial planning in general. It provides a methodological development that has the
potential to transform conservation planning practices and policies in many marine regions
24 / 29
worldwide. This very simple idea was technically difficult to implement, and time consuming,
but has delivered a plan that applies to the real world (by linking social and ecological systems
more tightly) and thus has increased potential to achieve political support and implementation
across different management spheres. Future technical developments of the method should
focus on easing the data management, and advancing the modelling and analytical processes,
exploring sensitivity to additional extraneous factors, and cost-benefit analyses. Further
development and refinement of methods to integrate social and ecological dimensions in
multiscale marine spatial planning is important, particularly if we wish to address real-world,
complex planning problems. These efforts should involve interdisciplinary teams, so that the
development of sector-specific outputs is avoided, and integrated multi-sector spatial plans are
enabled and promoted.
Our study shows the positive impact of a multi-scale and self-consistent approach to marine
spatial planning in terms of political and social buy-in at local ad national level. The overall
approach proposed in this study is a meaningful contribution to marine spatial planning in
general, i.e., not only in terms of methodology or from a conservation point of view. We hope
this study will contribute to help societies developing sustainable futures througout marine
spatial plans taking into account the increasing interactions among human activities and
biophysical phenomenon across multiple spatial scales, from earth-scale down to local-scale.
S1 Appendix. Data and methods for mapping biodiversity elements in the SeaPlan planning domain.
S2 Appendix. Data and methods for mapping human activities and the index of anthropogenic pressures in the SeaPlan planning domain.
S3 Appendix. List of biodiversity elements and conservation targets.
We thank Christopher A. Lepczyk and the two anonymous referees for their thorough reading
of the manuscript and their constructive and thoughtful comments which have helped to
improve the manuscript further. Authors acknowledge the African Coelacanth Ecosystem
Programme (ACEP). Erwann Lagabrielle and Amanda T. Lombard acknowledge the National
Research Foundation of South Africa (NRF-SA), the MASMA program funds and the
assistance of WIOMSA (Western Indian Ocean Marine Science Association). Jean M. Harris
thanks the Pew Fellows Program in Marine Conservation for support for this work. This study
was finalized in the framework of the MASMA MOZALINK project: Grant NÊ: MASMA/OP/
2013/02 ``Linking marine science, traditional knowledge and cultural perceptions of the sea in
the Mozambique Channel to build tomorrow's marine management using spatial simulation
tools and educational gameº. The publication of this research received support from the
European Union's European Maritime and Fisheries Fund (Grant NÊ: EASME/EMFF/2016/22.214.171.124/
Conceptualization: Erwann Lagabrielle, Amanda T. Lombard, Jean M. Harris.
25 / 29
Data curation: Erwann Lagabrielle, Amanda T. Lombard, Jean M. Harris, Tamsyn-Claire
Formal analysis: Erwann Lagabrielle, Amanda T. Lombard, Jean M. Harris, Tamsyn-Claire
Funding acquisition: Amanda T. Lombard, Jean M. Harris.
Investigation: Erwann Lagabrielle, Amanda T. Lombard, Jean M. Harris, Tamsyn-Claire
Methodology: Erwann Lagabrielle, Amanda T. Lombard, Jean M. Harris, Tamsyn-Claire
Project administration: Amanda T. Lombard, Jean M. Harris.
Resources: Erwann Lagabrielle, Amanda T. Lombard, Jean M. Harris, Tamsyn-Claire
Software: Erwann Lagabrielle, Amanda T. Lombard, Jean M. Harris, Tamsyn-Claire
Supervision: Erwann Lagabrielle, Amanda T. Lombard, Jean M. Harris, Tamsyn-Claire
Validation: Erwann Lagabrielle, Amanda T. Lombard, Jean M. Harris, Tamsyn-Claire
Visualization: Erwann Lagabrielle, Amanda T. Lombard, Jean M. Harris, Tamsyn-Claire
Writing ± original draft: Erwann Lagabrielle, Amanda T. Lombard, Jean M. Harris,
Writing ± review & editing: Erwann Lagabrielle, Amanda T. Lombard, Jean M. Harris,
26 / 29
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Impact on Marine Ecosystems. Science. 2008; 319: 948±952. https://doi.org/10.1126/science.1149345
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