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Assessment and management of multispecies multigear fisheries : a case study from San Miguel Bay, the.. Bundy, Alida 1998

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Assessment and Management of Multispecies, Multigear Fisheries: A case study from San Miguel Bay, the Philippines. by Alida  Bundy  B.Sc. (Hons) University of Edinburgh, (UK), 1985 M.Sc. University College of North Wales, (UK), 1990 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE STUDIES (Resource Management and Environmental Studies) We accept this thesis as conforming to the required standard  The University of British Columbia November 1997 © Alida Bundy  In presenting this thesis in partial fulfilment of the requirements for an advanced degree at the University of British Columbia, I agree that the Library shall make it freely available for reference and study. I further agree that permission for extensive copying of this thesis for scholarly purposes may be granted by the head of my department  or by his  or  her  representatives.  It is  understood  that  copying or  publication of this thesis for financial gain shall not be allowed without my written permission.  Department  o f - f e s w g C g flA^^fVlBX  The University of British Columbia Vancouver, Canada  DE-6 (2/88)  ^  £jvVlreOMrM€^  <^7UOl^~  11  Abstract This work uses ecological theory to explore the interactions between fishing and the ecosystem and examines the implications for fisheries assessment and management. The multispecies, multigear fishery o f San M i g u e l B a y , the Philippines is used as a case-study. Three approaches were taken. The first was to descriptively and analytically assess the fishery. Catch rates in 19921994 and 1979-1982 were similar, but all other indications are that the fishery suffers from growth, recruitment and ecosystem overfishing. Large scale effort has decreased, but small scale effort has intensified and diversified. The second approach was to model the ecosystem using E C O P A T H an equilibrium mass-balance model. The model described a relatively mature and resilient ecosystem, dependent on detrital and benthic flows. Different fishing gears have differential impacts on the ecosystem and these are modified by the interactive effects o f predation and competition. In the third approach, a dynamic multispecies model, E C O S I M was used. The impacts o f fishing by a multisector fishery on a multispecies resource were dynamically explored under top-down and bottom-up trophic hypotheses. The results demonstrated that the interplay o f fishing mortality, species interactions and flow dynamics have profound implications for fisheries assessment and management. The uncertainties concerning the resource dynamics were explored using an adaptive management approach. Four E C O S I M models o f the San Miguel B a y were used, top-down, bottom-up, immigration plus top-down and immigration plus bottom-up. Analysis o f the E V P I showed that there was no value in learning more about the uncertainty or distinguishing between the different resource models. It was concluded that although an active experimental adaptive management was not worthwhile, adaptive management, using feedback information from the response o f the resource to management actions as recommended. This thesis demonstrates the critical importance o f an ecosystem-based approach to understanding fisheries dynamics.  Table of Contents  Abstract  ii  Table o f Contents  iii  List o f Tables  viii  List o f Figures  xii  Acknowledgments  xvi  Chapter 1  Multispecies, Multigear Fisheries: Introduction and Overview Introduction Research Objectives Methods Thesis Outline  1 1 7 9 12  Chapter 2  The San Miguel B a y Fishery Introduction to San M i g u e l B a y Methods The Data Trawl Survey Data Fishery Survey Data Analysis o f Trawl Survey Data The Species Composition o f San M i g u e l B a y Estimating Density and Biomass from Trawl Survey Data Longitudinal Comparison o f Species Compositions from Trawl Survey Data Seasonal Analysis o f Trawl Survey Data Estimation o f Mortality Estimation o f Growth Parameters Mortality Estimation using the Length-Converted Catch Curve Mortality Estimation using Beverton and Holt's Mean Length Method Mortality Estimation using the Powell-Wetherall Plot Mortality Estimation using Fishing Mortality = Catch/Biomass Yield-per- Recruit Analysis Analysis o f Fishery Data Estimating Catch and Effort Distribution o f Catch, Effort and C P U E in the Fishery  17 17 24 24 24 24 26 26 27 30 31 32 33 34 38 39 39 40 42 42 44  IV  Distribution of Catch, Effort and C P U E across Gears Comparative Analysis of Species Composition from Landings Data Species Composition and Distribution o f the Catch by Fishing Gear and Season Status o f the Major Species in San M i g u e l Bay Results Analysis o f Trawl Survey Data The Species Composition of San M i g u e l B a y Estimating Density and Biomass from Trawl Survey Data Longitudinal Comparison o f Species Compositions from Trawl Survey Data Seasonal Analysis o f Trawl Survey Data Estimation of Mortality Leiognathus splendens Secutor ruconius Leiognathus bindus Scomberomorus commerson Otolithes ruber Trichiurus haumela Yield-per- Recruit Analysis Analysis of Fishery Data Estimating Catch and Effort Effort Catch The Distribution o f Catch, Effort and C P U E across Gears Comparative Analysis o f Catch Composition Species Composition and Distribution of the Catch by Fishing Gear and Season The Large-Scale Sector The Mini-trawlers TheGillnets . The Fixed Gears Line Gear Other Gears Status o f the Major Species in San M i g u e l B a y Sciaenidae Sergestidae Penaeidae Leiognathidae Engraulidae Portunidae Trichiuridae Mugilidae Carangidae  44 44 45 45 45 45 45 48 52 57 60 60 63 64 64 65 67 68 71 71 71 74 77 83 86 86 90 92 101 105 107 108 109 111 112 112 117 119 119 121 121  V  Trash Fish Value o f the Catch Discussion Chapter 3  A n Ecosystem Model o f San M i g u e l B a y Introduction Methods ECOPATH Aggregating Species into E C O P A T H Groups Parameterising the E C O P A T H model Estimation o f the Production Biomass Ratio, P / B Estimating P / B from P/B=Z Estimating P / B from other Ecosystems Estimation o f the Consumption Biomass Ratio, Q / B Estimating Q / B from an empirical formula Results o f Q/B estimates Q/B Estimates from the Literature The Final Q / B Estimates, Corrected for Fishing Mortality Estimation o f Biomass Estimation o f Detritus Biomass Export (Catch) Ecotrophic Efficiency Diet Composition Zooplankton Meiobenthos Macrobenthos Sergestid Shrimp Penaeid Shrimp Large Crustaceans Demersal Feeders Leiognathids Engraulids Pelagics Sciaenids M e d i u m Predators Large Zoobenthos Feeders and Large Predators The E C O P A T H Parameters Running and Balancing the E C O P A T H M o d e l Results Some Basic Results Indices o f Maturity: Is San M i g u e l B a y a Mature Ecosystem? Some Inconsistencies in the E C O P A T H Results Legend for Figures 3.2, 3.4-3.6 Sensitivity Analysis  123 123 124 135 135 138 138 141 144 144 144 145 149 149 151 152 156 156 156 158 158 160 160 161 161 162 162 163 163 166 166 167 167 167 168 168 169 172 172 175 180 184 186  vi Trophic Impact Routine Introducing the Fishery into the E C O P A T H Model as Large and Small-Scale "Predators" Method Results Discussion Interpretation and Discussion Hypotheses About the San Miguel B a y Fishery and Resource Chapter 4  Chapter 5  Dynamic Multispecies Modelling o f San M i g u e l B a y Introduction Methods ECOSIM The Delay-Differential Model Running the Model The Equilibrium Fishing Routine The Dynamic Run Routine Analyses Impacts on Equilibrium Biomass o f Changing Fishing Mortality Across A l l Fishing Gear The Equilibrium Y i e l d Curves, Species Interactions and F l o w Dynamics Multispecies Dynamics in the San M i g u e l B a y Ecosystem The Effects o f Fishing in San Miguel B a y Consistency Checks The Input Parameters Splitting Pools into Adults and Juveniles Results Impacts on Equilibrium Biomass o f Changing Fishing Mortality Across A l l Fishing Gear Equilibrium Y i e l d Curves, Species Interactions and F l o w Dynamics Multispecies Dynamics in the San M i g u e l B a y Ecosystem The Effects o f Fishing in San Miguel B a y Consistency Checks Impacts on Equilibrium Biomass o f Changing Fishing Mortality Across A l l Fishing Gear The Effects o f Fishing in San Miguel B a y Reducing Fishing Effort on Each Pool to Zero Implications o f the Consistency Checks Discussion Management Strategies for San Miguel B a y Introduction Management Objectives in San Miguel B a y  189 194 195 197 206 208 211 217 217 222 222 225 226 227 227 227 228 228 229 229 230 232 232 233 233 240 250 259 278 278 280 280 281 283 294 294 298  vii  Adaptive management in San M i g u e l B a y Identify Alternative Models of the Resource Potential Management Policies in San Miguel B a y Performance Criteria Assessment of Whether Further Steps are Necessary by Estimating the Expected Value of Perfect Information, E V P I E V P I for all Policies E V P I when Extreme Policies are Excluded The Role of Migration Six Performance Criteria and Five Best Non-Adaptive Policies Discussion Some Notes on the Non-Bayesian Approach Used When W o u l d an Experimental Approach be Worthwhile? Experimenting W i t h Fish and Crustacea Immigration Validating the E C O S I M M o d e l The Reality of Adaptive Management Adaptive Management in San M i g u e l B a y Chapter 6  Summary and Concluding Comments Implications for the Assessment and Management o f Multispecies, Multigear Fisheries  References Appendix 1  Appendix 2  Appendix 3  301 305 306 311 315 315 320 324 325 327 328 331 332 337 338 340 342 350 356  List of Species Present in San M i g u e l B a y (from Trawl and Landings Surveys)  378  Restructuring the E C O P A T H M o d e l to Include Adults and Juveniles  385  List of Acronyms  395  List of Tables  Table 2.1  Table 2.2  Table 2.3  Table 2.4  Table 2.5  Table 2.6  Table 2.7  Table 2.8  Table 2.9  Table 2.10  Table 2.11  Table 2.12  The ten most abundant species in San Miguel B a y (1992-1994 Trawl Survey data).  47  The ten most abundant families in San M i g u e l B a y (1992-1994 Trawl Survey data).  47  Monthly Catch Rate (Trawl Survey), Density and Biomass Estimates for San M i g u e l Bay, September 1992 to June .1993  49  Results o f Density and Biomass Estimation in San Miguel B a y using Monte Carlo Simulation  49  Comparison o f Species Composition o f Trawl Survey data from 1947, 1979-1982 and 1992-1994  54  Comparison o f Catch Rate from Trawl Survey data from 1947, 1979-1982 and 1992-1994  55  Results o f the length frequency analysis and mortality estimation for Leiognathus splendens  62  Results o f the length frequency analysis and mortality estimation o f Secutor ruconius.  62  Results o f the length frequency analysis and mortality estimation o f Leiognathus bindus  62  Results o f the length frequency analysis and mortality estimation o f Scomberomorus commerson.  66  Results o f the length frequency analysis and mortality estimation o f Otolithes ruber.  66  Results o f the length frequency analysis and mortality estimation o f Trichiurus haumela.  66  Table 2.13  List o f Fishing Gear in San Miguel B a y and a comparison o f Gear Number and Effort in 1979-1982 and 1992-1994. 72  Table 2.14  Results o f the Monte Carlo Simulation o f the catch estimate (tonnes) for 1992-1994 and 1979-1982.  75  ix Table 2.15  Catch distribution by Gear Group in 1979-1982 and 1992-1994.  82  Table 2.16  The ten most abundant species in the Total Catch o f San Miguel B a y 1992-1994.  82  The ten most abundant families in the Total Catch o f San Miguel B a y 1992-1994.  82  Distribution o f species between the large-scale Sector and the Small-Scale Sector.  87  Table 2.17  Table 2.18  Table 2.19  Comparison o f the Net Income derived from the catch figures in Padilla et. al (1995) and the catch figures calculated from the Landings Survey above.  132  Table 3.1  Grouping o f species found in San Miguel B a y for Ecopath M o d e l .  142  Table 3.2  P / B ratios from the G u l f o f Thailand compared to the estimates from San Miguel Bay.  146  Input parameters and Q / B estimates from (1) Pauly's Integral Equation and (2) Palomares and Pauly's Empirical Equation.  153  Results o f the Pauly (1986) Q / B estimation method compared to the Palomares and Pauly (1989) regression equation.  154  Table 3.5  The Final Q / B estimates corrected for fishing mortality.  154  Table 3.6 Table 3.7  Input parameters for the E C O P A T H model o f San M i g u e l Bay. Diet Composition for the E C O P A T H model. Figures in brackets were changed during the balancing process, figures in bold are the new values.  159  Table 3.3  Table 3.4  164  Table 3.7 (cont.)Diet Composition for the E C O P A T H model. Figures in brackets were changed during the balancing process, figures in bold are the new values.  165  Table 3.8  Selected results from the San Miguel B a y E C O P A T H model.  173  Table 3.9  Transfer efficiencies, T E , and related indices between trophic levels in San Miguel Bay.  181  Input parameters and results for the two "fishery predator" E C O P A T H models, LS-SSpredator and LS-SSgears predator.  198  Legend and Scale for Figures 4.2 - 4.10 and 4.14 - 4.15.  234  Table 3.10  Table 4.1  X  Table 4.2  Table 4.3  Table 4.4  Comparison o f yield curves and current fishing mortality produced by a single species yield-per-recruit analysis and a multispecies analysis.  241  Impact o f decreasing the biomass o f one pool on the other pools in the San M i g u e l B a y ecosystem when top-down control is assumed.  251  Percentage change in total fished biomass, for each type o f fishing gear after 10 years and a reduction in fishing effort to zero.  263  Table 5.1  List o f potential long term management options for San Miguel Bay.  308  Table 5.2  Performance Criteria for the first stage o f the adaptive management.  313  Table 5.3  Percentage Change in Total Fished Biomass after 20 years.  316  Table 5.4  Change in Biomass Diversity after 20 years.  316  Table 5.5  Percentage Change in Total Catch after 20 years.  317  Table 5.6  Change in Catch Distribution after 20 years.  317  Table 5.7  Percentage Change in Total Revenue after 20 years.  318  Table 5.8  Percentage Change in Profit per Small-scale Gear after 20 years.  318  Table 5.9  Percentage Change in Total Fished Biomass after 20 years.  321  Table 5.10  Change in Biomass Diversity after 20 years.  321  Table 5.11  Percentage Change in Total Catch after 20 years.  321  Table 5.12  Change in Catch Distribution after 20 years.  322  Table 5.13  Percentage Change in Total Revenue after 20 years.  322  Table 5.14  Percentage Change in Profit per Small-scale Gear after 20 years.  322  Table 5.15  Table summarising the changes in biomass o f the large crustaceans and juveniles sciaenids after an experimental policy lasting 5 years.  335  Table A2.1  N e w Adult and Juvenile Parameters for the E C O P A T H M o d e l .  389  Table A2.2  Diet Composition o f the Sciaenids in the Original M o d e l and Split into Adult and Juveniles Groups.  390  XI  Table A2.3  Table A 2 . 4  Table A2.5  Diet Composition o f the Sciaenids in the Original M o d e l and Split into Adult and Juveniles Groups.  391  Diet Composition o f the Large Predators in the Original Model and Split into Adult and Juveniles Groups.  392  Additional Input Parameters for the Delay-Difference E C O S I M  394  List of Figures  Figure 1.1  Flowchart showing thesis chapter layout and content.  13  Figure 2.1  M a p o f San Miguel Bay, Philippines, showing bathymetry, sediments and key locales ( I C L A R M 1995)  18  M a p showing the trawl stations used during the 1992-1994 trawl survey of San Miguel B a y (Cinco et al. 1995)  25  Seasonal variation in the C P U E o f the major groups in the 1992-1994 trawl survey.  58  Length-based yield-per-recruit curves, showing yield-per-recruit against exploitation. The fine vertical line represents the optimal exploitation rate and thick line is the current exploitation rate.  69  Comparison o f effort between 1979-1982 and 1992-1994 for 20 types of fishing gear in San M i g u e l B a y . Effort is estimated from the number o f trips made by each gear type per year times the number of units o f each gear type (Silvestre et. al 1995).  73  Comparison o f Catch between 1979-1982 and 1992-1994 for 20 types of fishing gear in San M i g u e l Bay. Catch is estimated from the C P U E and effort. The error bars represent the 95% confidence limit on the estimates from the Crystal B a l l Monte Carlo simulation.  78  Comparison o f C P U E between 1979-1982 and 1992-1994 for 20 types of fishing gear in San Miguel Bay. C P U E is estimated from the catch and effort recorded in the Landings Survey.  80  Figure 2.8  Comparison o f the catch composition in 1979-1982 and 1992-1994.  84  Figure 2.9  C P U E o f the top species and groups in the baby trawl catch from 19791982 and 1992-1994. The sharks and rays are combined following the procedure used in the 1979-1982 data.  89  C P U E o f the top species and groups in the mini-trawler catch from 1979-1982 and 1992-1994. The two types o f mini-trawler, the Pamalaw, which targets sergestid shrimps and the Pamasayan, which targets penaeid shrimps are shown for the 1992-1994 data.  89  Figure 2.2  Figure 2.3  Figure 2.4  Figure 2.5  Figure 2.6  Figure 2.7  Figure 2.10  Figure 2.11  C P U E per month for the selected species in the M i n i Trawler catch o f  xiii 1992-1994. Figure 2.12  Figure 2.13  Figure 2.14  Figure 2.15  Figure 2.16  Figure 2.17  Figure 2.18  Figure 2.19  91  C P U E o f the top species and groups in the ordinary gillnet catch from 1979-1982 and 1992-1994. A l s o shown are the hunting gillnet and shrimp gillnet C P U E for 1992-1994.  93  C P U E per month for selected species and groups in the ordinary gillnet catch o f 1992-1994.  95  C P U E per month for selected species and groups in the hunting gillnet catch o f 1992-1994.  96  C P U E o f the top species and groups in (a) the bottom-set gillnet catch and (b) surface gillnet catch from 1979-1982 and 1992-1994.  99  C P U E o f the top species and groups in the filter net catch from 1979-1982 and 1992-1994.  102  C P U E per month for selected species and groups in the filter net catch of 1992-1994.  102  C P U E o f the top species and groups in the fish corral catch from 1979-1982 and 1992-1994.  104  C P U E per month for selected species and groups in the fish corral catch of 1992-1994.  104  Figure 2.20  C P U E per month for selected species and groups in the set longline catch of 1992-1994. 106  Figure 2.21  Modal lengths o f Otolithes ruber in the catch.  110  Figure 2.22  Monthly C P U E o f the sciaenids by the main gears that catch them.  110  Figure 2.23  Monthly C P U E o f the penaeids by the main gears that catch them.  113  Figure 2.24  Modal lengths o f leiognathids in the catch, (a) Leiognathus and (b) Secutor ruconius.  115  splendens,  Figure 2.25  Monthly C P U E o f the leiognathids by the main gears that catch them.  116  Figure 2.26  Modal lengths o f the engraulid Stolopherous commersonii in the catch.  118  Figure 2.27  Monthly C P U E o f the engraulids by the main gears that catch them.  118  Figure 2.28  M o d a l lengths o f Trichiurus haumela in the catch. The mean is used  XIV  where the mode could not be calculated.  120  Figure 2.29  Monthly C P U E o f Trichiurus haumela by the main gears that catch them. 120  Figure 2.30  Modal lengths o f Mugilidae in the Catch. Length at Maturity is calculated from an empirical formula.  122  Modal lengths o f the carangid, Alepes djeddaba in the Catch. Length at Maturity is calculated from an empirical formula  122  Trophic mass-balance model o f San Miguel B a y , Philippines showing biomass and production, inflows and outflows and the trophic level for each eco-group. The units are in tkm" , and the area o f the B a y is 1115 k m .  174  Comparison o f (a) biomass estimates and (b) relative abundance from the E C O P A T H model and the San Miguel B a y 1992-1994 Trawl Survey.  185  Sensitivity Analysis for the Sciaenids. Each o f the input parameters is varied from -50% to +50% and its impacts on the unknown parameters plotted.  188  Figure 2.31  Figure 3.1  2  2  Figure 3.2  Figure 3.3  Figure 3.4  Results o f the Trophic Impact Routine. The eco-groups on the x-axis are responding to an increase in biomass o f the named group. The impacts are relative but are comparable between groups. 190-192  Figure 3.5  Results o f the Trophic Impact Routine when (a) the small-scale sector and (b) the large-scale sector are increased. The eco-groups on the x-axis are responding to the increase. The impacts are relative but are comparable. 200  Figure 3.6  Results o f the Trophic Impact Routine when different small-scale gears are increased. The eco-groups on the x-axis are responding to the increase. The impacts are relative but are comparable. 202-203  Figure 3.7  Comparison o f the "predation mortality" (= fishing mortality) imposed by each "fishery predator" (= fishing gear) on the fished eco-groups.  209  Figure 4.1  Pattern o f fishing mortality used in Dynamic simulations.  231  Figure 4.2:  Equilibrium Simulation o f Changing Total Fishing Mortality for Bottom-Up and Top D o w n Control.  235  XV  Figure 4.3:  Equilibrium Simulation of Changing Total Fishing Mortality for Intermediate Control and Strong Top D o w n Control.  239  Figure 4.4.  Equilibrium Y i e l d Curves for the Pelagics.  243  Figure 4.5.  Equilibrium Y i e l d Curves for the Penaeids.  244  Figure 4.6.  Equilibrium Y i e l d Curves for the adult M e d i u m Predators.  246  Figure 4.7:  Equilibrium Y i e l d Curves-no fishing showing ecosystem impacts of the Sciaenids.  254  Equilibrium Y i e l d Curves-no fishing showing ecosystem impacts of the Sergestids (a) and (b) and the Large Crustaceans, (b) and (d).  257  Dynamic simulations reducing the fishing effort o f each gear to zero over the first 2 years of a 10 year simulation under the Top-down Control assumption.  261  Dynamic simulations reducing the fishing effort o f each gear to zero over the first 2 years of a 10 year simulation under the Bottom-up Control assumption.  262  Percentage change in the biomass of each pool after 10 years for (a) Top-down control and (b) Bottom-up Control.  266  Percentage change in the biomass o f each pool after 10 years when Top-down control is assumed for all other gears.  268  Figure 4.8.  Figure 4.9.  Figure 4.10.  Figure 4.11  Figure 4.12  Figure 4.13  Percentage change in the biomass of each pool after 10 years when Bottom-up control is assumed for all other gears.  269  Figure 4.14.  Dynamic simulations reducing total effort to zero.  277  Figure 4.15.  Dynamic simulation o f the 1979-1982 fishing pattern.  290  Results of an experimental policy where the hunting gillnet is increased by 100% for 5 years.  334  Figure 5.1  xvi  Acknowledgments To my supervisor, Dr. Daniel Pauly I would like to give a very special thank you. H e introduced me to San M i g u e l B a y and I C L A R M , he has given me consistent support throughout my work, he has remained motivated, flinging ideas at me left, right and centre, and has been a source o f inspiration for this work. He has always been attentive to my ideas and promptly and efficiently returned chapters to me. I must also thank my committee members for their contribution to and interest in this work. Their advice was always sound and gave direction at times when it was needed. O f special note I would like to give my thanks to Dr. Carl Walters for encouraging me to attempt the dynamic multispecies model, which later, under his development became known as E C O S I M . I would also like to thank Dr. Tony Pitcher for his early interest in and support for my work, Dr. Les Lavkulich for his sound chairing o f meetings, Dr. Gordon Munro for his continued interest in my work, even though in the end, economics did not play a big role and Dr. M i k e Healey. I must also thank I C L A R M for giving me access to the San Miguel B a y database, D r . Daniel Pauly for suggesting San Miguel B a y as a case study, M r . Gerry Silvestre for his interest and support during my visits to I C L A R M and the Philippines, M r . Cesar Luna for interesting discussions about decision analysis and San M i g u e l B a y and all other I C L A R M staff with whom I had the good fortune to discuss ideas and methods. I would like to give special thanks Dr. V i l l y Christensen for his great help and advice with E C O P A T H in Chapter 3. The Fisheries Centre must be noted for its financial support during m y first two years at U B C . The Centre, through the able directorship o f Dr. Tony Pitcher has been an enjoyable and stimulating location to pursue this thesis. I would also like to mention my good friends Dr. Ramon Bonfil, with whom I shared an office for so long and Y i n g Chuenpagdee for their friendship and support during the years and the other Fisheries Centre students and visitors who have made the Centre interesting and fun. In addition I would like to thank the Science and Engineering Research Council ( U K ) for its financial support during the first 20 months o f my Ph.D., and the Renewable Resources Assessment Group, Imperial College, London, U K . On another, and more personal level I would like to thank Anthony Davis for his unwavering support, advice, and sagacity. H e has been a constant friend and companion and is invaluable to me. Finally I would like to thank my father, John Bundy, who has supported all my endeavours and has never failed to offer help.  In memory of my mother, Colleta Carolina Fransisca Leenhouts Bundy Whose spirit lives on.  1  Chapter 1 Multispecies, Multigear Fisheries: Introduction and Overview  "...despite their importance, tropical fisheries are most often badly managed (if at all) - the resources are generally over-exploited and the fisheries overcapitalised Compounding the problem is the fact that, inspite of recent advances, the biological basis of tropical fisheries is only beginning to be understood; indeed the relative neglect of tropical fisheries research in the international literature appears very clearly when the relevant journals are surveyed. This problem is further complicated by the enormous diversity of life histories and adaptations among the organisms exploited, as well as the diversity of fisheries, gear types and social conditions, which represent serious (although not overwhelming) constraints on resources assessments and fisheries management." Pauly (1994:15-16)  Introduction  About 60% o f the world's fish catch is taken from fisheries i n developing countries, yet the community dynamics and fishery dynamics o f these predominantly tropical resources are little understood. These fisheries are typically multispecies, with over a hundred or more species landed for immediate consumption, trade, export, fishmeal, animal food or fish sauce. This is especially the case in Southeast A s i a (Pauly 1994). These multispecies fisheries are usually exploited by a diverse small-scale sector, and, in many cases, a competing large-scale sector. Increased population growth, development o f fishing technologies and demand for fish have placed enormous pressure on fish stocks. Patterns o f exploitation have expanded and largescale, capital intensive fisheries have long since moved into areas, and targeted species, traditionally exploited by small-scale fisheries (Pauly 1997, 1979a, Smith 1983, Marr 1982, Gulland 1982). The inevitable differences in interests and exploitative power between these  different sectors have led to over-exploitation and competition over resource use. A t the same time, there has been little research into the ecosystem impacts o f fishing on these multispecies, multigear fisheries.  The aim o f this study is to use and explore a multispecies, ecological approach to the assessment and management o f multispecies, multigear fisheries. In all fisheries, both interspecific interactions, and interactions between the fishery and the ecosystem occur. Without doubt, fishing causes change in the ecosystem, but it does not do this in isolation. The impact o f fishing on one or more species, has impacts on other species in the ecosystem. Fishing has been described as having a top-down effect on the food web (Pauly 1979a, L a r k i n 1996), although fishing occurs at all trophic levels, and thus at all levels o f the food web, Christensen (1996). W h e n examining the effects o f fishing in a multispecies context, both the direct effect o f fishing and the effect o f inter-species interactions need to be taken into consideration.  Larkin describes ecosystem management, when applied to marine ecosystems as "scientific shorthand for the contemporary appreciation that fisheries management must take greater note o f the multispecies interactions in a community o f fish species and their dependence on underlying ecosystem dynamics" (1996:146-147). The problems o f managing multispecies fisheries on an ecological basis are still being addressed i n the developed world. Murawski (1991) amply demonstrated this i n his description o f issues and problems in the multispecies, multigear demersal fishery o f the G u l f o f Maine. Yet, there is arguably a greater demand and challenge to resolve these problems in tropical multispecies fisheries, where ecological and  3 fisheries data, are poor, but the fisheries catch many species and are fished simultaneously by very diverse fishing gear.  There has been very little research aimed specifically at the ecological assessment and management o f multispecies, multigear fisheries, particularly for the fisheries o f the developing world (Christensen (1996), Larkin (1996), Pauly (1994), Murawski (1991), Garcia (1989), Gulland and Garcia (1984)). Troadec (1983) suggests that historically, small-scale fisheries are regarded as unimportant and that emphasis in fisheries research is placed on largescale, money making operations. West African governments for instance have a laissez-faire attitude to small-scale fisheries, regarding them as an activity o f last resort, the situation also found in Southeast A s i a (Smith 1983). However, the example o f the Ivory Coast shrimp fishery, where small-scale fishers may have caused the economic collapse o f the large-scale sector vividly demonstrates that small-scale fisheries should not be ignored (Griffin and Grant 1981) . However, data from small-scale fisheries in developing countries are often sparse and uncertain. Data collection and analysis in many o f these fisheries are challenged by dispersed landing sites, lack o f trained personnel, lack o f funds, lack o f access, lack o f facilities (Marr 1982) and, as suggested by Troadec (1983), lack o f interest and management.  M u c h o f the research that exists for the assessment and management o f multispecies, multigear fisheries in developing countries concerns tropical penaeid shrimp fisheries, a resource with high value. Several studies have been conducted to examine the optimal fleet configuration for these fisheries (e.g., S W I O P / C N R O 1989, W i l l m a n and Garcia 1985, Jarrold and Everett 1991). Shrimp resources were traditionally exploited by small-scale fishers in countries such  as M e x i c o , West Africa, India and Southeast Asian countries, but since the 1950s, industrial shrimp fleets expanded into these areas (Willman and Garcia 1985). Penaeid shrimps are vulnerable to both gears at different stages o f their life history. The small-scale fishers target the juveniles, the large-scale sector targets the adults.  Studies which examine the effect o f fishing on the penaeids only are not truly multispecies studies, which are studies o f systems composed o f predator species, prey species and competing species. Other studies which have examined the effects o f different sectors o f a fishery on a single species or family conclude that a singular solution is best, that is, that the optimal solution is one where the fishery consists o f only one gear. For example, Medley et al. (1991) used optimisation theory to demonstrate that optimal exploitation o f the multifleet yellowfin tuna fishery occurred when only one gear existed. Clark and Kirkwood's (1979) bioeconomic model o f the G u l f o f Carpentaria prawn fishery (Australia) also produced the result that the optimal fleet composition consists o f only one o f type o f fishing gear. However, this was not considered to be an acceptable alternative since neither gear sector was likely to accept displacement by the other. Charles and Reed (1985) in their bio-economic analysis o f sequential fisheries found that co-existence would only occur optimally (for the maximisation o f total discounted rent) under a narrow range o f conditions, related to cost and price ratios o f the two fleets and the fraction o f the offshore stock which spawns each season.  However, in multispecies, multigear fisheries, fishing gears do not all target the same species and singular solutions are highly unlikely to be optimal for small-scale, multispecies fisheries. Gulland and Garcia (1984) list a series o f attributes which define multispecies, multigear  5 fisheries. These include (1) that the resource be multispecific, (2) that catch composition should depend on fishing strategy, (3) that the species composition o f the resource changes with time and (4) that the various fisheries interact. For example, probably everywhere a largescale shrimp fishery exists, the shrimp trawlers w i l l interact and compete with finfish fishers. The shrimp trawlers, which use a small mesh size, catch the juveniles o f the species which are targeted by the small-scale sector. This is particularly the case in Southeast Asia, where shrimp are found in the same shallow coastal areas as juvenile fish (Pauly 1994). In Australia, Haysom (1985) reported that conflict between shrimpers and sports fishers and crab fishers was caused by increased pressure by the shrimpers on whiting and sandcrabs. In West Africa, many small-scale fleets exploit the juveniles o f other stocks, for example, Sardinella, which are exploited by large-scale sectors when mature (Troadec 1983). In Senegal in 1969, all trips made by large-scale shrimp trawlers were directed at shrimps. In 1978, only 2 5 % o f trips were targeted specifically at shrimp - the rest o f the trips were primarily directed at high value species such as sole, kingclips and croakers (L'Homme and Garcia 1984).  There are many examples o f species composition changes as a result o f fishing. In West Africa the 'trash' fish Balistes has increased due to the exploitation, and consequent decrease in biomass, o f predatory demersal stocks (Gulland and Garcia 1984, Pauly 1979a). In Northwest Africa, the sparids were replaced by cephalopods (Gulland and Garcia 1984). The G u l f o f Thailand is the classic example o f the effects o f ecosystem overfishing (Pauly 1994). Huge declines occurred in the biomass o f slow growing fish such as rays, small demersals such as the pony fish (Leiognathidae), medium sized and large predators as a result o f the large-  6 scale trawl fishery. This biomass was replaced by generalists such as squid, prawns and the  .  hairtails (Trichiuridae) (Pauly 1979a).  One multispecies approach to fisheries assessment is the study o f technical in mixed-species fisheries (as opposed to multispecies fisheries). A mixed-species yield per recruit approach is used, for example, Brander (1983), Murawski (1984), Murawski et al. (1991), Pikitch (1989). These studies are premised on the fact that fishing gears vary in their selectivity patterns and exploit different age and size groups. However, although the mixed-species yield per recruit approach models the impact o f fishing on a mix o f species, by ' n ' gears, it does not model biological interactions, that is multispecies interactions. Brander (1983) modelled the technical interactions between the Nephrops norvegicus and cod fisheries o f the Irish sea, and the biological interactions between cod and TV. norvegicus. H e demonstrated that the yield o f TV. norvegicus  was affected by the biomass o f cod: when fishing pressure on cod was low,  predation mortality on N. norvegicus was high, thus reducing their yield to the fishery. The maximum single species yields o f cod and N. norvegicus were sub-optimal when the biological interactions between the two species were taken into account. A s Brander (1983) suggested, the results "call into question the validity o f management objectives based on single species yield-per-recruit criteria in a mixed fishery".  Yet, there is relatively little known about species interactions and the effects o f fishing. Sophisticated assessment methods used in temperate fisheries, such as multispecies Virtual Population Analysis (Magnusson 1995, Sparre 1991, see Larkin 1996, Hilborn and Walters 1992 for a review o f multispecies approaches), are inappropriate for many fisheries due to their  large data requirements. There are several impressive volumes on multispecies methods and theory (Daan and Sissenwine 1991, Mercer 1982, and Pauly and Murphy 1982). Yet, there is no attempt to understand, in a holistic context, both the interactive dynamics between fish and the impact o f fishing by diverse gears on a multispecies resource.  Research  Objectives  The overall objective o f this thesis is to use ecological theory to inform fisheries assessment and management. Its departure point is the knowledge that fishing takes place on an ecosystem, that fish do not live in isolation from other fish (and therefore they interact), that there are interactions between the fishery and the ecosystem, and that different fishing gears have different impacts. These interactions within the ecosystem and between the fishery and the ecosystem should be considered in fisheries management and assessment. A mass-balance, tropho-dynamic approach is used here to investigate their impact on fisheries assessment and management.  Specifically, the following aims were addressed in this thesis:  to study species interactions in a multispecies fishery; to examine the biological and ecological impacts o f a multigear fishery on a multispecies resource; •  to determine the effects o f ecosystem considerations on fisheries assessment and management;  8 To develop a systematic, integrated approach to the assessment o f multispecies, multigear fisheries using relatively simple methods with wide applicability; to develop tractable sustainable management strategies for multispecies, multigear fisheries; and to focus on developing countries, where fisheries are frequently multispecies, multigear and data sparse.  The last three aims are also intended to address another concern. M a n y o f the fisheries methods used to assess the multispecies fisheries o f developing countries were developed for fisheries in temperate, developed countries, usually in the 'West', from a single species perspective. In many cases they are not particularly suitable for tropical multispecies fisheries (Pauly 1994). For many fisheries in the West, skilled fisheries scientists develop very specific, often sophisticated models to assess fisheries. Again, these approaches are not very useful for the fisheries in developing countries, for reasons discussed above, particularly the lack o f trained personnel and lack o f infrastructure. The main fisheries statistic collected in fisheries in the developing world, after catch and effort, is length o f fish. Most o f the methods developed in the West, use age to structure their models, not length. There is thus quite a degree o f incompatibility between methods and approaches used to assess fisheries in the developed and developing world, or for temperate and tropical fisheries. For this reason, methods were used here that were designed for the type o f data available in the fisheries o f developing countries. One aim was to develop an overall approach which made maximum use o f data that were available. Another aim was to use methods which were likely to increase knowledge about multispecies, multigear fisheries.  9  Methods  The fishery o f San M i g u e l B a y , Philippines was used as a case study for this work. It is a multispecies, multigear fishery under stress due to excess fishing pressure from both large and small-scale sectors. It is typical o f many fisheries in the developing world where small-scale fishers are dependent on the resource for their livelihood. Three types o f trawlers operate regularly in San M i g u e l B a y , alongside a diverse range o f small-scale gears, including handlines, gillnets, and fish corrals. There is a history o f resource competition in the B a y dating from the 1940s, when there was already a trawl fishery for shrimp (Warfel and Manacop 1950). N o regular annual fisheries statistics have been collected in San M i g u e l B a y . A study o f the fishery conducted in the early 1980s concluded that the fishery was overexploited and a series o f management recommendations were made (Smith et al. 1983).  The data used in this work came from a multi-disciplinary study o f San M i g u e l B a y conducted by the International Centre for L i v i n g and Aquatic Resource Management ( I C L A R M ) from 1992-1994. I C L A R M was contracted by The Fisheries Sector Programme o f the Philippines (sponsored by the Department o f Agriculture and the Asian Development Bank) and as part o f a larger five year coastal resource management study in the Philippines, to conduct a 17 month "Resource and Ecological Assessment o f San M i g u e l Bay". This results o f this intensive multi-disciplinary project have been published as a C D - R O M ( I C L A R M 1995). This complements an earlier study from the 1980s which was also conducted by I C L A R M , in collaboration with the Institute o f Fisheries Development and Research o f the  10 University o f the Philippines (Smith et al. 1983, Bailey 1982a, 1982b, Pauly and Mines 1982, Smith and Mines 1982).  The research presented in this thesis represents an independent analysis o f the database from the 1992-1994 I C L A R M research project , kindly made available to the author, and the use o f 1  published results where appropriate. The results o f the 1979-1982 study o f the B a y were used for comparative purposes. In addition, to fill in gaps in available data, information from literature sources was also used.  A large and diverse range o f methods have been used in this thesis. Most were chosen for their applicability to the type o f data that are available for San M i g u e l B a y , and to the limitations that these data place on the type o f analyses that can be performed. For example, estimates o f mortality and yield-per-recruit analyses are made using length-based methods because only length-based data are available for San Miguel Bay. Length-based approaches to fisheries assessment have been criticised (see Hilborn and Walters, 1992 for example). However, for many tropical fisheries, this is the only type o f approach that is possible. Whilst this research takes an ecological approach to fisheries management, it was not possible to include the entire ecosystem. For example, mangroves fringe the coast o f San M i g u e l Bay, and are an important link between the terrestrial and marine ecosystems. Inorganic nutrients are imported from the land and organic nutrients are exported into the Bay. Mangroves also stabilise fresh water run-off from the land. In addition, forty two percent o f the fish in B a y  In some cases, because the initial analysis of the database occurred concurrently with ICLARM's analysis, the same parameters were calculated from the data, for example, total catch and biomass. Where this occurred, the results from my analysis were used. 1  11 spend part, or all, o f their life cycle in mangrove areas (Vega et al. 1995a). Thus mangroves are important in the ecology o f San M i g u e l B a y . However, there has been a trend o f mangrove destruction in San M i g u e l Bay: the current area o f mangroves is only 42 % o f the area covered by mangroves in the 1950s (Vega et al. 1995a).  Several rivers discharge into the B a y , the largest o f which is the B i c o l river. The rivers introduce freshwater, silt, nutrients and pollution (from industrial, agricultural and domestic sources) into the Bay. Mendoza et al. (1995b) report that the B a y is not polluted although there is nutrient enrichment. Siltation o f the B a y makes it shallower, and may also clog the gills o f fish (Mendoza and Cinco 1995).  The B a y is, by definition, open to the ocean. Suspended materials maybe carried into the B a y by tidal currents, although Villanoy et al. (1995) report that there is no net transport into or out of the Bay. It is also likely that there is movement o f fish in and out o f the Bay. Pauly (1982b) speculated that fish spawn outside o f the B a y and that larvae are carried into the B a y on tidal currents. Another feature which may influence the ecology o f the B a y are coral reefs which are found at the mouth o f the B a y . These are reported to be in fair to good condition (Garces et al. 1995c). The biotic and abiotic inputs described above are clearly part o f the overall ecosystem. However, whilst recognising that they w i l l have a modifying effect on the Bay, it was not feasible to explicitly include them in the mass balance, tropho-dynamic approach used here.  12  Thesis  Outline  The thesis begins with a description and assessment o f the San M i g u e l B a y fishery (see Figure 1.1). This is a background chapter which sets the scene for the three subsequent chapters. In Chapter 3, the ecosystem is modelled using a static mass-balance model ( E C O P A T H ) and in Chapter 4 it is modelled using a tropho-dynamic model, E C O S I M . The aim o f both these chapters is to model the ecosystem, gain an understanding o f species interactions, the impact of fishing by a multigear fishery on the ecosystem and the implications o f the results for fisheries assessment. In Chapter 5, the management implications o f the results in Chapters 2, 3 and 4 are examined using an adaptive management approach. Each chapter is described in more detail below.  The fishery o f San M i g u e l B a y is described in Chapter 2. The background o f the fishery is first assembled, then the current state o f knowledge o f the fishery described. The purpose o f this chapter is to lay the foundations for the subsequent chapters. It is a basic fisheries assessment chapter, using methods which avoid the constraints that the data place on analysis. For example, the standard practice o f analysing series o f catch and effort data was not possible, because these data do not exist. Data from the trawl survey and catch survey are analysed using single species and length-based methodologies, including mortality estimation and yieldper-recruit analysis.  13  Chapter 2 Description o f the • fishery o f San Miguel B a y  Chapter 2 Assessment o f the fishery o f San Miguel B a y  Analysis of T r a w l Survey D a t a Changes in species composition Estimation o f biomass (with Monte Carlo Analysis) Estimation o f mortality Yield-per-recruit analysis Analysis of L a n d i n g s Survey D a t a Estimation o f total catch (with Monte Carlo Analysis) Distribution and changes in catch and effort Status o f major fish stocks Economic analysis  Chapter 3 E C O P A T H massbalance model  Estimation o f parameters Balancing o f M o d e l Maturity analysis and comparison with other ecosystems Trophic Impact Routine (1)  Chapter 3 E C O P A T H with fishing gears as top predators.  Incorporate the fishery into the E C O P A T H model as top predators and model impacts o f fishery using the trophic impact routine. Trophic Impact Routine (2)  Chapter 4 Dynamic multispecies analysis using  Split pools to allow for trophic ontogeny Conduct following analyses using bottom-up and top-down control assumptions: Species Interactions Impact o f total fishing on whole resource Impact o f fishing on yield curves Impact o f different fishing gears  Chapter 5 Adaptive Management  |Identify alternative hypotheses or models o f the resource; [Assessment o f whether further steps are necessary by estimating the expected value of) |perfect information (EVPI); Develop Baseline Policies; Develop Adaptive, Probing Policy Options; Develop Performance Criteria to measure success; Formal Comparison o f Options.  Figure 1.1 F l o w chart showing thesis chapter layout and content.  14 A longitudinal analysis o f the species composition data is conducted and compared to historical data from 1979-1982 and an earlier survey in the late 1940s (Warfel and Manacop 1950).  Chapter 3 takes the first step towards a multispecies species analysis o f the fishery. The fishery is modelled using a mass-balance model, E C O P A T H (Christensen and Pauly 1992a). E C O P A T H is a mass-balance description o f trophic interactions. It is used to determine and describe the interactions between different components within the ecosystem. In this way, the major energy flows and pathways in the ecosystem, upon which the fishery is based, are identified. The state o f development or maturity o f the ecosystem is also examined using a series o f indices outlined in Christensen and Pauly (1993c) and Christensen (1995, 1994). In addition, the key areas where information is poor are also highlighted. E C O P A T H is a means to collate data about a system in a coherent form, enabling a better understanding o f the entire system. Knowledge is increased. A second model was then built, incorporating the fishery, large-scale and small-scale, as predators within the model. In this way it was possible to directly examine the impacts o f the fishing gears on the ecosystem.  In Chapter 4 the fishery o f San M i g u e l B a y is modelled using E C O S I M , a dynamic multispecies model (Walters et al. 1997). This model requires only a few more parameters than E C O P A T H , and is thus very appropriate for data poor fisheries. W i t h E C O S I M it is possible to directly examine the impact o f the fishery on multispecies resource, and, ask, "what if?" questions, that is, to make predictions. The model is used to examine the interactions between different components o f the ecosystem and between the fishery and the ecosystem.  15 Furthermore, E C O S I M is used to simulate the two "views" o f energy flow control in ecosystems: top-down control (Carpenter and Kitchell 1993) and bottom-up control (Hall et al. 1970, Hunter and Price 1992) are simulated and compared.  There are two major uncertainties concerning the resource dynamics in San M i g u e l B a y : the relative strength o f inter-species interactions and their response to fishing, and the importance offish immigration in the fishery. These uncertainties are explicitly addressed in Chapter 5 using adaptive management (Walters 1986). Adaptive management is essentially a feedback system whereby empirical information generated from the resource in response to management is used to shape future management strategies. It is proposed that experimental adaptive management leading to an increase in empirical data could result in more constructive management policies, particularly for data sparse fisheries in the developing world such as San Miguel B a y .  In Chapter 6, the preceding chapters are revisited in the light o f the aims o f this thesis. Finally, the question is asked, "Is the ecological approach developed here, whereby E C O P A T H and E C O S I M are used to model multispecies fisheries and adaptive management used as a means to resolve uncertainty and gain knowledge, a useful approach for other fisheries?  This thesis may be read as a thorough assessment o f a multispecies, multigear fishery from the Philippines. It may also be read as part o f an attempt to develop and use methodological approaches appropriate for multispecies, multigear fisheries in the developing world. Most fundamentally, it is an attempt to understand the biological and ecological interactions  *6 between a multigear fishery and a multispecies resource and to examine the implications for fisheries assessment and management.  17  Chapter 2 The San Miguel Bay Fishery  "We know that the assumption of a constant parameter system is never strictly fulfilled in real life. However, we are often in a situation which forces us to make assumptions, which are known to " be crude approximations to reality. It often happens that only by making such assumptions we are able to carry out an analysis of available data, and it is better to do a crude analysis than no analysis at all." Sparred al. 1989, p. 139 Introduction  to San Miguel  Bay  San M i g u e l B a y is a large shallow estuary in the B i c o l region o f the Philippines (Figure 2.1). It supports an important multi-species fishery, traditionally exploited by small-scale fishers using a wide range o f gear types. The B a y falls within the jurisdiction o f two provinces, Camarines Norte and Camarines Sur. The two provinces have seven municipalities adjacent to the B a y , Basud and Mercedes in Camarines Norte and Cabusao, Calabanga, Sipocot, Siruma and Tinambac in Camarines Sur. The most densely populated municipality is Calabanga and the least, Siruma. Around the coasts o f these municipalities are 74 coastal or fishing barangays (villages). The population o f the 74 barangays was reported as 92, 716 in 1990. This represents 2.4% o f the B i c o l population o f 3.9 million. B i c o l , whose economy is dependent on agriculture, is one o f the poorest provinces in the Philippines and small-scale fishers live at or below the national poverty level (Dalusung 1992).  18  Figure 2.1  M a p o f San M i g u e l Bay, Philippines showing bathymetry, sediments and key locales. ( I C L A R M 1995)  19 Since the 1970s and initially as a result o f government credit programmes, trawlers have exploited the B a y , mainly for shrimp. The B a y is ideally suited for trawling: it is wide, 1115 k m , shallow and 95% o f the B a y has a soft-bottom (Garces et al. 1995a). However, this was by no means the first instance o f trawling in San M i g u e l B a y . In the 1930s, three Japanese beam trawlers fished there (Umali 1937). In 1950, as a result o f a nationwide trawl survey, Warfel and Manacop (1950) suggested that San M i g u e l Bay, as one o f the most productive trawling areas in the Philippines, could sustainably support four or five trawlers. B y the late 1970s there were 88 commercial trawlers fishing i n the B a y (Simpson 1978), and this number has since increased.  Catch and effort data are not routinely collected in San M i g u e l B a y . There are thus no systematically collected fishery statistics, nor time series o f data. Management has been minimal over the years. Though mesh size regulations and closed areas for trawlers exist, enforcement has proven problematic. Conflict has developed between the small-scale fishers and large-scale trawl fishers who compete for the same, limited resource. This was noted i n other areas o f the Philippines as early as the 1950s. Rasalan (1957:53) reported that, in the opinion o f the fishers, "the extensive operations o f the otter trawl do not only destroy the eggs, larvae, food and the spawning ground o f fishes, but also fish corrals and other fishing gear which are set at the sea bottom to catch demersal species". A t this time, the annual catch landed by the commercial fishery was 1,000-2,000 tonnes (Rasalan 1957).  During the late 1970s and early 1980s, an estimated annual catch o f around 19,000 tons was caught by over 18 different types o f small scale gear and four types o f trawlers (Pauly and  20 Mines 1982). The small-scale gears include an array o f passive gillnets, such as drift, bottom, and shrimp gillnets, some using motorised boats (bancas); fixed gears such as fish corrals, filter nets, fish weirs and stationary lift nets; simple active gears such as push and pull nets, which do not require either a boat or engine, and handlines, longlines and spear guns. The trawlers include large, medium, "baby" and " m i n i " trawlers (Vakily 1982). Large trawlers are 19 to 25 meters long, varying in capacity between 27 and 117 tonnes with engines o f 275 to 555 horsepower. The medium trawlers are smaller vessels: their length is around 18 meters, their tonnage 3 to 6 tons and engine horsepower o f 200. Baby trawlers, as the name suggests, are significantly smaller. Their capacity is around 1.6 to 3 tonnes, their horsepower 68 to 160 and their length about 12 meters. M i n i trawlers are powered by a 10-16 horsepower engine, they are about 5 meters long and have a capacity o f less than one tonne. The small-scale sector employed 5, 100 out o f a total o f 5, 600 fishers in San M i g u e l B a y (Smith and Mines 1983).  In the Philippines, fishing vessels are divided into commercial vessels and municipal vessels (in this thesis, the terms municipal sector and small-scale sector are equated). Commercial vessels are those with a capacity o f over 3 gross tons and, in San M i g u e l Bay, include the large and medium trawlers. Accordingly, municipal or small-scale vessels are those that weigh less than 3 gross tons. This law dates back to 1932, when it was arbitrarily instituted for taxation and licensing purposes (Pauly 1982a). The unfortunate consequence o f this arbitrariness is that small commercial trawling vessels are classified as municipal because their capacity is less than 3 gross tons. Thus, in addition to the traditional gears in San M i g u e l B a y , the so-called small-scale sector also formally includes the "mini-trawlers", which mainly exploit sergestid shrimp, and the small or "baby" trawlers, which target penaeid shrimp and demersal fish. This  21 classification impacts the fishery regulations, which have different rules for commercial fishing and municipal trawling.  The Fisheries A c t o f 1975 (or P D 704) encompasses all fisheries regulations in the Philippines (Luna 1992). The Bureau o f Fisheries and Aquatic Resources ( B F A R ) have responsibility for commercial fisheries whilst municipal or small-scale fisheries are under the jurisdiction o f the municipality. A l l vessels must be licensed, although this is more to earn revenue for the government than to control fishing. In fact, according to Luna (1992), the whole Fisheries A c t can be considered to be primarily focused on the further development o f the fisheries sector.  The Fisheries A c t placed a 7 k m or 7 fathom ban on all commercial vessels. This meant that commercial trawlers, for example, could not fish within either 7 k m o f the shoreline or in waters less than 7 fathoms deep. However, there were alternative rules for municipal trawlers, that is, trawlers less then 3 gross tons, the baby and mini-trawlers. These vessels were allowed to fish in waters as shallow as 4 fathoms, i f given permission by the municipality and approval by the Department o f Agriculture secretary (Luna 1992). In 1991 the N e w L o c a l Government code was instituted and this increased the jurisdictional responsibility o f the municipalities. Principally, The Code extended the municipal boundaries to 15 k m from the shoreline. This placed San M i g u e l B a y in its entirety within the jurisdiction o f the municipal authorities, and thus the decision on whether to allow trawling in the B a y was completely in their hands. In the early 1980s a multidisciplinary research project involving I C L A R M (International Centre for L i v i n g and Aquatic Resource Management) and the Institute o f Fisheries Development and Research o f the University o f the Philippines conducted a study o f the  22 fisheries o f San M i g u e l Bay. This was the first real attempt to assess the status o f San Miguel Bay and to consider management options and realities. Data were collected on the biology, economic and socio-economics o f the fishery. The study concluded that the B a y was overfished, that there were reduced profits in the fishery, that catch and income were unequally distributed between the trawling and small scale sector (heavily i n favour o f the trawlers) and that there were too many people fishing. The overall conclusion was that the B a y was in dire need o f management. I C L A R M developed a series o f management objectives, including the restriction o f trawling activities and recommended the creation o f a San M i g u e l B a y Fisheries Authority, an authoritative body to manage the entire B a y (Smith et al. 1983).  Despite the findings o f this comprehensive study, and a subsequent 5 year ban on commercial trawling, little changed in San M i g u e l B a y : overfishing and lack o f management continued. In 1986, Smith and Salon (1987) conducted a follow up study. They reported that the number o f trawlers had increased by 50%, that the number o f small-scale gears and fishers had increased, that out-migration o f fishers had increased, and that demands to close the B a y to all trawling activities had grown to a clamour. So it appeared that not only did the results o f the I C L A R M study have no impact, the situation had, in fact, worsened.  More recently, a second comprehensive study o f San Miguel B a y was conducted by I C L A R M from 1992 to 1994. I C L A R M was contracted by The Fisheries Sector Programme (FSP) o f the Philippines (sponsored by the Department o f Agriculture and the Asian Development Bank) as  23 part o f a larger 5 year coastal resource management study in the Philippines, to conduct a 17 month "Resource and Ecological Assessment o f San M i g u e l B a y " . 2  A n assessment o f the San M i g u e l B a y fishery is made in this chapter using the data from this study. A s noted above, no regular fishery statistics are collected in San M i g u e l B a y . This means that there is no time series o f catch and effort data, no regular, systematic trawl surveys and no annual census o f boats or fishers. Thus, most methodological approaches commonly used in fisheries assessment are not applicable to this fishery. For example, even relatively simple methods such as surplus production models cannot be used because o f the lack o f a time series o f catch and effort data with which to fit the model.  For the above reasons, the assessment made here is both analytical and descriptive. It uses the data from the recent I C L A R M project in San M i g u e l B a y to assess the current state o f the fishery. The results o f the earlier 1979-1982 I C L A R M project are used for comparative purposes.  A CD-ROM "The San Miguel Bay Story" (ICLARM 1995a) has been published containing the results of this research. 2  24  Methods  The Data  The data for the assessment o f the fisheries o f San M i g u e l B a y came from three surveys conducted by the Capture Fisheries Assessment component o f the I C L A R M study (Silvestre et al. 1995, Cinco etal. 1995).  Trawl survey Data  The trawl survey was conducted from July 1992 to June 1993 (Cinco et al. 1995). A one hour drag, during daylight, was made monthly at each o f nine stations, distributed around San M i g u e l B a y (Figure 2.2). The vessel used for the survey was a 1.93 ton, 10 m trawler with outriggers and a 65 H P inboard diesel engine. The trawl net used, a 4-seam bottom trawl with a 12 m headline and 0.9 cm cod-end mesh size, was typical o f the trawl gear used in San Miguel B a y .  Fishery survey Data  The fishing gear inventory was conducted from January 1993 to June 1993. It covered all 74 fishing barangays in San M i g u e l B a y and thus the seven municipalities. Information on ownership o f gear and seasonality o f gear use were obtained from the local barangay officials, who are up-to-date on who is fishing, with what, and where.  Figure 2.2 M a p showing the trawl stations used during the 1992-1994 trawl survey of San Miguel Bay. (Cinco et al. 1995).  26 The landings survey took place from July 1992 to June 1993. It covered the large and the small-scale sector and the following data were collected for each gear type: catch landed per trip, species composition o f the catch, length composition o f the catch (by species or group). The large-scale sector was surveyed at three landing sites in three municipalities Sabang (Calabanga), Castillo (Cabusao) and Padawan (Mercedes) and the small-scale sector at five landings sites in four municipalities, Castillo (Cabusao), Sabang and Sibobo (Calabanga), Filarca (Tinambac) and Padawan (Mercedes). The landings o f the large scale sector were monitored once a week and the landings o f the small-scale sector every other day. In both cases, monitoring was done by team members resident at the landing sites.  The comparative data from the 1979-1982 I C L A R M study were taken from published reports . 3  Analyses  of Trawl Survey  Data  T h e Species C o m p o s i t i o n o f San M i g u e l B a y  The current species composition o f San M i g u e l B a y was determined from the 1992-1994 trawl survey data. Catch per unit effort ( C P U E ) was used as a direct measure o f abundance.  These are available on the San Miguel Bay CD-ROM (see footnote 1), or as a series of hard copy technical reports published by ICLARM (1995b). 3  27 The abundance o f each species present was expressed as the relative contribution o f the C P U E of that species to the total C P U E over the 10 month survey. Once the total species present were collated, they were grouped into families.  Estimating Density and Biomass from the Trawl survey Data  The density and total biomass o f San Miguel B a y was estimated from the trawl survey data by the swept area method. Following the procedure i n Sparre et al. (1989) and Cinco et al. (1995), the monthly density (t/km ) was first calculated from the total monthly catch using 2  equation (a) below. The mean annual density was then calculated as the mean o f the estimated density per month (equation (b)). The mean biomass was calculated by multiplying the mean annual density by the effective area o f the B a y (c).  Density  mont  h i = Catch R a t e  Density = mean ( D  mon  mont  h \l ( X i * X * L * H L )  th i)  Biomass = Density * A  where X i = escapement factor X2 = effective width o f the o f the swept area L = length o f the path swept by the trawl  2  (a)  (b)  (c)  28  H L = headrope length A = area o f San M i g u e l B a y  The density and biomass estimates from this equation are not very precise due to uncertainties in the equation parameters (Sparre et al. 1989). In an attempt to include these uncertainties in the biomass estimate, Monte Carlo simulation (Crystal B a l l Inc., Denver, Colorado) was used to estimate means and 95% confidence limits o f the estimates. In this technique, the error distributions o f the input parameters are specified. Then 2000 random samples are taken from the input parameter distributions and the model results calculated for each. Finally, the distribution o f these result values provides the band o f estimates and a sensitivity analysis. Error distributions were input for the parameters X\, X2 and L .  The escapement factor, X i , refers to the proportion o f fish in the path o f the trawl net that are actually captured and retained in the net. Underwater film footage o f trawl nets in operation clearly show that some fish can outswim the trawl net, at least for some time. The proportion of the fish caught varies with the speed o f the trawl, the height o f the fishing line from the from the seabed, the width o f the trawl opening, the species o f fish targeted and other species in the fishery, etc. Values for X i in the literature range from a recommended 0.5 for trawlers in southeast A s i a (Saeger et al. 1980) to 1.0 Dickson (1974). Differentiating between these values is difficult (Sparre et al. 1989). Indeed, Hilborn and Walters note that 'the bottom line is always "What proportion o f fish in the area swept were captured'" (Hilborn and Walters 1992:163). T o incorporate this uncertainty into the biomass estimate, a triangular probability distribution, with a minimum o f 0, and a maximum o f 1.0 was used. Since there are no means  29 to discriminate between the two literature estimates above, the mean value o f 0.75 was taken as the peak o f the triangle and thus the most probable value.  Estimates o f X2, the proportion o f the headline length that describes the width o f the trawl path, range from 0.4 (Shindo 1973) to 0.6 S C S P (1978). Pauly (1980a) recommended 0.5 as a compromise value. The uncertainty in this parameter originates in two areas, the variation o f wingspread during the trawl and the differences i n the way that the gear is rigged by fishers. This likely variation was represented by a triangular probability distribution, with a minimum value o f 0.4, a maximum value o f 0.6 and the "compromise" value o f 0.5 as the midpoint.  The effective distance, L , travelled in one hour by the survey trawlers was 5.49 k m (Cinco et al. 1995). To allow for variability in the distance travelled caused by environmental factors such as weather, tides, bottom topography and depth, a 10% error factor was introduced to the estimate. This was represented by a uniform probability distribution with a range o f 4.94 to 6.04 and 5.49 as the midpoint.  The area o f San M i g u e l B a y is 1,115 k m (Garces et al. 1995a) with a trawlable area o f 95%. 2  However, since the 1979-1982 study, and other earlier studies (see Pauly 1982a) used a value o f 840 k m as the area o f San M i g u e l B a y , this latter value was also used for comparative purposes . The biomass and density results were then compared with the results from 19794  The difference in these two estimates is due to different boundary definitions of San Miguel Bay. The estimate of 1115 km is derived from a boundary drawn further north than the estimate of 840 km . The latter was drawn from Pambuan Point in Camarines Norte, eastwards to Siruma Island and then the mainland of Siruma in Camarines Sur (Mines et al. 1982, Figure 1). The former was drawn from Grove Point in Camarines Norte east to Butauanan Island and southeast to Quinabuscan Point in Camarines Sur (Garces et al. 1995a), see Figure 2.1. 4  2  2  30 1982 trawl data and with the results from 5 earlier trawl surveys dating back to 1947 (Pauly 1982a).  Longitudinal Comparison of Species Compositions from Trawl survey Data  The theory o f "fishing down an ecosystem" is now well known. When a fishery first develops, the larger, more valuable fish are targeted. A s time proceeds and stocks dwindle, the fishery switches it attention to the next most valuable species and fishes this until another switch is forced and so on. In this way, a fishery tends to be fished at different trophic levels at different stages in the history o f the fishery. This fishing practise becomes problematic when fishing pressure increases to the point where all extant trophic levels are targeted simultaneously and there is no reprieve for declining fish groups. Usually this point is not reached unless largescale trawling activities, often for prawns, compete with the traditional small-scale sector for resources. The effect o f this mass harvesting o f trash fish and juvenile fish, in addition to prawns, can destabilise multispecies resources and cause massive changes in species composition (Pauly 1979a, Beddington and M a y 1982).  Pauly (1979a) examined the species composition changes that had occurred in the G u l f o f Thailand and the Thai waters o f the Malacca Strait Fishery as a result o f the large scale trawling activity that has occurred since the 1960s. H e proposed a general pattern o f change which included the loss o f slow growing fish such as rays, huge declines in small demersals such as the Leiognathidae, declines i n medium sized and large predators and the rise o f generalists such as squid, prawns and the Trichiuridae. When a fishery reaches this stage it  31 might suffer from "ecosystem overfishing" which is defined as "what takes place in an ecosystem when the decline (through fishing) o f the originally abundant stocks is not fully compensated for by an increase o f the biomass o f other exploitable animals" (Pauly 1979b).  A longitudinal comparison was made with the species composition data from the 1992-1994 trawl survey, the trawl data from 1979-1982 (Vakily 1982) and the trawl survey data from 1947 (Warfel and Manacop 1950). The aim was to determine what species composition changes have occurred i n San M i g u e l Bay, to determine whether they agree with Pauly's general pattern, and to determine whether San Miguel B a y is ecosystem overfished.  Seasonal Analysis of the Trawl Survey Data  San M i g u e l B a y is subject to seasonal environmental conditions. Most significant is the northeast monsoon which occurs from October to March (Villanoy et al. 1995). During this time, the average wind speed is 3 metres per second but it can be as high as 7 metres per second. The northeast monsoon also marks the beginning o f the rainy season, and the rate o f fresh water discharge from the 12 rivers that flow into the B a y is correlated with this. It is also the period when the plankton density is highest. The other main environmental force in San M i g u e l B a y is the southwest monsoon which occurs between June and September (Villanoy et al. 1995). This has much less effect than the northeast monsoon because the B a y is protected by the B i c o l Peninsula. M a y to August is the  32  hottest period o f the year, with temperatures in the 32°C to 37°C range and during this time, the plankton counts are low.  In order to examine the impact o f these seasonal conditions on the San M i g u e l B a y fishery the C P U E and species composition o f the 1992-1994 trawl survey data were examined for seasonal differences. They were then compared with the trawl data from 1979-1982, where appropriate, to determine whether there was any consistency between the two data sets. The 1979-1982 trawl data were obtained using commercial trawlers. A systematic trawl survey was not conducted.  E s t i m a t i o n of M o r t a l i t y  The instantaneous rate o f total mortality, Z , was estimated using three length based methods, the Length Converted Catch Curve, Beverton and Holt's method (1956), the Powell-Wetherall Plot (Gayanilo et al. 1996). A fourth method which estimated Z from the sum o f fishing mortality and natural mortality was also used.  33 Estimation of Growth  Parameters  The three length based methods all require the growth parameters, Loo, the asyptotic length in the von Bertalanffy growth function and K , the von Bertalanffy growth constant. These were estimated using the E L E F A N I program contained within the F A O - I C L A R M Stock Assessment Tools software package, F i S A T (Gayanilo et al. 1996). Growth curves are fitted by E L E F A N I through a time series o f length frequency data, using the von Bertalanffy Growth Function. The goodness o f fit o f the curves is assessed by the number o f peaks and troughs in the length frequency data that the growth curve passes through. Several options are available in E L E F A N I for estimating the growth parameters. Once representative values o f Leo and K are described for a given series o f length frequency data, the effects o f trawl selectivity are used to correct the length frequency distribution. Loo and K are then re-estimated using the new distribution. Inclusion o f the smaller size classes enables a more accurate estimate o f Leo and K (Pauly 1987).  Values o f Leo and K were also extracted from the literature for comparative purposes. FishBase (FishBase 1995), a global fish database with data on growth rates o f different fish species, was used for this purpose. For each species analysed using the above method, mean Loo and K values were calculated from comparable studies. These mean values, which can be considered "generic values" for these species, were calculated with the aid o f the auximetric parameter  (phi prime), which expresses the growth performance o f a species or family o f  species (Munro and Pauly 1983, Pauly and Munro 1984, Pauly 1991). The equation relating (j)' to Loo and K :  34  <))' = logioK+2 log 10 Leo  (j)' and Loo are normally distributed and K has a log normal distribution. In order to estimate the average K from a series o f Loo and K values, <))' is calculated for each combination and the mean o f the  and Leo values taken. The above formula for §' is then solved for K , to find the  mean K .  These "generic" values o f Leo and K were used to check the results o f the length frequency estimation o f growth parameters. In addition, they were used as a guide in the analysis i f the parameter definition was confused (for example, no clear definition o f K ) . Tandog et al. (1988) who also used this approach for Philippine fishes, concluded that values o f  are  normally and narrowly distributed within a species.  Mortality Estimation using the Length-Converted  Catch Curve  Length-converted catch curves have become one o f the standard methods o f estimating mortality (Gulland and Rosenberg 1992). Catch curves originated with the use o f length based data by T. Edser in 1908 and F . Heinke in 1913, who also incorporated growth rate information (Ricker 1975). However, the modern form is based on age data and derived from the catch equation:  35  C(t,,t ) = N(t) • F / Z • (l-exp(-Z(t -t,)) 2  (1)  2  where, C(ti,t ) = catch from time ti to time t , 2  2  N(t) = numbers at time t, F = instantaneous fishing mortality rate Z= instantaneous total mortality rate  The catch equation is log transformed and manipulated into a form called the "general linearised catch curve".  LnC(t,,t ) = In (N(t )-F/Z) + Z(t ) - Z(t,) + ln(l-exp(-Z(t -t,))) 2  r  r  2  (2)  where, N ( t ) = number at age o f recruitment r  Z(t ) = instantaneous total mortality rate at age o f recruitment. r  The first term on the right had side, "ln(N(t )-F/Z)+Z(t )" can be consolidated as a constant, r  r  leaving "-Z(ti)+ln(l-exp(-Z(t -ti))". The second part o f this term is non-linear unless the time 2  interval is constant. However, the time to grow from one length interval to the next is not constant in fish and in order to make this non-linear term linear, an additional assumption is made. For small values o f " x " , that is x < l , , the following holds true:  ln(l-exp(-x)) w ln(x)-x/2,  36 and therefore where Z(t2-ti) is small, it follows that:  ln(l-exp(-Z(t -t,)) = ln[Z(t -t,)]- Z(t -t,)/2 2  2  2  (3)  Substituting this into the general linearised catch curve, substituting delta "t" for (t2-t|) and incorporating InZ into the constant gives:  ln[C(t, t+At)/At = c - Z(t+ At/2)  (4)  or,  Y = c - Zx.  where "c" is the constant and " - Z "  is the slope o f the descending right arm o f the curve.  Equation (4) is written in terms o f age. In order to convert it into a length based catch curve equation, length is transformed to age using the inverse von Bertalanffy equation, that is:  tL = t - l / K * l n ( l - L / L c o ) , 0  where, K = constant o f the von Bertalanffy Growth Function ( V B G F ) , L o o = asymptotic length at infinite age.  37 Substituting this into equation (4) produces the Length Converted Catch Curve equation below.  l n [ C ( L , , L ) / A t ( L , , L ) = c - Z[(t(L,) + t(L ))/2] 2  2  (5)  2  where, L i = length at time 1, L  2  = length at time 2,  At = t(L i )-t(L ) = 1 / K * l n [ ( L o o - L , ) / ( L o o - L ) ] 2  2  L e o = asymptotic length at infinite age.  The catch o f fish o f length L ] to L is thus divided by the time it takes to grow through this 2  length class, and this is plotted against the age o f the fish which is ( t ( L i ) + t ( L ) ) / 2 . The slope o f 2  the descending right limb gives the estimate o f Z .  In the method just described, it is assumed that the ecosystem is in a steady state, that the sample represents the mean population structure, that Z is constant over all size classes, that recruitment fluctuations are small and random and that the gear used (in this case trawling gear) has a selection curve where only the smaller animals are selected against. In this case the trawl samples, taken over a period o f 10 months, were pooled to produce the sample for analysis. This should enable the second assumption to be met. The third and fourth assumptions are usually met i f the descending limb appears straight (Pauly et al. 1980). In the case o f San M i g u e l B a y , the mean size o f fish are sufficiently small and fishing pressure has been sufficiently intense to virtually guarantee that none o f the fish included in the sample  38 were large enough to be selected against. The assumption o f a steady-state is always precarious and should be used with caution.  The next three methods share the assumptions described for the Length Converted Catch Curve.  Mortality Estimation using Beverton and Holt's Mean Length Method  The Beverton and Holt M o d e l (Beverton and Holt 1956) uses mean length, L o o and K to estimate Z . This method is robust under conditions o f variable recruitment (Wetherall et al. 1987).  Z = K*(Loo-  L)/(  L-L ), c  where, K = constant o f the von Bertalanffy Growth Function ( V B G F ) , L = the length for which all fish that length and longer in the catch are under full c  exploitation, L = mean length o f fish in the sample from L to L c  m a x  .  39 Mortality Estimation using the Powell-Wetherall  Plot  Another method which uses mean length information, is the Powell-Wetherall Plot (Gayanilo et al. 1996). It requires the whole sample to be pooled and assumes that this pooled sample represents the equilibrium state. It is a graphical method where the mean length o f fish above L , L minus L ( L - L ) is plotted against L . The equation on which this method is based is: c  c  c  c  L -L =a+b * L c  c  where, L = (Loo  + L )/(l+(Z/K) c  Z / K = -(l+b)/b or b = -K7(Z+K), L o o - -a/b, or, a = -b * L o o  The slope o f the line gives an estimate o f Z / K and the intersect with the x-axis, L e o .  Mortality Estimation using Fishing Mortality = Catch / Biomass  Under equilibrium conditions, fishing mortality, F , is equal to the quotient o f the catch and biomass. Total mortality, Z , is the sum o f F and natural mortality, M . Natural mortality was estimated using Pauly's M equation (Pauly 1980b), and F was estimated from the catch and the  40 swept area estimates o f biomass (see below). This method was included as an alternative to the length frequency approaches above. However, the Pauly estimate o f M does require the growth parameters L o o and K .  Y i e l d - p e r - R e c r u i t Analysis  The mortalities estimated above provide a snapshot o f the current status o f some o f the main fish species in San M i g u e l Bay, but they do not give information on how these mortality rates relate to the optimal rates o f fishing, nor how much change in yield can be expected as a result of adjusting fishing mortality. This is obtained using a length-based yield-per-recruit analysis. The analysis was carried out using the relative yield-per-recruit model contained within the F i S A T software (Gayanilo et al. 1996). This model is based on a modified version o f the Beverton and Holt yield-per-recruit model (Pauly and Soriano 1986). The required input parameters are a selection curve, the asymptotic length, L o o , the von Bertalanffy growth parameter, K and natural mortality, M . The growth and mortality parameters were calculated in the Mortality Section above.  The selection curve should ideally be estimated from empirical data collected during the trawl survey . However, this type o f data was not collected. Instead a routine within F i S A T , was 5  used to estimate the selection curve. The routine first estimates the probability o f capture from the Length Converted Catch Curve (see above). The model uses the Z and the M estimates to  In order to estimate the selectivity of a trawl net, a fine meshed net is used to cover the cod-end and thus catch any fish which escape the cod-end of the trawl net. 5  41  back-calculate the numbers o f fish that would have been present in the sample i f no selectivity had taken place. The probability o f capture is calculated as the ratio o f the number o f fish present with selectivity to the number o f fish that would be present with no selectivity. The selection curve is then estimated using a moving average o f probabilities for the age corresponding to three adjacent length classes, i.e., Probability at time (t) is equal to the average o f probability at time (t-1), time (t) and time (t+1). The method assumes that the estimate o f Z is accurate and that the smallest fish caught are fully recruited to the fishery (Isaacs 1990). Otherwise, the curve thus estimated is a "resultant", that is, the product o f a selection with a recruitment curve (Gayanilo et al. 1996).  Yield-per-recruit analyses were conducted for the species in San M i g u e l B a y for which there were sufficient data. Current exploitation rates were compared to the optimal rates generated by the yield-pre-recruit analysis. Current rates were calculated as F(=Z-M)/Z, where Z is the resultant overall mortality calculated above and M is calculated from the empirical formula o f Pauly (1980b).  42  Analyses  of Fishery  Data  E s t i m a t i n g C a t c h a n d Effort  One characteristic o f tropical fisheries is that catch, particularly by the small-scale sector, is landed at small, numerous and often widely distributed landing sites. Although convenient for fishers and local buyers, this complicates the collection o f fisheries data. Indeed it can make it impossible to collect comprehensive data. This is the situation in San M i g u e l B a y , which has landing sites dispersed all along its coastline. Despite the detailed study conducted by I C L A R M , it was not possible to numerically account for all effort and all landings at all landing sites. Instead, both are estimated from samples.  Effort  For each gear type, Silvestre et al. (1995) calculated the total number o f gear in each municipality from the information gathered in the Fishing Gear Inventory. They then summed these totals to obtain the number o f each gear type i n San M i g u e l Bay. They estimated the average number o f trips made per year from information on seasonal use o f gear in the Fishing Gear Inventory. The total effort, by gear, was then calculated from the product o f the number o f gears operating per year and the average number o f trips made per year, that is:  Effortgear = number o f units o f gear * mean annual number o f trips  43 Catch  The total catch was estimated from the annual average catch per unit effort ( C P U E ) per gear multiplied by the average annual effort o f that gear, that is:  Annual catch per gear = C P U E (kg/trip)*effort (No. o f trips)*No. Vessels (gear)  C P U E per gear, was calculated from the sum o f all landings made per gear over the survey period, divided by the total number o f trips made per gear.  The total catch is simply the sum over all gears, that is:  Total annual catch = Z  g e a r  annual catch per gear  When estimating catch from sub-samples in such a diverse fishery, there is inevitable uncertainty around the resultant catch estimates. In order to include and examine these uncertainties, Monte Carlo simulation (Crystal B a l l Inc., Denver, Colorado) was used to estimate means and 95% confidence limits o f the estimates, as described above.  Error distributions were entered for all three parameters, that is, C P U E , Effort and Vessels/Gear. T w o approaches were used:  1. a 10 % uniform probability distribution was used for all o f the parameters  44 2.  a 20% uniform probability distribution was used for the C P U E parameters whilst a 10 % uniform probability distribution was used for the other two parameters.  In order to compare the 1992-1994 catch data to the 1979-1982 catch, the 1979-1982 catch estimate was also subjected to Monte Carlo simulation and analysis.  Distribution of Catch, Effort and CPUE in the Fishery  Once the catch and the effort were estimated, several aspects o f the distribution o f catch, effort and C P U E in the fishery were examined. Informative comparisons with similar data from the 1979-1982 study were then made. These fall under the following headings:  Distribution  of catch, effort and CPUE across gears  In this section, the distribution o f the total catch, effort and C P U E across different gears was examined. This was then related to the distribution between the large and small-scale sectors.  Comparative Analysis of Species Composition from Landings  Data  The overall species composition o f the total catch was estimated and compared to the 19791982 data. The species composition o f selected gears was examined and comparisons were made with the earlier data. Where possible, the changing patterns o f use o f fishing gear since 1979-1982 were also determined.  45 Species Composition and Distribution of the Catch by Fishing Gear and Season In the Fishing Gear Inventory, a survey was undertaken to determine the seasonal use o f fishing gears in San Miguel Bay. The results o f this are examined and compared to the seasonality o f the catch, the species composition and C P U E . The aim is to gain an understanding o f the seasonal nature and operation o f the fishery.  Status of the Major Species in San Miguel Bay  The C P U E s o f each o f the top ten species were compared across the major gears that catch them and compared to the 1979-1982 figures. The modal or mean lengths o f the fish in the catch were examined where possible. In addition, their seasonal abundance was examined.  Results  Analyses  of Trawl Survey  Data  The Species Composition of San Miguel Bay  In total, 55 valid trawls were made during the trawl survey over a period o f 10 months (September 1992 to June 1993). Although there was an intended standard trawl time o f 1 hour, (Cinco et al 1995) in practise there was considerable variability; the modal trawling time was  46 30-40 minutes (31 out o f 55 hauls) with mean o f 46 minutes and median o f 35. The number o f trawls made per month varied from 2 to 7 and trawling time per month varied from 2 hours to 6 hours.  A total o f 98 species, from 46 families were recorded and identified during the course o f the trawl survey. However, despite this large degree o f diversity, the leiognathid, Leiognathus splendens was easily recognised as the most abundant species (Table 2.1). It comprised 16.5% o f the total. Another leiognathid, Secutor ruconius was the second most abundant species and accounted for 11.1% o f the total abundance. In fact, over 60% o f the total C P U E was produced by only 10 o f the 98 species recorded in the trawl survey, as shown in Table 2.1. Three leiognathids are included in these 10 species.  In Table 2.2 the top ten families in San M i g u e l B a y account for 79% o f the trawl survey abundance. The families are the same as those represented in Table 2.1, with the addition o f the Tetraodontidae and a group, called here the Benthic Invertebrates. It is clear from these figures that the Leiognathidae are, by far, the most abundant family in the trawlable biomass of San M i g u e l Bay. The Trichiuridae, Sciaenidae and Engraulidae each have about 20% o f the abundance o f the leiognathids, whilst the rest o f the families shown in Table 2.2 are less than 14% as abundant as the leiognathids.  47 Table 2.1 The ten most abundant species in San M i g u e l B a y (1992-1994 Trawl Survey data). Family  Species Leiognathus splendens Secutor ruconius Trichiurus haumela Penaeid Shrimps* Otolithes ruber Scomberomorus commerson Stolephorus commersonii Nemipterus japonicus Drepane punctata Leiognathus bindus  Leiognathidae Leiognathidae Trichiuridae Penaeidae Sciaenidae Scombridae Engraulidae Nemipteridae Ephippidae Leiognathidae  % of total CPUE 16.5 11.1 7.4 4.9 4.4 4.3 3.7 3.0 3.0 2.8  Accumulative  % 16.5 27.6 35.0 39.9 44.3 48.6 52.4 55.4 58.4 61.2  * The Penaeid species are grouped together, following the procedure used in the 19791982 study.  Table 2.2 The ten most abundant families in San M i g u e l B a y (1992-1994 Trawl Survey data). Family Leiognathidae Trichiuridae Sciaenidae Engraulidae Penaeidae Scombridae Tetraodontidae Benthic Invertebrates* Nemipteridae Ephippidae  % of total CPUE 35.2 7.4 6.8 6.4 4.9 4.4 4.0 3.9 3.0 3.0  Accumulative  %  35.2 42.6 49.4 55.8 60.7 65.1 69.1 73.0 76.0 79.0  * Benthic Invertebrates include Bivalves, Shellfish, Brittlestars, Jellyfish and Nudibranchs.  48  Estimating Density and Biomass from the Trawl survey Data  The monthly catch rates and the results o f the density and trawlable biomass estimation without Monte Carlo simulation are given in Table 2.3. Using the most likely parameters, the •  2  mean annual density is 1.56 t/km . There is considerable variation in the monthly estimates from which this annual figure is estimated. Mean monthly density estimates range from 0.55 t/km in M a y to 3.34 t/km in October. 2  2  The Monte Carlo simulation produced a mean density estimate o f 2.72 t/km , with a 95% 2  certainty o f 1.21 to 8.67 t/km , and a standard deviation o f 2.96 (Table 2.4). This corresponds 2  to a mean biomass estimate o f 3304 t i f the San Miguel B a y area is taken as having a surface area o f 1,115 k m . The 95% certainty range was 1,351 to 9, 653 t with a standard deviation o f 2  3, 303. The corresponding mean biomass for an area o f 840 k m was 2, 281 t. Cinco et al. 2  (1995) estimated a density o f 1.96 t/km and a biomass o f 1, 646 tonnes (for an area o f 840 km), using the swept area method described above. This falls with the 95% certainty range o f the estimate produced here and can be reproduced by using a value o f 0.6 for either X i or X , 2  the escapement factor and the effective width o f the trawl respectively.  The results are very sensitive to the input parameters. The greatest sensitivity was to the value o f X i (the escapement factor) (93.6% o f variation), then to the value o f X  2  (the effective width  o f the swept area) (3.5%) and the least sensitivity is to L , the distance travelled in a one hour trawl (2.9%), measured by rank correlation. The parameter to which  49 Table 2.3 Monthly Catch Rate (Trawl Survey), Density and Biomass Estimates for San M i g u e l Bay, September 1992 to June 1993  Month  September October November December January February March April May June MEAN  Catch Rate (kg/hr) 69.64 82.63 47.02 18.89 30.51 27.03 31.58 14.94 13.48 49.34 38.51  Density (t/km ) 2.82 3.34 1.90 0.76 1.24 1.09 1.28 0.60 0.55 2.00 1.56 2  Area of San Miguel Bay 1115km 840km Biomass (t) 3142.9 2261.1 3729.5 2809.6 2121.9 1598.6 642.4 852.7 1037.4 1377.1 1219.9 919.0 1425.1 1073.6 674.4 508.0 608.3 458.3 2226.8 1677.6 1309.2 1737.8 2  Table 2.4 Results o f Density and Biomass Estimation in San Miguel B a y using Monte Carlo Simulation Statistic Mean Standard Deviation Lower 95% L i m i t Upper 95% L i m i t Range Width  Density (t/km*) 2.12 2.96 1.21 8.67 51.2  Biomass A=1115km 3,027 3,304 1,351 9,653 57,092  2  A=840km 2,281 2,489 1,019 7,290 43,011  2  50 the results were most sensitive, X i , is also the parameter which had the widest input range and the parameter for which there is least certainty. Indeed, given that the standard deviations produced by the Monte Carlo simulation are as large as the mean, there can be little certainty about any o f the estimates given above.  The density estimate o f 2.13 t/km from the 1979-1982 study was also estimated using the swept area method (Vakily 1982).' This value falls within the 95% certainty range o f the 19921994 estimate. However, given the uncertainty surrounding these estimates, it is not possible to comment on whether there has been a change in density or biomass since 1979-1982. Monte Carlo analysis o f the 1979-1982 trawl data would give a similarly wide distribution o f density estimates since the parameter to which the results is most sensitive, X i would remain the same.  Pauly (1982a) collated a table o f historical trawl surveys conducted in San Miguel B a y and calculated the density and biomass using the swept area method described above. The first trawl survey was conducted in 1948 (Warfel and Manacop 1950) in the month o f July. The survey consisted o f five drags o f approximately 1 hour duration using a 30 m, 400 H P trawler with a 10 cm meshed cod end. A l l o w i n g for differences in gear and mesh sizes, Pauly estimated a density o f 10.6 t/km and stock biomass o f 8,900 tonnes, figures he described as conservative. Although these figures should only be used as an indicator o f likely density (since the survey was limited to one month and there were differences in the gears used) the 1948 estimates do fall outside the confidence limits o f the 1992-1994 density and biomass estimates. This indicates that the density and biomass in San M i g u e l B a y have decreased since  51 1948. U s i n g the mean density estimate o f 2.72 t/km for 1992-1994 as a guide, the density in San M i g u e l B a y can be said to have decreased to around 26% o f the 1948 density.  The density estimates from the other trawl surveys described i n Pauly (1982a) ranged from 5.2 9  9  t/knT i n 1957-58 to 3.49 t / k m in 1977. These values fall within the 95% certainty range o f the z  1992-1994 density estimate. They therefore cannot be said to be different from the 1992-1994 estimate. In addition, most o f these surveys, like the 1948 survey, only took place over one month and should be interpreted with similar caution. The results suggest that there has been a downward trend in density with time. However, given the uncertainty o f density estimation using the swept area method shown above, the inconsistent sampling methodology between surveys and the sparsity o f seasonal coverage for the 1960s and 1970s surveys, it is not possible to make any conclusions about trends in density or biomass over the time period o f these surveys. O n the basis o f the data presented here, it is as likely as not that the total density and biomass in San M i g u e l B a y have not significantly changed since the late 1950s.  Given the inaccuracy o f the swept area method for density estimation, another, less direct means o f comparing biomass over time would be to use C P U E as a measure o f abundance. However, even this is fraught with difficulty since recent work concludes that for many fish, especially schooling fish, there is no direct relationship between C P U E and abundance (Hilborn and Walters 1992), that is, C P U E does not accurately track abundance. A dramatic decline in abundance can occur while C P U E remains stable (Pitcher 1997, Mackinson et al. in press). A decline in C P U E is a clear signal that abundance has decreased.  52 The C P U E o f the 1948 trawl survey was 636 lb/hr (Warfel and Manacop 1959), that is, 289 kg/hr. This is considerably higher than the largest C P U E o f 82.63 kg/hr in Table 2.3 above. The ratio o f the largest 1992-1994 C P U E to the 1948 C P U E is 28%; using the mean C P U E for 1992-1994, the ratio falls to 13%. So, using the C P U E data for comparative purposes, it appears that the 1992-1994 C P U E rate is about 13-28% o f the 1948 C P U E .  The mean C P U E rates in the 1979-1982 trawl data were 30.77 kg/hr for the period from March 1979 to February 1980 and 36.17 kg/hr for March 1980 to February 1981. These figures are both in close agreement with the 1992-1994 mean C P U E o f 38.51 kg/hr. These results indicate that there has been little change in the total C P U E since the early 1980s.  Longitudinal Comparison of Species Compositions from Trawl Survey Data  A more telling analysis o f changes in biomass over time can be had by examining the changes in species composition over time. The seminal work o f Pauly (1979a) on the G u l f o f Thailand and Andaman Sea fisheries demonstrated that changes in the species composition of a fishery can reflect changes in an ecosystem caused by fishing, potentially leading to ecosystem overfishing.  A comparison to the 1947 trawl survey data (Warfel and Manacop 1950; see also Cinco et al. 1995) and the 1979-1982 trawl data is made in Tables 2.5 and 2.6. The original species  53 recorded in the 1947 survey are used, plus those families which have substantially increased since 1947.  The relative abundance o f the leiognathids was twice as high in San M i g u e l B a y in 1947 as in 1992-1994 (Table 2.5). Furthermore, the catch rate has substantially decreased from 173 kg/hr in 1947 to 13.4 kg/hr in 1992-1994. These results correspond with Pauly's (1979a) conclusions about the species changes that occurred in the G u l f o f Thailand as a result o f a trawl fishery, that is, that the bulk o f small prey fish diminish at a much greater pace than the other species in the ecosystem. In San M i g u e l B a y , the Pristidae , sharks and rays, Pomadasydae, 6  Sphyraenidae, Ephippidae and Ariidae have all also markedly decreased in relative abundance and/or catch rate. Again, this is as predicted by Pauly, that is the rays and the medium predator fish, dependent on the small prey fish for food, decrease.  The relative abundance o f other groups, such as the Trichiuridae, Scombridae, and squids, have increased since 1947 (although the C P U E o f the Scombridae has decreased). These correspond to the pelagic fish group noted by Pauly as unlikely to be reduced by bottom trawl. Pauly also identified a group consisting o f species likely to increase as a result o f demersal trawl fishing. These include "r" strategists such as flat fish and benthivores. There are many species present i n the 1992-1994 trawl survey that fall into this category, including the Benthic Invertebrates, Psettodidae, crabs and Tetraodontidae. M a n y o f these were not recorded in either the 1947 or the 1979-1982 surveys. It is not clear whether these species  The Pristidae in the 1947 trawl survey consisted of one specimen weighing 400 pounds. Since it was the only sawfish caught during the 5 drags conducted in the survey, the abundance of sawfish may be less than suggested by the figure in Table 2.8  6  54 Table 2.5 Comparison o f Species Composition o f Trawl Survey data from 1947, 1979-1982 and 1992-1994.  Species Taxon  Leiognathidae Pristidae Sharks and Rays Pomadasyidae Sphyraenidae Sciaenidae Ephippidae Ariidae Synodontidae Mullidae Scombridae Carangidae Trichiuridae Engraulidae Penaeidae Mugilidae Squids Clupeidae Others SUM  1947 60.0 15.4 6.3 2.3 1.6 0.9 0.9 0.8 0.8 0.7 0.7 0.6 0.1  8.8 100.00  Composition %  Direction of Change  1979/80 31.1  1980/81 31.8  1992/94 35.2  -  -  -  0.7 0.7  0.5 0.3  -  -  12.2  10.8  -  -  0.7  0.1  -  -  0.4 2.7 2.1 19.5 5.9 7.1 2.8 4.4 9.7 100.00  0.4 0.9 3.9 21.7 6.6 4.7 3.3 3.0 12.0 100.00  0.0 0.2 0.8 6.8 3.0 0.2 1.6 2.4 4.4 2.8 7.4 6.4 4.9 1.6 1.5 0.3 20.5 100.00  Sources for historical data: Warfel and Manacop (1950), V a k i l y (1982).  u I I i I n t? i t? T? t? t t  I I i i i t  -  •  55 Table 2.6 Comparison o f Catch Rate from Trawl Survey data from 1947, 1979-1982 and 1992-1994.  Catch Rate kg/hr  Taxon  Leiognathidae Pristidae Sharks and Rays Pomadasyidae Sphyraenidae Sciaenidae Ephippidae Ariidae Synodontidae Mullidae Scombridae Carangidae Trichiuridae Engraulidae Penaeidae Mugilidae Squids Clupeidae Others SUM  1947 173.34 44.38 18.31 6.66 4.62 2.68 2.59 2.40 2.31 2.13 1.94 1.85 0.28  25.42 288.91  Change of Direction  1979/80 9.58  1980/81 11.50  1992/94 13.40  It  -  -  -  i  0.22 0.22  0.19 0.12  -  -  3.73  3.91  -•  -  0.20  0.03  -  -  0.11 0.84 0.66 5.99 1.83 2.18 0.86 1.34 3.01 30.77  0.15 0.32 1.42 7.84 2.40 1.68 1.20 1.09 4.32 36.17  0.02 0.08 0.30 2.70 1.49 0.06 0.60 0.91 1.65 1.07 2.81 2.42 1.94 0.16 0.58 0.11 7.66 37.97  -  Historical data sources: Warfel and Manacop (1950), V a k i l y (1982)  I i i  n i?  i i? i?  u u t  i i  I uI -  56 were not recorded because they were not present in the previous surveys or because they were not identified and therefore classified in the "others" category.  In general then, the species composition changes seen in San Miguel B a y confirm the pattern outlined by Pauly (1979a). Certainly by the time o f the 1979-1982 survey, San M i g u e l B a y could be said to be suffering from ecosystem overfishing. However, the results do not indicate that the situation has necessarily worsened since then. The changes in species composition and in catch rates that have occurred between 1979-1982 and 1992-1994 are less clear. However, a note o f caution should be sounded here regarding the 1979-1982 trawl data. These data came from the commercial fishery, not a systematic trawl survey such as was carried out in the 1992-1994 study, or indeed in 1947. Thus, there is a likely bias in the species composition o f the 1979-1982 trawl data. W i t h this in mind, some groups, such as the Ephippidae, Synodontidae, Mullidae, carangidae and Trichiuridae follow a clear upwards trend from 1947 to 1992-1994. However, the relative biomass and catch rate o f the leiognathids, for example, have marginally increased since 1979-1982. The relative biomass and catch rate o f others species which were not recorded in the 1947 survey , such as the Penaeidae, Engraulidae, Clupeidae and squids, have decreased since 1979-1982. This suggests that those species which succeeded the traditional species outlined above are now themselves being succeeded. This mixed picture which may be due to differences i n sampling methodology rather than real differences.  These species may have been present in the Bay, but not caught in the 1947 trawl survey because of the large mesh size used in the trawl net. 7  57 The Sciaenidae are worth a separate note. The relative biomass o f the Sciaenidae increased from 0.9% in 1947 to 10.8-12.2 % in 1979-1982, then decreased by almost 50% to 6.8% in 1992-1994. However, the catch rate o f 2.7 kg/hr in 1992-1994 is approximately equal to the catch rate o f 2.68 kg/hr in 1947. The catch rate in 1979-1982 was higher, at 3.73-3.91 kg/hr.  A s noted above many species are recorded in the 1992-1994 trawl survey which were not previously recorded. Does this mean that there is a greater diversity in 1992-1994 than there was in earlier years? Without definitive data for the earlier years it is not possible to answer this question categorically. However, i f the species/families are matched one for one in 1947, 1979-1982 and 1992-1994, the "Others" category has the highest abundance in 1992-1994 and the highest catch rate in 1947 (Tables 2.5 and 2.6). So, although there has been an overall reduction in C P U E since 1947, the abundance o f the "Others" groups has increased and so has, therefore, the abundance o f "Other" groups which are now identified in the 1992-1994 surveys (see Appendix for a list o f species present i n San Miguel Bay).  Seasonal Analysis of the T r a w l Survey D a t a  Table 2.3 above gives the monthly catch rate from the trawl survey. It is unfortunate that the survey did not cover the full 12 months o f the year, and even more so that the 2 months that are missing are reputed to be the most productive (Villanoy et al. 1995). However, despite  8  This is also seen in the landings survey.  0  1  -I  i  1  1  1  1  T  ,  ,.  1  SEP  OCT  NOV  DEC  JAN  FEB  MAR  APR  MAY  JUN  SEP  OCT  NOV  DEC  JAN  FEB  MAR  APR  MAY  JUN  Figure 2.3 Seasonal variation in the CPUE of the major groups in the 1992-1994 Trawl Survey.  59 the missing data, there are clear trends through the year (Figure 2.3).  The total C P U E is high in September and October, but begins a decline (beginning o f the northeast monsoon) to reach a low in December. This low C P U E prevails until M a y , creating a low C P U E plateau. In M a y , the C P U E rises again to June and the trend indicates that it would continue to rise to meet the September high. The trawl C P U E data from 1979-1982 (figure 3 in V a k i l y 1982) follows a similar pattern and supports this assumption. In September/October the C P U E decreased until November/December when it began to increase. In this case there is a C P U E high plateau from February/March until September/October.  The trend described above is determined by the leiognathids, and, to a lesser extent, the "Others" group. (Figure 2.3). The seasonal pattern for the other top ten Families is more variable. The Sciaenidae C P U E is l o w during March to June and variable between September and February. The Penaeidae C P U E peaks in October and then steadily declines through to A p r i l , M a y and June when it is very low. The Engraulidae C P U E peaks in January and June. The Trichiuridae C P U E basically increases from September to December (with a dip in November) and remains high until M a r c h when it decreases to a low rate in June. In contrast to the other groups represented in Figure 2.3, the Trichiuridae are most abundant during the northeast monsoon. The carangidae, the Scombridae, the Gobiidae and the Benthic Invertebrates show no clear trend. The C P U E o f these groups is sporadic.  Unfortunately there is no monthly trawl C P U E by species from the 1979-1982 study available in the literature with which to compare these results. The results indicate that for most o f the  60 more abundant species in San M i g u e l Bay, C P U E decreases during the northeast monsoon and rise to a higher level during the summer months.  Estimation of M o r t a l i t y  O f the 98 species in the trawl survey, it was only possible to estimate the growth parameters and mortality for 6 species. These were the most abundant species in the trawl survey, that is, Leiognathus splendens, L. bindus, Secutor ruconius, Scomberomorus  commerson,  Otolithes  ruber and Trichiurus haumela. The length frequency data for the other species in the trawl survey were not sufficient to define their growth parameters, even with the aid o f generic values from the literature as a guide.  Leiognathus  splendens  The length frequency analysis gave a range o f combinations o f L o o and K for Leiognathus splendens. U s i n g the surface scan option, a "banana" o f correlated L o o and K values, with high goodness o f fit resulted, stretching from an Leo o f 13 - 16 c m and K values from 1.5-1.0 year" . 1  These are in agreement with values in the literature. The average values from the literature were approximately Loo = 14 c m and K = 1.0 year" . Further analysis o f the data using the 1  automatic search routine and the K-scan produced estimates o f L o o = 13.2 cm and K = 1.45 year" . Both combinations o f growth parameters were used to refine the estimates by allowing 1  for selectivity producing, for each combination two further estimates o f L o o and K plus the  61  average values from the literature. Table 2.7 below lists the Leo and K estimates. Z estimates from the pooled original length-frequency sample were then calculated from the growth parameters and 95% confidence limits generated.  Using the Beverton and Holt mean length method it was not possible to definitively judge which o f two points were the cut-off length above which all fish could be considered to be fully selected. For this reason, the calculations were made using both points. The results in Table 2.7 for the Beverton and Holt method are similar to those o f the Length Converted Catch Curve, although using the slightly higher L o f 6.5 cm produces a Z estimate which is 2.6 times c  higher than that obtained by using a L o f 6.0 cm. This indicates that this method is sensitive to c  the value used as L . c  The catch/biomass estimate o f Z is considerably lower than the Z estimates just described: M = 2.61 year"' and F = 2.60 year" . The Wetherall et al. method did not produce realistic values 1  o f L o o and is thus not included with the results. Since the Length Converted Catch Curve and the Beverton and Holt's methods give similar results, the average o f these, Z = 11.3 year" , was 1  assumed to be a representative, i f high, estimate o f total mortality for L. splendens.  62  Table 2.7 Results o f the length frequency analysis and mortality estimation for splendens Growth  Parameters  Length Converted Curve  Catch  Beverton and Holt (1956) L = 6.0 cm Z (year 11.3 11.2 8.2 11.3 8.7 10.2  Z (year"^ 11.5 11.7 8.8 12.5 9.6  Lower CI 8.9 9.1 6.9 9.9 7.6  Upper CI 14.0 14.2 10.7 15.0 11.6  L = 6.5 cm Z (year 14.1 14.1 10.4 14.3 11.0  10.8  -  -  12.8  c  Loo (cm) 13.0 13.2 13.6 14.0 14.0  K (year" ) 1.5 1.45 1.0 1.3 1.0 1  Mean Estimate o f Z  Leiognathus  _1)  c  _1)  Table 2.8 Results o f the length frequency analysis and mortality estimation o f Secutor ruconius. Growth Parameters  Length Converted Curve  Catch  Z (year )  Lower CI  Upper CI  7.83  6.75  8.91  Beverton and Holt (1956) L =4.5 c m Z (year ) 6.44 c  Loo (cm) 8.4  K (year ) 1.25 -1  -1  Powell Wetherall Plot L =4.5 cm c  Z (year ) 6.78  1  -1  Table 2.9 Results o f the length frequency analysis and mortality estimation o f bindus Growth Parameters  Length Converted Curve  Catch  Z (year" )  Lower CI  Upper CI  7.96  6.56  9.37  ' Beverton and Holt (1956) L =3.5 cm Z (year" ) c  Loo (cm) 12.2  K (year" ) 1.2 1  1  Leiognathus  1  7.39  L =4.5 cm Z (year" ) c  1  8.4  63 Secutor ruconius  The length frequency analysis indicated that the growth parameters o f S. ruconius lie in the range o f Loo = 8.3 - 8.9 cm and K = 1.0 - 1.3 year" . Further analysis produced the optimal 1  combination o f Loo = 8.3 cm and K = 1.3 year" , and, after allowing for selectivity, Leo = 8.4 1  cm and K = 1.25 year" . The latter is in close agreement with the average growth parameter 1  values for S. ruconius in the literature, Lco=8.4 cm and K=1.41 year" . The Length Converted 1  Catch Curve gives a Z estimate o f 7.83 year" . 1  The Beverton and Holt method gives a lower estimate o f Z then the estimate by Length Converted Catch Curve and indeed does not fall within the confidence bounds (Table 2.8). However the Z estimate o f 6.78 year" produced by the Powell-Wetherall plot does lies within 1  the bounds o f the confidence limits o f the Length Converted Catch Curve estimate. A l l three estimates could be said to be comparable, in the range o f 6.5 - 7.8 year" . The catch / biomass 1  Z estimate o f 3.29 year" is much lower than this. This is because the estimated fishing 1  mortality o f 0.14 year" is low, due to the small catch recorded in the landing survey . For this 1  9  reason, the results from this method were not included in the Table 2.8. The overall total mortality derived for S. ruconius was Z=7.02 year" , the average o f the first three estimates. 1  This is something of an anomaly since S. ruconius is well represented in the trawl survey. Compared to the high Z estimates from the other 3 methods, a higher catch would be expected.  9  64 Leiognathus  bindus  The analysis o f the Leiognathus bindus length frequency data produced an estimate o f Z = 7.93 year" . It was not possible to discriminate any one set o f growth parameters from the 1  length/frequency data alone, although a "banana" o f values were identified using the surface scan routine. The average values from the literature were included however in the optimal range o f parameter combinations and an Leo = 12.2 c m and K = 1.2 year" were used for the 1  Length Converted Catch Curve and the Beverton and Holt Method (Table 2.9). T w o cut-off points were used for the Beverton and Holt method, giving an average Z o f 7.9 year" . Neither 1  o f the two other methods produced credible results. The catch/biomass method gave no result because the recorded catch o f L. bindus in the B a y is minimal. The Powell-Wetherall Plot produced a higher L o o o f 14.9 cm, but it was not possible to estimate K .  Scomberomorus  commerson  There were very few data for Scomberomorus commerson (123 observations over the 10 month period, with some months having as few as two records). However, the average growth parameters values from the literature fitted the length frequency data well. These generic values were used as the growth parameters for S. commerson for the mortality estimation. The results are given in Table 2.10.  The results are not very consistent. The Length Converted Catch Curve gives a Z value o f 5.27 year" , intermediate between the two other results. The catch / biomass Z estimate is low, and 1  65  outside the confidence limits o f the Length Converted Catch Curve. However, the Beverton and Holt total mortality estimate o f 9.64 year" is high. It is dependent on the maximum length 1  in the sample used, L  m a x  . The longer length groups are poorly represented in this sample and  were not included in the analysis. Reducing L  m a x  to the longest well represented length group,  ( T L = 24 cm) produces a high estimate o f Z . The mortality estimate for S. commerson is thus very uncertain. The length frequency data give widely different results when different methods are used.  Otolithes ruber  It was not possible to discriminate a set o f growth parameters with the length frequency data from the trawl survey. Growth parameters from the 1979-1982 study o f San M i g u e l B a y (Navaluna 1982), were fitted to the Otolithes ruber length frequency data. However, the fit was not good and the L o o o f 35.5 cm seemed to be too low. In the 1979-1982 length frequency data, relative age classes up to 6 years old were represented (Navaluna 1982). In the current data set, the oldest fish in the sample had a relative age, (t-to) o f 2 years.  Other values for L o o and K were taken from the literature and averages calculated as described above. A combination o f L o o o f 44.8 cm and K=0.40 year" gave the best fit to the data, after 1  allowing for selectivity. The K-scan indicated that there was a reasonable estimate o f K , given L o o . Scanning the data for other fits o f L e o and K did not improve the fit so the averages from the literature were used for mortality estimation.  66  Table 2.10 Results o f the length frequency analysis and mortality estimation o f Scomberomorus commerson. Growth Parameters  Length Converted Catch Curve  Beverton and Holt (1956) L =18 cm Z (year" ) 9.64 c  Loo (cm) 160  K (year" ) 0.2 1  Z (year" ) 5.27  Lower CI  Upper CI  1  1  2.97  7.56  Table 2.11 Results o f the length frequency analysis and mortality estimation o f Otolithes ruber. Growth Parameters  Length Converted Catch Curve  Beverton and Holt (1956) L =6 cm Z (year" ) 4.5 c  Loo (cm) 44.8  K (year" ) 0.398 1  Z (year" ) 4.27  Lower C I  Upper C I  1  1  3.69  4.85  Table 2.12 Results o f the length frequency analysis and mortality estimation o f Trichiurus haumela. Growth Parameters  Length Converted Catch Curve  Beverton and Holt (1956) L =37.5 cm Z (year" )  Z=M+F  c  Loo (cm) 65.3  K (year" ) 0.43  Z (year" )  Lower CI  1.63  1.34  1.94  1.90  135  0.26  4.40  3.63  5.18  4.68  1  Upper CI  1  1  Z (year" ) 3.84 (0.84+3) 3.45 (0.45+3) 1  67 The Length Converted Catch Curve and the Beverton and Holt method were the only two methods which produced credible results for the O. ruber data and these are give i n Table 2.11. The catch / biomass gave a total Z o f over 18 year" . Whilst the estimates using the other 1  two methods are high, this is too high to accept as a reasonable estimate. The PowellWetherall Plot produced unrealistic values for L o o , and it was not possible to determine K from these values. The overall estimate o f Z is taken as the average o f Z = 4.27 year" and Z = 4.5 1  year" . This value is considerably higher than the 1979-1982 total mortality values o f 1.891  2.67 year" (Navaluna 1982). 1  Trichiurus  haumela  The analysis o f the length frequency data o f 7 1 haumela did not enable discrimination o f any one set o f parameter values. Growth parameter values for T. haumela in the literature are variable. L o o estimates range from 34.4 to 154 cm and estimates o f K from 0.2 to 0.7 year" . 1  However, although variable, the estimates from the literature fell into three categories: those where the L o o was less than the L m a x o f T. haumela - these were not used; two sets o f data from M a n i l a Bay, the Philippines, with low L o o and high K and six estimates with high L e o and low K from other geographic areas. Their (j)' values are 3.3 and 3.7 respectively. The average growth parameters for the latter two groups are given in Table 2.12. Essentially, the data suggest that T. haumela in Manila B a y are smaller, but faster growing fish than those elsewhere. The mortality analysis was conducted for both sets o f parameters and the results are given in Table 2.12.  68 N o results were obtained with the Powell-Wetherall Plot because it was not possible to select any single K-value for a given L o o . However, the Length Converted Catch Curve and the Beverton and Holt method gave similar results for both sets o f growth parameters, in each case the Beverton and Holt estimate falling inside the confidence intervals o f the Length Converted Catch Curve. The catch / biomass method gave estimates very similar to one another. These results are also similar to the Length Converted Catch Curve and Beverton and Holt results for the slow growing, larger growth parameter set.  The results using the growth parameters which were not from the Philippines produce more consistent results than those from Manila Bay. However, since both M a n i l a B a y and San Miguel B a y are shallow, heavily exploited bays in the same geographical area, the Manila B a y parameters were assumed to be representative o f T. haumela in San M i g u e l Bay. The M a n i l a Bay growth parameters produce lower Z estimates. These were checked against available estimates o f Z in the literature and were comparable. The mean Z is 2.5 y e a r .  Y i e l d per R e c r u i t Analysis  Yield-per-recruit analyses were carried out for the six species above. Where there was more than one set o f growth parameters and no conclusion about which was best, generic values were used. The Zs from the Length Converted Catch Curve were used in the analysis since the selection curves were generated from the Length Converted Catch Curves. The results are shown in Figure 2.4.  69  Leiognathus splendens 0.016  Otolithes ruber  T  E  (=F/Z)  E(=F/Z)  Figure 2.4 Length-based yield-per-recruit curves, showing yield-per-recruit against exploitation. The fine vertical line represents the optimal exploitation rate and the thick line is the current exploitation rate.  70 The calculated mortality rates above are high and this is reflected in the yield-per-recruit curves and the current state exploitation. The results indicate that five o f the six species are overexploited. Optimal exploitation rates are around 0.5 (Gulland 1971) or below 0.5 (Pauly 1984). Actual exploitation rates range from 0.66 (71 haumela) to 0.92 (S. commerson). The sole species studied which is not overexploited is Secutor ruconius, which is currently exploited at the optimal rate. Since these species make up a large part o f the catch, this indicates that much o f the fishery is overexploited. The exploitation rates o f O. ruber, L. splendens, L. bindus and S. commerson are all more than 50% greater than the optimal rate. The results confirm the earlier results that there could be great increases in yield i f fishing mortality were reduced, particularly for the leiognathids and O. ruber, principal components o f the trawl survey and catch . 10  Clearly then, fishing is having a large impact on San M i g u e l Bay. The results o f the fishery analysis are now examined in detail.  The conclusions drawn from the mortality and yield-per-recruit analyses are very similar to those of Cinco and Silvestre (1995). They conducted a similar analysis although they managed to obtain results for a total of 15 species, although in less detail then presented here. 10  71  Analyses  of Fishery  Data  E s t i m a t i n g C a t c h and Effort  Effort  The results o f Silyestre et al. (1995) are shown in Table 2.13. For each gear type, the number o f gears, number o f trips per year and total effort are given. The comparative figures for 19791982 are also given and are shown graphically in Figure 2.5. Three things are immediately clear:  1. The large, medium and baby trawlers have all substantially decreased, both in absolute numbers and time spent at sea since the early 1980s"; 2. The number o f gillnet gears has increased by almost 100%, and the effort per unit has increased; 3. Total effort has increased in the fishery.  Since total effort (in terms o f total number o f trips) has increased and large-scale effort has decreased, the conclusion must be that there has been a substantial increase in effort by the  '' These results contrast with the findings of Smith and Salon (1987) who conducted a survey of key informants in San Miguel Bay in the mid-1980s. They reported that effort by the medium and baby trawlers had increased by 50% since 1981. They also reported that the number of gillnetters had increased, particularly the number of nonmotorised gillnetters.  72 Table 2.13 List o f Fishing Gear in San M i g u e l B a y and a comparison o f Gear Number and Effort in 1979-1982 and 1992-1994. Gear Type  Bicol Name  No. of units 1979/82  -  Large Trawl M e d i u m Trawl Baby Trawl  No. of trips per year 1979/82 1992/94  1992/94  Effort (trips per unit*units) 1979/82 1992/94  30  1  6  8  180  8  17  4  128  57  2176  228  72  50 260  128  133 202  9216  6650  35908  52520  M i n i Trawl  Ink-ink  Shrimp Gillnet  Lait  Surface Gillnet  Palataw  470  99  115  182  54050  18018  Bottom-set Gillnet Shark Gillnet  Palubog/ Patundag Pamating/ Pandaracol Pangasag  288  50  162  208  46656  10400  30  25  94  107  2820  2675  257  343  174  190  44718  65170  300  538  234  247  70200  132886  Crab Gillnet  188  651  Ordinary Gillnet Panke Hunting Gillnet  Timbog  Other Gillnet  —  Stationary Lift Net Crab Lift Net  Bukatot  191  — —  78120  120  288  —  220  —  63360  676  —  94  —  63544  171  60  53  115  9063  6900  71  164  132  192  9372  31488  Bintol  Set Longline  Kitang  103  236  120  156  12360  36816  Handline  Banwit  424  316  120  96  50880  30336  Filter Net  Biacus  60  260  225  240  13500  62400  Fish Corral  89  123  209  106  18601  13038  634  245  150  168  95100  41160  Fish Trap  Baklad/ Sagkad Hud-hud/ Kalicot/ Sakag Bubo  106  225  120  120  12720  27000  R i n g Net  Kalansisi  —  2  —  120  —  240  Pullnet  Bitana  —  3  —  224  —  672  Fish Weir  Sabay  Scissor Net  /Padbit  Stationary Tidal Ambak Weir Beach Seine Sinsoro  2  1  144  144  288  - 144  11  24  308  308  3388  7392  Spear G u n  51  95  156  156  7956  14820  3379  4739  3137  3913  499992  765985  TOTAL  5  Antipara  -  168  840  Table compiled from Pauly et. al (1982), Silvestre and Cinco (1992 ) and Silvestre et. al (1995).  140000  T  120000 +  Figure 2.5 Comparison of effort between 1979-1982 and 1992-1994 for 20 types of fishing gear in Miguel Bay. Effort is estimated from the number of trips made by each gear type per year times the number of units of each gear type (Silvestre et al. 1995).  74  small-scale sector o f the fishery. This is seen in the increase in gillnet gears, crab gear, filter nets and "others" gears.  In addition, it is also apparent that there is an increase in the diversity o f small-scale gears. In particular, the hunting gillnet (timbog) was not listed in the 1979-1982 study, nor were pullnets, ringnets or some o f the other gillnets. It is possible that these gears were present earlier, but were subsumed into larger categories. However, the hunting gillnet was not previously described (Pauly et al. 1982, Silvestre et al. 1995).  Catch  The estimated annual catch for 1992-1994 was 15, 871 tonnes. This is 3, 262 tonnes lower than the 1979-1982 figure o f 19,133 tonnes (Pauly and Mines 1982), and 984 tonnes lower than the estimate o f 16,855 tonnes by Silvestre et al. (1995)  . These figures indicate that the  total catch in San M i g u e l B a y has decreased since 1979-1982.  The results o f the Monte Carlo catch analysis using Crystal B a l l are presented in Table 2.14 for 1992-1994 and 1979-1982. U s i n g a 10% uniform input probability distribution for C P U E , vessels and trips, the 95% certainty range for the catch estimate does not include the estimate of 16, 855 (Silvestre et al. 1995), or the 1979-1982 mean catch. Similarly, the 95%  The difference between this estimate of 15,871 and that of Silvestre et al. (1995) is due to different CPUE values, particularly for the baby trawlers: despite various data manipulations, it was not possible to reconcile these figures. 12  75 Table 2.14 Results o f the Monte Carlo Simulation o f the catch estimate (tonnes) for 19921994 and 1979-1982.  Year  Probability Distribution Mean  Lower Limit  Upper Limit  Standard Deviation  Range Width  15,872  15,022  16, 806  452.1  2,994  20% uniform probability distribution C P U E  15,836  14,556  17, 267  691.7  4, 408  10% uniform probability distribution  19,078  17, 660  20, 605  770  5,086  20% uniform probability distribution C P U E  19,080  17, 040  21,360  1,090  7, 186  1992-1994 10% uniform probability distribution  1979-1982  95% Certainty Range  76  certainty bounds o f the 1979-1982 catch estimate (10% uniform distribution for all parameters) does not include the 1992-1994 mean catch estimate. The 95% certainty ranges o f the two estimates do not overlap at all.  Greater uncertainty was introduced to the analysis by increasing the variation in the C P U E probability distribution to 20%. This had the effect o f widening the range o f estimates. In this case, the 95% certainty ranges just overlapped, but as above, the mean catch estimates for the two time periods still lie outwith the other's 95% certainty range.  The Monte Carlo analysis does not reveal how much uncertainty there is in the estimate o f catch: it demonstrates the impact that including uncertainty has on the estimate. The uncertainty o f the C P U E parameter was increased because it was considered to be the most uncertain parameter.  C P U E was calculated from catch and effort data in the landings survey which took place from July 1992 to June 1993. However, not all o f the gears, nor all o f the specified landing sites, were systematically covered over the whole o f this period. Some months were only represented by one or two samples (see below). However, although increasing the uncertainty o f this parameter does increase the range o f possible values for the catch estimates, it is clear that there has been a decrease in total catch since the early 1980s (see also Silvestre et al. 1995).  77  T h e D i s t r i b u t i o n of C a t c h , C P U E and Effort Across Gears  The distribution o f the catch in the fishery in 1992-1994 and 1979-1982 is shown in Figure 2.6. and the distribution o f C P U E is given in Figure 2.7. Despite quite wide 95% confidence limits (using the results from run 2 in Table 2.14), there are very few cases o f overlap between the catch in 1992-1994 and 1979-1982. The only gears where there is some overlap are the ordinary gillnet (panke ), the shark gillnet (pamating), the crab lift net (bintol) and the lift net (bukatot).  Some considerable changes have occurred in the distribution o f catch since the early 1980s. For instance, the catch o f the trawling sector has decreased for all gears. In the case o f the large and medium trawlers, the catch declined dramatically to less than 20% o f the 1979-1982 estimate, whilst the catch o f the baby trawlers and the mini-trawlers declined to about 50% o f their previous value. The decrease in catch o f the large and medium trawlers mirrors the decrease in effort seen in Figure 2.5. However, a comparison o f C P U E (Figure 2.7) indicates that there was little change in C P U E for the large trawlers and that it increased for the medium 13  trawlers' \ In the case o f the baby trawlers, there was a decrease in catch, in effort and in C P U E . However, despite a large increase in effort by the mini-trawlers, there was a marked decrease in their catch and in C P U E .  The CPUE estimate is based on one sample per month from July to April, excluding November and January. The estimated catches for July to September were higher than those in the subsequent months by a factor of 10. Since the whole year was not represented in the sample, and one sample was taken per month, both the CPUE figure and the catch figure for the medium trawlers should be treated as quite uncertain. A CPUE estimate based on the samples from October to April produce a CPUE of 615 kg per trip, a figure more in line with that from 1980/81. 13  78  )8U||!0  >wens  eui|6uo-| jes  •— T J  19N  U ,3  si qBJQ  & «dBJX i|sy  c3  o  aunpuEH  «r< <2  cu oo  leuiiio qejQ 03  CO  (D  CD  M 00 — w  c *-  SJ8U1Q  CO  J9N JOSSpS  I  ha  © a  © -g CN  CM 00  jauiiio jes-ujojioa  g  ON i n •7 a s ( N CD ON - C ON ~ TJ C C3 (N oo ON  a  CD co  a CO  u 03  |MBJ] wmpey\| ON  g  sjaunio jaujo  ecu  jauiiio doiuus  %H  CD  jauiiio  BujiuriH  jaunio  AJBUIPJO  |MBJI  •° tj  -TO • -a c b  AqEg  «  S3 . 2 3 CL, C  S |MBJ}-!U!IA|  CD  cu  5 i  u  ^ TJ CM U cu eS  (sauuoi)  qojBO  60  \S CO  79 The total catch by the gillnet sector increased. This was not due to an increase in the catch o f the ordinary gillnet (panke), the main type o f gillnet used in San Miguel Bay. In 1979-1982 the ordinary gillnet represented 66% o f the gillnet catch; in 1992-1994 it represents 37%. There has been an absolute reduction in the catch and C P U E o f the ordinary gillnet, despite an increase in overall effort since 1979-1982 . The increase in total gillnet catch noted is largely 14  due to the use o f gears which were not recorded in 1979-1982. For example, there is a large catch by the hunting gillnet (timbog), the shrimp gillnet (lait) and other gillnets. The catch o f the gillnets recorded in 1979-1982 all decreased by 1992-1994, with the exception o f the crab gillnet (pangasag) and the shark gillnet (pamating). The increase in the catch by the crab gillnet is due to an increase in effort and an increase in C P U E . Shark gillnet effort decreased slightly, but an increase in C P U E appears to have enabled an increase in annual catch. The catch o f the bottom-set gillnet (palubog) and surface gillnet (palataw) decreased: however, effort was also severely reduced (Table 2.13) and C P U E in fact increased (Figure 2.7).  There are three main types o f fixed gear in San M i g u e l Bay, the lift net (bukatot), the fish corral (sagkad) and the filter net (biacus). A s noted above, the filter net effort has increased since 1979-1982, whilst fish corral and lift net effort have both decreased by about 33%. The catch o f the lift net has also increased, but this figure is based on only one sample. C P U E o f the filter net and the fish corral both decreased: an increase in effort by the former produced an overall increase in the filter net catch, but the catch o f the fish corral decreased.  There is however, an overlap in the 95% certainty ranges for the two time periods.  Figure 2.7 Comparison of CPUE between 1979-1982 and 1992-1994 for 20 types of fishing gear in San Miguel Bay. CPUE is estimated from the catch and effort recorded in the landings survey. See text for further details.  81 The crab lift net (bintol) catch remained relatively constant between 1979-1982 and 19921994. Effort increased by 300% and C P U E decreased. The catch o f the set longline (kitang) increased from 25 tonnes in 1979-1982 to 514 tonnes i n 1992-1994. Effort increased by some 300% and C P U E increased from 2 kg per trip to 14 kg per trip. A t the same time, the catch o f the handline (Banwit) decreased to 25% o f its 1979-1982 value, effort decreased by 40% and C P U E decreased by 50%. The push net (kalicot, huh-hud) catch increased by over 100%' . 5  The fish trap (bubo) catch decreased by 40%, the effort increased and the C P U E decreased. However, as with the lift net, these catch and C P U E estimates are based on one sample.  In sum then, the general trend in the distribution o f catch in San M i g u e l Bay, since 1979-1982, has been a decrease in the share o f the total catch by the large-scale sector and an increase by the small-scale sector. This is shown more clearly in Table 2.15. The gillnetters almost doubled their share o f the catch whilst the large scale sector, or trawlers' share decreased by more than 50%. I f the mini-trawlers are included in the large scale sector, the decrease remains as great. W i t h the exception o f the mini-trawlers, the catch share o f all small scale gears, as grouped below, has increased.  Meanwhile the C P U E , and in many cases effort, o f the gears recorded in the 1979-1982 study has decreased, whilst gear diversification appears to have led to increased C P U E for some non-trawl gears in the small-scale sector.  5  The catch estimate was based on only one sample however.  82 Table 2.15  Catch distribution by Gear Group in 1979-1982 and 1992-1994. Gear  Trawlers M i n i Trawlers Gillnets Fixed Gear Line Gear Others Totals  Catch (t) 1979-1982 1992-1994 6,897 2766 4,779 2,165 4,855 6,639 1,416 2,125 571 228 920 1,605 19,095 15,871  % of Catch 1979-1982 1992-1994 17.4 36.1 13.6 25.0 25.4 41.8 13.4 7.4 1,2 3.6 10.1 4.8 100 100  Table 2.16 The ten most abundant species in the Total Catch o f San M i g u e l B a y 1992-1994. Species Sergestid shrimp Otolithes ruber Penaeid Shrimp* Trash fish** Leiognathus equulus Dendrophysa russelli Portunus pelagicus Trichiurus haumela Stolephorus commersonii Stolephorus indicus  Catch (t) 2736.52 2349.03 1466.77 991.82 925.54 777.14 774.47 664.60 454.02 452.25  % of Catch Accumulative % 17.61 17.61 15.12 32.73 9.44 42.17 48.56 6.38 54.52 5.96 5.00 59.52 64.50 4.98 4.28 68.78 2.92 71.70 74.61 2.91  * The Penaeid species are grouped together, following the procedure used in the 1979-1982 study. ** The Trash Fish are unidentified.  Table 2.17 The ten most abundant families in the Total Catch o f San M i g u e l B a y 1992-1994. Family Sciaenidae Sergestid Shrimp Penaeid Shrimp Leiognathidae Engraulidae Trash Fish* Portunidae Trichiuridae Mugilidae Carangidae  Catch (t) 3332.7 2736.5 1466.8 1452.5 1089.1 991.8 774.5 664.6 574.0 484.3  * The trash fish are unidentified.  % of Catch 21.5 17.6 9.4 9.3 7.0 6.4 5.0 4.3 3.7 3.1  Accumulative % 21.5 39.1 48.5 57.9 64.9 71.2 76.2 80.5 84.2 87.3  83  Comparative Analysis of Catch Composition  The species composition o f the total catch in San M i g u e l B a y is similar to the species composition o f the trawl survey data. Table 2.16 lists the ten most abundant species, which comprise 75% o f the total catch and in Table 2.17, the ten most abundant families, comprising 87% o f the catch are listed. The sciaenids and the sergestids are the most abundant o f all groups in the catch, each accounting for approximately 20% o f the total catch. The leiognathids, trichiurids, engraulids and penaeids are again some o f the most abundant families.  The main differences between the trawl and catch composition are due to species which are not selected by the trawl gear. The sergestid shrimp have the greatest abundance o f any single species represented in the catch. The crab, Portunus pelagicus, is the sixth most abundant species. The sergestids however, were not represented in the trawl survey and P. pelagicus accounted for less than 1% o f the total abundance. There was no "trash fish" category in the trawl survey, since all fish were identified . 16  The catch composition is similar to the 1979-1982 catch composition. The catch figures from 1979-1982 and 1992-1994 are compared in Figure 2.8. The categories used for the 1979-1982 data were used for ease o f comparison. The main decreases in catch occur in the sergestids, the engraulids, the clupeids, the mugilids, the squids, the leiognathids and the  There is another curious difference. Leiognathus bindus was quite abundant in the trawl survey but only a very minute quantity was recorded in the landings survey. L. equulus was very poorly represented in the trawl survey (less than 1% of the leiognathid abundance) and yet is apparently caught in large quantities. These differences may be due to misidentification. 16  4500 -n—,  •1 9 7 9 - 1 9 8 2  11992-1994  Figure 2.8 Comparison of the catch composition in 1979-1982 and 1992-1994  85 sciaenids. A few groups such as the crabs, carangids, trichiurids, sharks and rays, and Ariidae increased in abundance.  The miscellaneous/other group in Figure 2.8 decreased by around 20% o f the 1979-1982 catch level. However, the miscellaneous/other groups i n the 1979-1982 and 1992-1994 data are not necessarily the same. In the 1979-1982 survey, many species were not identified, but simply labeled as "miscellaneous". Pauly (1982a) noted that the large size o f the category o f miscellaneous species i n the 1979-1982 study made it virtually impossible to do species-byspecies assessments. H e recommended that in future studies a greater attempt should be made to identify the unidentified species. This has been largely achieved in the 1992-1994 study. Because o f this, many species appear i n the 1992-1994 data that were not recorded in the 1979-1982 data. In Figure 2.8, the 1992-1994 miscellaneous/other group is comprised o f 17 species present i n 1992-1994 which were not recorded i n 1979-1982 plus "trash fish" .  O f the species recorded in 1992-1994 which were not recorded i n 1979-1982, the greatest proportion consists o f demersal feeders such as the Tetraodontidae and Sillaganidae, and predators such as eels and Lates calcarifer. Because these species were not recorded i n the 1979-1982 data, it is not possible to compare them directly. Some information can be gleaned from the historical trawl survey data, however. This data indicated that the abundance o f some groups, such as the Sphyraenidae, the Synodontidae and the Mullidae has decreased since 1947 (Table 2.6). The abundance o f others, such as the Sillaginidae, the eels and the Tetraodontidae may have increased. The latter three groups now constitute a significant  Trashfishinclude four categories: assortedfish,mixed species,fishmeal and trashfish.All other species were identified in the survey, at least to the generic level. 17  86 proportion o f the total catch with landings o f 200 t, 144 t and 84 t respectively. These groups may thus have increased. Alternatively, the increase in catch o f these groups could be explained by the fact that these species are now sought and marketed, whereas they were not previously.  Species Composition and Distribution of the Catch by Fishing Gear and Season  It was noted above that the large-scale share o f the total catch has decreased since 1979-1982. O n a species or group by group basis, the same story is told. Table 2.18 shows the distribution of catch between the large and small-scale sectors. Regardless o f whether the total catch for each species or group in the table increased or decreased, the small-scale sector share increased, with one exception. Only the catch o f the carangids, which increased i n both sectors, increased proportionately more in the large-scale sector. The small-scale catch o f the penaeids increased by 83%, the leiognathids to 66%, Otolithes ruber to 98%, the other sciaenids to 90%, the Scombridae to 78% and the sharks and rays to 74%. The small-scale share o f the Miscellaneous/other species also increased.  The Large-Scale  Sector  The large-scale sector lands 17.4% o f the total catch and is less selective than most o f the small-scale gears. The medium trawl and baby trawl catch around 70 species o f fish, elasmobranchs and crustaceans. O f these, 6% and 19% respectively are trash fish.  87  Table 2.18 Distribution o f species between the large-scale Sector and the Small-Scale Sector. Taxon  Sergestid Shrimp Penaeid Shrimp Crabs Squids Clupeidae Engraulidae Carangidae Leiognathidae Mugilidae 0. ruber Sciaenidae Trichiuridae Ariidae Pomadasyidae Scombridae Sharks and Rays M i s c . spp. SUM  Catch (0 1979/82 LS SS 0 4473 582 462 120 380 235 15 202 593 1369 731 212 57 74 2013 329 860 1594 410 313 1155 254 70 6 39 21 13 47 28 36 9 1041 1389 6897  12138  Catch (t) 1992/94 LS SS 0 2736.5 248.9 1217.9 55.1 758.7 57.3 21.8 62.7 242.4 472.9 616.3 170.6 313.7 961.7 490.8 97.5 476.5 50.9 2298.2 100.1 883.5 181.9 482.7 8.4 99.0 0 1.3 129.0 34.9 92.1 263.6 742.9 1166.4 2867  12669  %  % 1979/82 LS SS 0 100.0 44.2 55.8 24.0 76.0 93.9 6.1 25.3 74.7 65.2 34.8 78.6 21.3 96.4 3.6 27.7 72.3 20.4 79.6 78.7 21.3 78.5 21.5 13.2 88.0 38.5 61.5 37.9 62.1 20.1 79.9 57.1 42.9 36.1  63.4  1992/94 LS SS 100.0 0 17.0 83.0 93.2 6.8 72.5 27.5 79.4 20.6 43.4 56.6 35.2 64.8 66.2 33.8 83.0 17.0 2.2 97.8 10.2 89.8 27.4 72.6 92.1 7.9 0 100.0 78.7 21.3 74.1 25.9 38.9 61.1 18.5  L S = Large-Scale Sector (Large, Medium and Baby Trawls plus the R i n g Net) SS = Small-Scale Gears(all other gears)  81.5  88 Collectively, this is about the same as the amount o f trash fish landed by the entire small-scale sector, but in the small-scale sector, the trash fish only account for 4% o f their total catch. The baby trawl landing o f trash fish is also an increase from 1979-1982 when 10% o f landings were trash fish.  The top ten species/groups landed by the trawlers account for 80%-85 % o f the catch and include many o f the families listed in Table 2.17. A s in the 1979-1982 survey, the main groups caught are trash fish, leiognathids and engraulids. Figure 2.9 compares the C P U E between 1979-1982 and 1992-1994 for the baby trawlers. The top ten species/groups from the 19921994 data are shown plus any species/groups which were important in the 1979-1982 data. The C P U E o f the leiognathids, engraulids, sciaenids, mugilids, clupeids and squid all decreased. Only the trash fish, the trichiurids, the carangids and sharks and rays increased. There was little change in the C P U E o f the penaeids and the crab, Portunus  pelagicus.  The trawl gears operate throughout the year. There is seasonal variation in the catch composition and the C P U E . The leiognathids, engraulids, carangids and clupeids are most abundant in the trawl catch during July, August and September, the period o f the Southwest monsoon. During these months, the total C P U E o f the baby trawler is an order o f magnitude higher than during the rest o f the year . The greatest diversity in the catch o f the baby trawler was from December to February, although this could be due to a higher number o f samples. During these months, the trash fish make up the greatest component o f the catch (22%-41%).  Months included in the landings survey for the baby trawler were: August, September, October, December (1992)and January, February, March and June (1993). Landings data for July to October and December (1992), February, March and April (1993) were collected for the medium trawl, and July and August 1992 for the large trawl. 18  89 160  j  140 - -  120--  5100  f  • 1979-1982 • 1992-1994  I  °  Figure 2.9 C P U E o f the top species and groups in the Baby Trawl catch from 1979-1982 and 1992-1994. The sharks and rays are combined following the procedure used in the 19791982 data.  120  T  100 4-  QornoctiH  •  1979-1982 Mini Trawl  Q h r i m n  Troe[i pJ5fl • 1992-1994 Mini-trawl(pamalaw)  P o n a o i r l a o  • 1992-1994 Mini-trawl(pamasayan)  Figure 2.10 C P U E o f the top species and groups in the M i n i Trawl catch from 1979-1982 and 1992-1994. The two types o f M i n i Trawler, the Pamalaw, which targets Sergestid Shrimps and the Pamasayan, which targets Penaeid Shrimps are shown for the 1992-1994 data.  90 The medium trawl catch was also more diverse during these months, although the sample size did not change.  The  Mini-Trawlers  The mini-trawlers land mainly sergestid and penaeid shrimps and they account for almost 14% of the total catch. There is a strong seasonal aspect to the operation o f this gear. T w o different operations o f the mini-trawler exist, the pamalaw which targets the sergestid shrimp from October to M a y (during the months o f the Northeast Monsoon) and the pamasayan  which  targets penaeid shrimp during A p r i l to October . Penaeids comprise 52% o f the catch o f the 19  pamasayan, 25% o f the catch are trash fish, and 67 species are caught in total. The pamalaw is a little more selective. It catches 45 species in total, 77% o f which are sergestid shrimp and 12%, trash fish.  Figure 2.10 compares the C P U E in 1979-1982 and 1992-1994. The data for 1979-1982 are collated, while the data from 1992-1994 are compared separately. The total C P U E o f the mini trawl decreased in 1992-1994 (Figure 2.7) and the C P U E o f the sergestids and the trash fish decreased too. The C P U E o f the penaeids increased from the 1979-1982 value when compared to the pamalaw, which targets the penaeids, and decreased for the pamasayan.  The mini-trawler pamalaw and pamasayan differ in the material and mesh size used in the trawl net (Tulay and Smith 1982). It appears that the catch and effort of the two forms were combined in the 1979-1982 survey. For comparative purposes, their catch and effort are combined here too. The effort statistics (Sylvestre et al. 1995) refer only to the mini-trawlers collectively. Their effort was differentiated here on the basis of the number of months of operation. The pamalaw is the mini-trawler main mode of operation for 7/12 months and the pamasayan the main mode of operation for 5/12 months. 19  Figure 2.11 1992-1994.  C P U E per month for the selected species in the M i n i Trawler catch o f  92 Both the mini-trawlerpamalaw and pamasayan  were sampled from August 1992 to June 1993.  The C P U E from these data confirm that the mini trawl pamalaw operate during the period o f the year'when the sergestid C P U E is highest (Figure 2.11), that is, during the Northeast Monsoon. However, the 1979-1982 C P U E value o f 117 kg/trip was not achieved in any month in 1992-1994. The other species/groups caught by this gear are incidental and their C P U E is lower from October to M a y than during the rest o f the year. The C P U E o f the penaeids is more variable. It peaks i n the mini-trawler pamasayan  catch in March and in the pamalaw catch in  September. There is no clear indication that the C P U E or abundance o f the penaeids is higher during A p r i l to October, the period when they are targeted by the mini-trawler  pamasayan.  The Gillnets  In terms o f magnitude o f catch and effort, gillnets are the dominant sector o f the fishery. They are diverse in their operation and land 42% o f the total catch. Table 2.13 lists eight types o f gillnet, but the actual total is higher, since some are combined. Between them all, they catch virtually all species in the catch and operate all year.  The ordinary gillnet, the hunting gillnet and the shrimp gillnet all catch sciaenids, engraulids and penaeids, identified in the list o f the top ten species/groups in the total San M i g u e l B a y catch. For each gear, Otolithes ruber is the most abundant in the catch. The shrimp gillnet is the most selective. Only six species are recorded in its catch. The ordinary gillnet catches 93  25 •  Ordinary Gillnet 1979-1982  •  Ordinary Gillnet 1992-1994  B Hunting Gillnet 1992-1994 20  •  Shrimp Gillnet 1992-1994  Q.  10  5  4  Figure 2.12 C P U E o f the top species and groups in the Ordinary Gillnet catch from 19791982 and 1992-1994. A l s o shown are the Hunting Gillnet and Shrimp Gillnet C P U E for 1992-1994.  94  species and the hunting gillnet 68 species. This highly diverse catch is comparable to the trawler catch. The top ten species/groups in the ordinary gillnet catch account for 94% o f the catch and the top ten species in the hunting gillnet catch account for 97%. The C P U E o f these top ten species/groups are shown in Figure 2.12, together with the shrimp gillnet data. They are compared to the 1979-1982 C P U E for the ordinary gillnet.  It was noted above that the C P U E o f the ordinary gillnet has decreased since 1979-1982. This was due to a decrease in the C P U E o f the sciaenids, the clupeids and the mugilids. Some groups increased, such as the trichiurids, the Ariidae and the penaeids. Others, for example, Sillago sihama were not recorded in the 1979-1982 data, but may have been included in the miscellaneous group.  The ordinary gillnet is used throughout the year, though effort is higher during the months o f October to March. Figure 2.13 shows the total monthly C P U E and the monthly C P U E o f the sciaenids and Trichiurus haumela. The C P U E o f the sciaenids is relatively stable, especially from December to A p r i l . There are two peaks in the total monthly C P U E . One is caused by the sciaenids in October and the other by a large increase in the C P U E o f Trichiurus haumela in M a r c h . Without the latter, the monthly C P U E o f the ordinary gillnet would be relatively flat. 20  In almost every month, the sciaenids are the most abundant species in the catch. However, the average sciaenid C P U E o f 1979-1982 was 36 kg/trip and this value is not reached during any month in 1992-1994 data. The highest sciaenid C P U E is 15 kg/trip i n  This data comes from the landings from 10 vessels, mostly taken towards the end of March. The catch of two vessels, recorded on the same day, accounts for 83% of the March catch.  Figure 2.13 C P U E per month for selected species and groups in the Ordinary Gillnet catch o f 1992-1994.  96  Figure 2.14 C P U E per month for selected species and groups in the Hunting Gillnet catch o f 1992-1994.  97 M a y . Indeed the total C P U E exceeded the 1979-1982 sciaenid C P U E value only once, due to the large catch o f Trichiurus haumela in March.  The hunting gillnet also operates all year round. The C P U E o f the sciaenids is both higher and more variable than for the ordinary gillnet (Figure 2.14). Here the highest C P U E is reached in March, almost reaching the 1979-1982 average value . The C P U E o f Trichiurus  haumela  21  increases during M a r c h and A p r i l . Otherwise the total C P U E is shaped by the sciaenid C P U E . It is possible that the hunting gillnet was adapted in response to declining catches o f the sciaenids.  The main use o f the shrimp gillnet occurs from December to March, although 40-50% o f respondents in the Fishing Inventory also use the gear during the other months. The landings of the shrimp gillnet were sampled during December, February , M a r c h and M a y . Although 2 2  described as a shrimp gillnet, in December and February over 70% o f the catch were sciaenids, mostly Otolithes ruber. In addition, the total C P U E o f the sciaenids is almost twice that o f the penaeids. The C P U E o f the sciaenids declined from December to M a y , while the C P U E o f the penaeids was between 2-3 kg/trip for all months sampled except M a r c h when it peaked to 10 kg/trip. The total C P U E o f the penaeids and the engraulids is higher than for the ordinary gillnet or the hunting gillnet (Figure 2.12). This is because o f the high C P U E s recorded in the March sample.  21 22  However, this value came from one sample only. Sampling was rather sparse, with four samples in December and one sample in the other months.  98 The surface gillnet and the bottom-set gillnet have surprisingly similar catch compositions. Both gears catch 67 species in total. The mugilids account for 76% o f the surface gillnet catch and 67% o f the bottom-set gillnet catch. The top ten species/groups i n the catch account for 97.5% and 99.5% respectively. Other species in the top ten caught by both gears include Trichiurus haumela, engraulids, clupeids, penaeids, sciaenids, and trash fish. The C P U E s o f these gears are compared to the 1979-1982 values in Figure 2.15. Although the catch and effort o f these gears have decreased, the C P U E o f the mugilids increased by around 40% for both. The bottom-set gillnet mugilids C P U E is twice the C P U E o f the mugilids caught by the surface gillnet. A greater diversity o f species is recorded by both gears in 1992-1994 than in 1979-1982.  The surface gillnet and bottom-set gillnet are basically used year round, although effort by the bottom-set gillnet is concentrated during the months o f M a y to September. The landings were only sampled over 6 months though, and the bottom-set gillnet was not sampled from July to November, part o f the period o f its main use. There is a decline i n the total C P U E o f the surface gillnet from December to M a y , and an increase in the C P U E o f the rays from March to M a y . The C P U E o f the other species/groups in the catch is variable with no clear pattern. The monthly C P U E pattern o f the bottom-set gillnet is dominated by a large peak in the mugilid catch in A p r i l  . Unfortunately there is no data for March, and the C P U E falls rapidly again i n  M a y . There is thus considerable variability i n the C P U E o f the mugilids caught by the bottomset gillnet. During January and February, the engraulids and the sciaenids comprise the greatest part o f the catch. But this is the "off-season" for the bottom-set gillnets.  Four vessels, on four separate days spanning the whole month, landed the catch which caused this large CPUE.  Figure 2.15 C P U E o f the top species and groups in (a) the Bottom-Set Gillnet catch and (b) Surface Gillnet catch from 1979-1982 and 1992-1994.  100  The catch and C P U E o f the last two gillnets to be discussed, the crab gillnet and the shark gillnet increased from 1979-1982 to 1992-1994. For the crab gillnet, this was due to an almost 100% increase in the C P U E o f the portunid crab, Portunus pelagicus, which accounts for 96.5% o f the total catch . Although some crab gillnets are in operation all year, most operate 24  from M a y to October-December. Unfortunately, the 5 months during which this gear were sampled were February to June and so only 2 months o f its main operation are covered. This does provide contrast however. From February to M a y the C P U E ranges from 0.3 kg/trip to 8.6 kg/trip. The June C P U E is 43.3 kg/trip, taken from an average o f 29 samples  . The catch,  effort and C P U E o f this gear have increased since 1979-1982, indicating that this has been one of the few areas o f expansion in the fishery.  The shark gillnet catch is composed o f the centropomid Lates calcarifer (50%), the shark, Carcharhinus  melanopterus  Arius leitocephalus  (24%), the ephippid, Drepanepunctata  (17%) and the catfish,  (3%). The effort o f this gear has decreased slightly since 1979-1982, but  the catch and C P U E increased. This gear was sampled from December to M a y , excluding A p r i l and there are no clear trends in the C P U E . There is not much basis for comparison with the 1979-1982 species data either. The C P U E o f the shark group and the Ariidae decreased, but L. calcarifer  and D. punctata were not recorded in the 1979-1982 data.  In the 1979-1982 data, the crabs were not differentiated. Pauly (1982a) notes that the San Miguel Bay crabs were named Neptunus pelagicus (=Portunus pelagicus) by Umali (1937). It is assumed here that the crabs in the 1979-1982 catch are predominantly P. pelagicus. The implication is however, that the average annual CPUE of the crab gillnet would be higher if it were calculated from 12 months of CPUE data instead of the 5 months of data presented here. Consequently, the crab gillnet catch may be higher than presented in Figure 2.6. This case could also be made for several other gears. The confidence limits in Figure 2.6 are intended to represent this uncertainty. 25  101 The Fixed Gears  The fixed gears, the filter net, fish corral and lift net, land 13.4% o f the total catch (Table 2.15). They all operate throughout the year, although twice the number o f filter nets are used during January and February than in the other months. The filter net is a fine-meshed gear (1-2 cm, Silvestre et al. 1995) and 42.3% o f its catch is the sergestid shrimp. The engraulids, penaeids and trash fish account for another 45.5% o f the catch. Thirty three species/groups are recorded in the catch in total. The top ten species/groups in the filter net catch include eight o f the top ten species/groups in the total catch o f San M i g u e l B a y . In Figure 2.16, the C P U E for the top ten species/groups is compared to the 1979-1982 C P U E values. Most have decreased, especially the engraulids and the leiognathids. The exceptions are the sergestid shrimp and the penaeid shrimp, which increased. Note that when the sergestid C P U E was examined for the mini-trawler above, the sergestid C P U E had decreased.  The total monthly C P U E is shaped largely by the sergestid C P U E (Figure 2.17). The C P U E o f the sergestids fluctuated between 5 and 10 kg/trip from December to M a y . The C P U E o f the engraulids peak in December and then decrease to zero through March, A p r i l and M a y . In December and February, the total C P U E is higher than the 1979-1982 average C P U E . In November, it is much lower.  The catch o f the fish corral is more diverse that the filter net. However, the fish corral catch, effort and C P U E decreased between 1979-1982 and 1992-1994. There are 86 species recorded in the catch and the top ten species/groups account for 80%. W h e n compared to  102  Figure 2.16 C P U E o f the top species/groups in the Filter Net catch from 1979-1982 and 1992-1994.  30  T  Figure 2.17 C P U E per month for selected species/groups in the Filter Net catch o f 19921994.  103 the C P U E o f the 1979-1982 catch in Figure 2.18, the most notable change is in the trash fish, which decrease from 12 kg/trip to 1.4 kg/trip. This decrease and the decrease in the crab, clupeid and sciaenid C P U E account for the large decrease in total C P U E since 1979-1982. The C P U E o f the carangids, penaeids, engraulids, leiognathids, Trichiurus haumela and mugilids increased.  There is considerable variability in the monthly C P U E o f the fish corral (Figure 2.19). About 28 fish corral fishers in the Fishing Gear Inventory use their gear all year around. However, around three times this number are in use in any one month. The two peaks in total C P U E occur in November and June, but there are no data for the months between June and November . The pattern o f gear usage does not reflect these two peaks. The peaks are caused by the carangids, leiognathids and sciaenids in November and the carangids and penaeids in June. The sciaenids and leiognathids peak again between M a r c h and M a y and the C P U E o f all species/groups is variable.  The lift net is also used all year around by almost all operators. The effort o f this gear decreased considerably since 1979-1982. Unfortunately, only one landing sample was taken for the lift net during the landing survey. Its composition contrasts sharply with the 1979-1982 catch composition. In 1979-1982, 80% o f the catch were engraulids and 8% clupeids. In 19921994, 88% o f the catch are leiognathids, 7% engraulids and 5% clupeids. Given the seasonality of the catch seen in the gears above, little confidence can be placed in the 1992-  The sample size for October to February was 1, 2 in April, 3 in March and May and 4 in June.  104  Figure 2.18 C P U E o f the top species and groups i n the Fish Corral catch from 1979-1982 and 1992-1994.  Oct  Nc"  r ,  ~~  Total C P U E •  Sciaenids  1  Trichiurus haumela  A  •  Carangids  —O—Engraulids  "  Jun  —*—Penaeid — — —  Shrimp  Leiognathids  Figure 2.19 C P U E per month for selected species and groups in the Fish Corral catch o f 1992-1994.  105 1994 data being representative o f the annual lift net catch. The lift net is estimated, on the basis o f its C P U E and effort, to land 50% o f the fixed gear annual catch . 27  Line Gear  The set longline accounts for 3.2 % o f the total catch and the handline accounts for 0.4% o f the total catch. Although a small percentage o f the total catch, these gears, particularly the longline, catch the larger and older fish in San M i g u e l Bay.  Forty percent o f the longline catch is composed o f the carangids and 30% by rays. The shark, Carcarhinus  melanopterus  accounts for another 10%, the eels, Muraenidae and  Muraenesocidae, 12% and the centropomid Lates calcarifer  5% o f the catch. The only  comparison that can be made with the 1979-1982 catch is that then, 20% o f the longline catch were carangids and 20% pomadasyids (these were not recorded in the 1992-1994 catch). The rest o f the catch was not identified (Pauly et al. 1982).  The set longline is used all year around. There is C P U E data for the months o f December to June (Figure 2.20). A g a i n there is considerable month to month variability. In January and February, virtually only eels are caught and this is the only group which is caught in every month sampled. Carangids are only caught from March to June. From March to June the catch is more diverse.  In a parallel survey, designed to monitor fishing operations and conducted by the socio-economic component of the ICLARM project, the lift net was sampled from January to June 1993 and its species composition consisted mostly of engraulids, as in the 1979-1982 survey (Padilla et al. 1995).  Figure 2.20 C P U E per month for selected species and groups in the Set Longline catch o f 1992-1994.  107  The handline is also used year round. Surprisingly, for it is a simple and cheap gear to operate, the use o f this gear has declined since 1979-1982. It declined both in total number o f gear and the numbers o f trips made per gear (Table 2.13). Its catch i n 1992-1994 consists o f the sillago, Sillago sihama, (67%), the tetraodontid, Lagocephalus  (24%) and Gobiidae(5%). This  composition is derived from only one sample. There is no comparative data from 1979-1982 because the catch then was not identified into its components.  Other Gears  Other gears include the crab lift net, fish trap, push net (or scissor net), as well as the stationary tidal weir, beach seine, spear gun, pullnet and ring net. These gears land 10% o f the total catch. The number o f push nets decreased since 1979-1982 but the catch increased. A s noted above this catch estimate was based on only one sample, which consisted entirely o f sergestids. There are however, three types o f push net. One operates from January to July, one operates all year, but mainly from September to February and the third operated all year. Combined, the push nets land 7% o f the total catch in San M i g u e l B a y .  The data for the crab lift net and fish trap are similarly scant. The crab lift net operates all year. T w o landings were sampled and the catch consists o f the portunid crab, Scylla serrata. In 1979-1982 the catch was also composed o f 100% crabs although it is not specified whether the crabs were Scylla species or Portunus species. The fish trap is also used all year, but with more effort during A p r i l to September. Its catch was sampled i n March and was composed o f mostly o f the rabbit fish, Siganus javus and the snapper, Lutjanus russelli. There are no data for the  108 catch composition from 1979-1982. The fish trap is the only gear that catches either the lutjanids or the siganids.  There is no information on the catch distribution or seasonal changes in catch for the other gears listed above. Together, they account for 2.8% o f the total catch.  There is no record o f discards in the fishery. It is very likely, as suggested by Pauly (1994) that there are no discards or by-catch because all catch is used, be it as table fish or fishmeal, fishfeed or fish sauce. A s Pauly notes "most fish caught in Southeast A s i a are landed, even when taken incidentally with shrimp (Pauly 1994:99).  Status o f the M a j o r Species i n San M i g u e l B a y  For each o f the top ten families/groups listed i n Table 2.17 an assessment is made o f their status. A comparison is made between the modal length o f fish i n the catch and the length at maturity. For those species with no available length at maturity data, length at maturity is calculated from the Leo using an empirical relationship . Their seasonal abundance is also described, but this is often confounded by the lack o f adequate coverage o f all seasons. Where possible, the recruitment pattern  is compared to the seasonal abundance.  The empirical relationship was calculated from the species for which there were data on Loo and L . It was determined that Loo was between 1.5 and 2 times L . In order to indicate an approximate relationship between mean length in the catch and L , it was assumed that L = 0.5 * Loo. The recruitment pattern is determined from the trawl survey data length frequency data. FiSAT (Gayanilo et al. 1996) software was used. FiSAT has a routine which estimates the likely number of recruitment peaks from a length frequency sample. It is an approximate routine and should be used as a guide only. 28  m a t  m a t  nrat  29  m a t  109  Sciaenidae  The results o f the mortality analysis and the yield-per-recruit analysis from the trawl survey data all indicated that O. ruber  is a highly exploited species. Its relative abundance in the  fished biomass and the trawl survey C P U E has decreased since 1979-1982. The main gears which catch sciaenids are the ordinary gillnet (41%), the hunting gillnet (36%), the shrimp gillnet (14%) and, to a lesser extent in 1992-1994 than in 1979-1982, the baby trawl (4%). The combined sciaenid catch o f these gears has decreased since 1979-1982 and the C P U E o f sciaenids by each gear has also decreased. In addition, an analysis o f the length composition o f the sciaenids (O. ruber) in the catch o f these gears reveals that the trawlers, mini-trawler, bottom-set gillnet and filter net catch all o f their catch o f O. ruber  well below the length o f  maturity (Figure 2.21). The model length o f O. ruber in the catch o f the hunting gillnet, ordinary gillnet and shrimp gillnet, is not much greater than the length at maturity. Length at maturity was calculated from the empircial relationship between L o o and L (1990). A l s o shown is the L  m a t  m a t  in Mathews  estimated by Almatar (1993). This is higher than that calculated  from Mathews, and i f correct, would imply that virtually all O. ruber  are caught below the  length at maturity.  There is no clear seasonal phase o f abundance o f the sciaenids in Figure 2.22. The C P U E from the trawl survey, the ordinary gillnet and the hunting gillnet track each other well. There may be two peaks in abundance, one in February/March and the other in September/October, which coincide with the end and beginning o f the Northeast Monsoon.  110  Maximum length in the catch Modal length in the catch  - - - Length at maturity calculated from Linf/Lmat in Mathews (1990) and Linf=44.8 cm. Length at maturity (Almatar 1993)  Figure 2.21 M o d a l lengths o f Otolithes ruber in the catch.  Figure 2.22 Monthly C P U E o f the sciaenids by the main gears that catch them.  Ill  The recruitment pattern from the trawl survey length frequency data predicts only one recruitment peak.  These results are in accordance with the findings o f Navaluna (1982). What is surprising is that the catch o f sciaenids is as large as it is, more than 10 years after Navaluna concluded that Otolithes ruber was both over-exploited and that mesh sizes should be increased. Fishing mortality has not decreased nor mesh sizes increased since 1979-1982.  Sergestidae  Since the sergestids are not caught by trawl gear other than the mini-trawler, their assessment is based on information from the catch analysis. The results indicate that these shrimps are suffering greater fishing pressure than in 1979-1982. The main gears catching the sergestids are the mini-trawlers, the filter net and the scissor net. The total catch o f the sergestids has decreased since 1979-1982 and so has the sergestid C P U E o f the mini-trawler which catches almost 80% o f the total sergestid catch. The C P U E o f the filter net and scissor net increased, although the latter was poorly sampled. Since the mini-trawler catches the greatest part o f the sergestid catch and its landings were sampled throughout the entire year, the C P U E o f this gear is a more reliable indicator o f change. The seasonality o f this species was described above for the mini-trawler pamalaw (see Figure 2.11.), where the peak in abundance occurred during the period o f the Northeast Monsoon.  1.12 Penaeidae  The assessment results for the penaeids all indicate, that the penaeids stocks are doing well. Their abundance in the trawl survey has slightly decreased but the C P U E has remained at around the 1979-1982 level. Several gears catch considerable quantities o f the penaeids: the mini-trawler (27%), shrimp gillnet (19%), other gillnets (16%), baby trawl (15%), the ordinary gillnet (10%). The filter net and fish corral also catch smaller quantities o f sciaenids. In all cases, the C P U E o f the penaeids has increased, or remained stable, since 1979-1982 , despite 30  increased effort by many o f the gears. This contrasts with the results o f Pauly's assessment (Pauly 1982a) o f the penaeids which indicated that the yield-per-recruit o f the penaeids would decrease i f fishing mortality was increased.  The monthly C P U E s o f the various gears listed above shed little light on the seasonality o f abundance o f the penaeids. The trends shown i n Figure 2.23 indicate that there might be peak in abundance around M a r c h and another in September/October. The mini-trawler pamasayan C P U E describes a single peak however.  Leiognathidae  Although the relative abundance o f the leiognathids in the trawl survey has not changed since 1979-1982 and the C P U E has increased, other indicators suggest that this group is being overexploited. Mortality estimates were made for the three main species in the group. A l l  The CPUE of the other gillnets is not included because it is a composite group of gillnets.  11  Figure 2.23 Monthly C P U E o f the penaeids by the main gears that catch them.  114  were very high. The yield-per-recruit analysis indicated that the current rate o f exploitation is much greater than the optimum rate for two o f the three species. The leiognathids are caught by the lift net (62%) and baby trawl (30%) and medium trawl (4%). However, as explained above, the lift net catch estimate is highly uncertain. For this reason, the leiognathid catch o f the fish corral (1%) and the filter net (1%) were also examined. The total leiognathid catch decreased and the C P U E o f the baby trawl and filter net decreased. The lift net and fish corral C P U E increased.  The modal length o f L. splendens and S. ruconius in the trawl catch is around 6 cm and 5 cm respectively in Figure 2.24. A l s o shown is the length o f maturity for L. splendens and S. ruconius. Clearly, much o f the leiognathid catch is landed before it reaches maturity. There is no length information for the catch o f the lift net. However, since it is a fine-meshed gear, it is also, as with the filter net, likely to catch undersized fish. A s in the case o f the sciaenids, the high proportion o f juveniles in the catch is troubling. However it is possible, since generally, juvenile natural mortality is high, that the fishery is catching juveniles that would die anyway, from natural mortality.  The trends in monthly C P U E shown in Figure 2.25 indicate that there is a large peak in abundance from August to November. The August part o f the peak is caused by the medium trawlers. There is also a smaller peak in March. The separate recruitment patterns for the three species predict two peaks in recruitment for L. splendens and L. bindus and one peak for S. ruconius. The monthly C P U E o f L splendens and S. ruconius shows two peaks, one in  115  Secutor ruconius  —  Length at maturity (Arora 1952)  Figure 2.24 M o d a l lengths o f leioganthids in the catch, (a) Leiognathus splendens, and (b) Secutor ruconius. The mean is used where the mode cannot be calculated.  116  Figure 2.25 Monthly C P U E o f the leiognathids by the main gears that catch them.  117  October/November and one in March, while there is only one discernible peak in C P U E for L. bindus in A p r i l .  Engraulidae  The relative abundance and C P U E o f the engraulids in the trawl survey has decreased since 1979-1982. The total catch has also decreased and the C P U E o f the main gears that catch engraulids has decreased. A range o f gears catch engraulids. Almost 40% are caught by the baby trawl, 20% by the filter net, 16% by the shrimp gillnet, 6% by the lift net, 5% by the medium trawl and 4% by the ordinary gillnet.  In Figure 2.26 the modal length o f Stolephorus commersonii  in the catch o f the trawl, filter net  and ordinary gillnet is compared to the length o f maturity. A s in the cases described above, the modal lengths, and therefore much, i f not all o f the catch o f some gears, is caught before maturity. Pauly (1982a) reported that the cod-ends o f trawl nets were covered with a finemeshed net o f 8 m m stretched mesh. Length at first capture was estimated to be 2-3 cm, well below the length at maturity.  The monthly C P U E o f some o f the gears which catch engraulids are shown in Figure 2.27. The data indicate that there may be a peak in abundance during the summer, the period o f the Southwest Monsoon, and one in December/January, the period o f the Northwest Monsoon.  118  Figure 2.26 M o d a l lengths o f the engraulid Stolopherous commersonii  Jan  Feb  Mar  Apr  May  Jun  Jul  Aug  Sep  Oct  in the catch.  Nov  Dec  Trawl Survey —•—Filter Net —•—Ordinary Gillnet —a— Medium trawl —x—Baby Trawl  Figure 2.27 Monthly C P U E o f the engraulids by the main gears that catch them.  119  Portunidae - Portunus pelagicus  It was noted above that the main gear that catches Portunus pelagics is the crab gillnet which catches 80% o f the total catch. The baby trawl, mini-trawler, and several o f the gillnets take incidental catches. The catch and C P U E have increased since 1979-1982. In the 1979-1982 study, it was reported that crab fishers had complained that their catches had been decreasing (Pauly 1982a). However, the available data for 1992-1994 indicate that this species is not overexploited and indeed may have increased in abundance since 1979-1982.  Trichiuridae  Trichiurus haumela is another species which is not currently suffering from over-exploitation. Its abundance and C P U E in the trawl survey has increased since 1979-1982. Relative to the other species for which mortality was estimated, its mortality is low. The yield-per-recruit curve places the current rate o f fishing mortality at around the optimum level. The total catch of Trichiurus haumela has increased and the C P U E o f gears which exploit it, the ordinary gillnet (50% o f the catch o f Trichiurus haumela), the baby trawl (27%), and the hunting gillnet (17%) has increased. In addition, as shown in Figure 2.28, these gears catch mature fish.  The monthly C P U E s from the trawl survey and fishing gears are shown in Figure 2.29. There is a definite period o f high abundance which ranges from October/November through to  120  = Mean length in the catch  Parin 1993)  Figure 2.28 M o d a l lengths o f Trichiurus haumela in the catch. The mean is used where the mode could not be calculated.  Figure 2.29 Monthly C P U E of Trichiurus haumela by the main gears that catch them.  121  March/April, the period o f the Northeast Monsoon. The recruitment pattern from the trawl survey length frequency data has one strong peak, lasting 6-7 months.  Musilidae  The C P U E o f the mugilids increased in the two gears that each catch over 30% o f the total catch o f the mugilids. However, all data indicate that the mugilids are overfished. Their abundance and C P U E in the trawl survey decreased. The total catch decreased and the baby trawl and ordinary gillnet C P U E o f mugilids decreased. It is possible that the bottom-set gillnet and surface gillnet fishers have discovered means to improve their catches. In addition, since the effort o f these two gears has decreased since 1979-1982, there w i l l less competition amongst remaining fishers. In Figure 2.30, the modal length o f mugilids in the catch are shown. The length at maturity is calculated from the Leo using the empirical relationship described above. Although the  L t is thus only a hypothetical value, it is not likely to be ma  lower than shown in Figure 2.30, yet the modal length in the catch is lower. The monthly C P U E data are too sparsely distributed through the year to give any sense o f seasonal abundance.  Carangidae  The carangids are a large group, with over 14 species occurring in San M i g u e l Bay. A s a family the abundance o f the Carangidae in the trawl survey has remained stable and the C P U E has increased slightly. The total catch and catch C P U E have also increased. The set longline  122  Trawl  Bottom-  Ordinary  Survey  Set Gillnet  Gillnet  Maximum length in the catch  Filter Net  Mini Trawler  Length at maturity  Modal length in the catch Figure 2.30 M o d a l lengths o f Mugilidae in the catch. Length at Maturity is calculated from an empirical formula - see text for further details.  Trawl  Ordinary  Mini  Survey  Gillnet  Trawler  Maximum length in the catch  Length at maturity  Modal length in the catch Figure 2.31 M o d a l lengths o f the carangid, Alepes dejedaba in the catch. Length at Maturity is calculated from an empirical formula - see text for further details.  123  lands 42% o f the carangids, the ring net  19%, the baby trawl 13% and the fish corral 10%.  The modal length in the catch in shown in Figure 2.31. L  m a t  was calculated as described above.  For Alepes djedaba, the only carangid for which there were data on the length composition o f the catch, most o f the catch is above the estimated length o f maturity. A s in the case o f the mugilids, the monthly C P U E data o f the carangids are too sparsely distributed through the year to give any sense o f seasonal abundance.  Trash Fish  The baby trawlers land 47% o f the trash fish (see footnote 14), the mini-trawlers 33%, the filter net 9% and the hunting gillnet 5% o f the trash fish. The monthly C P U E data indicate that there might be a higher catch rate o f trash fish from November to March/April. However, the data are too scant to make a firmer statement on the seasonal distribution o f the trash fish.  Value of the Catch  The top families listed in Table 2.17 are also some o f the most valuable in the catches o f San M i g u e l Bay. In particular, the ex-vessel price o f the larger crustacean species, the penaeids and the portunids are the highest in the total catch, which the exception o f the Serranidae. The latter are priced at between 200 and 240 pesos/kg , while the penaeids make an average price of between 42 and 86 pesos/kg and the portunids between 14 and 122 pesos/kg (Padilla et al. 1995). The sciaenids have an average ex-vessel price o f between 12 and 46 pesos/kg, the  31 32  There are data for two landings for the ring net in the Catch data from San Miguel Bay from July. At the current, 1997 foreign exchange rate, US$1 = 26 pesos: 240 pesos is equivalent to $9.23.  124  carangids 33 to 60 pesos/kg and the mugilids 27 pesos/kg. The leiognathids, engraulids, trichiurids and sergestids are all less valuable. The species listed above that were not recorded in the 1979-1982 catch, such as the Mullidae and the Synodontidae, are some o f the lowest value species.  Discussion  Given the assessed status o f San M i g u e l B a y in the early 1980s, it is hard to conceive that the fishery could have improved in the intervening years, since none o f the recommendations o f the 1979-1982 survey were enacted. In 1982, a 5 year ban on the operation o f commercial fishing boats did commence, yet it apparently only affected a small number o f commercial operators (Luna 1992), and had little or no effect. A l l the evidence suggests that, for most species, the status o f the fishery has worsened. A series o f analyses were presented above, and essentially all confirm that the B a y is highly overexploited. Since there is no time series o f catch and effort data for San Miguel B a y , much o f this analysis was based on length data from the trawl survey and comparative analysis with data from the 1979-1982 survey. In the latter, comparisons were made between the composition o f the trawl survey, the catch, C P U E per gear and effort changes.  In their emerging fisheries classic, Hilborn and Walters (1992) devote a chapter to lengthbased methods in fisheries assessment. They do not hide the fact that they are not enthusiasts o f the approach, and indeed point to many o f its difficulties and uncertainties. Their principal  125  point is that obtaining a representative sample, covering all sizes and ages in the population, is confounded by gear selectivity and fish behaviour, including fish movement, gear avoidance and distributional changes related to ontogenic migration. M a n y o f these concerns have been addressed by researchers, who have examined the sensitivity o f length-based methods to some 1  of these problems (see papers in Pauly and Morgan (1987) for example).  In the San M i g u e l B a y survey, the B a y was sampled by trawl survey. Since the vast majority o f the B a y is sand and sandy-mud, few areas were inaccessible to trawlers. The trawl survey was distributed throughout the year (with the exceptions noted above) and throughout the B a y (see Figure 2.2). In the analysis o f the length data, the F i S A T program was used to allow for selectivity by the trawl gear. Only data from the trawl survey were used to estimate growth parameters and mortalities. In addition, growth parameters were only estimated for the speciesfor which there were visible modes that could be tracked over several months. It was assumed that there was no net movement o f fish in or out o f the B a y , in accordance with the steady-state assumptions o f the length-based methods used . The implication o f this assumption is discussed below. These measures were taken to ensure, as far as possible, a representative sample.  In order to estimate mortalities and yield-per-recruit, the growth parameters were first estimated using E L E F A N I. The E L E F A N I method was used because it is well tried and tested (Pauly 1987, Hampton and Majowski 1987, Majowski et al. 1987, Rosenberg and  In fact in all likelihood, emigration and immigration occur in San Miguel Bay. Pauly (1982b) has described the nursery role of San Miguel Bay. However, it is questionable, as a consequence of the observed level of fishing pressure in San Miguel Bay, whether many of the adult fish escape the Bay before they are captured. The question of immigration and emigration is returned to in Chapters 3 and 5. 33  126 Beddington 1987), it uses a time series o f data, it has routines which allow for the effects o f selectivity to be accounted for and the results can be used directly in the mortality estimation using the F i S A T software. In a sensitivity analysis o f E L E F A N I, Rosenberg and Beddington (1987) found E L E F A N I to be sensitive to variation in length at age (assumed constant in E L E F A N I). In addition, E L E F A N I consistently underestimated the growth parameter K , unless the true value o f K was known to within 20-25% o f its true value. Hampton and Majowski (1987) concluded that L o o was over-estimated and K underestimated when fishing is size selective. However, both o f these simulation trials were conducted on earlier versions of E L E F A N I, and the program has since been improved (Pauly 1987, Gayanilo et al. 1996). In any case, the rational o f using the generic values o f L o o and K (estimated from the literature) as guides in the analysis was to overcome such biases and to avoid the problem o f the correlation between L o o and K . O f the large number o f species for which there were length frequency data, growth parameters were only estimated for six, because it was not possible to discriminate a growth curve from the data for the other species. Whilst not wishing to appear too confident about the growth parameters estimated above, there is sufficient certainty that they are in the right "ball park".  The length-based data and growth parameters were used to estimate mortality and yield-perrecruit. Mortalities were estimated using the Length Converted Catch Curve, Beverton and Holt's M e a n Length Method, the Powell-Wetherall Plot, and the relationship between fishing mortality, catch and biomass. The first two methods were considerably more successful than the latter two methods. Four methods were used because o f the uncertainties associated with  127 length-based methods to estimate mortality. A l l are based on the equilibrium assumption and accompanying assumptions.  Hampton and Majowski (1987) found that Z was generally overestimated by the Length Converted Catch Curve Method and that the greater the variation in growth, the greater the positive bias. In addition, variation in cohort strength, which is assumed constant, can cause bias. In the catch curve analysis above, the samples were pooled in order to smooth out recruitment pulses and thus better simulate equilibrium conditions (Pauly 1987).  Recruitment variation also causes bias in the Beverton and Holt M e a n Length Method (Ralston 1989). A recruitment surge causes a negative bias and vice-versa. A s above, pooling samples, taken over a period o f time, reduces the bias. It is also sensitive to the input parameters, Leo, L , the length at first capture, and the difference between mean length and L e o , or L . The c  c  sensitivity to L was shown above in the mortality estimation for Leiognathus splendens. A L c  c  o f 6.5 c m produced a Z estimate o f 12.8 year" while a L o f 6 cm gave a Z estimate o f 10.2 1  c  year" . It is however, quite robust against variation in individual length at age since it uses the 1  mean length (Laurec and Mesnil 1987). Walters and Hilborn suggest that "only the most naive biologist would use this method with any confidence" (1992:425). Its use here is justified since it was used in conjunction with other methods. The Z estimates from the Beverton and Holt method were often in agreement with the estimate from the Length Converted Catch Curve.  The Powell-Wetherall Plot was unsuccessful and only produced one useful Z estimate. The estimation o f M from Pauly's equation produced realistic values, but the estimates o f F  128 generally resulted in total mortality estimates which were either much greater or much lower than the Length Converted Catch Curve or the Beverton and Holt estimates. Given the uncertainty surrounding the biomass estimate (see above), this result is not too surprising.  That length-based methods are more approximate than age-based methods leaves little room for dispute. M a n y authors begin their texts with an admonition about these methods, while recognising that for many fisheries, particularly in the tropics, the only option to not using length-based methods would be to use no methods at all, and to wait for better data. The same argument is used here. The hope, in estimating mortality, was not to get pin-point figures, but to obtain a sense o f the likely mortality that is imposed on San Miguel B a y fishes.  This was achieved for six important species in San Miguel Bay. Their levels o f mortality can be used as an indicator o f mortality levels in the fishery generally. The yield-per-recruit analysis confirmed that these species, with the exception i f Secutor ruconius and Trichiurus haumela are highly overexploited.  The estimated mortalities are very high. There are at least four explanations for these high mortalities. 1. The mortalities are over-estimated for the reasons discussed above, that is the sensitivity o f the methods. (For example, the Length Converted Catch Curve and Beverton and Holt methods gave Z estimates o f between 10 and 12 for Leiognathus Z = F + M method estimated Z to be half o f this value). 2. The Zs are high because o f emigration from the Bay.  splendens while the  129  3. The Zs are high because the samples are largely juvenile and they have higher mortality than adults. 4. The Zs reflect the actual mortalities. Such high Zs are known in other tropical systems. For example, the mortalities estimated by Tandog-Edralin et al. (1988) for heavily and moderately fished areas o f the Philippines have a similar range.  In essence the actual values o f Z are less important than their range. The relevant message is the recognition that the extent o f over-fishing o f San M i g u e l B a y has not improved since 19791982. The results from the analysis o f the trawl survey data and the catch survey data conclusively give the same result.  The fishery analysis above showed that while effort had decreased in the large-scale sector, both diversity and effort increased in the small-scale sector. Yet, Sunderlin (1995) reports that the number o f small-scale fishers has remained effectively unchanged since 1979-1982. A t that time, the total o f 5,600 fishers had grown rapidly since 1939 (Bailey 1982). In 1992-1994, there are 4,800 full-time fishers and 500 part-time fishers , a slight decrease from the 197934  1982 figures. Furthermore, although the population o f fishing villages has grown, the proportion o f fishing households decreased from 54% to 45% o f the total number o f households. There was a greater dependence in fishing households on non-fishing income and income from females and children (Sunderlin 1994). These socio-economic factors indicate that the increase i n small-scale effort and diversity is an effort by fishers to maintain incomes, not an effort to either increase incomes or to increase the number o f fishers. Sunderlin also  Sunderlin defines full-time fishers as those who get all or most of their income from fishing and part-time fishers as those who get all or most of their income from a non-fishing source. (Sunderlin 1995).  130  reports that 2/3 o f small-scale fishers interviewed, complained that their catches had decreased over the previous two years, 36% said it was the same and 14% said that it was better. Given that the total small-scale catch has increased slightly and the total number o f small-scale fishers decreased, on average there should be at least no change in catch.  The fishing population has stagnated, the total catch has decreased and the assessed status o f the fishery has worsened since in 1979-1982. However, Padilla et al. (1995) report that economic rents are still made in the fishery. Padilla et al. examined the economic performance o f eight gears, the large, baby and mini trawls, the lift net, filter net, fish corral, pushnet and gillnet . They concluded that pure profits were made by the mini-trawler, fish corral, filter net, 35  gillnets and pushnet, while the baby trawlers and lift nets made negative pure profits (a loss). In total, they estimated that the total value o f rents were equivalent to their value in 1979-1982.  In general the level o f investment required to start in the fishery decreased between 1979-1982 and 1992-1994. One exception was the Push Net. The relative increase in the start-up costs for this gear could explain why the number o f Push Nets have decreased since 1979-1982 (Table 2.13). The large increase in the number o f filter nets could be related to their relatively low start-up costs compared to the other fixed gears, trawl gears and gillnets. In addition, the startup costs o f gillnets, the number o f which has substantially increased since 1979-1982, decreased relative to the start-up costs o f the trawl gears, lift net and fish corral.  The decline in the large-scale trawling sector may be for economic reasons. Padilla et al. showed that, over a period o f 6 months, the large-scale sector made a slight positive income,  131  but when opportunity costs were taken into consideration, this sector suffered a loss. However, Padilla et al. used catch and effort figures which are different from those collected in the landings survey described above. They used catch and effort figures that were collected in a socio-economic survey and monitoring o f fishing operations, a survey that was carried out in parallel to the landings and trawl surveys. From this survey, information was collected on catch, operating expenses, crew remuneration and the dynamics o f fishing units over a period of 6 months from January 1993 to June 1993 (Padilla and Dalusung 1995).  Padilla et al. calculated the catch composition and C P U E for the eight gears monitored in the socio-economic survey. These figures do not correspond well with the results from the landings survey. For example, the C P U E o f the gillnets calculated by Padilla et al. was 9.8 kg/trip, whereas the average C P U E for the gillnets above was 15.8 kg/trip. The discrepancy between the C P U E s for the baby trawl between the two surveys was also very large. Padilla et al. concluded that the baby trawl made a negative pure profit.  In Table 2.19, the net income is calculated for six o f the eight gears using the C P U E and catch figures calculated from the landings survey above, and the economic figures in Padilla et al.  Padilla et al. (1995) combined all gillnets into one category.  132 Table 2.19 Comparison o f the Net Income derived from the catch figures in Padilla et. al (1995) and the catch figures calculated from the Landings Survey above. Value (Pesos *)  Fish Corral Filter Net Mini-Trawler Baby Trawl Large Trawl Gillnet (combined) * 26.4 pesos = US$1  3831 2713 11620 131435 122944 7716  Costs Value Pesos (Pesos) Padilla et. al (1995) 8272 3415 2300 1031 8361 6053 8782 8277 104, 501 498 5975 5011  Net Income  1  417 1683 5567 123,158 122,446 2705  Net Income Padilla et. al (1995) 4857 1270 2307 505 257 963  133 (1995).  The net income figures from Padilla et al. (1995) are also given for comparison.  They are quite different. Only the results for the filter net are comparable. The differences seen for the other gears are caused by the different catch rates and hence catches.  O n the basis o f the data presented in this thesis, it cannot be concluded that the trawl gears are uneconomic. When the catch figures estimated in this work are used in the economic analysis, the net income is much greater than the net income estimated by Padilla et al. This is also the case for the other gears listed in Table 2.19 with the exception o f the fish corral. Therefore, all gears must make pure profit, except the fish corral, which may make a negative pure profit.  In summary, it could be said that the fishery o f San M i g u e l B a y is in a state o f laissez-faire anarchy. Management regulations exist, but in practise internal and informal sources regulate the level and type o f effort. The main management measures have been mesh size and trawling restrictions, but neither o f these have had much effect. The results o f the analysis o f the mean length in the catch o f various gears attests to the former. That trawling regulations are not as effective as they should be is recognised by Luna (1992), M i k e Pido (pers. comm.) and personal observation o f fishers' complaints.  However, despite dire warnings at the beginning o f the 1980s, the fishery still operates, it still sustains about the same number o f fishers and it still generate profits i n most sectors o f the fishery examined. There is also greater equity i n the fishery. The distribution o f the catch and  ' The net income for the lift net and the scissor net were not re-calculated because the data from the landings survey are based on one sample and therefore highly uncertain. In the case of these two gears, the catch data in Padilla et al. (1995) is more complete.  134  profits has changed with the small-scale sector now taking a larger, and more equitable share. M a n y o f the species are over-fished, but other species are succeeding, for example, the portunid crabs and the trichiurids. The results indicate that, as in the G u l f o f Thailand, this fishery is being fished down and is following the classic pattern, described by Pauly (1979a). Other than a severe reduction in effort by all sectors or complete closure o f the fishery, it is difficult, on the basis o f this analysis and the lack o f adherence to the management regulations, to make specific recommendations. The increase in the fine-meshed gears such as the filter net, and the development o f the hunting gillnet, an active gear, are indications that, despite the reduction in trawling effort, this fishery is going down a one-way street.  135  Chapter 3 An Ecosystem Model of San Miguel Bay "The approach we propose is thus to use state and rate estimates for single species in a multispecies context, to describe aquatic ecosystems in rigorous quantitative terms, during the (arbitrary) period to which their state and rate estimates apply." Christensen and Pauly (1992a:2)  Introduction  San M i g u e l B a y has been described as a tropical multispecies fishery with a diverse smallscale sector and a large-scale sector. The latter has dominated the fishery, until recently. The B a y has been diagnosed as seriously overfished, but there is little real insight into how to ameliorate this situation, other than to reduce all fishing and prohibit trawling. The number o f trawlers operating in the B a y has already declined by around 50% since 1979-1982, yet there is no noticeable improvement i n the fishery resource as a consequence. The situation is complex because the multispecies resource is selectively and non-selectively exploited by a large number o f diverse fishing gears.  Little ecological detail about the ecosystem on which the fishery is based is available, yet alone the impact o f the different gear sectors. There are many species in the ecosystem, and some, probably many, are over-exploited. Pauly (1982a) speculated on the nature o f likely interactions between some o f the species in San M i g u e l B a y . He noted that the penaeids are predated upon and are in competition with a number o f fish species. Since the penaeids  136 command one o f the highest ex-vessel prices in the catch (Padilla et al. 1995), these relationships are important. Trawling has effectively reduced the biomass o f many o f the competitors and predators o f the penaeids, thus promoting the huge surge in penaeids seen in San M i g u e l B a y , and in other fisheries in Southeast A s i a (Pauly 1982a, Pope 1979). The leiognathids, for example, were and, although they have declined, are the most abundant species in San Miguel Bay. They are also competitors with the penaeids. Arguably, the penaeids have "profited" by the decline o f the leiognathids. Other changes that have occurred in the species composition o f San Miguel B a y since the late 1940s and the early 1980s were noted in Chapter 2. Directly and indirectly these changes are due to exploitation o f the fishery.  When assessing and managing a multispecies fishery such as San M i g u e l B a y it would be unwise not to consider such ecological interactions. Until recently though, no ecological or multispecies modelling o f San M i g u e l B a y had been conducted. The data from the 1992-1994 I C L A R M survey is used here to construct a simple, linear mass-balance model o f San Miguel Bay using E C O P A T H  3 7  (Christensen and Pauly 1992a, 1992b, see also Palomares et al. 1995a  ID  ). E C O P A T H has been applied widely to aquatic systems (see contributions in Christensen and Pauly 1993a). It has been used to describe and examine the energy flows in ecosystems (e.g., Jarre-Teichmann et al. In Press, Christensen 1994, 1995) and as a diagnostic tool (e.g. Pauly and Christensen 1995, 1996).  ECOPATH is available as a DOS version, ECOPATH II (Christensen and Pauly 1992a) and in a new Windows version, ECOPATH 3.0 (ICLARM 1995c). When this work was undertaken, only the DOS version of ECOPATH was available. When ECOPATH 3.0 became available results were checked and elaborations on method made see below. Palomares et al. (1995a) made a first attempt at an ECOPATH model of San Miguel Bay. However, they relied almost exclusively on the data and structure of the ECOPATH model for Brunei Darussalam (Silvestre et al. 1995). 37  38  137 E C O P A T H is a mass-balance description o f trophic interactions. It is used here to model San M i g u e l B a y and to determine and describe the interactions between different components within the ecosystem. In this way, the major energy flows and pathways in the ecosystem, upon which the fishery is based, are identified. The state o f development or maturity o f the ecosystem is also examined using a series o f indices outlined i n Christensen and Pauly (1993c) and Christensen (1995). In addition, the key areas where information is poor are also identified. E C O P A T H is a means to collate data about a system in a coherent form, enabling a better understanding o f the entire system. Knowledge is increased.  That the changes in species composition noted i n San M i g u e l B a y are due to the direct and indirect effects o f fishing bears little questioning . The trawling sector o f San Miguel Bay, 39  and the small-scale sector are equally likely candidates for altering the ecosystem o f the Bay. In order to directly model the effects o f fishing, the fishery was included in the E C O P A T H model as a "top predator". The fishery was modelled as a large-scale predator and a smallscale predator, and as a large-scale predator and a series o f different small-scale predators.  Environmental effects such as the destruction of mangrove habitats (Vega et al. 1995a,b), siltation (Mendoza and Cinco 1995), and pollution (Mendoza et al. 1995b) are also contributing factors.  138  Methods  ECOPATH  E C O P A T H is a mass-balance model, developed by Christensen and Pauly (1992a,b) from earlier work by Polovina (1984). The basic premise o f the model is that, over the time period for which the model is relevant, total production is equivalent to total loss, that is steady-state . That is, for 40  each group " i " in the model:  Production by (i) - all predation on (i) - non-predation losses o f (i) - export o f (i) = 0,  or,  B; P/Bi-Zj Bj * Q/Bj * DCjj - P / B i * B ( l - EE;) - E X ; = 0 s  where,Bj = biomass o f (i), P / B ; = production/biomass o f (i), EEj = ecotrophic efficiency o f (i), (that is the proportion o f the production that is exported or consumed by the predators in the system. It should be close to 1 for most groups ), Q/Bj = consumption biomass ratio o f (i), D C j= 41  E C O P A T H is not restricted to the steady-state. The user can enter a biomass accumulation term, although the model must maintain a mass-balance. In a model that contained more detail on the contribution of vegetation to the ecosystem, a primary producer such as reeds may contribute greatly to primary contribution, but not be consumed in the ecosystem. The E E would thus be close to 0, and detritus accumulation would occur as a result.  41  (1)  139 fraction o f prey (i) in the average diet o f predator (j), and E X ; = export o f (i), (Christensen and Pauly 1992a).  The ecosystem is modelled using a set o f simultaneous linear equations derived from the above relationship. Each group in the model is represented by one balanced equation.  B,P/B,EE, - BJQ/BJDC^, - B Q / B D C 2  B P/B EE 2  2  2  2  - BJQ/BJDC,^- B Q / B D C 2  2  B P/B EE - B ^ / B . D C , ^ - B Q/B DC n  n  n  2  2  ,  2  2  2  2 ) n  B Q/B DC N  N  N L  B Q/B DC  N  B Q/B DC  n n  N  n  N  n  2  - EX, = 0  - EX  2  = 0  - EX = 0 n  This system o f linear equations is solved using matrix algebra. The attractiveness o f the massbalance E C O P A T H approach is that it is not necessary to know all the parameters for all the groups represented in the ecosystem. For each group in the model there are six input parameters. The export and the diet composition o f each group must be entered. O f the four other parameters, B , P / B , Q / B and E E , three must be entered for each group . Since the linear 42  equations represent a balanced system, they can be solved for the unknown parameters.  The steady-state assumption o f the model is justified in the following way. E C O P A T H has no time dimension. The time period represented by the model is determined by the user. In this way,  140 it is assumed that there is no net difference between production and loss, over the time period for which the model is relevant (Christensen and Pauly 1993b). In the case o f San Miguel Bay, the time period is 1 year.  There are three key steps to modelling with E C O P A T H .  1. Aggregate the total number of species in the ecosystem into representative groups. These groups should reflect similarities in habitat, size and diet and importance in the fishery. Choose a representative species for each group, or take the mean o f a number o f representative species. 2.  Calculate the parameters P/B, Q/B, biomass, EE and diet for each group. In exploited ecosystems, the catch is modelled as an export from the system and the annual catch per group must be entered. The units for the energy related parameters are in t km" . The area o f San 2  M i g u e l B a y is taken as 1115 km (Garces et al. 1995b). 2  3.  Balance the model. It is highly unlikely that an E C O P A T H model w i l l be perfectly balanced when the parameters are first estimated. Balancing an E C O P A T H model requires enough knowledge about the ecosystem to make reasonable adjustments to the original parameters in order for the model run and balance.  Under certain conditions, the model will estimate more than one unknown parameter for group(i) if all the parameters are known for the other groups. See Christensen and Pauly (1992a) for further details.  141 E C O P A T H produces a range o f diagnostic statistics. These include gross efficiency ( G E ) , which is calculated as the ratio o f its production to consumption, for each group, respiration , 43  mortality coefficients, a matrix o f predation mortality coefficients, trophic levels, transfer efficiencies and a variety o f cycle and pathway information. Some o f this output can be readily used to check the validity o f a model. For example the E E should not be greater than 1; G E should be between 0.1 and 0.3; there can not be negative flows to detritus and the respiration/biomass ration (resp/biom) should be less than 100 (Christensen and Pauly 1992, V . Christensen, pers.comm).  Aggregating Species into E C O P A T H G r o u p s  More than 100 species o f fish and Crustacea were recorded in San M i g u e l B a y during the 19921994 trawl and landing surveys (Appendix 1). In order to make the E C O P A T H model tractable, this complexity had to be reduced, by aggregating the species into groups. Following the guidelines in Sugihara et al. (1984), these species were aggregated into eco-groups, according to similarities in habitat, body size, diet and co-occurence i n fishing gear. E C O P A T H models in Christensen and Pauly (1993a) were also consulted for comparative purposes.  Eleven eco-groups resulted from the aggregation, three o f which are crustacean groups (Table 3.1). The division o f some o f the groups was straightforward, because o f their high relative abundance in the trawl survey and their importance i n the catch. The sergestids and penaeids, for example,  Respiration is calculated in the Ecopath model and is the difference between assimilated food and production. It corresponds to the biological definition of respiration. If actual data are available, the model can be tuned to this data (Christensen and Pauly 1993b).  142 Table 3.1 Grouping o f species found i n San M i g u e l B a y for Ecopath M o d e l .  Ecopath Group  Family/species  Relative Abundance  Sergestid Shrimp Penaeid Shrimp  5.30  Large Crustaceans  0.80  Ecopath Group  Family/species  8.24  Pelagics Clupeidae Dussumieria Carangidae Chirocentridae Scombridae Loligo  Portunid Crabs Stomatopods Demersal Feeders  16.86 Mullidae Nemipteridae Haemulidae Priacanthidae Theraponidae Tetraodontidae Bothidae Apogonidae Lethrinidae Sparidae Silliganidae Gobbidae Muglidae Siganidae Cynoglossidae Polynemidae Platycephalidae Tricanthidae Soleidae Lactaridae  Leiognathids  Sciaenids  Medium Predators  Pennahia sp  11.11  Arridae Synodontidae Trichiuridae Muranaesocidae Sphyraenidae Formionidae Muraenidae Plotosidae Serranidae Psettodidae Lutjanidae Fistularidae Opicthidae  37.85 Large Zoobenthos Feeders  2.32 Rays Ephippidae  Large Predators  S. insidiator  6.13 Stolephorus commersonii Stolephorus indicus Thryssa se tiros tris  7.02 Otolithes ruber Dendrophysa russelli Pennahia macropthalmus  L. splendens L. elongatus L. equluus L. bindus Gazza minuta Secutor ruconius  Engraulids  Relative Abundance  0.31 Carcharhinus melanopterus Lates calcifer  143 were clear groups, as were the leiognathids, engraulids and the sciaenids.  The large crustaceans group is comprised mostly o f the portunid crabs Portunus pelagicus and Scylla serrata , but also includes stomatopods. P. pelagicus  is most abundant and is taken as the  representative species for the large crustaceans.  The demersal feeders is a more diverse group, representing 20 families, and almost 17% o f the trawlable biomass. The most abundant families include the Mullidae, Tetraodontidae, Nemipteridae and Apogonidae. Others include the Muglidae, Gobiidae and Siganidae.  The pelagics are another mixed group, and account for over 8% o f the trawlable biomass . This 44  groups includes the Clupeidae, Carangidae, Scombridae and Squids (Loligo sp.) The latter were included in the pelagics, following Pauly (1985) and Silvestre et al. (1993). Pauly (1985) compared the growth performance o f squid to fast growing scombrids.  The medium predators account for over 11% o f the trawlable biomass and include 13 families. The most abundant are the Trichiuridae which comprise over 60% o f the total group biomass and the Synodontidae which comprise 11% o f the biomass. Other families include the Sphyraenidae, Muraenidae, Muranaesocidae and Psettotidae. It also includes, at much lower levels, the traditional predators o f San M i g u e l Bay, the Ariidae and Serranidae.  Since pelagicfishare not the main target of demersal trawls, this figure probably underestimates the actual relative abundance of the pelagics.  144 The large zoobenthos feeders are comprised o f various species o f rays and the Ephippidae. They represent 2% o f the trawlable biomass. The large predators account for 0.3 % o f the biomass. They consist o f the shark, Carcharinus melanopterus and the Centropomid, Lates  calcarifer.  In addition to the above groups, there are five other eco-groups i n the E C O P A T H model. These are the phytoplankton, zooplankton, meiobenthos, macrobenthos and detritus.  Parameterising the E C O P A T H M o d e l  Estimation of the Production Biomass ratio, P/B The production biomass ration (P/B) is estimated in two ways. The first uses the assumption that the ratio o f annual production to mean biomass (P/B) is equal to the annual instantaneous rate o f total mortality, Z , under equilibrium conditions and assuming the von Bertalanffy growth function (Allen 1971). For the eco-groups for which it was not possible to estimate Z , a second method to estimate P / B was used. Values o f P / B were taken from comparative tropical shallow marine ecosystems described in the literature, using the available Z estimates as a guide.  Estimating P/B from P/B=Z  There are three estimates o f Z for the leiognathids (see Tables 2.7-2.9). Christensen and Pauly (1992a) recommend using a weighted mean to calculate combined parameters. This produced a P / B o f 9.42 year . There is only one Z estimate for the sciaenids, for Otolithes ruber. Since -1  145 this species represents 65% o f the trawlable biomass o f the sciaenids, it was taken as representative, giving a P / B o f 4.39 year" . The medium predators are represented by the Z 1  estimate o f 2.5 year" for Trichiurus haumela . The results o f the Z estimation for the pelagics 1  were inconclusive (Table 2.10) and ranged from 1.04 to 5.26 to 9.64 year" . Their P / B value 1  is estimated below.  Estimating P/B from other Ecosystems.  A s a first step, the P / B estimates for the leiognathids, the sciaenids, the medium predators and the pelagics were compared to estimates o f P/B for other ecosystems described in Christensen and Pauly (1993a) and Pauly and Christensen (1993). Comparisons were made between the San M i g u e l B a y estimates and estimates from the individual ecosystems, the mean o f the various shelf and lagoon ecosystems and the mean o f all ecosystems. The set o f estimates that compared best with the San M i g u e l B a y figures were those from the G u l f o f Thailand. Although with an area o f 300,000 k m (Pauly 1979a) the G u l f o f Thailand is much larger then 2  San M i g u e l B a y , the comparison with San Miguel B a y is reasonable. In addition to their similarity in geographic location, both water bodies have been intensively fished for several decades and have a similar fauna (Pauly 1979a, Pauly and Mines 1982). The comparison o f estimates, and the final P / B values are given in Table 3.2.  Some o f the groups in the G u l f o f Thailand model differ from the San Miguel B a y eco-groups. The G u l f o f Thailand intermediate predators include both the San Miguel B a y medium predators and the sciaenids. The latter are both compared with the intermediate predator  146 Table 3.2  P/B ratios from the Gulf of Thailand compared to the estimates from San Miguel Bay.  Gulf of Thailand Groups  Gulf of Thailand P/B (year ) 200 40 6.85 5 62 6  San Miguel Bay Z estimates (year )  6 6  7.5-11.3  -  -  Final P/B (year' ) 200 67 10 6.8 62 6.48 2.8 6 9.42 6  —  — —  1.04-9.64  5.45  1  Phytoplankton Zooplankton Benthos Molluscs Microcrustaceans Large Crustaceans Demersal Z B feeders Leiognathidae Small pelagics Loligo Medium Pelagics Intermediate Predators  6 3.1 4 4  Large Z B Feeders Large Predators  1  —  — —  -  -  1  -  -  — 1.3 2  -  2.5 4.4 1.3 2  Z B = Zoobenthos Data for the Gulf of Thailand from Pauly and Christensen (1993)  Phytoplankton Zooplankton Meiobenthos Macrobenthos Sergestids Penaeids Large Crustaceans Demersal Feeders Leiognathids Engraulids  -  2.5 4.4  —  San Miguel Bay Groups  Pelagics Medium Predators Sciaenids Large Z B Feeders Large Predators  147 estimate. The pelagics from San M i g u e l B a y include small pelagics, the Loligo and the medium pelagics. These are treated separately in the G u l f o f Thailand data, so the San Miguel B a y estimate was compared with each o f the G u l f o f Thailand P / B estimates and the weighted mean o f the estimates. The San Miguel B a y pelagics P / B values were comparable to all the other model P / B estimates, because they cover such a large range.  O n the basis o f the G u l f o f Thailand P / B figures, the P / B o f the demersal feeders is 6 year , -1  the large Zoobenthos feeders, 1.3 year" and the large predators, 2 year" . However, due to some 1  1  differences in the way that the species from San M i g u e l B a y and the G u l f o f Thailand were grouped, the derivation o f the other P / B values requires further explanation.  The G u l f o f Thailand microcrustacea include sergestids and in the absence o f other information, the microcrustacea P / B o f 62 year" was taken to represent the sergestids o f San 1  Miguel Bay.  The large crustaceans group o f San M i g u e l B a y is composed mostly o f portunid crabs whereas the large  Crustacea  o f the G u l f o f Thailand includes lobsters and shrimps in addition to crabs.  The P / B estimate from Campeche Bank, G u l f o f M e x i c o (Arreguin-Sanchez et al. 1993a), a tropical area also similar to San M i g u e l B a y , o f 2.8 year" for portunid crabs was used to 1  represent the large  Crustacea  o f San M i g u e l B a y .  There is no P / B estimate for the penaeids from the G u l f o f Thailand because they are included in the large Crustacea. However, there are two P / B estimates for penaeids in Christensen and  148 Pauly (1993a) from Campeche Bank, G u l f o f M e x i c o (Arreguin-Sanchez et al. 1993a) and the southwestern G u l f o f M e x i c o (Arreguin-Sanchez et al. 1993b).The mean o f these two estimates, 6.48 year" , was taken as the P / B o f the penaeids in San Miguel Bay. 1  The engraulids were not modelled as a separate group in the G u l f o f Thailand data and there were no P / B estimates in the other available data. In this case, the G u l f o f Thailand small pelagics P / B o f 6 year" was used. This was considered valid since the engraulids are small 1  pelagics and they are overfished in San Miguel B a y with exploitation ratios greater than 0.5 (Chapter 2, Pauly 1982a).  Since there was no reliable empirical estimate o f Z for the pelagics, their P / B was also estimated from the G u l f o f Thailand data. The G u l f o f Thailand P / B values for the small pelagics and L o l i g o both fall within the range o f Z estimates. The weighted average o f these, 5.45 year" , was taken as the representative P / B value for the San M i g u e l B a y pelagics. 1  The P / B value o f 200 year" for the phytoplankton and the zooplankton P / B o f 67 year" were 1  1  taken from Silvestre et al. (1993). The G u l f o f Thailand P / B estimate for the benthos could not be used directly since the benthos in the San M i g u e l B a y model is divided into meio and macro benthos. The P / B estimate o f 10 year" for the meiobenthos was taken from the only 1  estimate available, from Celestun Lagoon in the Southern G u l f o f M e x i c o (Chavez et al. 1993). The macrobenthos o f San Miguel B a y is composed o f 80.5% annelid worms, 3.8% molluscs (Garces et al. 1995b) and 15.7% o f other matter, termed here, heterobenthos. The macrobenthos P / B was calculated from the weighted mean o f the P/Bs for each o f these  149 components from the G u l f o f Thailand ecosystem The resultant weighted mean P / B was 6.8 year" , very similar to the benthos estimate o f 6.85 for the G u l f o f Thailand. 1  Estimation of Consumption Biomass Ratio, Q/B  The consumption biomass ratio is the quantity o f food consumed by the biomass o f fish in the population, within a given time period. It is estimated here in two ways. Where possible it is derived from the empirical formula described below. For the other eco-groups, comparative Q / B estimates were taken from the literature, as described for the P / B estimates.  Estimating Q/B from an empirical  formula  Palomares and Pauly (1989) derived an empirical formula to estimate Q / B , expressed as a daily rate. The annual form o f the equation, given in Christensen and Pauly (1992) is:  Q/B = 3.06 * W o o "  0 2 0 1 8  * Tc  0 6 1 2 1  *Ar  0 5 1 5 6  where, W o o = asymptotic weight (g) Tc = mean habitat temperature ( ° C ) A r = Aspect ratio = (height o f caudal fin) /surface area o f caudal fin H d = food type (0=carnivorous, 1= herbivores+detritivores).  * 3.53  Hd  (2)  150 The novelty o f this approach is the connection between the readily measurable physical proportions o f the tail, and fish energetics. The Palomares and Pauly (1989) equation was derived from a regression analysis o f 33 Q / B estimates. Pauly (1989) confirmed these results using a data set o f 75 Q / B estimates. The regression model explained 75% o f the variation in the data set and A r , the aspect ratio, explained 50% o f the total variance. Equation (2) provides a simple empirical formula to estimate Q / B , and requires only four., easy-to-estimate parameters.  The Q / B values used by the above authors in their regression analyses were estimated from an age-structured model, integrated over the life span o f the group (Pauly 1986). It requires six parameters including estimates o f growth rate and mortality, in addition to Woo and other constants. However, both Palomares and Pauly (1989) and Pauly (1989), used natural mortality, M , to model the abundance o f various age groups (D. Pauly, pers. comm.). Equation (2) is thus, strictly speaking, only applicable to unfished systems, where Z = M . The fishery analysis presented in Chapter 2 clearly demonstrated that San Miguel B a y is very far from an unfished system, and hence Q / B estimates derived from equation (2) w i l l tend to underestimate Q / B . 4 5  This creates a problem because there is no alternative to using equation (2) to empirically estimate Q / B . There are insufficient data to calculate Q / B from the integral equation for more than four species, and thus avoid the M = Z assumption. Instead, a three-step method was  Since, if Z>M, there will be relatively more youngfishin the population consuming more by unit weight than a population dominated by old fish.  151 developed to adjust Equation (2) to include the effects o f fishing mortality and thus counter the M = Z assumption.  1. Q / B was first estimated for the four species using the intergration model o f Pauly (1986) . 46  The growth and mortality results from the length-frequency analysis (Chapter 2) and length-weight parameters calculated from the length-weight data from the trawl survey data  47  were used.  2. Q / B was estimated from equation (2). The mean habitat temperature in San Miguel B a y during 1991-1992 was 29 ° C (Mendoza et al. 1995a). The aspect ratios were kindly provided by M r . Francisco Torres Jr., I C L A R M . Values o f W o o were calculated from the trawl survey data. For some species, values or mean values from the literature were used. In almost all cases, these were values from Philippine fisheries. 3. The two forms o f Q / B estimate were compared for the four species. The mean ratio between them was used as a raising factor for the Q / B estimates o f the other fished ecogroups.  Results of Q/B estimates  Estimates o f Q/B from the integral equation were made for Leiognathus splendens, L. bindus, Otolithes ruber and Trichiurus haumela, representing the leiognathids, sciaenids, and medium  Q/B was estimated using the ICLARM software program MAXIMS (Jarre et al. 1990). See also Cinco and Diaz 1995  152 predators respectively . Q / B estimates using Equation (2) were made for all fished groups apart from the large zoobenthos feeders and the large predators. The results are shown i n Table 3.3.  In Table 3.4 the results o f the Q / B estimates from the two methods are compared for the four species. The mean ratio between them is 1.63.  Q/B Estimates from the Literature  In order to obtain the Q / B estimates for the rest o f the eco-groups i n the San Miguel B a y E C O P A T H model, comparable Q / B values in the literature were consulted. A s for the P / B estimates, the Q / B estimates derived above  49  were compared to Q / B estimates from various  tropical marine ecosystems i n Christensen and Pauly (1993a) and Pauly and Christensen (1993). There was no one ecosystem to which all the San Miguel B a y Q / B estimates were comparable. For this reason, each eco-group was considered separately and where possible, estimates from southeast Asian coastal areas were used. The results are detailed in Table 3.5.  The Brunei Darussalam (Silvestre 1993) Q / B estimate o f 280 year was used for the -1  zooplankton. The meiobenthos Q / B estimate (50 year" ) was taken from Celestun Lagoon, 1  M e x i c o (Chavez et al. 1993), the same source as the P / B estimate. The macrobenthos Q / B o f  The Maxims program requires six parameters, Woo, K, the VGBF growth parameter, "b" the exponent of the length/weight relationship, Z, total mortality, W , the smallest weight in the population and W , the maximum weight in the population and p, a constant. W was assumed to be 0.1 and W , the value recommended by the program (D. Pauly pers. comm.). p was calculated by first making the modelfitthe data to the Palomares and Pauly (1989) estimates of Q/B using M instead of Z. This was an iterative process. The Q/B estimates from Equation (2) were used for the comparison.  4 8  r  r  4 9  n m  m a x  153 Table 3.3 Input parameters and Q/B estimates from (1) Pauly's Integral Equation and (2) Palomares and Pauly's Empirical Equation. Exponent ECOPATH Group  Demersal Feeders Leiognathids  Representative Species  Woo (g)  Pelagics  Upeneus sulphurus Leiognathus splendens Leiognathus bindus Stolephorous commersoni Stolephorous indicus Alepes djedaba  Sciaenids  Otolithes ruber  898.5  Medium Predators  Arius thalassinus Saurida tumbil  4217.8  Trichiurus haumela  760.6  Engraulids  K (year ) 1  145.3 ' b  •b'of  Ar  length/ weight relationship 3.00°  2.72  Q/B  Q/B  (year )  (year ) 14.7  c  87.5  a  1.0  3.07  a  2.06  25.2  14.2  27.6  a  1.2  2.96  e  2.45  27.6  19.5  15.1 '  3.19  a  1.21  15.1  40.5 '  a  a  b f  b  8  3.32  h  1.56  14.3  42.1  bJ  2.76  j  3.18  20.5  3.06  a  0.85  1  1.25  5.0  0.8  5.75  a  0.40  a  3.02  b,k  679.6 b,m  3  0.43  a  3.02  a  2.66  a  11.3  5.6  7.6  A r = Aspect Ratios. Values provided by M r . Francisco Torres Jr., I C L A R M .  Parameter calculated from length frequency and length weight data from the 1992-1994 trawl survey of San Miguel Bay (see Chapter 2). Calculated from Leo and length-weight relationship, using Leo and/or length-weight parameters from the literature. Leo an average of values from the Philippines, Armada and Silvestre (1980), Corpuz et. al (1985), Silvestre (1986), Sambilay (1991) and Frederizon (1993). Frederizon (1993) Philippines Murty (1983). India Ingles and Pauly (1984) Philippines Leo an average of three estimates from the Philippines, Sambilay (1991), Corpus et. al (1985) and Padilla (1991). Cinco(1982) 'Corpus et. al(1985) Cinco(1982) Loo an average of values from Menon (1986) India, Wahyuono and Budihardjo (1985) Indonesia, Bawazeer (1987) Kuwait and Dwiponggo et. al (1986) Indonesia. 'Bawazeer (1987) Loo an average of two estimates from Manila Bay, the Philippines, Tiews et. al (1972b), Ingles and Pauly (1984).  a  b  c  d  c f  8  h  J  k  m  154 Table 3.4 Results of the Pauly (1986) Q/B estimation method compared to the Palomares and Pauly (1989) regression equation.  Species  Q/B from Pauly (1986) integral equation (year ) 25.2 27.6 11.3 7.6  Q/B from Palomares and Pauly (1989)  Ratio between the two Q/B estimates  (year ) 14.2 19.5 5.6 5.7*  1.77 1.41 2.02 1.33  —  —  1  Leiognathus splendens Leiognathus bindus Otolithes ruber Trichiurus haumela Mean Ratio  1  1.63  *This is the weighted mean of the Q/B estimates for Arius thalassinus and Saurida tumbil.  Table 3.5 The final Q/B estimates corrected for fishing mortality. Ecopath Group Zooplankton Meiobenthos Macrobenthos Sergestid Shrimp Penaeid Shrimp Large Crustaceans Demersal Feeders Leiognathids Leiognathus splendens Leiognathus bindus Engraulids Pelagics Sciaenids Medium Predators Large Zoobenthos Feeders Large Predators  Q/B (year ) 192 50 25.9 310 19.2 . 8.5 14.7  -  14.2 19.5 14.7 17.9 5.6 5.7 8.2 8.4  Final Q/B (year ) 192 50 25.9 506 31.4 13.9 24.5 26* 25.2 27.6 24 28.9 11.3 7.6 11.7 11.9 1  Values in italics are those that are changed in the final analysis. This is the weighted mean of the two leiognathid species.  155 25.9 year" is the weighted mean o f the Q / B estimates for annelid worms, molluscs and "other" 1  benthos from the G u l f o f Thailand and Brunei Darussalam.  There was no Q / B estimate for the sergestid shrimp from the G u l f o f Thailand so the estimate o f 310 year" for the small Crustacea groups from the Brunei Darussalam ecosystem (Silvestre 1  1993) was used. The Q / B estimate o f 8.5 year" for the large crustaceans group was taken from 1  the Campeche Bank, G u l f o f M e x i c o (Arreguin-Sanchez et al. 1993a), the same source as the P/B estimate. The penaeid shrimp Q / B was also taken from the Campeche Bank ecosystem.  The G u l f o f Thailand Q / B estimates were used for the large zoobenthos feeders and the large predators.  Q/B was estimated above for the pelagics representative, Alepes djedaba using the empirical formula. However, A. djedaba represents only 25% o f the pelagic biomass. In order to obtain a more representative Q / B estimate for the pelagics group, literature values were also consulted. The following Q / B values were assumed for three other members o f the pelagics , 50  Loligo, Q/B=25 year" , (Pauly and Christensen 1993); small pelagics (Clupeids and 1  Dussumierids), Q/B=17.9 year" ; (Pauly and Christensen 1993) and other Carangids, mean 1  Q/B=9.2 year" (Arreguin-Sanchez 1993b, Mendoza 1993). The weighted mean Q / B o f these 1  values and the Q / B o f A djedaba is 17.9 year" . 1  Although 41% of the pelagics biomass is Scomberomorus commerson, only juveniles are found in the Bay (L reported in this study was 37 cm (TL), Pauly (1982b)). Since Q/B is the mean consumption biomass ratio over the life of a fish, S. commerson cannot be used to represent the group. max  156 The Final Q/B Estimates, Corrected for Fishing  Mortality.  The initial and final Q / B estimates are listed in Table 3.5. Both the empirically derived and the literature derived values were raised by 1.63 to allow for the effects o f fishing.  Estimation of Biomass  The biomass o f each eco-group is estimated from the biomass estimate in Chapter 2. The calculation o f biomass is made on the basis o f the relative abundance o f each eco-group in the trawlable biomass (Table 3.1). However, biomass could only be calculated for those groups which appear in the trawl survey. For the other groups, except the detritus, no estimate was made.  Estimation of Detritus  Biomass  The detritus biomass was estimated using an empirical relationship derived by Pauly et al. (1993). It relates detritus biomass to primary productivity and euphotic depth.  l o g D = -2.41 + 0.954 l o g P P + 0.863 l o g E 10  where,  l0  l 0  (3)  157 D = detritus standing stock (gCm" (grams o f Carbon per square metre)), PP = primary 2  productivity (gCm" year"'), E = euphotic depth (m). 2  The fit o f the regression equation to the data is not very good, but as suggested by Pauly et al, it "might be considered sufficient in cases where no other information is available" (1993:13).  Ricafrente-Remoto and Mendoza (1995) made three estimates o f primary production in San M i g u e l B a y using the 'light-dark bottle technique', nutrient data and chlorophyll ' a ' data. Their results were quite variable. The results using the nutrient data gave an average annual value o f 391 gCm" year"', which is comparable to values in the literature. This value was 2  51  assumed to represent the average annual primary production i n San M i g u e l B a y .  The euphotic depth is calculated from the Beer-Bouger L a w where,  l n I ( l ) - l n I ( 2 ) = k(D(2)-D(l))  and, I (1) = 100% irradiance (at the surface), 1(2) = 1% irradiance (at the euphotic depth), D (1) = depth at surface (Om), D(2) = euphotic depth, k = light attenuation co-efficient.  ' k ' is calculated from the relationship k= 1.45/Ds (Walker 1980), where Ds is the secchi depth. A n average secchi depth o f 2.4 m was estimated from Figure 3 in Mendoza et al. (1995a). The  The "light-dark bottle technique" produced an average annual PP of 2833.14 gCm" year"', and the chlorophyll 'a' method an average annual PP of 162.57 gCm" year".  51  2  2  1  158 range was 0 — 10 m. Solving the Beer-Bouger L a w for D(2) produces an average annual euphotic depth o f 1.92 m.  Substituting the primary production value o f 391 g C m ' year' , and the euphotic depth o f 1.92 2  1  m into equation (3) produced a detritus biomass o f 1.98 gCm" . This translates into 19.8 tkm' 2  2  using a conversion factor o f lOg wet weight = l g C (as suggested by Christensen and Pauly 1992a:20).  Export  (Catch)  The catch o f each eco-group was calculated from the catch figures in Chapter 2 and expressed as tkm" , taking the area o f San M i g u e l B a y as 1115km (Table 3.6). 2  Ecotrophic  2  Efficiency  The ecotrophic efficiency is a measure o f productivity that is not "other mortality". In other words, ecotrophic efficiency is the proportion o f the production that is exported or consumed by predators in the ecosystem. It has no units and is difficult to measure (Christensen and Pauly 1992a). Ecotrophic efficiencies o f 0.95 are commonly used as approximations (Christensen and Pauly 1992a, 1993, Polovina 1984). This procedure was adopted here.  Input values for the parameters P / B , Q / B , biomass, export and ecotrophic efficiency are given in Table 3.6.  Table 3.6 Input parameters for the E C O P A T H model o f San Miguel Bay. Ecopath Group Phytoplankton Zooplankton Meiobenthos Macrobenthos Sergestid Shrimp Penaeid Shrimp Large Crustaceans Demersal Feeders Leiognathids Engraulids Pelagics Sciaenids Medium Predators Large Zoobenthos Feeders Large Predators Detritus  P/B (year') 200 67 10 6.8 62 6.48 2.8 6 9.42 6 5.45 4.39 2.5 1.3 2  -  Q/B (year ) 1  192 50 25.9 506 31.4 13.9 24.5 26 24 28.9 11.3 7.6 11.7 11.9  -  Biomass (tkm- ) 2  -  0.107  0.144 0.022 0.458 1.03 0.167 0.224 0.191 0.302 0.063 0.008 19.8  -  Export/Catch (tkm )  EE  2  0.036 2.403 1.677 0.854 1.273 1.434 1.071 1.149 3.388 1.090 0.309 0.131  0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95  -  -  160 Diet  Composition  Empirical data from diet studies conducted in San M i g u e l B a y exist for five o f the 15 ecogroups represented in the E C O P A T H model. For the other eco-groups, information from the literature was used to estimate diet composition. The main source o f information were diet compositions from other E C O P A T H models i n Christensen and Pauly (1993a), although other sources were also used. O f the E C O P A T H models in the literature, two are derived from similar habitats to San M i g u e l B a y , that is the models for the G u l f o f Thailand (Pauly and Christensen 1993) and Brunei Darussalam (Silvestre et al. 1993). The latter is based on an earlier model from Malaysia (Liew and Chan 1987). A l l three models are from a similar latitude and longitude to San M i g u e l B a y and are also shallow. Where possible, the diet compositions from these two models were used to represent the diet o f the eco-groups in San M i g u e l B a y . The resultant diet matrix is shown i n Table 3.7, and the details o f the derivation for each group are given below. Diet composition is measured by weight . 52  Zooplankton  /  The G u l f o f Thailand and Brunei Darrussalam models had similar zooplankton diets comprising phytoplankton (70% and 65% respectively), 30% and 25% detritus and, in Brunei  MacDonald and Green (1983) discuss three methods by which stomach contents can be measured, % occurrence of food items, numerical abundance of food items and the weight or volume of food items. Their analysis demonstrated that the three measures are highly correlated on the first principal component of a PCA, indicating that there is some redundancy. This results indicates that it is not necessary to take all three measurements in order to accurately measure stomach content. However, the three measures do measure different things. Percent occurrence measures the variability in predator diets. Numerical abundance gives information on the density dependent prey eating behaviour of predators and can therefore give insight into feeding behaviour. The weight or volume of food items measures the nutritional value of a prey species.  161 Darussalam, 10% zooplankton. Since there was no basis for choosing any one set o f values, the values were averaged over the two systems.  Meiobenthos  The only diet composition data found for meiobenthos are from a lagoon in Veracruz, M e x i c o (Cruz-Aguero 1993). In the absence o f other data, these values were used for the meiobenthos in San Miguel B a y .  Macrobenthos  The macrobenthos i n San M i g u e l B a y is dominated by annelid worms (80.5%). The other components are 3.8% molluscs and 15.7% "others" (Garces et al. 1995). Data on diet composition for annelid worms and molluscs were taken from other E C O P A T H models (Arreguin-Sanchez et al. (1993a), Arreguin-Sanchez et al. (1993b) Chavez et al. (1993) and Vega-Cendejas et al (1993)) since the two models noted above did not have separate data on these groups. Data for the "others" were assumed to be represented by the heterobenthos groups in the G u l f o f Thailand and Brunei Darussalam. For each group, the average diet composition across models was calculated. A weighted average, based on the relative abundance o f each group in the macrobenthos, was taken as the final representative diet composition for the macrobenthos.  162 Sergestid  Shrimp  Sergestids are small pelagic crustaceans, essentially zooplankton (Omori 1975). However, there is no information on their dietary preferences. In all likelihood they w i l l feed on small herbivorous zooplankton, phytoplankton and detritus. Thus the sergestid shrimp diet is a "guesstimate" o f 40% zooplankton, 50% phytoplankton and 10% detritus.  Penaeid  Shrimp  Tiews (1976) provides information on the diet o f four penaeid species in San Miguel B a y (P. semisculatus,  P. merguensis, P. canaliculatus and Metapenaeus  monocerus). Unfortunately  these data were recorded as % occurrence in the stomachs and cannot be used directly since the diet composition is measured in % weight. However, they can be used as a guide when using quantitative data from other systems. Data from six E C O P A T H models were used (Abarca-Arenas and Valero-Pacheco (1993), Arreguin-Sanchez et al. (1993a), ArreguinSanchez et al. (1993b) Chavez et al. (1993), Cruz-Aguero (1993) and Vega-Cendejas et al. (1993)) and the mean diet composition compared to Tiews et al. 's figures. The resultant diet composition is given in Table 3.7 The same food groups appeared in both data sets, although in Tiew et al. 's data, there was less emphasis on the detritus and more on meiobenthos. This could be due to the different methods o f measurement.  163 Large  Crustaceans  The large Crustacea are composed almost entirely o f the swimming crab Portunus  pelagicus.  Diet data from two Mexican systems for crabs o f the Callinectes sp. were used (ArreguinSanchez et al. (1993a), Arreguin-Sanchez et al. (1993b)), plus the large Crustacea data from Brunei Darussalam and the G u l f of Thailand. The resultant mean data were compared to some data from Edgar (1990) and Wassenberg and H i l l (1987) who describe the diet of P. pelagicus as consisting mainly o f benthic invertebrates such as bivalves, polychaetes and crustaceans. The diet in Table 3.7 reflects this preference.  Demersal  Feeders  This group is composed o f 20 fish families, 80% o f which are the Mullidae, Nemipteridae, Tetraodontidae, Gobiidae and Apogonidae. In order to account for this diversity and the fact that there was no direct diet information from San M i g u e l B a y for any o f these species, diet information for similar groupings o f demersal feeders from the G u l f o f Thailand and Brunei Darussalam E C O P A T H models were used in combination with diet data for some o f the individual families from other E C O P A T H models (Abarca-Arenas and Valero-Pacheco (1993), Arreguin-Sanchez et al. (1993a), Arreguin-Sanchez et al. (1993b) Chavez et al. (1993), Cruz-Aguero (1993), Mendoza (1993) and Vega-Cendejas et al. (1993)). The resultant diet composition shown in Table 3.7 is based on the weighted mean diet o f the different families o f demersal feeders.  164 Table 3.7 Diet Composition for the E C O P A T H model. Figures i n brackets were changed during the balancing process, figures in bold are the new values. Ecopath Group Zooplankton  ZP  Meiob  Macrob  0.05  Meiobenthos  0.1  Macrobenthos Sergestid Shrimp Penaeid Shrimp Large Crustaceans Demersal Feeders Leiognathids Engraulids Pelagics Sciaenids Medium Predators Large Zoobenthos Feeders Large Predators Phytoplankton  0.70  Detritus  0.25  Serg  Pen  LC  DF  Leiog  0.40  0.05  (0.10) 0.04  0.10  0.275  0.05  0.050  0.45  0.425  0.10  0.150  0.05  0.10  0.05  0.40  0.13 (0.30) 0.38 (0.15) 0.04 (0.05) 0.04  0.05  (-) 0.01  0.9  0.05  0.50  0.05  0.85  0.10  0.40  (-) 0.00 (0.40) 0.36  0.050 0.25  0.050  Z P = Zooplankton, Meiob = Meiobenthos, Macrob = Macrobenthos, Serg = Sergestids, Pen = Penaeids, L C = Large Crustaceans, D F = Demersal Feeders, Leiog = Leiognathids, Some values are rounded to two figures, thus not all columns add up to 1.  165 Table 3.7 (cont.) Diet Composition for the E C O P A T H model. Figures in brackets were changed during the balancing process, figures in bold are the new values. Ecopath Group Zooplankton Meiobenthos Macrobenthos Sergestid Shrimp Penaeid Shrimp Large Crustaceans Demersal Feeders Leiognathids  Eng  Pel  Sci  MP  0.40  0.50  0.10  (0.10) 0.09  0.15  0.10  0.05 0.30  0.25  0.05  0.25  0.15  0.1  0.15  0.05  0.10  LIB  (0.10) -  (0.19) 0.14 0.15  0.9  (0.10) 0.09  0.05  (0.20)  0.03  (0.11) 0.18 0.10  —  Pelagics  0.10 0.02  Sciaenids Medium Predators  Detritus  (0.05) 0.03 (-) 0.03  Large Zoobenthos Feeders Large Predators Phytoplankton  (0.10) 0.04  (0.15) 0.13 (0.10) 0.14 (0.15) 0.16 (0.15) 0.20 (0.15) 0.20 (0.10) 0.13  0.10  Engraulids  LP  H 0.20 0.05  0.05  Eng = Engraulids, Pel = Pelagics, Sci = Sciaenids, M P = Medium Predators, L Z B = Large Zoobenthos Feeders, L P = Large Predators.  .166 Leiognathids  Data from Palomares et al. (1995b) indicate that crustaceans formed 46% o f the diet o f Leiognathus bindus, but no other food items were identified. There is data on the diet composition o f leiognathids from Tiews et al. (1972a) which is measured in % occurrence. These data for L. splendens, L. bindus, S. ruconius and S, insidiator  were compared to diet  data for leiognathids from the G u l f o f Thailand and the Brunei Darussalam E C O P A T H models. The diet data recorded by Tiews et al. are qualitatively comparable to the diet data in the two E C O P A T H models. The relative proportions o f the E C O P A T H models were used and the resultant diet is given in Table 3.7.  Engraulids  There are some data on the diet compositions o f Stolephorus commersonnii and S. indicus from San Miguel B a y (Palomares et al. 1995b) which indicates that crustaceans and plankton are an important part o f their diet. However, these data are not detailed enough to quantify the diet o f the engraulids. Diet data were taken from Abarca-Arenas and Valero-Pacheco (1993) and Arreguin-Sanchez et al. (1993b). About 40% o f the diet were crustaceans which fits with the San M i g u e l B a y data.  167 Pelagics  The pelagics represent six families. Since there were no empirical diet data, data for the clupeids, carangids, scombrids and loligo were taken from several E C O P A T H models (Arreguin-Sanchez et al. (1993a), Arreguin-Sanchez et al. (1993b), Cruz-Aguero (1993) and Mendoza (1993)). A weighted average was taken o f these groups plus an "others" groups representing the rest o f the pelagics. Data for the "others" group were taken from the pelagics in the G u l f o f Thailand and the Brunei Darussalam E C O P A T H models.  Sciaenids  The most important food item in the diet o f Otolithes ruber is crustaceans, followed by "other", then fish (Palomares et al. 1995b). This compares well with the fish and crustacean components o f the diet composition o f sciaenids from Venezuela (Mendoza 1993). It was assumed that the Venezuelan sciaenid diet is representative o f the San M i g u e l B a y sciaenid diet.  Medium  Predators  The medium predators are another diverse group, although over 60% o f the biomass is made up o f Trichiurus haumela (Table 3.1). However, there were no empirical diet data for any o f the families represented by the medium predators. Instead, diet data for the Synodontidae and the Ariidae were taken from E C O P A T H models (Arreguin-Sanchez et al. (1993b), Cruz-  168 Aguero (1993), Mendoza (1993)). The medium predators diet was then taken as the weighted mean o f these data plus the medium predator data from the G u l f o f Thailand and the Brunei Darussalam E C O P A T H models.  Large Zoobenthos Feeders and Large  Predators  In the absence o f other data, the diets o f the large zoobenthos feeders and large predators were taken as the average o f the diet compositions described in the G u l f o f Thailand and the Brunei Darussalam E C O P A T H models.  The E C O P A T H Parameters  It is recognised that some o f the parameters that have been determined for the E C O P A T H model are very approximate. Still the approach taken here is justified for three reasons. First, Hoenig et al. (1987:331), writing on the use o f indirect rapid assessment and the use o f comparative studies, conclude that comparative studies can "lead to better theoretical models and understanding o f the systems by suggesting structural relationships which require explanation". Secondly, Sugihara et al. (1984:139) suggest that "it is possible that coarse allometric estimates o f growth/mortality parameters may be more appropriate for certain (large-scale) models than are data obtained directly from individuals". They are suggesting that coarse, but representative measures o f parameters for aggregated groups may be more suitable than precisely defined parameters for single species. This approach was adopted here for the more heterogeneous eco-groups. Thirdly, i f approximate parameter values were not estimated  169 or taken from the literature, it would not be possible to even attempt an ecosystem model o f San M i g u e l B a y . Thus, instead o f probing and modelling, learning and gaining knowledge, with assumptions in full view, no knowledge would be gained nor new hypotheses formulated.  The final parameters which were used in the first run o f the E C O P A T H model are given in Table 3.6 above.  Running  and Balancing  the ECOPATH  Model  It was noted that E C O P A T H models rarely, i f ever, balance when first run with derived parameters. This was indeed the case here. The initial problem with the San M i g u e l B a y E C O P A T H model concerned the biomass estimates. The model was first run using the biomass estimates from the trawl survey and the P / B and Q / B estimates. E C O P A T H recalculates the E E when the other three parameters are entered. Some resultant E E s were above 1, and thus there were also negative flows to the detritus, G E s above 0.3, and some respiration/biomass ratios were too high. Essentially, the biomass was too low to support the catches being removed from the ecosystem.  This problem was first addressed by entering the E E estimates for all groups apart from the detritus, large predators, medium predators and large zoobenthos feeders and allowing the model to estimate biomass. This approach was more successful and corrected many o f the problems noted above. One consequence is that the estimated total fished biomass is now three  170 to four times higher than the biomass estimated from the trawl survey in Chapter 2. The approach was refined to the point where only the biomass o f the large predators was entered. Through an iteration method, the large predators biomass was reduced as much as possible in order to minimise the total fished biomass. Total biomass is reduced when the biomass o f the top predator is reduced because E C O P A T H is a top-down modeling method and all flows are scaled to the biomass levels at the top o f the food web. Christensen and Pauly (mimeo, Marine Ecosystem Management: A n ode to Odum) used this method to simulate climax ecosystems by increasing the biomass o f top predators, thereby causing increases in biomass at lower levels of the food web .  The fitting criteria noted above were used to constrain the model. This entailed several minor changes in the input parameters, including the diet matrix. The P / B ratios o f the leiognathids and medium predators were decreased in order to reduce their G E s below 0.3. Both new values remain within the confidence limits o f the Length Converted Catch Curve estimates o f Z in Chapter 2 . The P / B o f the pelagics was increased in order to increase the G E above 0.2. This 5 4  was considered reasonable since the Z estimate in Chapter 2 was highly uncertain, and the value o f 5.45 year" in Table 3.6 is a composite value. 1  The Q/Bs o f the sergestids was decreased by almost 50% in order to reduce their respiration/biomass ratio below 100. This value approximates the Q / B value o f the  The authors found that when climax ecosystems were simulated, the ecosystem took on many of the properties predicted by Odum's theory of ecosystem development (1969), particularly the retention and recycling of detritus. For the leiognathids, the new P/B value of 7.5 year"' is within the confidence limits of the Length Converted Catch Curve estimate of Z for Secutor ruconius and Leiognathus bindus. However, it only falls within the confidence limits of one of the Z estimates fori, splendens (Table 2.7). 54  171 zooplankton o f 280 year" . The latter was reduced to the same value as the sergestids, since 1  both are zooplanktivores . The Q / B o f the sciaenids was also changed. In this case, Q / B was 55  changed because the value o f G E was too high. G E was set to 0.27, the P / B and E E values were entered and the model allowed to estimate Q / B . This produced a Q / B estimate o f 16.3 5 6  year" . 1  Small changes to the diet o f the pelagics, medium predators and large predators were made because their predation mortality on some o f the lower eco-groups was too high. Changes were also made to the large crustaceans diet . The new diet composition in shown (in bold) i n 57  Table 3.7.  The balancing o f the model was performed manually, iteratively changing parameters in order to better fit the model. The latest version o f E C O P A T H , E C O P A T H 3.0 contains a routine which allows the user to input a range o f values and ascribe probability ranges to them. E C O P A T H 3.0 then fits the parameters using various criteria o f best fit ( I C L A R M 1995). The above parameters were tested using this routine, but essentially, these parameters are the "best fit" . 58  The proportion of food intake which is not assimilated was also increased from the default value of 0.2 to 0.4, for these two eco-groups, following Christensen (1994) and Christensen and Pauly (1992a), in order to reduce the resp/biomass ratio. The model was made to estimate Q/B, instead of P/B, because the latter was considered a more robust parameter since it was directly estimated from length-frequency data in Chapter 2. The changes to the large crustaceans diet were made in response to the testing of a prototype ECOSIM model (see Chapter 4), and on the basis of diet infonnation in Wassenberg and Hill (1987). Slightly differentfitswere produced, but included parameters which resulted in GEs above 0.3 and respiration/biomass ratios above 100. 56  57  58  172  Results  Some Basic Results  Some o f the E C O P A T H results are shown in Table 3.8. Figure 3.1 is a box model which shows the relation o f the eco-groups to one another, their trophic p o s i t i o n and the nature and 59  strength o f the flows between them. The ecosystem so described is one which is more dependent on its detritus-benthic components than the pelagic components o f the ecosystem. O f the total flow in the ecosystem, 56% originates from the detritus and 44% from primary producers.  The ecosystem spans more than four trophic levels. The large predators occupy a trophic level o f 4.1 and the fishery operates at an average trophic level o f 4.0. Thus the large predators occupy a higher trophic level than the fishery. Most o f the eco-groups however occur at a trophic level o f between 2.5 and 3.5. O f these, the engraulids and pelagics are mainly dependent on the pelagic strands o f the food web. The others, the sciaenids, leiognathids, demersal feeders, large zoobenthos feeders, large crustaceans and penaeids rely on the detritus food chains. Given the large number o f groups occurring at a similar trophic level, there is likely to be strong competitive interactions between groups, i n addition to predator/prey relationships.  ECOPATH calculates trophic levels in two ways (Christensen and Pauly 1992a). In Figure 3.1, fractional trophic levels are used. Thus, the trophic level of each eco-group is calculated as the weighted average of its prey's trophic level. Producers and detritus are assigned a trophic level of 1. ECOPATH also calculates discrete trophic levels for the entire system using an aggregation routine - see note 63 below.  173 Table 3.8 Selected results from the San Miguel Bay E C O P A T H model. B P/B Q/B (tkm') (year ) (year )  EE  GE  Harvest (tkm' year' ) 1  l.ZP  0.757  67  260  2. Meiob  3.973  10  50  3. Macrob  7.786  6.8  25.9  0.95 0.263  4. Serg  0.215  62  260  5. Pen  1.286  6.48  6. L C  1.154  7. D F 8. Leiogs  0.95 0.258  0  0.2  0  Trophic Flow to Omnivory Resp/ Index Biomass Level Detritus (tkm' year ) 81.237 89 2.05 0.053 2  2.11  41.718  0.111  30  0.036  2.11  42.98  0.111  13.92  0.95 0.238  2.403  2.42  23.008  0.266  94  31.4  0.95 0.206  1.677  2.60  8.495  0.303  18.64  2.8  13.9  0.95 0.201  0.854  2.75  3.371  0.332  8.32  0.336  6  24.5  0.95 0.245  1.273  2.88  1.747  0.279  13.6  0.348  7.5  26  0.95 0.288  1.434  3.03  1.943  0.132  13.3  0.39  6  24  0.95  0.25  1.071  3.18  1.987  0.119  13.2  10. Pel  0.296  6.65  28.9  0.95  0.23  1.149  2.96  1.809  0.274  16.47  11. Sci  0.972  4.39  16.3 0.95  0.27  3.388  3.38  3.374  0.097  8.617  12. M P  0.651  9. Eng  13. L Z B 14. L P 15. Ph/P 16. Detritus  0.95  2.2  7.6  0.95 0.289  1.09  3.70  1.061  0.188  3.88  0.25  1.3  11.7  0.95 0.111  0.309  3.08  0.602  0.073  8.06  0.095  2  0.131  4.14  0.285  0.112  7.52  12.825  70  0  -  0.283  -  19.8  -  11.9 0.689 0.168 0  -  0.2  -  0  1  717.795  0.462  -  0  1  -  Figures in italics are estimated by E C O P A T H . ZP = Zooplankton, Meiob = Meiobenthos, Macrob = Macrobenthos, Serg = Sergestids, Pen = Penaeids, L C = Large Crustaceans, D F = Demersal Feeders, Leiog = Leiognathids, Eng = Engraulids, Pel = Pelagics, Sci = Sciaenids, M P = Medium Predators, L Z B = Large Zoobenthos Feeders, L P = Large Predators, Ph/P = Phytoplankton.  n o >, c CQ  .2 £ O o  CD O O  n o  i  LI a.  CJ  o  o ..  U  3  CI.&  CD  i  CO  co  5 TO  •2 J  •II co  c  .2"  ;? o~, „ i-. o 3  >;  M-  ,2? o —.  i>  3  S 2 g cS  OO "o  <4-c  o  >  JU *a3 o  S o <u 5 -fl 03  -*-»  "53 T3 C  3 j-j  co  3  -2  a. o ^  O T3 —'  £  CD  3 OX)  c —  175 The omnivory index in Table 3.8 is a measure o f the variability i n the trophic level at which the eco-groups feed (Christensen and Pauly 1992a). A value o f 0 indicates complete specialisation in prey selection. Higher values indicate less specific feeding habits. The sciaenids and the large zoobenthos feeders have low omnivory indexes. The penaeids, large crustaceans and demersal feeders have the highest values. This makes sense for the former are recognised as generalists and the demersal feeders eco-group is composed o f a large number o f species. However, the omnivory index does not capture well, i f at all, the variability o f prey within trophic levels. For example, the prey o f the large predators includes (in almost equal proportions) demersal feeders, leiognathids, engraulids, pelagics, sciaenids and medium predators plus some macrobenthos. However, the engraulids have a slightly higher omnivory index, yet they feed on fewer eco-groups, but over a greater number o f trophic levels.  Indices of Maturity: Is San Miguel Bay a Mature Ecosystem?  There are several measures that indicate the level o f maturity o f an ecosystem. These measures are adapted from the maturity index o f Odum (1969) and systems theory developed by Ulanowicz (1986). Christensen (1995, 1994) and Christensen and Pauly (1993c) describe these measures and relate them to 41 E C O P A T H models. Christensen (1995) showed that for most of these measures, the articulation o f the model (the number o f boxes) does not matter, when the models include at least 8-12 boxes. Thus comparisons between models with different numbers o f boxes can be made. Their analyses can be usefully applied to the San M i g u e l B a y  176 E C O P A T H results. A n attempt is made here then, to compare, where possible, these measures for the 41 E C O P A T H models with the San M i g u e l B a y model.  A s ecosystems mature, they should become more dependent on detrital flows than on flows from primary producers Odum (1969). 56% o f the total flow in San M i g u e l B a y originates in the detritus.  The ratio o f the total primary production to total respiration (PP/R) is 2.35. Odum (1971) proposed that in immature systems, primary production would be much greater than respiration while in mature systems, the ratio would approach 1. Christensen and Pauly found that for the 41 E C O P A T H models, the modal P P / R value was between 0.8 and 1.6. They compared this to the work o f Lewis (1981) who found the model value to be between 1.6 and 3.2. The San M i g u e l B a y value is comparable to latter P P / R values.  The ratio o f the total system productivity to total system biomass (P/B) is high in developing systems and low in mature systems (Margalef 1968). The San M i g u e l B a y value o f 28.7 falls within the lower range o f those in Christensen and Pauly (1993c).  Christensen and Pauly concluded that cycling in ecosystems is related to maturity, as proposed by Odum (1969). C y c l i n g is quantified in E C O P A T H using the Finn Cycling Index (FCI, Finn 1976), expressing the percentage o f the total throughput which is actually recycled. The total throughput is the sum o f all flows, that is, consumption, exports, respiratory flows and flows to the detritus. The F C I in San Miguel B a y is 7.5%. When F C I was plotted against system  177 overhead , Christensen and Pauly found that ecosystems with very low cycling, such as 60  upwelling systems have low system overhead. System overhead is positively related to the stability o f the ecosystem. It was suggested by Christensen and Pauly that ecosystems with very high F C I may be less stable because they need to maintain a pattern o f intricate flows. However, Vasconcelles et al. (In press), compared the response o f 18 systems to perturbation, and concluded that F C I is positively related to system maturity. The San Miguel B a y F C I and system overhead (71.3%) place it at an intermediate level (Figure 6, Christensen and Pauly 1993c) which Christensen and Pauly suggest may be optimal.  Christensen and Pauly also plotted the F C I against the P P / R ratio and against mean path length. The mean path length is the average number o f eco-groups that a unit o f flow passes through on its way from inflow to outflow, and is calculated as Throughput/(S Export + 2 Respiration), (Christensen 1995). Path length w i l l be affected by diversity o f flows and cycling. Since these increase with increasing maturity, it is assumed that long path lengths are associated with mature ecosystem. San M i g u e l B a y again falls within the range o f the 41 E C O P A T H models described in Christensen and Pauly (1993 c Figures 8 and 9 ) . The mean path length in San Miguel B a y is 2.89. This would be classified within either the tropical shelves category or tropical estuaries o f Christensen and Pauly. The tropical systems so far examined, tended to have longer path lengths than the non-tropical ecosystems.  ' System overhead is a calculated statistic in ECOPATH. It is the difference between two measures, the ascendancy and capacity of an ecosystem. Ascendancy is a measure of the average mutual information in a system, scaled by system throughput. The upper limit of ascendancy is the capacity. The overhead, the difference between these two, is a measure of how much the ascendancy can increase. It is thus a measure of what the system has in reserve, a measure of its ability to cope with change or stress. It is a measure of stability (Christensen and Pauly 1992a).  178 The residence time o f energy in the ecosystem is also a measure o f maturity (Harmon 1979, Herendeen 1989). It is measured as the ratio between the total system biomass and the sum o f all respiratory flows and export. In San Miguel Bay, the residence time is 0.035 years. This places it at an intermediate level in the overall ranking, by residency time o f the 41 E C O P A T H models in Christensen and Pauly (1993c, Figure 15).  Christensen (1994, 1995) ranked the 41 models using a maturity index based on seven o f Odum's attributes. He then investigated the behaviour o f different goal functions with respect to this maturity ranking, again using the 41 models. One o f the goal functions used by Christensen was ascendancy. Ascendancy is assumed to be a measure o f the growth and development o f an ecosystem (Ulanowicz 1986). Growth is measured by the increase in the energy throughput o f an ecosystem while development is measured by the increase in information content o f the flows in an ecosystem. Ascendancy is calculated then as the product o f the energy throughput and the average mutual information (Christensen and Pauly 1992b, after Ulanowicz and Norden 1990). Christensen found that there was no good relation between ascendancy and maturity and concluded that only the growth part o f the function was reflected, due to a great variation in the system throughput between ecosystems. He suggests that the computational aspect o f ascendancy should be reconsidered.  However, Christensen (1995) found that another goal function, relative ascendancy was highly,  179 but negatively correlated with maturity . Relative ascendancy is the ratio o f ascendancy to 61  capacity (the upper limit of.ascendancy) and is a dimensionless function. The relative ascendancy in San M i g u e l B a y is 28.7. This places San M i g u e l B a y amongst the 12 most mature models, ranked by Christensen on the basis o f their relative ascendancy values  . The  other top 12 models included the G u l f o f Thailand, Brunei Darrussalam, Campeche and Yucatan, that is, other tropical shelf systems.  Compared to the 41 E C O P A T H models ranked by Christensen (1994, 1995) and Christensen and Pauly (1993c), the San M i g u e l B a y ecosystem is at an intermediate level to high level o f maturity. The results also indicate that the San M i g u e l B a y model is comparable to the other model results, giving it some validation. However, there are also some inconsistencies which are noted below.  This result is contrary to Ulanowicz's premise that ascendancy increases with ecosystem maturity. Christensen (1995) offers a rationale "Bearing in mind that the complementary measure to the relative ascendancy is the system overhead, and that the overheads are a measure of system stability (Rutledge et al. 1976) this can be interpreted to mean that the maturity ranking is strongly (positively) correlated with ecosystem stability", (see also footnote 60) The relative ascendancy values for the 41 models were kindly provided by V. Christensen 61  62  180 Some Inconsistencies in the E C O P A T H Results  The transfer efficiencies (TE) between trophic levels are high, ranging from 16.9 to 22.8 (Table 3.9)  . The classic value for T E is 10%, that is, one tenth o f the energy that flows into a  trophic level is transferred to the next trophic level (Lindeman 1942). E C O P A T H calculates the T E as the ratio between the total exports from and predation on a given trophic level and the energy throughput or consumption by that trophic level (Christensen and Pauly 1992a). This definition includes the fishery as predators o f the ecosystem. Herein lies the cause o f the high T E s . The fishery exerts a very high degree o f fishing mortality at trophic levels 3, 4 and 5 (Table 3.9). Fishing accounts for 40% o f the T E at trophic level 3, 72.5% o f the T E at trophic level 4 and 87% o f the T E at trophic level 5. The T E at trophic level 6 is entirely accounted for by mortality due to fishing. H i g h fishing mortality is also cited by Moreau et al. (1993) to explain the high transfer efficiencies found in Lake Victoria.  High fishing mortality does not explain the T E o f 17.7 for trophic level 2. However, this value is comparable to the TEs at trophic level 2 for other tropical coastal systems such as Brunei Darrussalam and several coastal ecosystems from Mexico. It is higher than the G u l f o f Thailand T E however. Christensen and Pauly (1993c) note that high G E s lead to high T E s , since G E is the T E o f the eco-groups . They also note that the high T E s found for several  TEs are calculated from the trophic aggregation routine in ECOPATH. This routine aggregates the ecosystem into discrete trophic levels and allocates the flows within the ecosystem between these trophic levels. The TEs thus refer to the discrete trophic levels, not to the fractional trophic levels in Figure 3.1. With discrete trophic levels, the eco-groups are not assigned to any one trophic level as was the case with the fractional trophic levels. Here, if 60% of the prey are detritus and phytoplankton and 40% are zooplankton, as in the case of the sergestids (Table 3.7), then these are the relative fractions of the flow through the group which are attributed to trophic levels 2 and 3 respectively. The total flow at each trophic level is thus the sum of the relative flows over the ecogroups.  181 Table 3.9 Transfer efficiencies, T E , and related indices between trophic levels in San M i g u e l Bay.  Trophic Level Transfer Efficiency (%)  II  III  V  IV  VI  17.66  21.77  22.75  21.10  16.67  104.44  13.63  0.85  0.02  0  3.30  9.10  2.25  0.16  0.004  107.74  22.73  3.10  0.18  0.004  609.99  104.44  13.63  0.85  0.024  3.07  40.03  72.50  86.67  100  Outflows Consumption by predators (tkm" year"') Consumption by fishery (tkrn" year"') Sum of all out flows 2  2  2  1  (tkm" year") Inflows Throughput/Consumption % of T E due to fishing mortality  182 tropical coastal shelf models may be a result o f lack o f independence in model construction. Since these models were used as a basis for some o f the input parameters o f the San M i g u e l Bay model, this reasoning may also help to explain the high T E s found for San Miguel B a y .  The T E s indicated that San Miguel B a y is an efficient ecosystem, with respect to the transfer of energy up the food web. The high level o f fishing mortality contributes to this overall efficiency.  The production/biomass (P/B) ratios are also high. Some o f the P/Bs were derived from the mortality estimates in Chapter 2. It was suggested that the high Zs may be due to inaccuracy in the estimation method, that they may be due to export or that they reflect high mortality due to the fact that a large proportion o f fish were juveniles. These explanations remain valid. However, some o f the P / B estimates were taken from the G u l f o f Thailand which also has high P/Bs (Table 3.2). It has been suggested by Margalef that high P/Bs are indicative o f ecosystems which are highly stressed (Rapport et al. 1985). If stress is equated with high levels of fishing mortality then both the G u l f o f Thailand and San Miguel B a y would be categorised as stressed. The key question here is whether there is a significant export factor in San M i g u e l Bay. This question is addressed below.  The biomass estimates from the trawl survey data (Chapter 2) were not large enough to support the fishery. In order to make the E C O P A T H model balance, it was necessary to allow the model to estimate biomass. The resultant biomass estimates differed from the biomass estimates from the trawl survey. The total fished biomass in the trawl survey was 2.72 tkm" .  183 The biomass estimated by the E C O P A T H model was 5.78 tkm" , about twice the trawl survey 2  estimate. This was estimated from a large predator biomass o f 0.095 tkm" , 10 times the trawl survey estimate o f 0.008 tkm" . The E C O P A T H total fished biomass estimate is within the confidence limits o f the trawl survey estimate (Table 2.4), although it was noted that the biomass estimate from the trawl survey was rather imprecise, that is, its confidence limits were wide. In addition, the trawl survey did not include the months o f July and August. Data on catch rates indicate that biomasses are very high during these summer months. Thus, i f the trawl survey had included data for the entire 12 months, the biomass estimate would have been greater than 2.72 tkm" . The total E C O P A T H fished biomass estimate is also comparable to the 2  total fished biomass estimates from other E C O P A T H models. For example, the total fished biomass in the G u l f o f Thailand was 9.35 tkm" (Pauly and Christensen 1993), Brunei Darussalam 10.53 tkm" (Silvestre et al. 1993) and i n Malaysia was 4.02 tkm" (Liew and Chan 2  2  1987). The San M i g u e l B a y estimate is at the lower end o f this range. In terms o f the total fished biomass therefore, the E C O P A T H estimate is acceptable.  However, the relative abundance o f the eco-groups i n the trawl survey and the E C O P A T H model are different (Figure 3.2). There are some serious discrepancies. The differences are due to two factors. First, not every eco-group is equally available to the trawl survey. It is not possible for a survey designed with only one type o f gear to target all species equally. Therefore some species w i l l be under or over represented in the trawl catch. Secondly, E C O P A T H uses catch estimates to estimate biomass. That is, E C O P A T H calculates the biomass required to support a given catch: the greater the catch, the greater the biomass  Legend for Figures 3.2, 3.4-3.6  Z P = Zooplankton, Meiob = Meiobenthos, Macrob = Macrobenthos, Serg = Sergestids, Pen = Penaeids, L C = Large Crustaceans, D F = Demersal Feeders, Leiog = Leiognathids, Eng = Engraulids, Pel = Pelagics, Sci = Sciaenids, M P = Medium Predators, L Z B = Large Zoobenthos Feeders, L P = Large Predators, Ph/P = Phytoplankton,  185  Pen 10  H  LC 1  DF 1  Leiog 1  Eng 1  Pel 1  •  Sci  MP  h———I  :  LZB 1  LP 1  •  •  0.1  t in re E o  m  0.01  0.001  -L  c  100  CD D_  O _J  Q  LU  10  CD D_  •  c u o c re  O  CO  CO  rsi  •  73  •  c  3 £1  re  0.1 -J-  •  ECOPATH  • Trawl Survey  Figure 3.2 Comparsion of (a) biomass estimates and (b) relative abundance from the E C O P A T H model and the San M i g u e l B a y 1992-1994 Trawl Survey.  1  186 estimate . The catch estimates in San M i g u e l B a y are derived from the landings o f over 20 64  different types o f fishing gear, all targetting different groups. Relative catches are therefore quite different from the relative abundance in the trawl survey and, consequently, the relative abundance o f the E C O P A T H estimates o f abundance are quite different too. The large crustaceans, for example, are hardly represented in the trawl survey, while they have a large catch, mostly by the crab gillnet. Similarly, the large predators are poorly represented in the trawl survey, but are caught in relatively large numbers by the set longline gear. Given these kinds o f differences, it seems unlikely that E C O P A T H could reproduce the relative abundance of a trawl survey from catch derived biomass estimates.  Sensitivity Analysis  A sensitivity analysis o f the input parameters was performed using a routine within E C O P A T H designed for this purpose. Each o f the basic input parameters is varied by -50% to +50% in steps o f 10%. The impact o f this on the other missing input parameters is estimated as:  (Estimated parameter - Original parameter) / Original parameter.  The general result for all the eco-groups is illustrated in Figure 3.3. Here the sensitivity o f the model to the sciaenid parameters is shown. There are several things to note.  Reducing the catch of the sciaenids to 0.5 tkm" for example, reduces the biomass estimate from 0.972 tkm" to 0.221 tkm"  64  2  2  2  187 1. The P / B and E E input parameters have the same impacts on the other parameters, 2. Reducing P / B and E E by 50% has a greater effect than increasing them by 50%, 3. Changing the input parameters o f most eco-groups, affects only that eco-groups, 4. The model results are not very sensitive to the Q / B input parameters.  Thus most o f the sensitivity i n the model is the sensitivity o f the biomass estimates to the P / B and E E parameters. There are some exceptions. The phytoplankton biomass is sensitive to the zooplankton P / B and E E parameters. The meiobenthos biomass is extremely sensitive to its P / B and E E ratio and Q / B ratio. The sergestids P / B and E E impact on the biomass o f the zooplankton and the E E o f the phytoplankton. The sciaenids, depicted in Figure 3.3, have the widest ranging effects. The biomass o f the large crustaceans, meiobenthos, macrobenthos, sergestids and penaeids undergoes at least a 50% increase when the P / B or E E o f the sciaenids is reduced by 50%.  In general then, the model is robust, although it would be improved by a better empirical base for the P / B and E E parameters. This is especially pertinent given the discussion o f high P / B ratios . 65  ' This further legitimates the procedure in the balancing of the model, where the model was allowed to estimate the Q/B of the sciaenids rather than the P/B. Allowing the model to estimate Q/B, produced a lower P/B and a much higher sciaenid biomass.  -0.4  % change in input parameter —11 •A---11 -e—11 •©—11 A- - -11 11  PB 1 B PB 3 B PB 5 B PB 9 B PB 15 EE QB3B -H—11 Q B 9 B  11 11 11 11 •11 11 -©—11  - A-  EE 1 B EE 3 B EE 5 B EE 9 B E E 15 E E QB4 B QB 11 B  •11 •11 •11 •11 •11 •11 •11  PB 2 B PB 4 B PB 6 B PB 11 B QB 1 B QB5B QB 15 E E  •11 EE 2 B -X- •11 EE 4 B •11 E E 6 B •11 E E 11 B • 11 QB 2 B •11 QB 6 B  Figure 3.3 Sensitivity Analysis for the Sciaenids. Each o f the input parameters is varied from -50% to +50% and its impacts on the unknown parameters plotted. For further details see the text. P B = Production/Biomass, Q B = Consumption/Biomass, E E = Ecotrophic Efficiency  189 T r o p h i c Impact Routine  E C O P A T H has a routine that enables the impact o f increasing the biomass o f one eco-group on the biomass o f the other ecogroups to be studied. Thus it begins to be possible to ask "what i f ?" questions. The trophic impact routine originates in economic input/output theory. Leontief (1951) developed a method to examine the direct and indirect interactions in the economy o f the U S A , using what has become known as the Leontief Matrix. The economic theory was adapted to ecology by Harmon (1973) and Hannon and Joiris (1989). Ulanowicz and Puccia (1990) used a similar approach and their methodology is incorporated into E C O P A T H (Christensen and Pauly 1993b).  The results o f the trophic impact routine are given in Figure 3.4. A l l eco-groups respond negatively to an increase in their own biomass, due to increased competition within the ecogroup for resources. The eco-groups with the greatest impact on other groups are the detritus, phytoplankton, zooplankton and macrobenthos. The detritus has a positive impact on all groups apart from the phytoplankton. The phytoplankton has a positive impact on the zooplankton, sergestids, leiognathids, engraulids, pelagics, sciaenids, medium predators and large predators. The zooplankton has also has a positive impact on the sergestids, leiognathids, engraulids, pelagics, sciaenids, medium predators and large predators, and a negative impact on phytoplankton. The positive impact o f the phytoplankton on these groups is probably due to its positive impact on the zooplankton. The macrobenthos has a positive impact on all groups apart from the zooplankton, meiobenthos, sergestids and detritus.  190 Zooplankton  i i—i  x> o  g  CO CL  CO  CL 2  CD CL  c  111  CO CD  CD  3  Q —  Meiobenthos  CD  g  CD  •E CO CD 3  CL  'CD  CD  sz  CO  Q  Sergestids  g  g>-  x> o  CD X>  CD CO  CD CL  g  CO N  CL  'CD  CL  -  -C CL  — to CD 3  Q  Penaeids  ,1 N  ' § x,  u  y-f>  J  g>  CD CO  CD CL  LL  Q  1  <3>—  .2  1  I, CD CL  CL 2  -COIN  i= co CD  Q  3  Large Crustaceans  Figure 3.4 Results o f the Trophic Impact Routine. The eco-groups on the x-axis are responding to an increase i n biomass o f the named group. The impacts are relative but are comparable between groups. See text for further details.  ery  Fish  1  ,1 1, us  DetriT  , Ph/P  1, LP  ,1  L  il  LZB  J  n  MP  ii  Sci  J  Ii  Pel  J  Eng  J  Leiog  i  1 1  O CD X! -2 1  DF  N  1 1'  LC  0-  Pen  1 <\  Serg^  Macrobenthos  191 Demersal Feeders  CL N  O CD  ,1 .O O  co  CD Q.  o  CD QL  g  I,  m  ~ CO <D D  N  CO  Q  u.  •fc <o <D 3 Q  x: co il  Leiognathids  ,1 o. N  g  'CD  O  CU CO  c CD CL  o  CQ  CD  '<D  I,  Engraulids  0-  D)  N  c LU  g cu  CD  CO  0.  CL  2  CO N  ~ CO CD 3  Q  Pelagics  Sciaenids  M e d i u m Predators  CL M  g  'CD  2  .O O  co  CL  o -i  'L^—l' a  S > 'LOIJ'LJCB—I  1  L.2_l  ,<=  a  O  ——I  co  s  m  N  CD  Q  Figure 3.4 (cont) Results o f the Trophic Impact Routine. The eco-groups on the x-axis are responding to an increase in biomass o f the named group. The impacts are relative but are comparable between groups. See text for further details.  192 Large Zoobenthos Feeders  a. N  o  CD  CD . Q  co  . CD CL  O) g '<D  O  CD CL  m  O CO  •C co  N  CD  Q  3  Large Predators  CL N  e CD .Q 2  o  "S S ° CO  g> cu CO  CD 0-  O  g 'CD  O)  c IXI  CD CL  o w  0  S  ^  m~ fS  — <D  Q  CO  3  CU  SZ  CO  il  Phytoplankton  CL N  g CD  .O O  OJ CL  g 'CD  CD  co  coo  "BT  ~  rsj  CD  o  CD  CO  3  W  C  il  Detritus  il CL N  g CD i D  o  C3> CD CO  o  LL Q  CD CL  g 'CD  CL  2  CQ N  H  CL  •-  <D  °-  Q  i l  •f  CD 3 CO  Fishery  Figure 3.4 (cont.) Results o f the Trophic Impact Routine. The eco-groups on the x-axis are responding to an increase i n biomass o f the named group. The impacts are relative but are comparable between groups. See text for further details.  193 None o f the other eco-groups have comparable impacts. This would suggest that the San M i g u e l B a y ecosystem is largely controlled by the lower trophic levels. Considering that the fishery has long since reduced the biomass o f the top predators (Chapter 2), this result is reasonable. It was noted in Figure 3.1, that many o f the eco-groups are clustered around trophic level 3. A n increase in the biomass o f any o f these groups, the leiognathids for example, leads to an increase in the medium predators and the large predators. The biomass of the other eco-groups at this trophic level, the demersal feeders, leiognathids, engraulids, pelagics decreases. The sciaenids and the penaeids also decrease. These groups probably decrease through the combined effects o f predation by the large predators and the medium predators and competition between trophic level 3 groups. A n increase in the biomass o f the medium predators leads to a decrease in most o f the other eco-groups and a small increase in the large predators. When the biomass o f the large predators is increased, it produces small decreases in all the fish groups and small increases i n the three crustaceans groups. This behaviour, and the behaviour noted above, resembles a trophic cascade (Carpenter et al. 1985).  The three crustacean groups, the sergestids, penaeids and large crustaceans each decrease in response to an increase in the biomass o f the other two crustaceans. This indicates that there is competitive interactions between these three groups. Only the engraulids, pelagics, sciaenids and medium predators respond positively to an increase in any o f the crustacean groups.  The trophic impact routine describes a fishery that is more affected by the lower trophic levels than higher trophic levels. It has a large number o f eco-groups at trophic level 3 which are in competition with one another for resources. These include two to three crustacean groups  194 which also compete with one another. The top predators in the system do impact on the intermediate groups, releasing lower eco-groups from predation pressure. Some trophic cascade effects are apparent. O n top o f this ecosystem, there is the fishery. The fishery always increases in response to an increase o f any o f the eco-groups, including those which are not fished. The fishery benefits from the unfished groups because o f the positive impact these groups have on the higher trophic levels. A n increase in the fishery has a negative impact on all o f the fished groups, except the sergestids and penaeids which benefit from the reduction in predation and competition. The zooplankton and macrobenthos also increase for the same reasons.  Introducing Small-Scale  the Fishery  into the ECOPATH  Model as Large Scale  and  "Predators"  The trophic impact routine indicates that the fishery has a big impact on the ecosystem. The fishery is composed o f many gear types (see Chapter 2) which are specifically designed to target certain fish or crustacean groups. Different fishing gears would therefore be expected to have different effects on the ecosystem. This is examined in more detail. Instead o f including the fishery in the E C O P A T H model simply by including catches as export, the fishery is broken down into gear groups and included as predators within the model structure.  The validity o f this approach has been questioned ( M c G o o d w i n 1991). M c G o o d w i n discusses the equating o f fishers as predators in ecological systems and concludes "Ecosystemic models  195 which conceptualize humans as any other predator i n a marine environment  assert or at  least imply erroneous parallels between human fishers and other marine predators by inhering questionable assumptions about the nature o f the relationships between the human fishers and their prey" (1991:68). M c G o o d w i n ' s premise is that humans and their marine prey have not co-evolved as predators and prey within the marine ecosystem have, humans are not recycled into the ecosystem, the link between human predator and marine prey does not include marine prey predating on humans at a different life cycle stage, and fishers predate on the marine environment for more and different reasons than marine predators predate.  This approach is nonetheless justified here because it is used i n order to understand the effects of fishing on the ecosystem o f San M i g u e l Bay. It is not used to predict or manage the behaviour o f fishers. E C O P A T H is a static model and the dynamic feedback cycles envisioned by M c G o o d w i n do not apply here.  Method  In order to include the fishery i n the E C O P A T H model as predators, certain assumptions are made concerning the input parameters o f the "fishery predators" ( V i l l y Christensen, pers. comm.). First, their biomass is arbitarily designated to be l , consumption is the catch and 6 6  production is the quantity o f fish landed. Unassimilated food is equated with discards, and thus flow to detritus, and respiration, the energy required by the system can be equated with fish  This is valid since biomass simply operates as a multiplier in the linear system of equations.  196 consumed aboard the vessel. The diet composition o f the "fishery predators" is equivalent to their catch composition.  The harvest in Table 3.8 is thus now "consumed" by the "fishery predators". There is no export from the original eco-groups. Instead, all exports are exported via the "fishery predators". The total export from the ecosystem remains the same. In effect, a fishery is fishing the "fishery predators". This is a ploy to fool the model; it does not affect the computation o f flows, although it does add an extra trophic level to the system.  The fishery was modelled as "fishery predators" in two ways. First, the fishery was included as a large-scale predator and a small-scale predator (LS-SSpredator  model). The large-scale  predators include the large, medium and baby trawlers, and the small-scale predators include all the other fishing gears (see Chapter 2). In order to examine the impact o f the small-scale sector in greater detail, it was then subdivided into the gear groups shown in Table 3.10 (LSSSgears predator model). This model consisted o f one large-scale predator and eight smallscale predators. The input parameters for the "fishery predators" are also shown in Table 3.10.  197 Results  The results o f the LS-SS predator and LS-SSgears E C O P A T H models for the biological ecogroups are as described above . The results for the "fishery predators" are shown in Table 67  3.10. The E E and G E are 1 since consumption and production are equivalent and there is no respiration.  The trophic levels at which the small-scale fishing gears fish are variable. The gears which fish at the highest trophic levels are the ordinary gillnet and the hunting gillnet The mini-trawler and the fine meshed gears fish at the lowest trophic levels. The former target sciaenids and the latter sergestids and penaeids. The large-scale sector fishes at an intermediate trophic level. It has a non-specific target catch. The small-scale sector collectively fishes at a lower trophic level than the large-scale sector.  The "fishery predators" have very low "omnivory indices", indicating that they do not fish across many trophic levels (cf. Table 3.8). This is particularly true for the crab gear which has an omnivory index o f 0.002 and catches almost 100% crabs. The hunting gillnet, mini-trawler and ordinary gillnet also have low indices. O f the small-scale gears, the highest omnivory index is incurred by the "other gear". This makes sense since it is a composite group o f gears, which vary from handlines to beach seines to spear guns. The "other gillnets" and the fine meshed gear also have higher omnivory indices. The small-scale sector has a much higher  In some cases there are very slight differences. For example the EE is estimated in the fishery predator model because all other parameters for the biological eco-groups are entered. The estimated EE for some groups is 0.949 or 0.951 instead of 0.95. These differences are due to rounding differences in the input parameters and the calculations.  6 7  198 Table 3.10 Input parameters and results for the two "fishery predator" E C O P A T H models, LSSSpredator and LS-SSgears predator.  FISHING GEAR  INPUT  Biomass P/B Q/B Export Harvest (tkm' ) (year ) (year ) (tkm' (tkm" year ) year ) 1 0.68 0.68 0.68 0.68 1 1.23 1.23 1.23 1.23 1 0.66 0.66 0.66 0.66 1 1.83 1.83 1.83 1.83 2  Other Gear Fixed Gear Crab Gear Fine Mesh Gear Other Gillnets Ordinary Gillnet Hunting Gillnet Mini-Trawler Large Scale Small Scale  RESULTS  PARAMETERS  2  EE  GE  Trophic Level  Omnivory Index  1 1 1 1  1 1 1 1  4.20 4.05 3.75 3.58  0.164 0.024 0.002 0.112  2  1 1  2.75 2.37  2.75 2.37  2.75 2.37  2.75 2.37  1 1  1 1  4.00 4.31  0.116 0.088  1  1.51  1.51  1.51  1.51  1  1  4.33  0.040  1 1 1  1.56 2.23 12.59  1.56 2.23 12.59  1.56 2.23 12.59  1.56 2.23 12.59  1 1 1  1 1 1  3.54 4.09 3.98  0.070 0.095 0.172  Other Gear = Set Longline, Handline, Fish Trap, Ring Net, Pull Net, Fish Weir, Beach Seine, Spear Gun. Fixed Gear = Lift Net, Fish Corral. Crab Gear = Crab Gillnet, Crab Liftnet. Fine Mesh Gear = Filter Net, Scissor Net. Other Gillnets = Shrimp Gillnet, Bottom-Set Gillnet, Surface Gillnet, Shark Gillnet, "Other Gillnets". Large Scale = Large Trawlers, Medium Trawlers, Baby Trawlers. Small Scale = A l l small-scale gear, including Mini-Trawlers.  199  omnivory index than the large-scale sector, thus signifying that it fishes at a wider, i f lower, range o f trophic levels.  These results could be anticipated from the fishery analysis presented in Chapter 2. More interesting is the impact on the ecosystem o f increasing fishing effort. This can be simulated using the trophic impact routine. Bearing in mind that this is a qualitative exercise, it is nonetheless possible to obtain a sense o f the response o f the eco-groups to an increase in fishing. The results o f the trophic impact analysis o f the LS-SS predator model are shown in Figure 3.5, and the results for the LS-SSgear predator model are given in Figure 3.6.  The two graphs in Figure 3.5 are drawn to the same scale. A s would be expected, there are not many positive responses to an increase i n fishing by either sector. It is immediately obvious however, that the small-scale sector has a much greater impact than the large-scale sector. The small-scale sector has a bigger impact on all the eco-groups except the leiognathids and the engraulids. The sciaenids suffer a large decrease when the small-scale sector is increased while they increase slightly when the large-scale sector is increased. The small-scale sector also indirectly impacts the non-fished groups, the zooplankton and macrobenthos, in the model. The latter increase in response to an increase in the small-scale sector. This manifestation is likely to be a response to a decrease i n the biomass o f their predators. The small-scale sector also has a greater negative impact on the large-scale and small-scale sectors themselves . 68  ' This result should be interpreted with caution. The fishery is not subject to the same kind of feed-back cycles that the eco-groups incur (McGoodwin 1991).  200  Small Scale Sector  Large Scale Sector  2  r JO  1acro  n  *  g> 0> co  c  Pe  i  CL N  eio  1  i  o _i  Q  '1 1l i r CU L _S> o_l c 0. cu —LU— _j 1  1  r  'o CO  CL  T  1 Lo 1  1 CO —Nl—1 _j _l  r  CO co  CO _l  a. CL  CO  •cu  a  Figure 3.5 Results o f the Trophic Impact Routine when (a) the small-scale sector and (b) the large-scale sector are increased. The eco-groups on the x-axis are responding to the increase. The impacts are relative but are comparable. See text for further details.  201 The graphs in Figure 3.6 are also drawn to the same scale, although it is smaller scale than in Figure 3.5. This is because the impacts o f the small-scale gear groups individually are larger than the impacts o f the small-scale sector collectively. This may be because when the smallscale sector as a whole is increased, the impacts o f the different gears buffer each other. When an individual gear is increased, its effect alone is felt. However, it may also not be valid to make this comparison across models with a different number o f eco-groups.  The individual small-scale gear (or more correctly, gear groupings) are more selective than the large-scale or small-scale sectors. This can be seen in their impact on the model and in the omnivory index. There is a differential response by the eco-groups to increased fishing by the different small-scale gears. For example, the ordinary gillnet and, to a lesser extent the hunting gillnet, target sciaenids and medium predators. These both decrease in response to increased fishing effort by the two gears. However, the eco-groups below them, with the exception o f the pelagics, all increase, even though some are "consumed" by the ordinary gillnet and hunting gillnet. This increase is due to a decrease in predation from the sciaenids and the medium predators. The large zoobenthos feeders also increase.  The impact o f the mini-trawler on the sergestids and the penaeids is negative. Since these are the target species o f this gear, this result conforms with expectations. However, several o f the other eco-groups increase. It is not immediately clear why these eco-groups should increase. The mini-trawler "consumes" all o f these groups, albeit in relatively small quantities, with the exception o f the large predators. It is possible that the demersal feeders and the leiognathids increase because they are competitors with the sergestids and penaeids which have decreased.  202 Other Gillnets  Ordinary Gillnet  0-  o  CD 2  .o o o co  E?  CD CL  CD GO  CU  O  'CD  CL CD  2  Q  Hunting Gillnet  Sci  r  Pej^  1. Eng  I.I DF  LC  Pen  .I  Leiog  1, Serg  Macrob  Meiob  ZP  ,1  1 1.  }'  cc— 2  '  I  1 1  1  1  CO  CL  N  -  -  1  1  1 1  1 1 CL  co  £  .3 iz  CL  CD  Q  Mini-trawler  Figure 3.6 Results o f the Trophic Impact Routine when different small-scale gears are increased. The eco-groups on the x-axis are responding to the increase. The impacts are relative but are comparable. See text for further details.  203 Fixed Gear  CL Nl  JQ O  co  O  CD  co  2  LL Q  CD CL  F  CD  L  g  CL  -5—  CL  CO  N  1  CL  '<D  CD Q  Fine Mesh Gear  CL Nl  g  'CD  2  .Q O  O CO  2  CD '  -CO—I  c  CD CL  CD CL  m  O CO  CL •—  Nl  CL  CD Q  Crab Gear  Other Gear i Detritus  1—2—1  i  i  1  Ph/P  co  i  i  LP  Pel  Eng  Leiog  DF  LC  Pen  Serg  Macrob  Meiob  ZP  Figure 3.6 (cont.)  i  LZB  1,  ,1  Results o f the Trophic Impact Routine when different small-scale gears  are increased. The eco-groups on the x-axis are responding to the increase. The impacts are relative but are comparable. See text for further details.  204 Since the former increase, they provide more food for their predators. There is mixed support for this hypotheses from the earlier trophic impact analysis shown in Figure 3.4. The impact o f an increase in the sergestids was a small increase in the leiognathids, demersal feeders and sciaenids and a decrease in the pelagics and large zoobenthos feeders. The impact o f an increase in the penaeids was a larger decrease in the leiognathids, demersal feeders and large zoobenthos feeders and a decrease in the pelagics and sciaenids. Reversing these impacts to mimic a decrease in the sergestids and penaeids produced a very unclear picture. It may not be possible to understand the impact o f a reduction in more than one eco-group by examining the impacts o f individual eco-groups.  The crab gear target large crustaceans and their impact is clear. A n increase in fishing by crab gear produces a large decrease in the large crustaceans. Other eco-groups increase. When the large crustacean biomass was increased in Figure 3.4, these eco-groups all decreased. Thus opposite impacts are seen when the biomass o f the large crustaceans are increased, or decreased due to fishing. The increases in the biomass o f the other eco-groups may be due to a reduction in competition with the large crustaceans for resources.  The fine meshed gear "consumes" a wide range o f the fished eco-groups. The "diet" is dominated by the sergestids and, to a lesser extent, the engraulids. Other eco-groups are "consumed" in small quantities, and the large crustaceans, sciaenids and large zoobenthos feeders are absent from the "diet". The impact o f the fine meshed gear on the model is mixed. The sergestids and the engraulids decrease, the engraulids more than the sergestids. The zooplankton increase, probably due to a reduction in predation by the sergestids and  205 engraulids. Some o f the eco-groups which are fished, such as the demersal feeders and leiognathids, increase in response to an increase in fishing.  The impact o f the fixed gear is dominated by a large decrease in the leiognathids . The small 69  increase in the demersal feeders and sciaenids may be a response to a reduction in competition. However, the fixed gear "consume" all the fished eco-groups except the large predators and the large crustaceans. So the mixed effects o f fishing, predation and competition are being seen.  The "other" gillnets "consume" all the fished groups except the sergestids. They have a mixed impact. The greatest impact is on the demersal feeders which decrease. This response can be attributed to the impact o f the bottom-set gillnet, the "diet" o f which is dominated by demersal feeders. The increases in the medium predators, leiognathids, large crustaceans, sergestids and macrobenthos may be due to a decrease in predation by or competition with the large predators and demersal feeders. However, this small-scale gear group consists o f many types o f gillnets and it is not possible to determine whether the impacts on the other eco-groups are due to the direct or indirect impact o f the Other gillnets.  This is also the case for the "other" gear. The large decrease in the large zoobenthos feeders and the large predators is due to the effect o f increasing the fishing effort o f the set longline. The leiognathids, the engraulids and the sciaenids increase. This may be due to a decrease in  It was noted in Chapter 2 that the large catch of leiognathids by the lift net may be an artifact of poor sampling for this gear.  6 9  206 predation by the large predators and the medium predators. The decrease in the pelagics and medium predators is due to the direct impact o f fishing.  Discussion  This analysis demonstrates the complexity o f the interactions that occur between the effects o f fishing mortality, predation mortality and competition. In some cases it is relatively straightforward to discover these interrelated effects, for example, the impact o f the crab gear. However, for most o f the other gears, the situation was more difficult. This might be reduced i f the gear groups were broken down further. For some small-scale gears it was possible to link the likely effects o f competition and predation and fishing. For others, for example, the minitrawler the possible interactions o f competition, predation and fishing were too interwoven to determine. A n increase in one eco-group (A) produced an increase in eco-group (B), but an increase i n eco-group (C) produced a decrease in eco-group (B). I f both ( A ) and (B) increase, and (C) also increases, does this mean that the impact o f group ( A ) is stronger, or are there other less obvious interactions occurring?  The trophic impact routine dramatically illustrates that the small-scale sector has a bigger and more widely ranging impact on San M i g u e l B a y than the large-scale fishery. The large-scale sector is non-selective and, although there are preferred catches, for example, penaeids, it catches a large range o f species in its nets.  207 When taken as a whole the small-scale sector also catches a large range o f species. Some o f the individual gears catch as many different species as the large-scale sector, for example, the mini-trawler and the ordinary gillnet (Chapter 2). However, there is a fundamental difference. Almost all o f the small-scale gears specifically and effectively target certain species or groups. Small-scale gears are more versatile than large-scale gears. In effect, the small-scale gears can and do fish any species in San Miguel B a y , in any niche, at any trophic level, anywhere. While the large-scale sector mass harvests non-selectively, the small-scale sector selectively mass harvests.  What this means is that there is no refuge in the Bay. When species " A " decreases, the smallscale sector has the versatility to switch to species " B " and so on. It was noted in Chapter 2 that the small-scale sector has diversified since the early 1980s. This diversification has increased the potential o f the small-scale gear to target fish. The hunting gillnet, for example, uses scaring devices to drive sciaenids into the net. Apparently this gear was not present in the 1980s and thus the sciaenids had to swim into the net in order to be caught.  The common, and perhaps entrenched, view is that the large-scale sector is bad and the smallscale is good and benign and creates a livelihood for fishers. While the latter part o f the statement is not contested, the benignity o f the small-scale sector is. The small-scale sector in San M i g u e l B a y has the ability to fish at all trophic levels and does (Table 3.10). In addition, the small-scale sector imposes a greater fishing mortality than the large-scale sector on every eco-group, except the engraulids (Figure 3.7). The small-scale threat could be regarded as clandestine. Recent management actions in San M i g u e l B a y have aimed at banning the large-  scale sector (San M i g u e l B a y Integrated Coastal Fisheries Management Plan (unpublished manuscript). In effect large-scale effort may have been reduced (Mike Pido pers. comm.) However, this is not sufficient. The trophic impact results presented here support the conclusion reached in Chapter 2. A l l sectors o f the fishery must be assessed and managed.  Interpretation  and  Discussion  The E C O P A T H models o f the San M i g u e l B a y fishery have increased knowledge about San Miguel B a y . The results o f the first model indicate that the ecosystem is relatively mature , 70  and thus relatively stable and able to respond to stress (Christensen 1995), that there are four trophic levels (Figure 3.1), that many o f the species are grouped around trophic level 3, that there is likely to be strong competitive interactions between eco-groups at this level and that 56% o f flows originate in the detritus. The trophic impact routine further indicated that considerable competition and predation occurs, although the eco-groups showing the greatest impact were those at the lower trophic levels. The results o f the LS-SS predator and LSSSgears predator models are an insight into the combined effects o f fishing and biological interactions. The results also demonstrate how the fishery has "fished down" the food web.  Relative to other models for which comparisons were possible.  209  l  7  6 +  5 +  * 4 o 2 c g ra  I  3  CL  2 +  o  m  X  •  •  •  •  I-  od-  -6-  •g  CO  CD CO  cu  c  cu  to  CL  o  <u o> to  <U  cu  cu D  X Other G e a r  •  •  A Mini-Trawler  Ordinary Gillnet  •g !c co c O) g  cu  T3  Fixed G e a r  •4  s •  g jo  (U  CL  X  • • •g 'c  cu CO o CO  -0-  CL  'cu  cu A Crab Gear  O Fine M e s h G e a r • Other Gillnets  •  O Hunting Gillnet  Large S c a l e  CQ N cu E? to  Figure 3.7 Comparison o f the "predation mortality" (= fishing mortality) imposed by each "fishery predator" (= fishing gear) on the fished eco-groups.  CD  o > o  o 03 T3 0)  -I cu CO  210 These results are based on the data input to E C O P A T H . B y comparative standards, the data requirements o f an E C O P A T H model are small. However, as the first part o f this Chapter attests, the parameter requirements are still quite demanding. The parameters used in this E C O P A T H model have mixed origins and some were derived by, perhaps, somewhat convoluted means. A few were estimated directly from empirical data (some P/Bs, most Q/Bs, few diet compositions), some were estimated from empirical data and data from the literature and some were derived entirely from the literature. A skeptic could be forgiven for asking whether a model so constructed bears any resemblance to reality. This quasi-empirical approach has been used in many o f the ecosystem models (Christensen and Pauly (1993a) and is recommended for ecosystems with insufficient data. A s far as possible, the empirically derived parameters were not altered in the balancing o f the model, thus ensuring that the model was fitted to the local data. In addition, the sensitivity analysis indicated that the most sensitive parameter is the P / B ratio and the E E . The P / B estimates from San M i g u e l B a y were comparable with other ecosystems, particularly the G u l f o f Thailand. Certainly with respect to the pattern o f fishing that has occurred in San M i g u e l B a y over the last two decades, they are feasible. The E E values were those recommended in Christensen and Pauly (1992a).  The time period o f the San Miguel B a y E C O P A T H model is 1 year. It was noted in Chapter 2 that there are seasonal effects in the fishery. Arguably, the dynamics o f the ecosystem would be better represented by two models, one for the months o f October to March, during the Northeast monsoon, and one from A p r i l to September, which would include the Southeast monsoon. Unfortunately there were insufficient data to estimate separate parameters for these two seasons. This was particularly so for the diet composition, most o f which was drawn from  211 comparable models in the literature. It is therefore assumed that the amiual model describes the B a y sufficiently.  Hypotheses About the San Miguel Bay Fishery and Resource  O n the premise that the E C O P A T H model is a useful construct, several hypotheses can be made about the fishery o f San M i g u e l Bay. There is a fundamental query about San Miguel Bay. In the 1979-1982 study o f the Bay, the fishery was declared highly/over-exploited. More than 10 years later, the fishery is still highly/over-exploited. The total catch declined by 17%. However, the fishery is still viable, perhaps more viable than would have been predicted in 1982. This is despite the fact that many fish are caught before maturity (Chapter 2). So the fundamental query is, " H o w is this resiliency possible?".  The trophic impact analysis o f the small-scale sector showed that these gears exploit all trophic levels, niches and eco-groups in San Miguel B a y using an array o f fishing methods. It was noted in Chapter 2 that there has been species succession, with " K " strategists being replaced by "r" strategists. The resilience o f the fishery could simply be due to there still being room for the fishery to be fished further down the food web. The small-scale sector is able to respond to the changes caused in the resource through succession and continues to do so. The question then becomes, "What is the end point?".  212 A complimentary explanation may be found in the important role that detritus plays in the ecosystem. In Figure 4.4, all eco-groups, except the phytoplankton, responded positively to an increase in detritus, including the fishery. Could detritus be sustaining the fishery? The level o f detritus in San M i g u e l B a y may have increased. Siltation in San M i g u e l B a y , from the river systems, loss o f mangroves, and mining and quarrying activities has increased over at least the last few decades (Mendoza and Cinco 1995). The main influence on the siltation is the B i c o l River. I f it is assumed that the silt carries organic matter, then this would lead to an increase in detritus. This steady increase in detritus may have promoted productivity in the fishery, especially in those eco-groups which are sustained by the detritus flows, as suggested by Pauly (1982a). However, Mendoza and Cinco (1995) also note a large list o f negative effects o f siltation such as modification o f bottom topography and clogging and abrasion o f gills. In reality then, the positive impact o f an increase in detritus may be modified by the negative physical effects o f siltation.  A more parsimonious explanation for the continuance o f the fishery at current levels, and one that bears some consideration, is that the species i n San M i g u e l B a y are continually replenished by larvae from outside the Bay. That is, San Miguel B a y is not a closed system. Pauly (1982b) suggested that San Miguel B a y acts as a nursery area. Pauly cites several pieces o f evidence to support this hypothesis, which include the lack o f larvae in plankton trawls during likely spawning periods (Weber 1976). Length data also indicated that larger fish are caught outside the B a y than inside (Pauly 1982b). Pauly schematically suggested that mature fish emigrate from the Bay, spawn outside the Bay, the larvae are carried into the B a y by tidal currents and gyres, they migrate towards the shallow coastal or nursery areas where the  213 juveniles mature. A s the juveniles mature they move into deeper water until they eventually leave the B a y as adults (1982b, Figure 4).  There is also other evidence to support the emigration/immigration hypothesis. In the catch data presented in Chapter 2, a large proportion o f the sciaenids and engraulids, and to a lesser extent the leiognathids in the catch were below the length o f first maturity. I f fish are caught before they are mature, it is difficult to conceive how they could reproduce i f there was no import from outside the B a y . However, there were also mature fish caught, indicating that not all mature fish emigrate from the Bay, or that they do not immediately emigrate. If emigration occurs, it could account for the high estimates o f apparent Z in Chapter 2.  Omori (1975) suggests that there is immigration by sergestids since they swarm and go offshore. Some penaeids also move offshore to spawn (Garcia 1988, Motoh 1981). Tiews and Carces-Boya (1965) conjecture, on the basis that no spawning leiognathids are caught by the fishery in Manila Bay, that mature leiognathids go offshore to spawn. However, there is also evidence that some species do not migrate offshore to spawn. In the Ragay G u l f (Philippines) for example, berried females o f Portunus pelagicus are found inside the Gulf. (Ingles and Braum 1989).  Johannes (1978) discusses the reproductive strategies o f tropical marine fish at some length. He identifies five types o f spawners, including migrating spawners. Carangids, sphyraenids, serranids, lutjanids and leiognathids, all fish species found in San M i g u e l B a y , are described as  However, an alternative explanation for this is that since M is very high below L , the fishery is only taking fish that would have died anyway.  71  mat  214 migrating spawners. Johannes further delineates a size category. Fish greater than 25cm are likely to migrate to spawn and those less than 25 cm are likely to spawn inshore. The L e o o f the engraulid and leiognathid species in San Miguel B a y is less than 25cm. However, Johannes emphatically states that he has not considered the tropical fish o f predominantly sandy and muddy continental shelves "because too little is known about their reproductivity" (1978:65).  This leaves a question mark concerning the issues o f immigration and emigration. It is more likely than not that immigration and emigration occur, at least to some extent, for some species. The presence or absence o f imports to the B a y profoundly affects our thinking about 72  the fishery. If fish spawn outside the Bay, there is a continual supply o f eggs into the B a y . Thus the B a y is stocked from outside, allowing high production and catches. What are the implications for the San M i g u e l B a y E C O P A T H model? A n d what are the management implications?  A n essential assumption o f any E C O P A T H model is that there is greater similarity within the defined system than between the defined system and an outside system. In other words, the interactions within the system should add up to a greater flow than the interactions between the system and the outside (Christensen and Pauly 1992a). The sum o f all flows in San M i g u e l Bay, that is, the throughput, is comparable to other systems. It could be argued, i f emigration occurs, that fishing mortality is so high inside San Miguel B a y that the fishery competes for the flows to the outside. That is, the fishery catches the mature fish before they emigrate. Thus export is retained within the model as fishing mortality, and there is no or little flow to the  The area outside the Bay might thus be considered a refuge.  215 outside.  It is not feasible to model larval import or immigration using E C O P A T H . This is because E C O P A T H is a biomass model and import is modelled as negative export. Thus to model import alone, a negative value would be input for the export. But the biomass o f the larval import would be very low, perhaps negligible. To model import and export, the difference between the biomass o f the import and export would have to be input. This would be effectively the same as entering export only.  On the premise, then, that fishing captures most mature fish before they emigrate from San M i g u e l B a y (which is likely), that i f any fish do "escape" the sum o f the interactions between San M i g u e l B a y and the export is less than the sum o f the interactions within San M i g u e l B a y , and that import cannot be effectively modelled using E C O P A T H , it is assumed that the models described above are still useful and relevant, even i f immigration and emigration do occur.  In summary, the E C O P A T H models indicate that predation and competition in the biological community are important determinants within the dynamics o f the ecosystem. The results also indicate that competition and predation interact with the effects o f fishing mortality. Thus not all species which are caught by a fishing gear necessarily decrease as a result o f an increase in fishing mortality. Different fishing gears have different impacts on the fishery and these are not simply a linear consequence o f the fishing activity.  216 It is not possible to investigate the effects o f predation, competition and fishing mortality further with E C O P A T H . E C O P A T H represents a static system. It is not dynamic and it is not quantitative. The trophic impact routine can only indicate direction o f change. It does not indicate the degree o f change necessary to produce a response, nor the degree o f the response, nor the time dynamics o f the response. The trophic impact routine cannot predict how biomass w i l l change with time. These questions are investigated further using a fully dynamic ecosystem model, E C O S I M (Walters et al. 1997) in Chapter 4. W i t h E C O S I M it is possible to quantitatively analyse the community dynamics and fishing dynamics in San Miguel B a y and to explicitly address the interactions between them. The question o f immigration and emigration is returned to in Chapter 5.  217  Chapter 4 Dynamic Multispecies Modelling of San Miguel Bay  "With the rising status of the Third World and rapid growth of its tropical fisheries, the limitations of the classical single species approach are becoming more apparent" Sugihara et al. 1984:132  Introduction  Tropical fisheries are reknown for their complexity and the multiplicity o f species which coexist in these productive marine ecosystems. The problems involved in assessing and managing these multispecies, multigear fisheries were introduced in Chapter 2, and an equilibrium biomass ecosystem model, E C O P A T H II was used in Chapter 3 to gain an understanding o f the fishery o f San Miguel B a y as a fishery o f an ecosystem. However, a static representation o f an ecosystem does not allow the asking o f "what if?" questions. For example, what would happen in San Miguel B a y i f the trawling sector were banned completely? H o w many years would the ecosystem take to recover? E C O P A T H was pushed to its limits to try to answer these types o f questions, but the equilibrium nature o f the model restricted the answers to mere indications o f the likely direction o f change. It was not possible to determine how species interactions would behave through time and how these might effect outcomes for the fishery. In order to address these types o f "what if?" questions, a multispecies dynamic modelling approach is necessary.  218 Multispecies approaches have been minimally applied i n fisheries management, despite the recognition by many that a multispecies approach to fisheries assessment is critical (Walters et a/. 1997, Christensen 1996, Hilborn and Walters 1992, Daan and Sissenwine 1991, Kerr and Ryder 1989, Pauly and Murphy 1982, Larkin and Gazey 1982, and Mercer 1982). There is however, a range o f multispecies approaches. They include: multispecies biomass models (eg, K i r k w o o d 1982, M a y et al. 1979); aggregate production models (eg, Ralston and Polovina 1982, Pope 1979); multispecies yield per recruit analysis (eg. Murawski et al. 1991, W i l s o n et al. 1991), and multispecies V P A (eg, Pope 1991, Pope and Macer 1991, Sparre 1991) and empirical methods (eg., Sainsbury 1991, 1988, Saila and Erzini 1987).  The aggregate production model, simply totals the biomass o f all species, ignores interactions between species and treats them as though they were a coherent whole. Ralston and Polovina (1982) vary this by first using cluster analysis to identify species assemblages and then treating each assemblage as a whole. This approach requires only catch and effort data for the fishery. The other multispecies methods listed above all have large parameter demands. This includes the multispecies biomass model which requires interaction terms between species: the more species in the model, the greater the demands.  But multispecies modelling is a data hungry exercise. The sparse use o f a multispecies approach in fisheries science and management is due i n large part to their data demands  . One  o f the few, i f not the only multispecies V P A , is o f the North Sea (Pope 1991): compared to tropical fisheries, this is a relatively simple ecosystem. None o f the above models are  It is also due to the economic and political consequences of multispecies assessment (May et al. 1979), which can confuse and confound an already complex management situation.  219 applicable to the type o f data available from San Miguel Bay. There is no time series o f catch and effort data with which to fit production or biomass models and the data simply do not exist for the other methods. The direct empiricism o f Sainsbury (1991, 1989), based upon the principles o f adaptive management, (Walters 1986) is a possible approach and is discussed in Chapter 5.  A s early as 1979, M a y et al. demonstrated the potential effects o f species interactions on yield estimates. They used direct interaction terms between species, and assumed a Lotka-Volterra model o f predation. However, the Lotka-Volterra model is not the only model o f flow control. Indeed, there is no unifying paradigm o f how energy flow between trophic levels o f an ecosystem is governed. T w o theories, top-down control with trophic cascades (Carpenter et al. 1985, Carpenter and Kitchell 1993) and bottom-up, donor control (Hall et al. 1970, Hunter and Price 1992), have dominated the ecological literature (Matson and Hunter 1992). However, the notions o f bottom-up and top-down control are not well-defined.  Operationally, a bottom-up regime is defined by a response to changes in productivity at the lowest trophic levels. A n increase in productivity at the bottom o f the trophic system leads to an increase in the productivity and abundance at all higher trophic levels. Each trophic level is food limited (Power 1992), not predator limited. This is an important concept in bottom-up control. That is, an increase in the abundance o f a predator species, does not lead to an increase in the mortality rate on the prey. Predation is proportional to the biomass o f the prey, flow is controlled by prey abundance and the mortality rate is stable over time. A n increase in the  220 abundance o f a top predator would not impact the rest o f the ecosystem because the mortality rate would remain stable.  The operational definition o f top-down control, is that an increase in the abundance o f a toppredator leads to the prediction o f a trophic cascade. That is, an increase in the abundance at trophic level 4, leads to an increase in the rate o f predation mortality on trophic level 3, and thus a reduction in its biomass. This produces a decreases in the predation by trophic level 3 on 2, and thus to an increase in the biomass o f trophic level 2. A n d so on. Here, flow is predator controlled. A n increase in productivity at the bottom would lead only to an increase in biomass at the top o f the food chain. The biomass o f lower levels would not increase because the mortality rate incurred by them would increase due to an increase in the abundance their predators. Only their productivity would increase.  The terms top-down and bottom-up control, are used here in the sense described above (see also Walters et al. 1997). M u c h o f the debate over top-down versus bottom-up control has occurred in the self-contained world o f fresh water lake systems. In the marine world, the picture is no less murky. Until recently, few marine studies contributed much further to the top-down, bottom-up debate. Those that did commonly supported the top-down control theory. However, some recent work has shown evidence for bottom-up control in some systems, such as benthic marine communities and rocky shores (Menge 1992).  The debate has moved on and it is recognised that top-down and bottom-up control are both likely to act on ecological communities (Neill, in press , Hunter and Price 1992, Matson and  221 Hunter 1992, Menge 1992, Powers 1992). Questions are being posed instead as to the nature of the links between bottom-up and top-down influences. Hunter and Price (1992) propose that the variation in flow types observed i n ecosystems is a consequence o f heterogeneity o f communities, ecosystems and species interactions.  The nature, or type, o f flow dynamics in San Miguel Bay, is unknown. San Miguel B a y is a large muddy bay with a broad mixture o f species, demersal and pelagic, vertebrate and invertebrate. The species composition o f the B a y has changed over the last few decades (Chapter 2), and it is possible that the flow dynamics have also changed. To date though, no empirical studies have been undertaken to examine this issue. The E C O P A T H model in Chapter 3 showed evidence for both flow hypotheses.  Despite the relative paucity o f data with which to make a multispecies analysis and assessment of San M i g u e l Bay, this has been made possible with the development o f a new multispecies model, E C O S I M (Walters et al. 1997). E C O S I M was developed from a simple mass-balance trophic model. E C O S I M proposes mechanisms for top-down and bottom-up control theories and it requires only a few more parameters than the E C O P A T H model used in Chapter 3. E C O S I M is used here to explore the community dynamics o f San M i g u e l B a y , the interactions between fishing, the ecosystem and species interactions and to examine certain "what i f ? " questions. Throughout the analysis, simulations were made for both top-down and bottom-up assumptions and the results were contrasted.  222  Methods  ECOSIM  E C O S I M (Walters et al. 1997) was developed from a mass-balance model. Walters (1996) recognised that the linear equations which describe the trophic fluxes in mass-balance, equilibrium assessments o f ecosystems (for example, E C O P A T H II) could be replaced by dynamic equations once the model was balanced. When an equilibrium model is balanced (see Chapter 3), the linear equations can be re-expressed as differential equations equivalent to changes in biomass through time. Perturbations to the equilibrium state are made by changing the exploitation regime. Thus without any extra parameters, the equilibrium ecosystem model is transformed into a dynamic ecosystem model and the impact o f changes in exploitation can be examined for each component o f the ecosystem.  The key step in the elaboration o f E C O S I M was the replacement o f the static consumption flows with functional relationships between consumption and biomass o f predators and prey. The basic E C O P A T H II linear equation is,  Bi.PBt  where,  - YBj.QBj.DCn-PBi.Bi(l j  -EEt)-FiBi-EXt  =0  223 Bj = Biomass o f (i), PBi - Production/Biomass ratio o f (i), QBi = Consumption/Biomass ratio of (i), DCjj = Proportion o f (i) in the diet o f (j), (1-EEj) = other mortality o f (i), EX = Export o f t  (0This is equivalent to,  g&j  Q(f -FiBi  - J^.Qi  -MoBi  = 0  where,  Qji = consumption o f (i) by (j) and  and, making the equation a differential,  —-  = giY Cij.{Bi,B )-MoB -F B J  j  l  l  -  l  j=\  j=\  where, Cjj{Bj, Bj) is a function used to predict consumption, Q , from the biomass o f the prey (/) and fj  the predators (J).  Initially, for simplicity, in the development o f E C O S I M , the Lotka-Volterra mass action equation was used as the functional relationship, where  224  cij (B i ,Bj)  = ciijB iBj  and, fly is die instantaneous mortality on the prey (Bi) caused by one unit o f predator biomass, (Bj), or, in ecological terms, the rate o f effective search.  The parameter a,y is determined by solving for a,y using the initial equilibrium model parameters. However, the Lotka-Volterra equation has only top-down control assumptions, and it does not take spatial or behavioural limiting mechanisms into account. In reality, there is likely only to be a certain amount o f prey available to a predator at any given time, due to predator avoidance behaviour by prey . In E C O S I M this is modelled as "vulnerability to predation", and provides one specific way to model bottom-up control o f biomass flow.  In this case, the functional relationship is  2vij +aijBj where, v,y = the maximum instantaneous mortality rate that By can exert on B,-.  T w o extra parameters, ay and v,y, are required in addition to the initial equilibrium model parameters. The maximum instantaneous mortality rate, v,y, is calculated from user input and the equation then solved for a,y.  225 With this functional relationship, E C O S I M is able to emulate the two energy flow control theories, top-down or bottom-up control. Top-down relationships are simulated by making v,y , the "vulnerability factor" large, meaning that a large part (or all) o f the prey biomass is vulnerable to predation. Bottom-up, donor control is simulated by making the value o f v,y very low. This limits the amount o f prey that are vulnerable to predation (thus keeping the mortality rate close to stable). In E C O S I M , when bottom-up, donor control is simulated by making vulnerability very low, the mortality rate is largely independent o f the abundance o f the next trophic level and for top-down control, the mortality rate is strongly dependent on the abundance o f the next trophic level.  The user thus determines the nature o f the flow relationships within the ecosystem, and can experiment with the effects o f different assumptions. When consumer biomass is low, the relationship is reduced to a Lotka-Volterra mass action flow and when consumer biomass is high, the relationship is donor control type.  In addition to the main functional relationship for consumers, functional relationships were developed for the producers in the ecosystem and for consumption and production by the detritus (see Walters et al. 1997 for further details).  77ze Delay-Differential  Model  In the E C O S I M model described, each pool is composed o f all age classes from juveniles to adults. Early work with the model unveiled a considerable shortcoming o f this approach. Top  226 predators were seen to increase unrealistically in response to decreased fishing mortality. These groups, first fully consumed the adult food groups, then were sustained by the invertebrate food o f the juveniles. The model had not accounted for the different diets o f juveniles and adults, that is, trophic ontogeny.  This was addressed by including the option to split pools into adults and juveniles (Walters et al. 1997). In the split pools, numbers o f individuals are tracked in addition to biomass. The adult pool receives numbers and biomass from the juvenile pool and numbers of juveniles are recruited from the adults. Each juvenile/adult pair is represented by five differential equations, based on Beverton and Holt type equations. In essence the model is akin to the delaydifference equations o f Deriso(1980): the juvenile pool has a mean age and weight and the juveniles grow to the age o f maturity when they become adults.  Three additional parameters are required for the "delay-differential" model, W*, the weight at which juveniles becomes adults, T, the age at which juveniles become adults and K , the von Bertalanffy growth parameter. The advantages o f the split pool model is that it allows for the effects o f both trophic ontogeny and cannibalism by adults on juveniles.  Running the Model  A l l that is required to run E C O S I M is the output from a balanced equilibrium mass balance model such as E C O P A T H II (and the split pool parameters). The model has two main modes of operation, the "Equilibrium Fishing Routine" and the "Dynamic Run Routine".  227 The Equilibrium  Fishing  Routine.  Fishing mortality, F , is varied incrementally and the equilibrium biomass o f each pool in the model is estimated for each value o f F. A Newton search method is used to find the equilibria by searching for zeros in the equations outlined above. F can be varied in three ways: on a single pool, for selected fishing gears or over total F in the fishery. The equilibrium catch for one pre-specified group is also calculated and both biomass and catch are plotted against F (see figure 4.2).  The Dynamic  Run  Routine.  The biomass o f each pool in the ecosystem is estimated through time in response to user imposed changes in the fishing pattern (see figure 4.9). The user can change the fishing mortality o f an individual group or an individual gear or o f all gears in the fishery. This is done graphically. Biomass is calculated using a fourth order Runge-Kutta numerical integration scheme.  Analyses  The community dynamics o f San Miguel B a y and the impacts o f fishing on the ecosystem were examined in several ways using E C O S I M . Throughout, the results o f the top-down and bottom-up assumptions about energy flow are contrasted. Bottom-up control was simulated using a vulnerability factor (vf) o f 1.5, and top-down control by a v / o f 10. Intermediate values were obtained using a vf oi 4, and extreme top-down values obtained using a vf oi 50. The scale and legend for Figures 4.2 - 4.10 and 4.14 are given in Table 4.1.  228 Impacts on Equilibrium Biomass of Changing Fishing Mortality Across All Fishing Gear  The Equilibrium Fishing Routine was used to investigate the gross effects o f total fishing mortality on the ecosystem and its response to change in F. Total fishing mortality (that is, fishing mortality summed over all gears) was incrementally reduced and increased from the current value. In addition to the top-down and bottom-up runs, two additional runs were made for an intermediate assumption o f flow control and an extreme top-down assumption.  The Equilibrium  Yield Curves, Species Interactions and Flow  Dynamics  In single species analyses, yield curves have played an important role in determining the status of fisheries and in helping to direct management. Here, three types o f yield curves are compared: the single species yield curves from Chapter 2; yield curves produced by the Equilibrium Fishing Routine that include the effects o f fishing on the whole fishery and yield curves produced by the Equilibrium Fishing Routine that only include the effects o f fishing on one pool.  Y i e l d curves which include the effects o f fishing on the whole fishery (yield curve - plus fishing) are produced by the routine described above. The shape o f the yield curve is influenced by the fishing mortality to which the pool is subject, indirectly by the fishing mortality that the other pools are subject to via species interactions, plus the assumptions made about flow dynamics in the ecosystem. The fishing mortality exerted on all pools changes simultaneously. Y i e l d curves which include only the effect o f fishing on one pool  229 (yield curve - no fishing) are produced when one pool is selected and fishing mortality varied only on that pool. The shape o f this yield curve is influenced by the specific fishing mortality for that pool and the assumptions made about flow dynamics. The fishing mortality on all other pools remains at the current fishing mortality rate. For the single species yield curves from Chapter 2, the only process effecting the shape o f the yield curve is the direct effect o f fishing mortality on that species.  Multispecies Dynamics in the San Miguel Bay Ecosystem  The flow diagram o f the San M i g u e l B a y (Figure 3.1) portrayed a hierarchically organised food web, ranging from trophic level 1 to 4. It also showed that many o f the pools were clustered around trophic level 3, indicating likely competitive interactions as well as predation links between trophic levels. The extent and strength o f these interactions, and thus their dynamics, could only be conjectured in Chapter 3. Here, it is possible to examine these interactions using the biomass results o f the single pool runs o f the Equilibrium Fishing Routine above, under both bottom-up and top-down assumptions. It is then possible to question and examine the association o f these interactions with the effects o f fishing.  The Effects of Fishing in San Miguel Bay  The impact o f fishing was directly examined for every gear using the Dynamic R u n Routine. The fishing mortality o f each gear was reduced to zero over the first 2 years and the simulation allowed to run for another 8 years (Figure 4.1), by which time most biomass transients had  230 stabilised (in cases where they had not, the simulation was run for 20 years as a check). Their impacts on the ecosystem was assessed in two ways, (i) the number o f pools impacted by the gear and the magnitude o f that impact, and (ii) the effective change in total biomass in the ecosystem.  Consistency Checlcs  A routine within E C O S I M allows the user to check the effect o f the above perturbations on the consumption/biomass ratios and the mean weights o f the split pools. This is a means to check the validity o f results. Extreme values o f either parameter would indicate that there is internal inconsistency in the model.  The simulations were ran as described above with the exception o f the Equilibrium Y i e l d Curves-no fishing. The effect on Q B and mean weight o f changing the biomass o f individual pools was checked using the Dynamic Runs routine. The fishing mortality on each pool was reduced to zero over the first 2 years o f a 10 year simulation, as in the dynamic runs where fishing effort was reduced to zero. This method was used for efficiency and because it gives the results as transients through time as opposed to equilibrium values.  Fishing Mortality Figure 4.2: Equilibrium simulation o f changing total fishing mortality for bottom-up and top down control. Thick red line indicates yield curve (for juvenile scieanids), coloured lines, biomass, dotted black line, current fishing mortality. See text for more details and Table 4.1 for legend and scale.  232 The Input Parameters  The initial data used for the E C O S I M model was the output runfile from the E C O P A T H model used in Chapter 3. To examine the effects o f fishing by different gear types in the fishery, an additional file containing a matrix o f the fishing mortality imposed on each pool by each gear type was also necessary. The twenty six gear types listed in Table 2.14 were combined into nineteen and included in the model. The large, medium and baby trawlers were combined simply as large scale trawlers (in contrast to the mini-trawlers) and the ring net, pullnet, stationary tidal weir, beach seine and spear gun were included as "others". Fishing mortality was calculated as catch (of pool a by gear x) over biomass (of pool a).  Splitting Pools into Adults and Juveniles  Early work with E C O S I M revealed that a "split p o o l " approach would be required. O f the 16 groups described in the San Miguel B a y ecosystem, eight are fish groups where the differences in diet o f adults and juveniles o f the same species might impose differential mortality effects on trophic groups lower in the ecosystem. However, only three o f these, the sciaenids, the medium predators and the large predators are likely to show any significant trophic ontogeny. Since the number o f pools i n the E C O P A T H model is already quite large, only these three groups were split in to adult and juvenile pools.  233 For the three split pools, each o f the E C O P A T H input parameters had to be re-calculated for both the adults and the juveniles, that is production/biomass, consumption/biomass, ecotrophic efficiency, exports/harvests, and diet. The new E C O P A T H model was then rerun and balanced. Since there were no explicit data available for the juveniles distinct from the adults, some assumptions had to be made on the basis o f the available data - full details are given in Appendix 2. Finally a file containing the split pool parameters, W*, T and K for each o f the split pools was required.  Results  Impacts on Equilibrium Biomass of Changing Fishing Mortality Across AU Fishing Gear  The overall impact on the San Miguel B a y fishery o f simultaneously increasing or decreasing fishing pressure over all fishing gears is shown in Figure 4.2. The results clearly show that, regardless o f assumptions made about the control o f energy flow, the biomass o f almost all fished groups in the ecosystem is currently less than 50% o f their biomass when no fishing mortality is incurred. The most striking feature o f both plots is the profusion o f coloured biomass lines on the left hand side o f the graph, indicating abundance and complexity compared to the much simpler array on the right. The results predict that i f fishing mortality were further increased, the fish groups would be replaced by the crustacean groups. The fishery would thus be reduced to essentially a crustacean fishery (a highly productive  Table 4.1 Legend and Scale for Figures 4.2 - 4.10 and 4 . 1 4 - 4 . 1 5 .  Ecosim Pool Zooplankton Meiobenthos Macrobenthos Sergestidae Penaeidae Large Crustuceans Juvenile Sciaenids Juvenile Medium Predators Juvenile Large Predators Demersal Feeders Leiognathids Engraulids Pelagics Sciaenids Medium Predators Large Zoobenthos Feeders Large Predators Phytoplankton Detritus  Equilibrium  Graph Maximum Dynamic Run Maximum 4.0 10 40.0 1 40.0 1 0.4 100 4.0 10 4.0 10 4.0 10 4.0 10 0.4 100 4.0 10 4.0 10 4.0 10 10 4.0 4.0 10 4.0 10 4.0 10 0.4 10 40.0 10 40.0 10  Fishing Mortality Figure 4.2: Equilibrium simulation o f changing total fishing mortality for bottom-up and top down control. Thick red line indicates yield curve (for juvenile scieanids), coloured lines, biomass, dotted black line, current fishing mortality. See text for more details and Table 4.1 for legend and scale.  •  236  crustacean fishery in the case o f the top-down assumption). These results once more emphasise that San M i g u e l B a y is suffering from ecosystem overfishing. The results also show that a reduction in fishing mortality would enable recovery o f biomass across most groups.  Although the broad equilibrium response to fishing mortality is similar for the bottom-up and top-down control assumptions, there are differences. There are also differences in the responses o f individual pools.  The top-down assumption produces more drastic changes in biomass than the bottom-up assumption: rates o f biomass change are faster and the range o f fishing mortality over which these changes occur, narrower. Thus biomass increases faster and higher, and decreases faster and lower. For some groups, such as the large crustaceans, the sergestids, the penaeids and the engraulids, this means that for low values o f fishing mortality, their biomass is negligible. O n the other hand, at high fishing mortality, the three crustacean groups are very productive. They suffer minimal predation, since all higher groups are virtually fished out. Pauly (1979a) predicts this increase in benthic invertebrates as a consequence o f overfishing. This crustacean biomass increase is also seen, although less dramatically, under the bottom-up control assumption. Their response is different however when fishing mortality is reduced. Here, these crustacean groups remain at relatively high biomass levels even when the biomass o f their predators also increases in response to the reduction in fishing mortality. Bottom-up control produces a more stable system that is more resilient to change. Generally, when fishing mortality is reduced, the biomasses do not rise to the levels seen in the top-down  237 simulation ' . However, the biomass o f the leiognathids, the pelagics, the demersal feeders, 74  75  the engraulids and the large zoobenthos feeders persist over a greater range o f high fishing mortality. This is because, under the bottom-up assumption, production at lower levels controls abundance at higher levels. A l l o f these groups feed on the crustacean pools which increase in abundance as fishing mortality is increased. Thus the biomass o f these groups persist, despite increased fishing pressure. The lack o f persistence o f these pools under the topdown control assumption is due to their intolerance to predation mortality and the increased fishing mortality. The biomass curve o f the pelagics and the demersal feeders has a small second peak (top-down control) to the right o f current F . These small peaks could be due to a release from predation pressure by the predator pools.  Under both control scenarios, the biomass o f the sciaenids, the medium predators and the adult large predators is reduced to an extremely low level when fishing mortality is increased much above the current fishing mortality. Conversely, when fishing mortality is reduced, their biomass increases quite rapidly. Surprisingly, the juvenile large predators do not follow this pattern. Their biomass persists at high levels o f fishing mortality.  In the top-down equilibrium fishing simulation, the biomass plots of the leiognathids, demersal feeders and pelagics initially increase in response to decreased fishing mortality, but subsequently, as F is further decreased, their biomass decreases. This would appear to be the result of predation by the higher trophic groups which continue to increase their biomass. However, trawl data (Warfel and Manacop 1950) indicate that the leiognathids comprised over 60% of the trawl biomass in the late 1940s (Table 2.6), when fishing effort was at a comparatively low level. It seems unlikely then that at low fishing mortality, the predators in the ecosystem would increase to such a high biomass as predicted by ECOSIM. Walters et al. (1997) note that the ECOSIM is unlikely to reasonably predict the ecosystem behaviour when extreme values of F are used since the model parameters originate in an equilibrium state assumption. The extreme results of the ECOSIM simulations should thus be interpreted with caution. When the equilibrium fishing simulation was run under the bottom-up control scenario, a runtime occurred. This was due to the sciaenids. The biomass of the juvenile sciaenids, after steadily increasing when F was decreased, suddenly dropped at very low F, crashing vertically whilst the adult sciaenids biomass exponentially increased. Further explanation is given in the text below of this phenomenon. In Figure 4.2, an intermediate value of vf=4 was used for the juvenile sciaenids, in order to conduct the bottom-up simulation. 75  238  The pools at the lowest trophic levels, the phytoplankton, zooplankton, meiobenthos. macrobenthos and detritus are very stable when bottom-up control is simulated and respond to predation by pools higher in the food web when top-down control is simulated.  For comparative purposes, two more simulations were made, one assuming that neither topdown or bottom-up control predominates, but that in reality, there is an intermediate level o f control. The other simulation assumes much stronger top-down control. The intermediate control simulation produced results, Figure 4.3 (a) which are in fact a kind o f half-way house between those described above. The strong top-down simulation, Figure 4.3(b), results in a biomass plot which resembles Figure 4.2, but shows less stability. For example, the penaeids decrease almost to zero and then take-off at high levels o f fishing mortality. Generally there is greater variation in biomass.  The results o f this multispecies equilibrium analysis demonstrate that there is not simply an increase in all groups in the ecosystem when fishing mortality is decreased and a decrease in all groups when fishing mortality is increased. In addition to the effects o f fishing mortality, the effects o f species interactions (predation and competition) and the assumptions made about flow dynamics are evident. The interplay o f fishing mortality, species interactions and flow dynamics has profound implications for fisheries assessment and management.  239  Fishing Mortality Figure 4.3: Equilibrium simulation o f changing total fishing mortality for intermediate control and strong top-down control. Thick red line indicates yield curve (for juvenile scieanids), coloured lines, biomass, dotted black line, current fishing mortality. See Table 4.1 for legend and scale.  240  Equilibrium Yield Curves, Species Interactions and Flow Dynamics  The interactive effects o f fishing mortality, species interactions and flow dynamics are perhaps even more evident in the equilibrium yield curves. Y i e l d curves were analysed with and without the effects o f fishing on other pools in the ecosystem, in addition to the effect o f multispecies interactions. Y i e l d curves without the effects o f fishing on other pools (yield curve-no fishing) were produced by varying fishing mortality on only one pool. Y i e l d curves that include the effects o f fishing on other pools (yield curve-plus fishing) were produced by varying fishing mortality across the whole fishery. The equilibrium yield was calculated from the equilibrium biomass for each pool in Figure 4.2. The latter is the more realistic scenario in a multispecies, multigear fishery. Although the yield curve-no fishing allows the analysis o f the effect o f fishing mortality on one pool, it would not be possible in practice to selectively fish on only one pool. Table 4.2 compares results o f the multispecies equilibrium yield curves with the single species curves (Chapter 2).  Some o f the pools in the model exhibit significant differences in the shape o f their yield curves, depending on whether fishing on other pools is included or not.  The (yield curve-no fishing) for the pelagics indicates a severely overfished fish group when top-down flow dynamics are assumed (Figure 4.4b), as does the single species yield curve for Scomberomorus  commerson (41% o f pelagic trawl biomass). A bottom-up assumption results  in the conclusion that current fishing mortality is at around the optimum l e v e l . However, the 76  ' Here optimum fishing mortality is interpreted as the area from the left of the top of the yield curve to the top of the curve, roughly comparable to the M E Y and M S Y points.  241  8 ftC o fc fc V V A Q V V h fci fc & ft o O o o U fc fc fc 1  a  PH  fc'  3  to 3  3  o  PQ  •i-F  o  *4Q  PH  PH  PH  • 3  PH  o  PH  PH  t  I  fc b 3  OH  3  CJ PH  o  3  PH  PH  fc  fc  ft  PH  A  A  3  O  ft  fc  oo  fc fc  V A  3  u  OH  3  CJ  3 F  CJ  fc fc fc  UH  A I  fc  3  3  cu  O  C  OH^  <•  3  fc fc  ^ I fc fc  I  O  fc  PH  CJ  t fc  CJ  Oj  OH  PH  PH  A  3  CJ  -  fc  PH  o  & c^  C  A A  o CJ o fc fc p_ fc 3  o  A A  3  PH  c  O. OJ  fc fc fc  fc  fc 3 o  o  P_  (  V  fc  PH  PH  PH  3  u  8"  OH  O  D. 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O S2 U 'ii <N g  ft  3  o  tu o  CO  3  PH  PH  Co  Co  o ft  PH  s s o  2  <3  8  to  U  to O  •2 ^ S  t*9  u  Ml  u,  ft  a 3  oo O  s  « i 2 3 ft 00 — N  C fc  1  3 C3  o  *  c  o 5 o  cu  O 48 <U t-<  -a  "3 <u  .2 '5 co  ft  s 3  •6  o  III OH  CO  e O  CU  E  •a  tu to fccu 3  £ .2  O ,  o fc  cu  3  J  •4—'  UH  cu  §  CC5  1-  u  cu  §  c •2 bO cu  A  ft  e o  3  3  UH  A  S a,  tu  C3  3  E  & O UH  1-  > .  . 2 2 c«  _tu  PH  fc' fc fc  R  e s O  PH  A A c  tu <+H  CH  D J  00  c  cn O  cn  «a  c CJ  CO _o -a o tu o  N  UH  *>. ft  tu  CU  H  HJ  CO  -1  c  CJ  fc 3  CJ  fc 3  CJ  fc  242 yield cmvQ-plus fishing produces quite different conclusions. For either flow assumption, the curve describes two peaks, one at a fishing mortality below the current fishing mortality and one at a fishing mortality higher than current fishing mortality (figure 4.4). The second peak results from the small increase in biomass at higher fishing mortality described above. If the optimum fishing mortality is associated with the highest peak, current fishing mortality is below optimum fishing mortality for both flow assumptions. The implication o f this is that i f fishing effort were increased across the fishery, there would be a greater return o f pelagics. A comparison o f the pelagic catch between 1979-1982 and 1992-1994 (Chapter 2) indicates that the level o f catch has not changed. Both plots in Figure 4.4 have a relatively flat area to the left of the current fishing mortality. So, at a lower total fishing mortality, such as there was in 1979-1982, a similar total catch o f pelagics would be predicted. The demersal feeders and engraulids have a similar response, although the latter does not show the double peak. The catch o f the latter was also higher in 1979-1982 than in 1992-1994. In summary, these three pools are unlikely to be able to sustain further mortality i f the effects o f fishing mortality are examined on them alone. However, i f the effects o f fishing on other pools in the ecosystem are included in the analysis, they would be able to sustain further fishing mortality.  Their revitalisation results from the decrease in their predators, and therefore a reduction in predation mortality, caused by further fishing.  This is more dramatically shown by the crustacean pools (sergestid, penaeids and large crustaceans). The yield curves for the penaeids are shown in Figure 4.5. For the yield curve-no fishing , current fishing mortality is either below optimum fishing mortality (bottom-up) or  243  Bottom-up  Top-down  Equilibrium Yield Curves-«o  fishing  Equilibrium Yield Curves-/?/«s  fishing  Fishing Mortality  Fishing Mortality  Figure 4.4. Equilibrium yield curves for the pelagics. In (a) and (b) fishing mortality is varied on only the pelagics. In (c) and (d) total fishing mortality is varied as in Figure 4.2. Thick red line indicates yield curve, coloured lines, biomass, dotted black line, current fishing mortality. See Table 4.1 for legend and scale. For further details see text.  244  Bottom-up  Top-down  Equilibrium Yield  Curves-/2o fishing b  ^  Equilibrium Yield  Fishing Mortality  Curves-plus  fishing  Fishing Mortality  Figure 4.5. Equilibrium yield curves for the penaeids. In (a) and (b) fishing mortality is varied on only the penaeids. In (c) and (d) total fishing mortality is varied as in Figure 4.2. Thick red line indicates yield curve, coloured lines, biomass, dotted black line, current fishing mortality. See Table 4.1 for legend and scale. For further details see text.  245 at optimum fishing mortality (top-down). However, for the yield curves-plus  fishing,  current  fishing mortality produces a yield far below the potential optimal yield produced when fishing mortality is increased and the other pools in the ecosystem are severely reduced by overfishing. Again this is the case regardless o f the flow assumption, although a much stronger response is seen for the top down assumption.  Some pools exhibit the opposite behaviour. For example, the adult medium predators yield curves-no fishing  and the single species yield curve for Trichiurus  haumela (62% medium  predators trawl biomass) indicate that current fishing mortality is at about optimum fishing mortality. The yield curves-plus  fishing  indicate that current fishing mortality is substantially  greater than the optimum fishing mortality (Figure 4.6). In the latter case, the optimum fishing mortality occurs at a much lower fishing mortality. This is because, when fishing mortality is reduced across the whole fishery, the biomass o f many other groups also increases, including prey species. The medium predators are by definition predators and thus an increase in prey abundance plus a decrease in fishing pressure enables a substantial increase in their biomass. The optimum fishing mortality corresponds with high biomass and the return from fishing mortality. For the yield curve-no fishing,  all predator groups are at low biomass levels:  decreasing fishing mortality only allows the increase in biomass o f the predator, not its prey. In fact, with the top-down assumption, the prey o f the medium predators decrease further with the increase in biomass o f the medium predators. Thus the medium predators are much more productive when fishing is reduced across the whole fishery and the abundance o f their prey is increased. Notably it is the pools at the highest trophic levels in the ecosystem, that is the adult large predators, medium predators and the juvenile medium predators, that show  246  Bottom-up  Top-down  Equilibrium Yield Curves-rco fishing  Equilibrium Yield Curves-plus fishing  Fishing Mortality  Fishing Mortality  Figure 4.6. Equilibrium yield curves for the adult medium predators. In (a) and (b) fishing mortality is varied on only the medium predators. In (c) and (d) total fishing mortality is varied as in Figure 4.2. Thick red line indicates yield curve, coloured lines, biomass, dotted black line, current fishing mortality. See Table 4.1 for legend and scale. For further details see text.  247 this response. The yield curves o f the juveniles medium predators are very similar to the adult medium predators suggesting that their abundance, at least in part, is a consequence o f the adult abundance.  The leiognathids and juvenile large predators respond similarly with both types o f multispecies yield curve, but differently with the assumptions made about flow dynamics. When a top-down assumption is made about flow dynamics, current fishing mortality is above the optimum fishing mortality, that is, the leiognathids are overfished. With bottom-up control, they are not overfished: current fishing mortality is below optimum fishing mortality for the yield curved/us fishing and at about fishing mortality for the yield curve-no fishing. The single species yield curves for L. splendens and L. bindus show these two species, which comprise 55% o f the trawl biomass o f the leiognathids, to be overfished. However, the yield curve for another leiognathid, Secutor ruconius (32% trawl biomass) indicates that current fishing mortality is at the optimal rate.  Finally, some pools give the same results regardless o f type o f yield curve or the assumptions made about flow dynamics. The juvenile sciaenids and the large zoobenthos feeders are always overexploited and the adult sciaenids are always just at optimum fishing mortality. The single species yield curve for Otolithes ruber (65% trawl biomass o f sciaenids) indicates that this species is highly overfished. Since most o f the catch consists o f juveniles (see chapter 2) this result is in accordance with the multispecies results.  248 The results from the two types o f multispecies yield curve analyses underline the critical importance o f the assumptions made about flow dynamics. Generally, the bottom-up assumption results in a current fishing mortality that is much closer to the optimum fishing mortality than the top-down assumption. Thus the predictions from a bottom-up analysis indicate, for many pools, a fishery in a reasonably healthy state. However, the top-down assumption results often indicate quite the opposite, where pools are seriously overfished. Clearly in this case, sustainable management would require measures that would act to conserve these pools and reduce fishing effort. Since it is not known what type o f flow dynamics prevail in San Miguel B a y , making the top-down assumption would be the more cautious approach to managing the fishery. This would be consistent with the precautionary principle ( F A O 1995a,b). The results from the bottom-up analysis could lead to the conclusion that there was potential for expansion in some sectors o f the fishery.  The analyses also demonstrate the contrasting results obtained from examining the yield curves when fishing mortality is held constant on all pools other than that pool being examined (yield curve-no fishing)  and the yield curves when fishing mortality is varied across all gears in  the fishery. Fishing other pools in the fishery affects the shape o f the yield curve. This can mean that the pool w i l l sustain more fishing than the yield curve-no fishing  indicates, it may  imply that less fishing can be sustained, or the two analyses may reach the same conclusions. In a multigear, multispecies fishery then, it is vital to examine yield curves produced by changing fishing patterns across the whole fishery, or selected fishing gears, rather than examining the yield curve produced by changing fishing mortality on one pool alone. In this analysis, fishing mortality was varied across all gears simultaneously: in reality, increases in  249 fishing mortality would occur differentially across gears. This was demonstrated in Chapter 2 where the changes in gear composition across the fishery and their implications were described. The consequences o f differential changes in fishing effort and fishing gear in San Miguel B a y are explored below.  The overall picture is that the crustacean pools would withstand further exploitation and indeed lead to a great increase in productivity, i f the biomass o f the other pools in the ecosystem were reduced further by increased fishing mortality. The pools at the top o f the ecosystem are overfished no matter which way you look at it, with the exception o f the medium predators which may not quite yet be overfished, although they are maximally fished. The state o f the pools at an intermediate trophic level, that is the demersal feeders, leiognathids, engraulids, and pelagics is less clear. These groups respond to both changes in their predators and prey and so their status, like that o f the crustacean groups, depends more heavily on what is happening in the rest o f the fishery. Certainly the suggestion from the yield curve-plus fishing analysis is that they could sustain a higher fishing mortality. However here caution should be exercised since the yield curve-no fishing so clearly contradicts this.  The results o f the multispecies yield curves broadly agree with the single species yield curves, (see Table 4.2). They give the similar relationships between current fishing mortality and optimum fishing mortality. They also produce similar parabolic shaped curves, although the shape o f some are distorted by the effects o f other pools. However, Figures 4.4-4.6 clearly show that changing the biomass o f one pool in the ecosystem can strongly impact on other pools in the ecosystem. The single species analysis can give no indication o f how changes in  250 one pool might affect other pools. This was briefly discussed above and w i l l now be examined in more detail.  Multispecies Dynamics in the San Miguel Bay Ecosystem  The equilibrium yield curves-no fishing  illustrate the changes in equilibrium biomass o f all  pools when the biomass o f one pool is changed (by incrementally increasing and decreasing fishing mortality on that pool). When bottom up control o f flow dynamics is assumed, most pools have little effect on the biomass o f the other pools in the ecosystem. Since the other pools are not subject to changes in fishing mortality and are limited only by food availability, their biomass would not be expected to change very much, unless their food availability changes. However, when top down control is assumed, because o f strong predator influences, there are often quite dramatic responses to the changes in biomass o f just one pool. Table 4.3 maps these responses for top-down control.  Surprisingly, the large predators, at the top o f the ecosystem have very little impact on the rest of the ecosystem, regardless o f flow assumption. The juveniles have more effect than the adults because they form the prey o f other pools. However, no pools consume the adult large predators, and because o f their low biomass, they do not eat much o f anything else.  In contrast, changes in biomass o f the sciaenids and medium predators have profound effects on the other pools in the ecosystem. Figure 4.6 a,b illustrates this for the adult medium  251 Table 4.3. Impact o f decreasing the biomass o f one pool on the other pools in the San Miguel Bay ecosystem when top-down control is assumed.. The biomass o f the pools in the left hand column are decreased. The pools across the table respond to the decrease.  Biomass Biomass Decrease Serg  Serg Pen  -  LC  Response  J_Sci J__MP J-LP  DF  Leiog  Eng  Pel  Sci  +  -  +  +  ++  -  -+  —  ++  -  ++  +  ++  —  -  -  ~  -  +  -  -+  +~+  +-+  +  +~+  —  ++  ++  ++  +  ++  -  +  -  +-+  ~  +  ++  ++  ~  -+  -  —  -+  +-  -  +-  +  +  +  +  +  -  ++  —  ++  ++  —  ++  —  ++  -  ++  —  +-  ++ -  —  -  -  —  +-  LC  ++  ++  J_Sci  ++  +-  ++  J_MP  —  —  —++  +-  J-LP  -  -  —  +  +  DF  ++  ++  +—  ++  -  —  Leiog  ++  -+-  +-+  ++  -  —  ++  Eng  ++  ++  —  ++  —  —  ++  Pel  ++  ++  ++  ++  -  —  ~  ++  Sci  +  +-  ++  —  -  -+  -+  —  ++  ++  MP  —  —  -++  —  -  ++  ++  ++  +  ++  -  LZB  ~  ++  +  ~  ~  -  +  +  +  LP  ++  ~  +--  -  + ~  ++  — + = positive response, ++ = strong positive response, negative response, ~ = no change.  LP  +  Pen  -  MP LZB  ~  ~+  ++ ++  -  ~  - = negative response,  — = strong  Serg = Sergestids, Pen = Penaeids, L C = Large Crustaceans, J_Sci - Juvenile Sciaenids, J _ M P = Juvenile M e d i u m Predators, J _ L P = Juvenile Large Predators, D F = Demersal Feeders, Leiog = Leiognathids, E n g = Engraulids, Pel = Pelagics, Sci = Sciaenids, M P = M e d i u m Predators, L Z B = Large Zoobenthos Feeders, L P = Large Predators.  252 predators. When medium predator biomass is decreased from the left to the right o f the plot (and F is increased), the biomass o f the leiognathids, the demersal feeders, the pelagics and the large predators all increase substantially. The biomass o f the engraulids also increases but more slightly. These pools are all prey for the medium predators. The sciaenids decrease with the medium predators, as do the sergestids and the penaeids. The large crustaceans decrease then increase. Essentially then, increasing the biomass o f the medium predators (either adults or juveniles) leads to a decrease i n the biomass o f most o f the other fish pools in the ecosystem. Under the bottom-up assumption there is little change in any group other than the adult medium predators. However, when the biomass o f the juvenile medium predators is decreased, there is a small increase in the biomass o f the leiognathids, the demersal feeders, the engraulids, the pelagics, the sciaenids and the large predators. These increases might be due to the release from competitive pressure for food by the juvenile and adult medium predators.  The sciaenids also exhibit a repressive effect on other pools in the ecosystem when top down control is assumed. In this case, the engraulids, the pelagics, the large predators, the sergestids and the large crustaceans are most affected and increase when the biomass o f sciaenids is decreased (Figure 4.7 d). The leiognathids, the medium predators and the large zoobenthos feeders decrease, while the demersal feeders increase slightly when the biomass is increased or decreased. This repressive effect is much weaker when bottom-up control is simulated however (Figure 4.7c). In this case the three crustacean groups all increase when the biomass o f the sciaenids is reduced. Most o f the fish pools also increase, but only very slightly.  253 The plots for the juvenile sciaenids are less easy to interpret (Figure 4.7 a,b). Both bottom-up and top-down interpretations present problems. Under the top-down assumption, the medium predators decrease with decreasing biomass o f sciaenids and the basic trend for the leiognathids and the large zoobenthos feeders is also to decrease. This result is qualitatively the same as for the adult sciaenids The engraulids, sergestids and large crustaceans increase. However, the demersal feeders, the pelagics, the large predators and the penaeids have a more complicated response. The trend in the biomass o f these pools changes at the point o f current fishing mortality, and it changes again at a much lower value o f fishing mortality on the juvenile sciaenids.  When the juvenile sciaenids are subject to the bottom-up assumption, the model crashes as it incrementally tracks its way down the fishing mortality range from current fishing mortality (Figure 4.7a). A s fishing mortality is decreased, the biomass o f the juvenile sciaenids and the adult sciaenids increase. However, before the fishing mortality reaches zero, the biomass o f the juveniles suddenly and rapidly crashes while the biomass o f the adult increases exponentially. A t this point the model fails. The failure is caused by a combination o f factors. One is that the numerical procedure for tracking the movement o f the system equilibrium breaks down when a parameter (including fishing rate) combination is encountered for which the system has either no stable state or a bifurcation to several possible dynamic patterns  254  Bottom-up  Top-down Juveniles  Figure 4.7: Equilibrium yield curves-no fishing showing ecosystem impacts o f the sciaenids. Thick red line indicates yield curve, coloured lines, biomass, dotted black line, current fishing mortality. See Table 4.1 for legend and scale. For further details see text.  255 (C. Walters, pers. comm.). However, in addition to this there is a more serious and fundamental problem.  This is a problem with the way that the two-pool delay differential model is structured. E C O S I M explicitly models the flow from adults to juveniles and juveniles to adults. The flow o f adult sciaenids to juveniles has reasonable values in the above simulation (high adult biomasses produce high flows into the juvenile pool as new recruits). However, the flow of surviving juveniles moving to the adult pool at presumed size W*, takes values that are much too large. The equation which calculates this flow is based on a steady-state approximation o f juvenile flow to adults. It is this approximation that fails, whereby juveniles would not be able to reach size W * in the time predicted by the model, or would reach that size very much sooner than predicted. Such failure is caused by extreme changes (or very rapid changes) in the biomasses (C. Walters,pers.  comm.) . 11  The model is thus unable to predict, when bottom-up flow dynamics are assumed, what would happen when the biomass o f the sciaenids, juveniles and adults, is increased through a decrease in fishing mortality. The increased abundance would presumably lead to increased competition for food and space. One hypothesis (C. Walters,pers. comm.) is that increased competition would lead to an increase in foraging time which would lead to increased vulnerability. Since bottom-up control is modelled as low vulnerability, it would not make any sense to assume bottom-up control when the juveniles are so abundant that they have trouble getting enough food to reach body size W ^ within a time span set in the input data. This  A new version of the model, ECOSIM II is now available to correct these problems for future studies; it was not available in time for this analysis. 77  256 approach was adopted here and in the bottom-up simulations, the juvenile sciaenids are given TO  an intermediate vulnerability factor (see footnote 74) .  Biomass changes o f the pools at the middle o f the trophic range, that is the demersal feeders, leiognathids, engraulids and the pelagics (Figure 4.4) also impact strongly on other pools in the ecosystem under top-down control. I f their biomass is decreased, the biomass o f the sergestids and the sciaenids increases while the medium predators and the large predators decrease. The former are largely competitors for food, the latter, predators on these four pools. The large zoobenthos feeders increase when the demersal feeders and the leiognathids are decreased but decrease when the engraulids and pelagics are decreased. Within this groups o f four pools, the pelagics always decrease when the biomass o f the other three pools is decreased and the engraulids increase. The demersal feeders and the leiognathids both decrease when the pelagics decrease. Thus these groups also have strong influences on the rest o f the ecosystem.  The crustacean groups have less o f a direct impact on the higher trophic pools in the ecosystem (Figure 4.8), even under the bottom-up assumption (one might think that large increases or decreases in the biomass o f these lower trophic groups would have impacts for those groups higher in the system i f their biomass is controlled from the bottom up). Under the bottom-up assumption, the three pools have almost identical effects on the other pools in the ecosystem (Figures 4.5 and 4.8). These crustacean pools are more indicators o f change than determ