© 2004 International Council for the Exploration of the Sea
Trophodynamic indicators for an ecosystem approach to fisheries
a IRD-CRHMT BP 171, 34203 Sète Cedex, France
b Marine and Coastal Management Private Bag X2, Rogge Bay 8012, South Africa
c Ministry of Fisheries and Marine Resources PO Box 394, Lüderitz, Namibia
d CEFAS Lowestoft Laboratory Pakefield Road, Lowestoft, Suffolk NR33 0HT, England, UK
e Danish Institute for Fisheries Research, North Sea Centre PO Box 101, 9850 Hirtshals, Denmark
f Marine Biology Research Institute, University of Cape Town Rondebosch 7701, South Africa
g Fisheries Centre, 2259 Lower Mall, University of British Columbia Vancouver, British Columbia, Canada V6T 1Z4
*Correspondence to P. M. Cury: tel: +33 0 499573234; fax: +33 0 499573295. e-mail: pcury{at}ifremer.fr.
Acknowledging ecological interactions, such as predation, is key to an ecosystem approach to fisheries. Trophodynamic indicators are needed to measure the strength of the interactions between the different living components, and of structural ecosystem changes resulting from exploitation. We review trophodynamic indicators derived from models, as well as from emergent patterns such as trophic cascades and regime shifts. From 46 indicators identified in the literature, six (catch or biomass ratios, primary production required to support catch, production or consumption ratios and predation mortality, trophic level of the catch, fishing-in-balance, and mixed trophic impact) were selected because of their ability to reveal ecosystem-level patterns, and because they match published criteria. This suite of indicators is applied to the northern and southern Benguela ecosystems, and their performance is evaluated to depict drastic and contrasted ecosystem changes. A few complementary indicators are suggested as needed to detect the trophodynamic impacts of fisheries and ecosystem changes. Trends in indicators are sensitive to the choice of trophic level made for certain species. Trophodynamic indicators appear to be conservative, because they respond slowly to large structural changes in an ecosystem. Application of the selected indicators to other marine ecosystems is encouraged so as to evaluate fully their usefulness to an ecosystem approach to fisheries, and to establish international comparability.
Keywords: Benguela, ecosystem, fishery management, foodweb controls, indicators, trophodynamics
Received 1 April 2004; accepted 8 November 2004.
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Introduction |
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 Top Introduction A selection of indicatorsComparing the northern and...DiscussionAppendix. NotationReferences |
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The Reykjavík declaration of 2001, reinforced at the World Summit on Sustainable Development in Johannesburg in 2002, requires nations to base policy governing exploitation of marine resources on an ecosystem approach. To fulfil this challenge, a strategy is needed that is based on the development of ecosystem-based indicators coupled with operational frameworks that bridge the gaps between scientific results, social needs, and a comprehensive and effective ecosystem approach to fishing (EAF). From an ecological point of view, this means that interactions between the different components within marine ecosystems have to be acknowledged, understood, and quantified (Cury et al., 2003).
The strength of ecological processes such as trophodynamic interactions, i.e. predation and competition, has been recognized as being of great importance in fish population dynamics (Bax, 1998). Trophic interactions raise two concerns for fisheries management. The first is the decline in the food resource upon which some component of the ecosystem subsists, necessitating its departure to other localities or causing its decline (Link, 2002). The second is the indirect effect of decreasing fish biomass on ecosystem functioning (e.g. regime shifts). To be useful in tracking progress towards sustainable development, indicators need to be closely linked to clear objectives (what is to be achieved, or what definitely needs to be avoided), and translated into reference points (Garcia and Staples, 2000). This review focuses on descriptive and performance indicators as a tool for cross-system comparisons. We briefly summarize the important processes and patterns, and review trophodynamic indicators derived from models of trophic interaction, as well as from observed patterns. A selection is then proposed on the basis of the criteria of Rice and Rochet (2005). Finally, this suite of indicators is applied to the northern and southern Benguela, two well-studied ecosystems, and their performance is evaluated.
An extensive, though not exhaustive, review found more than 200 different indicators of ecosystem status (Rice, 2000). However, the challenge is not to find indicators of ecosystem status, but rather to evaluate their performance (Rice, 2003). Potential indicators are obtained mainly from model outputs or statistical analyses. Trophodynamic indicators are grouped here according to their linkage to specific objectives, i.e. indicators used to characterize single foodweb components (e.g. population, species, functional group, trophic level) and indicators used to characterize ecosystem state.
Main quantitative indicators covering aspects of trophic ecology are presented in Table A1 of the Appendix. Most indicators are simple or composite (e.g. ratios) measures of flows of some currency (mass or energy), but some are based on other metrics, e.g. position in the food chain (trophic level), number of trophic links (connectance), or probabilistic considerations (information). Most system indicators (Table A1, part b, of the Appendix) have been related to ecosystem maturity (Odum, 1969; Christensen, 1995, 2000), and they depend upon the formulation of the underlying foodweb model (e.g. aggregation or disaggregation of trophic groups, diverse assumptions about structural and functional parameters; Rice, 2003).
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A selection of indicators |
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TopIntroductionA selection of indicatorsComparing the northern and...DiscussionAppendix. NotationReferences |
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The trophic indicators listed (Table A1 of the Appendix) were considered with respect to the criteria developed by ICES (2001), modified by Rice and Rochet (2005). Six were subsequently selected on the basis of their perceived suitability for fisheries management purposes. Their ability to quantify effects of fishing was then evaluated in a preliminary scoring procedure on the basis of the nine criteria (Table 1). The procedure was to ask participants of the SCOR-IOC Working Group 119 task force on trophodynamic indicators to score the different individual indicators, then to discuss the results collectively to obtain the final evaluation.
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The relative change in species (or functional group) composition within the catch or surveyed community can be quantified by means of biomass ratios (preferable to catch ratios), to characterize ecosystem changes (e.g. piscivorous:zooplanktivorous fish). Such ratios are easily understood and measurable, and are often, but not exclusively, sensitive to fishing. However, finding theoretical foundations for setting reference points is problematic, and these would have to be defined empirically based on historical data.
The primary production required (PPR) to support catches (Y) in a system is expressed as a percentage of the total primary production available in the system during a given period, and may be computed as
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The proportion of production by different components, and the proportion of the total consumption of each prey taken by each predator group, can be used to quantify the relative importance of prey or predators (consumers). The importance of predation and/or fishing mortality relative to total mortality in any particular group may be helpful in monitoring changes in trophic structure within or among systems. Predation mortality is often larger than fishing mortality (Bax, 1991; Jarre et al., 1991), but their relative importance may change through time, or differ between systems. Effects of fishing will be most apparent in cases of tight trophic coupling, such as between forage fish subject to heavy fishing pressure and seabird predation (Crawford and Dyer, 1995).
Trophic level (TL) expresses the position of organisms within a foodweb. The mean TL of the catch may be computed for each year, from
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The fishing-in-balance index (FiB) is computed as
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Mixed trophic impact (TI) is a measure of the relative impact of a change in the biomass of one component on other components of the ecosystem (Ulanowicz and Puccia, 1990). The analysis is based on an input–output method used to assess direct and indirect economic interactions (Leontief, 1951). Through matrix calculations, TI quantifies the net effects of one species on every other species in a system, taking into account positive effects of a prey species on its predator (weighted relative to its proportion in the diet), negative effects of a predator on its prey (weighted according to the fraction of the production of a prey that is consumed by the predator), and the indirect effects one species may have on another through trophic interactions. Matrices are constructed of relative net impacts of each group on every other, scaled between –1 and 1. An assumption is that the trophic structure remains constant, implying that TI should not be used in a predictive sense, but rather as a type of sensitivity analysis, to identify those groups that may have large trophic impacts on others, and so might be suitable indicators for monitoring fisheries effects across an ecosystem.
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Comparing the northern and the southern Benguela |
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TopIntroductionA selection of indicatorsComparing the northern and...DiscussionAppendix. NotationReferences |
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General description
The northern and southern Benguela are dynamic and comparable upwelling systems, in which the fisheries target largely similar demersal (Cape hakes, Merluccius capensis and M. paradoxus) and pelagic fish species (sardine, Sardinops sagax; anchovy, Engraulis encrasicolus; horse mackerel, Trachurus t. capensis). However, the two ecosystems have followed very different trajectories since the 1950s in terms of exploitation, species composition, structure, and dynamics (Figure 1). A regime shift was documented in the northern Benguela (Boyer and Hampton, 2001) as the result of overexploitation, whereas the southern Benguela exhibits variability that appears to be within natural limits (Cury and Shannon, 2004). Trophic models of the two ecosystems are available (Shannon and Jarre-Teichmann, 1999; Shannon et al., 2003; Roux and Shannon, 2004), and were standardized for comparative purposes according to the methods described by Moloney and Jarre (2003). Two models were used for each system to describe the foodwebs under different productivity levels of pelagic fish (northern Benguela, 1980–1989 and 1995–1999; southern Benguela, 1980–1989 and 1990–1997), and these were used as case studies for the application and interpretation of the six selected trophic indicators, to assist in testing their usefulness.
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The detailed recent history is reflected in the time-series of catches shown in Figure 2a, namely a sequential exploitation and depletion of the three main stocks in the northern Benguela (sardine, anchovy, and hake). Few management measures could be implemented effectively there until Namibia's independence and proclamation of a 200-mile EEZ in 1990. Since independence, hake catches have recovered only modestly, sardine catches have remained insignificant, and horse mackerel has continued to dominate the total biomass landed, as it has since the late 1970s, although it has declined slowly since the late 1980s. Total catches have steadily decreased from a peak of >2 million tonnes in 1968 to around 0.5 million tonnes in the late 1990s.
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In the southern Benguela, a bottom-trawl fishery targeting hake had been established even before 1950, catches slowly increasing from around 50 000 to 140 000 t between 1950 and 1977, when a 200-mile Fishing Zone was proclaimed by South Africa (Payne, 1995). Since then, hake catches have risen only slightly. Horse mackerel never dominated the South African catches, though sardine constituted the bulk of the landed biomass until 1965, before being replaced by anchovy until the mid-1990s. Recently, landings of both sardine and anchovy have increased. The total catch shows fluctuations around 0.5 million tonnes, without a clear trend, from the early 1960s to the present (Figure 2a). Fisheries have been carefully managed, for example by maintaining annual pelagic fish catches well below 25% of estimated annual biomass since the early 1990s.
Catch ratios
The ratio between demersal and total catch increased in both systems during the second half of the 20th century (Figure 2b), but only moderately so in the southern Benguela (from 20% to 36% between 1950 and 2000; observed range 16–45%). In comparison, the ratio in the northern Benguela increased from approximately zero before 1960 (when catches were mostly sardine, and there was little local or foreign interest or knowledge of demersal resources) to 90% in 2000, when catches of small pelagics were insignificant, and the bulk of the landings was large horse mackerel (largely demersal, in contrast to small horse mackerel, which are caught pelagically) and hake targeted by demersal trawlers. The difference in trends results from the earlier establishment of a trawl fishery in the southern than in the northern Benguela.
Mean trophic level (TL) of catch
The trajectory of the mean TL of catches in the northern Benguela underwent three distinct phases (Figure 2c, d). From 1950 to 1964, the TL remained around the low value of 2.7 (corresponding to the TL of sardine). The TL then increased rapidly to values in excess of 3.8 between 1965 and 1972, following the rapid development of the hake fishery and the decline of the sardine stock. Finally, from 1972 to 2000, the index remained stable at an average value of 3.7 (s.d. = 0.1), without a trend over the past 29 years (F = 0.69, p > 0.4). The apparent stability is due to the continuing decline of small pelagics in the catches, and the partial replacement of hake by horse mackerel (medium TL) during a period of general decline in overall catches.
In the southern Benguela, the trajectory displays a significant dip between 1958 and 1965, when catches were dominated by sardine (Figure 2d). From 1966 to 1991, the TL increased slowly (average slope 0.004 per year), but significantly (p < 0.005), but declined slightly thereafter. The rising trend is due to the slowly increasing hake catches and the replacement of sardine (TL = 3) by anchovy (TL = 3.5) as the dominant small pelagic species. The slight decline during the 1990s cannot be interpreted as an effect of "fishing down the foodweb", because hake catches remained remarkably stable. In fact, the decline reflects the increasing contribution of sardine following the successful recovery of that stock during the 1990s.
Fishing-in-balance (FiB) index
The trajectory of the FiB index (Figure 2e) in the northern Benguela shows a rapid increase between 1960 and 1972, reflecting the rapid expansion of the pelagic fishery, followed by an expansion of the trawl fishery and declining pelagic catches. From 1972 to 2000, the FiB shows a slight but significant overall decline (with a slope of –0.07 per annum, r2 = 0.49, p < 0.0001). The main factor contributing to this negative trend is the overall decline in total catches (Figure 2a), and the increasing relative contribution of horse mackerel to the catches.
Because the fishery in 1950, the reference year, was more developed in the southern than in the northern Benguela, the amplitude of FiB changes is much smaller in the southern Benguela. Nevertheless, the increasing trend apparent between the mid-1960s and 1988 seems to track the overall increasing catches of anchovy, which peaked in 1987 and 1988. The reverse trend in the 1990s reflects the increasing contribution of sardine (Figure 2a). FiB plots were also examined assuming a higher transfer efficiency (TE) than 10% (even as large as 20%; i.e. including the 12% used in Shannon et al., 2003), as well as a variable TE each year (according to the average TL of the catch in each year), but this made virtually no difference and the results are not included here.
Ratios of catch, biomass, production, and consumption
For both ecosystems, ratios of production, consumption, biomass, and catches of the various groupings of small pelagic fish relative to demersal fish were lower in the 1990s than in the 1980s (Table 2), indicating a decline in the relative biomass of planktivorous fish and/or an increase in that of the demersal fish assemblage, according to the models used. However, in the northern Benguela, catch ratios of planktivorous:piscivorous fish and small pelagics + small hake:large pelagics + large hake, increased as a result of the observed increase over the two decades in catches of horse mackerel relative to piscivorous hake. The small increase (4%) in the consumption of small pelagic fish (excluding small hake) by predators, relative to their consumption of demersal fish, in the southern Benguela reflects an increase in biomass of mesopelagic fish off South Africa between the 1980s and 1990s. Overall, the ratios indicate a shift towards greater importance of demersal fish in the Benguela over the time periods modelled.
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Primary production required (PPR) to sustain catches
In the northern Benguela, the PPR to sustain catches decreased by more than 50% in accordance with the reduction in catches, as indicated by the similar PPR:catch ratios in the two periods. By comparison, the PPR in the southern Benguela was similar in both periods, though catches were smaller in the later period. This meant a 23% increase in the ratio of PPR to catch, indicating that catches were more ecologically expensive in the later period, in accord with the estimated increased TL of the catch.
Mixed trophic impact (TI)
For the two northern Benguela models, the group displaying the largest differences in TI between periods is large horse mackerel (Figure 3a). Horse mackerel sustain a major commercial fishery off Namibia and, because they migrate vertically through the water column, horse mackerel trophically integrate to a certain extent the pelagic and demersal systems. More abundant in the 1980s, horse mackerel had, at that time, larger effects on their competitors and predators. TI also provides an indication of changes in the trophic importance of other groups in the ecosystem. For example, chub mackerel (Scomber japonicus), anchovy, and sardine stocks had all undergone severe declines by the second period, leading to a reversal from negative (competition for common prey being the dominant factor) to positive TI in the second period (large horse mackerel also feed to some extent on the other three species which, at low abundance, no longer strongly compete with horse mackerel for prey).
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In the southern Benguela, sardine abundance increased in the 1990s, reflected in the larger net negative TI on its competitors (e.g. anchovy, other small pelagic fish, horse mackerel), and larger positive TI on its predators (e.g. seabirds, cetaceans, large pelagic fish; Figure 3b).
Predation mortality
For all selected fish groups in the two systems, predation mortality relative to the sum of predation and fishing mortality increased between the two periods (Figure 4a). However, the indicator measures different things in the two systems. In the southern Benguela, predation mortality reflects support of a 24% larger biomass of pelagic fish predators (Table 2) in the 1990s than in the 1980s, whereas in the northern Benguela, the indicator is a measure of the increased pressure on the fish groups. Predator biomass off Namibia declined between the two periods (Table 2).
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In the northern Benguela, anchovy predation mortality (relative to total predation and fishing) attributable to seals and seabirds more than doubled in the later period as a consequence of a large reduction in stock size and the collapse of the anchovy fishery (Figure 4b). The decrease in predation pressure exerted on sardine by seals and seabirds off South Africa during the 1990s reflects the recovery of sardine (Figure 4b).
Differences within the region
Many differences between trophic indicators for the two subsystems appear to be related to the southern Benguela model not only constituting a typical upwelling region (west coast), but also a shallow bank with diverse demersal fish assemblages (Japp et al., 1994). Moreover, large parts of the northern Benguela shelf are subjected to recurrent low oxygen conditions, which might be limiting for some demersal species. The catch of pelagic fish compared with the catch in the demersal fishery (trawls and longline) is 1.5 times larger in the northern Benguela than in the southern Benguela. Similarly, ratios of consumption by and of pelagics vs. demersals are larger in the north. Conversely, catches of pelagic fish relative to demersal fish, and of small pelagic fish vs. large hake and large pelagic fish, are an order of magnitude smaller off Namibia than off South Africa. The smaller discrepancy between catch ratios of planktivorous:piscivorous fish arises from the large catch of large (planktivorous) horse mackerel in trawls. The importance of horse mackerel in the north also accounts for inflated catch ratios of planktivores:piscivores in the north, relative to those in the south (cf. pelagic:demersal ratios).
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Discussion |
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TopIntroductionA selection of indicatorsComparing the northern and...DiscussionAppendix. NotationReferences |
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The dynamics of the northern and southern Benguela ecosystems documented across different trophic levels show contrasting trajectories in various components during the past two decades. Therefore, the available information should be useful in testing the performance of trophodynamic indicators. Most of the indicators discussed (total catches, ratio demersal to total catches, ratio piscivorous to total catches) display interpretable trends in the northern Benguela (see also Willemse and Pauly, 2004), compared with their relative stability in the southern Benguela. This appears to be a reflection of the better "health" of the fisheries in south, and may be partly because management measures were implemented effectively in the south since the implementation of the 200-mile Fishing Zone in 1977 (more than 13 years earlier than off Namibia). However, the relative stability over the past 30 years of the mean TL of the catch in the south hardly reflects the important changes in the system (fluctuations of the anchovy stock and catches), suggesting that this indicator specifically is a conservative one that responds slowly to large structural change. The absence of a trend in the northern Benguela during the same period (when the system was subjected to sequential depletion of its most important stocks) highlights the bias that results from the use of an indicator based on catches if an ecosystem has shifted productivity towards non-exploited species (such as, in this case, pelagic goby, Sufflogobius bibarbatus, or jellyfish). This being said, in February 2004, the Conference of the Parties to the Convention on Biological Diversity (CBD) identified mean TL of the catch (which they term the Marine Trophic Index, MTI) as one of eight indicators to be tested immediately for their ability to measure progress towards achieving a significant reduction in the current rate of biodiversity loss by 2010 (CBD, 2004). To emphasize changes in the relative abundance of higher TL fish, and to reduce bottom-up effects of productivity changes (reflected in the large fluctuations of small pelagic fish stocks and catches), Pauly and Watson (in press) recently proposed that low TL catches be excluded from global computations of MTI. This would allow top-down effects of fishing to be identified. However, for the reasons just discussed, it is also important to consider changes at the lower TLs (bottom-up effects) when attempting to track changes (via top-down or bottom-up forces) in an upwelling ecosystem such as the Benguela.
The relative stability of the mean TL of the catch, despite the known changes, emphasizes the potential danger of interpreting a single indicator without analysing the causes of the observed trajectory, or understanding the dynamics of the fisheries. The FiB index (in combination with plots of the TL of catches against catches) seems to capture not only the historical development of the fisheries in both systems, but also the differences in the state of the fisheries over time between the two areas, more accurately than any other single index derived from catch statistics. One drawback of the FiB index is that it is heavily dependent on the catches and their trophic level in the reference year. The absolute value only has a clear interpretation relative to that reference year, but if the reference year is changed, the values also change. This accounts for the difference in the range of values obtained for the two systems (Figure 2d). However, the trends in a FiB series are conserved irrespective of the base year selected. Examining the FiB separately for sub-periods characterizing different fishing and/or ecosystem "regimes" is recommended if interannual changes are to be captured in greater detail.
Quantifying changes in an ecosystem is not straightforward, and no single trophodynamic indicator can track the complexity of the observed changes in fisheries and ecosystems. Although they appear useful for understanding ecosystem and fisheries dynamics, such indicators tend to be conservative, because they respond quite slowly to structural change. Moreover, trends are sensitive to calculated TL values, emphasizing a need to improve data collection to better understand fish feeding behaviour. One could wonder whether it might have been more informative to consider abundance and species composition from research surveys, rather than to compute the indicators used here. The use of trophodynamic indicators should not restrain scientists from using single-species metrics, but the trophodynamic indicators should be valued for improving evaluation of the dynamics of the whole ecosystem, by measuring the net effects of the underlying interactions between species groups. In that sense, they are useful in considering probable future trajectories of different ecosystem components. In the case of the southern Benguela, the indicators investigated tell us that the fisheries did not affect the structure and functioning of the ecosystem to a great extent, whereas the indicators for the northern Benguela provide a rather "optimistic" assessment in so far as they did not adequately depict the perceived "ecosystem collapse" (Bakun and Weeks, 2004).
Trophodynamic indicators have not been developed to capture bottom-up forces specifically. We propose that an environmental version of the FiB would be useful in quantifying how environmental effects propagate up through the foodweb. Such an Environment-in-Balance index (EiB) could constitute an analogue that would calculate how, for example, an increase in the planktonic production would propagate up the food chain.
Trophodynamic indicators are still descriptive, and reference points have not yet been identified, although historic time-series, even if data quality is not ideal, confirm massive changes in marine ecosystems. This highlights the need for long-term retrospective analyses to interpret trends and values correctly, and to avoid shifting baselines. Any change in the temporal dynamics or trajectory of an indicator must be interpreted in the light of other, complementary indicators, as well as general ecological knowledge.
One way to apply ecosystem indicators in fisheries management would be to focus on how they can be implemented in minimizing the adverse effects of fishing, rather than to focus on the precision of particular reference points or indicator values (Garcia and Staples, 2000). This may be a challenge beyond the scope of natural science alone. As a first step, the opportunity should be seized to apply the selected indicators to a broad selection of large marine ecosystems (LMEs), to evaluate fully their usefulness for an ecosystem approach to management, and to establish international compatibility of indicators for intersystem comparison. This may be a long-term task, but any step forward should facilitate the evaluation of the overall effectiveness of indicators used in the context of local ecosystems under local institutional management arrangements.
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Acknowledgements |
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The study is a SCOR/IOC WG-119 contribution, and part of the IRD-IDYLE Research Unit dedicated to the study and modelling of marine ecosystems. The document is based on work partially supported by the U.S. National Science Foundation under Grant No. 0003700. GMD acknowledges support from the UK Department of Environment, Food and Rural Affairs contract MF0323. DP acknowledges support from the Pew Charitable Trusts via the Sea Around Us Project and the Natural Sciences and Engineering Research Council of Canada.
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