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Ecosystem impacts of fishing forage fishes : an analysis of harvest strategies for the brazillian sardine Vasconcellos, Marcelo 2000

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E C O S Y S T E M I M P A C T S OF FISHING F O R A G E FISHES: A N A N A L Y S I S OF H A R V E S T STRATEGIES F O R T H E BRAZILIAN SARDINE  by  MARCELO VASCONCELLOS  B . Sc., University o f R i o Grande, 1991 M . S c , University o f R i o Grande, 1994  A THESIS SUBMITTED I N P A R T I A L F U L F I L M E N T OF T H E R E Q U I R E M E N T S F O R T H E D E G R E E OF  D O C T O R OF P H I L O S O P H Y in T H E F A C U L T Y OF G R A D U A T E STUDIES (Resource Management and Environmental Studies) W e accept this thesis as conforming to the required standard  T H E U N I V E R S I T Y OF BRITISH C O L U M B I A February, 2000 © Marcelo Vasconcellos, 2000  In p r e s e n t i n g t h i s t h e s i s i n p a r t i a l f u l f i l m e n t of the requirements f o r an advanced degree at the U n i v e r s i t y of B r i t i s h Columbia, I agree t h a t the L i b r a r y s h a l l make i t f r e e l y a v a i l a b l e f o r r e f e r e n c e and study. I f u r t h e r agree t h a t p e r m i s s i o n f o r e x t e n s i v e c o p y i n g of t h i s t h e s i s f o r s c h o l a r l y purposes may be g r a n t e d by the head of my department or by h i s or her r e p r e s e n t a t i v e s . I t i s u n d e r s t o o d t h a t c o p y i n g or p u b l i c a t i o n of t h i s t h e s i s f o r f i n a n c i a l g a i n s h a l l not be a l l o w e d without my w r i t t e n p e r m i s s i o n .  Department of The U n i v e r s i t y of B r i t i s h Columbia Vancouver, Canada  Abstract The ecosystem impacts o f fisheries have become an important concern i n fisheries management and one o f the most important issues in fisheries science. The objectives o f this thesis were i) to evaluate the use o f trophic models in the analysis o f the ecosystem responses to fishing small pelagic forage fish; ii) to forecast the 'fishing down marine food web' phenomenon in Brazil; and iii) to conduct an in-depth analysis o f the sardine, Sardinella  brasiliensis,  fishery off  southeastern Brazil to examine hypotheses o f ecosystem changes following stock collapse, and to evaluate how uncertainties o f ecological processes influence the choice o f harvest strategies and controls in this fishery.  Dynamic simulations o f mass-balance trophic models were used to  compare the ecosystem responses to fishing forage fishes across different types o f marine ecosystems, and to examine the stability characteristics o f ecosystems when impacted by fisheries. The comparative analysis among trophic models indicated that as 'wasp-waist' species in upwelling ecosystems, small pelagics w i l l only sustain much more conservative exploitation rates than the levels that have brought about historical stock-collapses. In the upwelling ecosystem off southeastern Brazil, sardine has been historically the main target o f commercial fisheries, although 'fishing down marine food webs' for small pelagic fish species is not yet an overall observed phenomenon in Brazil. The collapse o f the sardine fishery i n the late 1980s has apparently favored a competing small pelagic fish (anchovy, Engraulis ecosystem.  anchoitd)  i n the  The combined effect o f fishing and environmental effects makes it difficult,  however, to characterize the changes in the sardine population and the ecosystem, and to predict the results o f rehabilitation measures for the stock. O n the other hand, the explicit recognition o f these ecological uncertainties allows a sound choice o f precautionary strategies for the fishery, and a better scrutiny o f research programs to improve management.  In this context, trophic  models w i l l have a complementary role to single-species models i n the analysis o f the broad consequences o f fishing policies, and i n the test and formulation o f hypotheses about the causes of observed changes in marine ecosystems.  ii  Table of Contents Abstract  ii  List of Tables  V  List of Figures  vi  Acknowledgements  \X  Chapter 1. Introduction  1  Fisheries for small pelagic fish resources  1  Guidelines for Fisheries Management  4  1.1. Objectives  6  Problems in managing small pelagic fisheries A framework for fisheries assessment  6 10  Chapter 2. Ecosystem impacts of harvesting small pelagic fishes. A comparative analysis using trophic mass-balance models  17  2.1. Introduction  17  2.2. Methods  21  General Predicted Impacts  23  Ecosystem Stability  25  2.3. Results and Discussion  30  Ecosystem responses to fishing  30  Ecosystem stability and resilience  43  Model limitations  48  Management implications  50  2.4. Summary  51  Chapter 3. Fishing down food webs and the carrying capacity of marine ecosystems in southern Brazil  52  3.1. Introduction  52  3.2. Methods  54  3.3. Results  .'  59  PPR and Trophic Levels  59  Fishing Down the Food Web  63  3.4. Discussion  66  3.5. Summary  69  Chapter 4. The sardine fishery in the Southeastern Brazilian Bight 4.1. Introduction  70 70  Sardine fishery development  73  Overview of Management Context  75  4.2. Methods  79  Ecosystem structure and dynamics  79  Spawning Stock and Recruitment  84  Stock distribution, effort dynamics and catchability change  88  4.3. Results and Discussion Ecosystem structure and dynamics Stock distribution, effort dynamics and catchability change 4.4. Summary  90 90 114 125  Chapter 5. Analysis of harvest decisions and information needs in the management of the Brazilian sardine. Comparing multi-species and single-species modeling approaches  126  5.1. Introduction  126  5.2. Methods  127  Modeling approaches  127  5.3. Results and Discussion  137  Single-species approach  137  Expected Value of Perfect Information  143  Multi-species approach  144  Conclusions from model simulations  151  On the choice of a modeling approach  157  5.4. Summary Chapter 6. Conclusions  161 162  6.1. Evaluation of ecosystem responses to fishing using dynamic trophic models  162  6.2. Fishing down marine food webs in Brazil  165  6.3. The case of the sardine fishery off southeastern Brazil  166  Notes  169  Bibliography  170  IV  List of Tables Box 1.1. International cooperative programs on oceanography and productivity of marine fish populations  8  Table 2.1. Trophic models of upwellings used in the analysis of the impact of small pelagic fisheries.. 24 Table 2.2: Models used in the analysis of ecosystem stability  29  Table 2.3: Correlation among the ecosystem attributes defined in the text  44  Table 2.4. Trophic models used in the regression between quality rank and persistence rank  50  Table 3.1.Area, primary productivity and total primary production off southern Brazil  55  Table 3.2. Trophic level of the main species landed in Brazil  56  Table 3.3. Parameters of the trophic model of the pelagic ecosystem off southern Brazil  57  Table 3.4. Diet matrix of the model of the pelagic ecosystem off southern Brazil  57  Table 3.5. Trophic level, mean catch and PPR estimates for the southern shelf.  60  Table 3.6: Trophic level, mean catch and PPR estimates for the southeastern shelf.  60  Table 3.7. Summary statistics of the southern and southeastern shelves  60  Box 4.1. Ecopath models of the southeastern Brazil shelf ecosystem  82  Table 4.1. Parameters of the split pools in Ecopath used by the delay difference model in Ecosim  83  Table 4.2. Catch (millions of fish) at age data used in V P A  85  Box 4.2. Virtual Population Analysis procedure for reconstruct sardine biomass at age  86  Table 4.3. Parameters of the Ecopath trophic model of the Southeastern shelf ecosystem  90  Table 4.4. Diet matrix of the trophic model of the Southeastern shelf ecosystem  92  Table 4.5. Ecosystem attributes used in the comparison of trophic models of upwelling ecosystems  94  Table 4.6. Transfer efficiencies between trophic levels  95  Table 4.7. Trophic level, mean catch and PPR for the main species landed in southeastern Brazil  96  Table 4.8. Time-scale hierarchy of variables controlling the Southeastern shelf ecosystem  99  Table 4.9. Biomass at age estimated from V P A  109  Table 4.10. Parameters used in the representation of stock-recruitment relationships  112  Table 4.11. Effort, fishing mortality and catchability of sardine stock to purse seiners  115  Table 4.12. Results of partial correlation analysis between catchability, stock size and temperature.... 117 Table 5.1. Hypothesis, models and parameters used to predict sardine recruitment rates  129  Table 5.2. Percentage of individuals mature and and the reproductive output at age  134  Table 5.3. Decision table on the choice of harvest rate and effort level for the sardine fishery  143  Table 5.4. Results of the analysis of the expected value of perfect information  144  Table 5.5. Relative change in sardine biomass after 5 year under different fishing scenarios  145  Table 5.6. Relative change in sardine catches after 5 years under different fishing scenarios  146  Table 5.7. Relative change in sardine biomass after 5 years under two strategies for stock recovery.... 146 Table 5.8. Relative change in biomass of main harvested species in the Southeastern Brazilian Bight. 147 Table 5.9. Relative change in biomass of main harvested species in the Southeastern Brazilian Bight. 147  List of Figures Figure 1.1. Analytical framework for integrating fisheries stock assessment and management  11  Figure 1.2. Ecosystem models used to evaluate the effects of fishing small pelagic fish species  16  Figure 2.1. Schematic representation of marine food webs with waists at the mid-trophic level  21  Figure 2.2. Simulation of system recovery of the Venezuela shelf model  26  Figure 2.3. Predicted equilibrium biomass and catches of anchovy in the California System (65-72).... 31 Figure 2.4. Predicted equilibrium biomass and catches of anchovy in the California System (78-85).... 32 Figure 2.5. Predicted equilibrium biomass and catches of omnivorous fish in the Monterey System  33  Figure 2.6. Predicted equilibrium biomass and catches of sardine in the Namibia System (71-77  34  Figure 2.7. Predicted equilibrium biomass and catches of anchovy in the Namibia System (78-83)  35  Figure 2.8. Predicted equilibrium biomass and catches of sardine in the N W Africa System (70-79).... 36 Figure 2.9. Predicted equilibrium biomass and catches of anchoveta in the Peruvian System (64-71)... 37 Figure 2.10. Predicted equilibrium biomass and catches of anchoveta in the Peruvian System (73-81). 38 Figure 2.11. Predicted equilibrium biomass and catch of small pelagic fish in the Venezuela System... 39 Figure 2.12. Predicted fishing mortality at maximum yield under two trophic control hypotheses  42  Figure 2.13. Recovery time of the 23 models perturbed by pulse fishing wasp-waist populations  43  Figure 2.14. Relation between Finn's index of detritus recycling and system recovery  45  Figure 2.15. Effect of increasing detritus cycling on the stability of the Venezuela shelf model  46  Figure 2.16. Rank correlation between model persistence and quality  49  Figure 3.1. Shelf regions of Brazil  53  Figure 3.2. Flowchart of trophic relationships in the pelagic association off southern Brazil  58  Figure 3.3. Mean trophic level (A), and species composition (B and C)of total Brazilian landings  62  Figure 3.4. Equilibrium simulation of increasing fishing mortality for anchovy  64  Figure 3.5. Relationship between total catch and the mean trophic level of catches  65  Figure 4.1. Detail of the Southeastern Brazilian Bight  71  Figure 4.2. Sardine landings by the three main state fleets  74  Figure 4.3. Fishing areas (shaded) of the Santa Catarina fleet used in the analysis of effort allocation... 89 Figure 4.4. Trophic flows diagram of the Southeastern Brazilian shelf ecosystem  91  Figure 4.5. Holling's four phase model of ecosystem dynamics  97  Figure 4.6. Pattern of variation observed in many marine fish populations  100  Figure 4.7. Output predicted by Ecosim with a cyclic regime in primary productivity (PP)  103  Figure 4.8. Output predicted by Ecosim with selective predation mortality on juvenile sardine  107 vii  Figure 4.9. Reconstructed time-series of sardine biomass from VPA 110 Figure 4.10. Bayes marginal posterior probability distribution of natural mortality rate  110  Figure 4.11. Graphic representation of the two stock-recruitment hypothesis  112  Figure 4.12. Graphic representation of the "dome-shaped" regime hypothesis  114  Figure 4.13. Relationship between catchability to the purse seine fleet and sardine stock biomass  116  Figure 4.14. Changes in the catchability of sardine with stock size and the mean temperature  116  Figure 4.15. Sardine stock distribution area as estimated by acoustic surveys  119  Figure 4.16. Proportion of total sardine landings by fishing area  120  Figure 4.17. Fleet distribution by landing place  123  Figure 4.18. Test for the equalization of cpue among fishing areas off Santa Catarina  124  Figure 4.19. Monthly effort allocation by the Santa Catarina fleet  125  Figure 5.1. Marginal posterior probability distribution of catchability parameters  130  Figure 5.2. Conceptual structure of a decision analysis on the choice for fishing strategies for sardine..132 Figure 5.3. Monte Carlo procedure used in the evaluation of the outcomes of fishing strategies  134  Figure 5.4. Outcomes of fishing strategy that capture a constant catch  139  Figure 5.5. Outcomes of fishing strategies based on effort control  140  Figure 5.6. Outcomes of fishing strategies that harvest a constant proportion of the stock  141  Figure 5.7. Outcomes of fishing strategies based on a constant stock escapement  142  Figure 5.8. Dynamic simulation of fishing scenarios in 5 years  148  Figure 5.9. Predicted average yield of sardine under fishing mortality rates  149  Figure 5.10. Example of a pulse fishing strategy for sardine  151  Figure 5.11. Percentage spawning per recruit for equilibrium exploitation rate and age at first capture.. 154  vii J  Acknowledgements I would like to thank the members o f my supervisory committee, Les Lavkulich, Daniel Pauly, Carl Walters and Tony Pitcher for their guidance and invaluable inputs to m y work. I would especially like to thank my supervisor, Tony Pitcher, for giving me the opportunity to come to the Fisheries Centre, U B C , to pursue the doctorate degree. I thank the Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq/Brasil) for the financial support provided during the program, and for the assistance o f their staff, Jessy Alves, Ilka Oliveira, Marisa Cassim, and others, which greatly facilitated m y studies i n Canada. A number o f people have given me assistance during the course o f this study. Data for the case study was kindly provided by M a r i a Cristina Cergole and by the I B A M A / C E P S U L , Itajai. I also thank M a r i a Gasalla, Jorge Castello, V i l l y Christensen, Kevern Cochrane, Katherine Sloman, Manoel Haimovici and Humber Andrade for providing data, suggestions and/or criticism to much o f this study. I am especially grateful to friends and colleagues, Steve Mackinson, Ingrid Ross, Dave Preikshot, Trevor Hutton, Rashid Sumaila, and to the many 'coffee breaks', that helped create a friendly and exciting place to work at the Fisheries Centre. N o thanks would be too much to m y wife, Daniela, for her continuous support and inspiration.  IX  Chapter 1. Introduction One o f the major challenges o f natural resources management centers on the science-policy interface (Cicin-Sain and Kenecht, 1998; Walters, 1998). The main reason to relate science and policy is to improve the information available for making management decisions. Improvements in the management  o f natural resources  is largely dependent  on improvements in the  understanding o f the processes involved i n the resource problem. M a n y resource management situations have been aggravated because o f inadequate information or lack o f foresight in anticipating unexpected problems; yet, good information does not always guarantee reliable decisions, since very often information is available but no appropriate management choices are implemented.  Still, there is a perceived strong political benefit for grounding decisions on  science (Jasanoff, 1990; Cicin-Sain and Kenecht, 1998).  This thesis focuses on the use o f  scientific ecological information i n the management o f fisheries for small pelagic fish resources.  Fisheries for small pelagic fish resources Since the advent o f large-scale industrialized fisheries, small pelagic fishes have dominated world landings. Out o f the five leading species i n world catches between 1980 and 1990, four were either small or mid-size pelagics: the Peruvian anchoveta, Engraulis ringens, the Japanese and South American pilchards (Sardinops  melanosticta,  S. sagax) and the Chilean horse  mackerel, Trachurus murphyi (Csirke, 1995). The term 'small pelagic' is used to refer to species such  as  anchovies,  sardines,  pilchards and herrings, which  share  common  biological  characteristics, such as strong shoaling behavior, planktonic feeders, fast growth and short lives. These characteristics lead to stocks with large biomasses, a key role i n the transfer o f energy i n marine food webs, substantial temporal variability i n abundance and to their potential as target o f fisheries exploiting a cheap protein source for human use (Glantz, 1983; Csirke, 1995; Bakun, 1996).  Biological, economic and technological factors have played a role i n the development o f small pelagic fisheries under an 'unlimited resources' paradigm, in which the stocks were considered not prone to be overfished. Shoaling pelagic fish have highly aggregated distributions which makes them relatively easy to capture with modern and efficient fishing technologies, depriving 1  stock assessment and management o f early warnings o f overfishing and stock collapse. O n the other hand, their life history strategies often make small pelagic fish stocks appear to be among the most productive and abundant marine fisheries resources.  The Peruvian anchoveta, for  instance, once supported the largest fishery ever recorded, accounting alone for over 10% o f total world fish production. Fishmeal production is the main objective o f pelagic fisheries worldwide. The increase i n world demand for fishmeal to the feed industry following the W o r l d War II created economic conditions for the fast development o f reduction fisheries for small pelagics. One o f the best documented cases o f rapid economically-driven development was that o f the Peruvian anchoveta fishery i n the early 1950s, when U . S . factories for fishmeal production were built in Peru following the collapse o f the California Pacific sardine, Sardinops sagax (Glantz, 1983). Today, pelagic catches used worldwide by the reduction industry represents roughly one third o f world marine catches and about 80% o f total world pelagic catches (Durand, 1998).  The history o f many pelagic fisheries worldwide reveals, however, a different reality. Small pelagic stocks were depleted to the point o f collapse, i n some cases repeatedly, and often i n alternating 'regimes' between anchovy and sardine species (Lluch-Belda et al., 1989).  The  depletions caused not only economic and social difficulties but also undesirable and unexpected ecological disruptions, such as massive mortality o f guano birds after the collapse o f the Peruvian anchoveta (Tovar et al, 1987), and an increasing dominance o f jellyfishes after heavy exploitation o f small pelagics and intense eutrophication i n the B l a c k Sea (Mee, 1992). Today, cyclic 'boom and bust' dynamics o f pelagic fisheries is an widely recognized characteristic that is explained by alternative (but non-exclusive) hypotheses concerning climatic effects  on  recruitment, trophic interactions, and overfishing (Radovich, 1982; Caddy and Gulland, 1983; Lluch-Belda et al., 1989; Csirke, 1995; Bakun, 1996). In fact, vigorous debate on the extent that fisheries and environmental processes have contributed to cyclic regimes still persist (Radovich, 1982; Bakun, 1996; Steele, 1996).  A metanalysis carried by Patterson (1992) showed, for  instance, that most cases o f small pelagic stock collapse occurred when F (fishing mortality) was higher than 0.6 M (natural mortality).  The demise o f many small pelagic fisheries rouses a more fundamental rationale - that stock assessment research has not been providing adequate advice to managers, specially through the design o f fishery control systems that can respond to the types o f variability and uncertainties observed i n nature. Scientific debate around the causes o f collapses, recoveries and species shifts 2  in many ecosystems and the relatively short time series o f data for most fisheries, have been historically responsible for the lack o f a comprehensive framework for scientific advice to fisheries management.  Debate on the causes o f fisheries collapse has always permeated the  philosophies, research activities and conclusions o f fisheries scientists on different agencies, as portrayed by Radovich (1982) for the case o f the Pacific sardine:  "The Federal scientists, working for an agency whose fundamental assist the development and maintenance  charter was to  of U.S. commercial fisheries,  looked for  reasons other than fishing, for the sardine's declining condition, while the scientists employed by the State (whose basic role was protector supported  the premise  that overfishing  of the State's  was having a detrimental  resources)  effect on the  standing stock. These were capable, competent scientists using the same data and coming  up with different  conclusions  in part because  they were employed  by  agencies whose fundamental goals were different."  On the other hand, a perceived need for scientific consensus is present i n many resource management  occasions,  and  in  some  o f the  most  disastrous  examples  of  fisheries  mismanagement. F o r instance, i n the case o f the Atlantic cod, Gadus morhua, the insistence on consensus has led a government-administered assessment framework that suppresses scientific uncertainties and fails to document important differences i n scientific opinion regarding the risks o f stock collapse (Hutchings et al., 1997).  T w o types o f responses to the lack o f scientific  consensus are commonly encountered i n fisheries management: first to completely ignore uncertainties when making decisions; and second to direct more research efforts to better understand the nature o f processes affecting fish stocks before changes i n the status quo policies take place. More often, however, uncertainties have impeded scientific inputs into management decisions, which end up obeying strictly economic and political forces.  L u d w i g et al. (1993) asserted that we shall never attain scientific consensus concerning the systems that are being exploited because controlled and replicated experiments are impossible to perform i n large-scale systems.  Therefore there w i l l be always ample scope for differing  interpretations o f natural events. In this context, fisheries management should acknowledge the uncertain nature o f ecological processes and seek for robust strategies for exploiting populations  3  and ecosystems. Moreover, it is necessary to recognize that science and management must be coupled to better understand the nature o f processes affecting fish populations (Walters, 1986).  Guidelines for Fisheries Management Complications arising from the uncertain nature o f fisheries management, and the common realization o f the need for a broader, ecosystem perspective on natural resources management has lead to the development o f many principles and guidelines for effective fisheries management throughout the world (e.g. F A O , 1997; G E S P E , 1997; Canadian Ocean Act).  W i t h respect to uncertainties, the F A O Code o f Conduct for Responsible Fisheries ( F A O , 1995) adopts the precautionary approach which states that "...where there are threats of serious or irreversible damage, lack offull scientific certainty shall not be used as a reason for postponing  cost-effective  measures to prevent  environmental  degradation".  The proper implementation o f the precautionary approach w i l l require, among other issues identified by F A O Technical Guidelines for Responsible Fisheries ( F A O , 1997), "-consideration potentially  of the needs of future generations  and avoidance  of changes that are not  reversible;  - prior identification of undesirable outcomes and of measures that will avoid them or correct them promptly; - that where the likely impact of resource use is uncertain, priority should be given to conserving the productive capacity of the resource; - that harvestable and processing capacity should be commensurate levels of resource, and that increases in capacity should be further productivity is highly  with estimated  sustainable  contained when resource  uncertain".  The F A O Technical Guidelines for Responsible Fisheries ( F A O , 1997) also recognizes the need to place fisheries i n an ecosystem context; "....harvesting  any one species is almost certain to impact others,...The  impact of ecological  linkages (e.g. through the trophic chain) between species, may lead to changes in species dominance and affect the dynamic equilibria  of the resource system, potentially  affecting future  4  options.  These multi-species effects need to be considered in responsible fishing, which should  aim to ensure that no species, whether targeted, by-catch or indirectly affected by fishing, is reduced to below sustainable levels. " Moreover, " Responsible fisheries management should consider the impact of fisheries on the ecosystem as a whole, including its biodiversity, and should strive for sustainable use of whole ecosystems and biological  communities."  Mangel et al. (1996) proposed an independent set o f principles for the conservation o f w i l d living resources which summarized the point o f view o f 42 scientists. Three out o f seven principles explicitly recognize the need to account for human impacts on ecosystems, e.g.  "The goal of conservation biological  should be to secure present and future  diversity at genetic, species, population,  options by  maintaining  and ecosystem levels; as a general  rule  neither the resource nor other components of the ecosystem should be perturbed beyond natural boundaries of variation."  "Assessment of the possible ecological and sociological  effects of resource use should precede  both proposed use and proposed restrictions or expansion of ongoing use of a resource."  "Regulation  of the use of living resources must be based on understanding  the structure and  dynamics of the ecosystem of which the resource is part and must take into account ecological and sociological  the  influences that directly and indirectly affect resource use."  The adoption o f these principles w i l l challenge scientists and decision makers to consider the more diverse as possible consequences o f fisheries to marine populations and ecosystems. Therefore, a critical re-examination o f concepts, methods, tools, and regulatory measures commonly used in fisheries assessment and management is primarily needed.  5  1.1. Objectives It is the overall goal o f this thesis to evaluate fishing strategies for small pelagic fish stocks considering the inherent variability o f the ocean environment and the ecological role o f these species i n marine food webs.  The specific objectives o f the thesis are  - to make specific predictions about ecosystem responses to fishing small pelagic species, using dynamic trophic models;  - to analyze the characteristics o f stability and resilience o f exploited marine ecosystems when impacted by fisheries for mid-trophic level forage species; and  - to carry out a case study with the fishery for sardine, Sardinella  brasiliensis,  in the  Southeastern Brazilian Bight to examine how the combined effect o f environmental, behavioral and ecological processes, as captured by different modeling approaches, influence the choice o f harvest strategies and controls.  Problems in managing small pelagic fisheries The inability to predict the effect o f harvest policies given the lack o f understanding o f the processes affecting fish stocks has lead to major research initiatives into fisheries-oceanography, specially i n the analysis o f the effects  o f environmental variability on recruitment o f  commercially important marine populations.  International initiatives were supported by the  Intergovernmental Oceanographic Commission ( I O C - U N E S C O ) program on Ocean Sciences i n Support o f L i v i n g Resources ( O S R L ) , which aimed to promote the development o f plans for major oceanographic studies o f the physical-ecological interactions o f importance to fishery resource-related problems (Bakun et al,  1982). The rationale o f the program was that many  fisheries problems, such as recruitment, population availability and species interactions, could not be solved solely based on the analysis o f fisheries data. The accumulated evidence derived 6  from deposits o f fish scales in sediments (Soutar and Isaacs, 1974; Baumgartner et al., 1992), and the global synchrony i n the raising and falling o f the large populations o f anchovies and sardines i n the major upwelling regions o f the world (Kawasaki, 1983; Lluch-Belda et al., 1989), indicated that radical fluctuations i n abundance may be an intrinsic feature o f many marine fish populations. Moreover it pointed to the existence o f physical processes that teleconnect these widely separated systems. I f so, then the hope for successful fisheries management would appear to rely i n gaining scientific understanding o f the natural factors determining reproductive success and population dynamics o f fishery stocks. The rationale would have two main implications to fisheries management: first, it would show the need to develop robust strategies for fisheries viability under conditions o f radically varying resources; second, and most importantly, that fisheries management would directly benefit from the increased scientific ability to identify and predict transition periods or 'regime shifts', when distinct levels o f precautionary actions would be necessary (Bakun, 1998).  A research framework was proposed involving a combination o f a rational (cause-effect) approach, based on the understanding o f relevant physical and biological processes for the viability o f marine populations, and an empirical approach, i n which functional relationships between these processes were defined by analysis o f available data. The difficulty i n addressing some o f the physical-biological processes experimentally called for the combination o f a third approach based on the comparative method o f analysis (Bakun, 1996). Results o f the comparative studies o f geographical climatology o f fish reproductive habitats have tended to identify a triad o f physical factors capable o f exerting control on reproductive success o f marine populations (Bakun, 1996; Bakun et al., 1998). These are enrichment processes that lead to the production o f the zooplankton upon which the young stages depend for food;  concentration  processes that aggregate food products and therefore increase their availability for the growing larvae; and retention processes that keep the young in favorable nursery habitats. In coastal upwelling areas enrichment is basically driven by persistent alongshore winds that forces water to move offshore and, i n order to balance the superficial deficit, deep nutrient-rich water is forced into the surface illuminated layers where primary production is possible.  Frontal structures  (vertical and horizontal) are the main features responsible for the concentration o f plankton i n thin stable layers and for creating optimal larval feeding conditions.  Retention involves  mechanisms such as Taylor column, Ekman divergence and zones o f stability (Bakun,- 1996). Some corollaries o f triad theory have been tested by Cury and R o y (1989) who showed that the 7  reproductive success i n upwelling ecosystem is tightly associated to an optimum window o f moderate wind stress, thought weak winds are not sufficient to enrich the upper layers and winds too strong can otherwise disrupt stable structures i n the ocean. The "triad framework" is being applied i n diverse international programs aimed at identifying the links between climaticoceanographic processes and marine fish populations (Box 1.1).  Box 1.1. International cooperative programs on oceanography and productivity of marine fish populations.  • I R E P / S A R P . The Sardine-Anchovy Recruitment Project ( S A R P ) was established as the initial focus o f the International Recruitment Project (IREP) component o f the O S R L . The main objective o f S A R P was to promote a greater understanding o f the biologicoceanographic processes involved in controlling recruitment success i n sardine and anchovy populations (Bakun et al., 1982). • C E O S . The Climate and Eastern Ocean Systems ( C E O S ) project was a joint effort o f the National Marine Fisheries Service ( N M F S ) , the Institut Francais de Recherche Scientifique pour le Developpement en Cooperation ( O R S T O M ) , and the International Center for L i v i n g Aquatic Resources Management ( I C L A R M ) . The C E O S project was an international collaborative study o f the potential effects o f global climate change on the living resources o f the highly productive eastern ocean upwelling ecosystems and on the ecological and economic issues directly associated with such effects. A major focus o f the study were the clupeoid fishes (Durand et al., 1998). • G L O B E C / S P A C C . International Global Ocean Ecosystem Dynamics ( G L O B E C ) program on Small Pelagic Fishes and Climate Change ( S P A C C ) . The objective o f S P A C C is to identify how physical forces are linked with the growth o f pelagic fish populations, which is believed to be mediated through the dynamics o f zooplankton populations. The long-range goal o f S P A C C is to forecast how changes i n ocean climate w i l l alter the productivity o f small pelagic fish populations. To address this goal, S P A C C uses comparative retrospective and process studies (Hunter and Alheit, 1997).  The proposed improvement in management  practice using climatic/oceanographic process  studies has been fiercely criticized (Walters and Collie, 1988; Walters, 1998).  A s stated  previously, one direct benefit o f these studies would appear to be the improvement in the quality of decisions on harvest levels by allowing the adjustment o f policies according with predicted environmental conditions. For instance, i f short term variations could be accurately forecast, the best policy would be to adjust the target stock size so as to increase catch when positive variation is anticipated and to reduce when negative variation is anticipated. Conversely, it may involve allowing escapement to increase when good environmental conditions are forecast so as to take the maximum advantage o f the optimum conditions (Parma, 1990; Walters and Parma, 1996).  Walters and Collie (1988) argued, however, that i n most cases better understanding and predictive models are not that useful, since the same benefits could be met more cheaply by improved monitoring programs, i.e., recruitment and spawning biomass surveys, and existing feedback policies.  Better understanding o f the processes controlling recruitment success seems to be mostly needed for situations i n which the effects o f long-term environmental factors are confounded with the effect o f stock size and fishing (Walters and Collie, 1988). Environmental cycles o f intermediate periods superimposed on stock production dynamics can obscure any underlying relationship between recruitment and spawning biomass (Armstrong and Shelton, 1988).  In fact, most  accounts o f stock collapses during the last decades have at their core the endless debate about whether it was the result o f fishing or o f environmental effects. A m o n g the best documented examples are the sardine/anchovy collapses i n coastal upwelling systems (Pauly and Palomares, 1989; Barnes et al., 1992), the decline i n recruitment o f groundfish stocks off east coast o f North America (Walters and Maguire, 1996) and the "Thompson-Burkenroad" debate on the causes o f recruitment fluctuations i n the stock o f Pacific Halibut (Parma and Deriso, 1990). In these cases the understanding o f how environmental changes affect fish productivity would be very valuable to fishery management.  Yet, it is argued that such understanding w i l l not be achieved by  continued correlative and biological process studies and w i l l instead require sound management experiments i n which environmental studies are coupled with deliberate manipulation o f stock sizes through changes in harvest policies (Walters and Collie, 1988; Walters, 1998).  A more fundamental criticism o f the process-oriented studies is that they do not reflect necessarily the questions and information needs o f managers.  Walters (1998) stated that weak  communication between scientists and managers have repeatedly resulted i n the development o f models based on variables and factors that do not consider policy options and values identified by managers, the allocation o f scarce resources into research programs that are only vaguely related to policy questions, and, ultimately, the design o f policies that most commonly fail when implemented because important details were not properly accounted for. Relating research and management w i l l require a more thorough analysis o f information needs for making management decisions, and a better scrutiny o f research programs i n which to allocate resources.  9  A framework for fisheries assessment Fisheries stock assessment research has been recognized as a mean to provide some structured use o f available information to estimate the nature o f the tradeoffs when comparing fisheries management choices (Hilborn and Walters, 1992). Tradeoffs and choices are the core elements o f fisheries assessment advice, and imply an active role o f fisheries scientists i n aiding managers understand the responses o f fishery systems to alternative management  choices, being the  responses usually related to the impact o f fishing on fish stock and ecosystem, social desirability o f fishery regulations and economic returns to fishery sectors.  The main scientific input to  fisheries management is i n the evaluation o f the consequences o f alternative harvest policies for marine resources. This advisory work should consider the consequences o f fishing activities to marine populations and ecosystems, being ideally linked by a common framework (Mangel et al, 1996). A framework for fisheries stock assessment must be able to provide managers with information on (Butterworth et al, 1997; Cochrane et al, 1998): • tradeoffs among different regulatory mechanisms i n the short, medium and long term; • risks associated with each regulatory mechanism; • outputs that could be easily understandable for all decision makers; • guidance on what type o f data to collect for the purpose o f the chosen regulatory mechanism; and • definition o f a rank o f research priorities, and a time frame for revision o f both research and regulatory mechanisms, in light o f any changes i n understanding o f resource/fishery.  The achievement o f a basis for informed judgment on the costs o f postponing or attenuating decisions demands an integrated strategy for fisheries management and research.  Figure 1.1  outlines an integrated framework for fisheries assessment based on a set o f interrelated questions.  10  1 what are the decisions for which ecological information is required? policwrecommendations 2 which type of information would provide the basis for decisions?  3 how well cantTecisions be made with the available information?  Figure 1.1. Analytical framework for integrating fisheries stock assessment and management (based on Dorcey and Hall, 1983).  The first step i n the framework comprise the analysis o f the management or decision context. The achievement o f objectives is the sole reason for being interested i n any decision. Yet, unfortunately, objectives are not adequately articulated and explicit i n many  management  situations because o f either a lack o f involvement o f all fisheries interests i n the decision making process, or for the constant political pressure put on managers to produce short-term "tangible results" (Keeney, 1992). The values relevant to a decision context w i l l indicate what type o f information is important. In this respect, data w i l l have value only i f it w i l l help lead to better consequences, either through the creation o f better alternatives or through the wiser choice o f alternatives (Keeney, 1992). For fisheries management, decisions normally involve the choice o f harvest strategies, and ecological information w i l l be used to estimate the impact o f these regulatory mechanisms on the ecological sustainability o f the fishery. The ecological criteria for sustainability may involve an array o f indicators such as catch, the size (biomass) o f the spawning stock, and/or indicators o f fisheries impact on the ecosystem. Ecosystem impacts may include direct effects such as the catch o f non-target species (by-catch) and the destruction o f marine habitats, and indirect effects on non-target species due to trophic cascade effects through the food web.  11  M a n y possible combinations o f harvest strategies and controls are possible, and it is the role o f stock assessment to provide managers with advice on the possible consequences o f each regulatory mechanism (management decisions).  Once the decision context has been properly  outlined it is then possible to analyze how well can the choice for management strategies be made, what are the sources o f uncertainties and the opportunities for learning. A t this stage decision analysis methodology can be used to provide an "heuristic aid" (sensu Rowen, 1976) for relating means to ends, for thinking about ends, as well as for identifying new management alternatives (Morgan and Herion, 1990). Decision analysis involve processes subsequent to the analysis o f objectives, such as the identification o f alternative hypothesis about the system been managed, inference o f uncertainties on processes and states relevant for the decision context, and the assessment o f the consequences and risks associated to each strategy using criteria defined by the objectives.  The completion o f these steps w i l l ultimately lead to policy and research recommendations (Figure 1.1).  Research recommendations can be oriented for improving both descriptive and  functional knowledge (Dorcey and H a l l , 1983). Descriptive knowledge may include data on fish distribution and abundance, parameters,  species diet compositions, fleet characteristics,  etc., and involves research  oceanographic  activities such as inventorying and monitoring.  Functional knowledge involves understanding system relationships such as how recruitment is affected by reducing the spawning stock, how catch rates change with oceanographic conditions, etc. N e w functional knowledge can only be developed by carrying out experiments to test hypotheses through either experimental research, experimental management or desk analysis (Dorcey and H a l l , 1983). The later should precede experimental management or experimental research i n order to develop new hypothesis and guide experimental design. Very often, when experimentation is difficult or unfeasible, the comparative method o f analysis has been employed to acquire new functional knowledge o f ecological processes (Mayr, 1997). The two types o f knowledge are linked by the fact that new functional knowledge is only obtained when the data produced by inventorying and monitoring are used to test hypothesis about system processes. Characteristically, research recommendations w i l l have a longer time frame for involving the understanding o f processes operating at medium and long-term scales (e.g. oceanic regimes, recruitment variability, cycles o f investment, technological changes, etc.).  12  Policy recommendations, on the other hand, require short-term responses from scientists who are compelled to explicitly confront and communicate uncertainties to decision makers. There are usually competing hypothesis about the dynamics o f a natural resource, and the outcomes o f a given management decision may differ considerably according to which o f the alternatives come out to be true. Therefore when decisions are made under uncertainty, managers are bound i n effect to consider the risks associated with their actions and the impacts o f their choices on the 'legacy o f uncertainty' that their successors w i l l face. The emphasis on the need to account for uncertainties when making decisions is however not often transparent.  Morgan and Herion  (1990) describe situations in which accounting for uncertainties is crucial for a decision analysis, e.g., when people's attitude towards risk is likely to be important, such as i n many health and environmental issues; when multiple information sources and uncertainties must be combined; when a decision must be made on the allocation o f resources to obtain additional information on the problem being analyzed; and in situations where the process o f decision making involve multiple actors making explicit and implicit decisions over an extended period. In these cases the analysis w i l l be more useful i f it treats the uncertainty explicitly allowing users to evaluate its conclusions and limitations better i n the changing context o f the ongoing decision process (Morgan and Herion, 1990). Taking account o f uncertainties is on the other hand a fundamental requirement for the adoption o f sound precautionary measures i n fisheries management ( F A O , 1997).  Fisheries stocks assessment is usually faced with uncertainties on the biological production, and on the expected outcomes o f harvest strategies and controls. Uncertainties on the biological production, and hence i n the relationship between  current catches and the future state o f the  stock, frequently result from biases in fishery data as a descriptor o f stock dynamics, sampling errors, and by process errors created by environmental effects on recruitment and production rates. A l s o , the ecosystems supporting small pelagic fish populations undergo productivity changes o f decadal  frequencies,  often called 'regime shifts' (Steele, 1996; 1998). These  systematic shifts i n productivity can be driven by a variety o f factors including climate changes, variability i n the abundance o f preys, competitors, predators as w e l l as by changes in the internal structure o f the stock (Walters, 1987). The lack o f understanding o f processes controlling biological production and the  multi-interest nature o f resources  exploitation generates  uncertainties i n the choice for management strategies and i n the expected outcomes o f a chosen fishing strategy. Finally, the information required to achieve the objectives o f a fishing strategy 13  may include data on spatial distribution, temporal distribution and abundance o f the stock, and on fleet characteristics and movement on the fishing ground. The information requirements for harvest control are best remembered as the answers to the questions, "where, when and how many ?" (Mundy, 1985). Uncertainties i n the outcomes o f the implemented tactics arise whether the information on the state o f the resource is poor or catch rates vary independently o f stock size as a result o f changes i n catchability.  Fisheries advisory work can be based on two types o f modeling approaches: single-species and ecosystem or multi-species models. little used to date.  Multi-species models are i n their infancy and have been  Multi-species models have been proposed as tools for guiding the  implementation o f ecosystem principles i n fisheries management, but exactly how the approach could be used, and also what should be the role o f single-species approaches in this new paradigm is still unclear. Such comparisons have not been performed explicitly before.  This thesis is organized in four chapters.  Chapter 2 uses trophic models to examine the ecosystem impacts o f harvesting small pelagic fishes i n selected marine ecosystems. Results are used to discuss three main questions: i) what are the general predicted ecosystem responses to fishing small pelagics species?; ii) what is the role o f trophic cascades i n the replacement o f species at the pelagic planktonic niche? and; iii) what are the characteristics o f stability o f marine ecosystems when impacted by fishing at the mid-trophic level groups?  The validity o f the predicted results is evaluated with published  observations and limitations o f the modeling approach.  Chapter 3 analyzes the carrying capacity o f marine shelf ecosystems i n southern Brazil for harvestable species by (1) quantifying the amount o f available primary production appropriated by fisheries catches, (2) evaluating the trend i n the mean trophic level o f fisheries, and (3) simulating the ecosystem effects o f 'fishing down the food web' i n an intensively exploited shelf region. The analysis aims to forecast the 'fishing down marine food web' effects i n Brazil, and to compare the footprint o f fisheries i n two o f the most intensively exploited regions o f Brazil: the southern and southeastern shelf ecosystems.  14  Chapter 4 reviews the status o f fisheries assessment o f the Brazilian sardine in the Southeastern shelf ecosystem. The fishery was chosen as a case study because it exhibits many similarities with other pelagic fisheries worldwide, notably for its history o f development and collapse, as well as  for its regional importance as one o f the most  productive and well studied marine fisheries resources in Brazil. Ecological and fisheries data are used to describe the changes i n recruitment, stock catchability, and the structure o f the Southeastern Brazilian Bight ecosystem.  This information is used to formulate  hypotheses about the resilience o f the population and ecosystem when impacted by fisheries.  Chapter 5 centers on the assessment o f risks o f management decisions i n the sardine fishery with currently available information and according to two modeling approaches: a single-species and a multi-species model. The analysis aims to evaluate the short and long term predictions o f the impacts o f harvest strategies and controls; to discuss the relative values o f reducing current process uncertainties; to recommend the types o f research that would most likely provide the information needed to improve the quality o f decisions; and to suggest the precautionary measures that should be adopted i n face o f the ecological uncertainties.  Finally, Chapter 6 summarizes the general conclusions o f this work.  The map i n figure 1.2 identifies the trophic models used i n each chapter to evaluate the ecosystem responses to fishing small pelagic fish species.  15  Venezuela small pelagics  California anchovy Chapter 2  N W Africa sardine Chapter 2  Chapter 2  «  Peru anchovy Chapter 2  Southern Brazil anchovy Chapter 3  SE Brazil sardine Chapter 4 & 5  Namibia sardine & anchovy Chapter 2  Figure 1.2. Ecosystems for which trophic models were used to evaluate the effects of fishing small pelagic fish species. The boxes indicate the species fished and the correspondent chapters where the models were used.  16  Chapter 2. Ecosystem impacts of harvesting small pelagicfishes.A comparative analysis using trophic mass-balance models 1  2.1. Introduction Fisheries management has been urged to consider the ecosystem impacts o f fishing activities, given the signs o f human dominance and impact on the oceans. worldwide status o f marine capture  Recent assessments o f the  fisheries revealed for instance that, fisheries  alone  appropriate ca. 8% o f the total marine primary production and up to one third o f temperate shelf systems production (Pauly and Christensen, 1995); over 60% o f the most important fish stocks are either overexploited or at the verge o f becoming overexploited by current fishing intensity (Garcia and Newton, 1997); and that approximately 27 million tons o f nontarget animals are discarded annually as "trash" fish (Alverson et al., 1994). A l s o , present exploitation patterns are resulting in a "fishing down marine food webs" phenomenon, by which heavy commercial fisheries are causing a progressive simplification o f ecosystems i n favor o f smaller, highturnover, lower-trophic-level fish and invertebrate  species that are adapted to withstand  disturbance and habitat degradation (Pauly et al., 1998; Pitcher and Pauly, 1998).  The effect o f fisheries on ecosystems are usually classified i n direct and indirect impacts (Botsford et al., 1997; Goni, 1998). Direct impacts include overfishing, by-catch and discard o f non-target species, changes i n genetic diversity o f stocks, physical disturbances and habitat destruction by fishing gears. Fisheries, for instance,  have the potential to affect the genetic  diversity o f populations by selectively removing older and larger individuals (Goni, 1998), and by depleting small reproductive stocks o f metapopulations o f species such as salmon and herring (Policansky and Magnuson, 1998). Ultimately, the decrease i n genetic diversity by fishing can cause loss o f resilience o f fish populations to both human and natural impacts. Fisheries are also responsible for physically damaging important marine habitats, specially with towed gears (otter trawlers, beam trawlers, dredges). The effects o f trawling on the sea bed vary from destruction of suitable habitats for the settlement of juvenile and adult phases o f diverse marine organisms, changes in abundance and species composition o f benthic communities, and concomitant changes in the fish species composition associated with the physical alterations o f bottom structures (Goni, 1998). 17  Indirect ecosystem impacts o f fisheries are at least partly mediated through the food web, where the'effects o f fishing 'cascade' to other components o f the system. The ecosystem-wide effects o f catching fish attracted little attention and probably had minor importance before the advent o f large-scale industrialized fisheries. But, today, the scale o f fish production can considerably alter the structure o f marine food webs. Heavy commercial fishing has been often associated with drastic changes in species composition in marine communities. Examples o f major changes in marine ecosystems with intense fishing are present in almost every region o f the world (Goni, 1998), although in many cases it has proved difficult to separate the natural and anthropogenic causes o f changes (Steele, 1998). A m o n g the best documented examples are the cases o f switches i n dominance between sardines and anchovies i n coastal upwelling systems after the activity o f reduction fisheries (Lluch-Belda et al., 1989; Bakun, 1996). In the Bering Sea, human exploitation o f whales and other top predators is thought to be responsible for cascading effects on other components o f the ecosystem, such as declines i n sea lions and seals, and the dominance o f groundfish species such as pollock, Theragra chalcogramma, and large flatfishes ( N R C , 1996; Trites et al., 1999). O n Georges Bank, large scale disturbances caused by intense fishing and habitat destruction were also associated with apparent replacement o f gadids and flounders by species o f low commercial value, including dogfish sharks and skates (Fogarty and Murawski, 1998).  Over the last 70 years a dramatic catalogue o f stock collapses have involved small pelagic forage fish, but despite some pioneering analyses (Beddington and M a y , 1977) there have been few rigorous attempts to model and predict the potentially devastating ecosystem consequences o f overfishing. In the case o f the Peruvian ecosystem there is clear belief, but little evidence, that the loss o f ca. 5 million guano-producing birds with the collapse o f the anchoveta stock w i l l have had profound ecosystem impacts (Pauly and Tsukayama, 1987). Beverton (1990) warned against the subtle consequences to the ecosystem that may result from the collapse o f major small pelagic populations.  H e suggested  that there was "some inferential evidence that the  disappearance o f some 10 million tonnes o f Norwegian spring spawning herring and 2 million tonnes from the North Sea may have resulted i n reorientation o f the flow o f production into alternative stable states". Some believe this reorientation o f flow was responsible for the "gadoid outburst" (Cushing, 1980). In a review o f cases o f species replacement, Daan (1980) concluded that for the North Sea some sort o f replacement was likely.  However, under his rather strict 18  criteria for replacement, only one out o f nine candidate cases (Pacific sardine and anchovy) could be considered as true replacement rather than coincident changes.  While evidence o f ecosystem impacts o f fisheries accumulates throughout the world, we see the development o f fisheries management principles and guidelines which acknowledge the need to place fisheries i n an ecosystem context (e.g. Mangel et al., 1996; F A O , 1997).  Scientific  advisory work is now compelled to consider the more diverse as possible aspects or consequences o f fishing activities to marine populations and ecosystems (Mangel et al., 1996). Although the need for ecosystem management has been widely recognized, scientific advice is still hampered by the lack o f understanding o f the complex dynamics o f ecosystems, and the lack o f consensus on which framework should be used to account for the ecosystem effects o f fisheries. Despite the reality that fisheries are generally not restricted to affecting one species alone, the development o f single species models for fisheries management has centered around that very assumption.  A n d , due to our lack o f ability to model complex systems, such  methodology is still prevalent.  Attention during the last decades has been given to the development o f tools that describe patterns o f trophic interactions in the food web, mainly represented by multispecies virtual population analysis, M S V P A (Sparre, 1991), and ecosystem mass-balance models such as Ecopath (Polovina, 1984; Christensen and Pauly, 1992). The implementation o f the first type o f modeling tool has been hampered i n part by the need for extensive time series o f catch-at-age data, difficult parameterization, the high degree o f expertise required from the modeller, and the overall lack o f transparency i n the estimation procedure (Walters et al., 1997). Ecopath offers, on the other hand, a simpler approach for the reconstruction o f trophic interactions i n fished ecosystems, and has been widely applied to aquatic ecosystems (more than 80 Ecopath models have been published world-wide describing upwelling, shelf, lake, river, open ocean, and terrestrial farming systems; see the Ecopath homepage  at http:Wwww.ecopath.org).  The  approach has some advantages over other existent trophic models v i z . , it includes all trophic levels i n the analysis (from primary producers to top predators) as opposed to focusing only on the commercially important fish species; the emphasis on ecological relationships makes it intuitively simple; it incorporates an standardize large amounts o f scattered information, from data routinely collected by fisheries scientists and marine biologists to the traditional ecological knowledge (Pauly et al., 1998); and, more importantly, the widespread use o f the approach 19  creates opportunity for comparative studies o f ecosystem's  response to fisheries impact.  Moreover, further developments o f the mass-balance model, which originally focused on describing systems at steady-state conditions, has resulted i n a dynamic ecosystem model called Ecosim (Walters et al., 1997) that is capable o f answering " w h a t - i f questions about policy and ecosystem changes that would cause shifts i n the balance o f trophic interactions.  This chapter uses Ecosim to examine the ecosystem impacts o f harvesting small pelagic fishes in selected models o f marine ecosystems. M a n y marine ecosystems share a similar configuration o f their biological community structure, characterized by a crucial intermediate trophic level often occupied by a small plankton-feeding pelagic species (Rice, 1995; Bakun, 1996). Unlike typical food webs where different types o f predators feed upon different types o f prey, in these ecosystems one prey type usually dominates as the primary channel o f energy from lower to higher trophic levels (Fig. 2.1). Trophic dynamics i n these 'wasp-waist' ecosystems (sensu Rice, 1995) is thought to be largely dependent on this mid-trophic level species, often represented by important fisheries resources o f anchovy, sardines and herrings. Results o f model simulations are discussed around three main questions: i) what are the general predicted ecosystem responses to fishing small pelagic species?; ii) what is the role o f trophic cascades i n the replacement o f species in the pelagic planktivore niche? and; iii) what are the characteristics o f stability o f exploited marine ecosystems when impacted at the 'wasp-waist' species? Finally, the validity o f results is evaluated with published observations and the limitations o f the model. The practical utility o f the results and approach for defining ecosystem objectives i n fisheries management is discussed.  20  Figure 2.1. Schematic representation of marine food webs with waists at the mid-trophic level. A waist is characterized by a small number of taxa that transfer most of the energy between lower and higher trophic levels. In 'wasp-waist' ecosystems only one or a few species at the intermediate trophic level transfer most of the production from all lower levels to predators at the top of the food web. The flow diagram on the right shows a pelagic system where a small pelagic species (anchovy) is the 'wasp-waist' species. The thickness of lines is proportional to the biomass flow between species; double-head arrows indicate exports from the system.  2.2. Methods  Ecosystem impacts o f harvesting small pelagic fishes were examined with a dynamic trophic model, Ecosim (Walters et al., 1997), structured from mass-balance assessments with Ecopath (Polovina, 1984; Christensen and Pauly, 1992).  Ecopath provides a static picture o f the  ecosystem trophic structure by estimating trophic flows and biomasses w h i c h satisfy growth and mortality constraints. The model relies on the truism that for each group (i) i n the system, and to any time period:  Production by (i) = All predation on (i) - Fisheries catches - Other mortality - Losses to adjacent systems  This can also be articulated as  B .(^) .EE t  i  l  -J-Bj.^pj.DCji-(Y+EX)i  = AB  t  (1)  21  where i n a system o f i=l,...,n functional groups; P/Bj is the production/biomass ratio o f (i) (equal to the total mortality rate Zj under the assumption o f equilibrium); E E is the ecotrophic ;  efficiency, i.e. the fraction o f the production that is accounted for by consumption within the system (predation) or harvested; Y is the yield o f (i), i n weight, with Y = Fj.B , where F is the ;  {  ;  ;  fishing mortality; E X is other exports o f (i) from the system; Bj is the biomass o f the consumers ;  or predators; (Q/B)j is food consumption per unit o f biomass for consumer j , and DCjj is the fraction o f i i n the diet o f j .  A B i is biomass accumulation rate per time in cases where the  analysis do not use data from an initial equilibrium situation.  B y re-expressing the system o f linear equations (1) as differential equations, Ecosim provides a dynamic model suitable for simulation o f the effects o f F varying i n time on the biomass o f each group i n the system. The model provides dynamic biomass predictions o f each (i) as affected directly by fishing and predation on (i), changes in food available to (i), and indirectly by fishing or predation on other groups with which (i) interacts (Walters et al., 1997).  Constructing a  dynamic model from equation (1) involves three changes; a) replace the left side with a rate o f change o f biomass; b) provide a functional relationship to predict changes i n P / B with biomass ;  Bj and consumption, and c) provide a functional relationships predicting how the consumption w i l l change with changes in the biomasses o f B and Bj (Walters et al., 1997). Thus equation (1) f  is re-expressed as  dt  f(Bi)-M.B  -Fi.Bi-  t  n X  cij(Bj.Bj)  (2)  where f ( B J is a function o f Bj i f (i) is a primary producer or f(Bj) = g X Cjj (B;,Bj) i f (i) is a ;  consumer, where g is the net growth efficiency, and c^Bj.Bj) is the function used to predict i  consumption rates from B to Bj. Ecosim uses a function for Cy derived from assuming possible ;  spatial/behavioral limitations i n predation rates  (3)  where Cy is the trophic flow, biomass per time, between prey (i) and predator (j) pools; 22  B and Bj are the biomasses o f prey and predators, respectively; ;  a is the rate o f effective search for prey i by predator j ; and Sj  Vy and v'jj are prey vulnerability parameters  Parameters Vy and v'y represent the rate o f exchange o f biomass between two prey behavioural states: a state vulnerable to predation and a state invulnerable to predation. The rationale o f this representation is that at a given moment i n time not all prey biomass is vulnerable to predators; predator-prey relationships i n nature are often limited by behavioral and physical mechanisms, such as schooling behavior and diel vertical migration patterns i n clupeid fish or spatial refuges used by many reef fish that considerably limit exposure to predation. The model is designed so that the user can specify the type o f trophic control in the food web by hypothesizing the maximum consumption rates (and indirectly the rate o f exchange o f biomass Vy) that a predator can ever exert on food resources.  For low predator biomass or high exchange rates (vy) the  functional relationship approximates a mass-action flow, or Lotka-Volterra type o f model c = a B Bj, implying a strong top-down effect. For high consumer biomass or l o w exchange rates the ;  functional relationship approaches a donor-controlled (bottom-up) flow rate (c = VyBj), so v can ;j  be interpreted as the maximum possible instantaneous mortality rate that j can cause on i .  General Predicted Impacts The impact o f fisheries on upwelling ecosystems was examined by comparing equilibrium biomass predictions provided by Ecosim over a range o f fishing mortality rates for a small pelagic species i n five different upwelling ecosystems off California, Namibia, Northwest Africa,, Peru and Venezuela. Details on each model are provided i n table 2.1. T w o contrasting trophic control hypotheses were tested by setting the maximum instantaneous mortality rate (vy) that consumer j could ever exert on food resource i . For a 'bottom-up' control Vy was fixed as 4 times the baseline mortality rate; for a 'top-down' control Vy was fixed as 20 times the baseline mortality rate.  23  Table 2.1. Trophic models of upwelling ecosystems used in the analysis of the impact of small pelagic fisheries. California: Horse mackerel, Trachurus symmetricus; Hake, Merluccius productus; Mackerel, Scomber japonicus. Namibia: Mackerel, Scomber japonicus; Horse mackerel, Trachurus capensis; Snoek, Thyrsites atun; Hake, Merluccius capensis, M. paradoxus. NWAfrica: Anchovy, Engraulis encrasiculus; Sardinella aurita; Mackerel, Scomber japonicus; Horse mackerel, Trachurus trachurus; Hake, Merluccius merluccius. Peru: Sardine, Sardinops sagax; Mackerel, Scomber japonicus; Horse mackerel, Trachurus murphyi; Bonito, Sarda chiliensis; Hake, Merluccius productus; Pelican, Pelecanus thagus; Sea lion, Otaria flavescens. Venezuela: Small pelagics, Engualidids and Clupeids; Groupers, Serranids; Croakers, Cynoscion spp and Micropogonias furnieri; Squids, — r-rSystem/ Period California 1965-1972 California 1978-1985 Monterey  Fished group Anchovy, Engraulis mordax Anchovy, Engraulis mordax Omniv. fish  N° of groups 17  Commercially exploited groups Anchovy, Horse Mackerel, Hake, Demersals  Top predators  Reference  Marine Mammals Marine Birds  17  Anchovy, Mackerel, Horse Mackerel, Hake, Demersals  Marine Mammals Marine Birds  16  Carnivorous nekton, Demersals, Omnivorous fish Micronekton Anchovy, Sardine, Mackerel, Horse Mackerel, Snoek /Tuna, Other pelagics, Hake, Other demersals Anchovy, Sardine Mackerel, Horse Mackerel, Snoek/ Tuna, Other pelagics, Hake, Other demersals Anchovy, Sadinella sp, Sardine, Mackerel, Horse mackerel, Larg. Scombrids, Other pelagics, Hake, Demersals Macrobenthos, Anchovy, Sardine, Mackerel, Horse Mackerel, Bonito, Hake, Other pelagics, Other demersals Other mammals Macrobenthos, Anchovy, Sardine, Mackerel, Horse Mackerel, Bonito, Hake, Other pelagics, Other demersals, Fur seals Other mammals Small pelagics, Small sharks, Scombrids, Carangids, Groupers, Squid, Croakers, Shrimps  Sea Mammals Sea Birds  Jarre-Teichman and Christensen, 1998 Jarre-Teichman and Christensen, 1998 Oliviera et al., 1993  Namibia 1971-1977  Sardine, Sardinops ocellatus  17  Namibia 1978-1983  Anchovy, Engraulis capensis  17  NWAfrica 1970-1979  Sardine, Sardina pilchardus  18  Peru 1964-1971  Anchovy, Engraulis ringens  20  Peru 1973-1981  Anchovy, Engraulis ringens  20  Venezuela  Small pelagics  16  Marine birds Marine mammals  Jarre-Teichman and Christensen, 1998  Marine birds Marine mammals  Jarre-Teichman and Christensen, 1998  Marine Birds Marine mammals  Jarre-Teichman and Christensen, 1998  Pelican Sea lion  Jarre-Teichman, 1991  Pelican Sea lion  Jarre etal., 1991  Small sharks Scombrids Barracudas Snappers Groupers  Mendoza, 1993  24  Ecosystem Stability Fisheries exploiting the 'wasp-waist' populations have a potential disrupting effect on marine ecosystems communities with direct impact on dynamic stability. There are three general views of ecosystem stability i n the ecological literature (Holling, 1973; Peterman et al., 1978): one assumes that ecosystems are globally stable and tend to recover their original structure after disturbance; a second view is that ecosystems are highly unstable, and that disturbances w i l l lead to system collapse; a third and intermediate view between the two extremes believes that ecosystems may have more than one equilibrium state which are separated by boundaries i n the processes controlling the ability o f the system to respond to disturbance.  In this section  ecosystem models are used to analyze the first type o f stability, i.e., the ability o f system to return to an equilibrium state after a temporary disturbance. The presence o f narrow waists i n food webs make it possible to compare the impact o f disturbances on functionally similar groups in different ecosystems, which them allow us to derive stability properties from inter ecosystems comparisons.  Stability comparisons were carried out with 23 models o f marine ecosystems (Table 2.2).  In  each model a group or species was selected using a set o f criteria allowing direct comparison o f results among ecosystems.  The criteria define the characteristic role o f wasp-waist species,  which must: (i) occupy an intermediate trophic level; (ii) provide an important link between lower and higher trophic levels, indicated by a high energy throughput compared to other groups at similar trophic levels, as well as by the species importance i n the diet o f higher trophic level groups; and (iii) already be fished i n the baseline Ecopath model.  A fishing pattern was chosen which generated a 5 fold increase i n fishing mortality on the waspwaist group. Simulations were run with a bottom-up control setting (mean vij =4.0 over all i-»j flows).  Throughout the work we tested different levels o f increase i n F without noticing any  substantial difference i n results. W e decided for a 5 fold increase to create a situation where the group is severely depleted or displaced from its original steady-state condition i n a relatively short period o f time. Such a extreme scenario is routinely applied to many small pelagic fish populations and very often associated with stock collapse (Patterson, 1992). The higher F values  25  were kept constant for 10 years, then returned to the baseline, with the model running for further 80 years (Fig. 2.2). This allows estimation o f the time it took the system to return to its original state after having been impacted, that is, the time it takes the last impacted group to return to its baseline biomass level (Fig. 2.2). The recovery time was considered as a measure o f the internal stability o f the model and hence an index o f ecosystem stability.  Figure 2.2. Simulation of system recovery of the Venezuela shelf model after fishing impact imposing a 5 times increase in fishing mortality. Top graph shows changes in biomass (B) by group; fished group: small pelagics (Sardinella auritd); last group to recover: croaker (Cynoscion spp. and Micropogonias furnieri); ^ start of simulation with F baseline; interval t, - 1 sets the time the system was kept under higher fishing mortality rates (here 10 years); interval t - t corresponds to the system recovery time or time it took the last impacted group to recover to its original biomass. Time t equals 100 years. 2  2  3  4  To identify ecosystem attributes directly related to stability, a comparative analysis was carried out using a set o f ecosystem goal functions previously identified as representative o f Odum's attributes o f ecosystem maturity (Christensen, 1995a). The attributes were chosen to represent 26  three different aspects o f ecosystem development: i) complexity i n community structure, ii) community energetics and iii) overall community homeostasis. A more detailed description o f the indices listed below can be found i n Christensen and Pauly (1992) and Christensen (1995a). i)  Complexity i n community structure:  - Total number of ECOPATH pools (Npools); - Connectance Index (CI): The connectance index represents the ratio between the number o f actual trophic links to the number o f possible links in the system. Ecosystems are expected to evolve from linear to weblike trophic relationships as they mature; the more complex is the trophic structure the higher is the connectance index. - System Omnivory Index (SOI): The system omnivory is calculated as the average omnivory of all consumers i n the system, omnivory being defined as the variance o f the trophic level o f preys o f a consumer. The SOI measures how the feeding interactions are distributed between trophic levels and can be used along with the connectance index to describe the food web structure o f a system. ii) Community energetics: During the development o f ecosystems towards a climax stage the relationship between three energetic properties, namely production, respiration and biomass, are expected to change. In the early stages o f development, and hence i n immature systems, the total primary production is expected to exceed respiration, leaving an excess net production to accumulate as biomass in the system.  A s the system matures biomass is  expected to increase and production w i l l tend to balance respiration. Five indices are used here to describe these changes i n ecosystem energetics: the ratio between total primary production and total system biomass (Pp/B); system biomass and production (B/P); system respiration and biomass (R/B); the system throughput (SThr), which measures the size or total activity o f the system; and the ratio between biomass and system throughput (B/T). iii) Community overall homeostasis: Ecosystems w i l l tend to attain during the different stages o f succession large and diverse organic structures as a result o f community actions such as symbiosis, nutrient conservation, and the increase i n the information content o f flows. These mechanisms also are thought to increase the ecosystem stability. Four indices are used i n the analysis o f community homeostasis.  Ascendancy (Ascd) is a measure o f the degree o f  development o f an ecosystem, both in terms o f size (throughput) and organization (mutual information) (Ulanowicz and Wulff, 1991; Baird et al., 1991).  The system internal flow  overhead (Iovh) measures the fraction o f the development capacity that does not appear as organized structure due to the magnitude o f an uncertainty i n the imports, exports and 27  respiration flows, as well as to redundancies i n the pathways between organisms in the system.  The cycling index (FCI), developed by F i n n (1976), measures the fraction o f an  ecosystem's throughput that is recycled. The degree o f recycling o f energy and nutrients in an ecosystem is assumed to increase as ecosystems mature and develop routes for nutrient conservation (Odum, 1969). The path length (Path) is defined as the average number o f components or pools that a unit o f flow passes through on its way from inflow to outflow (Finn, 1980). A s diversity o f flows and cycling is expected to increase with maturity, and as these tendencies should result i n an increased path length it is assumed that the path length w i l l be highest for more mature systems (Christensen, 1995a).  28  Table 2.2: Models used for analyses of ecosystem stability. Group trophic level is computed as l+(the weighted average of the prey's trophic level) (Christensen and Pauly, 1992). F Baseline (year ) refers to the wasp-waist group's fishing mortality estimated from the corresponding mass-balanced E C O P A T H model. 1  Model Code  Location  Species/Group Fished  Bru  Brunei, South China Sea Campeche Bank, Gulf of Mexico California system, 1965 - 1972 California system, 1978 - 1985 Celestun Lagoon, Gulf of Mexico Northern shelf, Gulf of Mexico Lingayen Gulf, Philippines Maputo Bay, Mozambique Monterey Bay, California Benguella system, 1971 - 1977 Benguella system, 1978 - 1983 North Sea  Pelagic fish  Cmp Cal65 Cal78 Cel GMx Lng Mpt Mrey Nam71 Nam78 Nsea  Opisthonema  oglinum  Peru 60 Peru 70 Sch Thai 10 Thai50 Thau Trg Venz WMx Yet  Northwest Africa, 1970- 1979 Peru upwelling, 1960 - 1969 Peru upwelling, 1973 - 1979 Schlei Fjord Germany Gulf of Thailand, 10-50 m. South China Sea Etang de Thau, France Kuala Trengganu, Malaysia Venezuela shelf, 10-50 m Western Gulf of Mexico Yucatan shelf, Mexico  F Baseline  Source  2.9  0.28  Silvestre et al. (1993)  2.9  0.89  Vega-Cendejas et al. (1993)  Engraulis  mordax  2.6  0.05  Engraulis  mordax  2.6  0.09  3.1  0.04  Jarre-Teichman and Christensen (1998) Jarre-Teichman and Christensen (1998) Chavez et al. (1993)  Pelagic fish  2.0  0.11  Browder(1993)  Sergestids  2.5  0.31  Pauly and Christensen (1993)  Small pelagics  2.1  0.35  Paula e Silva et al. (1993)  Omnivorous fish  2.5  0.16  Olivirei et al. (1993)  Sardinops  2.4  0.29  2.4  0.56  3.7  0.45  Jarre-Teichman and Christensen (1998) Jarre-Teichman and Christensen (1998) Christensen (1995b)  pdchardus  2.2  0.13  Engraulis  ringens  2.2  0.78  Jarre-Teichman and Christensen (1998) Jarre etal.( 1991)  Engraulis  ringens  2.4  0.54  Jarre et al. (1991)  Planktivorous fish  3.1  0.28  Christensen and Pauly (1992)  Small demersal fish  3.3  3.86  Pauly and Christensen (1993)  Small demersal fish  3.2  3.70  Pauly and Christensen (1993)  Atherinids  2.8  0.02  Palomares et al. (1993)  Pelagic fish  3.2  2.71  Christensen (1991)  Small pelagics  2.6  0.08  Mendoza(1993)  3.0  0.06  Arreguin-Sanchez et al. (1993a)  3.2  0.01  Arreguin-Sanchez et al. (1993b)  Eucinostomus  spp  ocellatus  Engraulis  capensis  Sandeel, Ammodytes  NWAfrica  Trophic Level  Sardina  spp  Eucinostomus  spp  Herring, Opisthonema  oglinum  29  2.3. Results and Discussion  Ecosystem responses to fishing Figures 2.3 to 2.11 show the predicted equilibrium effects o f fishing mortality rates on the biomass and yield o f small pelagic species, and on the biomass o f low, medium and high trophic level groups. Altering the ecosystem control mechanism between assumptions o f top-down and bottom-up control results in marked difference o f the predicted biomass changes i n terms o f both rate and magnitude; biomass changes are more extreme under assumptions o f top-down control o f trophic interactions. For all systems, corresponding with depletion o f small pelagics, biomass of food groups (specially zooplankton) tend to increase. A t the mid-trophic level, predictions o f increase i n biomass o f a competing small pelagic species (e.g. sardine, anchovy or micronekton) occur i n all but i n the Peruvian system during the 1970s (Peru70 model; F i g . 2.10). The degree o f replacement o f one species by the other is more pronounced under assumptions o f top-down control for the California system (Figures 2.3 and 2.4), the Namibian system (Figures 2.6 and 2.7), and the Northwest Africa system (Figure 2.8).  In these cases it is the decrease i n the  biomass o f a common predator, following the depletion o f a forage species, that leads to a burst in the production o f the competing mid-trophic level species. This is less apparent under bottomup control, when competition for food resources were limiting interactions. Some o f the models presented small oscillations in the predicted equilibrium biomasses, specially i n the top-down cases (Figs 2.6 and 2.8). These types o f responses do not correspond to 'real' predictions, as they result from an approximation problem caused by not running the model long enough to reach the actual equilibrium.  30  Top-down  Bottom-up Biomass  ^  [  Figure 2.3. Predicted equilibrium biomass and catches of anchovy in the California System (1965-1972) under different fishing mortality rates (upper panel); lower panels show the predicted percent change in biomass (relative to the Ecopath baseline level) of all other groups in the system with the change in equilibrium fishing mortality rates for anchovy. Ecopath groups are organized according to their position in the food web (from lower to higher trophic levels).  31  Top-down 15 r 10 h  E  Bottom-up Biomass  Biomass  Catch  Catch  c • o  0.0  0.5 F anchovy (year )  0.0  1.0  0.5 F anchovy (year )  1.0  10 r  et  £ o s cu OX)  cu OX)  d  a  a JS  U  0.5  H. mack.  Lg. Scomb. "Willi I  T.O  300 200 100  Lg. scomb. Hake/Whit. & jniiiiiiilllllllllllllllllllllllllll  0 l  -100  Mar. birds  -200 Figure 2.4. Predicted equilibrium biomass and catches of anchovy in the California System (1978-1985) under different fishing mortality rates (upper panel); lower panels show the predicted percent change in biomass (relative to the Ecopath baseline level) of all other groups in the system with the change in equilibrium fishing mortality rates for anchovy. Ecopath groups are organized according to their position in the food web (from lower to higher trophic levels).  32  Top-down  Bottom-up Biomass  "Biomass  Catch  -Catch  1.5 1.0 F Omnv. fish (year )  0.0  2.0  0.5  1.0  1.5  2.0  F Omnv. fish (year ') 100  Mcrozoop.  mtlllllllllllllllllllllllllllliiiillllllllllllllllllllllllllll  50 S?0  i  1.5 f 2.0 Sm phyt.  -50  «5  CA CA  CS E CA  05  -100  s o  o  CQ _g  S  sa D£ fl S3  cu  OX) fl es J=  JS  U  u  Cam. fish  -200  -200  Figure 2.5. Predicted equilibrium biomass and catches of omnivorous fish in the Monterey System under different fishing mortality rates (upper panel); lower panels show the predicted percent change in biomass (relative to the Ecopath baseline level) of all other groups in the system with the change in equilibrium fishing mortality rates for omnivorous fish. Ecopath groups are organized according to their position in the food web (from lower to higher trophic levels).  33  Bottom-up  Top-down Biomass  Biomass  Catch  Catch  0.0 F sardine (year )  Zoop.  »  20 15 10 5 S? 0 -5 i -10 -15 -20  mi  0.5 F sardine (year )  1.0  Zoop  Rtyt  0.5  T.0  C«  ca  S ©  S  a CU DX>  o  200 H. mack.  pa  100  CU OJD  8  a  u  .a  ca  200  93  u  i  0  5  Anchovy  1  0  -100 -200 200 100 ^0  ( -100  0.5  Mar. mam Mar. birds  -200 Figure 2.6. Predicted equilibrium biomass and catches of sardine in the Namibia System (1971-1977) under different fishing mortality rates (upper panel); lower panels show the predicted percent change in biomass (relative to the Ecopath baseline level) of all other groups in the system with the change in equilibrium fishing mortality rates for sardine. Ecopath groups are organized according to their position in the food web (from lower to higher trophic levels).  34  Top-down  Bottom-up  Figure 2.7. Predicted equilibrium biomass and catches of anchovy in the Namibia System (1978-1983) under different fishing mortality rates (upper panel); lower panels show the predicted percent change in biomass (relative to the Ecopath baseline level) of all other groups in the system with the change in equilibrium fishing mortality rates for anchovy. Ecopath groups are organized according to their position in the food web (from lower to higher trophic levels).  35  Top-down  Bottom-up  Figure 2.8. Predicted equilibrium biomass and catches of sardine in the Northwest Africa System (1970-1979) under different fishing mortality rates (upper panel); lower panels show the predicted percent change in biomass (relative to the Ecopath baseline level) of all other groups in the system with the change in equilibrium fishing mortality rates for sardine. Ecopath groups are organized according to their position in the food web (from lower to higher trophic levels).  36  Bottom-up  Top-down  0.0  0.5  Biomass  Biomass  Catch  Catch  1.0 1.5 2.0, 2.5 F anchoveta (year )  0.5  3.0  1.0  1.5  2.0  2.5  F anchoveta (year')  Zoop.  0 mi mi mi inn i  o  s  700 600 500 400 300 LOther. pelagics  a es  JS  O  2oo L I i H. Mack. 100 0 -100  Sardine mil mi Hill llll II  a  S o  S a  CU  ec a ss  JS  U  Figure 2.9. Predicted equilibrium biomass and catches of anchoveta in the Peruvian System (1964-1971) under different fishing mortality rates (upper panel); lower panels show the predicted percent change in biomass (relative to the Ecopath baseline level) of all other groups in the system with the change in equilibrium fishing mortality rates for anchoveta. Ecopath groups are organized according to their position in the food web (from lower to higher trophic levels).  37  Top-down  Bottom-up  Figure 2.10. Predicted equilibrium biomass and catches of anchoveta in the Peruvian System (1973-1981) under different fishing mortality rates (upper panel); lower panels show the predicted percent change in biomass (relative to the Ecopath baseline level) of all other groups in the system with the change in equilibrium fishing mortality rates for anchoveta. Ecopath groups are organized according to their position in the food web (from lower to higher trophic levels).  38  Bottom-up  Top-down •Biomass  Biomass  •Catch  Catch  0.0  1.0  0.5  F Small pelagics (year )  1.0  0.5  F Small pelagics (year )  Zooi  Benth. prod.  _j  i  i 1.0  0.5  Phyt.  VI d E  o  s  j  d  W)  0.5  i  i  Croackers  i iQ  d  cs  JS  U  200  -100  200  r  tfiiiiiiiiiiiiiiiiiiililnilimiiiii  Scorrb. _J  I  L_  Sharks 0.5  1.0 Snappers Scomb  Figure 2 . 1 1 . Predicted equilibrium biomass and catch of small pelagic fish in the Venezuela System under different fishing mortality rates (upper panel); lower panels show the predicted percent change in biomass (relative to the Ecopath baseline level) of all other groups in the system with the change in equilibrium fishing mortality rates for small pelagic fish. Ecopath groups are organized according to their position in the food web (from lower to higher trophic levels).  39  Responding to the depletion o f small pelagic species, top predators display a decrease in biomass in all but i n the California system models (Figures 2.3, 2.4 and 2.5).  The degree to which  predators are impacted further depends upon the relative importance o f the small pelagic fish i n their diet. A s would be expected, those predators that rely solely upon small pelagics as their prey are impacted most heavily. O n the other hand, predators that do not rely primarily on small pelagics, such as horse mackerel and mackerel i n the Peruvian system o f 1970s (Figures 2.10) and pelicans i n the Peruvian system o f 1960s (Figure 2.9), tend not to be heavily impacted, the increase i n biomass o f other species compensating their diet loss. In some systems, competitors o f small pelagics represent important food items in the diet o f top predators and hence there are close links between changes i n biomass. For instance, while most top predators are negatively impacted by the depletion o f small pelagics i n the Peru, Namibia and Northwest Africa models (Figs. 2.6 to 2.10), sea mammals and predatory fish in the California system models appear to benefit (Figs. 2.3 to 2.5), probably as a result o f the increased biomass o f other prey groups. Evidence does exist o f top predators, specially seabirds, that have been able to successfully shift their diet between sardine and anchovy following collapse (Velarde et al., 1994).  But,  experience gained in the Peruvian and Northwest Africa systems show undoubting evidence o f drastic impacts on predatory fish, marine mammals and birds following the collapse o f forage resources (Pauly and Tsukayama, 1987; Cury et al., in press).  Cury et al. (in press) used this  information to infer that the dominant relationship between top-predator and prey i n upwelling ecosystems is bottom-up. Small pelagics evolved life-history strategies i n the form o f schooling and shoaling behavior to reduce mortality risks by predation, so it is expected that aggregation i n the pelagic environment does limit predation.  In all cases analyzed here, model simulations  suggest that changes i n the biomass o f top-predators following the collapse o f their prey occurs under both assumptions, and are i n fact more pronounced under assumptions o f top-down control.  M u c h scientific debate on the causes o f shifts i n species composition i n many marine ecosystems has focused on a 'bottom-up' perspective i n which the effect o f physical forcing, mediated through  climatic-oceanographic processes,  leads  to  changes  i n primary production and  reproductive success o f fish populations which i n turn w i l l trigger changes in the food web (Beamish, 1995; Bakun, 1996).  A n alternative 'top-down' perspective, mostly applied i n the  study o f lakes, asserts that predation affects directly and indirectly the structure o f populations and communities, and production processes at all trophic levels i n the food web (Kitchell et al., 40  1994). M o d e l simulations allowed us to explore the effects o f both assumptions on predicting the changes i n the food web accompanying the exploitation o f small pelagic fish.  Under top-  down control the system responded with a marked increase i n the biomass o f a competitor species due to cascade effects up and down the food web. These results differ from the early emphasis placed on food competition as the driven force o f species replacement (Cushing, 1980; Daan, 1980), and reinforce the potential role o f predation mechanisms and trophic cascades effects i n shaping the dynamics o f mid-trophic level, forage species. The fact that in the four major upwelling ecosystems analyzed, natural mortality o f small pelagic fish due to predation is much higher than their fishing mortality (Jarre-Teichman and Christensen, 1998) corroborates the above hypothesis.  The observed dynamics demonstrate that small pelagics play a central role i n upwelling ecosystems.  Their high throughput o f energy, their intermediate trophic level, and the high  connectivity to other components in the ecosystem indicate that depleting stocks o f small pelagics through fishing is expected to have important consequences components and for fisheries on other commercially important species.  for other ecosystem Where these other  species are dominant predators o f the small pelagics the likely outcome is a reduction in their biomass and catch.  The converse may be true when the target species is a competitor, the  increased biomass o f food sustaining greater biomass o f the competitor.  I f validated, these  results indicate that one o f the long term effects o f intensive fisheries for small pelagics might be the intensification o f the observed downward trend i n the mean trophic level o f fisheries (Pauly et al., 1998), by both accelerating the decline o f species at the top o f the food chain and favoring the outburst o f other mid-trophic level forage species.  Predicted equilibrium yields and fishing mortality at maximum sustainable yield ( F  msy  ) also vary  significantly between the two control scenarios (upper panels i n Figs. 2.3 to 2.11). Bottom-up control generally produces a catch curve that achieves an asymptote at higher Fs, predicting that stock can sustain much higher fishing pressure before it begins to decline. This occurs because under bottom-up control, predation mortality rate (My = Cy/Bj) tends to remain more stable, while the consumption rate (C/Bj) o f small pelagics increases more, to make them more productive per biomass due to donor control o f total food eaten (Cj). prevents higher yields under top-down control. scenarios are compared i n Figure 2.12.  F  m s y  Strong predation control generally  values predicted under the two control  A s stated before, using a bottom-up control predicted 41  higher F  m s y  values. W i t h the exception o f two models (Namibia78-83 and Peru 64-71), predicted  F ^ y values fall i n a conservative range o f fishing mortality rates, below the  sustainable  exploitation rate o f 0.4 suggested for small pelagic fish stocks by Patterson (1992).  o Top-down • Bottom-up  anch. Cal65-72  anch. Cal77-85  omn. fish sard. anch. sard. anch. anch. s. pel. Monterey Nam71-77 Nam78-83 NWAfrica Peru64-71 Peru73-81 Venezuela  2.5 Peru 64-71  „  Namibia 78-83  1.5  CD  ° -*  0.5  § • „  0.5  % o  1  1.5  M (year ) 1  Figure 2.12. Upper panel: Predicted fishing mortality at maximum yield (F ) under two contrasting assumptions on the dominant type of trophic control in the food web. The x-axis indicates the species fished in the respective models. Lower panel: relationship between F and the natural mortality rate of the small pelagic species fished in each model. The dotted line represent the exploitation rate (F/Z) of 0.4 suggested by Patterson (1992) as a sustainable fishing rate for small pelagic stocks. msy  m s y  42  Ecosystem stability and resilience O f the 23 models analyzed, one (Thai 10 model) did not recover the original stable state after being perturbed. Walters et al. (1997) suggested that such unstable behavior is expected when Ecosim is used to extrapolate to a state far away from that described by a given Ecopath model, or when diet composition data and group definitions are inadequate to represent actual trophic interactions among pools. Six out o f the eight upwelling ecosystem models used in the analysis had not recovered completely their original biomass levels 80 years after the end o f the perturbation (Figure 2.13). The low stability o f these models must reflect some characteristics o f upwelling ecosystems, either in terms o f the importance o f the impacted wasp-waist populations to other ecosystem components, or in terms o f intrinsic properties o f the food web captured i n the models. One immediate conclusion from these results is that strategies o f pulse fishing, historically applied to many small pelagics, have a potential destabilizing effect i n upwelling ecosystems on time scale responses far from most projections customarily provided by conventional stock assessment research. O n the other hand, results indicate an apparent lack o f biological 'mechanisms' i n these systems to cope with perturbations.  That is explored below  using ecosystem indexes calculated by Ecopath.  10  20  30  40 50 60 70 Recovery time (years)  80  More  Figure 2.13. Recovery time of the 23 models perturbed by pulse fishing at the mid-trophic level, wasp-waist populations. The recovery time of the models of upwelling ecosystems is highlighted.  43  System recovery time o f the remaining 15 models that recovered the baseline equilibrium biomasses was correlated against the different ecosystem attributes (Table 2.3).  Only Finn's  indices o f cycling and path length are statistically correlated to recovery time (r = -0.76 and 0.67, respectively). O n the other hand, the assumption that ecosystem organization is a more important aspect o f system stability (Ulanowicz, 1986) was not confirmed: none o f the attributes o f trophic web organization (Relative ascendancy and Internal flow Overhead) was correlated with recovery time.  Community complexity and energetics  attributes  also showed low  correlation, as may perhaps have been expected (Begon et al., 1990; Hansen and Jorgensen, 1990). M a y (1972) concluded that a simple relationship between stability and complexity may not be obtained, and in some cases, complex systems may fluctuate more than less complex ones. Here, we refute the hypothesis that stability and complexity (expressed i n terms o f connectance, web structure (SOI) and trophic groups richness) are correlated i n the 15 models compared.  Table 2.3: Correlation among the ecosystem attributes defined in the text. Numbers in bold indicate significance at the 5% level (d.f. = 13). Recovery time (Rec. time) is the measure of stability utilized in the analysis. CI: connectance index; SOI: system omnivory index; Npools: total number of system components; FCI: Finn's cycling index; Ascd: ascendancy; Iovh: system internal flow overhead; R / B : respiration/biomass ratio; Pp/B: primary production/biomass ratio; B/T: biomass/throughput ratio; B/P: biomass/production ratio; SThr: total system Attribute  Rec. time CI SOI Npools FCI Ascd Iovh R/B Pp/B B/T B/P  Energetics  Homeostasis  Complexity  CI SOI Npools -0.10 -0.15 0.12 0.28 -0.57 0.29  FCI  Path  -0.76  -0.67  0.13 0.23 -0.14  -0.09 0.48 -0.02 0.80  Ascd 0.09 -0.47 -0.35 0.22 0.27  lOvh -0.32 0.53 0.24 -0.51 -0.04 -0.83  R/B -0.10 -0.25 0.33 -0.19 -0.26 -0.46 0.29  SThr -0.28 -0.28  Pp/B 0.33 -0.23 0.25 -0.05 -0.34 -0.36 0.38  B/T -0.13 -0.13 -0.45 -0.37 0.20 0.44 -0.23  B/P -0.14 -0.06 -0.40 -0.16 0.24 0.50 -0.32  0.63  -0.86 -0.55  -0.83 -0.65 0.94  0.77  -0.01 0.49 -0.04 0.27 -0.04 0.16 0.06 -0.07  Finn's cycling index showed a consistent negative relation with system recovery time (Fig. 2.14). Systems with a low percentage o f energy recycled, simple recycling structures and short average path  length  (e.g.  the  upwelling systems  off Venezuela and  Monterey  Bay) exhibit  characteristically slow recovery time. O n the other hand, systems where recycling is important, such as coastal lagoons and fjords (e.g. Schlei Fjord and Etang de Thau models), appeared more stable.  To test the hypothesis that stability changes with recycling i n a single system, we  simulated the effect o f an increase in the importance o f detritus cycling i n the Venezuela shelf model. The steps were as follows: first we gradually increased the diet components o f detritus for heterotrophic benthos from 0 to 45% (thereby increasing the F C I from 0 to 2.8%); then we  increased the diet component o f detritus for zooplankton from 0 to 95% (the F C I thereby increased from 2.8 to 24.5%). Simulations were run for each level o f recycling, leading to the results shown in F i g . 2.15.  A negative relationship clearly emerges between the amount o f  detritus cycled and the recovery time o f the model.  M o d e l stability does not appear very  sensitive to increasing recycling up to a certain threshold from which small changes i n recycling lead to substantial reductions i n recovery time.  50  40  r  • Thau Lng  60  J3  o >. o  3  30  Sch NSea  20  • Thai50 • Trg Cel  'B Q  Bru.  Mrey  10  WMx  Yet  • Cmp  GMx • Mpt  • Vnz 10  20  30  40  50  60  70  80  Recovery time (years) Figure 2.14. Relation between Finn's index of detritus recycling and system recovery time (r=-0.76; p=0.001; d.f.=13). See Table 1 for models descriptions. Empty squares indicate the recovery time of the models of upwelling systems (Monterey and Venezuela).  45  30  e  2 0  o  ••5 10  .43 Q  • 20  30  40  50  60  Recovery time (years) Figure 2.15. Effect of increasing detritus cycling on the stability of the Venezuela shelf model (see text for procedure). The square corresponds to the system recovery time (t on Fig.l) for the baseline Finn cycling index value (2.7%). 3  A view o f the role o f recycling different from the presented here was proposed by R . E . Ulanowicz and co-workers who argued that it is perturbed systems which tend to do more recycling (Ulanowicz, 1984; Ulanowicz and Wuff, 1991; Baird et al., 1991); from this view, they concluded that recycling is a measure o f stress. Indeed, Ulanowicz and W u f f (1991) suggested that in ecosystem development, the number and length o f recycling routes is a more important attribute than the amount o f recycling, since i n some ecosystems recycling occur over short benthic cycles and do not involve loops through various trophic levels. Hence, the amount o f energy and nutrients recycled may result in overevaluating the importance o f recycling to the overall community. However, in the present study, recycling and path length were significantly correlated (r = 0.80) (Table 2.3), pointing to the substantial complexity and importance o f the recycling routes for the marine ecosystem models analyzed.  W e believe that Ulanowicz et al. interpretation o f the role o f detritus recycling as a result (and a measure) o f stress to be due to they having selected a priori ascendancy as a strong and positive correlate o f maturity, then evaluating all other system properties relative to ascendancy.  This  approach is problematic because ascendancy (or at least that o f its two component meant to express the 'information content' o f an ecosystem) is i n fact negatively correlated with maturity 46  (Christensen, 1995a). O n the other hand, relative overheads, a measure o f stability derived from Ulanowicz's theory does show a strong relationship with Finn's index (Christensen and Pauly, 1993b). Thus we conclude that recycling does indeed have an important role i n the maintenance o f ecosystem stability. Basically, the higher the recycling the more quickly w i l l the effects o f perturbations be eliminated from the system ( O ' N e i l l , 1976; DeAngelis, 1980).  Traditional views o f ecosystem development consider that succession culminates in a stabilized ecosystem i n which biomass and symbiotic functions between organisms are maintained per unit o f available energy (Odum, 1969). Odum interpreted the strategy o f succession as the one o f increase control and or homeostasis with the physical environment i n the sense o f achieving maximum protection from its perturbations. The net result o f community development would be symbiosis, nutrient conservation, stability, a decrease i n entropy, and an increase i n information. According to Odum, biotic control o f grazing, population density and nutrient cycling provide the chief positive feedback mechanisms that contribute to stability i n mature systems by preventing overshoots and destructive oscillations. Our results are i n agreement with the above theory to the extent it shows that systems with higher capacity to recycle detritus are systems with a higher ability to recover from perturbations. Taking, therefore, stability and recycling as directly related attributes during ecosystem development the results o f this analysis provide support to Odum's theory and to previous comparative studies o f marine food webs (Christensen and Pauly, 1993b; Christensen, 1995a).  Very often ecosystems develop what Odum called pulse stability, where a more or less regular physical perturbation can maintain an ecosystem at some intermediate point i n the development sequence toward maturity.  Connell (1978) showed that i n fact intermediate levels o f  disturbances are essential for the maintenance o f certain ecosystem characteristics, such as species diversity, even i n highly mature systems. Evidence gained i n ecological studies, mainly of terrestrial ecosystems, has shown that change during ecosystem development is not continuous and gradual, but it is episodic with slow accumulation o f biomass and nutrients punctuated by sudden releases and reorganization (Holling et al., 1995). According to Holling, the structure o f biological communities is therefore  controlled through the balance o f destabilizing and  stabilizing forces. While destabilizing forces are important i n maintaining diversity, resilience and opportunity, stabilizing forces, such as nutrient recycling, are important i n maintaining productivity and biogeochemical cycles. 47  The role o f destabilizing forces may be particularly important i n pelagic marine ecosystems. In the sea, short-term physical variability is damped out by the very large heat capacity o f the ocean. In turn, this large thermal capacity and the long period exchange rates between deep and near-surface waters lead to relatively large-amplitude changes at the long term scales (Steele, 1985). A s a result, less robust internal ecosystem processes are needed to handle the smaller amplitude variability at short periods. The possible absence o f such mechanisms, combined with increase variance with period, can mean that pelagic marine populations or ecosystems have to continually adapt to physical variability i n the short as well as the long term (Holling et al., 1995). The absence o f well structured recycling routes, the low recycling and reduced stability o f upwelling ecosystems (Figures 2.13 and 2.14) can be considered a result o f a longer-term adaptation o f biological community to the physical variability and transitory nature o f these systems. In fact, taking fisheries yield as an indicator, upwelling ecosystems can be considered the most variable oceanic systems (Stergiou, 1998). Bakun (1996) considered variability itself a key asset for the massive small pelagic wasp-waist populations inhabiting upwelling systems, which must rely on pulsing its abundance to cope with the temporal and spatial patterns presented by their prey, while simultaneously presenting patterns to their predators  that  overcome growth o f intolerable levels o f predation.  Model limitations Notes o f caution relating to the interpretation o f simulations were presented by Walters et al. (1997), but w i l l be summarized here. First, each Ecopath model may differ i n terms o f the time span which the data represent, the number o f groups used, and the parameters which are estimated.  It is difficult to determine what effect differences i n data quality have on the  simulations but it has been suggested that poor data may lead to models with a tendency for selfsimplification (i.e., low persistence), through competition or predatory exclusion o f some groups after disturbance by fishing (Walters et al., 1997). O f the 40 Ecopath models tested by Walters et al., those representing coastal and shelf marine ecosystems show a positive relationship between persistence and model quality (Pauly et al., in press; Figure 2.16; Table 2.4). In this analysis model persistence was ranked according to the maximum flow rate (mean v ) between predator;j  prey that led to persistence o f all groups i n the system after a disturbance.  M o d e l quality was  48  ranked according to the accuracy and quality o f data used as input i n the construction o f an Ecopath model. Walters et al. (1997) suggested that positive relationships between persistence and quality have two interrelated consequences; first, it indicates the possibility that strong trophic interactions do occur in nature and lead to a selective process such that "what persists to be studied as an 'equilibrium' i n the field is a very peculiar or particular set o f interaction parameter values"; second, and consequently, one can only estimate these interaction values in models built from data accurately measured.  These results differ from the studies o f trophic  interactions i n aquatic ecosystems (McCarthy et al., 1995; Scheffer and de Boer, 1995) which show that the patchy distribution o f organisms may lead to ratio dependence in predator-prey interactions, and consequently to relatively weak predator control (low Vy) i n aquatic food webs. Ultimately, the relationship between model quality and persistence indicates that results from dynamic simulations with Ecosim w i l l depend i n part on the quality o f information used to construct the original Ecopath model.  B at  6J3 e 2  B  2c  a  • _i  1  i  2  r =0.452  r =0.557  s  i  i  i  i  i  3  4  5  6  7  Quality ranking  s  1  2  3 4 5 6 Quality ranking  7  Figure 2.16. Rank correlation between model persistence and quality, for Ecopath models of (A) coastal (r=0.452) and (B) shelf (r=0.557) ecosystems.  Other limitations o f the approach relate to its inherent simplifying assumptions, which ignores spatial processes, does not allow for numeric responses, as opposed to biomass predictions only, and does not take into account environmental variability, a factor frequently ignored i n complex food web models (Hunter and Price, 1992). Some o f these limitations, which may as well apply for many single species models, were recently addressed by incorporating into Ecosim a delaydifferential model structure that is capable o f tracking both numbers o f individuals and biomasses o f juvenile and adult stages o f key species i n the ecosystem (Walters et a l , in press a), and an interface that allow for the representation o f spatial processes (Walters et al., in press b). Walters et al. (1997) also warned about the risk o f using Ecosim to extrapolate to circumstances 49  far from the equilibrium for which Ecopath data are available. However, i f we only project a short time horizon and consider only short term dynamics, long term effects are easily missed. Despite the reservations expressed about long term simulation periods, they still can be useful i n predicting the directions o f biomass change, and serve as a warning to keep i n mind potential delayed responses, a point that should be emphasized especially when considering complex and unstable ecosystems.  Table 2.4. Trophic models used in the regression between quality rank and persistence rank. Max v persistence Quality rank Shelf systems Brunei Darussalam 5 20 4 5 gulf of Thailand, 10-50 m 6 11 Kuala Terenganu, Malaysia 2 40 Northern Venezuela Shelf 1 35 North Sea 8 Northern Gulf of Mexico 7 8 Yucatan shelf 3 Coastal systems 6 Campeche Bank, Mexico 8 40 5 Celestun lagoon, Mexico 4 Etang de Thau, France 3 10 7 Lingayen Gulf, Philippines 1 43 Maputo Bay, Mozambique 2 46 Schlei Fjord, Germany 7 4 Shallow areas, South China Sea 6 8 Southwestern Gulf of Mexico (coast) M  Management implications The comparative analysis o f the ecosystem impacts o f harvesting small pelagic fish using multispecies trophic models showed that: i) small pelagic fish play a central role i n upwelling ecosystems; changes i n their abundance can have considerable consequences to species both at the top and the bottom o f the food web; ii) observed patterns o f species replacement at the pelagic niche can be explained by the effect o f fisheries and trophic interactions alone, being more evident when the system is dominated by predatory control, i.e., top-down; iii) upwelling ecosystems have characteristics o f unstable systems, lacking biological mechanisms (e.g. nutrient recycling) to damp the impact o f external forces created b y fishing and environmental factors; and iv) quantitative prediction o f the ecosystem effects  o f fisheries using  mass-balance  ecosystem models is very sensitive to the type o f dominant trophic control assumptions, and may be problematic for situations o f poor data quality. However, these models are capable o f output  50  robust qualitative responses which may guide the screening o f fisheries policies under ecosystem principles. In this regard, results o f the present work can have direct implications to the management o f small pelagic fish with ecosystem goals. It indicates that as 'wasp-waist' species in upwelling ecosystems, small pelagic forage fish w i l l sustain much more conservative exploitation rates than what has been historically applied i n the cases o f stock collapse. A l s o , fisheries for small pelagics have a potential disrupting effect on ecosystem dynamics and may cause shifts i n the species composition at the mid-trophic level. Ultimately, the characteristics o f unstable systems w i l l mean that variability is a key asset for the resilience o f upwelling ecosystems, and management should i n turn be prepared to cope with the unexpected changes resulting from both fishing and environmental effects.  2.4. Summary This chapter analyzes  the ecosystem impacts o f harvesting small pelagic fish i n upwelling  systems comparing the results o f simulations carried with trophic models. Results include the predicted responses o f the system to fishing, i n terms o f changes i n the biomass o f small pelagic fish and other ecosystem components; the recovery time o f marine ecosystems when disturbed at the mid-trophic level, wasp-waist populations; and the characteristics o f stability o f upwelling systems. These results are used to drawn conclusions on the effect o f fisheries and trophic cascades on species shift at the pelagic niche, and on the applications and limitations o f multispecies trophic models i n the design o f ecosystem principles and precautionary measures for small pelagic fisheries.  51  Chapter 3. Fishing down food webs and the carrying capacity of marine ecosystems in southern Brazil 2  3.1. Introduction Recent assessments o f the worldwide status o f marine capture fisheries revealed alarming signs o f human dominance and impact on the oceans, such as overfishing o f important stocks, by-catch and discard o f non-target species, and the fishing down o f marine food webs (Botsford et al., 1997; Pauly et al., 1998). In line with some o f these global trends, marine capture fisheries o f Brazil are i n a state o f crisis caused by the scarcity o f resources, over-capitalization o f fisheries activities and the lack o f sound fisheries management policies. The crisis i n Brazilian fisheries occurs concurrent with major efforts to assess the potential production o f fishery resources i n the Exclusive Economic Zone, triggered by the country's ratification to the United Nations Convention on the L a w o f the Seas. Early assessments o f the fisheries resources along the coast carried during the 1970s (Hempel, 1971; Neiva & Moura, 1977) pointed at a potential total catches over 1 million tons/year, when i n fact, since the mid-1980s landings have stabilized around 700 thousand tons/year while many o f the traditional fish stocks have become either fully exploited or overexploited (Dias-Neto, 1991a; 1991b; I B A M A , 1994a; 1994b; 1994c; Reis et al., 1994; Cergole, 1995; Matsuura, 1995; Haimovici et al., 1997). The current situation raises concerns on the limits o f marine ecosystems carrying capacity i.e., whether the level o f exploitation can be sustained  without impairing the productivity and integrity o f these  ecosystems.  Brazil has an extensive coast line that extends from 5 ° N to 34°S, including regions o f tropical and subtropical climate. Matsuura (1995) divided the Brazilian coast i n five regions with distinct environmental characteristics and types o f fishing activities (Fig. 3.1). In the north biological production is high as a result o f the continental runoff from the A m a z o n river (Teixeira and Tundisi, 1967).  The wide continental shelf and the rich benthic community favored the  development o f trawling activities i n this region, mostly for shrimps and large catfishes. The northeast and east regions present oligotrophic conditions due to the influence o f tropical waters from the Brazil Current.  R o c k y bottoms and a mostly narrow continental shelf induced the  development o f hook-and-line and longline fisheries for rockfishes, sharks and tunas.  In the  52  southeast, primary production is mainly driven by seasonal upwelling o f nutrient-rich, cold subtropical waters pumped by alongshore winds and by cyclonic vortexes originated from the Brazil Current (Bakun and Parrish, 1990; Matsuura, 1995). The southern part o f the Brazilian coast is under the influence o f the Subtropical Convergence between the southward and northward flowing Brazil and Malvinas currents. The confluence o f water masses and the high volume o f continental runoff provide physical and chemical conditions for high biological production on the shelf (Seeliger et al., 1997). Trawling is the main type o f fishing activity in the southeastern and southern regions, although the presence o f highly abundant pelagic stocks, mainly sardine, i n the southeast has also lead to the development o f an important purse seine fishery as early as 1950.  Figure 3.1. Shelf regions of Brazil. The southeastern shelf is considered from Cabo de Sao Tome to Cabo de Santa Marta Grande, and the southern shelf from Cabo de Santa Marta Grande to Chui.  The regions also differ i n the type o f fisheries production. While catches i n the north, northeast and east regions are mainly artisanal (Diegues, 1995), i n the southern regions it is the industrial fisheries that provide most o f the fisheries production, accounting for approximately half o f the 53  total Brazilian catches (IB A M A / I B G E , 1995). Historically, it was i n the south and southeast that industrial fisheries were mostly developed through a series o f government incentives, and it is where fisheries are best documented.  This chapter presents a comparative analysis o f fisheries i n the southeastern and southern regions o f Brazil which aims to assess the carrying capacity o f the marine shelf ecosystems  for  harvestable species. Carrying capacity has been defined as the maximum size o f a population or activity that could be  indefinitely sustained  without  degrading  the  ecosystem's  future  productivity or suitability for that use (Odum, 1997). In the oceans, carrying capacity is usually referred to as the upper limit o f biomass o f organisms that can be supported by a set o f primary production and food web structure (Christensen and Pauly, 1998).  Fisheries yield is directly  related to the carrying capacity o f marine ecosystems, since there is a maximum sustainable rate of fish production associated to the total fish biomass at the carrying capacity. A l s o , overfishing can directly affect the carrying capacity o f marine ecosystems by altering the structure o f food webs and changing their potential productivity. In this chapter I analyze the carrying capacity o f marine ecosystems i n three ways. Firstly by computing the total flux o f energy from primary producers available to different trophic levels i n the food web, and the total primary production required to sustain fisheries catches (Pauly and Christensen, 1995).  The ratio o f these two  quantities provides a straightforward measure o f the "appropriated carrying capacity" (sensu Rees, 1996) o f ecosystems, i.e. the amount o f the available energy i n a ecosystem already appropriated by fisheries catches. Secondly, I do a diagnosis o f fisheries for the 'fishing down the food web' phenomenon using trophic level estimates and national and regional catch statistics.  Finally, I analyze how fisheries are likely to alter the species composition i n  ecosystems structured by trophic relationships.  I explore this effect by simulating a 'fishing-  down-the-food-web' scenario i n the southern shelf region, where traditional demersal fish stocks are overexploited and the prospects for increasing yield relies on exploiting abundant small pelagic forage fish.  3.2. Methods The method used here to quantify the appropriated carrying capacity follows the approach developed by Pauly and Christensen (1995) for the analysis o f primary production required to sustain world fisheries. Primary production required by fisheries (PPR) is estimated based on the 54  trophic level o f the species caught, the energy transfer efficiency between trophic levels, and on the primary productivity o f the two shelf regions (Table 3.1). Primary production estimates for the southeast and south were obtained from Brandini (1990) and Odebrecht and Garcia (1997), respectively. Species trophic levels (Table 3.2) were computed according to Odum and Heald (1975) using available information on diet composition, and from trophic models.  In this  analysis primary producers have trophic level 1, and each higher order consumers has trophic level 1 + the weighted average trophic level o f its preys.  Rocha et al. (1998) constructed a  simplified trophic model o f the Ubatuba region i n the southeastern shelf that is here used to estimate the mean trophic transfer efficiency for the region. Transfer efficiency for the southern shelf is calculated from the trophic model described below.  P P R estimates are based on a  conversion factor o f 0.06 g Carbon = 1 g wet weight o f catches (Walsh, 1981) and on the mean transfer efficiency per trophic level, that is  PPR = catches  a^ '^ 1  where a = TE" , and T E is the mean trophic efficiency measured as the proportion o f the energy 1  transferred between  consecutive trophic levels ( T L ) .  P P R is commonly expressed as a  percentage o f the total primary production (%PP).  Table 3.1.Area, primary productivity and total primary production of southern and southeastern shelf regions of Brazil. Shelf areas were measured to the 200 m depth line using planimetry. Region Area PP Total PP m «10 gOm^year gOyearMO 2  Southeast South  10  17.14 11.40  1  100 160  12  17.14 18.25  Catch statistics obtained from Haimovici et al.(1997), Haimovici(1998) and from the Instituto de Pesca de Santos, Sao Paulo, were used in the analysis o f P P R for the two shelf regions. F A O catch statistics o f reported Brazilian catches were used to compute the mean trophic level o f landings i n Brazil from 1950 to 1994.  55  Table 3.2. Trophic level of the main species landed in Brazil. Trophic level estimates are from model in figure 3.2, from diet composition studies (references in table footnote), and/or from other published trophic models (Christensen and Pauly, 1993). Species Trophic Level Group 2.3 Shrimps Farfantepenaeus spp; 2.3 Xiphopenaeus kroyeri 2.6 Lobsters Panulirus argus; 2.6 Panulirus spp 2.8 Small and mid-size pelagics Sardinella brasiliensis" 3.0 Engraulididae 3.1 Scomber japonicus 3.3 Scomberomorus spp 3.4 Common squids Loligo spp 3.5 Miscellaneous marine fishes Osteichthyes 3.4-3.5 Micropogonias furnierf' 3.2 Umbrina canosai' 3.9-4.0 Cynoscion spp ^ 4.3. Macrodon ancylodon? 4.3 Trichiurus lepturus 3.4 Batistes capriscus' 4.2 Pomatomus saltatrix' 3.8 Pinguipes spp' 3.8 Ariidae* 3.8 Mugilidae' 3.4 -3.8 Sharks, rays, skates Elasmobranchs''' 3.7 Epinephelus spp Groupers 3.7 Mycteroperca spp 3.8 Lutjanidae Snappers 3.8 Ocyurus chrysurus 3.9 Coryphaena hippurus Common dolphinfish 3.9 Skipjack tuna Katsuwonus pelamis" 3.9 Large pelagic fish"'"''' Thunnus spp 3.9 Xiphias gladius 3.9 Other Scombroidei a. Goitein, 1983,Gasalla & Oliveira, 1997; b. Vazzoler et. al. (in press); c. Gasalla, 1995; d. Vazzoler, 1975; e. Haimovici et al., 1989; f. Vieira, 1990; g. Juras and Yamaguti, 1985; h. Martins and Haimovici, 1997; i. Froese and Pauly, 1998; j . Haimovici and Krug, 1992; k. Araujo, 1984; 1. Soares et al., 1992; m. Vilela, 1990; n. ZavallaCamin, 1982; o. Vaske, 1992; p. Vyalov & Ovchinnikov, 1980. b  d  c  h  The ecosystem effects o f 'fishing down the food web' for anchovy in the Southern shelf was explored with a simplified mass-balance model ( E C O P A T H , Christensen and Pauly, 1992) o f the trophic interactions i n the pelagic ecosystem (Tables 3.3 and 3.4; F i g . 3.2). The model was constructed based on the pelagic species association described by M e l l o et al. (1992) for the winter and spring, and depicts anchovy as the dominant planktivorous fish species, being responsible for most o f the consumers (Figure 3.2).  transfer o f energy  from  lower trophic levels to higher order  The system is defined by the coordinates 32°S - 4 3 ° 3 0 ' S and 5 1 ° W -  5 4 ° W , with a total area o f 28,661 K m . 2  56  Table 3.3. Parameters of the trophic model of the pelagic ecosystem off southern Brazil. Underlined values, trophic levels (TL), and omnivory index (01) were estimated by the model. A n omnivory index equals zero indicates the predator feeds on a single trophic level. Cutlassfish, Trichiurus lepturus; Hake, Merluccius hubbsi, Weakfish, Cynoscion guatucupa; Mackerel, Scomber japonicus; Jack mackerel, Trachurus lathami; Anchovy, Engraulis anchoita. Q/B EE Yield TL B P/B Species/Group OI year' year' tons»Km' • tons*Km year' 1  2  2  0.852 2.050 0.015 Cutlassfish 4.25 0.259 0.240 0.410 0.004 4.11 1.750 0.950 Hake 0.126 0.085 0.355° 0.130 3.94 1.172 0.342 0.400 4.000= 0.950 Sharks 5.300 0.950 0.405 4.02 1.080 0.748 0.570 Other pelagics 2.340 0.935 0.306 0.302 2.000 0.480 Weakfish 3.59 0.950 0.033 0.340 2.710" 3.11 0.000 1.329 Mackerel 0.958 0.054 0.350 3.000 0.000 0.300 Jack Mackerel 3.11 0.240 — 13.710' 5.155 Anchovy 3.00 0.111 1.290" 3.230 0.894 — 3.32 0.185 0.200 1.500 Squids 0.950 0.040 0.000 0.298 3.930' 19.130 Marine shrimps 2.00 324.600 0.619 Zooplankton 2.11 0.111 8.000 64.920 — 0.965 Phytoplankton 16.700" 100.000" 1.00 0.000 — — 1.148 — — — 1.00 0.179 150.000° Detritus a. Martins and Haimovici (1997); b. Haimovici (1998); c. based on Peterson and Wroblewski (1984); d. based on Palomares and Pauly (1989); e. based on other trophic models (Christensen and Pauly, 1993); f. Haimovici et al. (1997); g. I B A M A (1993a); h. Saccardo (1980); i . Lima and Castello (1995); j . Freire (unpubl.); k. Haimovici (1997); 1. DTncao (1991); m. Resgalla Jr. (unpubl.); n. Odebrecht and Garcia (1997); o.according to Pauly et al. (1993). a  c  d  b  d  b  b  e  c  e  f  s  c  d  b  h  h  d  f  f  e  d  k  c  m  m  e  e  f  Table 3.4. Diet matrix of the model of the pelagic ecosystem off southern B r a z i l . Values represent the proportion of the diet of a predator (column) made of a given prey (row). Some of the groups (mainly sharks, weakfish, and other pelagics) have several feeding habitats such as the outer shelf and benfhic habitats. For these groups an Import was included as a "prey" in the diet composition. Prey \ Predator 1 2 3 4 5 6 7 8 9 10 11 1. Cutlassfish 0.140 — — — — — — — — — — 2. Hake 0.020 0.100 — — — — — — — — — 3. Sharks _ _ _ _ _ _ _ _ _ _ _ 3  b  c  4. Other pelagics — — — — — — — — — — — 5. Weakfish" 0.120 — 0.050 0.050 0.050 — — — 0.050 — — 6. Mackerel — — — 0.100 — — — — — — — 7. Jack Mackerel" 0.030 0.030 0.020 _ _ _ _ _ _ _ _ 8. Anchovy 0.570 0.800 0.330 0.300 0.450 — — — 0.150 — — 9. Squids 0.050 0.030 0.030 0.050 _ _ _ _ _ _ _ 10. Mar. shrimps' 0.010 — 0.070 0.200 0.010 _ _ _ _ _ 11. Zooplankton 0.010 — — 0.050 0.200 0.990 1.000 0.900 0.800 — 0.100 12. Phytoplankton _ _ _ _ _ _ _ 0.050 — 0.200 0.550 13. Detritus — — — — — — — 0.050 — 0.800 0.350 Import 0.050 0.040 0.500 0.450 0.100 — — — — — — a. Martins (1992); b. Haimovici et al.(1993); c. Castello et al. (1997); d. Castello (1997); e. Vieira (1990); f. Schwingel and Castello (1995); g. Haimovici (1997); h. based on other trophic models (Christensen and Pauly, 1993). d  e  d  f  8  1  57  X  pelagics  Hake  Cut fish 3 0 Sharks Weakfish M  T Mackerel  Squids J Mack  ^shrimp j  Anchovy  Zoop  "* Flow Connector * Harvest Import —  Phyto  Figure 3.2. Flowchart of trophic relationships in the pelagic association off southern Brazil. It describes the flows between groups (boxes), the biomass of each group (area of boxes proportional to the log of biomass), and the respective trophic levels. Only the consumption flows are shown. For full parameter descriptions see tables 3 and 4.  'Fishing down the food web' was simulated by increasing fishing mortality (F) for anchovy from 0 to 1 year" , while maintaining F constant for other exploited groups. 1  A s some o f the high  trophic level species (e.g. sharks, weakfish and pelagics) are already fully exploited or overexploited (Haimovici et al., 1997), I considered fishing for anchovy as the most likely continuation o f the 'fishing down the food web' effect i n this system. Ecosim (Walters et a l , 1997) was used to calculate the predicted changes in equilibrium biomasses o f species/group and the total catch from the system over the range o f F values for anchovy. The model provides biomass predictions o f each group in the system as affected directly by fishing and predation, changes i n food availability, and indirectly by fishing or predation on other groups i n the system (Walters et al., 1997). Simulations were run under two contrasting trophic control hypothesis, bottom-up and top-down, as described in Chapter 2.  58  3.3. Results  PPR and Trophic Levels P P R estimates by shelf region and species landed are shown i n tables 3.5, 3.6 and 3.7. Fisheries in southern B r a z i l already use a large proportion o f the productive capacity o f the shelf ecosystems. In the south, primary production required to sustain catches has changed little from the 1970s to the 1990s, being in the order o f 2 5 % o f the total primary production. Little change is also observed i n the mean trophic level o f fisheries in the south which have been targeting mostly high trophic level species (Table 3.5). A n increase i n catches o f tunas and sharks was observed i n the southern shelf i n the early 1990s accompanying the depletion o f important demersal fish stocks, such as Umbrina canosai, Macrodon ancylodon and catfish species, Netuma spp (Table 3.5).  However, this alternation o f species i n the catches did not result in  major changes i n the P P R and i n the mean trophic level o f landings between the two periods. Landings in the Southeast are on the other hand dominated by low trophic level species, sardine and marine shrimps being the most important stocks i n terms o f catch volume (Table 3.6). With the collapse o f the Brazilian sardine during the late 1980s and early 1990s, and the increase i n tuna and sharks catches, there was an increase i n the mean trophic level o f fisheries from 2.81 to 2.93. Despite the fact that catches were considerably lower in the latter period, the change i n relative importance o f the species landed resulted i n an increase i n P P R from 25.8 to 33.4 % o f the total shelf primary production. Although substantially higher fishing yields are obtained i n the southeast than i n the south, the footprint o f fisheries is relatively the same between the two regions as a result o f differences i n the mean trophic level o f landings and the mean trophic efficiency (Table 3.7).  59  Table 3.5. Trophic level, mean catch and PPR estimates for the southern shelf. Species 1975 -1979 1990 -1994 PPR PPR Catches Trophic Catches Level tons tons gC»10 gC • 10 43.2 Micropogonias furnieri 3.5 14,308 42.0 14,709 14.7 3.2 16,900 25.7 9,629 Umbrina canosai 88.9 6,439 65.2 8,785 Cynoscion guatacupa 3.9 90.1 Macrodon ancylodon 7,941 180.4 3,966 4.3 13.9 4,052 15.9 4,143 Miscellaneous teleosteans 3.5 4.7 30.5 615 Netuma spp 3.8 3,983 441 10.8 75 1.8 Trichiurus lepturus 4.3 2,584 5,931 39.1 Demersal sharks" 3.8 17.0 460 3.4 1,010 2.9 Rhinobatus horkelli 1.3 3.1 116 0.5 746 Rays and skates 3.6 0.2 — — 1,148 Marine shrimps 2.3 3,848 5.1 Small and mid-size pelagics 3.2 ' 1,549 1.4 3,521 7.2 4.2 4,290 88.1 Pomatomus saltatrix 14.4 1,524 10.6 3.8 2,081 Mugil spp 70.4 — — 8,088 Katsuwonus pelamis 3.9 182 547 2.9 3.7 0.9 Pelagic sharks a. Pogonias cromis; Merluccius hubbsi; Paralichthys spp., Pagrus pagrus; Prionotus punctatus; Urophycis brasiliensis and Poliprion americanus. b. mostly Galeorhinus galeus; Mustelus schmitti and Squatina spp. c. Brevoortia pectinata; Scomber japonicus and Trachurus lathami. 1 0  10  3  0  Table 3.6: Trophic level, mean catch and PPR estimates for the southeastern shelf. Species 1977 -1980 1990 -1995 Trophic Level 3.4 3.7 4.0 3.4 2.8 3.4 2.3 3.8 3.9  Micropogonias furnieri Macrodon ancylodon Cynoscion jamaiscencis Batistes capriscus Sardinella brasiliensis Rays and skates Marine shrimps Sharks Katswonus pelamis  Catches (tons) 7,126 2,053 1,921  — 146,520  PPR Catches (tons) gC»10 4,541 39.9 32.7 1,870 2,245 105.6 19.4 2,144 54,414 70.3 504 4.6 13,997 4.0 2,144 52.2 243.6 7,197  PPR gC«10 62.6 35.9 90.4  1 0  1 0  — 189.4  — 17,371 517 1,380  5.0 12.6 46.7  Table 3.7. Summary statistics of the mean catch, mean trophic level (TL), mean transfer efficiency (TE), the primary production required by fisheries catches (PPR), and the percentage of the total primary production appropriated by fisheries (% PP) inthe southern and southeastern shelves. TE PPR %PP Catch TL Region (tons'year ) gCyear''10" % Southern 487 26.7 65,510 3.63 8 1975-1979 471 25.8 68,101 3.64 1990-1994 Southeastern 25.8 5 443 176,888 2.81 1977-1980 33.4 572 2.93 1990-1995 89,055 1  >  The increasing trend i n the mean trophic level o f catches observed i n the southeastern shelf is also observed i n the F A O fisheries statistics for Brazil (Fig. 3.3). Fisheries i n Brazil had a 60  relatively constant mean trophic level o f the species landed from 1950 to the early 1980s, but show a recent increase i n mean trophic level caused by the combined effect o f the collapse o f small and mid-size pelagics (mostly sardine), and the increasing landings o f large pelagic fishes (tunas and sharks) with the development o f offshore fisheries. Another factor that contributes to the increase i n the mean trophic level o f landings is the steady increase i n teleost catches. Teleosts are characterized by high trophic levels (from 3.2 to 4.3, Table 3.2), and represent the most important group i n total Brazilian fisheries landings. However, the proper evaluation o f the contribution o f the group to the average trophic level w i l l require higher resolution in the species catch composition statistics, which currently aggregate a large part o f teleosts species into Osteichthys (Table 3.2).  61  Figure 3.3. Mean trophic level (A), and species composition (B and C)of total Brazilian landings. Source F A O .  62  Fishing Down the Food Web A strategy very often proposed to increase catches i n exploited ecosystem is to fish down the food web for highly abundant, small pelagic planktivorous fishes after larger species are depleted. Simulation results o f a fishing down food web scenario for anchovy i n the southern shelf are shown i n figures 3.4 and 3.5. Figures 3.4 represents the predicted equilibrium yield and biomass o f anchovy, and the percentage change i n biomass o f all other groups in the system under "top-down" and "bottom-up" control o f trophic interactions.  The model predicts  considerably smaller yields and optimal fishing mortality rates for anchovy under top-down control (F  top-down - 0 . 1 year ; F _1  msy  bottom-up ~ 0.3 year ) . Both hypotheses generate a _1  m s y  similar pattern o f decrease i n biomass o f higher trophic level species, increase i n biomass o f midtrophic level groups and increase i n zooplankton biomass with increasing F for anchovy. Predictions o f biomass changes at the mid-trophic level are more pronounced under top-down control, where the release i n predation mortality due to the depressed biomass o f top predators leads to a sharp increase o f jack mackerel abundance.  63  "Top-down"  Cutlassf ish  "Bottom-up"  Hake  1  Sharks  O. P e l a g i c s — — Weakfish  -10  t  -10  F- anchovy (year ) Zooplankton —  Riytoplankton  L  F- anchovy (year" )  -1  1  Detritus  Figure 3.4. Equilibrium simulation of increasing fishing mortality for anchovy. Upper panel represents the predicted equilibrium yield and absolute biomass of anchovy. Lower panels show the predicted relative change in biomass of all other groups in the system. 64  <l -J I 0.0  I  0.5  I  I  I  1.0 1.5 2.0 Total catch (t.km" .year ) 2  I  1  2.5  3.0  1  Figure 3.5. Relationship between (A) total catch from the system and the mean trophic level of catches; and (B) total catch and the mean trophic level of the system (detritus excluded) with increasing fishing mortality for anchovy. The arrows indicate the direction of increase in F, and the dots correspond to 0.01 increments in fishing mortality (from 0 to 1 year" ). 1  Figure 3.5a shows the changes i n the total production (catches from all groups) at trophic level with increasing equilibrium fishing mortality for anchovy. Fishing down the food web has  the 65  effect o f increasing yield up to a threshold fishing mortality rate for anchovy ( F  msy  ) beyond which  fisheries production becomes gradually impaired by overfishing and by divergence or complete interruption o f major energy pathways to the higher trophic levels. W i t h the overfishing o f anchovy, total catches decrease and the mean trophic level o f catches increases (less catch o f low trophic level species). The backward bending curve between the mean trophic level o f catches and total catch suggests that production at trophic level becomes considerably smaller when anchovy is overfished, i.e. the system is unable to capitalize the energy previously available for fisheries and other organisms at the higher trophic levels.  The depressed abundance and  productivity o f top-predators in turn impedes the complete recovery o f the mean trophic level o f catches, which becomes composed mainly by mid-trophic level groups also targeted by fisheries. Parallel changes occur i n the mean trophic level o f the system (Fig. 3.5.b).  The mean trophic  level o f the system is smaller than that o f fisheries catches (due to the contribution o f zooplankton and phytoplankton), and show a progressive decrease with the increase in anchovy exploitation. W i t h "bottom-up" control, total system production at the end o f the simulation is smaller than that originally obtained before fishing down the food web. These generic effects are attenuated under "top-down" control when the model predicts that total catch may remain high after anchovy depletion as a result o f the sharp increase i n abundance o f other mid-trophic level species (e.g. jack mackerel, Figure 3.4) also targeted by fisheries.  3.4. Discussion The primary production required to sustain marine capture fisheries i n southern Brazil is estimated to vary between 25 and 33% o f the total shelf primary production. Results indicate a level o f fisheries impact i n this portion o f the Brazilian coast comparable to the most intensively exploited temperate shelf ecosystems o f the world (Pauly and Christensen, 1995). Our estimates may be conservative considering that discards were not included i n the calculations, and that part o f the catches may remain unreported in official fisheries statistics (Gasalla and Tomas, 1998). Haimovici et al. (1997), for instance, suggested that discards may represent ca. 25% o f total annual catches i n the southern shelf. Including discards i n our estimates for this region, with the same mean trophic level o f the species landed, would increase the expected P P R for the early 1990s from 471 to 666 x 10 gC-year" and from 25.8 to 36.5% o f the total primary production. 10  1  66  The high P P R values in the southern shelf corroborate to the belief that most commercially important estuarine, coastal, and shelf stocks are either fully or overexploited i n the region, and landings are expect to decrease with current fishing pressure (Haimovici et al., 1997).  The  prospect o f increasing catches i n the region has to come from two non-exclusive strategies: i) by better utilizing or recovering stocks which are currently overfished, such as most demersal stocks (Table 3.5), and/or ii) by "fishing down the food web" for alternative resources not yet utilized, mostly anchovy and jack mackerel (Haimovici et al., 1997).  Fishing down the food web has been shown to increase catches up to some threshold fishing intensity, beyond which fisheries production may become impaired by shifts i n major energy pathways in the system (Figure 3.5). Can this type o f fisheries-induced change i n the ecosystem happen? Fishing down the food web is not an observed phenomenon i n Brazil. Instead, fisheries have been targeting high trophic level species, with the exception o f sardine i n the Southeast, and show a recent increasing trend due to the development o f offshore fisheries for high trophic level species such as tunas and pelagic sharks. This increasing trend o f mean trophic level o f fisheries landings was also obtained by Pauly et al. (1998) for the Southwest Atlantic, and attributed to the development o f new fisheries, which, according to the authors tend to mask the fishing down the food web phenomenon. In Brazil, both national ( F A O ) and regional data indicates that although fisheries expanded into areas/stocks not previously exploited there is no underlying downward trend in the mean trophic level o f catches.  Regional experience with intensive fishing for a forage species i n southeastern Brazil has apparently shifted a system that once supported a large fishery for sardine into one occupied by an abundant population o f anchovy, Engraulis anchoita, that is not commercially harvested (Castello et al., 1991). Nonetheless, the extent to which the collapse o f sardine fishery and the switch to an anchovy dominated system was due to human or natural factors is still inconclusive (Rossi-Wongtschowski et al., 1996). M a n y marine ecosystems have shown major 'regime shifts' or changes i n species compositions and production rates apparently triggered by environmental factors but intensified by the effect o f fisheries (Steele, 1996).  A m o n g the best documented  examples are the sardine/anchovy switches i n coastal upwelling systems (Lluch-Belda et a l , 1989), the gadoid outbursts i n the North Sea (Cushing, 1980; Daan, 1980), and the decline o f marine mammals and outburst o f pollock in the Bering Sea (Trites et al., 1999). A l s o , recent global assessment o f the trophic level o f marine fisheries (Pauly et al., 1998) provide evidence o f 67  the fishing-down-food-web phenomenon and o f associated fisheries-induced changes i n the food webs similar to that predicted i n Figure 3.5.  M o d e l simulations allowed the exploration o f the effects o f trophic control assumptions on predicting the changes in the food web accompanying the exploitation o f anchovy. Under topdown control the system responded with a marked increase i n the biomass o f a competitor species due to cascade effects up and down the food web. This pattern was less marked under bottom-up control, where competition for food resources limits interactions. These results, also obtained for other systems (see Chapter 2), again differs from the early emphasis placed on food competition as the driving force o f species replacement (Cushing, 1980; Daan, 1980), but reinforces the potential role o f predation mechanisms and trophic cascade effects in shaping the dynamics o f mid-trophic level, forage species.  A s observed i n other trophic models, the  predicted F ^ y for anchovy is very sensitive to the assumed type o f control o f trophic interactions, being twice as high under bottom-up assumptions.  Considering the evidence that F ^ y for small  pelagics is i n the order o f 0.6 M (Patterson, 1992), model predictions indicate that top-down control can be relatively important i n marine pelagic food webs.  Results from this study indicate that fisheries i n southern Brazil already appropriate a large proportion o f the marine shelf ecosystems  carrying capacity.  In line with recent stock  assessment reports, it is suggested that the prospect o f increasing catches and rebuilding stocks must rely on better management o f the stocks currently overfished, fishing for offshore resources currently moderately exploited, and/or fishing down the food web for abundant short-lived, planktivorous fishes.  It is showed, however, that i n an intensively exploited ecosystem the  proposal for increasing fisheries production by harvesting at lower levels i n the food web has the potential risk o f aggravating the depletion o f high trophic level species besides altering the structure o f the ecosystem, and thus needs to be approached with caution.  The adoption o f  precautionary measures and ecosystem principles i n fisheries policy decisions has been, at least theoretically, unanimous i n fisheries literature and government agendas worldwide (see F A O Code o f Conduct for Responsible Fisheries ( F A O , 1995); Oceans A c t Canada; G E S P E , 1997). One such principles states that  "regulation o f the use o f living resources must be based on  understanding the structure and dynamics o f the ecosystem o f which the resource is a part and must take into account the ecological [...] influences that directly and indirectly affect resource use" (Mangel et al., 1996).  If ecosystem principles and precautionary measures are to be 68  effectively implemented, managers and decision makers have to take the possibility o f such ecosystem impacts o f fishing down the food web for granted when designing policies for the exploitation o f marine resources.  3.5. Summary The carrying capacity o f marine shelf ecosystems in southern Brasil for harvestable species is analyzed by (1) quantifying the amount o f available primary production appropriated by fisheries catches, (2) evaluating the trend i n the mean trophic level o f fisheries, and (3) simulating the ecosystem effects o f "fishing down the food web" i n an intensively exploited shelf region. Fisheries allocate between 25 an 33% o f total primary production i n the southern shelf regions o f Brazil. Overall, fisheries landings do not display a trend o f decreasing trophic level with time, due to the collapse o f the sardine fishery and the recent increasing o f offshore fishing for higher trophic level species, mainly tunas and sharks. However, the simulations show that fishing down the food web through fisheries that target small pelagic planktivorous fishes, while at first increasing catches i n intensively exploited regions, has the potential o f actually decreasing yields, by interrupting major energy pathways to exploited, high-trophic level species.  This  generic effect, corroborated by global assessments o f fisheries-induced changes in marine ecosystems, provides support for the design o f precautionary measures for future fishing policies.  69  Chapter 4. The sardinefisheryin the Southeastern Brazilian Bight  4.1. Introduction This chapter reviews the status o f fisheries assessment o f the Brazilian sardine, and characterizes the sources o f uncertainties on ecosystem, population and harvest processes o f relevance for current management practice.  Sardine, Sardinella brasiliensis, inhabits the coastal bight (Southeastern Brazilian Bight) that extends from Cabo Frio i n the north to Cabo de Santa Marta Grande i n the south, encompassing the coastal waters o f four states, R i o de Janeiro (RJ), Sao Paulo (SP), Parana (PR) and Santa Catarina (SC) (Figure 4.1). The main oceanographic characteristic o f the region is the seasonal presence o f cold, nutrient-rich South Atlantic Central Water ( S A C W ) on the inner shelf, i.e. 10 to 50 m deep (Pires-Vanin and Matsuura, 1993). Being on the western side o f the ocean, the large scale boundary current (Brazil Current) flows poleward. Large scale, alongshore wind stress is in the same direction, particularly during austral spring and summer, which favors the upwelling o f the S A C W into the coastal region. The two major centers o f wind induced coastal upwelling are i n Cabo Frio and Cabo de Santa Marta Grande (Figure 4.1). Bakun and Parrish (1990) found the spawning strategy o f the Brazilian sardine to be like that o f the California sardine i n the Southern California Bight: sardine spawning occurs mainly during spring and summer, i n the enriched environment formed downstream o f the upwelling center o f Cabo Frio (Bakun and Parrish, 1990). In this period, retention and concentration mechanisms are favored by the coastal configuration which shelters the inshore regions from the strong alongshore winds and, as a result, turbulent mixing is decreased to a minimum within the bight, as is the offshore Ekman transport.  A n enclosed gyral circulation tends to form inside the Bight interior, which further  prevents the advection o f eggs and larvae to offshore regions.  The intensity o f the upwelling decreases in autumn and it is absent i n winter when the water column is more homogeneous. Vertical mixing during this season is controlled by the frequency and intensity o f cold fronts which put nutrients back i n suspension to be utilized by phytoplankton.  Cyclonic vortexes are also observed i n the region, originating from  the  meandering o f the Brazil current on the shelf break (Pires-Vanim and Matsuura, 1993). Their 70  presence is associated with localized (5 to 10 nm) subsurface upwelling o f S A C W into the euphotic zone and enhanced primary production.  T w o oceanographic processes are therefore  responsible for controlling the productivity o f Southeastern Brazilian shelf ecosystem: the penetration o f the S A C W and the formation o f frontal vortexes. The first has seasonal frequency but show marked interannual variation in intensity. The vortexes, on the other hand, were registered in 3 out o f 7 years o f observations and seem to lack periodicity.  Figure 4.1. Detail of the Southeastern Brazilian Bight, which encompasses the distribution area of sardine, Sardinella brasiliensis, and the fishing area of the purse seine fleet. Sardine distribution area includes the coastal region of four states, Rio de Janeiro (RJ), Sao Paulo (SP), Parana (PR) and Santa Catarina (SC). Depth in meters.  The seasonal penetration o f the S A C W i n the coastal region has a direct influence on the variability o f primary production and on the quantitative and qualitative composition o f the pelagic and benthic community (Pires-Vanin et al., 1993). Oceanographic conditions prevalent during summer create conditions for localized new primary production by diatoms. However, nano and picoplankton are characteristically more abundant conditions encountered in the bight.  due to general oligotrophic  Salps are particularly abundant during summer.  Salps  consume considerable amounts o f phytoplankton and are apparently not important in the diet o f higher order consumers (fishes). Their presence and dominance i n the pelagic ecosystem during the summer thus represent an alternative trophic flow to the classic phyto-zoo-fish structure normally present i n these systems. Pires V a n i n et al (1993) also emphasize the role o f salps in 71  exporting organic matter (in the form o f fecal pellets) to the benthic community. In the pelagic system the most abundant fish species besides sardine are the anchovy, Engraulis anchoita, and the jack mackerel, Trachurus lathami.  In the benthos, the organic matter exported from the pelagic and other adjacent systems is processed by a diverse community o f bacteria and benthic organisms. Assessment o f species diversity i n the region indicated the presence o f 195 megafauna and 424 macro fauna species i n the benthos, and more than 180 demersal fish species (Rocha et al., 1998) which show remarkable variation i n dominance as a result o f seasonal variability o f food sources (Pires-Vanin et al., 1993). Pires V a n i n et al. (1993) divide the benthic community into 6 trophic groups, with occurrence and abundance varying seasonally. detritivores,  sub-surface  The groups are suspension feeders, surface  detritivores, carnivorous generalists,  carnivorous specialists and  omnivorous. In summer the organic matter settled from the pelagic system is mainly utilized by omnivorous suspension feeders and surface detritivores.  In winter, turbulent mixing o f the  bottom associated with the frequent passage o f cold fronts is responsible for disrupting the suspension feeders and surface detritivore fauna, and for benefiting the sub-surface detritivores and carnivores.  The demersal fish community has 4 main trophic groups: pelagic feeding fishes (eating mostly fishes and crustaceans); and benthic feeding fishes, which are divided among the species that eat mostly bottom surface invertebrates, sub-surface invertebrates and demersal fishes. The majority of species utilize the surface invertebrates as the main food source. However, during the summer there is an increase i n importance o f fish species that utilize the pelagic system as major trophic pathway.  The presence o f demersal fish stocks and the relatively smooth, sandy-muddy bottom favored the early development o f trawling activities i n the Southeastern Brazilian Bight. The main demersal stocks exploited i n the region are the white croaker Micropogonias furnieri, king weakfish Macrodon ancylodon, weakfish Cynoscion jamaiscencis, triggerfish Balistes capriscus and the marine shrimps Xiphopenaeus kroyeri, Farfantepenaeus brasiliensis and F. paulensis.  The  presence o f large pelagic stocks, mainly sardine, has also influenced the development o f an important purse seine fishery as early as 1950. N e w offshore fisheries for tunas, sharks and rockfishes were developed during the last two decades following the depletion o f many 72  traditional stocks. Tunas and sharks are mainly caught with longlines and pole-and-line, while bottom longlines are employed in the rockfish fisheries (Gasalla and Tomas, 1998).  Sardinefisherydevelopment U n t i l the beginning o f the century sardine catches were mainly used for subsistence o f coastal communities. This artisanal fishery still exists in most states, catching sardines i n bays and estuaries along the coast, using cast nets and seine nets. More recently, a fishery based on small purse seiners targeting juvenile sardines i n inshore areas has developed to supply live-bait for bonito pole-and-line fishing boats. However, today, most o f the sardine catches come from the industrial fishery based on purse seiners.  The first purse seiners appeared ca. 1910 and gradually diverged from the artisanal and small scale sector (Diegues, 1995), particularly during the 1930's with the introduction o f power engines. The first vessels were constructed by Portuguese, Spanish and Italian immigrants using traditional models from their homeland. The nets were originally made o f cotton, demanding a great amount o f effort for maintenance, but this was substituted by nylon during the 1960s (Diegues, 1995). The present day industrial fishery utilizes rectangular purse seine nets 700 to 900 meters long, 50 to 60 m high and a mesh size o f 12 m m (Valentini and Cardoso, 1991). Purse seiners usually fish i n areas up to 60 m deep, although sardines may occur i n areas up to 100 m deep. The fishery was originally mainly carried out at night, during darker periods o f the lunar cycle, approximately 18 days per month. Fishing time was then still determined by a visual search; shoals migrating to the surface waters during dusk excite the phosphorescence o f diatoms and allow visualization experienced fishers. Catch volume and handling time made the fishing operation considerably slow (up to 6 hours to complete a set) and determined that landings took place i n ports close to the catching areas. In fact that also determined a positive relationship between the areas o f sardine concentration and the landing volume i n ports along the coast (Valentini and Cardoso, 1991).  During the late 1960's government tax incentives (Codigo de Pesca, L e i 221, 1967) attracted a considerable amount o f resources to the fishing sector which expanded the number o f industries for catching and processing fish products for export. Government incentives lasted from 1967 to  73  1978,  and resulted i n unprecedented changes i n the fishery.  Sardine then became the main  Brazilian fishery resource in terms o f volume, with total annual catches increasing from ca. 38 thousand tons in 1964 to a historical peak o f 228 thousands tons in 1973 (Fig. 4.2). The average landings during 1983-1987 period were 124 thousand tons.year" and accounted for 31.8 % o f the 1  total fish catches i n the region and for about 25 % o f the national marine catches ( I B A M A , 1995).  250  r  64  67  70  73  76  79  82  85  88  91  94  Year Figure 4.2. Sardine landings by the three main state fleets in Rio de Janeiro (RJ), Sao Paulo (SP), and Santa Catarina (SC).  Another program o f government incentives aimed at the modernization o f the fleet lasted from 1983 to 1985.  It resulted i n an increase i n fleet capacity (tonnage) o f approximately 300%  compared to the 1970's, a doubling o f the number o f fishing vessels, and the introduction o f technological innovations (e.g. sonar and power block) that considerably increased the fishing power o f purse seiners (Valentini and Cardoso, 1991). The fishery then experienced a marked decrease i n catches, mainly from 1987 to 1990, which culminated with the collapse o f the stock and a crisis in the commercial/industrial sector ( I B A M A , 1995). W i t h the decline o f the stock and the low catches i n 1990, the canning industry started relying on imported sardines and some fishing vessels shifted their target to other less productive stocks, mainly Mugillidae, jack mackerel (Trachurus lattami), mackerel (Scomber japonicus) and bonito (Katswonus pelamis). Since the collapse o f the fishery there is no indication o f  stock recovery (R. Habiaga, pers.  74  comm), although catches in the southern distribution range o f the stock have been increasing during recent years (Fig. 4.2). Today, economic recovery is also hampered by the higher price o f sardine compared to imported fish, which i n turn increases the internal pressure for more government subsidies to the fishing and processing sectors.  The collapse o f the fishery was attributed to diverse causes (Valentini and Cardoso, 1991; Castello, 1992, Matsuura et al., 1992), here divided between proximate and ultimate causes following Clark and Munro (1997). A m o n g the proximate causes o f collapse are i) the excessive fishing effort exerted by almost 500 boats (licensed and illegal), ii) intense fishing on juvenile fish (length less than 17 cm) all over the area o f occurrence o f the stock, and iii) alterations in the oceanographic system which resulted in higher mortality o f larvae i n 1986, decrease i n recruitment in 1987, and low stock biomass i n 1988. Short-term economic interests, government incentives driven by the same interests, and the lack o f compliance with regulations all may have acted together as ultimate causes o f collapse.  Overview of Management Context The management o f marine fisheries i n Brazil is executed mostly by the federal government, which is responsible for assessing the status o f the stocks and for setting and enforcing regulations on the use o f marine resources i n the Exclusive Economic Zone.  Governmental  institutional arrangements for regulating fisheries activities has been changing over the years. Until 1989 fisheries were under the agenda o f a federal sub-secretary for fisheries development ( S U D E P E ) . From 1989 to 1997, fisheries became one o f the agendas o f a federal Institute for the Environment and Renewable Resources ( I B A M A ) , subsidiary o f the Ministry o f Environment. IBAMA  was created by the fusion o f four federal agencies responsible for issues on  environmental protection ( S E M A ) , forestry (IBDF), fisheries ( S U D E P E ) and rubber production ( S U D H E V E A ) . Recent institutional changes have modified the governmental involvement with fisheries management.  The recently established Department o f Fisheries and Aquaculture  ( D F A ) , a subsidiary o f the Ministry o f Agriculture, is now responsible for defining the policies and programs to foster the development o f fisheries and aquaculture activities i n freshwater and marine areas. The specific attributions o f D F A i n relation to fisheries management are:  75  •  to promote the implementation and assessment o f policies and projects that support the development o f artisanal, industrial and recreational fisheries;  •  to develop studies, procedures and rules for the proper exploitation o f fisheries resources;  •  to identify and indicate the need for new scientific knowledge necessary for the development o f fisheries and aquaculture;  •  to define and implement programs that incentive regional and de-centralized forms o f management, based on principles o f institutional interaction, community participation and cooperation.  In this new arrangement, I B A M A is responsible for the implementation or enforcement o f the policies defined by the Department o f Fisheries and Aquaculture. Policy execution is ideally carried out i n cooperation with regional and municipal agencies o f fisheries.  Fisheries assessment research also went through distinct phases over the years (Castello and Haimovici, 1991). The first strategy for assessing fish stocks was implemented i n the late 1950's with the establishment o f a national system o f fisheries statistics and assessment o f industrial fishing fleets. A next stage, initiated during the 1970's, aimed at the surveying and assessment o f the productive potential o f fish stocks along the coast (Neiva and Moura, 1977). During the 1980's a system o f expert consultation 'Grupo Permanente de Estudo' ( G P E ) was established for each o f the main fisheries resources, i.e., shrimps, demersal fishes, sardine, lobsters, snappers and tunas. The objective o f the G P E s was to provide recommendations for both management and research based on the analysis o f biological, technological and socio-economic information on these major resources.  However, management recommendations produced i n the G P E s were  very often ignored or not effectively implemented due to the lack o f political decisions, usually restrained by conflicting interests, and lack o f enforcement.  Overall, fisheries assessment and management have degraded considerably during the last years as revealed by the status o f overexploitation o f many resources, the lack o f updated information on stocks and fisheries caused by the interruption o f statistical sampling programs, and the lack o f resources for research and enforcement (Instituto de Pesca, 1993). Since the Earth Summit in 1992, an inter-institutional Fishing Sector Executive Group ( G E S P E ) was created to implement a National Fishing and Fish Farming Policy plan aimed at fostering a better integration among institutions (public and private) for the reorganization o f fisheries management i n Brazil ( G E S P E , 1997). The follow up o f this plan is still uncertain and beyond the scope o f this thesis. 76  The plan recognizes as ultimate goal o f fisheries management  to ordinate and foster the  sustainable use o f fisheries resources, which is defined as the maintenance o f the equilibrium o f ecosystems and preservation o f the species under exploitation; economic profitability o f fishing activities; generation o f jobs and a fair work compensation ( G E S P E , 1997).  The current regulatory mechanisms adopted i n Brazil to manage fisheries activities include (Castello, 1992): limits on mesh size; limits on size o f fish landed; limits on fishing effort by license control; temporal fishing closures; control o f the type o f gear allowed; and catch limits. The fishery for sardine is mostly regulated by temporal fishing closures and limit on the size o f fish caught.  Policies adopted from 1976 to 1990 included: i) a three months fishing closure  during the spawning period (December to February); ii) minimum size i n the catch o f 17 cm (size o f first maturation), with a tolerance o f 15% o f catch weight below the minimum size; and iii) limit o f the number o f fishing licenses. In 1991, with the evidence o f stock collapse, policies were reformulated to include another fishing closure during the recruitment period (June to August), to restrict the amount o f illegal size fish i n the catch to 5% o f total weight, and more rigid control o f licensing o f fishing boats.  The recruitment closure was revoked i n 1995 given  the relative recuperation i n the total catch volume to ca. 80 thousand tons i n 1994.  Overcapitalization created by repeated government incentives, and the lack o f long term planning and enforcement, resulted i n an overgrown fishery with a fleet capable o f catching more sardines than the most optimistic assessments o f stock production.  Debate on the legal size o f fish  caught, and the length o f fishing closures permeates the discussions on the current fishing regulatory mechanisms.  A t the same time evidence o f changes i n oceanographic conditions i n  the bight, and associated variations in larval survival and recruitment success, suggest a volatile fishery prone to periodic "boom and bust" cycles. Further, the marked increase i n anchovy biomass during the decline o f sardine stock awakens concerns on the impact o f sardine fishery on the ecosystem o f the Southeastern Brazilian Bight, and on the ability o f the stock to recover given a possible shift in the ecosystem (Castello et al., 1991).  In this scenario, fisheries  management requires ecological information to predict the effects o f fishing on the short and long term ecological sustainability o f the system.  Ecological information would, i n principle,  provide the basis for discussing the following management questions:  77  I.  i n the long term, what type o f regulatory mechanism would be more appropriate for managing the fishery, and what is the expected outcome (catches, chances o f collapse, etc.) under different levels o f exploitation?  II. i n the short term, what type o f strategies would be required to foster the rehabilitation o f the stock?  The fishery is also the target o f scientific questions which aim to understand the causes o f recruitment fluctuations and ecosystem changes, such as:  I.  are fluctuations i n sardine catches related to global-scale climate changes (RossiWongtschowski et a l , 1996)?  II. what are the biological-oceanographic factors responsible for the success or failure i n sardine spawning and recruitment (Saccardo and Rossi-Wongtschowski, 1991; I B A M A , 1995) III. can the apparent shift between sardine and anchovy result from fishing-induced changes i n the ecosystem (Castello et al., 1991)?  Scientific recommendations produced by the GPE/Sardine during the early 90's ( I B A M A , 1991; 1992; 1993b; 1994d) pointed to a list o f ecological research activities to be explicitly undertaken i n order to acquire new knowledge for managing the fishery. Research priorities included: •  to obtain independent estimates o f stock size using either acoustic surveys or egg production methods;  •  to continue the collection o f fisheries data and biological sampling o f the catches along the coast;  •  to update the information on licenses and boats currently fishing;  •  to monitor oceanographic conditions through surveys and remote sensing;  •  to analyze temporal oceanographic anomalies off the coast.  Research priorities are mainly aimed at increasing the descriptive knowledge about the status o f the stock and fishery and improving the understanding o f the oceanography o f the Southeastern Brazilian Bight.  F e w attempts have been made to design research programs to improve the  functional knowledge o f processes affecting the fishery, and to evaluate how the understanding 78  o f these processes would improve the management practice for sardine. A m o n g the processes scientists are uncertain about, those controlling biological production and those directly influencing harvest control deserve a closer attention i n order to improve the quality o f harvest decisions.  Studies on Sardinella brasiliensis carried since 1950 provided substantial information on the reproductive biology o f the species, early life history, estimation o f population parameters and stock assessment (Saccardo and Rossi-Wongtschowski, 1991; I B A M A , 1995). But monitoring o f stock biomass during the last decades has been quite erratic. The lack o f updated information on the stock and the confounding effect o f environmental signs and overfishing complicates the understanding o f critical processes for management.  This chapter reviews the status o f sardine  stock assessment and characterize the sources o f uncertainties on ecosystem, population and harvesting processes o f relevance for current management practice.  4.2. Methods The analysis is divided i n three sections. The first describes the structure and dynamics o f the Southeastern shelf ecosystem and analyzes the changes that followed the collapse o f the sardine fishery. The second section analyzes the observed changes i n the sardine population with data on stock and recruitment.  Finally, the third section analyzes how the catchability coefficient has  changed with time and stock size, and what the likely consequences are for harvest control.  Ecosystem structure and dynamics The Southeastern Brazilian Bight encompasses an area o f distinct environmental characteristics and types o f activities, and defines an appropriate unit for ecosystem management  purposes  (Matsuura, 1995). In this section, the analysis o f the structure and dynamics o f the Southeastern Brazilian shelf ecosystem is based on a trophic mass-balance model adapted from Rocha et al. (1998).  Rocha et al. (1998) constructed a trophic model (Ecopath) o f the Ubatuba shelf  ecosystem, in the Southeastern Brazilian Bight, which describes the structure o f the system at summer conditions during the late 1980s.  The model was built with ten functional groups  representing organisms with a similar role i n the food web, plus detritus (Box 4.1). This model 79  was adapted as follows to reflect annual conditions i n the Bight, to represent i n detail the main harvested species i n the system and, as modified, has 22 boxes. 1) Primary production, biomasses, P / B ratios and catches o f all groups i n the system were adjusted to a one year period. Annual primary productivity i n the Southeastern Bight was obtained from Brandini (1990).  Biomass o f plankton and benthic organisms were adjusted  according to their seasonal abundance described i n Pires-Vanin (1993).  Catches o f fish and  shellfish groups were adjusted to one year according to data from the Instituto de Pesca, Santos (see table 3.6, Chapter 3).  2) The box Small pelagic fish i n Rocha et al. (1998) was split among Sardine, Anchovy, and Other forage fish.  Opisthonema  The latter represents small and middle-sized pelagic species such as  oglinum,  Harengula  clupeola,  Brevoortia  spp,  Trachurus  spp,  and  Chloroscombrus chrysurys. Biomass and P / B data for sardine was obtained from V P A (section 2) and from Cergole (1995). Sardine was split between adult and juvenile pools to explore the effect o f harvest on recruitment rates i n the dynamic simulations (parameter details on Table 4.1).  A n c h o v y biomass is from direct acoustic assessments (Castello et al., 1991), and  production estimate is from the southernmost unpublished).  stock and anchovy i n B r a z i l - ( K . Freire,  Sardine and anchovy diets were obtained from Goiten (1983) and Schwingel  (1996), respectively. Biomass o f Other forage fish was considered the difference between the biomass o f Small pelagic fish i n Rocha et al. (1998), and the sum o f Sardine and Anchovy biomasses.  3) The box Benthic feeding fish i n Rocha et al. (1998) was split among individual demersal stocks o f white croaker, Micropogonias furnieri,  king weakfish, Macrodon  ancylodon,  triggerfish, Balistes capriscus, Rays/Skates, and other Benthic feeding fish. Parameters P / B and Q / B for the demersal stocks were obtained from data on growth and mortality i n FishBase (Troese and Pauly, 1998).  Diets were obtained from Juras and Yamaguti (1985), Gasalla  (1995), Soares et al. (1992) and FishBase.  Triggerfish was split between adult and juvenile  pools to represent differences i n feeding habits and distribution o f the species during its life cycle;  while adults  are  mostly demersal  and  benthic  feeders,  juveniles  are  pelagic  zooplanktivores (Zavala-Camin and Lemos, 1997; FishBase, 1998). Parameters P / B and Q / B for juvenile triggerfish were assumed similar to the fast growing species i n the model (i.e. small forage fish).  Ecotrophic efficiencies were considered 0.9 for all individual pools for which 80  biomass data was not available. This assumption, which implies that 90% o f the production is either consumed or exported (harvested) from the system, can be expected for groups that have abundant consumers or are fully exploited.  Biomass o f the box Benthic feeding fish was  considered the difference between the sum o f estimated biomasses o f all demersal pools, and the biomass o f the original Benthic feeding fish group in Rocha et al. (1998).  4) The box Pelagic feeding fish i n Rocha et al. (1998) was split between weakfish, Cynoscion spp., and other Pelagic feeding fish. To represent the trophic ontogeny reported for weakfish stocks (Haimovici, 1997), the group was split between Adult and Juvenile weakfish. Parameters (P/B and Q / B ) for Adult weakfish were calculated from growth and mortality data i n FishBase (Froese and Pauly, 1998).  Juvenile weakfish P / B and Q / B rates were considered the same  estimated for small forage fish.  Diets for adult and juvenile weakfish were obtained from  Gasalla (1996) and Haimovici (1997), respectively.  Eco trophic efficiencies o f both groups  were assumed 0.9. Biomass o f the box Pelagic feeding fish was considered the difference between the sum o f estimated biomasses o f Adult and Juvenile weakfish, and the biomass o f the original pelagic feeding fish group i n Rocha et al. (1998).  5) A box representing bonito, Katswonus pelamis, was added to the model. Bonito diet is from V i l e l a (1990), biomass estimates from Jablonski and Matsuura (1985), and parameters Q / B and P / B derived from growth and mortality estimates for the species i n FishBase (1998).  6) The box Omnivorous benthos i n Rocha et al. (1998) was split between marine shrimps and other Omnivorous benthos. Marine shrimps include stocks o f Farfantepenaeus brasiliensis, F. paulensis, and Xiphopenaeus kroyeri.  Parameters P / B and Q / B o f Penaeid shrimps were  obtained from growth and mortality estimates on DTncao (1991). Ecotrophic Efficiency was considered 0.9.  Biomass o f the box Omnivorous benthos was considered the difference  between the estimated biomass o f Marine shrimps, and the biomass o f the original Omnivorous benthos group i n Rocha et al. (1998).  7) Finally, harvest information was aggregated i n three fishing fleet types according to Gasalla and Tomas (1998): Purse seiners, catching mostly sardine; Bottom trawl, catching weakfish, Rays/Skates, white croaker, king weakfish and triggerfish; Shrimp trawl, catching marine shrimps; and Pole-and-line catching bonito. 81  Box 4.1. Ecopath models of the southeastern Brazil shelf ecosystem. Trophic groups identified by Rocha et al. (1998) for the model of the Ubatuba shelf ecosystem, southeastern Brazil. 1. Phytoplankton: composed by phytoflagellates and diatoms 2. Zooplankton: small calanoid copepods 3. Salps: mainly Thalia democratica 4. Bacterioplankton: dominated by free bacteria feeding on detritus 5. Omnivorous benthos: Penaeid shrimps, chitons and echinoids 6. Carnivorous benthos: Crabs, asteroids, polychaetes and some gastropods 7. Detritivorous benthos: Polychaetes, gastropods, bivalves, ophiuroids, cumaceans, amphipods and tunicates 8. Benthic feeding fish: Sciaenidae, Rajidae, Serranidae, Triglidae, Batrachoididae, Haemulidae, Paralichthyidae, Gerreidae, Bothidae, Lophiidae, Rhinobatidae, Sparidae and Mullidae. 9. Pelagic feeding fish: Cynoscion guatucupa, C. jamaicensis and Merluccius hubsii 10. Small pelagic fish: Sardine, Sardinella brasiliensis, and Anchovy, Engraulis anchoita 11. Detritus.  Trophic groups in the model of the southeastern Brazil shelf ecosystem adapted from Rocha et al. (1998). 1. Phytoplankton 2. Zooplankton 3. Salps 4. Omnivorous benthos 5. Marine shrimps 6. Carnivorous benthos 7. Detritivorous benthos 8. Other Benthic feeding fish 9. King weakfish 10. Croaker 11. Rays/Skates 12. Triggerfish 13. Juvenile triggerfish 14. Other forage fish 15. Anchovy 16. Adult sardine 17. Juvenile sardine 18. Other pelagic feeding fish 19. Adult weakfish 20. Juvenile weakfish 21. Bonito 22. Detritus  Parameter adjustments had to be made i n order to balance the model, i.e., to make estimates o f biomass and fluxes among groups consistent with biological and ecological constraints.  In  particular, two groups showed Ecotrophic Efficiencies (EE) larger than 1 after the first iteration: Benthos Detritivores, and the Detritus box.  T o balance the model biomass o f Benthos  Detritivores was increased b y 50%. The final biomass (30 tonnes.Km ) is lower than the value 2  o f 40.3 tonnes.Km" used i n the summer model (Rocha et al., 1998), and it is consistent with the 2  lower abundance o f benthos detritivores observed i n the other seasons (Pires-Vanin, 1993). The major problem i n making the rate o f detritus accumulation balance detritus consumption i n the system was due to the large consumption rates o f bacteria. Christensen and Pauly (1996) warned of the difficulties i n representing flows associated with bacteria i n Ecopath, which tend to completely overshadow the other flows i n the system. One option suggested b y the authors, and adopted here, was to exclude bacteria from the model since i n the present analysis no special emphasis needed to be given to the microbial food web (i.e., the microbial food web is considered as an adjacent ecosystem (Christensen and Pauly, 1996)). The importance o f bacteria in the diet o f organisms i n the system was then transferred to detritus. Finally, to make the Ecotrophic Efficiency of Detritus less than 1, the importance o f detritus i n the diet o f Marine 82  shrimps, Benthos omnivores and Benthos detritivores was lowered and compensated by a proportional increase i n the importance o f other prey i n the diet o f these groups. A l s o , the gross efficiency o f Benthos detritivores was also increased from 0.09 to 0.11.  M o d e l and catch data were used to reevaluate the total flux o f energy, originated from primary producers, available to different trophic levels in the food web, and the total primary production required to sustain fisheries catches i n two time periods: from 1977 to 1980, before the overfishing o f sardine and other demersal stocks; and from 1990 to 1995, after the sardine stock collapse.  The trophic model was also used to examine hypotheses about the dynamic responses o f the system to changes i n fishing rates.  For this purpose, simulations were carried with Ecosim  (Walters et al., 1997) following methodology described i n detail i n chapter 2.  Recent  improvements i n Ecosim (Walters et al., i n press) allow for a more realistic representation o f linkages between split pool pairs (juvenile and adult stages), through flow o f biomass and numbers o f individuals, using a delay difference model for each split pool case in Ecopath. Parameterization o f the delay difference model is carried by the user entering information on growth, age and weight at transition from juvenile to adult stage (see table 4.1 for some examples). Ecosystem linkages such as changes i n predation and consumption rates by juvenile and adult pools can therefore be captured i n a age-structured population dynamics model embedded in the trophic model.  Table 4.1. Parameters of the split pools in Ecopath used by the delay difference model in Ecosim. K is the von Bertalanffy growth parameter (year ), w is the weight (g) at the age t (years) fish graduate to the adult pool. Parameters for sardine were obtained from Cergole (1995). Parameter values for weakfish and triggerfish are from 1  k  Split pool Weakfish Triggerfish Sardine  K 0.3 0.5 0.5  k  wk 100 128 44  tk 2.0 1.0 1.5  The model also allows for explicit representation o f hypotheses about changes i n growth rates, foraging time and time at risk to predation with increasing (decreasing) feeding opportunity. These assumptions are critical to the form o f implied 'stock-recruitment' relationship generated for each split species by the model. M u c h o f the dynamics o f trophic interactions in a aquatic system has been shown to depend on how time spent feeding varies with foraging opportunities 83  in limited spatial refuges from predation (Walters and Juanes, 1993; Walters and Korman, 1999); foraging time by an individual fish must balance growth and predation risk i n order to increase the chances o f survival. That is particularly true during the early life stages most vulnerable to predation. The Ecosim interface allows for the input o f constraints on time spent feeding by any group in the system by asking the user to enter the maximum relative increase i n time spent foraging when food intake is low ( F  max  ) . Time spend foraging influence both the rate o f effective  search (a ) and the time spent vulnerable to predation (v^) from equation 3, Chapter 2 (see details (j  also i n Walters et al., in press). In summary, setting high F  m a x  allow for ample change in time  foraging so as to compensate any decrease i n food availability and consumption (relative to the baseline consumption estimated by Ecopath). L o w F  m a x  constrains the rates o f effective search  and represents the hypothesis that individuals already spend most o f their time foraging (Walters et al., i n press).  Spawning Stock and Recruitment In this section, data from sardine stock assessment is used to describe changes in stock biomass and recruitment. Cergole (1995) used a Virtual Population Analysis ( V P A ) to reconstruct the population abundance o f sardine from catch at age data obtained from 1977 to 1990 (Table 4.2). In this part o f the analysis direct biomass estimates from acoustic surveys and egg abundance data are used to 'tune' the V P A previously done by Cergole (1995). M o d e l tuning aims to obtain biomass and natural mortality estimates that are consistent with independent indexes o f stock abundance.  84  00 r o  o os os1  CQ  '-  os oo ON *"*  in o  o  CN o ——  ON  ro OS  OS SO  ro ro  oo  O  CO  T—1Os  o  o  CN 1 — 1  O  90 oo rt  so  o  o  oo  in in in rCN o —-  so oo Os  so  t~  CN  SO  in 00 OS  •<t 00 OS  o  ro  t-  in  o  ro  SO  in C rO o  CN  1 1  SO  in CN in •*r in o o CN CN in OS  in  r-  Os oo OS  Os oo  CN OS  o CN so Os  SO  ro  oo ro ro ro Os  CN CN CN o  oo OS t-~ SO oo  SD  o  •*  <  OS ro ro  o  r~-  ro ro ro  in CN SO  Os OS  in o O O O  ro  O o  C mO  oo  00 rt-  m in  ro  CN  in  oo  o  OS SO oo  SO  CN o in CN oo  ro  o  SO  _  i—*  in o  OS  ro O  o ro  CN t-ro  SO  SO  in  CN  ro  — t~-  o  SO  in  ro  so  o o  CN  O oO o m ^r o oino C in o o in o CO in in r o —  CN CN r-  SO  oo SO  o  1  in in r00 O S rt  CN CN CN SO  00 r o  oo  o  •*  CN in CN in oCN CN o SO o Os CN CN "3- o r o o oo Cs oo Os CN OS CN ITS ro r~ CN ro  CO  00 OS  ro  rt  ro  CN  oo ro  Os  CN 00 Os *H  ro  O  OS  r~ OS  00  m o I< CN m CN OS O O o Os SO OO in r o  ro  in  o oo  OO  o oo oo r o r~-  ro  rOS  -  CN in so *H  00  00 OS CN CN CN ro  o 00 OS  oo ro  SO  o o  OS SO or oo r- r os rr oo SO O r>on  oo rOs  SO so  — CN ro ro oo  •~~'  ox  <  in  <=>  O  CN CN OS  in so  in rso o OS so o r o CN — CO 00  in  CN  ON  in CN  oo  so SO  SO  ro oo ro Os  CN  O  o  o in  in SO  m  SO ro  in  oo  r- o o r- « os ^CN  ""  SO t-  oo so  oo  o  Os  o  CO  Os OS SO  00  CO  CN  so  CN  O OS r~ oH in CN ro so Os rOS- o o O  in CN in oo  CN O  m  CN in  oo SO ^J-  o  CN  r-  o  r- CN in o CN o t~m O o in in oo CN in Os ^ oo O in oo' oo CN oo ro in SO T* in o  OS  —  in  •n  CN CN  ro  in  CO  „  pa  To simplify the calculations it was assumed that the fishery takes place in a relatively short period o f time during which the population is subjected only to fishing mortality. The basic reconstruction equation for any cohort in a discrete fishery is (Hilborn and Walters, 1992): a+l,t+\ "  N  a,t  N  =  + C  a>t  where N , accounts for the number o f fish o f age a alive at time t, s is the annual survival rate (= a  e~ ), M is the natural mortality rate, and C M  a t  is the observed catch o f age a fish at time t. It was  also assumed that no fish are left alive after age 3.5. Fishing mortality is expressed in terms o f t  age-time specific exploitation rates u  at  =——,which  represent the fraction o f individuals  ™ a,t captured  during the  fishery. To initialize the backward recursion calculations, terminal  exploitation rates were assigned for the last age in all years according to the procedure in B o x 4.2.  Box 4.2. Virtual Population Analysis procedure for reconstruct sardine biomass at age (Walters and Punt, 1994). 1. set u (the exploitation rate for the fully vulnerable ages in the last year T of data) equal uT; 2. calculate the fully vulnerable harvest rate (u ) in each year (1 to T - l ) as the average exploitation rate of the ages 2 and 3 (considered fully vulnerable to the fishery); 3. set N = C j./u, for all t<T; 4. for ages not completely vulnerable in the last year T, calculate N =C _ /(v , .u ), where v are the terminal relative vulnerabilities. Relative vulnerabilities at age for the last year of data were estimated as the average vulnerabilities calculated for early years in the assessment. aT  t  3 5 t  3  aT  a  T  a  T  T  a T  Finally, the probability distribution for each possible terminal exploitation rate (uT) and natural mortality rate ( M ) were obtained by tuning the V P A with biomass acoustic assessments and indexes o f egg density, considered proportional to the spawning stock biomass (Table 4.2). Sardine biomass i n 1977 (388,000 tons) was considered the average estimation o f acoustic surveys carried out between 1974 and 1980 (Johannesson, 1975; Rijavec and Amaral, 1977; Saccardo, 1983).  The 1988 biomass (77,000 tons) refers to the upper limit o f the acoustic  estimation obtained by Castello et al. (1991).  Monitoring o f sardine spawning i n the  Southeastern Brazilian Bight (Matsuura, 1995) provided measures o f spawning intensity (eggs.m" 2  ), that were used as relative indexes o f spawning stock biomass.  86  Bayes posterior probability distributions were assigned to each parameter combination:  L(D/u ,Mj)P(u ,Mj) Ti  p(u , Af / Z» - _ _ Ti  L  {  Ti  D  M  j  )  p  {  u  n  f  M  ,  )  i j where p(u ,Mj/D) is the posterior probability o f terminal fishing mortality and natural mortality Ti  rates given the abundance time series data (D), L(D/u ,Mj) is the likelihood o f the data given that Ti  parameter combination is true, and P(u ,Mj) is the prior probability o f the parameters. xi  Uniform  prior probability distribution were assigned to uT (U(0,1)) and M (U(0.95,1.36). Pauly's (1980) empirical equation was used to compute the range o f natural mortality values expected for the Brazilian sardine, based on growth parameters and water temperature values suggested by Cergole (1995), and on a correction factor o f 0.8 suggested by Pauly (1980) for schooling fishes.  The likelihood for the relative spawning stock biomass index (y,) with log-normal error (y = q t  S S B e ) was calculated according to Walters and L u d w i g (1994) and Walters and Punt (1994) E  t  L{y, lu ,M)  = SS  T  where SS = from V P A .  2  - Z ) , Z = log( Jj 2  t  ) and S S B is the spawning stock biomass i n year t t  Sardine biomass assessments from acoustic surveys were considered direct  measurements o f absolute biomass o f adult fish (age 1+) with normal error. The likelihood for survey biomass ( B J was calculated as  -(B -B )  2  t  L(B Iu ,M) t  T  where B  =e  t  2 a Z  is the adult stock biomass from V P A and a  2  t  is the variance o f the observation  uncertainty. The total likelihood o f parameters u and M was obtained by combining the two x  likelihood functions, that is  L(D fuT,M) = L(y I u , M). L(B T  lu ,M) T  87  Stock distribution, effort dynamics and catchability change The reconstructed time series o f population abundance also provides information useful for the analysis o f biases on fishery-dependent index o f abundance (cpue), and for the evaluation o f stock catchability changes with time, temperature, stock size and distribution. Stock catchability (q) was computed based on the relationship between fishing effort (f) and the effective fishing mortality rate (F) from V P A , i.e. F=q»f. Effort data was obtained from log-book information from 1977 to 1983 i n the three main state fleets (Rio de Janeiro, Sao Paulo and Santa Catarina).  Information on fleet characteristics, effort allocation and stock distribution were used to analyze the observed changes i n stock catchability. Effort allocation data was used to test the hypothesis that purse seiners allocate effort spatially according to an Ideal Free Distribution (Hilborn, 1985; Gillis et al., 1993). The Ideal Free Distribution predicts that the foraging benefits i n an area w i l l be proportional to the availability o f resources divided by the number o f foragers i n the same area.  If an individual is free to move so as to maximize its own benefits, the result is an  equilibrium effort distribution where foragers (vessels) i n different areas have the same profit rate (cpue). The test for the Ideal Free Distribution is therefore used as an indicator o f the efficiency o f purse seiners i n allocating effort spatially across the stock distribution area. Following G i l l i s et al. (1993), we tested for the equalization o f cpue among fishing areas in the southern part o f Bight (Fig. 4.3) using effort allocation data from the Santa Catarina purse seine fleet.  The proportion o f the total sardine catch i n each area within each month is regressed  against the proportion o f the total effort i n the same area during the same month. I f effort is allocated so that cpue is equalized among areas, then  Q  Z  C  /  irTTr  cpue  i  where Q a n d / are the catch and effort i n area i , and cpue is the value equalized among all areas in the month considered.  In this analysis cpue is expressed as tons o f fish catch per time  searching. B y rearranging the equation above, we have  88  i  i  which is a linear regression with intercept 0 and slope 1. I f the Ideal Free Distribution applies to the data, all points should fall along a line o f slope 1, regardless o f the monthly cpue value. Cabo  Figure 4.3. Fishing areas (shaded) of the Santa Catarina fleet used in the analysis of effort allocation.  89  4.3. Results and Discussion Ecosystem structure and dynamics Figure 4.4 shows the flow diagram representing the major components and trophic flows i n the Southeastern shelf ecosystem. M o d e l parameters are shown i n tables 4.3 and 4.4. depicts phytoplankton and  The model  detritus at trophic level 1, as primary producers, planktonic and  benthic consumers at trophic level 2, small pelagics, juvenile stages o f weakfish and triggerfish, and benthos carnivores at intermediate trophic levels, and a l l demersal and pelagic feeding fish groups at the top o f the food web.  Table 4.3. Parameters of the Ecopath trophic model of the Southeastern shelf ecosystem. Underlined values, trophic Species/Group  Trophic Omnivory Biomass level index tons* Km"  2  P/B year"'  Q/B year"'  EE  Catches tons* Km" * year"' — — — — — 0.081 — — — 0.317 — — — — 0.027 0.003 0.013 — — 0.040 0.011 0.013 2  Phytoplankton Detritus Salps Zooplankton Benthos ornniv. Marine shrimps Benthos detrit. Anchovy Benthos carniv. Sardine Juvenile sardine Other forage fish Juv. Weakfish Juv. Triggerfish Croaker Rays/Skates Triggerfish Other Bent, fish Other Pel. fish Bonito King Weakfish Adult Weakfish  1.0 1.0 2.0 2.1 2.1 2.1 2.3 2.8 2.9 2.9 2.8 2.9 3.1 3.2 3.4 3.5 3.5 3.5 3.7 3.7 3.8 3.9  0.000 0.190 0.000 0.053 0.003 0.003 0.169 0.233 0.374 0.177 0.177 0.177 0.001 0.019 0.246 0.162 0.098 0.163 0.114 0.053 0.445 0.102  24.00 10.00 20.00 4.12 13.14 0.31 30.00 '2.33 35.00 0.63 0.27 5.00 0.12 0.02 0.32 0.01 0.10 0.29 0.34 0.41 0.12 0.02  70.00 -  5.40 60.00 0.40 3.93 3.00 1.29 0.96 1.92 7.00 1.29 2.00 2.00 0.40 0.40 0.90 0.92 0.48 0.98 0.90 0.90  18.00 288.00 2.84 18.00 27.27 11.20 3.28 11.20 23.33 11.20 10.00 10.00 3.88 4.00 6.13 5.20 5.60 4.51 6.16 6.70  0.93 0.90 0.00 0.84 0.55 0.95 0.88 0.16 0.30 0.47 0.25 0.07 0.90 0.90 0.90 0.90 0.90 0.52 0.85 0.10 0.90 0.90  90  O I  TIT  |eAa~|  oit|dojj_  CN  0\ CN O o o d  m  o d  so CN o d d  o d  o d  o SO CN CN d d  m m CN CN d d o © CN SO o © o d o d  • o d  o CO d  m  co CN q d d  CO o co m © CN CN o d d d d  o d  d  m p d  in o d  o m d  oo CN d  o  o  o  d  d  d  m  o in d  oo CN d  •  d  d  00 CN d  CN O d  CN o d  •"0o d  -* o d  O in o oo o <-i d d d  m O d  T—< o ©  CN o O d d  o d  ©  in  o d in o d  CN CN o O d d o  o  d  d  o d m o d  <n o o m d d  o  d  o d  CN CN o o d d  o d  o d  o d  CN o d  •  CN CN CN o O od d d  CN O d  •  o d  ©  oo  o  00 00 CO d  o CN CN o d d  oo o d  CN CO d  o O  co  in o  CN d  o\ Os  ON  in  o  ©  ©  OO  m o  J3 (H  00  IS  si  O  I  H  o  c3  £  "S, 3 o  .5  &  C  H  o JEJ  £ Q  a o o oo N CQ  —i  co  CN  in  ~  is  a  2 ^  o o o o >  1 so  IS  < OO  .  OS  O  — I  on  S3  •5 3  IS  a in  ^ A -A 55 10 a> IS  CNco-rj-insor—  .a 43  «  000s  7?  2  CJ  £B  CN CN  Three peculiar features o f the ecosystem are represented i n the model. First the partition o f the system between pelagic and benthic food chains at lower trophic levels (1 to 3) representing energy pathways originated from phytoplankton and from detritus.  Benthic and pelagic food  chains are mainly linked at the top o f the food web by predators, such as weakfish, that optimize foraging benefits by feeding from both systems, and by the juvenile stages o f demersal species that are active pelagic planktonic feeders (e.g. weakfish and triggerfish).  Second, the model  depicts the partition o f the mid-trophic level pelagic niche among Sardine, Anchovy, Other forage fish, and juveniles o f top predator groups. Finally, the model represents the relatively high abundance o f Salps noticed by Pires-Vanin et al. (1993) as major primary consumers that are apparently not utilized by higher order consumers i n the system.  Table 4.5 compares several ecosystem attributes obtained with network analysis in Ecopath. Comparisons are made with the original model o f Rocha et al. (1998) and with 9 other trophic models o f upwelling ecosystems  listed on Chapter 2.  Inter-ecosystem  comparisons  are  particularly relevant for global attributes, considered common to all upwelling systems (JarreTeichman and Christensen, 1998). These are ecosystem characteristics relative to ascendency, recycling, path length, trophic transfer efficiency, and also fishery related attributes, such as the mean trophic level o f catches.  For instance, the Southeastern shelf ecosystem share common  characteristics with other upwelling systems, such as the low recycling o f nutrients, low relative ascendancy, low trophic transfer efficiency, and fisheries that target mostly low trophic level species.  L o c a l characteristics, such as the primary production and total catches, are more  difficult to compare since almost all upwelling systems for which models were available represent very productive eastern boundary current ecosystems.  Differences between the  modified model o f the Southeastern shelf ecosystem and the original model o f Rocha et al. are mainly related to the size, activity and utilization o f the system.  The modified annual model  reflects a smaller system (lower primary production and system throughput) with higher catches o f lower trophic level groups. A l s o , recycling was relatively higher i n Rocha's model and may possibly result from the authors including bacteria as one explicit group i n the model.  The  models otherwise have similar attributes within the range o f values expected for upwelling systems.  93  Table 4.5. Ecosystem attributes used in the comparison of trophic models models refer to the trophic models described in Chapter 2. Attributes Rocha et al. 1998 Modified model Global characteristics 37.80 27.50 Ascendency 0.14 System Omnivory 0.10 0.24 0.30 Connectance Index 7.33 Finn's Cycling Index 14.51 3.54 3.16 Mean Path Length 5.10 Mean Transfer Efficiency 4.45 3.01 2.86 Fisheries Mean Trophic level Local characteristics 5,314 Total system throughput 6,373 1,802 1,680 Net Primary Production 0.10 0.60 Total Catches  of upwelling ecosystems.  Upwelling  Upwelling models Average Range 32.97 16.20-40.60 0.11-0.32 0.16 0.23-0.32 0.26 1.58-13.60 8.11 2.45-3.01 2.78 3.60-7.40 5.00 2.85 2.22-3.28 24,256 7,836 19.99  8,058-59,677 3,290-22,059 0.95-91.67  Ecopath calculates the transfer efficiencies between the successive discrete trophic levels i n an ecosystem as the ratio between the sum o f the exports plus the flow that is transferred from one trophic level to the next, and the throughput on the trophic level. The transfer efficiencies between trophic levels II and V (herbivores and third level carnivores) are shown i n table 4.6. A pattern emerges where  lower trophic levels, specially herbivores/detritivores, have a higher  transfer efficiency than groups at higher trophic levels. This pattern was also observed i n other models o f marine food webs (Christensen and Pauly, 1993) and it is consistent with higher respiration losses expected for long lived and more active groups at the top o f the food chain. The mean trophic transfer efficiency o f all flows in the system is 5.1 %, which is below the mean transfer efficiency o f 10% estimated by Pauly and Christensen (1995) across different types o f marine ecosystems, but in line with their estimates for upwelling systems.  Upwelling  ecosystems are considered relatively inefficient i n transferring energy up the food web. Their energetic inefficiency seems to be related to the characteristics o f primary productivity and food web organization. The high and variable new primary production characteristic o f upwelling systems yields a higher export o f carbon compared to more stable and less productive systems, where most o f the primary production is regenerated (Berger et al., 1989). The variability o f upwelling systems involves a continuous change i n conditions that result i n simpler food webs that lack fully co-adapted autotrophs, heterotrophs, and decomposers.  Thus sporadic and  seasonal nutrient injection stimulates rapid growth o f phytoplankton which w i l l bloom not being grazed sufficiently to prevent exponential increase; a large part o f this unutilized primary production w i l l end up being exported from the euphotic zone towards the seafloor. Part o f the detritus exported from the pelagic system is recycled back into the food web by the activity o f 94  benthos detritivores and omnivores. It is estimated that i n the Southeastern shelf about 34% o f the flows originates from detritus, although recycling (including detritus recycling) represents only 7.33% o f the total system throughput.  Table 4.6. Transfer efficiencies between trophic levels calculated using the trophic aggregation routine in ECOPATH. Trophic level IV V Source II III 2.4 4.6 Producers 11.5 3.9 4.1 2.4 3.5 Detritus 6.7 4.5 9.9 3.6 2.6 All flows  Estimates o f the total primary production required to sustain catches (PPR) during the 1970s and 1990s are shown i n table 4.7.  P P R estimates for the 1970s were obtained by changing the  catches i n the trophic model with the values observed from 1977 to 1980 (as i n Chapter 3). Notably low P P R values (-4%) are estimated for fisheries i n the Southeastern shelf i n both time periods, compared to values between 25 and 33% estimated i n Chapter 3 using a different method. Ecopath calculates the P P R o f catches o f a given group by first identifying all the paths in the trophic network that lead primary production (or detritus) to the harvested group. For each path, the catches are then raised to the primary production equivalents using the product o f the catch, the gross growth efficiency o f each path element (=production/consumption), and the proportion the next element o f the path contributes to the diet o f a given path element (Christensen and Pauly, 1996). A t each consecutive step down the path, the calculated flow is divided by the ecotrophic efficiency o f the path element (so that the resultant P P R reflects the fact that not all biological production is utilized at consecutive trophic levels). Ecopath thus offers a more rigorous method to calculate P P R s , compared to the approach used i n Chapter 3 where the mean trophic efficiency between trophic levels is used to raise catch values to the primary production equivalents.  Assuming an average trophic efficiency o f 5% causes, for  instance, an overestimation o f the P P R o f low trophic level species (that make up the bulk o f the catches) for which transfer efficiencies are substantially higher (Table 4.6).  The relatively low footprint o f fisheries i n the region is on the other hand expected from the low trophic level o f catches, and may be conservative considering that discards were not included i n the calculations, pelagic sharks were not included i n the model, and that part o f the catches may remain unreported i n official fisheries statistics (Gasalla and Tomas, 1998).  95  Changes i n the catch composition between the two periods are related to a modest increase in catches o f triggerfish and rays/skates, decrease i n catches o f croaker, sardine and marine shrimps, and an increase i n landings o f bonito, Katswonus pelamis, which resulted i n an increase i n the mean trophic level o f fisheries from 2.81 to 2.86.  Table 4.7. Trophic level, mean catch and PPR estimates for the main species landed in southeastern Brazil. 1990 -1995 1977 -1980 Species  Bonito White croaker King weakfish Weakfish Triggerfish Sardine Rays and skates Marine shrimps Total Catch and PPR %PP Fisheries Mean T L  Trophic Level 3.73 3.32 3.74 3.80 3.37 2.84 3.41 2.00  PPR Catches tons tons»Km" «year" 4.35 1,380 7,126 24.43 18.10 2,053 1,921 13.26 2  —  146,520 —  17,371 174,991 2.81  —  20.79 —  0.47 61.70 3.67  PPR Catches tons tons'Km^year" 7,197 23.09 4,541 15.95 18.42 • 1,870 2,245 16.68 2,144 3.70 7.72 54,414 504 2.59 13,997 0.38 86,912 68.38 4.07 2.86  1  In terms o f ecosystem change between the two time periods a question remains on whether the energy previously available at lower trophic levels in the form o f sardine biomass was loss due to stock overfishing, was reallocated to other species in the ecosystem (e.g. anchovy, triggerfish), or simply reflect a decrease i n the productivity o f the system. A n d also whether this new system configuration is i n a stability domain that would impede the recovery o f the sardine stock. Simply put, can the current system configuration support a larger biomass o f sardine?  The understanding o f how ecosystems are structured and how they change i n response to human impact has at its core the understanding o f the dynamics o f ecosystems succession and resilience. Holling (1986, 1992, 1995) suggested four primary phases i n an ecosystem succession cycle which synthesize common features observed in both terrestrial and aquatic systems (Fig. 4.5). The phases are: exploitation, in which rapid colonization o f recently disturbed areas by opportunist species (r-strategy) occur and lead to a growth i n the size o f the system (stored capital); conservation, i n which the system slowly accumulates and stores energy and material, and develop a more complex structure (increase in connectedness) until a climax is attained. Characteristics o f this phase is the highly efficient processes  o f energy utilization and  conservation, and the presence o f K-strategy species; release, i n which the tightly bound  96  accumulation o f biomass and nutrients becomes increasingly fragile (overconnected) until it is suddenly released by physical or biological disturbances and unexpected events such as forest fires, storms, insect pests, intense pulses o f grazing, etc.; and reorganization,  i n which processes  of nutrient mobilization minimize losses and reorganize nutrients to become available for the next phase o f exploitation. The model depicts two aspects o f stability. First the ability and speed with which systems recover from perturbations which, according to the inter-ecosystem comparison carried i n Chapter 2, is mostly influenced by mechanisms o f nutrient recycling in the food web. The second aspect, that o f the resilience o f ecosystems, is represented by an arrow i n the top left corner o f the model which suggests that changes into another ecosystem structure following perturbation is most likely to occur during the release and reorganization phases.  A  2. Conservation  Q.  co O X} CD O  W  1. Exploitation  3. Release  Weak  Strong Connectedness  Figure 4.5. Holling's four phase model of ecosystem dynamics. During the cycle of exploitation, conservation, release and reorganization, biological time flows unevenly. It is slow from the exploitation to the conservation phase, then very rapid to the release (when the overconnected system triggers sudden changes by agents such as fire, disease, etc.), rapidly to reorganization and back to the exploitation phase. Resilience and recovery are determined by the fast release and reorganization sequence, whereas stability and productivity are determined by the slow exploitation and conservation sequence. The arrow in the top left corner of the model suggests the phase where change into another ecosystem structure following perturbation is most likely to occur (source Holling, 1995).  The  four phases model considers that the dynamics o f ecosystems is organized across scales i n  time and space around the operation o f a small number o f nested cycles o f exploitation, conservation, release and reorganization, each driven by a few dominant variables (plants, animals and abiotic processes) (Holling, 1992).  Examples o f regional resource management  (Gunderson et al. 1995) also suggest that institutions and societies achieve periodic advances i n understanding and learning through similar cycles o f growth, production (greatest efficiency), release (crisis) and renewal that shape the spatial and temporal dynamics o f ecosystems.  97  Holling's four phases model thus provides a framework that is useful for understanding both the dynamics o f ecosystems (its structure and change) and the functioning o f institutions bound to resilience.  The study o f the structure and dynamics o f the Southeastern Brazilian shelf ecosystem (PiresVanin, 1993) indicates that this system is conditioned by cyclical physical events that control seasonal changes i n the trophic structure and i n the patterns o f energy flow i n the biological communities. The seasonal upwelling cycle is i n fact nested i n a hierarchy o f cycles o f different speeds, or time scales, as suggested i n table 4.8-.  A t faster speeds there are bacteria and  phytoplankton organisms, which have characteristically higher turnover rates.  Individual  phytoplankton live for a few days and succession o f populations i n a patch or bloom can change significantly i n the same time scale. Physical processes such as pulses o f favorable wind stress, storms, small scale vortexes o f low residence time, and transient upwelling events caused by internal waves and fronts can all influence biological succession at this scale. Aidar et al. (1993) showed that phytoplankton biomass i n the Southeastern bight is usually limited by the lack o f nutrients, specially nitrogen, and it is mostly dominated by species o f the nano-plankton which are better adapted, with a higher surface-volume ratio, to explore the general oligotrophic conditions. Once the system is disturbed by the intrusion o f nutrient rich South Atlantic Central Water ( S A C W ) , species o f the nano-plankton are substituted by other larger phytoplankton species better adapted to the new conditions.  Physical processes operate at faster speeds to  control small-scale water turbulence and influence the rate o f encounter between planktonic predators and their prey (Mann and Lazier, 1991). Zooplankton and benthic invertebrates occupy an intermediate level i n the time-scale hierarchy between phytoplankton and fishes.  The  structure o f benthic communities i n the southeastern shelf is mainly influenced by the seasonal cycles o f upwelling which favor distinct types o f organisms during summer and winter (PiresVanin, 1993).  98  Table 4.8. Time-scale hierarchy of physical and biological variables controlling the dynamics of the Southeastern shelf ecosystem. Equivalent physical force Time scale Speed of change Variables Turbulent mixing, tides, Bacteria Hours Days Fast breeze, storms, vortexes Phytoplankton Seasonal cycles of upwelling Intermediate Zooplankton Days - Months Benthic organisms Cycles in upwelling intensity Years - Decades Slow Mid-trophic level fish and E l Nino (ENSO) Higher trophic level fish Climatic-oceanographic Marine mammals regimes Management institutions Programs of economic incentives Variability in ocean Scientific understanding circulation Global warming Decades -Centuries Very slow Large Marine Ecosystems Human myths of nature  Most fish species fit into the category o f slower variables, with turnover rates i n the order o f years and cycles i n production in the order o f years and decades.  Cycles i n marine fish  populations bear close correspondence to the long term climatic-oceanographic regimes o f the oceans.  Characteristic o f the sea is the "red noise" type o f variability spectrum, in which the  variance o f physical processes increases significantly with the time scale from hours to decades (Steele, 1985).  This inherent dynamics o f the oceans is thought to influence marine fish  populations i n two ways: first, the damped short and medium time scale variability create conditions that favor reproductive strategies such as high fecundity and absence o f parental care, that rely on a relatively predictable combination o f enrichment, concentration and dispersal mechanisms at these scales. Second, the absence o f mechanisms to cope with the short-term variability may make marine fish populations susceptible and adaptable to variability at longer time scales. Examples o f fish populations that show decadal oscillations i n abundance are found in almost every marine system. Bakun (1996), for instance, compiled examples o f synchronous oscillations i n several fish populations geographically isolated from each other, but apparently driven by a common environmental property.  These low-frequency population cycles varied  among species, but were generally characterized by a period o f rapid population growth i n the decade from the mid-1970s to the mid-1980s followed by stock declines after the mid-1980s (Fig. 4.6). According to Bakun the Brazilian sardine followed the pattern o f "crashing" after the mid-1980s.  99  "Dome-shaped" population curve Sardines (Japan, Peru-Chile, California) Benguela anchovy North Pacific groundfish (Alaska pollock and other stocks) Lobsters, Sea birds, Seals, Reef fishes in tropical NP Newfoundland-spawning northern cod stock In opposite phase Anchovies (Japan, Peru-Chile, California) Benguela Sardine North Pacific Albacore Population expansion beginning in Mid-1970s Sardinella aurita (Gulf of Guinea)  Population Size  "Crashing" following the Mid-1980s Brazilian Sardine Northern Cod stocks Balistes (W. Africa)  * Period of 'enhanced FJ Nino characteristics  70  72 74  76  78  80  82  84  86  88  90  92  Figure 4.6. Pattern of variation observed in many marine fish populations according to the "Dome-shaped" hypothesis (source Bakun, 1996).  Management institutions, such as fishing policies, programs o f economic incentives to fisheries development,  and  the  scientific understanding  o f ecological phenomena,  also  present  characteristics o f slow change, as has been the case with the development o f the sardine fishery in Brazil.  A t even slower speeds there are the changes i n the configuration o f Large Marine  Ecosystems, both in terms o f structure (number o f species, trophic organization) and functioning (processes controlling production, recycling, etc.). That correspond to the speed o f long term events such as those accompanying global warming effects. For instance, it is suspected that an increase i n greenhouse effect would enhance the land-oceanic temperature contrast and intensify the upwelling along continental margins (Bakun, 1992).  This phenomenon has been already  reported for several regions o f the world using multi-decade data on the intensity o f upwellingproducing wind stress (Bakun, 1992), although its likely effects on small pelagic fish populations and marine ecosystems is still debatable. A t the human dimension, that is also the scale at which myths about nature endure, as stated by Light et al. (1995) i n the case o f water management i n  100  the Everglades: "[tjhe myth o f "quest for control" has persevered through the past century and is characterized by a rational decision maker model that assumes that institutions can solve resource problems based on "objective knowledge" and the exploitation o f technology in the name o f progress".  Accompanying the increase i n the time scale up the food web there is also an increase i n area occupied by an individual species during its life cycle. F o r instance, certain fish species swim hundreds o f kilometers during their life span, while most phytoplankton and zooplankton organisms have a distribution range confined to patches o f a few kilometers. The combination o f time and space hierarchies in the processes controlling biological variability result i n a generic pattern i n which larger organisms smooth out the effects o f fluctuations i n the smaller prey populations (Mann and Lazier, 1991).  Connections do exist between processes at different scales . A critical feature o f such hierarchies is that larger, slower levels usually maintain constraints within which faster levels operate, i n other words, slower variables control faster ones (Holling, 1992). H o l l i n g (1995) also suggests that at certain conditions slower and larger levels i n ecosystems may become briefly vulnerable to small events and fast processes.  In forest systems, for example, fast variables can dominate  slow ones particularly at climax stages when the system is so overconnected that it lacks the resilience necessary to cope with forest fires or insect outbreaks (Holling, 1995). A l s o , during the phase o f reorganization, when the system is underconnected and with weak regulation, there are opportunities for the establishment o f a diversity o f pioneer species which can result in completely different final ecosystem configurations (Holling, 1995). Therefore, sometimes fast variables can control slower ones. The analog o f this process i n the sea can be represented by the concept o f Lasker windows.  The success o f a year class o f a fish population depends on a  diverse array o f events that influence the survival o f an individual fish from the egg stage to the recruitment size. Lasker (1975, 1980) and collaborators suggested that recruitment would be largely determined during a critical period i n the early life history when the first feeding larvae have to find food o f appropriate concentration and quality to avoid starvation. The formation o f fine-scale patches o f highly concentrated food particles depends on the existence o f a temporal window, typically i n the order o f 5 days, during which turbulent mixing energy by the wind remains low so that particles can accumulate. The frequency o f these calm periods o f short time scale, called Lasker windows (Bakun, 1996), is thought to be responsible for most variability i n 101  anchovy larvae survival and recruitment success i n the California Current system, with potential consequences to the population size and to other species i n the ecosystem.  A central question for fisheries management is whether the switches i n species composition observed i n heavily exploited marine ecosystems represent a natural and reversible change caused by oceanic regimes or do they reflect a loss o f resilience o f ecosystems and a change o f state caused by excessive exploitation. Several biological-oceanographic processes may be at play to cause the observed regime shifts i n marine ecosystems following disturbance by fisheries and natural events. A review o f the last 18 years o f publications (referenced i n the Aquatic Sciences & Fisheries Abstracts, A S F A ) on the causes o f fluctuations i n small pelagics showed that the majority o f the papers (30 out o f 52) associate changes to direct fishery and environmental effects. These include processes such as long term changes i n temperature, wind intensity, and food enrichment mechanisms, such as the ones proposed by Bakun and co-authors, that directly affects recruitment success o f fish populations.  In the following section I used  Ecosim to examine the effect o f long term changes i n food enrichment, food competition and predation on small pelagic forage fish (sardine) production.  The ecosystem model was best  suited to investigate possible relationships between changes in sardine recruitment rates and these ecosystem processes, specifically the interactions between sardine and anchovy. The simulation consisted o f adding to the trophic model o f the southeastern shelf a time forcing function that generates a 10 year cycle i n primary productivity, as suggested i n figure 4.6. During this time period, sardine fishing rate was maintained constant at twice the baseline fishing mortality. Figure 4.7 shows the predicted changes i n the biomass o f phytoplankton, sardine and anchovy, as well as i n sardine recruitment.  Results indicate that relatively small changes i n  primary production (+-50% the baseline primary productivity) can lead to larger changes i n fish biomass.  Sardine and anchovy are equally affected by changes i n food enrichment, although  sardine biomass declines faster than anchovy when food conditions worsen, due to the high fishing mortality. The combined effect o f food enrichment and fishing generates a cyclic nonstationary pattern i n sardine recruitment rates that culminates with the collapse o f the stock.  102  Biomass/biomass baseline  1/3  PP/PP baseline  1.5 1 0.5  !  Fishing rate (relative to baseline)  years  Figure 4.7. Output predicted by Ecosim with a cyclic regime in primary productivity (PP). Simulation run under a bottom-up trophic control. Not all groups in the system are represented. A fishing rate for sardine twice as high as the baseline value was applied during the simulation. The panel on the right represent the relationship between stock and recruitment for sardine obtained with the simulations on the left.  Trophic mechanisms are also used to explain apparent regime shifts i n the productivity o f small pelagics. The most frequently visited hypotheses are food competition, and predation o f adult anchovy/sardine on the early life stages o f the competing species (Santander et al. 1983; Alheit, 1986; Butler and Pickett, 1988; Butler, 1991; Valdez Szeinfeld, 1991). Cury et al. (in press) also point at behavioral causes such as the "school trap" phenomenon commonly observed in small pelagics. The "school trap" is caused by behaviour mechanisms that drives sardines, anchovies and sardinellas species to school together with other species when their relative abundance is diminishing.  B y doing so they subordinate  their specific needs to a different  set o f  environmental preferences, such as migration paths, habitat types, etc., not necessarily optimal for the species. Under these conditions stock productivity is expected to remain low for longer periods.  The hypothesis o f species replacement by food competition has been also used to explain shifts in species composition other than sardine-anchovy.  Caverivieri (1991) and Bakun (1996), for  instance, suggest that the collapse o f sardine, Sardinella aurita, i n the upwelling system o f the 103  G u l f o f Guinea led to a rapid increase in the abundance o f triggerfish, Balistes capriscus, which is a semi-pelagic species as adult and pelagic planktonic feeders as juvenile. The apparent shift in dominance may result from climatic changes (more specifically to changes i n continental runoff) but it is also considered that juvenile triggerfish effectively replaced the collapsed sardine at the mid-trophic level pelagic niche. Parallel changes have been observed i n the Southeastern Brazilian Bight, where an inverse relationship between Balistes capriscus and Sardinella brasiliensis is noted i n catch data from 1977 to 1995 (Table 4.6; Zavalla-Camin and Lemos, 1997). The hypothesis o f replacement by food competition implies two interrelated assumptions; first that the mid-trophic level pelagic niche is occupied by a dominant species, and that a competing species may " f i l l the environmental v o i d " created by the depletion o f the dominant one (Turner and Bencherifi, 1983); second that food is limiting production, so that even in the absence o f fishery the depleted population would not recover its biomass.  In many cases, the  hypothesis o f food competition between sardine and anchovy has been refuted by considerable differences between the diets o f the two species.  In the Benguella system, for instance,  competition between the two species seems to be limited by differences in the size composition o f the diet, where sardine usually consume smaller prey that anchovy (Louw et al., 1998).  Evidence o f predation o f adult sardine/anchovy on eggs and larvae o f the competing species would point to the existence o f "trophic triangles" i n the pelagic niche. In this case adults o f sardine and anchovy share a common prey pool, while also feeding on the early life stages o f the other. Thus reducing the biomass o f one species o f the pair would lead to an increase in prey availability and i n the abundance o f the competing species, which i n turn would cause an increase in larvae and juvenile mortality due to predation.  M o r e common in marine and  freshwater systems are however the "trophic triangles" among adult and juvenile stages o f top predators and a third forage species that is both competitor/predator o f juveniles and prey o f adult stages (Walters and Kitchell, i n press). A classic example is the cod-clupeid system in the Baltic sea (Rudstam et al., 1994 in B a x , 1998), where adult cod actively preys on adult and juvenile herring, while adult herring has cod eggs and larvae as a major food source.  Therefore  the depletion o f adult cod may lead to planktivorous fish dominance that may prevent cod recruitment.  Walters and Kitchell (in press) suggest that this type o f trophic mechanism may be  responsible for depensatory recruitment changes observed i n marine fish populations that have been subjected to overfishing, including many clupeid species.  The authors also noted the  possibility o f "trophic quadrangles" i n zooplanktivorous fish, where adults feed selectively on 104  larger zooplankton than juveniles; reductions i n adult fish biomass would cause an increase i n large zooplankton that feed on smaller zooplankton groups also utilized by juvenile fish.  There are two reasons to not support the hypothesis o f predation relationships between sardine and anchovy in the Southeastern Brazilian shelf. First, the lack o f evidence o f eggs and larvae i n stomach contents o f both species (Goiten, 1983; Scwingel, 1996) . Second, which corroborates the first, is that although sardine and anchovy inhabit the same region, spawning behavior differs between species (Matsuura et al., 1992). While sardine spawning occurs i n the surface warm mixed layer during spring and summer, anchovy spawns all year round  mainly beneath the  termocline, inside the cold S A C W which is present i n the shelf bottom layers during spring and summer. Eggs and larvae o f both species are found mainly above the termocline i n the coastal regions. Therefore adults and early life stages seems to be spatially segregated during the peak spawning season. There is also no evidence o f marked changes i n food (zooplankton) selection with size between juvenile and adult stages o f sardine (Saccardo and Rossi-Wongtschowski, 1991) which would make the case for "trophic quadrangles" at the bottom o f the food web.  Dynamic simulations with Ecosim were used to evaluate the effect o f changing purse seine fishing rates on sardine biomass.  In the following scenario it is assumed that most sardine  predation mortality occurs during the species early life stages.  Adult and juvenile sardine  compete for food with other small forage fish as well as with juvenile stages o f some apex predators (weakfish).  Simulations were run under different hypotheses on the type o f trophic  control i n the system and on the maximum relative foraging time o f apex predators. The fishing scenario consisted o f imposing high fishing rates on sardine for 4 years and then having the fishery closed to let the stock rebuild (Fig. 4.8). When the system is bottom-up controlled or when the maximum foraging time o f apex predators is low ( F  m a x  = 1 ) sardine promptly (within 2  years) recovers its baseline biomass once the fishery is closed.  Consequently, food does not  apparently limit production and any decrease i n fishing mortality would be expected to result i n the stock recovery. However when predation is controlling production at lower trophic levels and higher relative foraging time is assigned to apex predators (F =2) sardine biomass is max  maintained at a depressed level even after the fishery is closed; the population usually takes up to 10 years to recover its baseline biomass.  Figure 4.8 represents the two types o f responses  obtained with Ecosim; the depensation scenario results from allowing apex predators (specially Pelagic feedingfish)to increase substantially its feeding time and consequently its consumption 105  rates to compensate for the decrease in sardine biomass; the high consumption rates i n turn impede the prompt recover o f the depressed population by imposing high mortality rates on juvenile fish. In the simulations increase i n the biomass o f competing forage fish with a decrease in sardine biomass was never observed. Results however imply that apparent regimes o f low productivity i n small pelagic fish could be explained as w e l l by increasing predation mortality and consequent depensation in recruitment rates following stock collapse. Myers et al. (1995) found some evidence o f depensation i n 5 out o f 128 marine fish populations for which time series o f stock and recruitment data were available. A m o n g them there are three clupeid fish stocks, the Spring spawning Icelandic herring, Pacific sardine and Georges Bank herring, which were severely overfished and remained commercially extinct for decades. The authors consider as possible causes o f depensation multispecies effects such as nonlinear feeding responses o f predators to changes in abundance o f preys, as well as reduced reproductive success at low population densities (Allee effect).  106  Biomass / biomass baseline 3r Top-down control  rel feeding tue  Figure 4.8. Output predicted by Ecosim with selective predation mortality on juvenile sardine. Not all groups in the system are represented. The model includes a Delay-difference representation of changes in numbers and biomass of sardine, weakfish and triggerfish. The panels on the right represent the relationship between stock and recruitment for sardine obtained with the simulations on the left; after 4 years of heavy exploitation (2 times the baseline level) the sardine purse seine fleet is shut down to let the stock recover. Dotted lines represent the change in foraging time of pelagic feeding fish.  Walters et al. (in press.a) warned about the possibility o f strong depensatory effects, and multiple equilibria in community structure, when predators in the system are assigned higher limits to the time spend feeding ( F  max  ) with decreasing food availability.  That creates, according to the  authors, results analog to the effect o f including a Type II functional response o f predators to the availability o f preys. Empirical and theoretical studies o f predator-prey systems have tended to identify three types o f functional responses  (types I, II or III as i n H o l l i n g 1959) relating  predation mortality rate and prey density. Type I response assumes that search by predators is random, and that the number o f prey eaten by a predator would be directly proportional to prey density. Conversely, types II and III assume non-linear responses o f predators to prey density as a result o f behaviour processes, e.g. when predators are able to aggregate to areas where prey 107  numbers are high, as well as for the result o f handling and satiation. In both cases predation rates may i n effect increase with the decrease in prey density to cause depensation i n production rates at low prey population size and multiple stability states. Field and laboratory studies have been able to demonstrate the existence o f the latter two types o f responses i n both terrestrial (Holling, 1959;  1973) and aquatic systems (Peterman, 1977; Peterman and Gato, 1978; Peterman et a l ,  1978). In these systems resilience seems to be conditioned by lower thresholds, e.g. in fish stock abundance, beyond which disturbance w i l l lead to completely different system stable states.  Verifying this hypothesis i n the field is however very difficult because it w i l l require estimating how predation mortality changes with the biomass o f consumers and preys under uncontrolled and variable conditions determined by other  environmental factors.  Alternatively,  the  depensation hypothesis can be checked against the reconstructed time series o f sardine spawning biomass and recruitment from V P A (Table 4.9; F i g . 4.9). Several processes can influence the productivity o f an exploited fish population. Hilborn and Walters (1992) argued that sum o f environmental and multispecies effects on a stock are likely to be most important during the fish early life stages, when larvae and juveniles are more vulnerable to sub-optimal environmental conditions, predation and competition for food. For convenience, i n a population analysis the net effect o f juvenile survival (i.e., recruitment) is usually represented by a stock-recruitment relationship. The focus on stock-recruitment relationships has also a practical reason, since it directly links a control variable (stock) and a rate o f future stock production (recruitment).  108  T3  C3  ^—N -4-»  2 -9  ca u 0) < >N  ,SP*  0  M  OS  Os 00 00 in in in CN O m OS O  CO CO t>  ^_ OS CO  CO Os OS  CO  "a00  — . 1 'ar0  1  in 00 00  ON  CO  CN  so  00 s o 00 in in Os s o Os so 1 — 1 in r~ in  t~ CN  OS  00 in -a- OS 0 in r o ro ro 0 in OS r o in 1 —•  00  r~ r o rCO O  Os  t~ O CN SO  00 Os 00 0 0 r~  OS CO  00  -a  T3  I 00 O  1 1 00  m CN in 00 00 in SO 0 CO Ti- la- CO CO r o in  OS OS  13"  I  OS OS  co  t~-  1  CN  00  U  (-1  3 3  o ca £3  ^J-  00 in  <= ca  •5 "o § SP  00 OS  1—.  r-  Os  •a «  Os Os  in  ~— 00  »n t > CN 00 Os Os OS 00 O O SO  IS 41 la ap o ca  2  CN  00  03 <D  C/5 CO  D on  J3 a .SP £ ^ .2 tu Xi JS In o  £ I  in 00 Os  OS  ro  11  OS  0  CN OO Os  w  s  f  OO OS  S •>.  K«  * .s <; s  0 00  OS  OS r-~ Os  u 'o  ro  CN CO  m  Os  CN O CN  Os SO CN ro  Tt  OS t~ SO  OS r> ^«  in  so OS SO  00  Os  in  1 — 1 ro  in  r~ so in  .—•  -a- r o ro SO so  m  0  CN  in  00 ' ' ' '  SO CO OS  t~CN CN  0 00 in 00 r-  00  •aCN CO CO  in  CN so CO  — inH  CN  SO  OS CN CN  CO  r~ O CN ro  Os  CO O CN  Os t-co  1 — 1CO CN  CN CN  OS ro Os  00 so co  1 — 1  SO CO  00  so SO CO CO  CO  0  ro OS OS  in 0 m  ro  •a- m in 00 CO  r-  O CN •«t  in  CN  SO  CN CO  OS  _  CN  m 0  CO  CO  0  m 0  OS  00 CN O SO SO CN  CN  00 CO CO  OS CN O O CN CO  00 m  CN  CN OS CN t> O CN  in  •  CN SO  ~  Os  OS  SO  ^  in in r-~ r o •a- ^1 r CO CN  ro  in  CN  m 0  00 00 OS ro  OS r>  CN  -aCN  CN  ro OS OS  OS CN  Os  SO CN  ro  Os  r~ 00 r-~ O  00 in  ro OS  in  CN  ro CO  — .<  so  e-  CN  1 — 1 O OS CN OS CN  in  SO so 00 00 0 CN s o Tt 00 CN s o SO 00 m Os CN in •a- 00 0 Os CO in so SO  CO  — <  CN  in CN  ro  OO  t> ro  „  in  vo  CN OS  OS  ^1 ^-1 CO  O  ~  t 0 0 la- 0 in 00 00 — . 1 00 so CN SO  CN SO Os  0O  00  CN SO O  OS  _  CN  OS  <!  ~  1 — 1CO CO m s o CN —  — . 1 00 •<* 0CO  ro  l ~  \C  £ "ft  00  CO  1-1  OS  CO so CO CN  in  00  r> so CN CO so 00 00 SO 00 00 SO CO CN in -a- 0 SO SO CO CO 00 CN CN r~ CN 0 SO 00 — ,1 SO SO — . 1 00 SO in — .1  CN  CQ c . o  00 0  Os  SO  CN  .2 2  CN  in in  CN  0 0  00 SO C^ r Os 00 — . 1 000  £  OS  SO SO  so "a-  "8 3 g 2 '3 O  CO  OS  00 0  SO OS CN CN  00  ca <« -u o ca ^  in  CN  0O  SO  S -2  to f_| "60,—  CO  OO  CN  Cu ca  >s  C~  so  CN  ca ca C o  N—'  SO co CN ro  Os  00 so so OS o s  o CN 00 s—  t3 ca  S i 3 «  ~  m SO m ~ 00 OS iOs 0 CN  •"3- „ 00 CN  r00  CN  CN  •& ca i3 on  CN  ro O  O  in CO  "b 0 H  O  0 I  1  1  1  1  1  '  '  '  1  1  1  1  1  1  1  1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990  Year Figure 4.9. Reconstructed time-series of sardine biomass from V P A with annual natural mortality rate (M) of 0.95 year" (parameter value with maximum likelihood). Also shown in the graph are the estimated spawning stock biomass (SSB) and the observed egg densities and stock biomass from surveys. 1  0.25  r  0.05  h  0  oo  1  1  0.4  1  1  1  0.5  ' ' i i i i ^ 0.6  0.7  0.8  1  0.9  1  1.1  1  1.2  1  1  1  1.3  1.4  M(year" ) 1  Figure 4.10. Bayes marginal posterior probability distribution of natural mortality rate (M) from V P A analysis. Shaded bars between dashed lines represent the range of M values expected for sardine using Pauly (1980) equation.  Stock and recruitment data obtained from V P A supports equally w e l l three different hypothesis that explain the changes in sardine production observed during the 1970s and 1980s.  These  110  hypotheses are represented below by functions relating stock size and recruitment (Figs. 4.11 and 4.12):  Hypothesis 1: Recruitment decreased in response to a gradual decline of the spawning stock biomass due to overfishing. In this case recruitment can be properly described as a function o f stock size. Environmental processes impose an interannual recruitment variability described by a log-normal statistical distribution. This hypothesis is consistent with the decrease i n recruitment that accompanied the decrease i n spawning biomass from 1977 and 1990 (Fig. 4.11). In this time period it is estimated that the total stock biomass declined from ca. 350,000 tons to 80,000 tons, with an average exploitation rate o f 0.5 (Table 4.8).  Hypothesis 2: Recruitment declined in response to overfishing and is forced to stay at low levels due to depensatory mechanisms Recruitment is modeled as a function o f stock size and according to different degrees o f depensation i n the average stock-recruitment relationship (Fig. 4.11). Interannual recruitment variability is also assumed to have a log-normal statistical distribution. Several processes can contribute to depensation i n stock production including competitive exclusion at the pelagic niche, increased predation mortality at low stock size, reduction o f intraspecific diversity, or even behavioural processes such as the "school trap" phenomenon.  Hypotheses 1 and 2 are represented by a Beverton-Holt stock recruitment function modified to include depensatory effects (Myers et al., 1995):  aSSB  x v  Re emits =  —e SSB  X  1  +  —  where parameter a is the maximum recruits per spawning stock biomass ( S S B ) as S S B approaches 0, the product a*K is the maximum number o f recruits, and x controls depensation i n the recruitment curve (depensation occurs when JC>1). A l l recruitment variability is described by a log-normal error (v).  M o d e l parameterization was carried as follows. For both hypothesis, the maximum number o f recruits was considered known from the historical experience, being set to 12*10 , thus 9  eliminating one unknown parameter (K). Parameter x was set to 1 to represent the hypothesis o f no depensatory mortality (Hypothesis 1) and to a value o f 2 to represent strong depensation  ill  (Hypothesis 2). M o d e l parameterization was completed by finding the maximum likelihood estimator o f parameter a, i.e. the maximum recruitment rate at low spawning stock biomass. The kernel o f the likelihood function with log-normal error was calculated according to Walters and L u d w i g (1994). Final model parameterization is shown i n table 4.10.  Table 4.10. Parameters  Parameters used in the representation of density-dependent hypothesis of stock-recruitment Hypothesis 2: depensation  Hypothesis 1: no-depensation  867 0.0138 2  135 0.0888 1  a K  X  14 r 12 O  10 8 -  w  '5i_  6 -  o <D  Ct 4 2 oL 0  0.1  0.2  0.3  0.4  SSB (tons) Hyp 1 -  — Hyp 2  •  Observed  Figure 4.11. Graphic representation of the two hypothesis used to describe the relationship between spawning stock biomass and recruitment. The replacement line represent the number of recruits needed to replace the correspondent spawning stock biomass.  Hypothesis 3: Recruitment declined as a result of overfishing and recruitment failures caused by long-term, low-frequency environmental effects, according to Bakun's  "dome-shaped" regime  hypothesis. According to Bakun (1996) the Brazilian sardine showed a pattern o f "crashing" following the mid-1980s which is consistent with synchronous oscillations observed i n several other marine populations from the mid-1970s to the mid-1980s (see F i g . 4.6). In this case, recruitment is described by a nonstationary relationship between stock and recruitment driven by low-frequency environmental cycles (Fig. 4.12).  Recruitment time series present a decadal  signal superimposed on the interannual variability.  112  Hypothesis 3 was represented by a modification o f the Beverton and Holt model according to Walters and Parma (1996):  Recruits =  aSSB aSSB  1+ -  K  where parameters a and K are expressed as function o f density-independent and density-  M  x  dependent mortality risks. Accordingly, o=exp  1 and K = M (exp 2  tt , where M , represents i-l)  density-independent mortality risk and M density dependent mortality risk. M , influences both 2  a (maximum recruits/spawner) and K (maximum recruitment) and hence was considered the parameter subjected to external environmental cycles. The model was parameterized by fixing the density-dependent mortality risk and making M „ the density-independent mortality risk, follows a sinusoidal trend with period o f 10 years and maximum and minimum value o f -3.576 and -4.576 (range = 1), i.e.  \,t  M  t •K = \ + range •Sin(-^~) M  Optimal environmental conditions were assigned to the mid-1980's to make the model consistent with Bakun's "dome-shaped" regime hypothesis (Figure 4.12).  113  Figure 4.12. Graphic representation of the "dome-shaped" regime hypothesis (hypothesis 3). Upper panel shows the two extreme stock-recruitment relationships modeled to represent a "good" and a "bad" environmental regime. The model is used to generate a sinusoidal trend in the marine carrying capacity which results in a dome-shaped relationship of recruitment with time (lower panel).  Stock distribution, effort dynamics and catchability change  Table 4.11 lists the total effort (number o f trips), corrected by the average vessel tonnage i n each state, and the calculated catchability coefficient obtained from the comparison between fishing effort and the effective fishing mortality rate. Effort data was obtained from log-book systems effective from 1977 to 1983 i n all three state fleets.  114  Table 4.11. Effort, fishing mortality and catchability of sardine stock to purse seiners off Rio de Janeiro (RJ), Sao Paulo (SP) and Santa Catarina (SC). Number of trips was considered a reliable effort index for this time period, considering that there were no significant changes in the number of fishing days per trip, and that trips were usually of short duration (average 1 day). Total F Year RJ SP SC q Effort (trips, tons, year) Effort" tonnage Effort Effort trips .tons year' tripsx WOO tripsx J 000 tripsx WOO 3.484 0.923 0.00175 7.604 38.2 2.074 527.3 1977 0.00212 518.9 1.103 1978 7.977 34.9 2.439 3.210 0.00452 2.872 1.475 32.0 3.792 346.9 1979 1.990 0.00349 399.9 1.395 29.1 2.232 5.703 1980 2.158 0.00448 4.984 2.602 360.0 1.615 1981 1.510 24.9 1.454 0.00387 4.237 3.433 375.3 1982 2.029 24.3 399.2 0.984 0.00246 20.2 5.728 2.479 1983 2.495 a. Source: PDP - Doc. T e c , n.07; Instituto de Pesca de Sao Paulo - Divisao de Pesca Maritma; Relatorio do Sistema de Mapa de Bordo. b. Mean tonnage was used as a factor to compensate for changes in effort efficiency due to changes in boat size off RJ. The mean tonnage of the boats off SP and SC was assumed constant (42.5 tons) during the period (SUDEPE, 1986). b  3  3  During this brief period o f time the catchability coefficient did not show a clear increasing trend, as one would expect as the result o f technological innovations to locate and capture fish. This is, however, consistent with the fact that the major technological innovations occurred in the sardine fishery after 1985 with government incentives to renovate the fleet ( I B A M A , 1995).  The  catchability coefficient does show an inverse relationship with stock biomass (Fig. 4.13). Saccardo (1983) also emphasized the role o f the oceanographic configuration i n conditioning the catchability o f the stock to purse seiners. For instance, temperatures below normal off R i o de Janeiro i n 1979 an 1981 seems to have caused the displacement and concentration o f sardine shoals to the south thus increasing the catchability o f the stock to the operating fleet (Fig. 4.14). It is therefore possible that the correlation between stock biomass and catchability is i n fact conditioned by a third variable (e.g. Sea Surface Temperature).  T o isolate the effect o f  temperature on catchability, a partial correlation analysis (Blalock, 1972) between biomass and catchability was done allowing the mean sea surface temperature i n the Bight explain all it can o f both variables. Partial correlation coefficients (Table 4.12) indicate that temperature has only a slightly effect on the correlation between B and q, which are strongly negatively correlated. Similar trends have been observed in other small pelagic species ( M a c C a l l , 1976; Csirke, 1989), and explained as a result o f three interacting processes ( M a c C a l l , 1990; Pitcher and Parrish, 1993; Pitcher, 1995). First, schooling and shoaling, which are behavioural adaptations evolved to cope with the volatility o f the pelagic environment, cause fish to maintain a roughly constant shoal size by interchange o f individual fish from depleted shoals to intact ones. Second, shoals 115  tend to occupy preferably the areas o f the sea with high habitat suitability, so that as the number o f fish and shoals decrease, the area occupied by the species is also reduced (range collapse). The third factor, which is technological, involve the fact that purse seiners are very efficient i n locating and capturing fish shoals, so that individual fish i n an area become more catchable as stock density decreases.  0.005 r 0.004 -  79  81 • 82 • 80  >*  0.003 5 to J= o 0.002 ro O 0.001 0 100000  83 • 78 • 77  200000  300000  400000  500000  Biomass (tonnes) Figure 4.13. Relationship between catchability to the purse seine fleet and sardine stock biomass. labeled with years.  Data points  Figure 4.14. Changes in the catchability of sardine with stock size and the mean sea surface temperature in the Southeastern Brazilian Bight. Temperature was obtained from the C O A D S database and the average computed for the whole shelf area.  116  Table 4,12. Results of partial correlation analysis between catchability, stock size and sea surface temperature. Correlation Variables -0.887 Catchability vs. Stock biomass -0.459 Catchability vs. Temperature 0.335 Temperature vs. Stock biomass -0.877 Partial Correlation of Catchability and Stock biomass  In the case o f the Brazilian sardine, the decrease i n stock biomass observed from the mid-1970s to the early 1990s appeared to be followed by a substantial reduction i n the distribution range o f the species (Fig. 4.15), which tended to concentrate in smaller areas on the southern and central parts o f the bight.  This decrease in spatial range is also consistent with the changes i n the  regional distribution o f landings (Fig. 4.16) which became highly concentrated i n the south after the mid-1980s. However, it is still unclear whether the collapse i n distribution range was due exclusively to the interaction between environmental and behavioural mechanisms, or a result o f a depletion o f geographically separated stocks. The hypothesis o f two separated groups o f sardine i n the Southeastern Bight is supported by differences i n size composition, spawning locations and time (Rossi-Wongtschowski, 1977), and protein biochemical types (Vazzoler and Phan, 1976), which all indicate the existence o f one group i n the north, between 22° and 25° S, that spawns during spring and summer, and a second group i n the south (26° to 28° S) spawning mainly i n spring. O n the other hand, results o f egg and larvae surveys indicate that spawning has taking place i n different locations throughout the years and seems to be mostly determined by oceanographic conditions (Matsuura, 1988). In this sense, the observed concentration o f sardine to the south would be a result o f better oceanographic conditions i n the area, and the depressed total biomass, which would force shoals to occupy a fairly restricted geographical range.  The effect that the collapse in distribution range had on stock catchability is still unknown given the lack o f information on fishing effort by each state fleet since the mid-1980s. Ultimately, the increase i n stock catchability depends on how efficient are purse seiners i n following the fish with the range collapse, that is, on how fishing effort is allocated. This question is addressed i n the next sections by looking at the fleet characteristics and movement over the Bight.  Fleet characteristics The purse seine fleet is basically divided among three states, R i o de Janeiro (RJ), Sao Paulo (SP) and Santa Catarina (SC). 117  Rio de Janeiro In 1977 a total o f 300 boats was fishing sardine i n the Southeastern Brazilian Bight. Wiefels & Jablonski (1979) concluded from sampling 2/3 o f this total that ca. 52 % o f the boats were fishing on waters off R J . From the sampled boats, 14.5 % had less than 15 m , 60.5 % between 15 and 22 m and 25 % between 22 and 27 m . Purse seiners targeted sardine and other pelagic species, specially mackerel Scomber japonicus.  The importance o f mackerel in the catches  became more important after 1982, when the species made up to 70% o f the total catches. From 1982 to 1985 the fleet was composed by an average o f 167 boats constantly i n activity and by 123 boats eventually in activity for less than 3 months per year. B y 1985 ca. 40% o f this fleet had more than 20 tons (average size 18.6 m) and 60% had less than 20 tons (average size 12.3 m). Most o f the sardine catches came from the "constant" boats and from those with tonnage larger than 20 tons. Both fleet components also fished on alternative species, although this activity was more important for the smaller boats usually restricted to nearshore areas (less than 50 m). A t that time there were at least 8 major landing ports along the coast. Variations i n the oceanographic conditions and fish distribution determined the importance o f each o f the landing ports, as boats were highly mobile i n the search for the most productive sites within the state waters ( S U D E P E , 1986). Larger boats (tonnage larger than 20 tons) were at least 3 times more efficient i n finding and capturing fish shoals than smaller boats ( S U D E P E , 1986).  118  0.8  0.6 0.4 0.2  South 0 1964  1969  1974  1979  1984  1989  1994  Figure 4.16. Proportion of total sardine landings by fishing area. South refers to landings in Santa Catarina, central to Sao Paulo, and north to Rio de Janeiro (source I B A M A , 1995).  In 1987 the number o f boats with tonnage superior to 20 tons decrease to 69 (Valentini and Cardoso, 1991). This decrease was balanced by an increase i n the number o f smaller boats, who had sardine as one o f the target species. Part o f the larger boats that used to fish sardines were adapted to fish bonito, Katswonus pelamis.  Valentini and Cardoso (1991) suggested that the  number o f purse seiners licensed on R J remained relatively high even after the decrease o f the sardine catches after 1981, since sardine was not anymore the mainly elected species i n the fishery, but one o f the alternative pelagic fishes. The first attempt to regulate the number o f fishing licenses came i n 1989 with the Portaria IBAMA 1347/89. A t that time there were a total of 105 licensed boats and about 180 illegal boats fishing for sardine o f f R J . Vessel numbers and characteristics did not change significantly until 1992, although a decrease i n the number o f active boats with the decrease i n catches was observed. O f the 162 licensed boats, 131 were considered artisanal due to their small size and tonnage (< 20 tons), and the lack o f technological devices. The 31 remaining boats were more equipped (sonar and power block), with tonnage between 20 and 81 tons and power between 110 and 150 H P . Even after the efforts to legalize all the purse seiners there are still boats fishing sardine without license.  120  Sao Paulo The number o f purse seiners in Sao Paulo increased from 55 i n 1973 to 120 boats in 1988 (Valentini and Cardoso, 1991).  W i t h the collapse o f the fishery i n the late 1980s and early  1990s, there was a decrease i n the number o f boats actively fishing. In 1989, only 77 o f the 113 licensed vessels were active; 47 i n 1990; 35 i n 1991; and almost all fleet was inactive i n 1992 ( I B A M A , 1991; 1992; 1993; 1994). Illegal boats were also observed i n SP, making up to 34 boats in 1989 and only 6 i n 1990. Overall, boat characteristics remained similar along the years, mean size o f 19 meters, mean tonnage o f ca. 55 tons and 260 H P power. However, some o f the vessels acquired technological innovations such as sonar and power blocks after government incentives for fleet modernization during the 1980s.  Santa Catarina Since the early 1970s the Santa Catarina's fleet was characteristically the most technologically equipped and with larger vessels among the three states. The number o f boats increased from 54 to 105 between 1973 and 1988. Fleet mean characteristics were: size 21meters, tonnage 73 tons and 262 H P o f power. The S C fleet significantly increased fishing power after 1985, following the government incentives, with the introduction o f sonar and power-block.  Technological  improvements to locate and catch fish were particularly improved i n 1988. W i t h the collapse o f the stock i n the late 1980s the number o f active boats progressively declined. Only 74 o f the 107 licensed boats were actively fishing in 1991. A total o f 12 boats still remained illegal in the fishery i n 1991.  Fleet movement and effort allocation Typically, fishing operation by purse seiners is very slow, involving sometimes up to 6 hours to complete a set. Catch volume and the long handling time determine that landings take place in ports close to the catching areas, thus creating a straight relationship between areas o f sardine concentration and the landing volume in different ports along the coast (Valentini and Cardoso, 1991). Therefore, data on the origin o f purse seiners at different landing ports along the coast can 121  provide information relevant for understanding fleet movement i n the fishing ground.  Figure  4.17 describes, for each state fleet, the proportion o f licensed vessels that landed sardine in the different states between 1989 and 1991. The first conclusion from this analysis is that fishing boats distribute differently according to their origin; boats from R J maintain a fairly restricted fishing area, mostly concentrated in their own state waters; boats from SP appear to utilize ports from the other two states more regularly; and the S C fleet landed sardines either i n SP or in S C . The second conclusion refers to changes i n fleet distribution with time, where the two most mobile fleets (SP and S C ) seemed to concentrate their activity (landings) to the southern part o f the stock distribution during the period analyzed . In 1989 ca. 6 5 % o f the SP fleet were actively fishing i n the waters o f both SP and S C , while i n 1991 this proportion was more than 80%. A s for the S C fleet, the southernmost state, the proportion o f the fleet landing exclusively in the state waters increased from 30 to ca. 65% between 1989 and 1991. This information is consistent with the observed increase in the proportion o f catches from the south (Fig. 4.16), and seems to corroborate to the hypothesis that purse seiners (at least those from SP and S C ) followed sardines with the collapse i n distribution range to the south in this time period. Results also indicate the possibility that the collapse i n the distribution range limited access to the stock to roughly one third o f the fleet, that from R i o de Janeiro, which is mostly composed by smaller and less technologically equipped boats that operate almost exclusively i n the northern end o f the species distribution area.  122  Fleet  RJ  RJ  RJ/SP  SP SP/SC Landing Place  SP  SC  R/S/S  RJ  "•"SP  SP U n d i n  SP/SC °  P l a c e  SC  SC  R/S/S  RJ  RJ/SP  SP SP/SC L>n*0 PLace  SC  R/S/S  Figure 4.17. Fleet distribution by landing place. Landing places were grouped in 6 categories: restricted to Rio de Janeiro (RJ); occurring in both Rio de Janeiro and Sao Paulo (RJ/SP); restricted to Sao Paulo (SP); occuring in both Sao Paulo and Santa Catarina (SP/SC); restricted to Santa Catarina (SC); occuring in the three states (R/S/S).  Another likely consequence o f an efficient process o f search and effort allocation is that boats w i l l move to equalize catch per unit o f effort across the stock distribution area according to an Ideal Free Distribution (Hilborn, 1985; G i l l i s et al., 1993). Figure 4.18 shows the results o f the regression o f the proportion o f catch i n area i (c/C) on the proportion o f the effort i n area i (f/F) during 1990 and 1991. The ideal free distribution appears to be a good approximation to the dynamics o f purse seiners off the coast o f Santa Catarina, although slopes smaller than 1 were observed in the two years analyzed.  That indicates higher than average return rates when  proportional effort is high and smaller than average return rates when proportional effort i f low. Such small deviations from the I F D line can be explained by diverse factors, including increasing information exchange between boats with increasing effort, and by patterns o f exploratory behaviour adopted by skippers, which by searching areas with low cpue for better fishing opportunities may cause a decrease in average return rates i n areas with low proportional effort (Gillis et al., 1993). Overall, results confirm that purse seiners are highly efficient i n following  123  shoal distributions, and that fleet dynamics may play an important role i n creating the observed increase i n catchability with decrease stock size and range.  1  1991 0.8 0.6 0.4 r =0.95 2  0.2  slope=0.88  0.2  0.4  0.6  0.8  c/C  Figure 4.18. Test for the equalization of cpue among fishing areas off Santa Catarina (see map on figure 3).c/C is the proportion of the total catch in area i, and f/F is the proportion of the total effort in area i. Regression slopes are statistically different from 1 (1990: df = 26; t = 3.69; P=0.001; 1991: df= 17; t=2.33; P=0.032).  The analysis o f stock catchability, range collapse and effort allocation has two general consequences for sardine stock assessment.  First it indicates that cpue and other fishery  dependent indexes o f abundance are potentially biased by the behaviour o f fish and by the characteristic non-random search o f fishing vessels. harvest  control by effort  Second, it questions the effectiveness o f  limitation i n preventing stock overfishing, specially with  the  overcapacity o f the fleet. Besides being affected by the increasing catchability, tactics o f effort control by fishing closures are particularly inefficient when fishers reallocate effort temporally (Fig. 4.19).  For instance, up to 1990 effort was controlled by one fishing closure during the  spawning season.  W i t h the prospect o f stock collapse i n 1991, a second fishing closure was  established during the recruitment months. A s a consequence, effort (measured by the number o f trips o f the S C fleet) was simply reallocated to the other months o f the year, total effort being almost the same between the two years. These results indicate that much insight can still be gained i n controlling harvest by understanding fishers dynamics i n allocating effort i n space and time.  124  Figure 4.19. Monthly effort allocation by the Santa Catarina fleet before (1990) and after (1991) the establishment of a recruitment closure.  4.4. Summary This chapter reviews the status o f sardine stock assessment and analyze the current ecological uncertainties in the management o f the fishery. The ecosystem o f the Southeastern Brazilian shelf is structured around the operation o f physical-biological cycles o f different speeds, the most documented ones being the seasonal upwelling cycle, and the decadal regimes determined by multi-year variability i n the intensity o f physical forces controlling biological production. The latter is particularly important for the management o f fishery resources because it operates at the time scale commensurate with the life span o f most marine fish populations. Fisheries i n the Southeastern shelf have been targeting mostly low trophic level species and have since the late 1970s experienced an increase i n the mean trophic level o f landings. The scale o f fisheries impact and the effect o f environmental regimes are often associated to changes i n the structure o f the ecosystem and to a decrease i n recruitment rates o f the sardine population. The combined effect o f fishery and natural processes make it difficult however to characterize the productivity of the population and to predict the results o f rehabilitation measures for the sardine stock. A l s o , given the current regulatory mechanisms, the implementation o f any measure to control harvest w i l l encounter difficulties created by the behavior o f the fish and the dynamics o f the fishing fleet, which seems to be responsible for changes in the stock catchability.  125  Chapter 5. Analysis of harvest decisions and information needs in the management of the Brazilian sardine. Comparing multi-species and singlespecies modeling approaches  5.1. Introduction This chapter aims to evaluate the short and long term predictions o f the impacts o f harvest strategies and controls for the Brazilian sardine, and to discuss the relative values o f reducing current uncertainties on ecological processes.  Results are used to recommend on the types o f  research that would most likely provide the type o f information needed to improve the quality o f decisions, and on the precautionary measures that should be adopted i n face o f the ecological uncertainties.  One the major tasks o f fisheries assessment is to estimate the tradeoffs o f management choices. A l s o , as a scientific activity, fisheries research involves a process o f learning about nature in which alternative ideas, or hypotheses, are contrasted against observations and experiences, so that a better understanding o f the potential responses o f systems being managed can be achieved through time. In these activities models are usually employed as tools to test our understanding o f the mechanisms i n the system, to provide the basis for both understanding o f known patterns and prediction about situations not yet encountered, and to provide the means for evaluating the potential effects o f various kinds o f decisions (Hilborn and Mangel, 1997).  Fisheries assessment can be based on two types o f modeling approaches: single-species and ecosystem or multi-species models. Ecosystem models are i n their infancy and have been little used to date. Single-species models are based solely on the analysis o f population processes such as growth, mortality and recruitment, and has been the most widely applied approach to provide advice on the consequences o f fishery regulatory tactics.  B y definition, multi-species models  consider not only the biological processes o f the target population but also the ecological (e.g. predation, consumption, competition) and technological (e.g. by catch) interactions with other species i n the ecosystem. Ecosystem impacts o f fisheries has become an important concern in fisheries management and one o f the most important issues i n fisheries science. Therefore, a  126  central question for fisheries assessment today is on how to evaluate and communicate the impacts o f alternative fishing policies on marine resources and ecosystems.  Multi-species  models have been proposed as tools for guiding the implementation o f ecosystem principles i n fisheries management, but exactly how the approach could be used, and also what should be the role o f single-species approaches i n this new paradigm, are still unclear. Such comparisons have not been performed explicitly before.  Following the framework outlined i n chapter 1, this chapter analyzes how management decisions in  the  sardine  fishery can be made  under  current  ecological  uncertainties,  and  how  recommendations differ according to two modeling approaches: a single species and a multispecies model. Models are employed in this analysis to evaluate two types o f decisions. The first concerns the evaluation o f strategies for sardine stock recovery. The analysis aims to evaluate the short term predictions o f stock recovery according to different fishing strategies. The second type o f decision relates to the choice o f long term fishing strategies.  I discuss the value o f  reducing uncertainties about ecological processes, the opportunities for improving understanding o f these processes, and the precautionary measures that should be adopted i n face o f ecological uncertainties.  5.2. Methods  Modeling approaches The evaluation o f the performance o f fishing strategies was carried with two modeling approaches: a single-species delay-differential model (Deriso, 1980; Fournier and Doonan, 1987) and a multi-species trophic model (Ecosim, Walters et al., 1997).  Single-species approach Fishing strategies were evaluated with a Delay-Differential model used as an operating model i n Monte Carlo simulations. The model predicts next year's biomass ( B i ) and numbers ( N ) t+  t+1  according to the equations (Hilborn and Walters, 1992):  127  B =s [aN t+x  t  +pB ] + w R  t  t  k  t+l  N =s N +R t+l  t  t  (1)  (2)  t+l  where w is the weight at recruitment age k (years), s = A,(l-h,) is the total survival rate, X is the k  t  natural survival rate (A=e" ), M is the instantaneous natural mortality rate (=0.95 year" ), and h is M  1  t  the exploitation rate. The exploitation rate can be calculated as the ratio catch/biomass i n a given year, or as a function o f fishing effort, i.e., h = l- e~  qE  , where E is effort and q the catchability  parameter. Catchability was represented b y an inverse function o f stock abundance: q =q,B  q 2  (3)  where q l is a proportionality constant and q2 is the degree to which catchability increases with declining stock size.  The delay-differential model also represents growth and recruitment processes. Growth i n mean body weight at age is described by a Ford-Walford plot o f the type w = a + pw _i, where a a  a  and p are constants from the regression between weight w at consecutive age classes a = k, k+1, ...fully vulnerable to the fishery. Sardine parameter values for a (= 0.025) and p (= 0.896) were obtained by regressing data on weight at consecutive ages from Cergole (1994).  Recruitment  (Rt ) is included i n the model as functions representing different stock-recruitment hypotheses +1  (Table 5.1).  Uncertainties are included i n three processes/states: (1) the relationship between spawning stock and recruitment; (2) the relationship between stock biomass and catchability; and (3) the assessment o f stock biomass at sea.  Uncertainties i n the relationship between stock and  recruitment were represented according to the hypotheses about recruitment changes outlined i n chapter 4. F o r decision analysis, all hypotheses were assigned equal degree o f belief, i.e., they are assumed to fit the available date equally well.  The hypotheses and models used i n this  analysis are described i n table 5.1.  128  Table 5.1. Hypothesis, models and parameters used to predict recruitment rates in the Delay-differential model (source chapter 4). Hypothesis Model Parameter values a= 135 x 1. Recruitment is a function aSSB v of stock size K = 0.0888 Recruits = —e x= 1 SSBX 1+ v = 0.4  K  2. Recruitment is a function of stock size with depensation at low stock sizes.  Recruits =  3. Recruitment is a function of stock size and low frequency environmental regimes.  Recruits =  aSSB  a = 867 K = 0.0138 x=2 v = 0.4  x  SSB' 1+ K  —e  aSSB ttt^t e aSSB  1+-  v  M = -3576 + 1 • Sin(-^-) u  v  M = 0.152 v = 0.4 2  K  a = exp' l M  M,  K =-  M (exp l-l) M  2  Uncertainties i n the relationship between stock biomass and catchability were described by marginal posterior probability distributions for parameters q, and q  2  (equation 3) based on  catchability data from chapter 4. Probabilities were computed using Bayesian analysis, D  ,  , , , ,  L(datalq ,q )*p(q ,q ) l  Pyq\><72 < data) = „  T  /  ,—;  1  l  2  -——  ZL(data I q\,q )*p(q\ 2  )  where the likelihood o f a given parameter combination L(data/q,,q ) is calculated assuming that 2  catchability values present a log-normal distribution (i.e. assume only positive values), and the prior probability distribution of parameters p(q q ) have an uniform distribution. The likelihood l5  2  o f a given parameter combination was calculated as  ss L(data Iqi,q ) 2  =e  -l/2(—)  where, SS = X(Inq - Inq)  t  0  , SSq = X(Inq - Inq)  t  , q is the catchability coefficient calculated  from V P A and observed effort data (see chapter 4), q is the estimated catchability from q, and q , 2  and qis the estimated catchability at best fitting q, and q . Figure 5.1 shows the estimated 2  marginal posterior probability distribution o f catchability parameters.  129  0.15  0.15  ^ 0.10  0.10 -  s ro  -O  "o " ,—I—Tl—I—I £ 0.05 -  ro o °- 0.05  "~J ]— -  o.oo I I M I M II II II I II M —  0.00 0.1 0.3 0.5 0.7 0.9 1.1 1.3 1.5 1.7 1.9  0.0001 0.0004 0.0007 0.001 0.0013 0.0016 0.0019  q2  qi  Figure 5.1. Marginal posterior probability distribution of catchability parameters q l and q2 (from equation 3) for the Brazilian sardine fishery based on data from 1977 to 1983 (see chapter 4).  Finally, uncertainties i n the estimation o f stock biomass by direct methods (e.g., acoustic surveys, egg production, etc.) were introduced by including a normally distributed error around the true stock biomass (Frederick and Peterman, 1997), where B  e s t  B  e s t  = B + ( B - C V -w) t  t  is the estimated stock biomass i n year t, B is the true biomass, C V is the coefficient o f t  variation o f the biomass estimation procedure (0 <CV>0.5), and w is a normally distributed variable with mean 0 and variance 1.  Monte Carlo simulations were used to evaluate the expected outcomes o f fishing strategies for sardine. Three criteria are used in the evaluation o f strategies: average catches, catch variability, and the probability o f stock collapse. These criteria were selected because they represent three types o f objectives commonly observed i n fisheries management: - M a x i m i z e yield. In effect, the increase i n fisheries catches represent more fish to the industry, more economic opportunities to the capture sector and, consequently, more job offers. - M a x i m i z e catch stability. Very often, the major interest with a management plan is to guarantee the stability or low variability o f catches, and therefore maintain a constant supply o f fish to the industry. - M i n i m i z e the chances o f fishery collapse.  That is a fundamental objective to any fisheries  management plan, considering the ecological and economic costs associated with the collapse of fisheries.  130  Two types o f fishing strategies are evaluated: - Catch control: the total catch allowed i n a given year is defined i n advance. Three types o f catch control options are considered: constant allowable catch; total allowable catch defined by a constant exploitation rate; and total allowable catch defined by a m i n i m u m stock escapement.  - Effort control: that represents the status quo policy o f fishing control i n the sardine fishery, involving the definition o f a limit to the total effort allowed i n a given year, and a minimum size o f fish i n the catches.  In this specific case the effort unit used was the number o f trips  corrected by the mean tonnage o f boats, as in chapter 4. Ideally the analysis o f a purse seine fishery should use as effort unit any measure o f fishing time such as fishing days, time searching, etc.  Nonetheless, the characteristic short duration o f  purse seiners trips in the  sardine fishery allows for a reasonably good approximation between the number o f trips and the number o f fishing days.  Figure 5.2 represents the structure o f the decision problem analyzed. The choice for one fishing strategy i n the left hand side w i l l depend on the objectives considered and on the uncertainties on diverse processes and states (depicted as branches diverging from the circles). For instance, the choice for an strategy o f catch control is made difficult by uncertainties on population characteristics, such as virgin biomass and productivity, as well as on the size o f the stock i n the sea.  The choice for an allowed level o f effort is on the other hand influenced by the stock  productivity (i.e. recruitment) and the catchability to the purse seiners. A l l decisions are subjected to the inherent uncertainties about the future environmental regime.  131  Fishing Strategies  Uncertain processes and states  Figure 5.2. Conceptual structure of a decision analysis on the choice for fishing strategies for sardine. The squares on the left hand side represent decisions, the circles represent the main uncertainties on population processes and states.  Expected outcomes of fishing strategies The expected outcome o f the different combination o f fishing strategies and controls were obtained following the steps below (Figure 5.3). Starting conditions for simulations were set to be consistent with the observed status o f the fishery in the late 1970s. 1) A number o f alternative strategies i n each strategy type (catch or effort control) was defined by test runs o f the model and also by using historical data from the fishery. Specifically i n the case o f strategies o f effort control, effort levels applied between 1977 and 1983 (Chapter 4) were used as reference points to define alternative fishing strategies. A n age at first capture o f 1.5 years (approximate age o f first maturity) was used as auxiliary measure to effort control. 2) Simulations were run for 10 years having as starting conditions the average spawning stock biomass between 1977 and 1983 from V P A (-250,000 tons), and a dome-shaped regime o f environmental conditions as proposed by Bakun (1996) for the 1980s (see Chapter 4 ) . 3) The expected outcome o f a given fishing strategy was calculated as the average o f the 10 years runs obtained in 1000 Monte Carlo simulations, weighted by probabilities o f parameters, models and variables used i n the model. 4) The performance o f each fishing strategy is defined by the expected average catch, catch variability, and the frequency o f cases (simulations) i n which the spawning stock was driven 132  below the historical lowest size, that is approximately 50,000 tons. This frequency was used as a measure o f the probability o f stock collapse.  Predictions of stock recovery Simulations were carried also to examine the expected responses o f the stock to strategies o f stock rebuilding after the collapse. Stock biomass and exploitation rates for the. late 1980s were used as starting conditions for simulating biomass recovering trajectories using the same procedure outlined i n Figure 5.3.  Spawning stock per recruit Data on survival, maturity and reproductive output at age (Table 5.2) were used i n the analysis o f spawning per recruit 3.5 A50%  SPR  where  SPR  =  A 5 0  ^(hfi i) R  o  is the number o f spawners produced per recruit for a given combination o f age at  / o  first capture ( A  5 0 %  ) and exploitation rate (E=F/Z; [0.1]); /, is the survivorship to age i (/,=/„s(l-  VjE), l is the survivorship to age 0 (=1), s = e x p 0  (M)  , M is the instantaneous natural mortality rate;  Vj is the age specific vulnerability , and R; is the age specific reproductive output.  V is a ;  function o f the age at first capture and a concentration parameter (b=10), i.e.  50%  A  +  1  The reproductive output weights the relative reproductive contribution o f an age class be accounting for differences i n relative fecundity and frequency o f spawning (Parrish et al., 1986). S P R is expected to decrease with E , and is normally expressed as the percentage o f the S P R value calculated for and unfished stock (%SPR; Goodyear, 1993).  133  Table 5.2. Percentage of individuals mature and the reproductive output (A) at age for Sardinella brasiliensis (source Cergole, 1994). The reproductive output (A) was calculated using the equation proposed by Parrish et al. (1986) to E. mordax. The equation was adjusted so that the relationship A = 0.0000432W; generates a factor A = l when the weight W = 45g, the approximate mean weight at age at first maturity 264  ;  Age %Mature A  0 0 0  0.5 0 0  1 0.5 0.83  1.5 0.75 1.00  2 1 1.95  2.5 1 3.21  3 1 5.19  3.5 1 10.58  Start  A Choose fishing strategy type  For each strategy  1  For each hy/pothesis  For each  loop over  hypothesis  loop over M.C. trials  3 Carlo trial  Initialize stock abundance  loop over  years  For years = 1 to 10  Estimate stock abundance (for strategies based on biomass estimations)  t Harvest  i Calculate remanining S S B (t) and recruitment (t)  t Average outcome i.e., average catch catch variability prob. of collapse  A Degree of belief on hypothesis  Expected outcome for strategy  Figure 5.3. Monte Carlo simulation procedure used in the evaluation of the outcomes of fishing strategies using a single-species approach.  134  Multi-species approach with Ecosim A trophic model o f the Southeastern shelf ecosystem (Fig. 4.4; chapter 4) is used i n this section to evaluate fishing strategies for sardine when the population is placed i n an ecosystem structured by trophic (i.e., predator-prey) relationships. The model has 22 trophic groups, and tracks changes i n number and biomass o f three groups (sardine, triggerfish and weakfish) that were split between juvenile and adult pools.  Ecosim (Walter et al., 1997) was used to evaluate the  predicted effects o f short-term harvest strategies on the main harvested species i n the ecosystem, and also to assess optimal fishing rates for the sardine stock. M o d e l details are given i n chapters 2 and 3.  Uncertainties were accounted for the type o f control o f trophic relationships i n the ecosystem, which has been shown to influence the magnitude and direction o f changes i n the ecosystem when subjected to fisheries (Chapter 2). Three hypotheses are considered to have the same degree o f belief: a) bottom-up control, where the amount o f prey available to predators is limited so that the mortality rate o f a species i n the ecosystem is largely independent o f the abundance o f predators.  b) top-down control, where a larger part o f prey biomass is vulnerable to predation, and mortality rate is largely dependent on the abundance o f predators.  c) "wasp-waist" control (Bakun, 1996; Cury et a l , i n press), where the abundance o f small pelagic fish control both their predators and prey.  Following Cury et al., the interaction  between small pelagic fish (sardine, anchovy, and other forage fish) and their prey (phyto and zooplankton) is assumed to be top-down controlled, while the interaction between small pelagics and their predators is bottom-up controlled. Top-down and bottom-up control were set in the model according to the scenarios a and b above.  Trophic control hypotheses were combined with two contrasting assumptions about the maximum relative foraging time o f apex predators in the system. The combination o f hypotheses thus produced 6 different models used i n the analysis o f fishing policies.  135  To examine the short term effects o f fishing strategies, dynamic simulations were run for 5 years according to fishing scenarios that halve, completely stop, and double the fishing rates o f the four major fleets in the region, i.e., Purse seiners, Bottom trawlers, Shrimp trawlers, and Pole-and-line vessels. Scenarios 1, 2 and 3 represent fishing strategies solely directed to the Purse seine fleet, resulting i n a 50% decrease, 100% decrease, and a 100% increase i n F for sardine i n 5 years, respectively. Strategies were also tested where all fleets (Purse Seiners, Bottom trawlers, Shrimp trawlers, and Pole-and-line vessels) are decreased by 50% (scenario 4); decreased by 100%, i.e., all fisheries closed (scenario 5); and increased by 100% (scenario 6) during a 5 years period. N o by-catch is included i n the model, as the objective o f the simulations was to investigate the likely effects o f changes i n fishing mortality rates for the target species.  To assess the optimal fishing mortality for the sardine stock, simulations were run for 10 years and the average sardine yield compared among constant fishing rates.  Valuing new information: the Expected Value of Perfect Information The quantitative decision analysis described above provides information useful for the computation o f risks associated with harvest decisions, either i n terms o f the expected consequences o f a given action or i n terms o f the odds o f obtaining an outcome different from that prescribed as the most probable. That i n turn allows evaluation o f how well decisions can be made with present available information and uncertainties. Simulation results are thus used to evaluate the benefits o f reducing uncertainties on the processes relevant for the decision problem. The objective o f this analysis is to calculate the expected value o f a decision made with perfect information about each o f the hypothesis is true.  This value, termed the expected value o f  perfect information (EVPI), represents the maximum, or upper bond, on what we should be willing to pay for research that w i l l generate new information that reduces uncertainties.  Two steps are necessary to calculate the E V P I (Walters, 1986; Morgan and Herion, 1990). First the expected value o f a decision made with perfect information must be calculated. Imagining that we could forecast which state o f nature exists, so that we could choose the optimal action for that state, then the value o f a decision made with perfect information would simply be the forecasted result o f that decision. However, at this point i n time we are uncertain about which  136  state o f nature exists, so the expected values o f outcomes for each optimal action for each possible state o f nature must be weighted by the probability o f occurrence o f those states. Therefore the expected value with perfect information ( E V W P I ) is calculated as  EVWPI = T.[p(i) • Max{i)\ i  where, p(i) is the probability o f state i occur, and Max(i) is the best outcome for state o f nature i . To calculate the expected value o f perfect information, then E V W P I needs to be subtracted from the maximum expected value from a decision analysis made under uncertainty, i.e. that takes uncertainty into account (ERJ).  EVPI = EVWPI -EIU  In this sense, E V P I indicates the total cost or value loss resulted from being uncertain.  In the  present analysis the expected value o f perfect information is computed with both modeling approaches using the probabilities placed on each hypothesis or model, and the conjunct o f decisions outlined above.  For the single species approach, E V P I is computed for two types  decisions: i n the choice o f an optimal exploitation rate; and i n the choice o f an optimal effort level. For the multi-species approach E V P I is computed for the choice o f optimal fishing rates for sardine. The objective to be maximized i n each case is the average catch i n the time period specified.  5.3. Results and Discussion  Single-species approach Simulations were run for 10 years, having as starting conditions the spawning stock biomass and environmental conditions hypothesized for the late 1970s. Results are shown i n figures 5.4 to 5.7. Strategies o f catching a constant proportion o f the stock and allowing a constant escapement produce the highest yields among the strategies tested. Changes i n stock catchability with stock size decrease considerably the performance o f strategies o f effort control. Similarly, constant catch strategies resulted i n the lowest average yield among the strategies tested.  The  performance o f constant escapement strategies are particularly sensitive to errors i n biomass  137  estimates. The general effect is o f decrease i n average yield, increase i n catch variability and i n the chances o f stock collapse with the increase i n coefficient o f variation o f the estimation method (Figure 5.7). The adoption o f more conservative escapement levels does not reduce the relatively high chances o f stock collapse when the error i n the estimation procedure is larger than 30%. Variations o f this magnitude are commonly observed i n acoustic estimation methods as a result o f variations i n the adjustment o f ecosounders, changes in the acoustic properties o f fishes, and the efficiency with which surveys cover the complete distribution area o f the stock (MacLennan and Simmonds, 1992). Misund (1997), for instance, reported differences o f ca. 100%) i n the estimation o f herring biomass obtained between surveys i n the same year. Similarly, acoustic assessment o f sardine biomass i n the 1988 spawning season (Castello et al, 1991) produced a confidence interval for stock biomass between 38 and 77 thousand tons, corresponding to a coefficient o f variation i n the order o f 30%o.  Strategies o f catching a constant proportion o f the stock annually are less sensitive to errors i n biomass estimates.  The increase i n the coefficient o f variation o f estimations has very little  influence over the expected average yield, but increase the variability o f catches and the chances o f stock collapse (Figure 5.6).  For a harvest rate o f 0.5, the chances o f bringing the stock to  collapse increases from less than 1% to more than 50% when cv increases from 0 to 0.5.  The expected yield under effort control peaks at ca. 350 effort units, with a probability o f stock collapse o f less than 20% (Figure 5.5). Figure 5.5 also represents the approximate fishing effort applied to the stock i n the period from 1977 to 1983, which was very close to the predicted optimal effort level. Considering that since then the purse seine fleet has doubled i n size and became more technologically equipped with sonar and power blocks, it is suggested that chances o f stock collapse increased considerably during the following decade.  138  Expected Yield 0.2  0.1 to c o  0 0  0.1  0.2  0.3  Catch (tonnes. 10 ) 6  Catch Variability 8 _ 6 x  3 > O  4  2 0 0  0.1  0.2  0.3  Catch (tonnes. 10 ) Probability of Collapse 6  1 0.8 ~  0.6  °- 0.4 0.2 0 0  0.1 0.2 Catch (tonnes. 10 )  0.3  6  Figure 5.4. Outcomes of fishing strategy that capture a constant catch of sardine as predicted by the single species model.  139  Expected Yield  0.25 i  0  200  400 600 Effort  800  1000  Probability of Collapse  Effort Figure 5.5. Outcomes of fishing strategies based on effort control as predicted by the single species model. The arrow indicates the approximate effort level in the period from 1977 to 1983. Effort index calculated as the product of number of trips and the mean tonnage of boats.  140  0.4 i  Expected Yield  Probability of Collapse  Harvest rate Figure 5.6. Outcomes of fishing strategies that harvest a constant proportion of the stock as predicted by the single species model. C V is the coefficient of variation of the biomass estimation procedure.  141  0 I 0  i  i  i  i  I  0.1  0.2  0.3  0.4  0.5  Escapement (tons.10 ) 6  Catch Variability 10 i  1  8 h  0 l 0  i  i  i  i  l  0.1  0.2  0.3  0.4  0.5  Escapement (tons.10 ) 6  Probability of Collapse  Figure 5.7. Outcomes of fishing strategies based on a constant stock escapement as predicted by the single species model. C V is the coefficient of variation of the biomass estimation procedure.  142  Expected Value of Perfect Information In the present analysis the expected value o f perfect information is computed for two types o f decisions: i n the choice for an optimal exploitation rate and for an optimal effort level. The objective is to find the fishing strategy (harvest rate or effort) that would maximize the yield from the fishery during 10 years after the collapse, i.e. after the early 1990's when the spawning stock biomass was about 70,000 tons.  Uncertainties were included i n three independent  processes for each type o f strategy (Table 5.3). Uncertainty on future environmental regimes represent a situation where decisions are made under complete ignorance on the environmental conditions and recruitment success i n future years.  Table 5.3. Decision table on the choice of harvest rate and effort level for the sardine fishery. Uncertain processes/states Harvest strategy Harvest rate h=0 to 1  Stock production 3 hypotheses  Effort f=0to 1000  Stock production 3 hypotheses  Estimated biomass coefficient of variation of estimations Stock catchability parameter uncertainty  Future env. reeime favorable or not Future env. regime favorable or not  Table 5.4 shows the calculated optimal fishing strategies, the correspondent average expected yield, and the expected value o f perfect information, which measures the expected gain to be obtained i f we could suddenly resolve all uncertainty about each o f the hypothesis is correct. Higher expected yields are predicted with constant harvest rate strategies, as has been previously suggested. W i t h constant harvest rates strategies the expected gain o f reducing uncertainties is less than 5% o f the expected yield obtained when decisions are made under uncertainty. Walters (1986) suggests three reasons why the value o f learning, measured as a upper bound by E V P I , is often not as large as one would intuitively expect: first, because optimal policies for each o f the various hypothesis considered need not to differ substantially from the policy that provides the maximum expected value; second, stock-recruitment hypothesis may predict nearly the same yield across a wide range o f harvest policies; and finally, because the optimal harvest rate tend to be close to the optimal policy for those hypothesis that were assigned high initial probabilities. The latter reason is particularly unlikely i n this case considering that the three stock production hypothesis were assigned equal probabilities.  It seems that i n this case uncertainties on  143  recruitment processes and i n the estimation o f stock biomass have very little influence on the choice for the optimal constant harvest rate strategy.  The  situation is quite different when effort control is employed.  Uncertainties on stock  catchability influence considerably the choice for the optimal effort level. The expected value o f perfect information is i n this case ca. 40% o f what is expected to be obtained with a decision made under uncertainty. That indicates that research on the factors controlling stock catchability would cause a considerable improvement i n the current management practice for the Brazilian sardine, more than that expected from oceanographic research aimed at increasing the ability to forecast future environmental regimes controlling recruitment.  Table 5.4. Results of the analysis of the expected value of perfect information for harvest strategies for the Brazilian sardine after the collapse. EVPI EVPI Best h or f Expected yield Harvest strategy (tons.year" ) (tons .year" ) .(%) 3.75 7,800 0.4 208,015 Harvest rate 40.81 34,886 150 85,467 Effort 1  1  Multi-species approach Results o f simulations o f the effect o f fishing strategies on the biomass and catches o f sardine are shown i n tables 5.5 and 5.6. It is predicted that closing the purse seine fishery (scenario 2) w i l l lead to an average increase i n sardine biomass ca. 6 times compared the baseline level o f the late 1980s. Halving the current fishing rate (scenario 1) would lead to a biomass increase o f ca. 2 times, and it is predicted to increase catch on average by 18% i n five years (Table 5.6). Results vary however with the type o f trophic control, being higher biomasses predicted to be recovered with top-down and wasp-waist control. In both cases releasing the fishing pressure would enable sardine to fully benefit from the available food by efficiently foraging on phyto and zooplankton. For a comparative analysis, results obtained with the single-species approach for the same short term policies are listed i n table 5.7; for instance, stopping the purse seine fleet is expected to result i n an increase i n sardine biomass between 1.27 and 2.66 times the baseline biomass, depending on the environmental conditions prevailing during the 5 years.  Table 5.5 and 5.6 also allow for a direct comparison o f the effect o f decisions in other fisheries (scenarios 4, 5 and 6) on the resulting sardine biomass and catches. For the rebuilding strategies  144  (4 and 5) results point to smaller biomass and catch than what is predicted under the purse seine only scenarios (1 and 2). O n the other hand, doubling all fisheries has a smaller effect on sardine biomass than that predicted by increasing the purse seine fleet. These results illustrate the type o f indirect effects expected when fish stocks are linked by predator-prey relationships, i.e., allowing the rebuilding o f species at the top o f the food web cause a reduction i n the benefits expected from releasing fishing pressure on sardine, specially when the fisheries target species that directly affect sardine production, either by competing for the same food resources or by being a predator. The latter situation occurs with Bottom trawl and Pole-and-line fisheries, for harvesting adult weakfish and bonito which are predators o f small forage fish i n the model. However the most important sardine predator group in the ecosystem model (Other pelagic feeding fish) is not fished by any o f the fleets considered i n the simulations.  Table 5.5. Relative change in sardine biomass after 5 year under different fishing scenarios. Values are relative to the Ecopath baseline level described in Chapter 3. F stands for the maximum relative feeding time of apex predators. Scenarios 1, 2 and 3 involve changes in the purse seine fleet only. Higher F Lower F Scenario Top-down Bottom-up Wasp-waist Top-down Bottom-up Wasp-waist Average 7 3~41 L51 2T16 3A1 L51 2A6 2.36 2 12.45 2.26 5.29 12.45 2.26 5.29 6.67 3 OJ 5 044 0.2_9_ __0A2 0.61 0.46_ 0.40_ 4 5 6  2.48 6.62 0.19  1.30 1.59 0.53  1.75 3.20  0.37  2.48 6.62 0.60  1.34 1.69 0.69  1.79 3.31 0.54  1.86 3.84 0.49  145  Table 5.6. Relative change in sardine catches after 5 years under different fishing scenarios. Values are relative to the Ecopath baseline level described in Chapter 3. F stands for the maximum relative feeding time of apex predators. Scenarios 1, 2 and 3 involve changes in the purse seine fleet only. Higher F Lower F Scenario Top-down Bottom-up Wasp-waist Top-down Bottom-up Wasp-waist Average 1 1/71 076 L08 TTl 0/76 L08 U8 2 — — — — — — 0.00 3_ 0.30 0.87_ 0.58 0.84_ 1_2_1 0.92_ 0/79 4 1.24 0.65 0.88 1.24 0.67 0.89 0.93 5 — — — — 0.00 6 0.39 1.05 0.73 1.19 1.37 1.08 0.97  —  —  Table 5.7. Relative change in sardine biomass after 5 years under two strategies for stock recovery. Simulations Future, regime  Stop fishing  Halve fishing  Bad  1.27  0.82  Good  2.66  2.20  Table 5.8 and 5.9 compare the predicted results o f closing (scenario 4) and doubling (scenario 6) all fisheries on the biomass o f the main harvested species i n the ecosystem. Predicted changes in anchovy biomass are also shown. Closing all fisheries positively benefit all harvested groups with the exception o f marine shrimps, anchovy and triggerfish. Conversely, doubling the current fishing rates negatively affect all groups i n the system but marine shrimps and anchovy, which are predicted to have a net biomass increase i n almost all trophic control hypotheses tested. Having small and fast growing groups such as marine shrimps benefiting from the overfishing o f higher order consumers is not surprising given that reducing all predators i n the system is expected to increase the abundance o f preys, as has been the case with overfished tropical marine ecosystems (Pauly, 1979; Christensen, 1998). A l s o the higher turnover rates o f marine shrimps allow them to withstand higher exploitation rates than that supported by slow growing fishes. The most affected groups are i n both scenarios adult weakfish, rays and skates, and sardine. These species are either heavily fished (weakfish and sardine) or not being predated i n the system (rays/skates and weakfish) so that a release i n fishing w i l l lead to a prompt increase i n biomass; or are slow growing species (rays and skates) so that an increase i n fishing w i l l rapidly reduce their biomasses. Figure 5.8 illustrates these generic effects for a bottom-up control.  146  Table 5.8. Relative change in biomass of main harvested species in the Southeastern Brazilian Bight as predicted by Ecosim after 5 years with all fisheries closed. F stands for the maximum relative feeding time of apex predators. Shaded are groups that show a decrease in biomass. Higher F Lower F Groups Top down Bottom up Wasp-waist Top down Bottom up Wasp-waist Average Bonito 1.67 1.52 1.59 1.67 1.47 1.59 1.59 Adult Weakfish 14.45 4.53 4.57 14.44 4.50 4.54 7.84 Rays/Skates 4.14 5.01 4.13 5.01 4.14 4.13 4.43 Croaker 1.24 1.40 1.25 1.40 1.23 1.25 1.30 King Weakfish 1.27 1.48 1.50 1.27 1.46 1.40 1.41 Triggerfish 1.05 0.88 0.91 1.05 0.91 0.88 0.95 Marine shrimps 0.00 0.49 0.50 0.00 0.51 0.52 0.34 Anchovy 0.89 0.87 0.87 0.89 0.90 0.88 0.88 Sardine 6.62 1.59 3.20 6.62 1.69 3.31 3.84  Table 5.9. Relative change in biomass of main harvested species in the Southeastern Brazilian Bight as predicted by Ecosim after 5 years with all fishing fleets doubled. F stands for the maximum relative feeding time of apex predators. Shaded are groups that show an increase in biomass. Higher F Lower F Groups Top down Bottom up Wasp-waist Top down Bottom up Wasp-waist Average Bonito 0.63 0.65 0.64 0.66 0.66 0.65 0.65 0.22 0.37 Adult Weakfish 0.12 0.31 0.31 0.37 0.28 0.20 0.20 0.20 0.20 0.20 0.20 0.20 Rays/Skates 0.80 0.80 Croaker 0.69 0.78 0.78 0.73 0.76 King Weakfish 0.81 0.75 0.85 0.85 0.78 0.63 0.81 1.02 1.02 Triggerfish 0.27 1.02 1.02 0.33 0.78 1.51 1.24 Marine shrimps 0.37 1.39 1.38 1.26 1.51 1.14 1.15 1.22 1.26 Anchovy 1.07 1.36 1.20 0.69 0.54 0.49 Sardine 0.19 0.53 0.37 0.60  147  Stop fishing  weakfish  rays/skates  3x •triggerfish  1x LO CO  o1/3x bo  0)  marine shrimps  Double fishing  CO  00  co E o  3x marine shrimps  in  1x  iggerfish bonito  1/3x rays/skates  1  weakfish  5 years  Figure 5.8. Dynamic simulation of fishing scenarios where all fishing is closed (upper panel) and doubled (lower panel) in 5 years. Simulation ran under a bottom-up trophic control.  Figure 5.9 compares the expected yield o f sardine under constant fishing mortality rates. Results vary with the type o f trophic control; higher yields are predicted with top-down control; topdown and wasp-waist control predict lower optimal fishing rates (F=0.3 year" ) compared to 1  bottom-up control (0.9<F>1.2 year" ). 1  Assuming lower maximum foraging time o f apex  predators result in the model predicting higher yields for fishing rates higher than the Ecopath baseline. This result has to do with the functional representation o f consumption rates in Ecosim, where food consumption per time varies proportionally to the decrease i n availability o f resources and its is limited by a maximum foraging time. Decreasing the availability o f preys (e.g. sardine) relative to the baseline level leads to an increase i n the time apex predators spend feeding to balance the deficit i n food intake; the end result being the increase i n predation mortality when the stock is overfished. The higher the maximum foraging time the lower the yield obtained from the prey species.  In one case, bottom-up control, the choice for the  maximum foraging time directly influenced the predicted optimal fishing rates for sardine.  148  Ecopath baseline F Bottom-up  Top-down  "Wasp-waist"  0.5  1 F (year ) t  1  1.5  Figure 5.9. Predicted average yield (10 years) of sardine under fishing mortality rates (F). Dashed line across graphs indicates the baseline F for sardine in the original Ecopath model (chapter 4). In the graphs the continuous line represents the predicted yield expected when the model allows for higher maximum relative foraging time of apex predators in the system. The dashed thin line represents results with lower maximum relative foraging time.  Differences i n production rates with each hypothesis do not imply however that much gain is expected to be obtained by reducing uncertainties on the operating type o f trophic control in the system; the expected value o f perfect information ( E V P I ) indicates i n this case a gain o f only 149  7.36% over the optimal decision that takes uncertainty into account, which is to fish at a F o f 0.3 year" . This value, which would also indicate an optimal harvest rate o f approximately 0.3, is 1  similar to that calculated with the single-species approach (h=0.4; Table 5.4) under a different set of constrains.  The type o f trophic control may however influence the transient responses o f the ecosystem and consequently the output o f a different set o f fishing strategies. Predicted ecosystem response to overfishing sardine, for instance, raises concerns on the definition o f minimum escapement levels and on the possible effects o f pulse fishing strategies commonly adopted for small pelagics. Pulse fishing strategies, which basically rely on intensively fishing a stock to the point o f overfishing and then let it recover before a new regime o f intense fishing is initiated, have been applied to small pelagic stocks mainly as a result o f the activity o f Distant Water Fleets ( W W F , 1998). Some authors have i n fact suggested that management o f small pelagic stocks should be targeted to favor recoveries when favorable environmental conditions prevail, rather than try to prevent depletion which is normally accompanied by unfavorable environmental conditions (Beddington and M a y , 1977). The objective o f promoting stock rebuilding is also intrinsic to escapement thresholds, defined by Quinn II et al (1990) as "the population level below which the stock may be unable to rebuild its optimal level over an acceptable period o f time". Figure 5.10 exemplifies the type o f response obtained i n Ecosim when sardine is subjected to pulse fishing strategies. When the system is completely bottom-up controlled the stock is predicted to bounce according to the intensity o f fishing, with minimal ecosystem-wide effects. When the system is top-down controlled, pulse fishing can however impose more drastic ecosystem effects and cause a delayed response o f the sardine stock to the release i n fishing pressure.  150  feiomass/original biomass  0  5  10  15  20 years  Figure 5.10. Example of a pulse fishing strategy for sardine; upper panel refers to a top-down scenario, lower panel refers to a bottom-up scenario. A l l trophic groups are represented to show the scale of system wide effects of fishing under the two trophic control hypothesis.  Conclusions from model simulations  Policies o f effort control currently applied in the management o f the Brazilian sardine are particularly inadequate in avoiding stock collapse due to the combined effect o f a large fleet capacity, changes i n catchability with stock size, and the variability i n production rates driven by environmental regimes.  The prospect o f reducing uncertainties i n the output o f harvest  decisions, and in effect reduce the chances o f stock collapse, for fisheries controlled by effort 151  limitation is still restricted by the proper understanding o f stock catchability. That w i l l imply, i n the case o f the Brazilian sardine, that more effort should be put on evaluating the current fleet capacity, estimating the catchability o f the stocks at contrasting stock sizes, and analyzing the population spatial dynamics (spatial range) and its interaction with the dynamics o f effort allocation by purse seiners. In the latter context, two concurrent lines o f thought are currently developing.  M a c C a l l ' s (1990) approach describes the spatial dynamics o f schooling pelagic  stocks according to a density dependent habitat selection model where a direct relationship between stock abundance and stock area is created by differences i n habitat suitability (which is the result o f environmental conditions combined with effects o f local competition).  His  hypothesis has some interesting corollaries useful for fisheries assessment, particularly that stock area could be a good indicator o f the population state and thus could be used together with catch rates in the stock assessment. This approach has been successfully applied to the Pacific sardine, when the population was small and difficult to assess with conventional techniques (Barnes et al., 1992). A second approach (Pitcher, 1997) suggests that shoaling behaviour alone can cause range collapse i n the absence o f significant environmental gradients i n space and time.  If  validated, this approach raises the prospect o f obtaining cost-effective diagnostics o f range collapse by monitoring behavioural parameters o f shoaling fish.  The consideration o f stock  spatial attributes into fisheries research w i l l require the gathering o f auxiliary information not only from surveys but also from the fishery activity such as time searching, density o f schools, school size, inter-school distance, etc.  The adoption o f precautionary measures i n the management o f the fishery with effort control w i l l demand an effective reduction i n the effort and fleet capacity at least to the level observed during the late 1970s. A l s o , strategies o f effort control are more likely to succeed i f accompanied by auxiliary measures o f control o f the minimum age o f recruitment to the fishery.  Figure 5.11  shows the reproductive potential o f the stock (measures as % S P R ) for different combinations o f age at first capture and the exploitation rate. When the first spawning age class (approximately 1.5 years) is effectively protected the total reproductive potential o f a cohort is only slightly reduced with increasing fishing pressure.  The combination o f high natural mortality rate and  early maturity makes the stock highly dependent on the first spawning age classes, which usually make the bulk o f the reproductive capacity i n many small pelagic stocks (Fig. 5.11c). In this sense protecting the first spawning age classes can help prevent early collapses o f the stock, and encourage more rapid responses to favorable environmental conditions. M a c e and Sissenwine 152  (1993) analysis o f replacement thresholds indicated that to persist, i.e. for successive generations replace each other on average, small pelagic populations must maintain an average 40 to 60% o f their unfished spawning per recruit (%SPR). This relatively high % S P R led the authors to infer low resilience o f these stocks to fishing mortality, since a small reduction i n S P R would compromise the future replacement o f the stock. Clearly, exploitation must be lower in order to maintain the stock for lower ages at first capture (see 40 to 60% bands, F i g . 5.11a,b).  But  protecting the first spawning age class (1 to 1.5 years) provides the stock with high resilience, i n the sense that a broader range o f fishing mortality rates could be sustained without substantially diminishing the capacity o f the population to react to favorable oceanographic events. For stocks with a dominant controlling influence by environmental regimes, that seems to be the most appropriate strategy that could be supported on biological grounds (Winters et al., 1985). Yet, such strategy is probably untenable under most conditions for pelagic species due to the difficulty in controlling the age at entry i n the fishery.  153  0.9 0.8 0.7  %SPR  0.6  • 80-100  0.5 E  • 60-80 • 40-60  0.4  • 20-40  0.3  •  0-20  0.2 0.1 0  -0.9 -0.8 -0.7 -0.6 -0.5 E  %SPR • 80-100 • 60-80 • 40-60  -0.4  • 20-40  -0.3  • 0-20  -0.2 - 0.1 -0  50  • Accounting for A  40  No age variation in A  30 20 10 0 0  20  40  60  80  100  %Total Spawning Potential Figure 5.11. Percentage spawning per recruit (% SPR) for equilibrium exploitation rate (E) and age at first capture (A o ). A ) taking into account the age specific reproductive output (A); B) i f reproductive output were constant with age; C) percentage contribution of sardine, Sardinella brasiliensis, first spawning age classes (ages 1 to 1.5) to the total reproductive potential of the stock. The y-axis represents the percentage of years from data compiled between 1979 to 1989 (Cergole, 1993). 50  /o  Better trade-offs between average catch, catch variability and the probability o f collapse can be achieved with strategies o f catch control, such as the one obtained with a constant harvest rate  154  policy. Under this type o f policy the expected yield o f a best decision made under uncertainty on the productivity o f the population is within a 10% difference from the expected result o f a best decision made with complete knowledge about  future  environmental regime controlling  recruitment or the type o f control o f trophic relationships. The predicted optimal harvest rates are very conservative compared to the ones usually applied to small pelagic stocks, but are consistent with the sustainable fishing rates defined by Patterson (1992). Optimal harvest rates between 0.3 and 0.4 are obtained according to the modeling approach used, and appear to be conditioned by two set o f independent processes traditionally overlooked by fisheries assessment: i n the single species  approach  conservative  fishing  rates result  from  the  possibility o f sub-optimal  environmental conditions represented i n the model by low frequency regimes i n recruitment success. In the ecosystem model more conservative fishing rates are predicted for sardine when predation is a major mechanism controlling production at lower trophic levels. The response o f the system to the interaction o f both processes (trophic and environmental) still need to be evaluated. Constant harvest rate strategies are considered very robust strategies to cope with the inherent uncertainties created by climatic effects on marine fish populations (Walters and Parma, 1996). They are usually implemented by fishing control systems that rely on annual biomass estimates and on simple feedback rules that specify the proportion o f the adult stock, or the total allowable catch, to be harvested each year.  The success o f catch control systems is however  dependent on the accuracy o f the stock assessment which often suffers from large uncertainties i n parameters and variables (e.g. catch at age, relative index o f abundance) used i n the estimation procedure.  The critical information for the success o f catch control systems is therefore the  frequency and accuracy o f stock abundance estimates, which may call upon a combination o f data from surveys (e.g. acoustic assessments o f spawning biomass and recruitment), better monitoring o f catch composition (age and size) used i n virtual population analysis, tagging experiments, and may as well rely on the active participation o f resource users i n data collection (Walters and Pearse, 1996).  Improvements i n the understanding o f biological and oceanographic processes controlling sardine production seems mostly needed i f the fishery is to be managed by  escapement  thresholds. The possibility o f depensation at low spawning stock sizes complicates the definition of escapement policies for increasing the risk o f shifts i n stability domains.  Environmental  effects can have i n this case either a positive or a negative impact by pushing the overfished stock i n or out o f a stability domain, or even by shifting the boundary o f stability domains. In 155  this situation a precautionary strategy that aims to diminish fishing and allow the stock to recover may not be very informative since the recovery o f the stock might be interpreted both as a result of reduced fishing mortality or due to the occurrence o f better environmental conditions; on the other hand, the failure o f the stock to recover might be interpreted both as a result o f depensation or due to a less favorable environmental regime. The trophic model provides i n this case a tool to test and formulate different hypotheses about the possible causes o f depensation and multiple ecosystem stability domains, such as the effect o f increasing predation mortality with decreasing stock size. The testing o f these hypotheses i n the field w i l l require data on recruitment, juvenile fish survival and abundance o f potential predators; yet, i n most cases the correct identification o f mechanisms w i l l be problematic due to the confounding effect o f other environmental factors on juvenile survival. In the model o f the Southeastern Brazilian shelf ecosystem the Pelagic feeding fish group is the most important sardine consumers. Pelagic feeding fish is, however, one o f the less documented trophic components i n the system. The group was originally included i n the model to represent demersal fish species that actively feed on the pelagic system, such as weakfish and hake (Soares et al., 1993; Rocha et al., 1998), but may as well include a diversity o f other demersal and pelagic species also targeted by fisheries and with important role at the top o f the food web.  A short list o f other pelagic feeding fish species include sharks, cutlass fish,  Trichiurus lepturus, and pelagic predators such as Auxis spp. and Sarda sp.  A better  characterization o f this group, which w i l l require data on biology and feeding habits, may lead to a more complete and accurate characterization o f the effect o f fisheries on the southeastern shelf ecosystem.  The evaluation o f harvested strategies for the Brazilian sardine considered only the biological and ecological trade-offs involved between two types o f widely applied fishing  strategies,  namely input (effort limitation) and output (catch limitation) controls. It was out o f the scope o f this work to discuss the likely socio-economic consequences o f adopting one or other type o f strategies and controls, neither was the objective o f the analysis to examine all the possible combinations o f strategies and controls for this fishery. Nonetheless, the results obtained here are expected to enrich this discussion which w i l l require, to be effective, the active participation of resource users.  156  On the choice of a modeling approach A central question for fisheries assessment today is on how to evaluate and communicate the consequences  o f alternative fishing policies to marine resources and ecosystems.  For a  quantitative analysis the question consequently concerns the choice for the modeling approach.  Models have many possible purposes and uses, and no one model is right for the entire range o f uses. In principle, the choice for which type o f approach to be used depends on the type o f products or outputs it is expected from the analysis, i.e., on what type o f information is needed for deciding among harvest decisions. Costanza et al. (1993) suggests three criteria forjudging model performance: realism (simulating system behavior i n a qualitatively realistic way); precision (simulating behavior i n a quantitatively precise way); and generality (representing a broad range o f system's behavior with the same model). N o single model can do well on all three of these criteria, and the choice o f which objective to pursue depends on the fundamental purposes o f the model. In modeling complex systems tradeoffs must be made among realism, precision, and generality. For instance, when seeking for generality, models must give up some realism and/or precision. H i g h precision (quantitative correspondence between data and model) w i l l often sacrifice realism and generality. When the goal is to develop realistic assessments o f a system, generality and precision must be relaxed.  H i g h realism models are concerned with  accurately representing the underlying processes i n a specific system, rather than with precisely matching quantitative behavior or being generally applicable. Costanza et al. (1993) argued that in many types o f system modeling, the desired outcome is to accurately determine the overall magnitude and direction o f change, trading off realism for some moderate amount o f generality and precision.  Conventional fisheries stock assessment models strive for quantitative precision when describing time series o f population abundance, calculating catch quotas, or predicting the consequences o f policy options. model"  Cochrane (1998), for instance, pointed to the need o f an "ecosystem operating  i n management procedures for multi-species resources.  A management procedure is  defined as a set o f rules which specify how a management recommendation is set and what data are used for this purpose (Butterworth et al., 1997). Rules are selected based on their anticipated  157  performance as estimated by simulation on an operating model o f the resources and fishery. Successful implementation o f formal management procedure for multi-species or ecosystem management w i l l require from the operating model (Cochrane, 1998): precision, so that the expected system response to a management strategy is quantitatively similar to that predicted by the model; realism in simulating adequately the fishery-resources interactions; the incorporation o f all sources o f uncertainties to enable robust forecasts o f ecosystem responses to a management strategy; and the output o f meaningful performance criteria.  A model with high precision would be able to describe perfectly w e l l the quantitative changes i n the variables o f interest with changes i n controls and strategies. Ideally, to the test the precision o f the model it would be necessary to contrast predicted and observed values. Alternatively, we can compare the consistency o f quantitative predictions o f changes i n sardine biomass obtained with the two modeling approaches i n the same scenario. For instance, according to the single species model closing the purse seine fishery for five years is expected to cause an increase in sardine biomass between 1.27 and 2.66 times (Table 5.7). The same scenario i n the ecosystem model produces an average increase i n sardine biomass o f 6.67 times the baseline level.  A  greater consistency between models is obtained when the ecosystem model is run under a bottom-up control o f trophic relationships (sardine biomass 2.26 times higher than the baseline level; Table 5.5).  Walters et al.(1997) also noted that Ecosim predictions approach that o f a  single-species model i f the trophic model is bottom-up controlled. In this sense, the precision o f short term predictions may be quite similar between approaches, depending on the weight put on the trophic control hypothesis i n the ecosystem model.  Realism varies between approaches.  The Ecopath/Ecosim model provides a more realistic  description o f the resource and ecosystem by representing not only the characteristics o f the sardine stock but also the trophic interactions with other ecosystem components, and the possible interactions between fishing  fleets. The ecosystem model however still  lack a proper  representation o f important fishery-resource interactions such as the effect o f changes i n gear selectivity, which is often one o f the most applied policy variables i n fisheries management, and the effect o f environmental factors on the availability and recruitment success o f keystone species i n the ecosystem. Experience with the use o f the approach to explain the ecosystem changes observed in the Bering Sea suggested for instance that environmental factors, affecting recruitment or primary production, may be more important in determining the dynamics o f the 158  ecosystem than predator-prey interactions alone (Trites et al., 1999).  Lack o f realism i n  representing important processes and uncertainties may i n this sense apply for both modeling approaches, as models w i l l always involve some degree o f simplification o f the observed processes i n nature.  The critical question is therefore not which model is more realistic, but  which level o f simplification imposed when using a model is less adequate to the type o f problem and policy variables in hand.  When the objective o f quantitative modeling is prediction, very simple models very often outperform more complex models. For instance, L u d w i g and Walters (1985) showed that nonage-structured models produce better predictions o f management  actions than detailed age  structured models. A s a rule, the increase i n model details makes it more difficult to specify how the components functionally interact, and each additional model parameter becomes less well specified by the available historical data. Walters (1986) showed that uncertainty about a policy parameter is likely to be minimized, for a fixed data set, by basing its calculation on parameter estimates from a model o f intermediate complexity. The increase i n model complexity decreases the prediction error (increase the ability to fit historical data) but increases uncertainty on parameters used to calculate the policy o f interest. In summary, the choice for model complexity depends basically on the purpose o f the model and on the amount o f available information, i.e., i f the model is too simple there is a risk o f lacking realism, whereas i f the model is too complex there w i l l be not sufficient information in data to distinguish between the possible parameter values o f the model.  A l s o , the increase i n model complexity should never compromise the  transparency o f arguments and conclusions o f the model, and ideally, should be preceded by the analysis o f its likely contribution to the final qualitative argument  (Walters, 1986). Therefore,  prediction o f the dynamics o f multi-species systems or ecosystems makes single-species models of little use, but detailed models involving all important species may not be the solution either, since the sensitivity o f complex models to errors i n parameters needed for their construction w i l l make them unreliable (May, 1984; Hilborn and Mangel, 1997).  From a manager's point o f view, the difference between the analysis o f scientific advice based on single or multi-species models may be irrelevant since the questions faced by managers concerning the quality o f science and the political acceptance o f regulations are exactly the same, regardless o f the modeling approach (Brugge and Holden, 1991). In this respect, Brugge and Holden suggest situations where the use o f multi-species models w i l l tend to go wrong. A m o n g 159  the possible operational reasons for the failure are first, when recommendations based on multispecies models differ radically or are totally contradictory to those proposed i n the immediate past with single species models.  A s stated by Gulland (1991), new scientific advice is most  likely to be used i f it implies changes in current management practice that are straightforward and preferably minor. Second, i f the use o f multi-species models cause an increase i n the complexity o f both management and assessment.  Complexity i n the management system is  likely to increase i f the conflicting interests are to discuss how the relative abundance o f the multiple stocks should be changed.  A s for assessment, data requirement w i l l most likely  increase with multi-species models. Finally, multi-species models are inclined to fail i f the lack of data force extreme simplifications i n overall system structure and process, thus compromising realism (an argument also constantly used against single-species models).  The achievement o f a comprehensive understanding that is useful for both realism and prediction w i l l perhaps call upon the integration o f the different approaches. Not that the "truth w i l l lie in the intersection o f independent lies" (Levins, 1966), but that the combination o f approaches can provide complementary valuable results.  Multi-species ecosystem models produce a more  complete caricature o f the system, explicitly recognizing its major components and processes, which enable us: i) to visualize the broad consequences o f fishing policies (e.g. removing all the prey w i l l impact the predators) and hence provide guiding principles i n which to ground ecosystem goals when defining conservative exploitation rates, catch quotas, etc.; and ii) to test and formulate hypotheses about the causes o f the observed changes i n marine fish populations, and about the functioning o f marine ecosystems. The latter is particularly relevant i n the scrutiny of research questions to which resources are to be allocated to improve the understanding o f the processes affecting fish populations.  The simpler structure and data requirement o f single-  species models, on the other hand, make them particularly useful for prediction o f policy variables and monitoring purposes i n the management o f fisheries for single stocks, as is the case with most small pelagic fisheries.  A n example o f the use o f a combination o f approaches to  fisheries management is found i n the case o f the Pacific herring off the west coast o f Canada (Vandermeulen, 1998). From the ecosystem point o f view, herring is an important source o f food for higher trophic level species and act as an indicator o f habitat loss due to their need for relatively pristine nearshore areas for spawning.  The measure and monitoring o f herring  spawning biomass (undertaken with a single-species model) is thought to provide an indicator o f overall marine ecosystem health and the effects o f decisions regarding the protection o f 160  nearshore habitats. In the case o f the Brazilian sardine the trophic model provided the means to evaluate alternative hypothesis about the causes o f species switches and changing stability domains that might have followed the stock collapse, besides indicating reasons other than environmental variability to the adoption o f more conservative fishing rates for the species i n the shelf ecosystem.  5.4. Summary This chapter assess the ecological risks o f management decisions i n the sardine fishery off southeastern  Brazil with currently available information and according to two modeling  approaches: a single-species and a multi-species model. It is evaluated the short and long term predictions o f the impacts o f harvest strategies and controls, and the relative values o f reducing uncertainties on ecological processes. Better understanding o f the processes controlling fisheryresource interactions are mostly needed i f the fishery is to continue being managed by effort control, or i f escapement thresholds have to be defined when adopting catch control strategies. A n alternative set o f catch control strategies, particularly feedback rules for a constant harvest rate provide better trade-offs among the ecological indicators tested and appears robust to uncertainties on the prevailing ecological processes controlling production. Consistency between modeling approaches varies according to the weight put on the hypotheses represented i n each model.  A complementary role is suggested for the different modeling approaches i n order to  balance realism and prediction. Results are used to recommend on the type o f research that would most likely provide the information needed to improve the quality o f decisions, and on the precautionary measures that should be adopted i n face o f ecological uncertainties.  161  Chapter 6. Conclusions Marine fisheries have historically exploited the abundant forage fish resources as source o f food and fishmeal. Small pelagics have increased i n importance for world fisheries particularly during the last decades as a result o f the depletion o f important stocks o f long-lived and high trophic level fish and the rapid increase i n the demand for fishmeal for the feed industry.  The  exploitation pattern o f capture fisheries has caused changes i n marine ecosystems, evident as major shifts i n the composition o f species, which raises concerns on the limits o f ecosystems carrying capacity to fisheries. Confronted with this reality, fisheries scientists have been faced with the need to evaluate and communicate the ecosystem consequences o f fishing given an incomplete understanding o f the complex dynamics o f ecosystems.  The objectives o f this thesis were threefold: i) to evaluate the use o f trophic models in the analysis o f the ecosystem responses to fishing small pelagic forage fish; ii) to diagnose the fisheries i n Brazil to the fishing down marine food web phenomenon; and iii) to provide an i n depth analysis o f the sardine fishery off southeastern Brazil to examine the hypotheses o f ecosystem changes and stock collapse, and to evaluate how uncertainties about ecological processes influence the choice o f harvest strategies and controls i n this fishery.  6.1. Evaluation of ecosystem responses to fishing using dynamic trophic models. The comparative analysis o f the ecosystem responses to fishing forage fish using trophic models indicated that small pelagics can play a central role i n upwelling ecosystems, changes i n their abundance can have considerable consequences to species at the top and the bottom o f the food web. A l s o , as 'wasp-waist" species i n these ecosystems, small pelagic forage fish w i l l sustain much more conservative exploitation rates than what has been historically applied i n the cases o f stock collapse. Predicted results are generally in accordance with observed qualitative changes in upwelling ecosystems with the collapse o f a forage species, which were normally followed by a decrease i n the abundance o f top predators and an increase i n the abundance o f a competing mid-trophic level species. Predictions o f optimal fishing rates for the species are also generally in agreement with the suggested sustainable fishing rates for small pelagics, but are very sensitive to the type o f trophic control (bottom-up or top-down) between predators and prey. 162  M o d e l predictions are generally sensitive to the type o f dominant trophic control assumptions and also appear influenced by the quality o f the data used i n the mass-balance assessments. A s a general rule, poor quality models w i l l perform badly i n simulations run under top-down control, when models w i l l tend to self-simplify their structure through competition or predatory exclusion of some groups.  The trophic model also allowed the testing o f hypotheses about the functioning o f ecosystems, their stability and resilience when impacted by fisheries.  Comparing the recovery time o f  systems disturbed by fisheries with the structural, energetic and homeostatic characteristics o f selected models it was possible to test hypotheses about the chief mechanisms controlling the stability o f marine ecosystems.  Results were i n agreement with Odum's theory o f ecosystem  development, which suggests that nutrient recycling is one the main mechanisms developed by biological communities to increase control or homeostasis with the physical environment and achieve protection from its perturbations. Systems with a high percentage o f the energy recycled are usually characterized by higher stability, or ability to recover from perturbations.  In this  analysis models o f upwelling ecosystems were by far the most unstable systems, lacking well structured recycling routes to cope with perturbations imposed on the 'wasp-waist'  forage  species. A second type o f stability, that related to the resilience o f ecosystems or their ability to respond to perturbations without shifting to a different stability domain, was analyzed when exploring the effects o f fishing sardine under different assumptions about the type o f trophic control and the feeding time o f apex predators i n the system. Results indicate the possibility o f depensation i n sardine recruitment rates and consequent multiple stability domains i n systems characterized with strong non-linear responses o f predators to changes i n prey densities. In these systems resilience appears to be conditioned by thresholds i n the abundance o f a forage species (sardine), beyond which disturbance w i l l lead to completely different system stable states.  Another type o f prediction explored with the trophic model relates to the long term effects o f fishing down marine food webs on systems that have historically sustained catches o f high trophic level species but that present the potential to sustain higher yields from unexploited forage species.  Simulations showed that fishing down the food web for small pelagic  planktivorous fishes, while at first increasing catches i n intensively exploited regions, has the potential o f actually decreasing yields by interrupting major energy pathways to already overexploited, high trophic level species.  The diagnosis o f this generic effect, described by a 163  backward bending curve between catches and trophic level o f fisheries, corroborates global assessments o f fisheries-induced changes i n marine ecosystems where fishing down the food web is an observed phenomenon.  The trophic model was also used to investigate the ecosystem effects o f contrasting exploitation scenarios for the major fisheries resources i n the southeastern  Brazilian shelf ecosystem.  Scenarios describing future options for managing fisheries i n the region focused on the ecological effects o f allowing the recovery o f stocks or intensifying even more fishing by the main fleets.  The predicted qualitative changes show that increasing exploitation w i l l lead to a  decrease in biomass o f high trophic level species and an increase i n the importance o f low trophic level species i n the ecosystem. Conversely, allowing the recovery o f stocks w i l l lead to major gains i n abundance o f commercially harvested species, but shrimps which are predicted to decrease i n abundance.  Predicted changes in sardine biomass were tested for consistency with  the values obtained with a single-species model under the same scenarios.  Biomass recovery  values obtained with the trophic model converges to that predicted with the single-species model when the system is bottom-up controlled. However, simulations run under a bottom-up control produce very optimistic fishing mortality rates for sardine, usually above that predicted with the single-species model.  Consistency between optimal fishing rates predicted with both models  occurs when uncertainties on the type o f trophic control are taken into account i n the multispecies model. The models therefore justify the need to adopt more conservative fishing rates for the species by two interdependent reasons: i n the single-species model conservative fishing rates result from the possibility o f sub-optimal environmental condition controlling recruitment success i n the future, while i n the multi-species model conservative fishing rates are predicted for sardine when predation is considered as a major mechanism controlling production at lower trophic levels.  A complementary role is suggested for the different modeling approaches i n  order to balance realism and prediction.  Multi-species models produce a more complete  caricature o f the ecosystem which is. useful to examine the broad consequences o f fishing policies and to test and formulate hypotheses about the causes o f observed changes i n marine ecosystems.  O n the other hand, the simpler structure and data needs o f single-species models  make them particularly useful for prediction o f policy variables and monitoring purposes i n the management o f fisheries for single stocks, as is the case with many small pelagic fisheries.  164  The analysis o f the ecosystem impacts o f fishing small pelagics using dynamic trophic models (Ecosim) can be expanded i n several ways. A question that still needs to be evaluated is how the impacts o f fishing  small pelagics compares  with those resulting from exploiting other  groups/species i n the ecosystem. A systematic comparision o f the predicted ecosystem effects among exploited groups may provide a better characterization o f the ecological role o f these species i n marine food webs. Another question to be evaluated is how sensitive are the predicted optimal fishing mortality rates o f small pelagics to parameter uncertainties other than the trophic control hypotheses tested in the thesis.  Other candidate parameters for analysis are the  productivity (P/B) o f small pelagics, the rate o f effective search and consumption rates o f predators i n the system.  Improvements o f the trophic model as a tool to test and formulate  hypotheses about the ecosystem effects o f fishing w i l l involve, i n the specific case o f the southeastern Brazilian Bight, a better representation o f groups at the top o f the food web, such apex fish predators, marine mammals and birds.  A l s o , improvements i n the quality and  credibility o f the model w i l l occur i f other scientists and local fisheries managers are fully involved i n data input and evaluation o f model structure.  In general, model predictions are  expected to be improved with prior information (hypotheses)  on how trophic interaction  strengths (v^) are distributed i n the food web, and how environmental processes other than predation affects recruitment rates o f individual trophic groups.  That is expected to provide  better insights on the dynamics o f resilience o f marine ecosystems when impacted by fisheries.  6.2. Fishing down marine food webs in Brazil In line with some o f the trends i n world fisheries, marine capture fisheries i n Brazil are in a state of crisis, with many o f the traditionally harvested fish stocks either fully exploited or overexploited. Today, the prospect o f increasing catches and recovering the status o f fisheries activities rely on better management o f the overfished stocks, fishing for offshore resources currently moderately exploited, and/or fishing down the food web for abundant short-lived, planktivorous fishes. Fishing down the food web is not yet an observed phenomenon in Brazil; fisheries i n B r a z i l had a relatively constant mean trophic level o f the species landed from 1950 to the early 1980s, but show a recent increase i n mean trophic level caused by the combined effect of the collapse o f small and mid-size pelagic species (mostly sardine) and the increasing landings of large pelagic fishes with the development o f offshore fisheries for tunas and sharks.  165  Characteristic o f Brazil is however the wide diversity o f marine ecosystems and types o f fishing strategies adopted regionally.  In the southernmost region o f the country, where the shelf  ecosystem is under the influence o f the subtropical convergence o f Brazil and Malvinas currents, fisheries have targeted mostly high trophic level stocks o f demersal and pelagic species. In the upwelling ecosystem off the Southeastern Brazilian Bight sardine has been historically the main target o f commercial fisheries, and sustained the largest fishery i n the country. The collapse o f this fishery i n the late 1980s has apparently shifted the ecosystem into one dominated by an abundant population o f anchovy, Engraulis anchoita, that is not commercially harvested.  The  extent to which the collapse o f sardine stock and the switch to an anchovy dominated system was the result o f fishing or environmental factors is still inconclusive. It was therefore the focus o f this case study to examine hypotheses o f ecosystem changes and to analyze how uncertainties on the ecological processes, as captured by different modeling approaches, influence the choice o f harvest strategies and controls i n this fishery.  6.3. The case of the sardine fishery off southeastern Brazil The analysis o f hypotheses o f ecosystem changes i n response to fishing impacts relied on the identification o f ecological processes controlling the dynamics o f ecosystem succession and resilience. The ecosystem o f the Southeastern Brazilian shelf is structured around the operation o f physical-biological cycles o f different speeds.  A m o n g the most documented cycles are the  seasonal upwelling o f the cold, nutrient-rich South Atlantic Central Water on the inner shelf, and the decadal regimes determined by multi-year variability i n the intensity o f physical forces controlling biological production. The latter is particularly important for the management o f fisheries resources because it operates at time scales commensurate with the life span o f most marine fish populations. A central question in the case study was therefore whether the collapse o f the sardine stock and the apparent switches i n species composition represent a natural and reversible change caused by oceanic regimes, or whether they reflect a loss o f resilience o f the ecosystem and a change o f state caused by excessive exploitation. Literature review, model simulations and stock-recruitment data for the Brazilian sardine pointed at three generic hypotheses that could be used explain the changes i n the structure o f the ecosystem and the collapse o f the sardine fishery. The overfishing hypothesis considers that fishing was the chief mechanism responsible for the collapse o f the stock, so that once the intensity o f fishing is  166  reduced the stock w i l l recover its original abundance.  The depensation and ecosystem shift  hypothesis considers that the stock declined i n response to overfishing, but the recovery is hampered b y depensation i n recruitment rates and a change i n state o f the ecosystem caused possibly by ecological and behavioral processes.  Finally, the regime shift hypothesis considers  that the stock collapsed i n response to overfishing and recruitment failures caused by long-term low  frequency environmental effects. This hypothesis accepts that radical fluctuations i n  abundance may be an intrinsic feature o f small pelagics inhabiting upwelling ecosystems. I f so the recovery o f the stock is less dependent on managing the fishery, but on favorable environmental conditions prevailing i n the future.  In summary, the combined effect o f both  human and natural effects make it difficult to characterize the dynamics o f the population and ecosystem, and to predict the results o f rehabilitation measures for the stock. The combination o f a third process, result o f the interaction between the behavior o f the fish and the dynamics o f the fleet, further contributes to degrade the performance o f current regulatory measures for stock rebuilding using effort control.  On the other hand, the explicit recognition o f uncertainties about the ecological processes controlling the dynamics o f interactions between fisheries and ecosystem may allow for a sound choice o f precautionary strategies for the fishery, and a better scrutiny o f research programs i n which to allocate resources to improve management. in the management  Policies o f effort control currently applied  o f the Brazilian sardine are particularly inadequate i n avoiding stock  collapses due to the combined effect o f a large fleet capacity, changes i n catchability with stock size, and the variability i n production rates driven by environmental regimes.  The prospect o f  reducing uncertainties about the output o f harvest decisions, and i n effect reduce the chances o f stock collapse, for fisheries managed with input control (e.g. effort limitation), is still restricted by the proper understanding o f stock catchability. precautionary measures i n the management  In the current situation the adoption o f  o f the sardine fishery w i l l imply an effective  reduction i n the effort and fleet capacity at least to the level observed during the late 1970s, and a more effective control o f the minimum age o f fish recruiting to the fishery, which has been shown to increase the resilience o f the stock to overexploitation.  A n alternative set o f output control strategies (e.g. catch limit), particularly feedback rules for a constant harvest rate, provide better trade-offs among the ecological indicators tested and appears robust to uncertainties on the prevailing ecological processes controlling production.  O n the 167  other hand, the performance o f another type o f output control strategy based on minimal escapement levels deteriorates considerably i n response to errors i n the estimation o f stock biomass.  A l s o , the definition o f escapement thresholds is complicated by the possibility o f  depensation i n sardine recruitment rates at low spawning stock sizes and the risk o f causing shifts in ecosystem stability domains. Consequently improvements i n the understanding o f biological and oceanographic processes controlling production and in the accuracy o f biomass estimation methods seems mostly needed i f the fishery is to be managed by escapement thresholds.  The implications o f this work to fisheries management were discussed i n the individual chapters but w i l l be summarized here. A s 'wasp-waist' species i n upwelling ecosystems, small pelagics should not be managed in isolation from other ecosystem components since changes in their abundance can have consequences throughout the food web.  Their ecological role i n marine  food webs determines that small pelagics w i l l sustain much more conservative fishing rates than what has been historically suggested and i n effect applied i n the cases o f fisheries collapse. The upwelling ecosystems inhabited by small pelagics have characteristics o f unstable systems, lacking well developed biological mechanisms to dump the impact o f fishing and environmental factors. The characteristics o f unstable systems have served as a reason for the adoption o f less restrictive fishing policies for the species, and helped to perpetuate management actions that continuously failed to allow the effective recovery o f collapsed stocks. Conversely, seen from another perspective, the unstable characteristics o f upwelling systems w i l l mean that variability imposed by nested physical-biological cycles is a key asset for the resilience o f these ecosystems, and that management should in turn be prepared to cope with these dynamic conditions. In this respect fisheries that adopt more conservative exploitation rates for the species w i l l be more robust to the variability and ecological uncertainties inherent i n these systems, and are more likely to be successful i n the long run.  168  Notes 1. Part of Chapter 2 is published in Vasconcellos, M.; Mackinson, S.; Sloman, K. and D. Pauly. 1997. The stability of trophic models of marine ecosystems: a comparative analysis. Ecological Modeling 100: 125-134. 2. Chapter 3 is based on a manuscript in press in Fisheries Research: Vasconcellos, M. and M. Gasalla. 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