Elsevier

Journal of Marine Systems

Volume 88, Issue 2, November 2011, Pages 298-311
Journal of Marine Systems

Use of sensitivity and comparative analyses in constructing plausible trophic mass-balance models of a data-limited marine ecosystem — The KwaZulu-Natal Bight, South Africa

https://doi.org/10.1016/j.jmarsys.2011.05.006Get rights and content

Abstract

Ecosystem modelling allows for an understanding of the structure and functioning of data-limited ecosystems provided that models undergo extensive sensitivity analyses to explore the levels of uncertainty. We explored one such data-limited system, the KwaZulu-Natal (KZN) Bight, a river-influenced bight on the east coast of South Africa. Potential system states of the KZN Bight were created by constructing multiple models in Ecopath with Ecosim, carrying out sensitivity analyses and comparing outputs. Sensitivity analyses showed that models were most sensitive to apex predator parameters and a comparison of outputs showed the important influence of riverine detritus on system functioning. To demonstrate the KZN Bight models could reproduce known differences to other ecosystems a comparison of the nutrient-poor KZN Bight to the nutrient-rich Southern Benguela was carried out. This confirmed that the KZN Bight was considerably smaller in biomass, productivity and fishery landings than the Southern Benguela with the systems being detritus-driven and phytoplankton-driven respectively. The KZN Bight relied on large detritus imports from rivers and had higher cycling through the system. The reliance on detritus import from rivers has riverine and fishery management implications as a decrease in riverine detritus caused a decrease in biomass of biotic groups, including targeted fishery species.

Research highlights

► Plausible ecosystem models can be constructed using local and non-local data. ► Models showed sensitivity to non-local apex predator data due to top-down balancing. ► Models reproduce known differences in functioning with a Southern Benguela model. ► Models described an ecosystem reliant on import and cycling of riverine detritus.

Introduction

Understanding how marine ecosystems function as a whole is a challenge for data-poor systems. However, a holistic overview of an ecosystem is necessary for understanding how they function and how anthropogenic activities could potentially impact them. Bays and bights are ecosystems often influenced by anthropogenic activities due to their proximity to the coast. Firstly, most are affected by commercial, recreational and/or subsistence fisheries (Jennings and Kaiser, 1998, Pauly et al., 1998, Pauly et al., 2005). Secondly, some are influenced by the outflow of large rivers (Diaz and Rosenberg, 2008, Gillanders and Kingsford, 2002, Lamberth et al., 2009).

River-influenced bays and bights are usually more productive than the adjacent ocean due to an inflow of nutrients and detritus from topographical upwelling and rivers (Wollast, 1998). However, there is an increasing need to place impoundments onto large rivers for inland water-use, and to increase inshore fishery catches for local consumption and export. Therefore it is important to understand how these ecosystems function at present in order to predict what effects these activities will have on production in the system.

The KwaZulu-Natal Bight (KZN Bight) on the east coast of South Africa is an example of a river-influenced bight (Fig. 1). It is also a data-poor ecosystem with sparse quantitative data on biotic and abiotic components. Biomasses are available for plankton groups and other quantitative data are available for important linefishing species, large sharks, and cetaceans only. Oceanic waters off south-east Africa are typically oligotrophic (Lutjeharms, 2006a). However, the waters of the bight are slightly less so than the bordering Agulhas Current (Lutjeharms et al., 2000, Meyer et al., 2002). This is due to nutrient inputs from an episodic upwelling off Richards Bay, a lee eddy off Durban and rivers along the coast, particularly the Thukela (Carter and D'Aubrey, 1988, Lutjeharms et al., 1989, Pearce et al., 1978). The Thukela River is the third largest river in southern Africa and has a sediment output of 6.79 × 103 m3 year 1 (Birch, 1996). This outflow creates a turbid area in the central bight which is home to South Africa's only prawn fishery (Fennessy and Groeneveld, 1997). The rivers and estuaries along the coast aid the recruitment of many targeted fishery species by providing nursery grounds (Lamberth et al., 2009, Wallace et al., 1984, Wallace and van der Elst, 1975, Whitfield, 1998). To understand and predict the impact of current and future anthropogenic activities (e.g. fishing and water impoundments on the Thukela River (DWAF, 2004)) on this ecosystem as a whole it is important to understand the functioning of this ecosystem as a whole.

Ecosystem modelling allows a system to be studied as a whole. The system can be constructed as a network using information on nodes (biomasses), links between nodes i.e. trophic flows (diet compositions), production, consumption, and fishery landings of biotic groups. If biomass, production or consumption is missing for a group then this can be estimated using the mass-balance approach, most commonly used in Ecopath with Ecosim (EwE) software (Christensen and Pauly, 1992). Thus this approach lends itself to the modelling of ecosystems such as the KZN Bight where many biomasses are unknown. For example, EwE has been used to model coral reef ecosystems and historical representations of ecosystems (Heymans and Pitcher, 2002, Morato and Pitcher, 2005, Polovina, 1984). From this network system metrics can be calculated which describe the system in terms of energy flows, energy cycling and ecosystem services provided by the system (Ulanowicz, 1986). These metrics enable an understanding of how the ecosystem functions and the identification of system level characteristics. In addition, comparisons of marine ecosystems from different areas and of different spatial scales can be carried out. These comparisons can aid the modelling of a data-limited ecosystem if functional differences to another ecosystem are known a priori.

The east coast of South Africa is typically oligotrophic with low fishery catches and is known to be influenced by the many rivers/estuaries flowing into coastal waters. This is in contrast to the west coast of South Africa, comprising the Southern Benguela upwelling ecosystem, which is nutrient rich with large plankton biomass and fishery landings (Shannon et al., 2003). A direct comparison of models of these systems would aid in assessing the plausibility of models of the data-limited KZN Bight. Plausible models of the KZN Bight would produce known differences in functioning to the Southern Benguela.

In this paper, we describe the development and analysis of models of the KZN Bight. The aims of this study are to demonstrate that plausible representations of data-poor river-influenced bights can be constructed, to better understand how the KZN Bight functions by gaining a holistic overview of the system and to build a framework for future models of the KZN Bight. We make comparisons between:

  • a)

    several versions of KZN Bight models to determine the levels of uncertainty associated with non-local input data,

  • b)

    the KZN Bight models and a model of the southern Benguela, an upwelling system on the west coast of South Africa, to determine if the KZN Bight models reproduce known differences in functioning between these systems.

Section snippets

Modelling approach

Mass-balanced models of the KZN Bight were constructed and analysed using Ecopath with Ecosim software, version 5.1 (Christensen et al., 2005). Underlying Ecopath with Ecosim are two equations ensuring the mass-balance or energy-balance of each biotic group. The first describes the production of a group:P/BiBi=ΣQ/BjBjDCij+P/BiBi1EEi+Yi

P/Bi is the production/biomass ratio of group i; Bi is the biomass of group i; Q/Bj is the consumption/biomass ratio of predator j; DCij is the proportion

Parameterization

Ecopath routines allowed missing parameters, biomasses and EEs, to be estimated. These were similar, if not the same, between models for all groups except macrobenthos which ranged between 25.6 and 38.94 t km 2 (Fig. 2). The system was clearly dominated by macrobenthos in terms of biomass.

Sensitivity of models

KZN Bight models were most sensitive to changes in the input parameters of apex and benthic-feeding chondrichthyans. Small changes (±10%) of these parameters caused the largest number of missing parameters

KZN Bight models

This was the first set of ecosystem models of the entire KwaZulu-Natal Bight on the South African east coast. Due to the scarcity of local data, non-local data were used for many functional groups. In cases of high uncertainty or several poorly defined functional groups, the development of multiple models spanning the range of potential system states is important (Essington, 2007, Fulton et al., 2003). It should be noted however that due to limited data this set of models may not necessarily

Conclusions

Whilst the models may not have predicted ‘accurate numbers’, they have identified data gaps in the literature for the KZN Bight and given an overview of its functioning. Expanding on the steps for ecological network construction in Fath et al. (2007), the following summarises steps taken in this study to construct the KZN Bight models which can serve as recommendations on how models for data-limited ecosystems may best be achieved:

  • 1.

    Define model domain (spatial boundary and time period).

  • 2.

    Define

Acknowledgements

We are thankful to the African Coelacanth Ecosystem Project (ACEP II) and the River Influenced Bays and Bights Project (RIBBS), CSIR, for financial assistance; Sean Fennessy from the Oceanographic Research Institute (ORI), Durban for advice; Bruce Mann and Jade Maggs (ORI) for facilitating access to recreational NMLS data; Geremy Cliff and Sheldon Dudley at the Natal Sharks board for shark net landings and advice; and finally the librarians at ORI for their assistance.

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