African Journal of Marine Science
ISSN: 1814-232X (Print) 1814-2338 (Online) Journal homepage: http://www.tandfonline.com/loi/tams20
Spatial characterisation of the Benguela ecosystem for ecosystem-based management SP Kirkman, L Blamey, T Lamont, JG Field, G Bianchi, JA Huggett, L Hutchings, J Jackson-Veitch, A Jarre, C Lett, MR Lipiński, SW Mafwila, MC Pfaff, T Samaai, LJ Shannon, Y-J Shin, CD van der Lingen & D Yemane To cite this article: SP Kirkman, L Blamey, T Lamont, JG Field, G Bianchi, JA Huggett, L Hutchings, J Jackson-Veitch, A Jarre, C Lett, MR Lipiński, SW Mafwila, MC Pfaff, T Samaai, LJ Shannon, Y-J Shin, CD van der Lingen & D Yemane (2016): Spatial characterisation of the Benguela ecosystem for ecosystem-based management, African Journal of Marine Science, DOI: 10.2989/1814232X.2015.1125390 To link to this article: http://dx.doi.org/10.2989/1814232X.2015.1125390
Published online: 01 Mar 2016.
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Date: 03 March 2016, At: 07:32
African Journal of Marine Science 2016: 1–16 Printed in South Africa — All rights reserved This is the final version of the article that is published ahead of the print and online issue
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AFRICAN JOURNAL OF MARINE SCIENCE
ISSN 1814-232X EISSN 1814-2338 http://dx.doi.org/10.2989/1814232X.2015.1125390
Review Paper
Spatial characterisation of the Benguela ecosystem for ecosystem-based management SP Kirkman1,2, L Blamey3,4, T Lamont1,3, JG Field3,4*, G Bianchi5, JA Huggett1,3, L Hutchings3, J Jackson-Veitch3,4, A Jarre3,6, C Lett7, MR Lipiński8, SW Mafwila9, MC Pfaff1,3, T Samaai1,3, LJ Shannon3,6, Y-J Shin3,7, CD van der Lingen3,10 and D Yemane3,10 Branch: Oceans and Coasts, Department of Environmental Affairs, Cape Town, South Africa Animal Demography Unit, Department of Biological Sciences, University of Cape Town, Cape Town, South Africa 3 Marine Research Institute, University of Cape Town, Cape Town, South Africa 4 Department of Oceanography, University of Cape Town, Cape Town, South Africa 5 Food and Agriculture Organization, Rome, Italy 6 Department of Biological Sciences, University of Cape Town, Cape Town, South Africa 7 Institut de Recherche pour le Développement [IRD], UMR MARBEC 248, Sète, France 8 Department of Ichthyology and Fisheries Science, Rhodes University, Grahamstown, South Africa 9 Department of Fisheries and Aquatic Sciences, University of Namibia, Pioneerspark, Namibia 10 Branch: Fisheries Management, Department of Agriculture, Forestry and Fisheries, Cape Town, South Africa * Corresponding author, e-mail:
[email protected] 1
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The three countries of the Benguela Current Large Marine Ecosystem (BCLME), namely Angola, Namibia and South Africa, have committed to implementing ecosystem-based management (EBM) including an ecosystem approach to fisheries (EAF) in the region, to put in practice the principles of sustainable development in ocean-related matters. There is also recognition of the need for marine spatial planning (MSP) as a process for informing EBM with regard to the allocation and siting of ocean uses so that ecosystem health is ensured and trade-offs between ecosystem services are appropriately dealt with. Marine spatial planning is both an integrated and an area-based process, and this paper produces a spatial characterisation of the BCLME for achieving a common basis for MSP in the region, focusing on the oceanography, biology and fisheries. Recognising spatial variation in physical driving forces, primary and secondary production, trophic structures and species richness, four different subsystems are characterised: (1) north of the Angola–Benguela Front, (2) from the Angola–Benguela Front to Lüderitz, (3) from Lüderitz to Cape Agulhas, and (4) from Cape Agulhas to Port Alfred on the south-east coast of South Africa. Research and monitoring requirements of relevance for MSP and EBM in the region are identified, focusing on understanding variability and change, including with regard to the boundary areas identified for the system. To this end, 14 cross-shelf monitoring transects are proposed (including seven that are already being monitored) to estimate fluxes of biota, energy and materials within and between the subsystems. The usefulness of models for understanding ecosystem variability and changes is recognised and the need for fine-scale resolution of both sampling and modelling for adequate MSP as input to EBM for the often-conflicting interests of conserving biodiversity, and managing fisheries, recreation, offshore oil and gas exploration and exploitation, offshore mining and shipping routes, is emphasised. Keywords: biology, drivers, ecosystem approach to fisheries, large marine ecosystem, marine spatial planning, models, monitoring, physical oceanography, variability
Introduction The contributions of the oceans to poverty eradication, sustained economic growth, food security and sustainable livelihoods are receiving increasing recognition (e.g. Costanza et al. 1997, 2014). In this regard, the importance of the responsible use of the oceans and of their resources for sustainable development has received much attention in recent years, together with the need to protect biodiversity and the marine environment and address the potential
impacts of climate change (Worm et al. 2006). Ecosystembased management (EBM), which includes an ecosystem approach to fisheries (EAF), is being promoted as a necessary approach to put in practice the principles of sustainable development in ocean-related matters (Douvere and Ehler 2009; Long et al. 2015). Ecosystem-based management is an integrated approach that takes into account fisheries and all other anthropogenic activities that
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affect the marine environment (Levin and Lubchenco 2008). When multiple activities occur in a given area, EBM should ensure that the different sectors share common sustainability goals and objectives, that there is a fair process of allocation of user rights and that a decision-making process is in place to reconcile conflicting interests across the sectors. The approach takes into account the interactions between ecosystem components and all human activities and considers the cumulative impacts across space and time (Long et al. 2015). An EAF expands from the scope of conventional fisheries management to include aspects related to the impacts of fishing on aquatic biodiversity and on ecosystem structure and functioning. These are balanced with social and economic well-being that can be derived from fisheries and take into account impacts of drivers other than fishing, as well as the characteristics of management (FAO 2003). An essential part in building an EAF is to inform managers about the status of the ecosystem in general, including changes occurring in the environment as well as changes in the exploited resource and vulnerable components of the ecosystem. This is the role of ecosystem indicators which, when carefully selected for their ecological meaning or their performance in indicating fishing pressure (Shin et al. 2012), are based on the best available data collected to monitor the ecosystem, and are meant to raise red flags both at the single- and multifishery level. Ecosystem and environmental data that are potentially useful in this regard include: (i) annual spatial distributions of target and non-target species, and spatial information on particular life stages or processes such as spawning areas, retention areas and migration routes; (ii) data and indicators (including spatial information) characterising the impacts of fisheries (direct or indirect) on target and non-target species, on the trophic structure of the ecosystem and on habitats; and (iii) data and indicators tracking the effects of climate change and variability of other external drivers (e.g. mining) on populations (e.g. range shifts), ecological processes (e.g. recruitment) or habitats. The importance of determining spatial characteristics of the ecosystem is apparent from these data requirements, which link with some of the priority areas for future research in the Benguela Current Large Marine Ecosystem (BCLME) that have been identified by Jarre et al. (2015a) during a thorough review of the state of knowledge of this ecosystem as part of the NansClim project (www.nansclim.org) synthesis. These include (among others) investigating spawning and early life history of the most important fish species (including location), assessing changes in broad-scale forcing and boundary conditions on the BCLME, and modelling the interactive effects of environmental and human drivers on ecosystem change. The three countries of the BCLME, namely Angola, Namibia and South Africa, have committed to implementing EBM, including EAF, in the marine ecosystem of the region (Benguela Current Commission Strategic Action Plan 2015– 2019; www.benguelacc.org/index.php/en/publications). The need to integrate science, socio-economics and management across the component countries, especially given that the distributions or movements of many resources transcend national boundaries, as do the effects of climate variability and change on the system, has for long been recognised
Kirkman, Blamey, Lamont, Field, Bianchi, Huggett et al.
(O’Toole et al. 2001). Logically this also extends to planning. In this regard, the utility of marine spatial planning (MSP) as a process for informing EBM with regard to the allocation and siting of ocean uses, so that ecosystem health is promoted and trade-offs between ecosystem services are appropriately dealt with, is increasingly being recognised (Carpenter et al. 2006; Daily et al. 2009). Indeed, a project ‘Marine Spatial Management and Governance (MARISMA) of the BCLME’, supported by the German Federal Environment Ministry, was recently initiated in the region in order to develop capacities at various levels to institutionalise and implement MSP in the BCLME (www.benguelacc. org). An aim of this paper is to produce a spatial characterisation of the BCLME for achieving a common basis for MSP in the region, focusing on the physical oceanography, biology and fisheries. A common understanding of the spatial characteristics of the ecosystem is applicable to the several spatial data requirements for EBM in the region discussed above and is especially important for MSP considering that it is both an area-based and an integrated process (Douvere 2008). Marine spatial planning is also ecosystembased and adaptive (Douvere 2008); thus research and monitoring requirements of relevance for MSP and EBM in the region are identified, focusing on understanding variability and change (including with regard to boundary areas identified for the system) and including the use of appropriate models. Methods The Benguela Current Commission (BCC; www.benguelacc. org), an intergovernmental regional organisation that was established to harmonise research and provide advice for management of human activities in the BCLME, convened a workshop titled ‘Benguela Ecosystems: Review of the Past, Challenges of the Present and Plans for Future Research’ held in Cape Town, 16–19 September 2014. The workshop, which comprised marine scientists, ecologists and fisheries biologists familiar with the BCLME (Appendix 1), considered research requirements for EBM including EAF in the region. The workshop focused on obtaining a common understanding of the system’s boundaries including identifying and describing subsystem boundaries, as well as identifying further research and monitoring requirements including the use of models for understanding variability and change in the system or subsystems. With regard to the latter, some of the major issues that need to be addressed in order to achieve an EBM approach in the BCLME and to quantify the ecosystem effects of impacts (e.g. one or several fisheries) were formulated into a conceptual model. Research and monitoring requirements to address these needs were itemised and discussed, including the use of trophic and ecosystem models for understanding Benguela ecosystem dynamics and multiple drivers (e.g. Shannon et al. 2008; Travers-Trolet et al. 2014). To conceptualise the different spatial and temporal scales of variability in the ecosystems, the crossscale dynamics of the BCLME were summarised using a Stommel diagram (Stommel 1963), based on the expert knowledge of workshop participants and existing published
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those of commercially and ecologically significant stocks, rather than encompassing all those described in the BCC framework (Exclusive Economic Zone to high-water mark). The characterisation of each subsystem, the locations of their boundaries and transect lines, and the associated rationales, are presented and discussed below.
information (Hutchings et al. 2009; Jarre et al. 2015a). The resulting space–time diagram was used to assist with the demarcation of subsystems that would take into account spatial variation in physical driving forces, primary and secondary production, trophic structures, and species richness and relative abundances of different species, and to identify requirements for further research and monitoring. The proposed subsystems and their boundaries were mapped, and important physical features were overlaid. Wind roses were calculated from Advanced Scatterometer Surface Wind fields (ASCAT) (http://manati.star.nesdis. noaa.gov/products/ASCAT.php) at 0.25° resolution over the period 2009–2015, using defined rectangular coordinates for the centres of each of the subsystems. Positions of potential transect lines for monitoring of biological resources and processes were also mapped. Their locations were based on where they would be best situated to improve biological knowledge of each subsystem. For this exercise, the cross-shore boundaries of the system were limited to
1 year
1 season
1 hour
1 min
Poleward undercurrent and SECC
Seasonal cycle
Eddies and fronts
Coastal upwelling Planetary waves
Winddriven upwelling
Wind-stress-curldriven upwelling Divergence-driven upweling
Submesoscale Microscale
1 day
Solar insolation/ stratification Basin-scale variability
1 month
1 week
Spatial characterisation of the BCLME Understanding of the spatial and temporal scales of variability and how these influence the different parts of the BCLME are summarised in Figure 1. It was recognised that physical drivers exist across a range of spatial and temporal scales, and their relative importance differs among biological groupings (Figure 1) and possibly also among the different subsystems. For example, fine-scale processes such as turbulence affect fish larval feeding, zooplankton, phytoplankton and other microorganisms such as bacteria
Planetary scale
10 y
Results and discussion
Climate change
Mesoscale
100 y
TIME
Shelf--edge jet
Tides
Sediment loading
Internal waves and inertial motions
Agulhas rings, filaments, eddies
Turbulence Gravity waves
Coastal-trapped waves Turbulence and wind-mixing
10 5 km
Planetary scale
10 4 km
km 00
km 10
SPACE
10 0
km
Mesoscale
10
1
m 10 0
m 10
m 1
cm
km
Submesoscale
Microscale
10
1
m
cm
Molecular processes
m
1 sec
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African Journal of Marine Science 2016: 1–16
Microbes and phytoplankton
Ichthyoplankton
Macroalgae
Zooplankton
Adult fish
Megafauna/top predators
Figure 1: Stommel space–time diagram summarising the diverse scales of physical processes that drive variability and steer biological responses in the Benguela Current Large Marine Ecosystem. Generic physical processes are circled (based on Dickey 2003) and processes of particular importance in the Benguela are superimposed by colour-shaded rectangles. Space–time domains of the most dominant scales of variability in the spatial distribution of major biological groupings are depicted as differently hatched rectangles, scaled to the vertical and horizontal axes. A colour key to the interaction between physical and biological processes is given on the right (the sizes of these areas are not to scale). SECC – South Equatorial Counter Current
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Kirkman, Blamey, Lamont, Field, Bianchi, Huggett et al.
Table 1: Physical features relevant to each of the four subsystems of the Benguela Current Large Marine Ecosystem. The most important features in each subsystem are shaded
Physical feature or process Kelvin waves
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Poleward undercurrent
South Equatorial Counter Current Solar insolation and stratification
Subsystem 1. North of Angola– Benguela Front (ABF) Kelvin waves propagating from tropical Atlantic; large scale; interannually variable LOW (low oxygen water) bottom current thought to be driven by wind stress curl, penetrates below ABF; large scale; interannually and seasonally variable Surface current
Kelvin waves propagating from tropical Atlantic; large scale; interannually variable LOW bottom current thought to be driven by wind stress curl, penetrates below ABF; large scale; interannually and seasonally variable
3. Lüderitz to Cape Agulhas Kelvin waves propagating from tropical Atlantic; large scale; interannually variable Southward flowing (cf. fish movement); large scale; interannually and seasonally variable
4. Cape Agulhas to Port Alfred
Large scale; seasonal, interannual and decadal variability Large scale with localised intensification, especially at Lüderitz; seasonal, interannual, decadal and eventscale variability Large scale; seasonal, interannual and decadal variability
Large scale; seasonal, interannual and decadal variability Small scale (capes); seasonal, interannual, decadal and event-scale variability
From Orange River; localised intensification with large-scale impact; seasonal, interannual and event-scale variability
Large scale; seasonal, interannual, decadal and event-scale variability Small scale, inshore; seasonal, interannual, decadal and event-scale variability Synergy between inertial motion and diurnal forcing at 30° S; small scale; seasonal, diurnal, subdiurnal and event-scale variability
Large scale; seasonal, interannual, decadal and event-scale variability
Wind-stress-curl-driven upwelling
Rossby waves
Sediment loading (riverine/ aeolian)
Advection (alongshore/ cross-shelf)
H2S eruptions
Wind-mixing on shelf
Large scale; seasonal, interannual and decadal variability Large scale with localised intensification, especially at Lüderitz; seasonal, interannual, decadal and eventscale variability Large scale; seasonal, interannual, decadal and event-scale variability Large scale; interannual and decadal variability From Cunene River and aeolian dust plumes; localised intensification with large-scale impact; seasonal, interannual and event-scale variability Alongshore and cross-shelf; large and small scale; seasonal, interannual, decadal (?) and event-scale variability Small scale, inshore (
