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UBC Theses and Dissertations

Strategic marine ecosystem restoration in northern British Columbia Ainsworth, Cameron H. 2006-12-31

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STRATEGIC MARINE ECOSYSTEM IN N O R T H E R N BRITISH  RESTORATION  COLUMBIA  by  C A M E R O N H. AINSWORTH B . Sc., The University o f British Columbia, 1997  A THESIS S U B M I T T E D IN P A R T I A L F U L F I L L M E N T OF THE REQUIREMENTS F O R THE D E G R E E OF  DOCTOR OF PHILOSOPHY  in  T H E F A C U L T Y OF G R A D U A T E STUDIES  (Resource Management and Environmental Studies)  THE UNIVERSITY OF BRITISH C O L U M B I A M a y 2006 © Cameron H . Ainsworth, 2006  11  ABSTRACT  Innovative methodology is developed for Back to the Future ( B T F ) restoration policy analysis to aid long-term strategic planning o f ecosystem-based restoration in marine ecosystems.  Mass-  balance and dynamic ecosystem simulation models (Ecopath with Ecosim: E w E ) are developed to represent the marine system o f northern British Columbia as it appeared in 1750, 1900, 1950 and 2000 A D .  Time series statistics are assembled for biomass and catch, incorporating local  ecological knowledge ( L E K ) from  community interviews and new estimates  o f illegal,  unreported and unregulated (IUU) fishery catch. The dynamic behaviour o f the historic models is fitted to agree with this time series information, when driven by historic catch rates and climate anomalies.  Each historic period is evaluated in an optimal policy analysis for its  potential to supply sustainable harvest benefits.  Harvest benefits  are quantified using  socioeconomic and ecological indicators, including novel measures such as the Q-90 biodiversity statistic. Candidate goals for restoration are drafted based on these historic ecosystems. A new conceptual goal for ecosystem-based restoration is introduced, the optimal restorable biomass ( O R B ) that represents an optimized form o f the historic ecosystems. It is structured to maximize sustainable harvest benefits, and to achieve a compromise between exploitation and the maintenance o f historic abundance and biodiversity. Finally, restoration plans are drafted using a novel addition to Ecosim's policy search routine, the specific biomass objective function, which determines the pattern o f fishing effort required to restore the depleted present-day ecosystem into one resembling a more productive O R B state. Cost-benefit analysis indicates that northern B C ecosystem restoration to an O R B state based on the 1950 ecosystem can deliver a rate o f economic return, in terms o f increased fisheries yields, that is superior to bank interest. The effect  o f fleet  structure  is paramount;  effectiveness o f the fleet as a restoration tool.  reducing bycatch w i l l  greatly enhance  the  Restoration plans that sacrifice immediate  fisheries profits tend to restore more biodiversity in a given amount o f time, but a convex relationship between profit and biodiversity suggests there is an optimal rate o f restoration.  iii  TABLE OF  CONTENTS  Abstract  ii  Table of Contents  iii  List of Tables  v  List of Figures  vii  List of Equations  x  Acknowledgements  xi  1 Back to the Future  1  1.1 Introduction  1  1.2 Ecopath with Ecosim  7  1.3 Northern British Columbia  10  1.4 Structure of thesis  14  2 Harvest Policy Evaluation Techniques  19  2.1 Introduction  19  2.2 Economic index: Net present value (NPV)  19  2.3 Social utility index: Employment diversity  22  2.4 Ecological indices  23  2.5 Q-90 case study: NE Pacific ecosystems  26  3 Community Interviews  35  3.1 Introduction  35  3.2 Methods  36  3.3 Results  :  42  3.4 Discussion  55  4 Estimating Illegal, Unreported and Unregulated Catch  59  4.1 Introduction  59  4.2 Methods  61  4.3 Results  .'  71  4.4 Discussion  76  5 Modeling the Past and Present  5.1 Introduction......  ,  82  ;  •  82  5.2 Fisheries  121  5.3 Ecosim parameterization  122  5.4 Assembling time series data  132  5.5 Analysis of fitted vulnerabilities  134  5.6 Validation of dynamic function  138  5.7 Discussion  142  iv 6 Evaluating Restoration Goals  146  6.1 Introduction  146  6.2 Methods  154  6.3 Results  165  6.4 Discussion  188  7 Achieving Restoration  197  7.1 Introduction  197  7.2 Methods  199  7.3 Results  215  ;  7.4 Discussion  :  244  8 Conclusions  250  8.1 Summary 8.2 An ecosystem approach to management  250 :  :  251  8.3 The developing role of ecosystem models  264  8.4 Policy recommendations  265  8.5 Concluding remarks  266  References  268  Appendices  298  Appendix 2.1 The Effect of Discounting on Fisheries  298  Appendix 2.2 Cost-benefit Analysis of Education  310  Appendix 3.1 LEK Trends of Relative Abundance  313  Appendix 4.1 BC Fisheries Timeline  314  Appendix 4.2 IUU Influences Table  322  Appendix 4.3 BC Reported Landings  330  Appendix 4.4 Average Species Weight  331  Appendix 5.1 Ecopath Parameters  332  Appendix 5.2 Ecosim Parameters  346  Appendix 5.3 Time Series  351  Appendix 5.4 Dynamic Fit to Data: 1950-2000  357  Appendix 5.5 Equilibrium Analysis of 2000 Model  361  Appendix 5.6 Comparison of Derived 2000 Model with Proper 2000 Model  363  Appendix 6.1 Policy Search Parameters  365  Appendix 6.2 Evaluation of ORB Ecosystems  367  Appendix 7.1 Input for Restoration Scenarios  372  Appendix 7.2 Candidate Restoration Trajectories  373  Appendix 8.1 Gwaii Haanas Spatial Investigations  376  Appendix 8.2 Ecospace Parameters  402  Appendix 9.1 References cited in the Appendices  .'  407  V  LIST OF  TABLES  Table 1.1 Published materials appearing in this thesis  18  Table 2.1 Eight EwE models of the NE Pacific  28  Table 3.1 Percentage of interviewee comments that agree with stock assessment records  43  Table 3.2 Biomass estimates (tkm") used in Ecopath models compared to LEK trend  50  2  Table 3.3 Place names mentioned during interviews  53  Table 4.1 Incentive ratings  67  Table 4.2 Anchor point range  67  Table 4.3 Mean reported catch  67  Table 4.4 Absolute ranges of IUU catch rate for each incentive rating  72  Table 4.5 Monte Carlo input: IUU catch range  74  Table 4.6 Monte Carlo output: Mean IUU catch with 95% confidence intervals  74  Table 5.1 Data sources forNE Pacific environmental indices  128  Table 6.1 List of nine criteria for sustainable and responsible 'lost valley^ fisheries  153  Table 6.2 Lost valley fleet catch  156  Table 6.3 Lost valley fleet discards  157  Table 6.4 Rank order of ORB ecosystem performance in various evaluation fields  177  Table 6.5 Fishing rates of ORB solutions, analysis of response surface geometry and ecosystem stability  184  Table 7.1 Available settings for the specific biomass rebuilding objective function  203  Table 8.1 Criticisms of MSY and their applicability to the ORB concept  255  Table A2.1.1 CBA of education  312  Table A4.1.1 BC fisheries timeline  314  Table A4.2.1 IUU influences table  322  Table A4.3.1 BC reported landings  330  Table A4.4.1 Average species weight  331  Table A5.1.1 Species aggregation by functional group  332  Table A5.1.2 Basic parameters for all periods  336  Table A5.1.3 Diet composition  338  Table A5.1.4 Landings data for all time periods (tkm )  342  Table A5.1.5 Discard data for 1950 and 2000 (tkm )  344  Table A5.1.6 Market prices ($jkg" ') for 2000 BC fleet  345  2  -2  Table A5.2.1 Juvenile/adult stage transition parameters for all models  346  Table A5.2.2 Feeding parameters for 1950  347  Table A5.2.3 Trophic flow parameters for 1950  348  Table A5.3.1 Biomass time series data (t-km ): 1900-1950  351  Table A5.3.2 Biomass time series data (tkm"): 1950-2000  352  2  2  vi Table A5.3.3 Catch time series data (tkm"): 1900-1950  353  Table A5.3.4 Catch time series data (tkm ): 1950-2000  354  TableA5.3.5 Fishing mortality time series data (yr"'): 1900-1950  355  Table A5.3.6 Fishing mortality time series data (yr ): 1950-2000  356  2  2  1  Table A5.6.1 Comparison of derived 2000 model with proper 2000 model  363  Table A6.1.1 Market prices ($kg ) for lost valley fleet  365  Table A6.1.2 Biomass/production (B/P) ratios by functional group  366  Table A6.2.1 Functional group biomass (tkm ) for selected ORB ecosystems  369  Table A6.2.2 Fisheries landings by gear type (tkm ) for selected ORB ecosystems  371  Table A7.1.1 Catch profile for maxdex fleet  372  _l  2  2  Table A8.1.1 Ecospace habitat definitions  383  Table A8.1.2 Group behaviour guidelines used to standardize Ecospace functional groups  390  Table A8.2.1 Habitat occupancy  402  Table A8.2.2 Fishery activity by habitat  403  Table A8.2.3 Salmon straying rates  403  Table A8.2.4 Dispersal parameters  404  Table A8.2.5 Ecospace output region definitions  405  Table A9.2.1 IG discounting case study references cited in Appendix 2.1  407  Table A9.2.2 Cost-benefit analysis of education references cited in Appendix 2.2  408  Table A9.4.1 BCfisheriestimeline references cited in Appendix 4.1  409  Table A9.4.2 Average species weight references cited in Appendix 4.4  414  Table A9.4.3 Illegal catch anchor point references cited in Appendix 4.2  415  Table A9.4.4 Discard anchor point references cited in Appendix 4.2  416  Table A9.4.5 Unreported catch anchor point references cited in Appendix 4.2  417  Table A9.5.3 Biomass, catch and effort time series data references cited in Appendix 5.3  418  Table A9.8.1 Spatial investigations for Gwaii Haanas references cited in Appendix 8.1-.  421  LIST OF  FIGURES  Figure 1.1 Biodiversity and species abundance decline caused by  fisheries  2  Figure 1.2 The Back to the Future approach to marine ecosystem restoration  4  Figure 1.3 Northern BC study area  12  Figure 2.1 Q-90 statistic definition  25  Figure 2.2 Dynamic ecosystem biodiversity (Q-90) of three example Ecopath with Ecosim simulations  29  Figure 2.3 Absolute Q-90 value at baseline (year zero) for eight northeastern Pacific Ecopath models  30  Figure 2.4 Change in Q-90 index after 30 years offishingfor eight EwE models of the NE Pacific  30  Figure 2.5 Q-90 sensitivity to changes in system biomass structure  31  Figure 2.6 Q-90 sensitivity to changes in ecosystem structure using three depletionfilterthresholds  32  Figure 3.1 Fraction of comments that agree with DFO records by functional group  44  Figure 3.2 Interviewee agreement with stock assessment data by career length  45  Figure 3.3 LEK abundance trend versus stock assessment  48  Figure 3.4 Rank correlation of LEK abundance trend versus stock assessment  49  Figure 3.5 Correlation of LEK relative abundance trend and stock assessment with model ouputs  52  Figure 3.6 A map of the study area showing the number of LEK comments indicating species presence  54  Figure 4.1 A time series of numerical influence factors assigned semi-quantitative 'incentive' ratings  64  Figure 4.2 Salmon recreational catch estimates  66  Figure 4.3 Cumulative probability distribution of missing catch  70  Figure 4.4 Likely range of groundfish discards  70  Figure 4.5 Estimates of missing catch for salmon and groundfish fisheries  75  Figure 4.6 Total estimated extractions in BC salmon and groundfish fisheries  76  Figure 5.1 EwE's predicted climate anomalies versus their strongest correlating environmental indices  ::'  128  Figure 5.2 Correlation of primary production and herring recruitment anomalies with environmental indices  129  Figure 5.3 Predicted and observed herring trend (1950-2000) under three conditions of climate forcing  130  Figure 5.4 Predicted and observed variance of group biomass trajectories (1950-2000)  131  Figure 5.5 Rank order of vulnerabilities in thefitted1950 model versus predator and prey trophic level  135  Figure 5.6 Log vulnerabilities in fitted 1950 model versus predator and prey trophic level  135  Figure 5.7 Evaluation of short-cut methods used to parameterize Ecosim vulnerabilities  137  Figure 5.8 Group biomass predicted in 2050 by derived and proper 2000 models after fishing release  140  Figure 5.9 Biomass change predicted by the derived and proper 2000 models after fishing release  141  Figure 5.10 Direction of biomass change predicted by proper and derived 2000 models after fishing release  142  Figure 6.1 Optimal Restorable Biomass (ORB) concept  148  Figure 6.2 Cluster analysis of group biomass configurations for two example ORB ecosystems  166  Figure 6.3 Value equilibriums for ORB ecosystems based on various historical periods  167  Figure 6.4 ORB equilibrium catch value per gear type  170  Figure 6.5 ORB equilibrium value by group under various harvest objectives  171  Figure 6.6 Social utility provided by ORB ecosystems based on various historical periods  173  Figure 6.7 Biodiversity of ORB ecosystems based on various historical periods  175  Figure 6.8 Biodiversity of historic ecosystems under optimal fishing policies using lost valley fleet structure  176  Figure 6.9 Profit and biodiversity of ORB equilibriums based on 1750, 1900, 1950 and 2000 periods  179  Figure 6.10 Social utility provided by ORB ecosystems based on 1750, 1900, 1950 and 2000 periods  181  Figure 6.11 Response surface geometries  183  Figure 6.12 Biomass depletion risk of ORB solutions, considering Ecopath parameter uncertainty  186  Figure 6.13 The effects of data uncertainty on ORB equilibrium values determined by Monte Carlo  187  Figure 7.1 Controls added to Ecosim's policy search interface for SB algorithm  203  Figure 7.2 Three models describing marginal improvement in SB function as group biomass approaches goal.... 206 Figure 7.3 Constrained marginal improvement model (MIM) Figure 7.4 Dynamic progress display form to monitor rebuilding success of the SB algorithm  208 211  Figure 7.5 Conceptual diagram showing cost-benefit analysis  214  Figure 7.6 Performance of SB algorithm towards achieving historic 1950 ecosystem structure  217  Figure 7.7 Commercial biomass increase under various restoration plans  218  Figure 7.8 Principle components analysis showing ecosystem configurations after restoration  219  Figure 7.9 Average improvement in functional group biomass towards target level after restoration  221  Figure 7.10 End-state group biomass after rebuilding relative to target 1950 goal biomass  222  Figure 7.11 End-state group biomass after rebuilding relative to target 1900 goal biomass  223  Figure 7.12 End-state profit and biodiversity of restoration plans targeting the historic 1950 ecosystem  225  Figure 7.13 Progress towards goal ecosystems 1950 and 1900 for all diagnostic optimizations  226  Figure 7.14 End-state ecosystem condition of nine restoration plans targeting the biodiversity ORB  228  Figure 7.15 End-state profit after 50 years of restoration versus sum of squares against goal ecosystem Figure 7.16 Change in average system trophic level and biodiversity following restoration Figure 7.17 Best reduction in sum of squares versus target system achieved by SB algorithm  229 231 232  Figure 7.18 End-state profit and biodiversity after restoration for all 50-year restoration plans tested  233  Figure 7.19 Worked example of a 30 year ecosystem restoration plan  234  Figure 7.20 Net present value of restoration plans achieving a minimum reduction in residuals versus goal  237  Figure 7.21 Equilibrium level profit and biodiversity achieved by restoration scenarios  241  Figure 7.22 Net present value of restoration scenarios  242  Figure 7.23 Internal rate of return (IRR) making restoration/harvest scenarios economically worthwhile  243  Figure A2.1.1 Stability analysis of dynamic ecosystem model  302  Figure A2.1.2 Real price of cod based on harvest from Atlantic Canada  304  Figure A2.1.3 Historic cod biomass trajectory estimated from VPA versus EwE optimal trajectories  305  Figure A2.1.4 Optimal end-state biomasses after 16 years of harvest under various discounting methods  306  Figure A2.1.5 Optimal end-state catches after 16 years of harvest under various discounting methods  307  Figure A2.1.6 Net present value of 40-year harvest profile based on real-world data and optimum solutions  308  Figure A2.1.7 Generational share of catch after 40 years for three harvest profiles  308  Figure A2.1.8 Sensitivity analysis showing the effect of discount rate on the optimal end-state biomass  309  Figure A2.2.1 Costs and benefits of education in BC discounted from a 1981 time perspective  311  .  Figure A3.1.1 LEK trends of relative abundance  313  Figure A5.4.1 Biomassfitto data (tkm )  357  Figure A5.4.2 Catchfitto data (tkm )  360  Figure A5.5.1 Equilibrium analysis of 2000 model  361  Figure A6.2.1 Equilibrium harvest benefits from ORB ecosystems derived from 1750, 1900, 1950 and 2000  367  2  2  Figure A7.2.1 Restoration scenarios using the BC fishing fleet  373  Figure A7.2.2 Restoration scenarios using the lost valley fishing fleet  374  Figure A7.2.3 Restoration scenarios using the maxdexfishingfleet  375  Figure A8.1.1 Ecospace habitats  382  Figure A8.1.2 Bathymetry  382  Figure A8.1.3 Tidal speed  382  Figure A8.1.4 Primary production forcing pattern used in Ecospace Figure A8.1.5 Modeled current circulation Figure A8.1.6 Ecospace output regions used to summarize results by area  385 387 391  Figure A8.1.7 Catch by output region  393  Figure A8.1.8 Regional effects of NMCA area closures on landings  394  Figure A8.1.9 Equilibrium trophic level of catch in regions adjacent to MPA  395  Figure A8.1.10 Group biomass change within MPA resulting from area closures.:  397  Figure A8.1.11 Equilibrium biodiversity in MPA and adjacent regions following fishery closure Figure A8.1.12 Equilibrium state changes within the MPA under zero to twelve month area closures Figure A8.2.1 Value of catch per gear type  398 399 406  X  LIST OF EQUATIONS Equation 1.1 Ecopath production equation  8  Equation 1.2 Ecopath consumption equation  8  Equation 1.3 Ecosim biomass dynamics  9  Equation 2.1 Conventional discounting model  20  Equation 2.2 Discount factor  20  Equation 2.3 Intergenerational discounting model  21  Equation 2.4 Intergenerational discount factor  21  Equation 2.5 Shannon entropy function  22  Equation 2.6 Shannon-Weaver biodiversity model  22  Equation 2.7 Q-90 statistic definition  25  Equation 2.8 Q-90 10th percentile  26  Equation 2.9 Q-90 90th percentile  26  Equation 4.1 Likely error range used for IUU Monte Carlo analysis  68  Equation 4.2 Probability density function of triangular IUU catch error distribution  68  Equation 6.1 Policy search routine objective function....!  159  Equation 7.1 SB algorithm summation term  200  Equation 7.2 Proximity to goal index (9) used by SB algorithm  201  Equation 7.3 Proximity to goal index (9) modified for biomass unit of improvement  202  Equation 7.4 Proximity to goal index (9) modified for combined unit of improvement  202  Equation 7.5 Linear marginal improvement valuation model  204  Equation 7.6 Quadratic marginal improvement valuation model  204  Equation 7.7 Gamma marginal improvement valuation model  :  Equation 7.8 Biomass term substitution for functional groups already close to target in SB algorithm Equation 7.9 Fast-track modification to SB algorithm summation term Equation A2.1 Cost-abundance relationship of fishing  205 207 210 303  XI  ACKNOWLEDGEMENTS  I extend deep gratitude to my supervisor, Tony Pitcher, for his advice and assistance, and the many opportunities he gave me. This project could not have been done without the support o f the Fisheries Centre.  I thank Daniel Pauly, V i l l y Christensen, Rashid Sumaila, Carl Walters,  N i g e l Haggan, Sheila Heymans, Sylvie Guenette and many other friends and colleagues who supported m y work. M a n y thanks go to A l a n Sinclair and my research committee. I especially want to express m y most sincere gratitude to Les Lavkulich, who went beyond his obligation to help me.  Interviews were conducted under the guidelines and approval o f the U B C Ethical  Review Committee. O n behalf o f myself, Coasts Under Stress and the U B C Fisheries Centre, I would very much like to thank all our interviewees for lending their time and expertise to this project.  I also thank the following organizations for project funding: the University o f British  Columbia Graduate Fellowship; Natural Sciences and Engineering Research Council; Coasts Under Stress, W o r l d Wildlife Fund Canada and the B C Ministry o f Water, Land and A i r Protection.  I offer thanks and love to E r i n Foulkes for her support, her patience and  encouragement.  To m y dear parents, Herb and V i Ainsworth, who did everything to help me, I  dedicate this report.  1  1 B A C K TO THE  FUTURE  The significant problems we face cannot be solved at the same level of thinking we were at when we created them.  Albert Einstein Qu. Dukas and Hoffman (1979)  1.1 Introduction For thousands o f years, humans have been exploiting the seas for food.  Paleoecological and  archaeological evidence records the significant impacts that we have caused (Jackson et al. 2001).  Fishing is thought to have become important to humans during the Upper Paleolithic  period, 10 to 30 thousand years ago (Bar-Yosef, 2004), although fish may have contributed to 'our diet much earlier than that (Yellen et ai,  1995; Fiore et al,  2004).  From the earliest  harpoons, nets and bone hooks, each advancement made in capturing fish must have opened up new habitats and new species to exploitation. But it was not until the development o f industrial fisheries, less than 200 years ago, that the major depletion o f marine systems began (e.g., Myers and W o r m , 2003; Pauly et al, 2005). W i t h the advent o f sail, steam and diesel powered boats, areas became accessible that were once out o f reach. The end o f the Second W o r l d War saw the modernization o f fleets, including the addition o f at-sea freezers, radar navigation, acoustic fish finders and other conveniences that increase catching power (Pauly et al,  2002).  The trend  continues today with satellite navigation systems and communication networks that make fishing easier, safer and more efficient than ever before.  Unfortunately, a step up i n technology has  proven to be a step down in the biodiversity and abundance o f marine ecosystems (Pitcher and Pauly, 1998) (Fig. 1.1). The effect is cumulative. Globally, fisheries are i n crisis (Pauly et ai, 1998; Myers and W o r m , 2003).  M a n y factors can potentially contribute to the decline o f fish stocks and the failure o f fisheries. Climate is known to influence productivity o f fish populations (e.g. Beamish et al,  1995;  2 Polovina, 2005), and changes in climate may be related to long-duration environmental cycles that are poorly understood (Finney et al, 2002). Other culprits like coastal development, landbased pollution and marine industries are also identified. In some cases, scientific error may contribute to fishery declines (e.g., Hutchings, 1996).  However, it is overfishing that many  scientists now believe has been the primary driver o f fisheries collapse world-wide.  Pleistocene  Recorded history  Present day  Near future  TimeC^> Figure 1 . 1 Biodiversity and species abundance decline caused by fisheries.  The  stepped downward line represents the serial depletion of marine ecosystems. Each fishing innovation, from simple harpoons to factory trawlers, opens up new species and habitats to exploitation. Horizontal arrows show sustainable use, which could have been achieved, in theory, at any level of ecosystem abundance. The three-way arrow shows policy options currently open to us. Modified from Pitcher and Pauly (1998).  Fishing overcapacity is viewed by some as the single greatest threat to sustainable fisheries (Mace, 1997; Greboval and Munro, 1999; Ward et al, 2001).  L u d w i g (1993) suggested that  overcapitalization in the fishing fleet is driven by a dangerous bioeconomic ratcheting effect, where good fishing years encourage over-investment and bad fishing years demand government subsides to keep the industry afloat. Compounding the problem, investors in the fishing industry may also expect a rate o f return that is comparable to other types o f enterprises, but cannot be supported sustainably by the natural growth rate o f fish populations (Clark, 1973). Therefore,  3 overfishing is driven by complex social, economic and political factors.  A n y lasting solution  w i l l require cooperation across disciplines, and the commitment o f many stakeholders groups.  To form this alliance we w i l l need tools that can weigh the interests o f all resource users, we w i l l need to improve our understanding o f human impacts on marine systems, and we w i l l need to agree on a proper goal for fisheries management. Although sustainability is usually pursued as an explicit objective in regulated fisheries, repeated failures indicate that it is rarely achieved in practice (Ludwig et al,  1993; Botsford et al., 1997). When environmental conditions are  favorable, sustainable fisheries may be achieved without careful restraints on human activities. But when climate turns against the interests o f people, which may be increasingly o f our own causing, our management systems need to operate according to strict precautionary principles. Sustainability is now too low o f standard to aim for; we realize this when we look to the past as a reference point and understand the enormous benefits that a healthy ecosystem is capable o f providing.  A new perspective onfisheriesmanagement M a n y traditional target species have declined to only a fraction o f their abundance prior to the industrialization o f fisheries (Christensen et al., 2003; W o r m and Myers, 2003; Reid et al, 2005; Rosenberg et al, 2005; Ward and Myers, 2005). The public, and scientists as well, are generally unaware o f the magnitude o f the historic decline. It is perhaps because o f Pauly's (1995) shifting baseline syndrome. H e suggested that one's concept or perception o f ecosystem abundance is based on a mental benchmark set at the beginning o f the career.  A s the ecosystem is slowly  degraded, each generation accepts a lower standard as the rule.  This can apply to fisheries  scientists as well as the general public. Considering the poor state o f the oceans, it has been argued that the proper goal for fisheries management should not be to sustain current fish populations, but rather to restore them to historic levels (Pitcher et al. 1998; Pitcher and Pauly, 1998; Pitcher, 2001).  The Back to the Future ( B T F ) approach to restorative marine ecology offers a new perspective on what management objectives should be (see Pitcher, 2001a, 2004, 2005; Pitcher et al, 1999,  4  2004, 2005; Ainsworth and Pitcher, 2005b). Under the B T F approach, an initial objective for any ecosystem-based restoration initiative should be to establish long-term goals for restoration. Candidate goals should be quantitatively evaluated for their potential to provide benefits to stakeholders and maintain ecological health.  Using ecosystem models, BTF simulates fishing of historic ecosystems to determine their longterm sustainable production potential. From this we can estimate what resource value has been lost due to human influences, and what a restored ecosystem might be worth to society. Fig. 1.2. shows a schematic illustration of the BTF concept. The symbols in Fig. 1.2 document many new and unconventional sources of information that must be relied upon to create whole ecosystem models of the past.  Although there will be some aspects of historical ecosystems that are  unknowable, multidisciplinary data on fish stocks and the environment can be used to form a picture of what the ecosystem looked like before heavy exploitation.  ANCIENT PAST  PAST  PRESENT  ALTERNATIVE FUTURES  Figure 1.2 The Back to the Future approach to marine ecosystem restoration. Triangles represent  trophic pyramids; height is directly related to biomass and internal connectance. Internal boxes show biomasses of representative species through time, with closed circles indicating extirpations. Ecosystems of the past contained longer trophic chains than they do now, greater biodiversity and predator biomass. The BTF approach advocates setting restoration goals based on historic ecosystems (right). Ecosystem models are constructed to evaluate various periods using historical documents (paper sheet symbol), data archives (tall data table symbol), archaeological data (trowel), the traditional environmental knowledge of indigenous Peoples (open balloons) and local environmental knowledge (solid balloons). Reproduced from Pitcher et al. (2004).  5  Historic ecosystems may hold special resonance with stakeholders as restoration goals if people can appreciate the long-term impacts that fisheries have had (Pitcher, 2000; Pitcher and Haggan, 2003). There may also be a scientific rationale for selecting restoration goals based on historic ecosystems. Because they existed, their relative species compositions may represent workable ecosystem goals, more so than an arbitrary design. If we can allow for environmental changes that have occurred since their time, then historic ecosystems can serve as an analogue for the future. The study of historic ecosystems can inform us as to what level of abundance and productivity can be expected from a natural system, given any constraints that regional oceanographic conditions impose. Pitcher et al (2004) imagined a bright future for marine fisheries, where the ecosystem is restored to something resembling a historic condition. They likened the reconstituted ecosystem to a lost valley , an untouched area as discovered in Sir Arthur Conan Doyle's "The Lost World". 1  This lost valley offers humans a second chance to responsibly use the marine ecosystem. BTF asks the following questions: what might this lost valley look like, how might we sustainably harvest it, and what would be the costs and benefits of rebuilding to this goal? To answer these questions, a new methodology has been developed that makes use of the ecosystem simulator, Ecopath with Ecosim (EwE: Christensen and Pauly, 1992; Walters et al, 1997; Christensen and Walters, 2004a).  A quantitative goal for ecosystem based approaches Quantitative techniques are often called upon to help set safe removal rates. Numerical targets and reference points have been established to guide fisheries management and allow the responsible use of living marine resources. Historically, a widely used paradigm has been the maintenance of maximum sustainable yield (MSY) from fisheries. For a given stock size, it is the theoretical amount of catch that can be taken each year, under average environmental conditions, without influencing the abundance of the stock.  The "puritanical philosophy"  identified by Larkin (1977), to take only surplus stock production and forever maintain MSY 1  The term lost valley was suggested by Prof. Daniel Pauly (Pitcher et ai,  2004).  6 once promised to solve all fisheries issues. N o w people question whether M S Y has ever been achieved i n practice and whether it is achievable in theory (Larkin, 1977; Sissenwine, 1978; Punt and Smith, 2001). Amendments have been proposed to address the well-known inadequacies o f M S Y ; for example, optimum sustainable yield ( O S Y : Roedel, 1975), maximum economic yield ( M E Y ) and Fn.i (see Hilborn and Walters, 1992).  However, some question whether proper  fisheries management is at all possible through a reductionist approach (Ludwig et al. 1993), which is the traditional mechanism o f single species science.  More and more, scientists are  turning towards ecosystem based approaches in the hopes that a holistic view o f ecosystem functioning w i l l provide a better foundation for fisheries management.  Ecosystem based management ( E B M ) could benefit from a new objective reference point; one that considers the health and productivity o f the ecosystem as a whole. Such a standard could do for E B M what indices like M S Y , O S Y , M E Y and Fo.i did for single species management provide a quantitative policy goal that can potentially set the benchmark for sustainable use. This volume presents a new conceptual target for ecosystem based approaches. It is the optimal restorable biomass ( O R B ) , an equilibrium biomass configuration for the ecosystem that maximizes sustainable harvest benefits, and is designed to meet specific criteria for ecosystem health.  O R B is calculated based on historic ecosystems. It is the species biomass vector, defining the relative abundance o f each ecosystem component, that would naturally result after the long-term responsible use o f historic ecosystems. Sidestepping the serial depletion o f stocks witnessed in reality, it takes into account the activities o f fisheries and determines the best compromise between maintaining historic abundance and diversity, while still providing for the needs o f humans.  Mace (2001) pointed out that even i f we could establish suitable goals for whole-ecosystem restoration, it is doubtful whether we would have the capability to manipulate the ecosystem into the desired state. The work presented in this volume offers a first step towards developing an integrated approach to management that can accomplish just that.  Tools and techniques  developed here for use with E w E models provide a strategic aid to help draft restoration plans  -  7 that would use selective fishing as a tool to manipulate the marine ecosystem, and ultimately restore it to some former level o f abundance and productivity.  1.2 Ecopath with Ecosim E w E provides a fresh tool to explore the complex interactions o f marine organisms. To enable multi-sector fishery policy analysis, the competing effects o f fisheries must be considered, as well as trophic interactions throughout the food web.  Single species models, versatile and  informative, are completely indispensable to whole ecosystem work, as they form the basis o f our understanding for key ecosystem components. Nevertheless, they are limited i n scope. Even traditional multi-species models can isolate and examine only a small number o f interactions, and strict data requirements limit these analyses to well understood ecosystem components. Although ecosystem models offer no panacea, they can provide a new perspective on population dynamics and help us understand unintuitive processes.  They can complement well-established  analysis methods and provide an integrated overview o f ecosystem functioning and the impact o f fisheries.  The mass-balance approach, in particular, makes it possible to construct a virtual  ecosystem without the need for exhaustive supporting science.  Invented by Polovina (1984) and advanced by Christensen and Pauly (1992, 1993), Walters et al. (1997, 1998) and Christensen and Walters (2004a) among others, E w E is a mass-balance trophic simulator that acts as a thermodynamic accounting system.  Summarizing all ecosystem  components into a small number o f functional groups (i.e., species aggregated by trophic similarity), the box model describes the flux o f matter and energy in and out o f each group, and can represent human influence through removals and other ways'.  There are now dozens o f  published articles that use E w E to describe ecosystems, qualify data, test hypotheses and demonstrate other applications (see review in Christensen and Walters, in press). E w E has been used i n actual fisheries management, but to a limited extent. Reviews and criticisms o f the E w E approach are provided by Fulton et al. (2003), Christensen and Walters (2004a), and Plaganyi and Butterworth (2004).  8  Ecopath The static model Ecopath (Polovina, 1984; Christensen and Pauly, 1992) implicitly represents all biotic components o f the ecosystem. The model operates under two main assumptions. The first assumption is that biological production within a functional group equals the sum o f mortality caused by fisheries and predators, net migration, biomass accumulation and other unexplained mortality. E q . 1.1 expresses this relationship:  B, • {P/B), = Y, + £ Bj • (Q/B)j • DC, + E, + BA, + B, {p/B), • (l - EE,)  Equation 1.1  7=1  Where B , and B , are biomasses o f prey (/) and predator (/"),• respectively; P/B, is the production/biomass ratio; Y , is the total fishery catch rate o f group (/); Q/B, is the consumption/biomass ratio; DC,y is the fraction o f prey (/) in the average diet o f predator (/'); E, is the net migration rate (emigration - immigration); and B A , is the biomass accumulation rate for group (i). E E , is the ecotrophic efficiency; the fraction o f group mortality explained in the model;  The second assumption is that consumption within a group equals the sum o f production, respiration and unassimilated food, as in eq. 1.2.  B-(Q/B) = B-(P/B)+(l-GS)-Q-(\-TM)-P  + B(Q/B)-GS  Equation 1.2  Where G S is the proportion o f food unassimilated; and T M is the trophic mode expressing the degree  o f heterotrophy;  0  and  1 represent  autotrophs  and  heterotrophs,  respectively.  Intermediate values represent facultative consumers.  Ecopath uses a set o f algorithms (Mackay, 1981) to simultaneously solve n linear equations o f the form in eq. 1.1, where n is the number o f functional groups. Under the assumption o f massbalance, Ecopath can estimate missing parameters. This allows modelers to select their inputs.  9 Ecopath uses the constraint o f mass-balance to infer qualities o f unsure ecosystem components based on our knowledge o f well-understood groups.  It places piecemeal information on a  framework that allows us to analyze the compatibility o f data, and it offers heuristic value by providing scientists a forum to summarize what is known about the ecosystem and to identify gaps in knowledge.  Ecosim Ecosim (Walters et al, simulation.  1997) adds temporal dynamics to turn the mass-balance model into a  It describes biomass flux between groups through coupled differential equations  derived from the first Ecopath master equation. The set o f differential equations is solved using the Adams-Bashford integration method by default. Biomass dynamics are described by eq. 1.3.  dB  "  ^ - g ^ f \ B  i p  B ) - Y  \ J  " f \ B  i l  , B ^ l  \ l  - ( M  l  4 - F ^ e y B  i  Equation 1.3  Where dB/dt represents biomass growth rate o f group (i) during the interval dt; gi represents the net growth efficiency (production/consumption ratio); I is the immigration rate; t  Mt and F , are natural and fishing mortality rates o f group (i), respectively; e, is emigration rate; and f(Bj,B,) is a function used to predict consumption rates o f predator (J) on prey (/) according to the assumptions o f foraging arena theory (Walters and Juanes 1993; Walters and Korman, 1999; Walters and Martell, 2004).  The principle innovation in Ecosim considers risk-dependant growth by attributing a specific vulnerability term for each predator-prey interaction.  The vulnerability parameter is directly  related to the carrying capacity o f the system, and it describes the maximum increase in the rate of predation mortality on a given prey.  A high value represents a top-down (Lotka-Volterra)  interaction, a low value represents a bottom-up (donor-driven) interaction, and an intermediate value indicates mixed trophic control. Variable speed splitting enables Ecosim to simulate the  10 trophic dynamics o f both slow and fast  growing groups  (e.g., whales/plankton), while  juvenile/adult split pools allow us to represent life histories and model ontogenetic dynamics. A new multi-stanza routine in Ecopath (Christensen and Walters, 2004a) back-calculates juvenile cohorts based on the adult pool biomass and on life history parameters. The multi-stanza routine has replaced former the split-pool method; however, it was not available at the time o f this work. A s such, recruitment to juvenile stanzas in this model are determined by Ecosim using a DerisoSchnute delay difference model (Walters et al, 2000).  Ecospace Ecospace (Walters et al 1998) models the feeding interactions o f functional groups in a spatially explicit way. A simple grid represents the study area, and it is divided into a number o f habitat types. Each functional group is allocated to its appropriate habitat(s), where it must find enough food to eat, grow and reproduce - while providing energy to its predators and to fisheries. Each cell hosts its own Ecosim simulation and cells are linked through symmetrical biomass flux in four directions; the rate o f transfer is affected by habitat quality. habitat  Optimal and sub-optimal  can be distinguished using various parameters such as the availability o f food,  vulnerability to predation and immigration/emigration rate. B y delimiting an area as a protected zone, and by defining which gear types are allowed to fish there and when, we can explore the effects o f marine protected areas ( M P A s ) and test hypotheses regarding ecological function and the effect o f fisheries. M a n y authors have used Ecospace in this capacity (e.g., Walters et al, 1998; Beattie, 2001; Pitcher and Buchary, 2002a/b; Pitcher et al, 2001; Salomon et al, 2002; Sayer et al, 2005).  1.3 Northern British Columbia Whenever viable fisheries are lost, communities and cultures that have traditionally relied on the sea can be impacted in deep and lasting ways. This is especially true when social and cultural values are tied closely to the sea. That is the case in northern British Columbia (BC). Fishery failures, such as the herring collapse o f early 1960s, the Northern abalone collapse o f the 1980s,  11 and the present decline o f the salmon fisheries displaces workers, disrupts communities and sabotages a sustainable source o f revenue.  This volume evaluates restoration scenarios for northern B C that would return the ecosystem to historic conditions o f biodiversity and abundance.  F o r this, I create ecosystem models o f  northern B C at various points in history: 1750, 1900, 1950 and 2000 A D . The models are described in Chapter 5. The 1750 model represents the marine ecosystem prior to contact by Europeans. It contains the most abundant array o f marine fish and animals, although it does not represent an unexploited system since indigenous coastal human populations are thought to have relied on the sea to a great extent (Haggan et al, in press; Turner et al, in press).  A model o f  1900 represents the ecosystem as it appeared prior to the industrialization o f fisheries, and before the advent o f major advances i n fishing technology such as steam trawlers. The 1950 model demonstrates what the ecosystem looked like during the heyday o f the Pacific salmon fisheries, and before most major depletions o f commercial fish populations.  Finally, the present-day  model, 2000, provides a contemporary representation o f the ecosystem. It is from this vantage point that restoration plans are drafted.  12  Physical area This  study  models  the  marine  environment o f northern B C , from the northern tip o f Vancouver Island to the southern tip o f the Alaskan panhandle,  si  Dixon Entrance  including the waters o f D i x o n Entrance (DE),  Hecate  Strait (HS) and  '/  Queen  Charlotte Sound ( Q C S ) (Fig. 1.3).  It G r a h a m Is  covers the shelf and continental slope, about 70,000 k m  z  M o r e s b y Is  ;  hfecate Strait  o f ocean, using the  same delineation as in Beattie (2001), including Department Oceans  o f Fisheries and  ( D F O ) statistical  Oceanography  of  4  the  areas  1-10.  region  was  «? i Queen Charlotte Sound  c  described by Crean (1967), Thomson (1981), Ware and McFarlane (1989) and Crawford (1997). 280  The  area  roughly corresponds ° J  eastern  region  to the  r  of  the  Coastal  Downwelling Domain identified by Ware  „. Figure 1.3  Kilometers  „ . , „ , Northern BC study area. The study area t  includes the shelf waters of Queen Charlotte Sound, Hecate S t r a i t  a n d D i x o n  E n t r a n c e  (  D F O  statistical areas l-io).  and McFarlane (1989). Water movement is influenced by the counterclockwise flow o f the Alaska gyre, which creates a northeastern flowing Alaskan current year round. The Alaskan current enters Q C S and extends northward along the coast into H S . In the south o f the study region there is a transitional zone, where the clockwise flowing California Current diverges from the Alaska current and flows south. Coastal convergence occurs mainly on the west coast o f Haida G w a i i and along the mainland shoreline of Q C S and H S . The shelf area is relatively shallow, more than two thirds o f the total area is less than 200 m in depth.  Three major gullies transect the continental shelf from east to west.  Crossing H S and  13 terminating south o f Moresby Island (S. Haida Gwaii) is the Moresby Trough. Q C S is divided twice, by Mitchell's Trough in the north and Goose Island Trough in the south. The mainland coastline is rugged, with many islands and inlets.  Biological system The waters o f northern B C host a diverse marine biota. W i t h the greatest human populations concentrated in the south o f the province, the marine ecosystem o f northern B C remains relatively intact compared to the Strait o f Georgia and Southern B C . The complex coastline provides a range o f habitats including rock, sand and mud flats, with various degrees o f wave exposure.  W i t h its large expanse open to the Pacific Ocean, Q C S offers an 'oceanic' habitat  which is subject to oceanographic intrusions. H S and D E provide a more shallow and protected zone. Deep troughs and productive banks in Q C S support large populations o f rockfish, flatfish and demersal fish species.  The coastal corridor is migrated annually by five salmon species,  each an important commercial asset.  Important nesting areas for seabirds, like cormorants  (Phalacrocoracidae), gulls (Laridae) and auklets (Alcidae), are located along the coastal islands and on the mainland. Large kelp beds covering much o f the coast provide habitat for juvenile fish, and support a large population o f benthic invertebrates. Echinoderms like urchins, sea stars and sea cucumbers are common. A l s o occurring in the tidal and subtidal zones are massive beds o f bivalves and barnacles.  Seals and seal lions occur throughout northern B C . There are five  species o f pinnepeds: two Phocidae (true seals) and three Otariidae (eared seals). species like killer whale (Orcinus  orca), minke whales (Balaenoptera  Cetacean  acutorostrata)  and  dolphins can occur throughout the year, and there are seasonal populations o f migratory gray (Eschrichtius  robustus) and humpback whales (Megaptera  novaeangliae).  Four hexactinellid  sponge reefs in central Q C S and H S are noted for their uniqueness and conservation utility (Conway, 1999; Sloan et al. 2001; Ardron, 2005).  14  Fisheries Commercial fisheries in northern B C are conducted by seine boats, gillnetters, trawlers (or draggers), trailers, demersal traps, hook and line, scuba diving and other gear types. Commercial capture fisheries yielded a value o f $359 million in 2004 ( D F O , 2004d), contributing a meagre 0.1% to the provincial gross domestic product. B y comparison, recreational fisheries and their supporting industries contributed an estimated $675 million in the same period, while aquaculture, mainly for Atlantic salmon (Salmo solar), contributed another $287 million. Pacific salmon constitutes the most valuable component o f the commercial catch.  Salmon species  include sockeye (Oncorhynchus nerka), pink (O. gorbuscha), chum (O. keta), chinook (O. tshawytscha) and coho (O. kisutch). The large majority o f salmon captures is achieved by the seine net fishery, followed by gillnets and trailers. The halibut (Hippoglossus stenolepis) fishery is second in importance after the salmon species.  It mainly uses longline gear and trolling  methods. Herring roe purse seine fisheries and shrimp trawl and trap fisheries follow. Fisheries for rockfish, sablefish (traps), crabs, lingcod and other invertebrates also contribute to the coastal economy.  1.4 Structure of thesis Chapter 1 summarizes the Back to the Future approach to restorative marine ecology.  It  describes the E w E ecosystem modeling software and provides background on the study area o f northern B C . A new conceptual and quantitative target for ecosystem restoration is introduced: optimal restorable biomass ( O R B ) .  Chapter 2 introduces quantitative indices used throughout this volume to evaluate harvest benefits in economic, social and ecological terms. Case studies are provided to demonstrate the use o f these indices within the E w E framework and their application to restoration ecology. Economic valuation indices include net present value ( N P V ) , calculated using conventional and intergenerational discounting approaches.  A case study examines the Newfoundland cod  collapse, and demonstrates that intergenerational valuation o f fisheries resources advocates better maintenance o f fish stocks than conventional valuation.  A n employment diversity index is  15 developed to help quantify social benefits o f fishing, and a new ecological index is introduced to describe species biodiversity, the Q-90 biodiversity statistic. A case study compares biodiversity impacts o f fishing policies using the Q-90 index across eight E w E models o f the N E Pacific, and demonstrates that the index is invariant to model structure.  In Chapter 3, I describe the B T F community interviews conducted i n northern B C , and explain the methodology used to turn the subjective comments o f interviewees into a relative abundance trend for E w E functional groups. These trends help set the dynamics for data-poor functional groups i n the northern B C models. The perceived changes in biomass are compared with stock assessment information and with preliminary model outputs as a diagnostic tool used to identify problem dynamics.  Chapter 4 quantifies illegal, unreported and unregulated ( I U U ) catch i n B C for salmon and groundfish fleets using a new subjective methodology. It is part o f a larger effort to establish reliable estimates o f extractions, which can be used to tune the dynamic models. A timeline o f B C fisheries is compiled that includes regulatory, technological and political changes likely to have affected the quantity o f unreported catch.  From this, a semi-quantitative Monte Carlo  procedure provides estimates o f I U U catch for each 5 year period between 1950 and 2000 based on qualified anchor points (i.e., real-world estimates o f misreporting from the literature and expert opinion).  Chapter 5 explains the northern B C models in detail, including basic parameterization and all fitting procedures used to improve dynamic predictions. Climate factors are addressed that may have influenced observed ecosystem dynamics, and some generalizations are drawn concerning predator-prey trophic vulnerabilities: Ecosim's chief dynamic parameters.  A novel procedure is  introduced whereby the dynamics o f ancient E w E models are tuned based on the fitted dynamics o f more recent models. This assumes stationarity in density-dependent foraging tactics.  It is  demonstrated that this method improves predictions by the 1900 northern B C model over other common parameterization methods.  In Chapter 6, I demonstrate O R B as a new ecosystem-based goal for restoration. Various O R B restoration targets are determined from historic ecosystems. O R B equilibriums are structured to  16  maximize socioeconomic or ecological benefits in varying degrees, and a trade-off spectrum of available benefits is presented for each historical period. This analysis demonstrates what wealth we have sacrificed over the last 250 years through our unsustainable fishing practices, and it also demonstrates what restoration could be worth to stakeholders in monetary and non-monetary terms. New techniques are used to relate the geometry of the optimization response surface to various policy considerations. Uncertainties surrounding historic model parameter estimates are also considered in the ORB solutions through use of a Monte Carlo routine.  Chapter 7 describes a new procedure integrated into Ecosim that can be used to determine optimal restoration plans to transform the current ecosystem into a desired configuration. A new objective function called specific biomass is created for EwE's policy search routine, and possible restoration policies are evaluated that would turn the present-day depleted system into one resembling a more productive ORB state. Plans are tested that provide various degrees of continued harvest benefits during the restoration period. economic feasibility of ecosystem restoration.  A cost-benefit analysis tests the  A conservative approach to restoration is  demonstrated to provide a better economic return than bank interest.  Chapter 8 offers conclusions on the strengths and weaknesses of this restoration approach, and suggests new avenues of research that could take this integrated methodology from theory into practice.  A comparison is made between O R B as an ecosystem management target and  Maximum Sustainable Yield (MSY), an analogous single species index. Criticisms of the B T F approach are addressed, and comment is made on the usefulness of EwE as a policy aid for restoration ecology.  Finally, policy recommendations are provided based on the general  conclusions of this study.  The appendices provide results and supporting information for each chapter.  Appendix 2  includes a cost-benefit analysis of education, as an existing example of a multigenerational enterprise, that can be used to set the intergenerational discount rate for valuation of fisheries resources.  Appendix 3 presents qualitative trends of relative abundance for EwE functional  groups based on L E K information. Appendix 4 provides supporting materials for the I U U analysis, including a timeline of B C fisheries, a table summarizing influences in the rate of misreporting, as well as reported landings and species weights used to estimate the IUU trend.  17 Appendix 5 presents parameters used in the Ecopath and Ecosim models o f northern B C for all time periods. Time series data for biomass and catch are presented; other information includes model outputs such as dynamic biomass and catch, and an equilibrium analysis o f the presentday (2000) model. Appendix 5 also compares the present-day 2000 model with the one derived from the 1950 model (following a 50 year simulation). Appendix 6 first presents the parameters used in the policy optimization routine in Chapter 6, and then presents the results o f the optimizations, listing harvest benefits o f O R B ecosystems measured using various indices o f harvest utility.  Appendix 7 provides supporting information used to parameterize the policy  search in Chapter 7, and biomass trajectories are presented for restoration plans that vary the speed o f restoration and the level o f sustained harvest benefits. Appendix 8 provides a spatial analysis o f the consequences o f marine protected area ( M P A ) zonation i n northern B C . Various harvest strategies are analyzed for the National Marine Conservation Area ( N M C A ) surrounding Moresby Island in southern Haida G w a i i . Appendix 9 lists references cited in the appendices.  The published materials appearing in this thesis are presented in Table 1.1.  Table 1.1  Published materials appearing in this thesis.  Articles in review or in preparation are available from this author (contact:  c. ainsworth@fisheries. ubc. ca) Thesis section  Subject  Reference  Journal or publisher  Chapter 1  Back to the Future policy approach.  Pitcher et al. (2004)  American Fisheries Society Symposium  * Conference procedings  Chapter 2  Application of Q-90 biodiversity statistic to EwE models of NE Pacific.  Ainsworth and Pitcher (inpress)  Ecological Indicators  * Primary literature  As above.  Ainsworth and Pitcher (2004b)  Fisheries Centre Research Reports  Economic valuation technqiues.  Ainsworth and Sumaila (2004a)  Fisheries Centre Research Reports  Grey literature  Employment diversity index.  Ainsworth and Sumaila (2004b)  Fisheries Centre Research Reports  Grey literature  Use oflocal ecological knowledge in ecosystem models.  Ainsworth and Pitcher (2005a)  Alaska Sea Grant  Chapter 3  Description  Grey literature  * Conference procedings  Interview methodology.  Ainsworth (2004)  Fisheries Centre Research Reports  Chapter 4  Estimation of IUU catch in BC As above.  Ainsworth and Pitcher (2005c) Ainsworth and Pitcher (2005d)  Fisheries Research State of the Environment Reporting (MWLAP)  Chapter 5  Preliminary Northern BC models for 1750, 1900, 1950 and 2000 AD.  Ainsworth et al. (2002)  Fisheries Centre Research Reports  As above.  Alcock et al. (in prep.)  McGill-Queens University Press  Analysis of predator-prey vulnerabilities for northern BC models  Ainsworth and Pitcher (2004a)  Fisheries Centre Research Reports  Evaluation of restoration goals based on ORB concept.  Ainsworth and Pitcher (2005b)  Alaska Sea Grant  As above.  Pitcher et al. (2005)  NATO Science Series IV: Earth and Env. Sciences.  Introduction of ORB concept.  Baker et al. (inprep.)  McGill-Queens University Press  i Book  Trade-off analysis of ORB benefits.  Pitcher and Ainsworth (in review)  Procedings of the 4th World Fisheries Congress  * Conference procedings  Policy search methods.  Ainsworth et al. (2004)  Fisheries Centre Research Reports  Restoration policy optimization; introduction of specific biomass algorithm.  Ainsworth and Pitcher (in review)  Procedings of the 4th World Fisheries Congress  * Conference procedings  Cost benefit analysis of ecosystem restoration to various ORB states. Demonstration of ecosystem-based population viability analysis.  Ainsworth and Pitcher (in review)  ICES Annual Science Conference Proceedings (2005)  * Conference procedings  Pitcher et al. (in review)  ICES Annual Science Conference Proceedings (2005)  * Conference procedings  Intergenerational discounting case study: Newfoundland Northern cod collapse.  Ainsworth and Sumaila, (2005)  Canadian Journal of Fisheries and Aquatic Sciences  * Primary literature  As above.  Ainsworth and Sumaila (2003)  Chapter 6  Chapter 7  Appendix 2  Appendix 8  Spatial analysis of Gwaii Haanas N M C A zonation Ainsworth (2004) options.  * Peer reviewed contribution  Grey literature * Primary literature Internal government report Grey literature * Book Grey literature * Conference procedings Book  Grey literature  Fisheries Centre Research Reports  Grey literature  (Available from author)  Workshop proceedings  19 2 HARVEST POLICY EVALUATION TECHNIQUES  The prudent heir takes careful inventory of his legacies and gives a faithful accounting to those whom he owes an obligation of trust.  John F . Kennedy State of the Union Address, 1961  2.1 Introduction To evaluate economic, social and ecological benefits o f harvest policies for the B T F procedure, I have adapted standard evaluation techniques and developed new ones for use with E w E models. The indices described in this chapter include an economic index, net present value ( N P V ) calculated under conventional and intergenerational discounting equations (Ainsworth and Sumaila, 2004a; Sumaila, 2004; Sumaila and Walters, 2005), a social utility index based on employment diversity (Ainsworth and Sumaila, 2004b) and an ecological index used to represent biodiversity (Q-90 statistic) (Ainsworth and Pitcher, 2004b; in press).  Chapters 6 and 7 use  these indices along with standard E w E outputs to compare candidate ecosystem goals for restoration, and evaluate the success o f fishing plans to achieve those goals.  2.2 Economic index: Net present value (NPV) Although benefits o f marine ecosystem restoration may be measured in ecological and social terms, economic considerations w i l l likely weigh heavily in determining the practicability o f any long-term restoration agenda.  The N P V term is used to summarize the economic success o f  harvest plans because it condenses the flow o f future benefits into a single expression, while introducing a time component through discounting that reflects the preference o f an investor for immediate benefit and delayed payment.  The conventional discounting N P V term weights  immediate harvest benefits heavily, but the present value o f benefits to be received far in the future is reduced exponentially with time.  Under the intergenerational discounting approach  20 (Sumaila, 2004; Sumaila and Walters, 2005), future benefits are discounted less, and the welfare o f future generations is considered explicitly in the present value term.  Conventional discounting Under the conventional model discounting, the flow o f fishery benefits is summarized i n the N P V term using the expression in eq. 2.1.  T  NPV  = Y {d'xNB ) /=o J  t  Equation 2.1  Where N B is net benefit accruing in year t; d is the discount factor defined in eq. 2.2,  d — T; rr (i + s)  Equation 2.2  Where 5 is the discount rate.  Intergenerational discounting The intergenerational discounting equation considers a continuous interlacing o f generations, where the discounting o f future benefit is countered each year by the addition o f 1/G stakeholders ( G is generation time). These new entrants bring with them a renewed perspective on future earnings, partially resetting the discounting clock.  The equation requires a standard  annual discount factor (d) and a discount factor to evaluate benefits destined for generations (<if ). N P V is represented as in eq. 2.3. g  future  21 1-A' 1-A  if 8*5,fg  NPV=\  Equation 2.3  r  I-  (1+S)'X  +  G)  otherwise  G is assumed to be 20 years, the average age at which a Canadian woman has her first child, and  A =  Equation 2.4  The conventional approach to discounting w i l l favour fishing policies that provide immediate benefits to stakeholders, while the intergenerational approach w i l l assign a higher N P V to policies that spread out benefits over several decades.  The need for a new resource valuation method In cost-benefit analysis ( C B A ) , standard discounting is often unable to sanction long-term environmental policies that fulfill the frequently stated mandate to provide for the needs o f future generations  (e.g., D F O , 2001; E C , 2002).  Scaling down the value o f future  benefits  exponentially through time ensures that immediate costs w i l l outweigh far-off benefits at any practicable level o f discounting, so that only myopic policies can result (Clark, 1973; Sumaila, 2001;  2004).  In valuing the stream o f benefits from a fisheries resource, use o f conventional  discounting may lead to early profit taking at the expense o f sustained productive potential. Evidence o f this type o f 'front-loading' o f fisheries benefits is clear in the harvest record o f Northern cod (Gadus morhua) in the years before the 1992 collapse.  Appendix 2.1 presents a case study on the Newfoundland cod fishery that suggests conventional valuation o f fishery resources may have contributed to the decline and collapse o f the Atlantic cod fishery.  The case study also demonstrates that intergenerational valuation o f fisheries  resources could make long-term conservation an affordable prospect. The discount rates I use to  22 evaluate fishery benefits are based on a C B A o f education (Appendix 2.2). children serves as an example o f an existing multi generational investment.  Schooling o f  B y applying the  apparent discount rate that people use to value the education o f their children, I implicitly account for a variety o f non-monetary benefits which could also apply to resource conservation.  2.3 Social utility index: Employment diversity Ainsworth and Sumaila (2004b) used an employment diversity index to evaluate harvest plans after the methodology o f Attaran (1986). Based on the Shannon's entropy function (Shannon and Weaver, 1949), this measure describes the diversity o f employment across fishing sectors. The entropy function is defined as in eq. 2.5:  n  D(E  = -^T E log  ,E ,...E )  X  2  N  t  2  E-  Equation 2.5  /=1  Where n is the number o f (possible) fishing sectors active in the ecosystem, and E is the proportion o f total employment that is located in the i fishing sector. . th  The measure is normalized across sectors with respect to their maximum possible diversity so that D ( E i , E2,... E ) ranges from 0 to 1, as i n eq. 2.6. n  D(E ,E ...E )= ]  2  n  - X / v l o g  V  1=1  2  £ ,  /MAX(D(E E ,..E )) v  2  n  Equation2.6  )i  When D - 0, this indicates that all fishing activity is concentrated in a single sector; D = 1 indicates the maximum possible employment diversity with all sectors contributing equally to employment (all Ej are equal).  23  Application to Ecosim A V B algorithm uses this descriptor to assess the annual employment diversity o f the dynamic 50-year harvest schedule for each optimal policy suggested b y the E W E policy search routine. Beginning with Ecosim's output C S V file, total value per gear type is calculated as the sum o f all functional group landings, multiplied by gear-specific prices (Chapter 5; Appendix Table A5.1.6).  Total value per gear type is converted to relative number o f jobs using an estimated  "jobs per catch value". It is considered equal for all fleets, so employment is proportional to landed value. Employment per sector ( E i ) is then calculated as a fraction o f total employment.  2.4 Ecological indices Although the commercial value o f fishing a restored ecosystem may offset the costs o f rebuilding (Chapter 7), any practical restoration agenda w i l l need to include ecological criteria for ecosystem improvement. A range o f ecological indicators is useful for forecasting non-monetary benefits in fishing scenarios, and many have been developed or adapted for use with E w E models.  It can be difficult to define appropriate indices to summarize ecosystem model outputs (Fulton et al, 2003), but considering the generic nature o f E w E , its wide availability and comparatively simple implementation, there is a need to develop standardized outputs that can help users interpret ecosystem effects o f experimental harvest scenarios. Ecosystem modelers have begun to realize that functional group aggregation styles and other nuances o f model structure can have significant impacts on the dynamic predictions (Fulton et al,  2003; Pinnegar et al,  2005).  Output indices therefore need to be robust and deliver consistent results despite subjective variations in model structure.  Ecosystem modelers must often make judgments on the applicability o f imperfect data, but there are also fundamentally subjective components in E w E . 1) Functional groups o f species are aggregated depending on the objectives o f modeling, fishery and policy targets and availability o f data.  2) When time-series data are unavailable for fitting, flow parameters may be set  according to rules o f convention. 3) The model diet matrix is usually based on incomplete and  24 imprecise data, and arbitrary manipulation o f the matrix may be required to achieve massbalance.  Some attempts have been made to standardize the E w E model construction process  (e.g., automatic mass-balance:  Kavanagh et al,  2004; semi-automated  data retrieval from  Fishbase: Froese and Pauly, 2005).  Existing EwE outputs Ecological indicators automated i n E w E include the Finn cycling index (Finn, 1976), indices relating to emergy and primary production required (Odum, 1988; Christensen and Pauly, 1993), trophic flow indices (Ulanowicz, 1986), resource niche overlap (based on Pianka, 1973), system omnivory index (Pauly et al, 1993), fishing-in-balance index (Pauly et al, 2000), mixed trophic impacts (Ulanowicz and Puccia, 1990), among other system state and trophodynamic indictors. Indicators developed for the B T F approach include an ecosystem resiliency index based on information theory (Heymans, 2004), a fuzzy logic algorithm to estimate local extinction risk based on fish life history parameters (Cheung and Pitcher, 2004; Cheung et al 2005) and a biodiversity statistic, Q-90, which is described here (also see Ainsworth and Pitcher, in press).  Q-90 biodiversity statistic The Q-90 biodiversity statistic is a variant on Kempton's Q index (Kempton and Taylor, 1976) that has been adapted for use with E w E , where taxonomic species are grouped into aggregate biomass pools o f functionally similar organisms.  When used i n conjunction with other  indicators, the Q-90 index offers a useful method to evaluate consequences o f alternative fishing plans, track the effect o f climate fluctuations and changes on biodiversity, estimate the nonconsumptive value o f ecosystems, and generally inform the ecosystem-based approach to marine science.  Although ecological indicators o f all varieties are o f scientific interest, biodiversity  holds special appeal to the public and is often addressed directly by policy - even though the appropriate scientific definition may not be made explicit (Harper and Hawksworth, 1994; Hamilton, 2005). In this section, I refer to biodiversity as organismal diversity at the level o f species functional groups.  25  Definition Kempton's Q index describes the slope o f the cumulative species  abundance  curve (Fig. 2.1). A s applied here, each functional  group in the E w E model  represents  one  'species',  and  the  biomasses o f these groups, sorted into bins, serves as a proxy for the number o f individuals in that species.  Kempton  and Taylor (1976) suggested using the inter-quartile abundance  slope  curve  of  the  in order  species to  log  avoid  log R  2  log-abundance  problems arising from the inclusion o f tails, which, in field sampling, may be  Figure 2.1 Q-90 statistic definition.  long and include a high number o f low-  functional groups in reference model; R| and R are lower  abundance  species.  In  applying  this  methodology to Ecosim, tails are less o f a  problem  since  modelers  do  S is number of 2  and upper  10-percentiles of the species abundance  distribution. Modified from Kempton and Taylor (1976).  not  normally represent a large number o f low abundance functional groups.  I therefore used the  slope between the upper and lower 10-percentiles rather than quartiles.  The Q-90 statistic is defined as in eq. 2.7.  g90 = 0.85/[log(i2  Equation 2.7  2  Where S is the total number o f functional groups in the model; R i and R are the representative 2  biomass values o f the 10 and 9 0 percentiles in the cumulative abundance distribution. th  th  26 The 10 and 90 percentiles are determined by eq. 2.8 and eq. 2.9, respectively,  R  Equation 2.8  < 0 . 9 - S < £ «R  Equation 2.9  R -\  R  2  Y,n  2  R  Where n is the total number o f functional groups with abundance R. R  Magurran (1988) describes the qualities o f Kempton's index that make it well suited to this application.  Kempton's index is not dependent on the assumption o f a particular species  abundance model, which makes it generically applicable to a wide variety o f ecosystem types. It is not biased by very abundant or very rare species, and this can be advantageous i f there are highly aggregated functional groups, as is sometimes the case with data-poor models.  It  expresses both species richness and evenness, which allows it to discriminate ecosystem effects among harvest  plans  (since exploitative fishing  strategies  can result  in depletions  or  extirpations), while also capturing changes in the ecosystem that occur outside o f harvested functional groups. In field studies, Kempton's index is robust against changes in sample size i f very small samples are avoided, but this is not critical with E w E models since the entire ecosystem is represented explicitly or implicitly.  The following case study evaluates the effect o f fisheries on ecosystem biodiversity, and demonstrates that the Q-90 statistic delivers consistent results regardless o f model structure.  2.5 Q-90 case study: N E Pacific ecosystems I use the Q-90 statistic to evaluate biodiversity after 25 years o f fishing for eight ecosystem models o f the northeastern Pacific under a variety o f fishing plans. I test the ability o f Q-90 to respond to fishing influence on the ecosystem, and compare predictions made using simple and complex ecosystem models.  B y choosing similar shelf ecosystems, biodiversity predictions  27 should be comparable across models. A n y real differences in biodiversity among the ecosystems should be minimized so that we can examine the effects o f model structure on the index.  Applying index to Ecosim output Using a Visual Basic algorithm, a user-defined number o f bins is established that represents the complete range o f functional group biomasses.  The biomass o f each functional group is then  sorted into its appropriate bin as a count; this serves as a proxy for the number o f individuals in that group. Bins may be linear or logarithmic. The Q-90 index is the slope o f the cumulative species abundance curve is determined between the 10- and 90-percentiles; the Q-90 value may be plotted for each year i n the simulation.  A t present, E w E does not permit absolute extinctions; it returns a low but non-zero biomass value for critically depleted groups.  Therefore, every fishing scenario at its conclusion w i l l  contain the same number o f functional groups as the base model. To increase the sensitivity o f the index to group depletions, a filter is passed over group biomasses for each year o f the simulation. If the biomass o f a given functional group falls below a reference value, that group is omitted from the~Q-90 calculation, reducing the overall biodiversity score.  In previous applications o f this index, the depletion filter threshold has been set as an arbitrary 60% o f the unfished biomass (Bo) and pristine biomasses represented in models o f ancient ecosystems have been used as a proxy for Bo (e.g., Ainsworth and Pitcher, 2005b; also see Chapter 6). Setting a high threshold makes the index more sensitive to group depletions; the Q90 value therefore drops off quickly as fishing plans tend towards heavy exploitation and the index provides greater discrimination between conservative and exploitative fishing plans. The filter threshold may be reduced when evaluating severely depleted ecosystems; alternatively, one may set the threshold at a fraction o f the baseline biomass.  28  Methods U s i n g eight E w E models o f present-day ecosystems in the northeastern Pacific (Table 2.1), I compare the effects o f three simple fishing policies on biodiversity: a reduction i n fishing mortality to one-half the model baseline (0.5 F), baseline fishing mortality (1 F ) and a five-times increase i n fishing mortality (5 F). Baseline represents an estimate o f current real-world fishing mortality.  In lieu o f biomass estimates for unfished populations (i.e., that correspond to the  species group aggregation style used by the original modelers), the depletion filter is set here as a proportion o f baseline group population size for all simulations. I f groups fall below 80% o f their initial biomass, they are removed from the Q-90 calculation.  Table 2.1 Eight EwE models of the NE Pacific. Abbreviation  1  Model area  # of groups  Reference  WCVI  West Coast Vancouver Is.  15  Pauly et al. (1996)  SNBC  Northern BC - small model  26  Ainsworth, C.  SOG  Strait of Georgia  27  Dalsgaarde/a/. (1998)  ALU  Aleutians  40  Heymans (2005)  HEC  Hecate Strait  50  Beattie(2001)  PWS  Prince William Sound  51  Okey and Pauly (1999); Okey and Wright (2004)  LNBC  Northern BC - large model  53  Ainsworth et al. (2002)  NCC  Northern California Current  65  Field (2004)  {unpublished manuscript)*  ' •  Contact: c.ainsworth@fisheries.ubc.ca  Results Long-term fishing simulations show a relationship between biodiversity maintenance and the overall level o f fishing mortality applied. F i g . 2.2 shows Q-90 biodiversity predictions from the Ecosim model o f the Northern California Current (Field 2004). A s we increase extractions from the ecosystem, biodiversity is sacrificed. Under the exploitative fishing policy described b y the (5 F ) scenario, there is an initial 50% drop in ecosystem biodiversity.  29  10  140  8 O  G  Q  O  O  O  O  o<~, o  a>  5  0  0 0 0  O  O  O  O  O  O  O  0  oooooooo oooooooOooooooooooooo  0 0 0 o o o  E 120  •••••••• ro 100  10  20 Years  Figure 2.2  30  0.5  F  1F  5F  Fishing mortality  Dynamic ecosystem biodiversity (Q-90) of three example Ecopath with Ecosim  simulations. A.) White circles show reduced fishing mortality from model baseline (0.5 F); grey circles show baseline fishing mortality (1 F); black circles show increased fishing mortality (5 F). B.) Bar graph shows total catch for these policies. Model of Northern California Current ecosystem (Field, 2004).  Fig. 2.3 compares baseline biodiversity among Ecopath models o f the N E Pacific constructed by various authors using independent group aggregation criteria. The absolute value o f the Q-90 statistic increases i n direct relation with the total number o f functional groups.  The scatter  around the trendline represents differences in functional group aggregation style and real ecological differences, although I have tried to minimize this factor by using models o f similar ecosystems.  F i g . 2.4 suggests that the relative change in the Q-90 statistic is not dependant on  model complexity. However, model complexity itself can affect dynamic function i f functional groups are over- or under- aggregated and a key ecological interaction is misrepresented (Fulton et al, 2003). In that case, the Q-90 index w i l l report the errant model behaviour. Because o f this, we may expect a small degree o f variation around the trend line i n F i g 2.3, owing to inherent behavioural differences between models o f varying complexity.  However, Q-90  measurements for complex models (containing many functional groups and interactions) should be resistant to the compounded data uncertainty (see Hakanson, 1995) i f errors surrounding the slope line in F i g 2.3 tend to cancel.  10  0 0  20  40  60  80  Number of functional groups  Figure 2.3 Absolute Q-90 value at baseline (year zero) for eight northeastern Pacific Ecopath models. The simplest Ecopath model represents the ecosystem using only 15 functional groups, while the most complex model uses 65 functional groups.  20  -60  J  WCVI # groups  15  SNBC 26  SOG  ALU  HEC  PWS  LNBC  27  40  50  51  53  NCC 65  Figure 2.4 Change in Q-90 index after 30 years of fishing for eight EwE models of the NE Pacific. From left to right, models increase in number of functional groups. White bars show reducedfishingmortality from model baseline (0.5 F); grey bars show baselinefishingmortality (1 F); black bars show increasedfishingmortality (5 F). West Coast Vancouver Is. (WCVI); small-Northern British Columbia (SNBC); Straight of Georgia (SOG); Aleutian Islands (ALU); Hecate Strait (HEC); Prince William Sound (PWS); large-Northern British Columbia (LNBC); Northern California Current (NCC).  31  Fig. 2.4 also shows the effects o f fishing on the biodiversity o f the ecosystem. A t five times model baseline fishing mortality, every E w E model predicts a drop i n biodiversity over 30 years. Except for S N B C , L N B C and P W S , which were designed to be steady state under baseline fishing mortality, all models predict a biodiversity decline under baseline fishing mortality. Several models suggest that even halving the exploitation rate w i l l not prevent biodiversity from declining over the long-term. However, the fishing scenarios tested are simplistic because all assume a constant level o f fishing mortality without regard to changing stock size and the fishing rates tested also assume a uniform change in fishing mortality across all gear sectors.  Index resolution The  Q-90 statistic tends to 20  change in a step-wise fashion with  dynamic  biomass  predictions. containing groups  Models many  allow  the  functional  o cn• O c  15  (/) CD cn c ro  index to  0  10 0  resolve more precise changes in  species composition, but  models  containing  functional  groups  B cn  fewer tend  to  produce coarse changes in the  15-40  50-65  # functional groups  biodiversity index over time  Figure 2.5 Q-90 sensitivity to changes in system biomass structure.  reflecting  only  large-scale  Q-90 sensitivity is compared in small (< 40 functional groups) and large  in  species  models (> 50 groups). Y-axis shows mean number of step-wise changes  changes composition. power  of  Resolving the  index  is  therefore reduced in models containing  fewer  in Q-90 value (i.e., resolving power) for a standard set of harvest simulations (30 year simulations at 0, 0.5, 1 and 5 times baseline fishing mortality). Closed circles show logarithmic bins, open circles show linear bins (error bars; 1 SD).  functional  groups (Fig. 2.5). A one tailed Student's t test indicates that resolving power is significantly less for small models (< 40 functional groups) than large models (> 50 groups) (p < 0.05). Linear  32 bins provide better resolution for small models than logarithmic bins (t test; p < 0.01), but logarithmic bins produce less variable results overall (F test; p < 0.05).  Fig. 2.6 shows application o f the depletion filter at 30%, 50% and 80% o f baseline functional group biomass.  A high filter threshold causes functional groups to fall out o f the Q-90  calculation, and increases the sensitivity o f the index to ecosystem changes.  Under high  depletion filter thresholds, linear bins may be slightly better at resolving biodiversity changes than logarithmic bins (t test; p = 0.104).  Discussion This application o f Kempton and  20  Taylor's (1976) index to ecosystem  o  models  O  considers  both  evenness  and richness in the biodiversity  CD i  15 0  to CD  score.  Although most ecological  studies  determine  based  on,  biodiversity  occurrence  the  03  10  fixed  species  number  population  and  CO  of  30  changes  species  represented.  are  not  are  explicitly  The  method  introduced here therefore provides an approximation to the original Kempton  index,  50  80  Depletion filter threshold (%)  species-level  considered in the dynamics unless those  0  0) CO  functional groups in a E w E model is  0  and  abundance o f taxonomic ('speciosity'),  c  which  developed for field studies.  was  Figure 2.6 Q-90 sensitivity to changes in ecosystem structure using three depletion filter thresholds. Thresholds are set at 30%, 50% and 80% of baseline functional group biomass. Y-axis shows mean number of step-wise changes in Q-90 value (i.e., resolving power) for a standard set of harvest simulations (30-year simulations at 0, 0.5, 1 and 5 times baseline fishing mortality). Closed circles show logarithmic bins, open circles show linear bins (error bars; 1 SD).  33 Evenness can be represented i n the ecosystem models, with biomass serving as a proxy for the number o f individuals in each functional group.  Under some circumstances this proxy could  produce a bias; for example, i f the average weight o f animals changes suddenly as a result o f fishing, as new technologies are introduced or in response to market influences.  When  comparing ecosystem models o f different time periods, evolutionary changes in response to fishing could also cause a bias. Calculating richness is less straightforward. Since the number o f model functional groups is fixed, the depletion filter is used to drop groups from the calculation and the total number o f functional groups active in the calculation is therefore analogous to species richness.  B y setting a high depletion filter threshold we increase the contribution o f  species richness to the overall biodiversity score, but without the filter the index solely represents evenness.  Eliminating groups from the computation with the filter increases the sensitivity o f the index to depletion events or effects and reduces the overall Q-90 value. However, as functional groups are removed, the remaining biodiversity calculation is based on fewer groups and the ability o f the index to recognize small changes in biodiversity is compromised.  I suggest using a high  threshold to increase sensitivity o f the index for models containing many functional groups, which can stand to loose a few from the calculation, or to exaggerate small differences in ecosystem biodiversity when comparing similar models or fishing plans.  The algorithm could be adapted to work with any static or dynamic multispecies or ecosystem model that represents species biomass in aggregated functional groups; see Fulton et al. (2003), Hollowed et al. (2000) and Whipple et al. (2000) for reviews o f multispecies and ecosystem models. Model dynamics do not need to be based on trophic flows, but the biomass o f functional groups must be accessible. Models which are primarily oceanographic or biogeochemical likely could not benefit; nor could E w E models that use nutrients as the currency o f group exchange instead o f biomass (e.g., Watkinson, 2001).  The Kempton Q index is now automated in E w E V5.1 and is available as a dynamic output for simulations (Christensen and Walters, 2004b). However, the integrated version is not exactly as described here. It considers only high trophic level functional groups (> T L 3), it uses the interquartile slope o f the cumulative abundance curve rather than 90-percentiles.  It can also  34 accommodate only linear species biomass bins, and as it does not employ a depletion filter it mainly serves as an indicator o f biodiversity evenness.  Contribution to ecosystem studies The use o f ecological indicators is recognized as a critical component o f E B M (e.g., F A O , 2003; Cury et al, 2005; Garcia and Cochrane, 2005), although firm ecological theory is needed to relate changes in ecological indices to proper remedial management actions (Hall and Mainprize, 2004). A s the relatively new field o f ecosystem modeling continues to advance, facilitated by an increase in inexpensive computing power and the current drive towards- ecosystem-based approaches in marine systems (Link, 2002), standardized indices w i l l make ecosystem models tools that are more effective toward understanding fisheries and climate effects on marine communities.  Not only can ecosystem models be used to evaluate potential repercussions o f fishing on nontarget organisms, broad indicators which describe the state o f the natural environment may hold special resonance with the general public (Rogers and Greenway, 2005); and public buy-in is critical since fishery stakeholders become a far more encompassing group once the entire marine ecosystem is factored in to management decisions.  The next chapter w i l l summarize work done with communities in northern B C . Community members helped evaluate candidate restoration goals and suggested fisheries that could be used to harvest a restored ecosystem. Through interviews, they provided local ecological knowledge to supplement scientific information and help satisfy the vast data requirements o f the ecosystem models used in B T F research.  3 5  3 COMMUNITY  INTERVIEWS  All our knowledge has its origins in our perceptions.  Leonardo de V i n c i Qu. E. MacCurdy(1954)  3.1 Introduction In modeling whole marine ecosystems, data deficiencies become especially apparent among species that hold no commercial appeal.  Stock assessment records exist for only a small  minority o f species so modelers must borrow parameters from other ecosystems, or rely on guesswork.  Although E W E grants modelers some reprieve by automatically estimating  biomasses o f data-poor groups based on the assumption o f mass-balance, there is a clear need to reduce uncertainty in our estimates by incorporating supplemental information, particularly for historic ecosystems. Local ecological knowledge ( L E K ) held by fishing community members is one such resource.  L E K can be used to fine-tune static Ecopath models, to confirm dynamic Ecosim function, or to inform us how the ecosystem might have been structured decades ago - before time-series data began for most species.  L E K therefore holds obvious application for B T F , which seeks to  quantify ecosystem changes over time. The key step in adapting L E K to our modeling needs comes in producing a quantitative data series from qualitative accounts. This section describes how that was done for the northern B C models, and how the L E K trends are used to improve dynamics in the northern B C models. I also compare L E K trends with stock assessment in the hope that fishers' perceptions can help establish criteria by which we can assess the quality o f scientific data - by challenging it with an independent authority and identifying where fishers' perceptions depart from the scientific understanding. Interview methods used in this chapter are published in Ainsworth (2004); results are in Ainsworth and Pitcher (2005a).  36  3.2 Methods Interviews Under approval o f the University o f British Columbia Ethical Review Committee, workers from the Fisheries Centre interviewed forty-eight community members from the Prince Rupert region and Haida G w a i i , B C in two community workshops in 2002 (Pitcher et al, 2002b; 2004).  The  processed  anonymous  [www.fisheries.ubc.ca/projects/btf/].  Interviewees  data  is  represented  searchable a  broad  Pitcher,  online cross-section  at of  commercial, recreational and aboriginal fishers as well as processors and others who are familiar with the marine system in Hecate Strait, D i x o n Entrance and Queen Charlotte Sound. A s the aim was to improve the northern B C models, participants were not selected randomly; snowball sampling was used to find the most knowledgeable contributors as recommended by partners and participants.  One hundred and eighteen flashcards o f marine mammals, birds, fish and invertebrates were shown to each interviewee.  L E K information recorded included species population changes,  fisheries interactions and spatial information - such as animal aggregations and seasonal movements.  These data along with career and demographic information were processed to  ensure anonymity, and entered into the B T F Historical and Interview Database (Erfan,. in press):  Creating a time-series of relative abundance Respondents were asked whether the abundance o f marine creatures had increased, remained the same or decreased during their careers.  This method assumes that respondents made implicit  allowance in their answers for any changes in catchability arising from new methods or fishing technology.  To create a numerical trend, an interviewee's comment o f increase, stable or  decrease is assigned the numerical value o f +1, 0 or -1, respectively. respondent fished receives one numerical 'vote' for that organism.  2  Every year that the  Summing votes from all  Dr. Tony Pitcher (P.L), Dr. Ussif Rashid Sumaila, Dr. Sheila Heymans, Dr. Melanie Powers, Nigel Haggan, Russ  Jones (Haida Fisheries Council), Eny Buchary, Cameron Ainsworth, Pablo Trujillo, Louisa Wood, Richard Stanford, Erin Foulkes and Aftab Erfan.  37 respondents, the annual total is assumed to indicate the average perception for that year. A value greater than zero therefore indicates that the fishers perceived an increase in biomass during that period, while a value less than zero indicates a perceived decline. The resulting time-series provides an index o f the rate o f change for each organism, which is converted into a running total to serve as a proxy for relative abundance.  Data trends for organisms are compiled into Ecopath functional groups (see Chapter 5 for group descriptions). Some functional groups include multiple species, so I assume that the abundance trend o f the group closely follows the species mentioned most often by respondents.  For  example, only eight comments out o f 59 concerning the functional group Odontocetae mentioned the Northern right whale dolphin, while 36 comments were made for orca. The abundance trend o f Odontocetae therefore more closely reflects the trend for orca; it is a weighted average o f the relative number o f comments. Ideally, one would weigh the contribution o f each species to the overall  functional group abundance  abundance.  trend using some independent  estimate  o f relative  However, i n the base Ecopath model, important and commercial species (i.e.,  species for which independent abundance data are available), are typically assigned their own dedicated functional group.  38  Weighting by expertise The interview data captures a diverse sample o f local knowledge.  M a n y fisheries (and  industries) were represented at the interviews, and each sector is expected to carry its own special expertise in species o f particular importance to the specialization. I therefore applied weighting to the votes offered by each participant according to their expertise. 'Expert' opinions were taken to be worth twice as much towards calculating the average (i.e., +2 and - 2 for increasing and decreasing votes), ' N o v i c e ' opinions were taken to be worth half as much (i.e., +0.5 and -0.5).  The following criteria are used to define 'expert' and 'novice' comments:  1.  Fishers are considered expert in their target functional groups;  2.  Group interviews are novice in all functional groups;  3.  First Nation group interviews remain unchanged in First Nation specialties;  4.  Non-fishers are novice in all functional groups;  5.  Recreational fishers are novice on all functional groups except their specialty, in which they are expert;  6.  Interviewee #20 was judged expert i n all functional groups;  7.  Interviewee #21 was judged expert in all rockfish functional groups.  Group interviews operated on consensus; their vote is reduced in importance to limit the effect o f influence between respondents in the analysis.  However, one exception is made.  Since the  majority o f First Nations respondents participated in group interviews, I do not want to reduce the impact o f their comments on the L E K abundance trends.  Comments made during First  Nations group interviews therefore remain 'unchanged' in importance regarding the abundance of traditionally harvested species. I assume that non-fishers and recreational fishers spend less time at sea than commercial harvesters, so their contribution to the overall trend is weighted half as much.  In addition to years o f fishing experience, interviewees 20 and 21 had formal  ecological training.  .  '  39  Alternatively, a weighting scheme based on years of experience could be used, although some degree of ranking by gear specialization should still be included. Information from experienced fishers does actually influence the LEK abundance trend more than information from less experienced fishers under the current methodology, since their comments apply to more years in the analysis.  Qualitative agreement of L E K versus stock assessment To determine how often comments agreed with stock assessment records, I compare the qualitative change in abundance offered by each interviewee with time series biomass data assembled from stock assessment. For the period in which a respondent fished, an Excel macro consults time series stock assessment records assembled from various DFO publications . The 3  algorithm determines whether the abundance of the subject functional group had increased, stayed the same or decreased in the stock assessment record. It compares this result against the suggested population change provided by the interviewee to determine agreement.  This  procedure is conducted for functional groups that have continuous stock assessment information. Comments are used from only the respondents whose career spanned a period covered by stock assessment data.  For each comment made concerning a particular functional group, the span of the interviewee's career at sea is divided into two halves. The average abundance of that functional group in the first and second halves of the fisher's career is determined from stock assessment records. If the average abundance was greater in the second half than in the first, the functional group is said to have increased.